TỔNG HỢP CÁC BÁO CÁO KHOA HỌC VỀ CUNG CẤP ĐIỆN CỦA BỘ MÔN HỆ THỐNG ĐIỆN (ĐẠI HỌC BÁCH KHOA HÀ NỘI)

CÁC BÁO CÁO BAO GỒM: 1. Fault Distribution Modeling Using Stochastic Bivariate Models for Prediction of Voltage Sag in Distribution Systems (Thầy Bạch Quốc Khánh). 2. Voltage Sag Index Using Stochastic Method with Considering Protection Coordination and Sensitive Equipment (Lê Việt Tiến). 3. Subsequence Action to Eliminate Blackout after Detecting Islanding using Solid State Transfer Switch Implemented in PSCADEMTDC (Thầy Nguyễn Đức Tuyên).

ĐẠI HỌC BÁCH KHOA HÀ NỘI

BỘ MÔN HỆ THỐNG ĐIỆN

Tổng hợp các bài báo khoa học giai đoạn 2007-2012

IEEE TRANSACTIONS ON POWER DELIVERY, VOL 23, NO 1, JANUARY 2008 347

Fault Distribution Modeling Using Stochastic

Bivariate Models for Prediction of Voltage

Sag in Distribution Systems

Bach Quoc Khanh, Dong-Jun Won, Member, IEEE, and Seung-Il Moon, Member, IEEE

Abstract—This paper presents a new method regarding fault

dis-tribution modeling for the stochastic prediction study of voltage

sags in the distribution system 2-D stochastic models for fault

mod-eling make it possible to obtain the fault performance for the whole

system of interest, which helps to obtain not only sag performance

at individual locations but also system sag performance through

system indices of voltage sag By using the bivariate normal

dis-tribution for fault disdis-tribution modeling, this paper estimates the

influence of model parameters on system voltage sag performance.

The paper also develops the modifiedSARFIX regarding phase

loads that create better estimation for voltage sag performance for

the distribution system.

Index Terms—Bivariate normal distribution, distribution

system, fault distribution modeling, phase loads, power quality

(PQ), stochastic prediction, voltage sag frequency.

I INTRODUCTION

AMONG power-quality (PQ) phenomena, the voltage sag

(dip) is defined in IEEE1159, 1995 as a decrease in rms

voltage to between 0.1 and 0.9 of the nominal voltage at the

power frequency for the duration of 0.5 cycle to 1 min There has

been a greater interest in voltage sags recently due to problems

caused by the performance of sensitive electronic equipment

that is widely used

Research about the voltage sag is usually related to a basic

process known as a “compatibility assessment” [1], [2] which

includes three steps

Step 1) Obtain the voltage sag performance of the system of

interest

Step 2) Obtain equipment voltage tolerance

Step 3) Compare equipment voltage tolerance with the

voltage sag performance and estimate the expected

impacts of the voltage sag on the equipment

Current research has shown evidence that obtaining the

voltage sag performance still needs more improvement The

Manuscript received August 2, 2005; revised December 5, 2006 This work

was supported by the Korea Foundation for Advanced Studies’ International

Scholar Exchange Fellowship for the academic year of 2004–2005 Paper no.

TPWRD-00456-2005.

B Q Khanh is with the Electric Power System Department, Faculty of

Elec-trical Engineering, Hanoi University of Technology, Hanoi, Vietnam (e-mail:

bq_khanh-htd@mail.hut.edu.vn).

D.-J Won is with the School of Electrical Engineering, INHA University,

Incheon 402–751, Korea (e-mail: djwon@inha.ac.kr).

S.-I Moon is with the School of Electrical Engineering and Computer

Sci-ence, Seoul National University, Seoul 151-742, Korea (e-mail: moonsi@plaza.

snu.ac.kr).

Digital Object Identifier 10.1109/TPWRD.2007.905817

information about the voltage sag is mainly obtained bymonitoring and stochastic prediction With recently advancedcomputer-aided simulation tools, the stochastic prediction ofvoltage sag becomes the preferable approach that can obtainthe results at required accuracy for various network topologiesand operational conditions “The method of fault positions”

and “the method of critical distances” are known as the mostwidely used methods for stochastic prediction studies

It is notable that regardless of which method is used, a chastic prediction study always has to solve two critical prob-lems: 1) the modeling of causes leading to voltage sags and2) the simulation of the power system for computing voltage sagcharacteristics Among important cause of voltage sags, short-circuit faults in the power system account for the largest part andthe assessment of the voltage sag performance based on faultdistribution modeling is a well-known approach However, it isvery difficult to build up “accurate” fault modeling because thedata of faults can only obtained by monitoring and, thus, it hasthe same uncertainties as to what the monitoring of voltage sagscan generate

sto-This paper presents a new approach on fault distribution eling for the stochastic prediction of voltage sags in the distri-bution system using the method of fault positions The simula-tion of the distribution system and fault distribution modelingare made on MATLAB for computing not only site indices, butalso system indices of voltage sags

mod-II FAULTDISTRIBUTIONMODELINGModeling the fault distribution is to determine the short-cir-cuit fault frequency (i.e., fault rate or the number of short-circuitfaults per year) for all fault types at all possible fault positionsthroughout the system of interest It consists of the selection offault position and fault type and the distribution of fault rate forthe selected fault positions and fault types

Fault positions are generally chosen in a way that a fault sition should represent short-circuit faults leading to sags withsimilar characteristics [2] For the distribution system with typ-ical radial network topology, small line segments, and distribu-tion transformers along the trunk feeders, it is possible to applyonly one fault position for each distribution transformer and onefault position for each line segment

po-Different fault types should be applied to each fault positionmainly depending on the number of phases available at the se-lected fault positions The fault rate of each fault type is nor-mally referred from the observed historical data

0885-8977/$25.00 © 2007 IEEE

348 IEEE TRANSACTIONS ON POWER DELIVERY, VOL 23, NO 1, JANUARY 2008

The fault rate mainly depends on fault position, fault type,

and fault cause While two earlier factors have been discussed

at length in past research, the distribution of the fault rate for

the selected fault positions has received less interest The most

common assumption that has been argued so far is that because

the fault can occur anywhere in the system, stochastically, it is

possible to model the fault rate as the uniform distribution [3],

[4] In this sense, the fault rate at each position is identical to

the component failure rate that is based on component

relia-bility However, in reality, many factors can lead to faults, not

just the component failure, and fault rates at different positions

in the system are rarely the same Recently, a report [5]

pro-posed some interesting 1-D models of fault distribution along

individual line segments (between two nodes) However, this

re-search could not consider the distribution of transformer faults

Furthermore, by using 1-D fault distribution, it is hard to

ob-tain a system index about voltage sag performance since there

are plenty of line segments in the distribution system The new

method of fault distribution modeling proposed by this paper

carefully analyzes concerned fault causes and builds up a

suit-able modeling of the fault distribution for the whole system of

interest from which system indices can be obtained

III NEWFAULT DISTRIBUTIONMODELINGBASED

ONFAULTCAUSES FORDISTRIBUTIONSYSTEMS

Although there are a variety of causes that result in faults in

distribution systems, it is possible to group them into two parts:

namely 1) equipment failures and 2) external causes

Equipment failure is basically due to defects that are

prob-ably created during manufacture, transportation, and

installa-tion Equipment failure depends on the time of being placed

into operation, the aging period, and maintenance conditions

According to the reliability theory, it is often characterized by

the component failure rate There are several distribution

func-tions to model this parameter but the most common one is the

exponential distribution which assumes the component failure

rate to be constant This value is equal to the average failure

rate during the useful life of the “bathtub” curve [6] Therefore,

if the same type of equipment is used throughout the system

(e.g., the same type of distribution transformers used in the

dis-tribution system), it is possible to assume that the failure rate

of equipment follows the uniform distribution depending on the

equipment type although it still may cause some errors (e.g., not

all equipment is put into operation at the same time or has the

same maintenance conditions)

Besides equipment failure, there are many other causes from

the ambient environment that also may lead to faults in power

systems This paper calls them the external causes Some can

in-fluence the fault performance of the power system in a large area

such as severe weather (wind storms, lightning, etc.)

Mean-while, others mainly have local impacts, such as trees and

ani-mals (birds, mice, etc.) Human factors (scheduled interruption,

human errors, mischief, and vandalism) can cause faults that

only influence the power system in small parts as well as

se-vere faults for a large power system All of these causes occur

randomly and they can be simulated by stochastic models 1-D

Fig 1 Example of bivariate normal distribution.

This paper proposes the idea of using 2-D stochastic models stead (e.g., the bivariate normal distribution model as illustrated

in-in Fig 1)

For large power systems, it is hard to obtain a converged 2-Dfault distribution model for various causes in a large area How-ever, for small-to-medium-size networks, such as the section ofdistribution network fed from a bulk-point distribution substa-tion, of which the monitored historical data of fault performanceshows that faults due to external causes occur concentratively onone location (e.g., some lines pass through a small area which

is at high risk for faults due to industrial pollution or trunk fall),

it is the favorite condition to obtain a converged 2-D fault tribution model

dis-IV PROBLEMDEFINITION ANDSOLUTION

A Case Study Definition

To illustrate the new method of fault distribution modeling

in the stochastic prediction of voltage sag in the distributionsystem, this paper uses the IEEE 123-bus radial distributionfeeder [7] as the test system It can be seen as the distributionsystem is fed from a bulk point It does not narrow the scope ofapplication of the study with the following assumptions

• Since line segments in the test system come in one, two,and three phases, distribution transformers at load nodesare the single phase type for separate single-phase loads

For three-phase loads, the connection of the tion transformer is 4.16-kV grounded wye—low-voltagegrounded wye

distribu-• Voltage sags are only caused by faults in the test system

• If the test system is supposedly a section of a large bution system, only faults occurring in it are considered

distri-The faults in sections fed from other distribution tions can be skipped as the transformer impedance in dis-tribution substations, in reality, is rather high Similarly, thefaults in low-voltage networks are also ignored because ofthe large impedance of distribution transformers This as-sumption only neglects voltage sags caused by faults in thetransmission system It will be considered if the stochasticprediction of voltage sag in large transmission systems [4]

substa-is included

• In terms of reliability, the test system is modeled on twomain components: lines and distribution transformers Thereliability of any other distribution equipment is suppos-edly included in the reliability of these two components

• The fault positions are selected as mentioned in Part II Fortransformers, one fault position at each load node (i.e., theTổng hợp các bài báo khoa học giai đoạn 2007-2012

KHANH et al.: FAULT DISTRIBUTION MODELING USING STOCHASTIC BIVARIATE MODELS 349

For lines, one fault position is also applied for each line

segment Due to the short line segments, this paper selects

the fault position at the end of each line segment (For the

test system, there are 122 line segments and 87 load nodes

Therefore, 209 fault positions in total are selected)

• Fault types (single phase to ground, phase to phase, two

phases to ground, and three phases to ground) are applied

to fault positions depending on the number of available

phases The fault impedance is assumed to be negligible

• The fault rate of a distribution transformer is a random

variable depending on the position of the load node it is

connected to The fault rate of a line segment is also a

random variable depending on the fault position and the

length of this line segment

Based on the previous definitions and assumptions, the

com-putation of voltage sags at all load nodes on the primary side

of distribution transformers throughout the test system is

per-formed on MATLAB [8] The voltage sag frequency at each load

node is obtained when applying the fault rate to each fault

posi-tion The fault rates at the fault positions are calculated based on

the new fault distribution modeling presented in Part B Finally,

related voltage sag indices are calculated

B Fault-Rate Modeling

Faults are random events and as previously indicated, they

can be simulated by stochastic distribution models According

to the analysis in Section III, the fault rate of each fault type at

each fault position is equal to the sum of equipment failure rate

and fault rate due to external causes The equipment failure rate

is supposed to follow the uniform distribution model Therefore,

for the fault position of the transformer , the failure rate is

cal-culated as follows:

(1)where

number of transformer faults of the test system;

total distribution transformers;

contributory percentage of equipment failure

The line failure rate is normally expressed in the number of

faults per year per foot (or meter) length However, because of

the short length of line segments, the line failure rate is

calcu-lated for the whole line segment as follows:

(2)

where

number of line faults of the test system;

total line segments;

length of the line segment (in feet)

The distribution of the fault rate due to external causes

de-pending on fault positions is supposedly in compliance with the

2-D stochastic model This paper uses bivariate normal

distribu-tion because it is the most common stochastic model which hassuch critical advantages as it accepts continuous variables and iseasy to build up the distribution based on monitored historicaldata Besides that, it is also simple to convert to other modelsusing continuous variables

So the fault rate at each fault position is as follows

For the transformer

(3)For the line segment

(4)where

contributory percentage of faults due

, weighted factors of the fault rates of

the transformer and the line segmentthat follow the bivariate normaldistribution model depending on faultpositions

The joint probability density function of bivariate normal tribution is expressed as follows:

dis-where

(5)

, , , means and standard deviations of two

variables , ;correlation coefficient If thecoordinates of fault positions areindependent variables

The probability for a fault to occur at the fault position

(6)

then the distribution is normalized as follows:

(7)

For the distribution system, geographically, if network nodesare disposed relatively uniform, it will be possible to apply the

350 IEEE TRANSACTIONS ON POWER DELIVERY, VOL 23, NO 1, JANUARY 2008

following approximation where and are the coordinates of

the fault position

• Faults rate for the transformer

(8)

• Fault rate for the line segment

(9)

C Development of Voltage Sag Indices

PQ indices are used to estimate the quality of supplied

elec-tric energy for the power system To date, many PQ indices

have been proposed for various PQ events A well-known index

of voltage sag is the system average rms voltage variation

fre-quency index for voltage sag down to under X% of the nominal

voltage value It is often used for evaluating the PQ

of a three-phase power system based on monitored limited

seg-mentation [3] The assessed system is segmented so that every

point in the system is contained within a section monitored by

an actual PQ measuring instrument

In distribution systems, because various phase loads (phase

to neutral, phase to phase, three-phase loads) are available,

asymmetrical faults, which account for most faults, never result

in voltage sags to all single-phase loads (e.g, phase A-to-ground

faults may not cause voltage sags to the loads connected between

phase B and neutral or phase C and neutral or loads connected

between phase B and phase C) Therefore, using

regardless of the number of phases involved, may not exactly

reflect the voltage sag performance of the distribution system

From the demand sides, the indices are more interesting because

they can estimate the voltage sag performance for phase loads

In order to take the availability of various phase loads in the

distribution system into account, this paper newly develops

in regard to phase loads as follows:

(10)

(11)

(12)where

X% that phase-to-neutral(A,B,C), phase-to-phase (A-B,B-C, C-A), or three-phase loadexperiences;

(A,B,C), phase-to-phase (A-B,B-C, C-A), or three-phasecustomers served from the

TABLE I

Fig 2 Mapping of the IEEE—123-bus radial distribution test feeder.

V RESULTDEMONSTRATION ANDANALYSIS

A Procedures of Stochastic Prediction

The process of stochastic prediction study is performedthrough the following steps

First, the system fault rate (the total of faults occurring in thetest system over a certain period of time) is assumed to be anarbitrary number, say 500 faults This value is just for calcu-lation and easier graphic demonstration of the results Besidesthat, contributory percentages of different fault types are alsoassumed as follows:

• single phase to ground (N1): 80%;

• two phase to ground (N11): 10%;

• two phase together (N2): 8%;

• three phase to ground (N3): 2%

and the component fault rates are supposed to be

• transformer: 50%;

• line: 50%

The listed percentages shown are, in fact, based on actual surveydata [9] Based on the aforementioned assumptions, the systemfault rates of transformers and lines for different fault types due

to different fault causes (equipment failure or external causes)are calculated and shown in columns 2 and 3 of Table I Param-eters ( , ) that are included make it possible to considerthe influence of fault causes due to external factors

Second, the fault rate of each fault type is calculated for eachTổng hợp các bài báo khoa học giai đoạn 2007-2012

KHANH et al.: FAULT DISTRIBUTION MODELING USING STOCHASTIC BIVARIATE MODELS 351

Fig 3 Sag frequency spectrum and SARFI of different phase loads for the case the mean value is at node 13 and deviation  =  = 0:51

Table I The test system with actual dimensions in feet is mapped

out in Fig 2 The fault positions are assigned with coordinates

Third, the voltage sag magnitude and phase shift at all load

nodes are computed for all selected fault positions With the

ap-plication of fault rates to the selected fault positions, the voltage

sag frequencies corresponding to different characteristics are

obtained The voltage sag frequency is calculated for the

fol-lowing:

• individual load nodes;

• all possible phase loads, including phase-to-neutral,

phase-to-phase, and three-phase loads;

• the whole test system

B Evaluation of Influences of the Fault Distribution Modeling

on the Voltage Sag Performance

The fault distribution modeling uses several parameters In

practice, it is possible to adjust these parameters so that the

re-sulting model is suitable for the fault performance of the

distri-bution system of interest However, the variation of these

param-eters also makes the voltage sag performance change

accord-ingly In modeling fault distribution, this paper also considers

the following options of fault distribution for estimating the

in-fluences of fault distribution on voltage sag performance

• Change contributory percentages of the fault due to

ex-ternal causes (change or ) In this paper, three

op-tions , 50%, and 100% are considered

• Switch the position of the mean value ( , ) of the

bi-variate normal distribution This paper considers four

op-tions of the mean value at nodes 13, 51, 67, and 85 as

in-dicated in Fig 2

• Vary the deviations , of the bivariate normal

distri-bution This paper also considers the options of the

devi-ation that are equal to 0.2, 0.5, and 0.8 of the maximum

value among deviations

from 10% to 90% of the nominal voltage are shown In this

Besides that, for the whole test system for ferent mean values (at nodes 13, 51, 67, and 85) of the faultdistribution models regardless of the number of involvedphases are also depicted in Fig 4 Obviously, there are bigdifferences between of different phase loads or

because the number of single-phase loads on each phase aredifferent are rather low as single-phase loadsjust experience sags due to single-phase-to-ground faults on

phase-to-phase loads are impacted by more faults (faults ontwo phases) than phase-to-neutral loads (faults on one phase)

For phase-to-phase loads, there is a little deep sag frequency;

meanwhile, the shallow sag frequency rises greatly because most phase-to-ground faults (80% system fault rate) just causeshallow sags to phase-to-phase loads for three

to 500 sags per load because three-phase loads will experiencevoltage sag for any fault type The aforementioned remarksalso explain why , defined for phase loads, is for moreuseful indices for estimating the voltage sag performance in thedistribution system where many single-phase loads exist

Fig 4 also shows that different positions of the mean value offault distribution models result in different spectrums of voltagesag frequency It is notable that if the position of mean value getscloser to the bulk point of supply, the deep sag frequency willincrease, that is, mainly because of the radial network topology

of the distribution system

352 IEEE TRANSACTIONS ON POWER DELIVERY, VOL 23, NO 1, JANUARY 2008

Fig 4 Sag frequency spectrum and SARFI of the whole system for different mean positions for the case that the deviation is  =  = 0:51

Fig 5 Voltage sag frequency spectrum of the load-bus 63 on phase A for

dif-ferent deviations The mean value is at node 67 (upper) and node 13 (lower).

Fig 6 Voltage sag frequency spectrum for loads on phase A for different

de-viations The mean value is at node 67 (upper) and node 13 (lower).

Figs 5 and 6 plot the voltage sag frequency for load node

Fig 7 Voltage sag frequency spectrum and SARFI for the whole system for different deviations for the mean value at node 67.

Fig 8 Voltage sag frequency spectrum and SARFI for the whole system for different deviations for a mean value at node 13.

different deviation values of fault distribution

in the case the mean values are identical tothe coordinates of node 13 and node 67 Similarly, Figs 7 and 8Tổng hợp các bài báo khoa học giai đoạn 2007-2012

KHANH et al.: FAULT DISTRIBUTION MODELING USING STOCHASTIC BIVARIATE MODELS 353

Fig 9 Voltage sag frequency distribution for sags lower than 10%, 40% to 50%, 60% to 70%, and 70% to 80%,  =  = 0:51 , = 50%, mean at

node 67.

for the whole test system also for different deviation values

and for the mean values atnode 13 and node 67 Increasing the deviation values and

will turn the normal distribution into the uniform

distribu-tion It causes shape variations to the voltage sag frequency

spectrum The clear increase of the frequency of deep sags is

shown in all cases of the sag performance demonstration If

the mean position of the distribution model is located at node

13, which is very near the bulk point, the frequency of sags

below 10% is even raised by about 50% for the small deviation

That is also explained as the result ofthe radial network topology of the distribution system

The spectrum of the voltage sag frequency for different case

studies (from Figs 3–8) is quite similar in which deep sags

ac-count for a large number mainly due to short feeders in the

dis-tribution system The frequency of 40% to 60% sags is also high

as the network topology consists of one trunk line with many

lat-eral taps in the middle That means the point of common

cou-pling of many load nodes is on the middle of the trunk line Few

load nodes connected to the trunk line near the bulk point of

supply (the distribution substation) explain why the shallow sag

frequency is very low Fig 9 gives us a closer look at the voltage

sag frequency distribution for different sag magnitudes It is,

without doubt, that deep sag frequencies appear at the nodes

on branches connected close to the far end of the trunk line

Voltage sags 40% to 50% are distributed rather uniformly

ex-cepting nodes near the bulk point The shallow sag frequencies

mainly occur at several nodes near the bulk point of supply

VI CONCLUSIONThis paper presented a new method of fault distribution mod-eling in the stochastic prediction of voltage sag for the distri-bution system using 2-D distribution models When using 2-Ddistribution models for modeling fault distribution, parameters

of the distribution model should be selected properly to matchthe monitored historical data of fault performance of the system

of interest By using the bivariate normal distribution for eling fault distribution, this paper also analyzed the influences

mod-of its parameters on voltage sag performance It is notable thatthe alteration of the deviation value of the distribution has amuch stronger impact on sag performance, especially for thedeep sag frequencies pattern than switching the position of themean value The more concentrated occurrence of faults on onelocation in the distribution system of interest will increase thenumber of deep sags The results are also evidence that the typ-ical radial network topology of the distribution system is alsoanother important reason for the high frequency of deep sags

2-D stochastic models, such as the bivariate normal tion used for modeling fault distribution, can provide a goodoverview of fault performance of the whole system of interest

distribu-Thus, it is possible not only to analyze the relation betweenfaults and voltage sags at individual locations of the system,such as a specific load node or a segment of line, but also tocompute system indices of voltage sags, such as The application of 2-D stochastic models has some limits tothe size of the system of interest For the sections of the dis-

354 IEEE TRANSACTIONS ON POWER DELIVERY, VOL 23, NO 1, JANUARY 2008

tribution system, of which the size is so large as the one

sup-plied from a bulk distribution substation, it is practical to use

this fault distribution modeling The accuracy will be further

improved for the distribution systems, of which the topology

features the uniform arrangement of components In addition,

the stochastic prediction of the transmission system should be

included if the influence of fault occurring in the transmission

system on voltage sag performance in the distribution system of

interest is considered

The presence of different phase loads in the distribution

system indicated that for the whole system without

considering the number of phase of the loads cannot reflect

voltage sag performance properly To have a better assessment

of the voltage sag, this paper develops modified

regarding phase loads The results proved that there are

for different phase loads and for the

whole system This modification of is more practical

from the customer’s point of view when power-supply contracts

are set up

REFERENCES

[1] R C Dugan, M F McGranaghan, and H W Beaty, Electric Power

System Quality. New York: McGraw-Hill, 1996.

[2] M H J Bollen, Understanding Power Quality Problems—Voltage

Sags and Interruptions. New York: IEEE Press, 2000.

[3] D L Brooks, R C Dugan, M Waclawiak, and A Sundaram, “Indices

for assessing utility distribution system RMS variation performance,”

IEEE Trans Power Del., vol 13, no 1, pp 254–259, Jan 1998.

[4] M R Qader, M H J Bollen, and R N Allan, “Stochastic prediction

of voltage sags in a large transmission system,” IEEE Trans Ind Appl.,

vol 35, no 1, pp 152–162, Jan./Feb 1999.

[5] J V Milanovic, M T Aung, and C P Gupta, “The influence of fault

distribution on stochastic prediction of voltage sags,” IEEE Trans.

Power Del., vol 20, no 1, pp 278–285, Jan 2005.

[6] R E Brown, Electric Power Distribution Reliability. New York:

Marcel-Dekker, 2002.

[7] IEEE Distribution Planning Working Group Report, “Radial

distribu-tion test feeder,” IEEE Trans Power Syst., vol 6, no 3, pp 975–985,

[10] G Olguin, “Voltage dip (sag) estimation in power system based on

sto-chastic assessment and optimal monitoring,” Ph.D dissertation, Dept.

Energy Environ., Div Elect Power Eng., Chalmers Univ Technol.,

Gotteborg, Sweden, 2005.

[11] M R Qader, M H J Bollen, and R N Allan, “Stochastic prediction

of voltage sags in reliability test system,” presented at the PQA-97 rope, Elforsk, Stockholm, Sweden, Jun 1997.

Eu-[12] J A Martinez-Velasco and J Martin-Arnedo, “Stochastic prediction

of voltage dips using an electromagnetic transient program,” presented

at the 14th PSCC, Sevilla, Spain, Jun 2002, Paper 4, Session 24.

Bach Quoc Khanh received the B.S and Ph.D

de-grees in power network and systems from Hanoi versity of Technology, Hanoi, Vietnam, in 1994 and

Uni-2001, respectively He received the M.S degree in system engineering from the Royal Melbourne Insti- tute of Technology (RMIT), Melbourne, Australia, in 1997.

He is currently a Lecturer with the Faculty of Electrical Engineering, Electric Power System Department, Hanoi University of Technology He was a Postdoctoral Fellow with the Power System Laboratory, School of Electrical Engineering and Computer Science, Seoul National University, Seoul, Korea His special fields of interest include power distribution system analysis, DSM, and power quality.

Dong-Jun Won (M’05) was born in Korea on

Jan-uary 1, 1975 He received the B.S., M.S., and Ph.D.

degrees in electrical engineering from Seoul National University, Seoul, Korea, in 1998, 2000, and 2004, re- spectively.

Currently, he is a Full-Time Lecturer with the School of Electrical Engineering with INHA Univer- sity, Incheon, Korea He was a Postdoctoral Fellow with the Advanced Power Technologies Center, Department of Electrical Engineering, University of Washington, Seattle His research interests include power quality, dispersed generation, renewable energy, and hydrogen economy.

Seung-Il Moon (M’93) received the B.S degree

in electrical engineering from Seoul National versity, Seoul, Korea, in 1985 and the M.S and Ph.D degrees in electrical engineering from The Ohio State University, Columbus, in 1989 and 1993, respectively.

Uni-Currently, he is an Associate Professor of the School of Electrical Engineering and Computer Science at Seoul National University His special fields of interest include power quality, flexible ac transmission systems (FACTS), renewable energy, and dispersed generation.

Tổng hợp các bài báo khoa học giai đoạn 2007-2012

Paper No PS-1

Abstract This paper presents a method of assessing a

power quality phenomena in distribution systems - voltage sag

The voltage sag performance is obtained by the problem of

stochastic prediction of voltage sag in power systems [2] basing

on System Average RMS variation Frequency Index (SARFI X ).

However, SARFI X is modified into SARFI X-CURVE that

considers not only the magnitude of voltage sag, but also its

duration The resulting SARFI X-CURVE provides a better

understanding of the influence of voltage sag on the electric

loads The duration of voltage sag is modeled regarding the

tripping time of protective devices in distribution systems The

paper also applies this method to assess voltage sag

performance of the 22kV feeder 482-E14 of 110/35/22kV Giam

substation in Hanoi city, Vietnam

Index Terms power quality, voltage sag, distribution

system, equipment compatibility curve, fault distribution

modeling, tripping time

I INTRODUCTIONMONG power quality phenomena, the voltage sag

(dip) is defined at IEEE1159, 1995 as a decrease in

RMS voltage to between 0.1 and 0.9 of the nominal voltage

at the power frequency for the duration of 0.5 cycle to 1

minute Interests in the voltage sag have been getting much

greater recently due to its problems causing on the

performance of sensitive electronic equipments that are

widely used

Researches about the voltage sag are usually related with

a basic process known as a “compatibility assessment” [1]

which includes three steps: i Obtain the voltage sag

performance of the system of interest, ii Obtain equipment

voltage tolerance, iii Compare equipment voltage tolerance

with the voltage sag performance and estimate expected

impacts of the voltage sag on the equipment Researches to

date have already evidenced that obtaining the voltage sag

performance is still needing much further improvement The

information about the voltage sag is mainly obtained by

monitoring and stochastic prediction [1] This paper

presents a method of predicting voltage sags in distribution

system using SARFIX-CURVE that is derived from SARFIX

with regard to tripping time of protective devices currently

used in power distribution networks in Vietnam

Bach Quoc Khanh is with Electric Power System Department,

Electricity Faculty, Hanoi University of Technology, 1 Dai Co Viet Rd.,

Hanoi, Vietnam (e-mail: bq_khanh-htd@mail.hut.edu.vn ).

II INDICES FOR VOLTAGE SAG ASSESSEMENT Voltage sag assessment often bases on its characteristics:

magnitude and duration There are many indices proposed for voltage sag quantification [1], [2] and one of frequently used indices is SARFIX that is defined as follows

N

N

i X X





)

(1) where

X rms voltage threshold; possible values – 10-90%

nominal voltage

N X(i) Number of customers experiencing voltage sag with

magnitudes below X% due to measurement event i.

N number of customers served from the section of the system to be assessed

Despite being widely used, SARFIX only considers the magnitude of voltage sag and, of course, its value is maybe much greater than the actual number of tripping electrical appliances, especially when the duration of sags is small enough (less than a half second) To take the sag duration into account, SARFIX is developed into SARFIX-CURVE [2], [4], [6] which is defined below

Figure 1 ITI curve for susceptibility of computer equipment

Prediction of Voltage Sags in Distribution

Systems With Regard to Tripping Time of

Protective Devices Bach Quoc Khanh (Hanoi University of Technology)

A

The 2009 ASEAN Symposium on Power and Energy Systems - EEE.RC.ASPES 2009

N

N SARFI

m i i X CURVE

(2) where

'

)

(i

X

N :Number of customers tripped when experiencing

voltage sag with magnitudes below X% due to measurement

event i.

SARFIX-CURVE corresponds to voltage sags below an

equipment compatibility curve So far, frequently used

curves are CBEMA, ITIC and SEMI [1] Obviously,

SARFIX-CURVE can provide a better understanding of the

influence of voltage sag on the operation of electric

equipment in electric networks This paper presents the

method of calculating SARFIX-CURVE using ITI curve (Figure

1) as a case study

III REDICTION OF VOLTAGE SAG IN DISTRIBUTION SYSTEM

A Problem definition

The problem of stochastic prediction of voltage sag can

obtain the voltage sag performance of a specific electric

system by using data of events leading to sags In fact, more

than 90% sag events are resulted from short-circuits and it is

possible to use fault modeling and short-circuit calculation

tools to predict voltage sags in the power system (Figure 2)

This work uses the method of “fault position” [1] for

voltage sag prediction in distribution systems with following

significant steps

- Modeling the fault distribution on of a given

segment of distribution system (see part B)

- Calculating the short-circuit current and voltage

sags at all influenced load nodes

- Cumulating system voltage sags with different

characteristics and obtaining SARFIX

- Cumulating system voltage sags that cause

equipment to trip and obtaining SARFIX-CURVE

To obtain SARFIX-CURVE, this work uses the typical

tripping curve (tPD = f(IF)) of protective devices like fuses,

feeder circuit breakers currently used in distribution

systems Each sag is plotted as a point characterized by a

pair of co-ordinates (magnitude of voltage sag and tripping

time) If the point falls out of voltage tolerant envelop

(Figure 1), the sag is cumulated to calculate SARFIX-CURVE

B Fault Distribution Modeling

Modeling the fault distribution is to determine the

circuit fault frequency (i.e fault rate or the number of

short-circuit faults per year) for all fault types at all possible fault positions throughout the system of interest [3] It consists of the selection of fault position and fault type and the distribution of fault rate for selected fault positions and fault types

Fault positions are generally chosen in the way that a fault position should represent short-circuit faults leading to sags with the similar characteristics [1] For the distribution system with typically radial network topology, small line segments and distribution transformers along the trunk feeders, it is possible to apply only one fault position for each distribution transformer and also one fault position for each line segment

Different fault types should be applied to each fault position mainly depending on number of phases available at the selected fault positions The fault rate of each fault type

is normally referred from the observed historical data

Fault rate mainly depends on fault position, fault type and fault cause For a segment of distribution system that is geographically seen as small area, it possible to assume that fault rate of each fault type follows uniform distribution for all fault positions [3] In this sense, the fault rate at each position is identical to component failure rate that is based

on component reliability In reality, uniform fault distribution is a practical assumption for distribution systems because the service area of a certain distribution line outgoing from a distribution substation is normally small

of the large impedance of distribution transformers This assumption only neglects voltage sags caused by faults in the transmission system It will be considered if the stochastic prediction of voltage sag in large transmission systems [7] is included

- In terms of reliability, the distribution system is modeled on two main components: lines and distribution transformers The reliability of any other distribution equipment is supposedly included in the reliability of these two components

- The fault positions are selected as mentioned in the Part III.B For transformers, one fault position each load node (i.e the nodes connected with distribution transformers) is applied For lines, one fault position is also applied for each line segment Because of short line segments, the paper selects the fault position at the end of each line segment

- Fault types (single phase to ground, phase to phase, two phases to ground and three phases to ground) are applied to fault positions depending on the number of available phases

Tổng hợp các bài báo khoa học giai đoạn 2007-2012

Paper No PS-1

The fault impedance is assumed to be negligible

D System voltage sag calculations

Short-circuit calculations and resulting voltage sag

magnitude at load nodes in distribution systems is

performed by MatLab programming that used in [3] The

program consists of two modules

- Short circuit calculation

- Fault distribution modeling

Its block diagram is briefly depicted as Figure 3

IV A CASE STUDY

A Case study definition

This work illustrates the method by predicting voltage

sag performance and resulting SARFIX-CURVE for a 24kV

feeder network in Hanoi, Vietnam Preliminary data is as

follows

The network segment under consideration: Feeder

482-E14, 24kV, underground cable, outgoing from 110/35/22kV

Giam substation It’s a radial network with 99 nodes and 98

branches Fault positions can be selected at load nodes for

distribution transformer fault and at all nodes for line fault

Besides, contributory percentages of different fault type

are also assumed as follows

- Single phase to ground (N1) : 65%

- Two phase to ground (N11) : 10%

- Two phase together (N2) : 20%

- Three phase to ground (N3) : 5%

and the component fault rates are supposed to be

- Transformer : 50%

- Line : 50%

The tripping curve used for this work is the typical inverse curve of in-service protective devices in distribution systems like fuse-cutout for distribution transformer protection, overcurrent relay for 24kV line feeder The common formula of tripping curve is

1)

I*: Ratio of fault current IN and pickup current IP

a, b: Constants that are selectable

V RESULT DEMONSTRATION AND ANALYSISFirstly, the system fault rate (the total of faults occurring

in the test system over a certain period of time) is assumed

to be an arbitrary number, say, 100 faults This value is just for calculation and easier graphic demonstration of the results The system fault rate is then distributed uniformly to all fault positions as assuming in Part III.B Short-circuit calculation is made at every fault positions and resulting voltage sags at all load nodes are identified by their magnitudes Besides, the fault current is used to determine voltage sag duration as per (3) and each voltage sag identified above are again checked to see whether it is to fall inside the voltage tolerant envelope of ITI curve or not If it

is inside, it is taken into account for calculating SARFI CURVE Finally two indices SARFIX and SARFIX-CURVE are obtained and plotted in the same graphics for analysis The results are depicted on two graphics Figure 5 depicts the system voltage sag frequency spectrum Figure 6 depicts SARFIX and SARRFIX-CURVE

X-The results also indicate some following remarks

- Deep sag frequency rises highly due to the radial network topology with short distances of cable lines in distribution systems

- 40-50% sag is also a little greater than other sags because the feeder consists of one trunk line with many lateral taps in the middle That means the

Figure 3 Block-diagram of voltage sag prediction

and SARFIX-CURVE in distribution systems

START

DETERMINE FAULT LINE

Find nodes and branches on fault current carrying line

STOP

ON FAULT-LINE CALCULATION

Calculate fault current I N and sags V S at nodes on fault line

SARFIXCALCULATION

Sag quantification by magnitude

calculation

OFF FAULT-LINE CALCULATION

Calculate voltage sags V S at nodes not on fault line

SARFIX-CURVE CALCULATION

Sag quantification by duration

TRIPPING TIME

tPD = f(IN)

24kV bus of 110kV Giam substation

Circuit

Distribution transformer

Distribution transformer

Figure 4 Brief description of 24kV feeder protection system

The 2009 ASEAN Symposium on Power and Energy Systems - EEE.RC.ASPES 2009

point of common coupling of many load nodes is

on the middle of the trunk line

- Voltage sags with X greater than 70% are very few

because the system and distribution transformer

impedances normally are much higher than

distribution lines

- SARFIX and SARFIX-CURVE are slightly different

because the tripping time of protective devices in

distribution systems is typically 0.5 seconds and

frequencies of voltage sag of 70-80% and 80-90%

are very small In ITI curve, sags with the

magnitude X lower than 70% nominal voltage

feature very short duration (less than one cycle)

and thus they are certainly taken in to account for

calculating SARFIX-CURVE

VI CONCLUSIONS This paper presented a method of assessing voltage sags

in distribution systems with regard to tripping time of

protective devices The assessment bases on SARFIX-CURVE

that combines SARFIX and equipment compatibility curves

Therefore, the results of assessment provide a better

understanding of the influence of voltage sag on loads

This method is also found useful for power quality

assessment and power supply contracting principles for

power distribution utilities in Vietnam in the process of

electricity market establishment because the management of

distribution system is becoming financially separated from

the power system

The application of the method has some limits that can be

developed in further researches For a larger network, a more suitable fault distribution should be considered [3], [5] In addition, a combination of the problems of predicting voltage sags in distribution systems and transmission system [7] will provide a more comprehensive understanding of voltage sag performance of a power system

VII REFERENCES [1] M.H.J Bollen, Understanding power quality problems - voltage sags and interruptions, IEEE Press, 2000

[2] Recommended practice for the establishment of voltage sag indices, Draft 6, IEEE P1564, Jan 2004

[3] Bach Quoc Khanh, Dong Jun Won, Seung Il Moon, “Fault Distribution Modeling Using Stochastic Bivariate Models For

Prediction of Voltage Sag in Distribution Systems”, IEEE Trans

Power Delivery, pp 347-354, Vol.23, No.1, January 2008

[4] Juan A Martinez, Jacinto Martin-Arnedo, “Voltage Sag Studies in Distribution Networks - Part II: Voltage Sag Assessment, Part III -

Voltage Sag Index Calculation”, IEEE Trans Power Delivery, pp

1679-1697, Vol 21, No 3, July 2006

[5] Jovica V Milanovic, Myo Thu Aung, C P Gupta, “The Influence of

Fault Distribution on Stochastic Prediction of Voltage Sags”, IEEE

Trans Power Delivery, pp 278-285, Vol 20, No 1, Jan 2005

[6] D L Brooks, R C Dugan, Marek Waclawiak, Ashok Sundaram,

“Indices for Assessing Utility Distribution System RMS Variation

Performance”, IEEE Trans Power Delivery, vol.13, no.1, pp.254-259,

Jan 1998

[7] M.R.Qader, M.H.J.Bollen, and R.N.Allan, “Stochastic Prediction of

Voltage Sags in a Large Transmission System”, IEEE Trans Industry

Applications, vol.35, no.1, pp.152-162, Jan./Feb 1999

[8] M.R.Qader, M.H.J Bollen and R.N.Allan, “Stochastic Prediction of Voltage Sags in Reliability Test System”, PQA-97 Europe, Elforsk, Stockholm, Sweden, Jun 1997

VIII BIOGRAPHIES

Bach Quoc Khanh received B.S and Ph.D degrees in power network

and systems from Hanoi University of Technology, Hanoi, Vietnam in 1994

and 2001 respectively He received M.S in system engineering from RMIT, Melbourne, Australia in

1997 He is a teaching staff of Electric Power System dept., Electrical Engineering Faculty, Hanoi Univeristy of Technology His special fields of interest include power distribution system analysis, DSM and power quality

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TẠP CHÍ KHOA HỌC & CÔNG NGHỆ CÁC TRƯỜNG ĐẠI HỌC KỸ THUẬT 667 SỐ 77 - 2010

Bài báo trình bày phương pháp đánh giá một hiện tượng chất lượng điện năng (CLĐN) trên lưới

truyền tải điện (LTT) là sụt áp ngắn hạn (SANH - voltage sag) [1] Mô hình đánh giá SANH dựa trên

phương pháp dự báo ngẫu nhiên SANH [2] trong hệ thống điện (HTĐ) Việc đánh giá này dựa trên chỉ

tiêu tần suất SANH trung bình của HTĐ với đặc tính X (SARFI X ) và SARFI X-CURVE [3] cho phép xét đến

không chỉ đặc trưng biên độ của SANH mà còn cả đặc trưng thời gian tồn tại SANH Đối tượng tính

toán là hệ thống truyền tải điện 220kV của Việt Nam theo tổng sơ đồ 6 với tỷ lệ suất sự cố ngắn mạch

thực tế của năm 2008 Việc đánh giá này là một cố gắng đầu tiên định lượng hóa tình hình một hiện

tượng chất lượng điện năng phổ biến trên một lưới điện diện rộng thực tế giúp cho việc đánh giá chất

lượng điện năng nói chung của hệ thống điện Việt Nam hiện nay

ABSTRACT

This paper presents a method of predicting a power quality phenomena in distribution systems,

voltage sag [1] The calculation of voltage sag performance follows the model of stochastic prediction

of voltage sag in power systems [2] The voltage sag performance is predicted basing on the System

Average RMS variation Frequency Index (SARFI X ) and SARFI X-CURVE [3] that considers not only the

characteristics - magnitude, but also the characteristics – duration of voltage sag The objective of

research is the whole 220kV transmisson systems in Vietnam as per the 6 th master-plan with actual

data of fault rate of the year 2008 This prediction is the first effort of quantifying the voltage sag

performance for such a large transmission system that helps assess the power quality of the electric

power system in Vietnam now

I ĐẶT VẤN ĐỀ

Theo IEEE-1159, 1995, SANH (voltage sag) là hiện tượng CLĐN trong đó giá trị điện

áp hiệu dụng của lưới điện sụt giảm còn từ 0,1

đến 0,9 điện áp định mức trong thời gian từ 0,5

chu kỳ đến 1 phút [1] SANH có thể làm cho

các thiết bị điện nhậy cảm như điện tử công

suất, các bộ điều tốc hay máy tính cá nhân

ngừng hoặc làm việc không mong muốn Hiện

tượng này lại rất hay xảy ra, trong khi để nâng

cao hiệu suất quá trình hay việc ứng dụng các

công nghệ mới, các thiết bị điện ứng dụng điện

tử công suất ngày càng được sử dụng nhiều, do

đó SANH ngày càng được quan tâm nghiên

cứu Trước khi xem xét những giải pháp khắc

phục tác động của SANH, yêu cầu đánh giá

SANH trong HTĐ luôn được đặt ra Quá trình

đánh giá CLĐN nói chung và SANH nói riêng

thường trải qua ba khâu chủ yếu [1] là i Nhận

dạng tình hình CLĐN được cung cấp, ii Xác

định yêu cầu CLĐN của các phụ tải, iii So

sánh yêu cầu CLĐN của phụ tải với tình hình

CLĐN được cung cấp và đánh giá tác động của CLĐN đối với phụ tải Việc xác định yêu cầu CLĐN của các phụ tải thuộc về các nhà sản xuất thiết bị dùng điện mà điển hình là đặc tính chịu điện áp của phụ tải CBEMA, ITIC hoặc SEMI [1] (Hình 1)

Trong khi đó, việc nhận dạng tình hình CLĐN là nhiệm vụ của phía cung cấp điện

Ở Việt Nam, đã bắt đầu có những nghiên cứu chuyên sâu về đánh giá tình hình SANH trong HTĐ [2, 3], tuy nhiên việc định lượng hóa tình hình SANH trên HTĐ thực tế ở Việt Nam vẫn chưa được thực hiện Nguyên nhân chính hiện nay là không có một cơ sở dữ liệu về CLĐN nói chung và SANH nói riêng của HTĐ Việt Nam do hệ thống giám sát và lưu trữ thông tin về CLĐN vẫn còn rất thiếu Bên cạnh việc giám sát CLĐN, một cách gián tiếp để xác định tình hình SANH trên HTĐ có thể dùng mô hình

dự báo CLĐN dựa trên các nguyên nhân sinh ra

nó Trong các nguyên nhân này, trên 90%

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73

SANH là do sự cố ngắn mạch trong HTĐ Do

đó, có thể đánh giá SANH thông qua mô phỏng

và tính toán ngắn mạch trên HTĐ theo phương

pháp điểm sự cố [1, 2]

Hình 1 Đường cong chịu điện áp của nhóm

thiết bị SEMI (Semiconduactor Equipment and

Materials International group)

Bài báo trình bày phương pháp dự báo

SANH trên toàn bộ lưới điện truyền tải 220kV

của HTĐ Việt Nam theo Tổng sơ đồ VI dùng

phương pháp điểm sự cố với số liệu sự cố ngắn

mạch trên lưới truyền tải 220kV thực tế của

năm 2008 và sử dụng chỉ tiêu SARFIX và

SARFIX-CURVE [3,4,8]

II XÂY DỰNG MÔ HÌNH BÀI TOÁN

2.1 Phương pháp điểm sự cố dùng cho dự

báo SANH trong lưới điện truyền tải 220kV

Theo phương pháp này, giả thiết SANH

gây ra là do ngắn mạch trong HTĐ Khi đó, đặc

trưng biên độ của SANH (Hình 2) được xác

định bởi vị trí và loại sự cố ngắn mạch [1, 4]

Đặc trưng thời gian tồn tại SANH thì phụ thuộc

vào thời gian loại trừ ngắn mạch của các thiết bị

bảo vệ

Các đặc trưng trên đây của SANH được

xác định tại các nút phụ tải của lưới truyền tải

220kV là các trạm biến áp 220kV để từ đó xác

định các chỉ tiêu SARFIX và SARFIX-CURVE cho

cả hệ thống truyền tải điện 220kV của Việt

Nam

Hình 2 Các đặc trưng của SANH

2.2 Xây dựng mô hình điểm sự cố đối với lưới điện truyền tải 220kV của Việt Nam

- Chọn vị trí sự cố : Chỉ xét sự cố ngắn mạch

trên lưới 220kV Các ngắn mạch xảy ra ở lưới

có điện áp thấp hơn có thể giả thiết là ít ảnh hưởng đến lưới 220kV do tổng trở các máy biến

áp khu vực và địa phương là khá lớn Vì bản thân lưới 220kV đã rất lớn nên trong nghiên cứu này chưa xét các sự cố ngắn mạch trên lưới 500kV và tại các nguồn điện Biên độ SANH tại các nút phụ tải phụ thuộc vào vị trí điểm ngắn mạch Về nguyên tắc sự cố có thể xảy ra tại bất cứ đâu trên lưới 220kV, tuy nhiên nếu trong một phạm vi của lưới điện mà ngắn mạch

ở đó đều dẫn đến SANH tại các nút phụ tải có cùng đặc trưng biên độ thì chỉ cần chọn một vị trí điển hình Sơ đồ hệ thống truyền tải điện 220kV theo tổng sơ đồ VI [9] tính đến năm

2008 gồm 66 trạm 220kV, 98 nhánh đường dây 220kV với tổng chiều dài là 7988km Sự cố ngắn mạch trên lưới điện truyền tải được xét cho cả đường dây và trạm biến áp nên đối với

sự cố trạm biến áp xét ở tất cả 66 nút có trạm 220kV, còn sự cố trên đường dây, tùy theo tổng chiều dài mỗi nhánh đường dây mà xét một hoặc vài điểm sự cố trên nhánh đó Nhìn chung, các điểm sự cố cách nhau từ 10km đến 40km

Tổng số điểm ngắn mạch trên đường dây là 169 điểm

- Chọn loại sự cố ngắn mạch : Lưới điện cao áp

là 3 pha, vai trò các pha như nhau nên khi xét các dạng ngắn mạch thì xét 4 dạng với tỷ lệ phân bố suất sự cố [10] :

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74

- Phân bố sự cố ngắn mạch : Sự cố ngắn mạch

mang tính ngẫu nhiên phụ thuộc vào nhiều yếu

tố [2] nên suất sự cố nhìn chung khác nhau đối

với từng loại sự cố và vị trí sự cố Trong nghiên

cứu này, do số liệu thống kê về phân bố sự cố

trên lưới truyền tải 220kV chưa đủ chi tiết nên

sự phân bố sự cố được đề xuất theo mô hình

phân bố đều Theo thống kê của tổng công ty

truyền tải điện quốc gia lưới truyền tải 220kV

trong năm 2008 có tổng số 45 sự cố xảy ra tại

các nút trạm 220kV và 143 sự cố trên các

đường dây 220kV Suất sự cố đường dây là

0,0179 sự cố/km/năm và của trạm biến áp là

0,682 sự cố/trạm/năm Phân bố sự cố cho từng

loại ngắn mạch đối với đường dây và máy biến

Trên lưới truyền tải, nút phụ tải là các nút có

trạm 220kV cấp điện xuống các lưới có điện áp

thấp hơn Lưới 220kV lại có dạng mạch vòng

nên nhìn chung trên mỗi nhánh đường dây

220kV, bảo vệ được đặt tại cả hai đầu và khi

xảy ra sự cố ngắn mạch trên nhánh đường dây

nào thì nhánh đó sẽ bị cô lập riêng Do đó, tất

cả các nút (66 trạm 220kV) trên lưới điện đều

bị SANH khi sự cố, không có nút nào bị mất

điện duy trì và ta phải tính SANH cho 66 nút

này

2.3 Tính toán ngắn mạch và xác định đặc

trung biên độ SANH trong lưới điện truyền

tải 220kV của Việt Nam

Việc tính ngắn mạch và SANH tại các nút phụ tải trong lưới truyền tải 220kV được

thực hiện bằng chương trình PSS/E Sơ đồ khối

các bước tính toán như hình 3

- Xác định SARFI X : Việc chọn vị trí và xác

định suất sự cố cho từng vị trí và từng loại sự

cố được thực hiện như ở 2.2 Dùng chương

trình PSS/E tính ngắn mạch tại từng điểm sự cố

với từng loại sự cố và từ đó xác định biên độ

SANH tại tất cả 66 nút phụ tải do từng điểm và

từng loại sự cố ngắn mạch gây ra Gán suất sự

cố cho từng vị trí và từng loại sự cố sẽ rút ra được tần suất SANH tại từng nút phụ tải do sự

cố đang xét gây ra Lặp lại việc tính ngắn mạch

và SANH với các điểm sự cố khác rồi tổng hợp lại ta được tần suất SANH với các đặc tính biên

độ khác nhau do sự cố tại tất cả các điểm ngắn mạch trên lưới truyền tải 220kV gây ra, và cuối cùng ta rút ra được chỉ tiêu SARFIX của toàn hệ thống

Hình 3 Sơ đồ khối đánh giá SANH trên lưới truyền tải điện 220kV Việt Nam

- Xác định SARFI X-CURVE : Để xác định SARFI

hệ thống bảo vệ lưới 220kV và dạng đặc tính chịu điện áp lựa chọn Đối với lưới 220kV của Việt Nam hiện nay, bảo vệ chính là bảo vệ cắt nhanh (so lệch dòng điện hoặc tổng trở cực tiểu) với tổng thời gian cắt ngắn mạch từ 120ms đến 150ms Trong nghiên cứu sử dụng đặc tính chịu điện áp của các phụ tải nhậy cảm là SEMI,

và với thời gian loại trừ sự cố như trên, các SANH có biên độ dưới 70% đều rơi vào vùng mất an toàn và làm các phụ tải nhậy cảm ngừng làm việc Do đó, khi xác định SARFIX-CURVE, với X từ 70% đến 100% điện áp định mức thì SARFIX-CURVE không đổi

Mô phỏng phân bố sự cố trên lưới truyền tải 220kV

Mô phỏng lưới điện, tính ngắn mạch bằng PSS/E

TẠP CHÍ KHOA HỌC & CÔNG NGHỆ CÁC TRƯỜNG ĐẠI HỌC KỸ THUẬT 667 SỐ 77 - 2010

75

III PHÂN TÍCH KẾT QUẢ

Thực hiện trình tự tính toán như sơ đồ

khối ở hình 3, sau đây là tóm tắt một số kết quả

đáng lưu ý :

- Tần suất SANH trung bình của một nút phụ tải

bất kỳ :

Hình 4 và 5 biểu diễn kết quả tính toán

tần suất SANH tại nút trạm 220kV Mai Động,

Thành phố Hà Nội

Hình 4 Tần suất SANH nút 220kV Mai Động

theo từng khoảng đặc trưng biên độ

Hình 5 Tần suất SANH nút 220kV Mai Động

theo đặc trưng biên độ lũy tiến

Hình 6 Tần suất trung bình SANH theo từng

khoảng đặc trưng biên độ SANH

truyền tải điện 220kV tính cho năm 2008

Hình 4 biểu diễn tần suất SANH theo từng khoảng đặc trưng biên độ của SANH

Hình 5 biểu diễn tần suất SANH khi SANH có biên độ nhỏ hơn từng mức đặc trưng biên độ

- Tần suất SANH trung bình hệ thống :

Đối với mỗi phụ tải, tần suất SANH trung bình theo từng khoảng đặc trưng biên độ SANH được cho ở Hình 6 Và cuối cùng là chỉ tiêu SARFIX của toàn bộ lưới truyền tải điện 220kV Việt Nam được cho ở Hình 7

Từ kết quả cho ta một số nhận xét đáng chú ý sau :

- Tần suất SANH ứng với từng loại sự cố tương ứng với tần suất của từng loại sự cố

- SANH nông (70%-90%) có tần suất khá lớn

và tần suất SANH dù của nút cụ thể là 220kV Mai Động hay trung bình cho từng nút chỉ khoảng 25 lần/năm rất nhỏ so với tổng số sự cố trên lưới 220kV là 188 Đó là vì lưới 220kV trải toàn quốc nên ngắn mạch xảy ra ở từng miền ít ảnh hưởng đến các phụ tải tại các miền khác

- Tần suất SANH ở nút 220kV Mai Động lớn hơn SARFIX của toàn hệ thống vì lưới 220kV ở miền Bắc có nhiều phụ tải hơn do địa bàn rộng hơn

Tổng hợp các bài báo khoa học giai đoạn 2007-2012

TẠP CHÍ KHOA HỌC & CÔNG NGHỆ CÁC TRƯỜNG ĐẠI HỌC KỸ THUẬT 667 SỐ 77 - 2010

76

V KẾT LUẬN

Bài báo đã trình bày phương pháp đánh giá SANH trên lưới truyền tải điện 220kV của

Việt Nam thông qua chỉ tiêu SARFIX và

SARFIX-CURVE Đây là cố gắng đầu tiên để định

lượng hóa việc đánh giá tình hình SANH nói

riêng và CLĐN nói chung trong HTĐ Việt Nam

tố ảnh hưởng đến phân bố sự cố khi lưới truyền tải điện của Việt Nam trải trên một phạm vi rộng lớn với tình hình sự cố khác nhau Các mô hình ngẫu nhiên với các luật phân bố xác suất phù hợp với tình hình xảy ra sự cố thực tế có thể được xem xét [2, 6, 8]

TÀI LIỆU THAM KHẢO

1 M H J Bollen; Understanding power quality problems - voltage sags and interruptions; IEEE

Press, 2000

2 Bach Quoc Khanh, Dong Jun Won, Seung Il Moon; Fault Distribution Modeling Using Stochastic

Bivariate Models For Prediction of Voltage Sag in Distribution Systems; IEEE Trans Power Delivery, Vol.23, No.1, pp.347-354, Jan 2008

3 Bach Quoc Khanh; Prediction of Voltage Sags in Distribution Systems With Regard to Tripping

Time of Protective Devices; Proceeding, EEE.CR.ASPES2009, Tech Section 2.1., Hua Hin, Thailand, Sep 28-29, 2009

4 D L Brooks, R C Dugan, Marek Waclawiak, Ashok Sundaram; “Indices for Assessing Utility

Distribution System RMS Variation Performance”; IEEE Trans Power Delivery, Vol.13, No.1, pp.254-259, Jan 1998

5 M.R.Qader, M.H.J.Bollen, and R.N.Allan; “Stochastic Prediction of Voltage Sags in a Large

Transmission System”; IEEE Trans Industry Applications, Vol.35, No.1, pp.152-162, Jan./Feb

1999

6 Juan a marTíNez-Velasco; “Computer-Based Voltage Dip Assessment in Transmission and

Distribution Networks”, Electrical Power Quality and Utilisation, Journal Vol.XIV, No.1, 2008

7 J.V.Milanovic, M.T.Aung and C.P.Gupta; “The Influence of Fault Distribution on Stochastic

Prediction of Voltage Sags”; IEEE Trans Power Delivery, vol.20, no.1, pp.278-285, Jan 2005

8 Recommended practice for the establishment of voltage sag indices, Draft 6, IEEE P1564, Jan

2004

9 Tổng sơ đồ phát triển Hệ thống điện Việt Nam, Bản IV, Viện Năng lượng, 2006

10 T A Short; Electric Power Distribution Handbook, CRC Press, 2004

Địa chỉ liên hệ : Bạch Quốc Khánh - Tel: 0904.698.900, email: bq_khanh-htd@mail.hut.edu.vn

Bộ môn Hệ thống điện, Khoa Điện, Trường Đại học Bách khoa Hà Nội

Số 1, Đại Cồ Việt, Hà Nội

6

Abstract— In this paper, a novel effort for prediction of

voltage sag in the entire transmission system of Vietnam is

presented As the Vietnamese electricity industry moves toward

the electricity market, prediction will help utilities have early

assessment of power quality in transmission system The

proposed prediction approach uses a fault position method in

which the fault distribution in the transmission system is created

based on an actual fault occurrence in the entire 220kV and

500kV transmission system throughout Vietnam that took place

ITIC and SEMI curve, which takes into account of the actual

fault clearing time of protective devices used in transmission

voltage sag performance is obtained in the transmission system

with regard to load’s voltage tolerance.

Index Terms transmission system, power quality, voltage sag

frequency, stochastic prediction, fault distribution, fault clearing

time, ITIC, SEMI curve

I INTRODUCTIONmong power quality phenomena, voltage sag (dip) is

defined by IEEE 1159 (1995) as a decrease in RMS

voltage to between 0.1 to 0.9 of nominal voltage at power

frequency for duration of 0.5 cycle to 1 minute Interests in

voltage sag has been getting much greater recently in Vietnam

due to its impact on the performance of sensitive electronic

equipment like variable speed drives, computer-controlled

production lines that are widely used, especially in industry

Although voltage sags are more common in distribution

system, many causes leading to voltage sag are derived from

transmission systems An assessment of voltage sag in

transmission systems is important for utilities and customers

in Vietnam now

Voltage sag assessment normally comes prior looking for

the solution of voltage sag mitigation Voltage sag assessment

is usually related with the basic process known as a

“compatibility assessment” [1] which includes three steps: (i)

Obtain the voltage sag performance of the system of interest,

(ii) Obtain equipment voltage tolerance and (iii) Compare

equipment voltage tolerance with the voltage sag performance

Bach Quoc Khanh is a faculty member with Electric Power Systems

Department, Electrical Engineering Faculty, Hanoi University of Science and

Technology, 1 Dai Co Viet Rd., Hanoi, Vietnam (e-mail:

bq_khanh-htd@mail.hut.edu.vn )

Nguyen Hong Phuc is a master student with Electric Power System

Department, Electricity Faculty, Hanoi University of Science and Technology,

1 Dai Co Viet Rd., Hanoi, Vietnam (e-mail: cllx2003@gmail.com )

and estimate expected impact of voltage sag on the equipment

The permissible voltage tolerance for electric equipment, normally defined by the manufacturers and the well-known

PQ curves for susceptibility of computer equipment displays are CBEMA, ITIC or SEMI [1] whereas power quality assessment of power supply system is utilities duty This paper

is the first effort to assess the voltage sag performance in the transmission system of Vietnam by using the method of stochastic prediction of voltage sags [1], [2], [3] using SARFICURVE-X that is derived from SARFIX with regard to fault clearing time of protective devices currently used in the transmission system in Vietnam

II INDICES FOR VOLTAGE SAG ASSESSMENT Voltage sag assessment often relies on voltage sag characteristics: magnitude and duration There are many indices proposed for voltage sag quantification [1], [4] In this paper the authors use one of the frequently used indices, SARFIX It is defined as follows

N

N

i X X

8

%

)

(1) where

X 96rms voltage threshold; possible values – 10-90% nominal voltage

N X(i) 96Number of customers experiencing voltage sag with

magnitudes below X% due to measurement event i

N96number of customers served from the section of the system

to be assessed Despite being widely used, SARFIX only considers the magnitude of voltage sag Unfortunately, the magnitude value maybe much greater than the actual number of tripped electrical appliances, especially when the duration of sags is small enough (less than a half second), such as for transmission system in Vietnam where the total fault clearing time of protection system is typically less than 5 to 7 cycles of the mains frequency To take the voltage sag duration into account, SARFIX is developed into SARFICURVE-X [5], [6]

which is defined below

N

N SARFI

m

i i X X

CURVE

8%

' )

(2) where

' )

(i

X

N :6Number of customers tripped when experiencing voltage sag with magnitudes below X% due to measurement

Prediction of Voltage Sag in The Transmission

System of Vietnam, A Case Study

Bach Quoc Khanh, Nguyen Hong Phuc

A

Tổng hợp các bài báo khoa học giai đoạn 2007-2012

event i

If we plot voltage sag as a point with co-ordinates being its

magnitude and duration on the graph of the equipment

compatibility curve, SARFICURVE-X corresponding to voltage

sags falling out of the equipment voltage tolerant area (Fig 1)

will be obtained So far, well known curves are CBEMA, ITIC

and SEMI [1] Obviously, SARFICURVE-X can provide a better

understanding of the influence of voltage sag on the operation

of electric equipment in electric networks This paper presents

the method of calculating SARFIX-CURVE using ITIC and SEMI

curve (SARFIITIC-X and SARFISEMI-X) as case studies

Fig 1 ITI curve for susceptibility of computer equipment

III PREDICTION OF VOLTAGE SAG IN

THE TRANSMISSION SYSTEM OF VIETNAM

A Problem definition

The problem with stochastic prediction of voltage sag is

that it can only obtain the voltage sag performance of a

specific electric system by using data of causal events leading

to sags In fact, more than 90% sag events are resulted from

short-circuits, hereby called faults, and it is possible to use

fault modelling and short-circuit calculation tools to simulate

and predict voltage sags in the power system This work uses

the method of “fault position” [1] for voltage sag prediction in

the transmission systems with following significant steps

1 Modeling the fault distribution of the transmission system

of Vietnam – event modeling (Sub section B)

2 Calculating the short-circuit current and voltage sags at all

influenced load nodes – event indices (Sub section C)

3 Quantifying voltage sag frequency at load nodes (site

indices) and cumulating system sags with different

characteristics and obtaining SARFIX (system indices)

4 Cumulating system voltage sags that cause equipment to

trip and obtaining SARFICURVE-X

To obtain SARFIX-CURVE, the voltage sag duration that

depends on the fault clearing time of protective system should

be considered This work takes the typical tripping time of

protective devices (instantaneous protective relay) and high

voltage circuit breakers currently used in the transmission

system in Vietnam into its calculation

B Fault Distribution Modeling and Assumptions

- Fault distribution modeling: Fault distribution modeling

considers the occurrence of all faults in the whole transmission system of Vietnam that cover 500kV and 220kV networks

The scope of the transmission system of Vietnam starts from the points of energy receiving from generating centers or interconnection points with the transmission system of South China to load nodes that are step-down 220kV substations An individual fault (short-circuit) is characterized by a pair of parameters: fault position, fault type and its occurrence is assigned a fault rate All faults with their assigned rate of occurrence build up a fault distribution model Following are analyses of each fault characteristics for the transmission system of Vietnam

- Fault position: The fault can occur anywhere in the

transmission system including 500kV and 220kV networks

Since load nodes of the transmission system are 220kV down transformers, faults in 110kV networks and distribution networks should not considerably impact on voltage sags in transmission system because of large impedance of 220kV step-down transformers Faults at the power generating sources should be included in the faults at the 220kV step-up transformers Therefore, this work only considers faults that occur in the transmission system According to [1], [3], [7], basing on the concept of “area of vulnerability”, fault positions should be generally chosen in the manner that a fault position should be the representative for other nearby short-circuit faults in a portion of network that cause voltage sags to load nodes with the similar characteristics (similar magnitudes) Voltage sag magnitude normally divides in 9 ranges : 0-0.1, 0.1-0.2,…, 0.8-0.9 p.u Similar manitudes mean the magnitudes that fall inside a same range of magnitude above said Faults in the transmission system are divided into two groups That are overhead line OHL faults (or faults on branches) and transformer faults (faults on substations) In the transmission system of Vietnam given in VI Master Plan [10]

step-for the year 2008, 63 substations 220kV will be seen as load nodes for voltage sag assessment The transmission system (Fig 2) includes the 500kV network (11 nodes as 500kV substation and 17 branches of OHL with total length of 3246km) and the 220kV network (63 nodes as 220kV substations and 103 branches of 220kV OHL with total length

of 6414km) In Figure 2, the number of 220kV substation is 51 that are under the management of National Power Transmission Corporation (NPT) Other twelve 220kV substations are under the management of power generation

Therefore, transformer fault positions will be 11 for 500kV substations and 63 for 220kV substations respectively For OHL faults, fault positions are selected depending on the length of each branch According to the above said principle

of fault position selection, we divide the line branches into some segments and each segment is represented by one fault position, normally at one of two ends of the line segment For 220kV OHL, the line segment length shoud be from 10km to 40km depending on the line branch length For 500kV OHL, each line segment should be 50km In this case study, fault positions are selected at 76 locations for 500kV OHL and 169 locations for 220kV OHL Therefore, there are 319 fault positions in total

SARFIX-CURVEqualified

SARFIX-CURVEdisqualified

Fig 2 The Transmission System of Vietnam in 2008

Vietnam National Power Transmission

Corporation

Total 500kV OHL length: 3441km

Total 220kV OHL length: 76541km

Number of 500kV substation: 11

Number of 220kV substation: 51

Total 500kV transformer capacity: 8756MVA

Total 500kV transformer capacity: 14761MVA

220/110/35kV Mai Dong substation, 2x250MVA, Hanoi Tổng hợp các bài báo khoa học giai đoạn 2007-2012

- Fault type: This calculation considers all types of short

circuit with well known contributory percentages of different

fault type are assumed as follows

Single phase to ground (SP-G): 65%

Two phase to ground (PP-G): 10%

Two phase together (P-P): 20%

Three phase to ground (3P-G): 5%

For the transmission system that requires high reliability

and stability, short-circuits are prone to permanent fault

Therefore, in this work, transitory faults are not considered

- Fault rate: The occurrence of short circuits depends on

many factors [3] and the rates of occurrence of different faults

(fault position, fault type) are normally not the same

However, because, in reality, recorded fault data does not

consider detailed fault distribution, this work assumes that

fault distribution for each fault type follows uniform model

within each regions in Vietnam For example, phase-to-ground

faults remain unchanged anywhere in the section of

transmission system within a region The transmission system

is Vietnam is divided in four regions The data of fault

performance recorded by NPT and its subsidiary agencies

(Power Transmission Companies, PTC) for 2008 is shown in

the Table 1 below

TABLE 1 REGIONAL FAULT RATE PERFORMANCE

It is noticeable that the fault rates stated in Table 1 are for

all four fault types as mentioned above Therefore, for each

fault type, the fault rate should multiply by contributory

percentage of different fault types For the fault that represents

OHL faults within a line segment, fault rate should be

calculated based on the length of the line segment

- Selection of load nodes for voltage sag calculation: In the

transmission system, load nodes are 220kV substations

feeding to downstream 110kV and medium voltage networks

The topology of transmission network is complicated and

many branches also have switching devices at both ends

When a fault occurs on a certain branch (a line or a

transformer), the two switching devices at both ends of that

branch will trip and isolate it from the network Therefore,

many load nodes normally experience voltage sags Only the

loads on or nearby the fault position (for transformer fault)

suffers an interruption So, voltage sags at all 63 load nodes

had to be considered in this work

- System loading condition when faults occur: It is also

notable that for short-circuit calculation in the transmission

system where limited power sources are connects to, the short-

circuit current and voltage sags depend heavily on the

pre-fault loading condition when the pre-fault occurs The heavier the

True

False True

False True

False

True

Select the load node (among

63 nodes) for sag calculation

Fault distribution modeling, determine fault rate of the fault under calculation

Calculate the frequency

of voltage sag at the selected load node

Are all fault type selected ?

Are all fault position selected ?

Are all load nodes selected ?

Sag frequency spectrum by the fault under calculation (event index)Sag frequency spectrum at selected load node by all faultsSag frequency spectrum at all load nodes by all faults

calculation

Check ITIC curve ?

(system index)

load on the system is, the higher short-circuit current will be

generated and the deeper voltage sags will be at load nodes

Therefore, the most interested prefault loading condition is

obviously that of full loaded and this work performs the

short-circuit calculation in the maximum loading condition

C Short circuit calculation and voltage sag determination for

the transmission system of Vietnam

Short circuit calculation and voltage sag determination for

the whole transmission system of Vietnam is carried out by

program PSS/E (Power System Simulation for Engineering)

The block diagram of the calculation is depicted in Fig 3

- SARFI X calculation: With fault distribution modeling for

the transmission system proposed in Part B, this work

performs short-circuit calculation using the program PSS/E for

a certain individual fault (fault position, fault type) and then

voltage sag magnitude at a selected load node is calculated

After assigning fault rate to this fault, the frequency of sag at

the selected load node resulted by this fault will be obtained

By repeating this calculation for all other faults (fault position

and fault type), and gather them together, we obtains the

frequency spectrum of voltage sag with different magnitude

characteristics at the selected load nodes caused by all faults in

the transmission system Fig 4, Fig 5 and Fig 6 show an

example of voltage sag performance for an individual load

node (220kV Mai Dong substation in Hanoi, Fig 3) Fig 4

shows voltage sag frequency spectrum by sag magnitude

NEW

Fig 4 Voltage sag frequency spectrum (per year)

by fault types at load node 220kV Mai Dong substation

Fig 5 Voltage sag frequency spectrum (per year) for all fault

events at 220kV Mai Dong Substation, Hanoi, Vietnam

(per unit) intervals for different fault types Fig 5 is voltage sag frequency spectrum for all fault types Fig 6 is the cumulative voltage sag frequency

Fig 6 Cumulative Voltage Sag Frequency (per year)

at 220kV Mai Dong Substation, Hanoi, Vietnam For other load nodes, the calculation is similarly performed and then we obtain voltage sag frequency spectrum of all other load nodes Finally, the average frequency spectrum per load node is calculated and plotted on the Fig 7 and SARFIX of the whole transmission system of Vietnam is calculated as the formula (1) The voltage sag performance of transmission system – SARFIX is shown in Fig 8

Fig 7 Transmission system average voltage sag frequency

Sag magnitude (p.u)

SP-G PP-G P-P 3P-G Tổng hợp các bài báo khoa học giai đoạn 2007-2012

- SARFI ITIC-X calculation: SARFIX-CURVE can be achieved by

taking fault clearing time of protective system into account

For the transmission system of Vietnam, the primary functions

currently used for transformer protection is biased differential

protection using differential relays of SIEMENS (SIPROTEC

7UT613) or ALSTOM (MiCOM P340) For OHL line

protection, the primary functions currently in use are also the

differential protection as above said using the

tele-communication links of power line carrier or fibre-optical

ground wire integrated in power carrying lines or the distance

protection using differential relays of SIEMENS (SIPROTEC

7SA6) or ALSTOM (EPAC 3000, MiCOM P440) All those

protective relay system is of instantaneous tripping type that is

typically less than 100ms The switching devices are almost

SIEMENS, SCHNEIDER or ABB products manufactured in

Europe with typical breaking time of 40ms for 500kV to 60ms

for 220kV circuit breakers Besides the above mentioned

operating times of protective relays and circuit breakers,

additional time delays are also included for auxiliary relay

trips and operating time of tele-protection with total additional

operating time not exceeding two more cycles (20-24ms)

Therefore, the total fault clearing time is 160ms to 180ms that

defines the voltage sag duration If posing this duration on the

ITIC curve, it’s obviously that only sags lower than 0.7 p.u

will be out of load voltage tolerance and qualified for

SARFIITIC-X The upper 0.7 p.u sags with duration defined by

the above said fault clearing time definitely fall inside the

voltage tolerance envelope and thus, they are not qualified as

SARFIITIC-X Therefore, SARFIITIC-X is a part of SARFIX with

X lower than 0.7 p.u as also shown on the SARFIX chart (Fig

8) For X from 0.7 p.u to 0.9 p.u, the value of SARFIITIC-X

remains unchanged and equal to SARFIITIC-0.7

If we use SEMI curve for assessment of sag duration, it is

noticeable that there is a small difference between ITIC curve

and SEMI curve for X from 0.5 cycle to 10 cycles (Fig 9)

Figure 9 The difference between ITIC curve and SEMI curve

Within this range, ITIC ridethrough voltage is 0.7 p.u whereas

this voltage level for SEMI F47 is just 0.5 p.u Therefore, with

the total fault clearing time (160ms to 180ms) for the

transmission system in Vietnam, only voltages sag with X

lower than 0.5 p.u are qualified for SARFICURVE-X using the

SEMI curve (SARFISEMI-X) With X greater than 0.5 p.u,

voltage sags fall inside SEMI’s ridethorugh area and not

qualified for SARFISEMI-X So, for X from 0.5 p.u to 0.9 p.u,

the value of SARFISEMI-X remains unchanged and equal to

SARFIITIC-0.5 SARFISEMI-X is also shown on Fig 8

D Result Analysis

From the results, there’re some following remarks:

- The SARFIX and SARFICURVE-X values obtained from this calculation are useful for utilities as a benchmark for reducing the frequency of fault for solving the problem of voltage sag

This result also helps customers know the voltage sag performance and choose suitable location for less voltage sag frequency

- The frequency of voltage sag as the result of an individual fault type is proportional to fault rate of that fault type for shallow sags (Fig 4)

- Shallow sags (0.7-0.9 p.u) feature a rather high frequency while the frequency of deep sags is very small Furthermore, the frequency of voltage sag with X lower than 0.9 for either the 220kV Mai Dong substation (about 33 times, Fig 5) and the system average load node (about 22 times, Fig 7) is also

khanh

Fig 9 Voltage sag frequency of selective load nodes (220kV substations) throughout of Vietnam

Sag magnitude (p.u)

Sag magnitude (p.u)

Sag magnitude (p.u)

much lower than total faults in transmission system (about 110

times per year) That’s because the goegraphical shape of

Vietnam is rather long (about 1700 km) and thin in the middle

(the narrowest is just 60 km) and the short-circuit faults occur

on one region has almost no impacts on voltage sag for loads

in other regions

- Cumulative frequency of voltage sag for the node 220kV

Mai Dong substation in Hanoi (33 times for X < 0.9 p.u) is

higher than the SARFI0.9 (22 times) because there’re more

load nodes (220kV substations) located surrounding Hanoi

and vicinity In the center and in the south of Vietnam, the

density of 220kV substation is lower than in the north and

faults has less impact on voltage sag of load nodes Fig 10

shows sag frequency charts for selective load nodes in the

north (upper), in the center (in the middle) and in the south

(lower) of Vietnam that indicates the above said difference

- Also because of high frequency of 0.7-0.9 p.u voltage sag,

SARFIX-CURVE is very much lower than SARFIX despite

voltage sags with the magnitude up to 0.7 p.u are qualified

enough for SARFIX-CURVE Therefore, voltage sags due to

faults in the transmission system of Vietnam have less

influence on loads than faults in distribution system when the

frequency of deep sag is normally very high [6] It is a

remarkable finding in power quality assessment in the power

system of Vietnam

IV CONCLUSIONS This paper presented the first effort of predicting voltage

sag performance for a large transmission system as a case

study of Vietnam In this work, fault distribution modeling is

proposed basing on actual fault performance for different

regions in Vietnam Using SARFIITIC-X gives a better

assessment of voltage sag influence on loads operation The

results of this work will be a useful reference for utilities in

power system quality assessment toward electricity market

operation This research still needs to develop as faults in the

generation part has yet to take into consideration Besides, if a

better fault data is achieved (by monitoring), a more detailed

fault distribution can be made and finally a better voltage sag

performance can be obtained

V ACKNOWLEDGEMENT

It is acknowlegded that this work received helps from

Phung The Anh (Msc), Nguyen Anh Tu (Msc) with Power

Engineering Consulting Joint-Stock Company No 1, Hanoi,

Vietnam for data collection and power system simulation as

well as technical consultancy from Professor David A Cartes,

Senior Member, IEEE, and Dr Bhuvaneswari Ramachandran

with the Center for Advanced Power Systems, Institute for

Energy Systems, Economics and Sustainability, Florida State

University, USA

VI REFERENCES [1] M.H.J Bollen, Understanding power quality problems - voltage sags and

interruptions, IEEE Press, 2000

[2] M.R.Qader, M.H.J.Bollen, and R.N.Allan, “Stochastic Prediction of

Voltage Sags in a Large Transmission System”, IEEE Trans Industry

Applications, vol.35, no.1, pp.152-162, Jan./Feb 1999,

[3] Bach Quoc Khanh, Dong Jun Won, Seung Il Moon, “Fault Distribution Modeling Using Stochastic Bivariate Models For Prediction of Voltage

Sag in Distribution Systems”, IEEE Trans Power Delivery, pp

347-354, Vol.23, No.1, January 2008

[4] D L Brooks, R C Dugan, Marek Waclawiak, Ashok Sundaram,

“Indices for Assessing Utility Distribution System RMS Variation

Performance”, IEEE Trans Power Delivery, vol.13, no.1, pp.254-259,

Jan 1998

[5] Juan A Martinez, Jacinto Martin-Arnedo, “Voltage Sag Studies in Distribution Networks - Part II: Voltage Sag Assessment, Part III -

Voltage Sag Index Calculation”, IEEE Trans Power Delivery, pp

1679-1697, Vol 21, No 3, July 2006

[6] Bach Quoc Khanh, Prediction of Voltage Sags in Distribution Systems

With Regard to Tripping Time of Protective Devices, Proceeding,

EEE.CR.ASPES2009, Tech Section 2.1., Hua Hin, Thailand, September 28-29, 2009

[7] J.V.Milanovic, M.T.Aung and C.P.Gupta, “The Influence of Fault

Distribution on Stochastic Prediction of Voltage Sags”, IEEE Trans

Power Delivery, vol.20, no.1, pp.278-285, Jan 2005

[8] P Saninta, S Premrudeepreechacharn “Assessment and prediction of voltage sag in transmission system in northern area of Thailand”,

Proceeding, 13th International Conference Harmonics and Quality of

Power, ICHQP, Sept.28-Oct.1 2008, Wollongong, NSW, Australia

[9] E Inan, B Alboyaci, C Leth Bak, “A Case Study Of Turkish

Transmission System For Voltage Dips”, The Journal on Power and

Energy Engineering, Vol 1, No 2, April 2010

[10] National Institute of Vietnam, Master Plan VI, 2006

VII BIOGRAPHIES

Bach Quoc Khanh received B.S., M.S

and Ph.D degrees in power network and systems from Hanoi University of Science and Technology, Hanoi, Vietnam in 1994,

1997 and 2001 respectively He has been a faulty member of Electric Power System dept., Electricity Faculty, Hanoi University

of Science and Technology since 2002 He

is currently a visiting scholar in the Center for Advanced Power System, IESES, Florida State University His research interests include power system analysis, DSM, power system quality, distributed generation

Nguyen Hong Phuc received BS in Electrical Engineering Faculty,

University of Thai Nguyen in 2006, Vietnam He is currently a master student

in the Electric Power Systems Department, Electricity Faculty, Hanoi University of Science and Technology, Hanoi, Vietnam

Tổng hợp các bài báo khoa học giai đoạn 2007-2012

Tổng hợp các bài báo khoa học giai đoạn 2007-2012

Tổng hợp các bài báo khoa học giai đoạn 2007-2012

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