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ICSET 2008

Evaluation of Genetic Algorithm based Solar

Tracking System for Photovoltaic PanelsSyamsiah Mashohor, Khairulmizam Samsudin, Amirullah M. Noor and Adi Razlan A. Rahman

AbstractThe maximum power supplied by a Photovoltaic(PV) panels system change over time. It depends onenvironmental factors such as the solar irradiation and thetemperature of these panels. The average solar energy harvestedby the conventional solar panels during the course of the day,is not always maximized. This is due to the static placementof the panel which limits their area of exposure to the sun. Inpractice, there are three possible approaches for maximizing thesolar power extraction in medium and large scale PV systemsare sun tracking, maximum power point (MPP) tracking orcombination of both. In this paper, a Genetic Algorithm (GA)has been proposed utilizing sun tracking approaches to maximizethe performance of PV panels. Literature suggested that the PV

panels could produce maximum power if the panels have angle ofinclination zero degree to the sun position. This work evaluate thebest combination of GA parameters to optimize a solar trackingsystem for PV panels in terms of azimuth angle and tilt angle.Simulation results demonstrated the ability of the proposed GAsystem to search for optimal panel positions in term of consistencyand convergence properties. It also has proved the ability ofthe GA-Solar to adapt to different environmental conditions andsuccessfully track sun positions in finding the maximum powerby precisely orienting the PV panels.

Index Termsgenetic algorithm, solar tracking, PV panels

I. INTRODUCTION

The photovoltaic (PV) technology is expanding due to

the growing demand for renewable energy mainly due to

the depletion of fossil fuel. Solar panel is the fundamentalenergy conversion component of photovoltaic (PV) systems

[1]. PV technology offers safe and clean energy sources [2].

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 [3]. As the solar panels are relatively expensive, much

research work has been conducted to improve the utilization

of solar energy. Physically, the power supplied by the panels

depends on many extrinsic factors, such as insolation levels

(incident of solar radiation), temperature and load condition.

To ensure the economical viability of this energy system,

the development of an adaptive system that always obtains

the maximum power from these solar panels is essential. Theaverage solar energy intercepted by the conventional solar

panels, during the course of the day, is not always maximized.

This work was funded by Universiti Putra Malaysia under the NewAppointed Lecturer Grant Scheme, grant no. 5315958.

Dept. of Computer and Communication Systems Engineering, UniversitiPutra Malaysia, 43400 Serdang, Selangor, Malaysia (phone: +603 89464348;fax: +603 86567127; e-mail: syamsiah@eng.upm.edu.my)

This is due to the static placement of the panel which limits

their area of exposure from the sun [4]. An automatic sun

tracking system using a number of Light Detecting Resistors

(LDRs) is proposed in [4, 5]. The principle idea is to sense the

sun light using LDR and the differential signal representing

angular error of the panel is used to rotate the panel in such

a way that the angular error is minimized. The usage of LDR

is to ensure that the solar panel is always aligned towards

the direction of the sun. However, a cloudy day and shading

condition around the PV panels may reduce the intensity of

light and effect the performance of the tracking system. Proper

placement of LDR in the system and the correct quantity ofLDR are also important in order to provide correct positioning

information for the solar panel at all time.

Researcher in [6, 7] has presented the design and

implementation of a computer-controlled dual-axis sun

tracking system to obtain high precision positioning of the PV

panel. The control of dual axis tracking system is complex

due to nonlinear dynamics and unavailability of the model

parameters. A PC-based fuzzy logic control algorithm utilizing

the knowledge of the system behavior is designed in order to

achieve the control objectives. Implementation of fuzzy logic

reasoning is limited to the predetermined conditions which

may not satisfy all conditions in the PV panel environment.

Therefore, sun tracking only occurs when these conditions are

met, which will not necessarily maximize the solar energy

absorption.

In this work, GA based solar tracking is proposed to

overcome the limitation of current method. GA is an

evolutionary algorithm which can adapt itself to the different

conditions, not only suitable for solar farm but also applicable

for harvesting energy for aerospace industries application,

electric vehicles, communication equipment and mobile

robots.

This paper will discuss the development of GA-based solar

tracking system (GA-Solar) by orienting PV panel on two axis

(azimuth and tilt angles) to obtain maximum power. In the

following section, the methodology of this system is discussed

which comprise of system description, GA architecture and

solar tracking simulator. Then, the results of the simulation

are discussed and finally, the conclusion is presented.

I I . METHODOLOGY

A. System Description

This paper is a preliminary work for the implementation of

sun tracking using our specially-tailored GA for maximizing

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PV panel

180 degree

180 degree

Surface

Figure 1. The geometry of the PV panel for GA-Solar system

performance of PV systems. The details of GA architecture

that is implemented in this simulation is discussed in [8]. The

geometry of the PV panel that will be explored by the GA

technique is shown in figure 1. The azimuth and tilt angle can

varies from 0 to 180 degrees which can fulfill any location

condition either the PV panel is installed on a high building

or on the ground. If both tilt and azimuth angle is 90 degrees,

the PV panel is normal to the ground surface.This work assumes that the orientation search by GA-Solar

is done during the first time calibration, right after a PV

system has been installed. GA-Solar would only require the

power measurement from the PV panel to assist the GA search

of the optimal position. After acquiring the maximum power

value from the PV system, GA-Solar will only start searching

again after power obtained from PV systems fell below some

threshold value. Subsequent search would be much faster as

the GA would start searching from the best individual.

B. GA Architecture

In this work, the GA is used to track the best tilt and

azimuth angle of the PV panel that generate the maximum

power from the sun. For this work, real integer coding hasbeen implemented considering the position of the sun in the

sky. The tilt and azimuth angle are both ranged from 0 to

180 degrees. Every individual represents a combination of

both position parameters which describes position of the PV

panel in relation to the sun. The domain of search for these

parameters is large (180180=32400) which is suitable for

the GA to explore.

The fitness function in this work is evaluated from the power

value measured from the PV panel according to the selected tilt

and azimuth angle. There should be only one maximum power

value at one time assuming that the panel angle of inclination

is zero degrees to the sun, while the minimum value is 0W.

Iteratively, the whole population for the next generation is

formed by selected individuals from the parents and offsprings

in current generation. These individuals are ranked based on

their fitness performance and only the top fittest individual are

selected for a new population. The population will perform GA

activities such as selection, mutation and crossover in every

generation until the termination requirements are fulfilled. The

GA search will be terminated if an ideal individual which

gives the maximum power value has been found or maximum

generation has been reached.

Table ITHE VALUES OFG A PARAMETERS INVOLVED IN EXHAUSTIVE SEARCH

GA parameter Values

Po pulatio n s iz e 3 0, 50 , 1 00 , 2 00Maximum generation 50, 100, 150, 200, 250

Probability of crossover 0.1 to 1.0Probability of mutation 0.001 to 0.01

C. Solar Tracking Simulator

In this work, to facilitate the development of the GA-

Solar system a solar tracking simulator has been developed.

The initial orientation of the panel are generated randomly.

The GA is used to search for the best tilt and azimuth

angle that gives the maximum power value (10W) that has

been randomly generated into a Look-Up Table (LUT). The

simulator also imitate environment factors such as insolation

level and temperature that degrade the performance of PV

system. As there is only one source of light which is from

the sun, we use a bi-variate normal distribution function to

initialize the LUT. The maximum power value and the angular

position parameters that have the maximum power value arerandomly picked and changed every time a new simulation

starts.

Figure 2 illustrate four generated LUT to simulate the power

obtained by a PV system oriented to a specific tilt and azimuth

angle ([tilt,azimuth]). From figure 2(a) and figure 2(b), a

maximum power of 10W from a PV system can be obtained by

orienting the panel to [90,90] and [0,0] position respectively.

Orienting the panel to [180,90] and [120,80] positions would

obtain the maximum power of 6.5W and 4.9W from LUT of

figure 2(c) and figure 2(d) respectively.

D. Experimental setup

To maximize the capability of GA to position the PV panel

to obtain the maximum power, the values of GA parameters

must be chosen carefully. Hence, an exhaustive search has

been conducted on all combination of GA parameters as listed

in table I to find the best combination of GA parameters. The

exhaustive search is possible due to the availability of the solar

tracking simulator which is able to simulate the two angular

axis positioning of PV panel very fast. The total combination

of GA parameters are 2000 combinations ( 4 x 10 x 10 x 5) and

each combination is repeated for consistency evaluation. The

maximum power generated by the PV system is fixed at 10W

to simplify the performance comparison between different GA

parameters setting. The effect of every GA parameter in thisGA-Solar also can be assessed based on this simulation.

The best GA parameter obtained from the exhaustive search

is then used to simulate 1000 GA-Solar search considering

the influence of different environmental conditions and sun

positions. The maximum power generated by the PV system

will vary randomly between zero and 10W due to the

environmental factors.

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Figure 2. Generated Look-Up Table for solar tracking simulator using bi-variate normal distribution function

III. RESULTS AND D ISCUSSIONS

A. Optimization of GA

The goal of the GA-Solar is to obtain maximum power from

PV system at all the time and in any condition. Therefore,

selected values of GA parameters that will be used in the futuresimulation or real-environment experiments must consistently

give the maximum power to optimize the utilization of the

solar energy. The selected GA parameters should also require

less generation to produce the findings as each measurement

of fitness function requires positioning of the two axis of

the PV panel. To evaluate each combination of the GA

parameters, the consistency and convergence properties of

the GA-Solar aligning the PV panel to obtain the maximum

power is considered. Consistency is defined as the frequency

of finding the maximum power, while the convergence is

defined as the least number of generation required to find the

maximum power. Each GA parameters combination simulation

is repeated 10 times.

The consistency in finding the maximum power is the

most important feature of the GA-Solar due to adaptability

to different conditions, PV panel material and system designs.

0

2

4

6

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10

0 400 800 1200 1600 2000

Frequency

GA with different parameters

69%

Figure 3. Consistency of GA-Solar in finding the maximum power

In the exhaustive search simulation, the best parameters give

consistent best results in finding the maximum power point

from the Look-Up-Table that emulate power measurement

from PV panels that are exposed to the sun. As shown in

figure 3, 69% of the simulation results give good consistency

in finding the maximum power, which is 10W in all 10

sets. With regard to this findings, the consistency of findingmaximum power in all 10 sets and convergence (sum of

generation) properties of these combination of GA parameters

are analyzed in order to choose the best GA parameter values.

The GA parameters are analyzed in sequence of population,

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Table IITHE NORMALIZED CONSISTENCY AND CONVERGENCE FOR DIFFERENT

POPULATION SIZE

Population size Consistency Convergence

30 0.58 1.0050 0.92 0.59

100 1.00 0.39

200 0.94 0.40

maximum generation, probability of crossover and probability

of mutation.

The population size have significant impact on embedded

system memory footprint. Table II summarized the consistency

and convergence for different population size. Based on the

table, the population size of 100 gives the most consistent

results in finding the maximum power in every set of

simulation and the results are found in the least convergence

value.

The maximum generation determine the limit of time

that GA will spend to search for the best solution.

There is no significant difference of consistency and

convergence properties of maximum generation parameter forthe population size of 100. However, a maximum generation of

50 is chosen since the population size of 100 is already large

enough to give enough time for the GA to find the solution.

It would also ensure that the GA would be terminated if the

duration taken to obtain the optimal position for maximum

power is too long.

Detail analysis on the probability of crossover shows that

the probability of 0.7 has the most consistency and the least

convergence values compared to other probability values. With

this value of probability, the crossover operation will happen

moderately frequent to produce better offsprings for the next

generation. For the probability of mutation, the value of 0.001

is selected based on the least convergence required to find the

same consistency as the other values. The value of 0.001 gives

enough occurrence of mutation for the generation to evolve

but excessive probability of mutation may increase the time to

compute.

B. Influence of Environmental Factors

In the simulation of 1000 different environmental

conditions, maximum power from PV systems may varied

caused by various environmental factors such as cloudy

condition, raining condition, dusty PV panels, shading by other

objects and temperature of the PV systems. Hence, the ability

of the GA-Solar to adapt and find the best panel position are

tested in these conditions.

From the simulation findings, the GA-Solar has performed

very well when it is able to find all the maximum power values

initiated randomly in every set. The initial maximum power

values generated and the GA-Solar solutions that have been

found are shown in figure 4. From the figure, it is obvious that

every maximum power initialized randomly in the look-up-

table, overlapped with the GA-Solar solution. Detail analysis

shows that GA-Solar is able to accurately track the correct

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Power[W]

Simulation runs

InitializedGA-Solar

Figure 4. Comparison of maximum power initialized by the solar trackingsimulator with GA-Solar search solution

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GenerationGain

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Figure 5. Generation gain for GA-Solar search

position of the PV panel in finding the maximum power 99.99

percent of the time.

Figure 5 shows the analysis on the generation gain required

by the GA-Solar to find the best solutions in every set ofsimulation. In average, the GA-Solar found the best solutions

after the third generation and the fastest is after the first

generation while the slowest is after the 21st generation. The

standard deviation is 1.55 indicates that the generation gain

required by the GA-Solar is nearly similar in any conditions. In

hardware implementation, short generation gain is important

for the system due to the repositioning of the solar panel.

The speed of motor to position the PV panel also has to be

considered in overall system performance.

IV. CONCLUSION

This paper described the simulation of GA-based solar

tracking system for PV panels. The solar tracking simulator

developed is an important tool to simulate and evaluate the

performance of the GA-solar system. The best value for GA

parameters have been selected according to the simulation

results. The most significant difference of frequency and

convergence occur in different population size, followed

by different probability of crossover. For various values of

probability of mutation, only the convergence yields the

significant difference. From the observation, there is no

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significant difference in consistency neither the convergence

properties when the maximum generation is varied. The

GA parameter that will be used in future simulation

and hardware implementation are population size of 100,

maximum generation of 50 and probability of crossover

and mutation are 0.7 and 0.001 respectively. The simulation

findings also show that the GA-Solar can be implementedto maximize the power harvested by the PV panel systems

considering the various influence of environmental factors and

different sun positions.

Future work will concentrate on

the hardware implementation to prove that the GA-Solar is

capable of maximizing the power obtained by the PV panel in

real-environment experiments and posses the ability to adapt

to different installation location and PV system design. The

hardware setup is nearly completed and later, more factors

that normally affect the solar panel system performance will

be added to the GA module.

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