genetic algorithm based solar tracking system

genetic algorithm based solar tracking system

Genetic Algorithm Based Solar Tracking System

D.F.Fam & S.P Koh & S.K Tiong & K.H Chong

Department of Electronic & Communication Engineering, Universiti Tenaga Nasional,

Km 7, Jalan Kajang-Puchong, 43009 Kajang, Selangor

se20597@uniten.edu.my,johnnykoh@uniten.edu.my,siehkiong@uniten.edu.my,chongkh@uniten.edu.my

Abstract: The current trend in solar concentrator tracking system is to use an open-loop local controller that computes the direction of the solar vector based on geographical location and time But it is not accurate because it has error from computing the sun’s position, mechanical, controller systems and installation Literature suggested that the photovoltaic panels could produce maximum power if the panels have angle of inclination zero degree to the sun position In this research, genetic algorithm is one

of the optimization techniques used to maximize the performance of solar tracking system This work evaluates the best combination of GA parameters by always fine-tuning the position of solar tracking prototype to receive maximum solar radiation Simulation results demonstrated the ability of GA solar to produce consistent result despite of different environmental conditions

Index Terms—genetic algorithm, solar tracking, photovoltaic panel

1 Introduction

The solar tracker, a device that keeps PV or

photo-thermal

panels in an optimum position perpendicular

to the solar radiation during daylight hours,

increases the collected energy The first

tracker introduced by Finster in 1962, was

completely mechanical One year later,

Saavedra presented a mechanism with an

automatic electronic control, which was used

to orient an Eppley pyrheliometer [1]Solar

tracking can be implemented by using

one-axis, and forhigher accuracy, two-axis

sun-tracking systems For a two-axis sun-sun-tracking

system, two types are known as: polar

(equatorial) tracking and azimuth/elevation

(altitude–azimuth) tracking.[2]

High-concentration solar requires the sun to be

tracked with great accuracy for maximum

output voltage The accuracy required

depends on the specific characteristics of the

concentrating system being analyzed

In general, the higher system concentration needs the higher accuracy tracking system The current trend in solar concentrator tracking system is to use an open-loop local controller that computes the direction of the solar vector based on geographical location and time But it is not enough accurate because it has error from computing the sun’s position, mechanical, controller systems and installation [3]The abundance of solar energy throughout the whole year in Malaysia due to the geographic location near the Equator line provides strong reason for the implementation

of an efficient PV energy system

Studies show that solar panels constitute a large portion (57%) of the total cost to install

PV energy system [4] Since the purchase of solar panel is quite expensive, therefore research has been heavily invested by Ministry of Science, Technology and Innovation on few local universities to study

on the implementation of PV technology as a renewable energy in Malaysia to replace coal and gas which form the primary resources to

generate electricity by Tenaga Nasional

Berhad.The conventional solar panel which is

used to produce power is not maximized to its

peak performance due to its static placement

which limits the area of exposure to the

sunlight[5]Abdallah et al designed and

constructed a two-axes, open loop,PLC

controlled sun-tracking system Their work

principle is based on mathematical definition

of surface position that is defined by two

angles: the slope of the surface, and azimuth

angle The slope was considered to be equal

as zenith angle of the sun Two tracking

motors, one for the joint rotating about the

horizontal N–S axis and the other for the joint

rotating about the vertical axis were used.The

daylight divided into four intervals and during

each of them the solar and motors speed were

defined and programmed into PLC.They

predicted that the power consumption to drive

motors and control systems hardly exceeds

3% of power saved by the tracking system

Fig 1 shows energy comparison between the

tracker and the fixed surface inclined at 32 °

They concluded that the use of two-axes

tracking surfaces results in an increase in total

daily collection of about 41.34% as compared

proposed, implemented and tested a

microcontroller based two-axis solar tracking

system They used light dependent resistors

(LDR) as sensors, stepper motors as actuators

and a microcontroller In addition, the system

was connected to a PC via RS232 for sun

position monitoring A crystal with a

frequency of 4 MHz was used as a clock

signal generator for the microcontroller The

panel degree from vertical axis was fixed at

50 ° The experimental study for two solar

collector panels, one stationary and the other

rotary were employed in the test Temperature

of the panels versus time was measured with

a minute interval and 50 data were captured

The angle of intervals was almost 5.2 ° A

distinction of 9 ° between rotary and

stationary panel was observed This result verified that the rotary panel containing solar tracking system took more light density than the stationary panel[8]

Fig 1 Energy comparison between tracking and fixed solar system [9]

In this research, solar cell polycrystalline and

GA has been used to fully maximize the performance of the solar tracking prototype

GA is used to overcome the current limitation

of the method that had been used by other researchers where the best GA parameters will be chosen based on the intensity fitness function and both angular axis will be simulated to face the solar panel at the right angle for maximum power generation In the following section, methodology will be discussed followed by results of the simulation and detailed discussion Finally, conclusion is presented for further improvement in the future

2 Methodology

Methodology part is divided into few areas which include initial population, evaluation, selection, crossover and mutation

In this paper, the flowchart of the system development is highlighted as shown in Fig 2

Fig 2: Flowchart of the system development

Initial population is randomly generated in 8

real values as follow:

T1 = [ 0, 10, 11, 20, 1, 10,15,19]

T2 = [ 1, 9, 15, 19, 1, 8,11,16]

T3 = [ 0, 5, 14, 19, 1, 7,12,18]

T4 = [ 1, 5, 13, 18, 1, 8,15,19]

T5 = [ 0, 8, 13, 18, 1, 9,13,17]

T6 = [ 1, 4, 11, 17, 1, 8,12,19]

T7 = [ 1, 7, 15, 16, 0, 9,14,18]

T8 = [ 1, 3, 14, 16, 1, 8,15,17]

T9 = [ 1, 2, 12, 17, 1, 3,12,16]

T10 = [ 1, 8, 12, 18, 1, 9,13,18]

The first gene and fifth gene of each chromosome are used to indicate the direction

of solar tracking axles, both horizontal and vertical axles to be left indicated by 0 or right indicated by 1

Second to fourth gene of each chromosome are used to indicate the different angles of horizontal axle to be positioned in order to get the maximum sun intensity

At the same time, sixth to eighth genes of each chromosome are used to indicate the different angles of vertical axle to be positioned in order to get the maximum sun intensity

Both motors are used to control the horizontal and vertical axles which will be directed by PLC controller

to be evaluated by measuring the voltage from solar cell polycrystalline

Tk =(A^B) exp[-C X (dk )E]

polycrystalline, A and B are fixed coefficient while C and E are tuning parameters

Each chromosome will be evaluated based on the objective function and fitness is simply equal to the value of objective function

F (Tk) , k = 1,2… k+1, where k = population size

From the evaluation, strongest chromosome and weakest will be identified

Program Initialization

Selection

Start

Evaluation

Crossover

Y Solution found

End

N

Gen > Max Gen

Y

End

N

2.3 Selection

A roulette wheel selection approach is used in

this research where it belongs to the fitness

proportional selection and new population is

selected with based on probability distribution

according to fitness values

Total fitness equation for the population will

be as below:

K+1

K=1

For each chromosome, the probability

population size (2)

F(Tk )

For each chromosome, cumulative probability

K+1

population size (3)

K=1

Roulette wheel is spanned for each selection

process and a new single chromosome will be

selected based on its fitness value

Crossover used in this research is one cut

point method which randomly select on cut

point location and each side of chromosome

will be exchanged between two parents to

generate offspring Consider two

chromosomes as follow and cut point is

T1 = [ 1, 10, 15, 20, 1, 9,11,19]

T2 = [ 1, 9, 14, 18, 1, 8,13,17]

The resulting offspring by exchanging the right part of their parents would be as below: T1 = [ 1, 10, 14, 20, 1, 9,11,19]

T2 = [ 1, 9, 15, 18, 1, 8,13,17]

The probability of crossover for each experiment is set as

Pc= 0.8, therefore, 80% of chromosomes will undergo crossover

Mutation changes one or more genes according to probability

chromosome T1 is selected for a mutation Since the gene is 15 and 20, therefore, both number will be interchanged and chromosome after mutation is as below :

T1 = [ 1, 10, 15, 20, 1, 9,11,19]

T1m = [ 1, 10,20,15, 1, 5,13,20]

The probability of mutation for each experiment is set as

Pc= 0.025, therefore, 2.5% of chromosomes will undergo mutation

After each chromosome have finished the cycle of crossover and mutation, the process will go back to evaluation procedure to continue the GA operations until the experiment ends

3 Simulation

A solar tracking has been developed to evaluate the application of genetic algorithm

as depicted in Figure 3 It would explore the intensity of sunlight at different angles and

locate the highest intensity with the GA

simulation The simulation has been carried

out using the GA parameters as given in 3

tables below, Table 1, Table 2 and Table 3

with the objectives to study

Table I: GA simulation parameter

Maximum Generation

Population, po

Chromosome length

Selection Method

Crossover Rate, pc

Mutation Rate, pm

Mutation Point, mp

No of Best

Chromosomes Kept, kb

Crossover Type

50

10

8 Roulette Wheel 80%

0.025

2

1 Dynamic

The solar tracking is placed at the origin

point of (Xo=45 °, Yo=45 °) The default

base point is at the centre of the workspace

searching the highest intensity location with

GA searching method Both stepper motors controlling X and Y axis of solar tracking will receive the signals through motion controller

to determine the angles of movement for both axis Highest intensity that is absorbed by solar cell will convert the digital voltage to analogue signal to be transmitted to Visual basic program via Programmable logic controller Panasonic FPX-C14R

Figure 3 : A solar tracking has been developed to evaluate the application of genetic algorithm

4 Preliminary Results

From the voltage measurement on the

solar cell at different angles manually, the

results which had taken on shiny day around

11am are shown in Table 2

Y AIXS

(°)

X AIXS

0 0 10.05

15 0 9.64

30 0 9.58

45 0 9.82

-15 0 9.86

-30 0 9.77

-45 0 8.93

Table 2 : Voltage measurement corresponding

to different angles X & Y

0 10 20 30 40 50 60 70 80 90 100

0.015

0.02

0.025

0.03

0.035

0.04

0.045

0.05

0.055

0.06

0.065

Generation

Best fitness Mean fitness

Graph 1 : Best Fitness Value- 0.017065

using GA

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

Generation

Best, Worst, and Mean Scores

Worst Score

Graph 3 : Best, worst and mean score for

each generation using GA

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45

Generation

data1

Graph 2 : Average distance between individuals in each generation using GA

0 0.5 1 1.5 2 2.5

3

Selection Function

Individual

state.Selection

Graph 4 : Number of children that is produced by each individual using GA

0 10 20 30 40 50 60

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

Iteration

Graph 5 : Best Function Value- 0.01720

produced using Simulated Annealing

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

Iteration

Best Function Value: 0.017072

Graph 7 : Best Function Value- 0.01707

produced using Threshold acceptance

0 10 20 30 40 50 60 0

0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16

Iteration

Current Function Value: 0.01724

Graph 6 : Function Value for each iteration using simulated annealing

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16

Iteration

Current Function Value: 0.037029

Graph 8 : Function Value for each iteration using threshold acceptance

5 Discussion

From the result, graph 1 shows the best

fitness value 0.01706 that could be achieved

using Genetic Algorithm through 50

generation

Graph 2 shows various average distances

between each individual for 50 generation

Initial generation involves more activities in

searching for optimum value which causes a

larger distance split among each individual

Once the search space is narrowed down

towards achieving global optimum value,

distance between individuals is getting

smaller and convergence moves to the

vicinity of global minimum value, which is

indicated in graph 2

Graph 3 indicates best, worst and mean

score for each generation where respective

scores achieved by each fitness value during

iteration is recorded

Graph 4 shows number of children that is

produced by each parent along 50

generation It is clear that fourth parent is

having the highest tendency of producing

highest off springs compared to other 9

parents As compared to Graph 5, 6 and 7

and 8 where results are obtained using

simulated annealing and threshold

acceptance respectively, this is to prove that

Genetic Algorithm is converging to the

global minimum value by having lowest

fitness value as shown below:

Optimization

Genetic

Algorithm

0.01706 10.05

Simulated

Annealing

0.01720 10.045

Threshold

Acceptance

0.01707 10.050

6 Conclusion

The proposed algorithm is used to control both X and Y motors so that solar tracking can be used to track the highest intensity

This experiment has been carried out in the outdoor working space to test the efficiency

of this solar tracking In this research, a simulator package has been developed and comparison between few other optimization methods has been done and best fitness value show that Genetic Algorithm performs better than Simulated Annealing and Threshold Acceptance The proposed method improves search speed, good accuracy and approximate solution

7 Reference

[1] Roth P, Georgiev A, Boudinov H Cheap two-axis sun following device, Energy Conversion and Management 2005;46:1179–92

[2] Pitak Khlaichom Kawin Sonthipermpoon, Optimization of solar tracking system based on genetic algorithm, 3rd Conference of the Energy Network of Thailand,2007

[3] Hossein Mousazadeh,Alireza Keyhani, Arzhang Javadi, Hossein Mobli,Karen Abrinia,Ahmad Sharifi, 2009 A review of principle and sun-tracking methods for maximizing solar systems output, Renewable and Sustainable Energy Reviews

13 (2009) 1800-1818 [4] J Enslin, “Renewable energy as an economic energy source for remote areas,”

in Renewable Energy, vol 1, pp 243–248,

1991

[5] B Koyuncu and K Balasubramanian,

“A microprocessor controlled automatic sun tracker,” IEEE Transactions on Consumer Electronics, vol 37, no 4, pp 913–917,

1991

[6] Abdallah S, Nijmeh S Two axes sun

tracking system with PLC control Energy

Conversion and Management

2004;45:1931–9

[7] Mamlook R, Nijmeh S, Abdallah SM A programmable logic controller to control two axis sun tracking system InformationTechnology Journal