Nghiên cứu ứng dụng các phương pháp hiện đại nhận dạng sự cố ngắn mạch trên đường dây truyền tải điện tt tiếng anh

Research focuses of the thesis: Research on signal analysis and processing algorithms using Matlab tools, wavelets, neural networks, correlation functions, Domain Reflectometry TDR and

INTRODUCTION

1 The thesis justification

The power electric system is a complex system in both structure and operation so the faults of any element in the system will affect the power supply reliability and power quality Therefore the main topic of

this thesis is “Research and apply modern methods to detect the fault on the transmission line” The

proposed methods will help to quickly identify and locate the faults on transmission lines to reduce the economic losses and to improve the reliability and quality of electricity supply to the consumers is very necessary

The problem of detecting the type of fault and the location of the fault on the power transmission line is a basic problem of circuit theory and power system Currently, many researchers has been working on this issue However, the results are still limited due to the fact that many fault events and faulty element values cause phenomena similar to the variations of parameters of the line, so methods such as distance relays will cause big errors The development of new measuring devices as well as new signal processing algorithms can further improve the accuracy of the fault location estimation

A new solution to analyze and detect fault locations will have practical implications If results can be applied, it will bring about high economic and technical efficiency due to the increased accuracy to support the faster fault process

Research purposes: The purpose of the thesis research is to develop a new method using modern

algorithms to allow the faults location on the power transmission lines (without branching and with branches) more accurately with as few measuring devices as possible

Research scope: The thesis focuses on researching and providing methods to locate the faults on

non-branched and non-branched transmission lines The thesis hasn’t considered the influence of environmental factors such as temperature and humidity on accuracy of the method

Research focuses of the thesis: Research on signal analysis and processing algorithms using Matlab

tools, wavelets, neural networks, correlation functions, Domain Reflectometry (TDR) and Domain frequency Reflectometry (TDFR) to identify fault locations and types of fault on transmission lines that single branch and transmission lines have many branches Study the effect of fault resistance, fault inductance to the accuracy of the method

Time-Research Methods: Analyze the system and identify the characteristics of the study object through many

different approaches Select and build the mathematical tools needed for research Select evaluation tools and verify the research results, as simulation modeling with Matlab software and test fault identification algorithms

Scientific and practical significance of the thesis:

The main scientific meaning of the thesis is: proposing a new method of identifying the fault location on the transmission line to supplement the existing methods, built and solved the problem of accurate fault location with different types of faults

Practical significance of the thesis

The research results of the thesis can be added to solutions to locate fault on transmission lines with one

or more branches The method only requires at least the measurement signals from the ends of the power transmission line, so the measurement and data collection stages are simple and highly economical

CHAPTER 1 OVERVIEW OF FAULT IDENTIFICATION AND LOCATION ON

TRANSMISSION LINE

1.1 Introduction

1.2 Overview of methods to detect faults on the transmission lines

1.3 Method of measurement from one side

1.3.1 Single reactance method

1.3.2 The Takagi method

1.3.3 Improved Takagi method

1.4 Method of measurement from two ends

1.5 The method uses neural network

1.6 Method of wave propagation

1.6.1 The method of locating incidents is based on the principle of propagation from the fault

1.6.2 Method of wave propagation from line ends

1.7 Conclusion:

When reviewing the methods of fault location on the tranmission line, it can be summarized that there are classic methods such as the measurement method from one end of the line and the measurement methods from two ends of the line As new methods we can list the neural networks and wave propagation methods Each of methods and algorithms is different, as its advantages and disadvantages

The types of the transmission lines are very diverse: there are transmission lines with different voltage levels, one source or multiple supplies, single lines, double lines, lines with one or many branches

The nature of the fault is also different as the resistance and inductance of the fault change Therefore one method can’t be applied to all types of transmission lines Simple solutions such as the single reactance method are as easy to implement but the accuracy isn’t high

The measurement method from two ends of the line or the method based on the wave from the fault point is more accurate but it uses many devices and requires synchronous time, leading to complicated and costly

The thesis focuses on researching solutions for three-phase (single branch and multiple branches) three-phase power transmission systems with the requirement to use as few measuring devices as possible and do not require time synchronization

In the following chapters, the thesis will focus on the method of proactively generating pulses from the beginning of transmission lines to identify faults Because the method uses few devices, no synchronization

is required This thesis researchs time domain reflectometry (TDR) and time frequency domain reflectometry (TFDR) method basing on the analysis of reflected waveform to detect fault on the transmission lines

CHAPTER 2

SOLUTIONS ON THE BASIC ANALYSIS OF THE WAVE

PROPAGATION COMPONENTS

2.1 Mathematical models of wave propagation on transmission lines

2.1.1 Transmission line model

In order to simulate transmission lines according to [2], [22] often use model  and model of distributed line parameters (Distributed Parameter Line)

a) Model :

Fig 2 1: Single-phase  transmission line model

An approximate model of the distributed parameter line is obtained by cascading several identical  sections, as shown in the following figure

Fig 2 2: Three-phase  transmission line segment model

b) Model parameters transmission lines

According to [2], [22] state equation of long

line is:

Fig 2 3: Diagram of Distributed Parameter Line

where R, L, C, G is parameters of lines per unit length

2.1.2 Principle of wave propagation on the transmission lines

According to [6], [22] wave propagation on the line includes forward u+(x,t) and reflective wave u-(x,t), Parameters typical for long-distance transmission are included: the surge impedance ZC, coefficient off , phase factor , Speed of wave v

j C

     where  is wavelength, f is the frequency

According to [3] when the line has characteristic impedance of the line Z0 and load impedance Z2 The  (reflection coefficient) and  (refraction coefficient) can be expressed by the following formula:

2.1.3 Wave propagation on a faul-free transmission line

When t = 0, we switch on a voltage source V inc( )t to the beginning of the line According to [6] when the line has characteristic impedance of the line Z0 and load impedance Z2

The  (reflection coefficient) and 

(refraction coefficient) can be expressed

by the following formula:

Fig 2.4: Equivalent Petersen model for solving the wave propagation

where, Vref is amplitude of the reflected signal, Vinc is amplitude of the forward signal

When the line has no fault, the time of wave spreads from beginning to end of line is calculated as in the following formula:

a) Wave propagation on a faul-free transmission line with resistance load

According to [6], when switching on a

voltage source V inc( )t at the beginning of the

line, if the line has characteristic impedance of

the line Z0 and load impedance Rt, the 

(reflection coefficient) can be expressed by the

following formula:

0 0

Fig 2.5: Equivalent Petersen model for lines with resistive load

b) Wave propagation on the transmission line does not have fault with resistance serial

inductance load :

On Fig.2.6, the circuit solution has the voltage

signal measured at the beginning of the line after 1st

c) Wave propagation on a faul-free transmission line with R-L parallel load

The circuit on Fig 2.7 has:

d

1

t T

e) Wave propagation on a faul-free transmission line with R-C serie load

On Fig.2.9, the circuit solution has the

voltage signal measured at the beginning of

the line after 1st reflections as:

Fig 2.9: Equivalent Petersen model for lines with R-C serie load

2.1.4 Wave propagation on a faulty transmission line:

When the forward wave spreads from the beginning of transmission line to the fault location, it will cause

a reflective wave back to the beginning of the transmission line we consider the case of temporary the short circuit with fault resistance and fault inductanceZ fault  R f  j X f

The reflection coefficient at the fault location is: 1 0 0

0 2

0

t t



2.2 The proposed solutions in the thesis

2.2.1 Diagram of the block estimating the fault location

The thesis proposes two methods of reflected wave analysis TDR and TFDR

sion

line

Pulse Signal feedback

From free fault line

Signal feedback from fault line

Block collected, storage

Detect feedback time when the line

is free faulty

Calculation of propagation speed

Detect the time of feedback from the fault point

Estimated results fault location

Fig 2.10: Block diagram of method overview to identify fault locations on power transmission lines

2.2.2 Time domain reflectometry method basing on the analysis of reflected waveform for lines without branches:

The thesis proposes to use TDR method for transmission lines without branching This method will use a pulse generator circuit (voltage /current) at the beginning of the transmission line After sending the pulse into the line, we will track and record the reflected signal The analysis of reflected waveforms on the transmission lines to detect the fault location

This thesis proposed using wavelet to

determine the time point of reflected signal from

the transmission line which causes a sudden

variable voltage signal at the beginning of

transmission line

The signal after wavelet analysis preliminarily

determines the time point of reflected signal will

be put into the neural network or use analytical

algorithm to estimate the fault location, as on Fig

2 11

Fig 2 11: Block diagram to identify fault locations on the

transmission lines

With the tranmission line has no branch but requires high accuracy (or the line may have many lines in

a serialized system), the thesis proposes to use TFDR method The main content of this method use a circuit

to generate a chirp signal (signal with amplitude and frequency changes over time) at the beginning of the line, then analyze the feedback signal to locate the fault

2.2.3 Methods of analyzing feedback waves with multi-branch lines

2.3 Simulation method to test research results based on Matlab / Simulink tool

2.3.1 Simulating wave process on the transmission lines:

The thesis uses Matlab/Simulilnk software to

simulate the wave transmission process on the

transmission line in case the line has no branch

and the line has many branches with different

types of fault parameters The idea for this

model is shown in Figure 2 12

Fig 2 12: Model of simulating wave propagationon the

transmission lines

2.3.2 Building elements used in the simulation

Fig 2 13: Block diagrams simulating fault forms, DC

sources, chirp signal sources

Fig 2 14: A block model measures the feedback signal from the fault point and the end of the line.

2.4 Conclusion

Based on the analysis of advantages and disadvantages of previous studies, the thesis has proposed solutions to identify fault on 3-phase transmission lines:

 Using the TDR application method to detect locations of fault based on analysis time and wave shape

of the reflected signal on the transmission line using Wavelet and neural network analysis,

 Using the TFDR application method to detect locations of fault based on analysis time and wave shape of the reflected signal on the transmission line using correlation function analysis,

 Proposing the application of Matlab / Simunlink software as a simulation tool to test the research results

Chapter 3:

TDR METHOD TO DETERMINE FAULT ON THE TRANMISSION LINE

3.1 Method description

When faults occurred, the protection element

reacted to isolate the faults Later we need to locate

the position of the fault One of the proposed

methods is the time domain reflectometry (TDR)

This method will use a pulse generator circuit

(voltage /current) at the beginning of the

3.2 Application of wavelet decomposition in detecting the sudden change time of sign:

3.2.1 Spectrum analysis by wavelets:

3.2.2 Wavelet transform algorithm discrete:

Continuous Wavelet Transform - CWT of a function f(t) is started from a function wavelet (Mother

Wavelet) ψ(t)

3.2.3 Wavelet algorithm analyzes the reflected signal:

Wavelet is a very effective tool to detect the time point of sudden signal changes When using wavelet, a time-dependent signal can be analyzed as follows: f t ( )  a t ( )  d t ( ) Where: a(t) is component

“approximation” that contains slowly variable components and d(t) is component “detail” that contains fast variable components We can continue the same analysis for the component to get multistage wavelet spectrum analyzer as follows:

For example, we use wavelets to analyze the

signal of the function as follows:

Fig 3.4 shows detail component d1 and

approximation a1 component of the signal from Fig

3.3 From the results of the analysis, the signal y(t) is

analyzed to the detailed component d1 and

approximation When we calculate the detailed

component level 1 (component d1 of the signal) as

shown in Fig 3.4 We can see all the sudden

variation of the signal in Figure 3.4 will correspond

to the sudden huge increase of component d1 So

wavelet is a very effective tool to determine the time

-0.5 0 0.5 1

Approximation A1

0 100 200 300 400 500 600 700 800 900 1000 -0.2

-0.1 0 0.1 0.2

When we calculate the detailed component level 1 (component d1 of the signal) as shown on Fig 3.5 and (and zoomed in on Fig.3.6), we can see the sudden variation of the signal in Fig 3.5 will correspond to the sudden huge increase of component d1 So wavelet is a very effective tool to determine the time of this fault

Detail D1

Times(s)

Fig 3.6: Form of the reflected voltage signal at the beginning of the lines when there is a 3-phase resistive fault at 20km

(the load is a R load in series with a L load ) and detail component d 1 of the voltage

x 10 4

-5 -4 -3 -1 0 1 2 4

Detail D1

Times(s)

Fig 3.7: Detail component d 1 of the voltage signal from Fig 3.5 is zoomed in

Steps calculate to determine specific time of voltage signal at beginning of transmission line as follows:

Step 1: At time t0, using a pulse generator circuit

(voltage/current) at the beginning of the transmission

line after the fault has occurred and the protective

0

0(t)

input wave propagated to the end of the line and

reflected back to the beginning of the line

Step 2: Measure the reflected signals at the

beginning of the line with sampling frequency  and

measurement time t> T0

Step 3: Perform wavelet transform to get W(a, b)

from u (t0, T0);

Step 4: Calculate component d1 of 4-th order

Daubechies wavelet expansion

Step 5: Determine the time at which d1 values are

greater than the threshold 0.1

Bước 6: The first time t0corresponds to the time of

closing the pulse generator (in this thesis, it is chosen

  then go back to step 7

Step 10: If there is no point, where value d1 exceeds the threshold on the line, then the line hasn’t fault The location of the fault point (if it exists) will be

t t t

x v   v  where:

v  wave speed on the transmission line, mean as:

0 0

2 l v

3.2.4 Factors contributing to the accuracy of wavelet analysis in detection of the reflectd waves:

3.3 Fuzzy neural network and application to correct the time of wave response

3 3.1 TSK fuzzy logic rules

TSK (Takagi – Sugeno - Kang) fuzzy neural network model is built with learning algorithm to adjust the network parameters to fit a given sample data sets [10] This network is characteristic in parallel processing

of a set of inference rules The TSK model uses fuzzy logic rules as:



   where: q are linear constants, x is the input vector ij xx x1, 2,,x N

3.3.2 TSK fuzzy neural network

model

The TSK model was implemente

as a straight-forward network as on

Fig 3.7 The network is

characterized with 3 parameters (N,

M, K) where N is the number of

inputs (the components of input

vector x), M is the rules number, K

is the outputs number In general,

TSK can be considered as a 5-layer

network

3.3.3 Mathematical formulas of TSK fuzzy neural network

[10] proposed an adaptive adjustment algorithm into two processes of adjusting linear parameters and adjusting nonlinear parameters The algorithm is described as follows:

 Step 1: Initialize the initial values of nonlinear and linear parameters

 Step 2: Maintain the value of linear parameters, using the maximum step reduction algorithm to

adjust the nonlinear parameters

 Step 3: Maintain the values of non-linear parameters, using algorithm to adjust linear parameters

 Step 4: Check the objective error function, if E <Ereq results meet the requirements, stop the learning process, otherwise return to step 2

3.3.4 Initiating neural networks for learning process

3.3.5 Fuzzy clustering algorithm

3.3.6 TSK network to correct the time of the feedback wave

Let denote the reflected signals y j   y1j, y2j,  , yNj  Using the wavelet decomposition to find the sudden changes in the signal (marked as t1) as shown above, in this thesis 20 values were sampled around t1

with sampling step 1ms t1h ms( ) for h  1,0,1, ,18., corresponding to 20 values

Fig3.8: TSK fuzzy neural network

Fig 3.9: Form of the reflected voltage signal at the

beginning of the lines when there is a 3-phase fault at

10km (the load is a R series with a L).

2.085 2.09 2.095 2.1 2.105 2.11 2.115 2.12 2.125

x 10 4

20 30 40 50 60 70

3-The thesis has proposed the TSK fuzzy logic

neural network with 20 inputs (corresponding to 20

 (1 ), ( ), (1 1 18 )

output s Where s is the error between the time of

arrival of the reflected signal from the point of fault

at the beginning of the transmission line and the

time t1 estimated by wavelets

25 30 35 40 45 50 55 60 65 70 75

Time (micro second) y(to)

Fig 3.10 Example of sampling 20 probes starting from the

beginning of the signal

3.4 Simulation results and calculations when using TDR method

3.4.1 Simulation model of propagating waves using Matlab Simulink

This thesis uses Matlab – Simulink to build models which simulate the wave propagation on the lines The parameters of 171-110kV Lào Cai the simulation line are: l AB  46, 7km; the length of the line;

L   H km the inductance per unit; R0  17, 43m /km;the resistance per unit length, and

C   F km the capacitance per unit length

Simulation model as shown in

Figure 3.11 with a pulse generator

and fault-free transmission line

Figures 3.12 and 3.13 show the

simulation model to find the

incoming wave and the reflected

wave of a faulty 3-phase

transmission and a faul-free load –

free 3-phase transmission line Fig 3.11: Simulation model to find the incoming wave and the reflected

wave of a faul-free 3-phase transmission line with

Fig 3.12 Simulation model to find the incoming wave and

the reflected wave of a faulty 3-phase transmission

Fig 3.13 Simulation model to find the incoming wave and the reflected wave of a faul-free load –free 3-phase

transmission line

3.4.2 Results of simulation wave propagation from Matlab-Simulink

a) The line without fault

Using the model in Figure 3.11 to simulate the load in cases R, R series L, R parallel C and combine with running the program in Matlab as Appendix 1 We get the results as shown in Figs 3.14, 3.15 and 3.16

Fig 3.14: The form of the voltage at the beginning of

the line when the load is purely resistance

R load =100()

x 10 -3

0 10 20 30 40 50 60 70

Fig 3.12: The form of the voltage at the beginning of

the line when the load is series R-L(R load =100(),

L=10mH)

x 10 -3

40 50 60 70 80 90 100 110

is calculated by formula (3.20) The calculation results are shown in Table 3.1

T ABLE 3.1: Speed of wave on the transmission line

t 0 (s) t 2 (s) v (km/s)

b) Tranmission line with fault:

Single-phase to ground account for 60% of faults in electrical systems A 1-phase fault with fault resistance Rfault = 10 (Ω) and Lfault = 0,5mH has the results shown in Figure 3.18a and Figure 3.18b