Enhanced Energy Output From a PV System Under Partial Shaded Conditions Using Fuzzy Logic

SINGARAVELAN.S Final year, EEE department

M.KUMARASAMY COLLEGE OF ENGINEERING,KARUR.

SUBRAMANI.K Final year, EEE department

M.KUMARASAMY COLLEGE OF ENGINEERING,KARUR

Abstract—For the maximum utilization of solar energy, photovoltaic (PV) power generation systems are operated at the maximum power point (MPP) under varying atmospheric conditions, and MPP tracking (MPPT) is generally achieved using several conventional methods. However, when partial shading occurs in a PVsystem, the resultant power–voltage (P–V) curve exhibits multiple peaks and traditional methods that need not guarantee convergence to true MPP always. This paper proposes an fuzzy logic algorithm for global MPP (GMPP) tracking under conditions of in-homogenous insulations. The formulation of the problem, application of the ABC algorithm, and the results are analyzed in this paper. The numerical simulations carried out on two different PV configurations under different shading patterns strongly suggest that the proposed method is far superior to existing MPPT alternatives. Experimental results are also provided to validate the new dispensation.

Keywords—Maximum power point tracking (MPPT), Fuzzy logic optimization methods, photovoltaic (PV) systems

I. INTRODUCTION

The increased energy demand coupled with limited stock and rising cost of conventional sources, such as coal, petrol, etc., has increased the contribution of renewable energy sources to the total energy consumption. The photovoltaic (PV) energy becomes a promising alternative as it is omnipresent, freely available, environment friendly, and has less operational and maintenance costs. To optimize the utilization of large PV modules, maximum power point tracking (MPPT) is generally employed in conjunction with a power converter (dc–dc converter and/or inverter). The MPPT scheme ensures that the system can always harvest maximum power generated by the PV system independent of change in environmental conditions namely ambient temperature and solar insulation. Since the power–voltage (P–V) characteristic curve varies nonlinearly with solar irradiation and atmospheric temperature conditions, tracking MPP is a challenge and has been successfully achieved using several methods. In large PV installations, several PV modules are interconnected in series and/or parallel to cope with desired voltage and current capacity. At times, due to moving clouds, shadows of trees, buildings, and other neighboring objects, some parts of the PV array receive nonuniform sunlight leading to partial shaded condition (PSC). The PV modules are equipped with bi-pass

diodes under PSC to avoid hotspots, and this result in multiple maxima in the P–V curve of the PV system. The presence of multiple peaks reduces the effectiveness of conventional MPPT techniques due to their inability to distinguish betweenthe local and global peaks. Thus, if a conventional MPPT technique such as multisearch perturb and observe (PO) method or modified versions of PO method is employed under PSC, this may result in the significant reduction of generated power and further brings down the reliability of PV power generation systems. The drop in PV power generation due to PSC can be alleviated either through PV array reconfigurations, system architectures, converter circuit topologies, or improved methods of MPPT techniques. The development of enhanced MPPT algorithms is more attractive due to simplicity of implementation, reduced cost, and the immediate adoption to existing system. Several MPPT techniques are recently developed under PSC and are reported in. Among these, particle swarm optimization (PSO) has been extensively employed to track the global MPP (GMPP) in a PV system under PSC.

II. CHARACTERISTICS OF PV SYSTEM

The basic photovoltaic device is the Structure for PV modules. All modules contain cells. The group of panels comprises the complete PV generating unit. A photovoltaic cell is a semiconductor p-n junction expose to light into electricity. Single cells are connected in series or parallel combination to form a module to achieve certain voltage or current. Photovoltaic cell is made from different types of semiconductors using manufacturing processes. Generally, mono crystalline and poly crystalline are used in commercial level. When light falls on the cell, it generates charge carriers that originate the electric current, if the cell is short-circuited. To draw the real model of a PV cell, it is necessary to take into the account, the losses due to leakage current in the diode. So, one resistor connected in series, its value will be low and another resistor will be connected in parallel, its value will be high It is clear that the current I that flows to the external circuit is flows through the diode and produces an opencircuit voltage Voc of about 0.5-0.6V. If the solar cell is short circuited, then no current flows through the diode, and all of the short-circuit current ISC flows through the short circuit.

Figure-1. Equivalent circuit of Solar Panel.

III. EFFECT OF PARTIAL SHADING

When one or more PV cells are shaded, bypass diodes are added in parallel for protection, prevent from the damage due to overheating, when cells are connected in series

Figure-2. Characteristics of different irradiance conditions.

Partial shading may occur due to environmental conditions, such as clouds, dirt and dust, trees and buildings. However, in power voltage characteristics curve also changes rapidly, multiple peaks are obtained. When the PSC occurs, the shaded PV cell act as a load instead of power flow,

generating multiple peaks in the I-V characteristic curve and multiple peak values in the P-V curve. To prevent this problem, PV module is comprised of parallel connected bypass diodes. In multiple peaks, one GMPP are obtained in the curve.

IV. DC-DC CONVERTER

In the solar PV system, the obtained output is of DC which is unregulated in nature. Therefore, the unregulated output has been converted into regulated DC output by means of a converter called DC-DC Converter. According to DC-DC converter, the unregulated DC voltage takes as input and converts the DC in to AC voltage. After the conversion process, the obtained voltage is transformed and rectified to desired DC output voltage. It is provided to regulate the constant output voltage under various operating conditions. Electrically, this converter Can produce high power, light weight and noise free. The features of converters are

Wide range of input voltage

Over voltage protection.The converter has two tasks, interface a PVmodel, grid and drive the operating point of the PV panelto MPP. Converter classified into categories of application, types of switching and types of current modes. DC-DC converter types are Non-isolated andIsolated. Isolation refers to the electrical barrier separatingthe input and output of the converter.In the buck converter, the output voltage will be less than that of input voltage. It can be used for connecting high module, voltage to low load. Theseconverters can modulate the input voltage through PWMto generate the output voltage required to cause the panelto operate in MPP . The step up converter, the outputvoltage is higher than that of input voltage magnitude.This converter used to connect high load and low modulevoltages. Many research workers developed the applications for Boost converter in PV systems. There are four different categories for low-cost and high efficiency boost converters.

Coupled inductor

Switched capacitor

Inductor and Switched capacitor and

Coupled inductor and switched capacitor.The three phase system has three levels, boosting

of MPPT control. Three level boost converters reduce diode reverse recovery losses and reduce the input filter size. Several controlled voltage levels are needed with self-balancing and unidirectional current flow, such as PV systems. When applied to the MOSFET, boost converter balance the voltage. It avoids over voltage due to the leakage inductor. In buck-boost converter, the output voltage magnitude may be higher or lower than the inputvoltage; it can be used in connecting nearly matched battery and module voltage. In Cuk converter performs like buck-boost converter. It is capable of stepping up or down input voltage with reverse polarity through the common terminal of

input voltage. In SEPIC Converter, the input current will be continuous and it draws the ripple free current from the PV panel. SEPIC Converter will use, when the battery voltage will be higher than the PV module voltage. In front of the inverter, high step up converter will be required to improve power conversion efficiency and stable DC link to invert from the panel low voltage to a high voltage level. This converter achieves a high step up voltage conversion ratio; the leakage inductor energy of coupled inductor is recycled to load . The converter has been built in two differentways, such as, to maximize the efficiency, the possibility of implementing MPPT, low price, reliability, flexible converter run with a wide range of input, output voltage and power .The bidirectional Cuk converter used as the bypass converter and a terminal Cuk converter was referred. In bypass converter, performance can be evaluated, and efficiency will be better under partial shading conditions. Due to the additional power converter, cost of building the type of PV system will be higher than conventional ones using only by-pass diodes. When connected to a variable current source like a PV panel, behaviors will not be expected, but this converter designed to operate with constant voltage source. For converting the low panel voltage in to high DC-link voltage with a high voltage conversion ratio is necessary,and this converter reduces the switching power losses by soft- switching operation of power devices. High voltage gain interleaved boost converter is a nonisolated boost converter, level up 12V DC input voltage to 36V DC output voltage. In long time operation, it gives better reliability and small size due to the simplicity of installation; switching devices can be controlled. Two switch buck boost DC-DC Converter and low cost 8bit micro controller are referred . This converter is more flexible and it can perform both step-up and step-down functions. It is able to bend through an entire voltage range of PV panel. In novel boost-half bridge micro inverter and repetitive current controller were explained . The minimum use of semiconductor devices, circuit simplicity, and easy control was achieved. The boost half-bridge micro inverter possesses features of low cost and high reliability. In the boost-half-bridge dc-dc converter over the wide operation range, high efficiency (97.0%-98.2%) is obtained; current injected to the grid is regulated precisely and stiffly. The variable step size technique provides a fast tracking speed and high MPPTefficiency. In a low-cost, high efficiency current measurement technique using a resistor and bypass switch for PV power systems with MPPT control. Because of this technique, it can reduce the power loss significantly for feedback control systems using a DSP decreases the size and material cost. 80W prototype hardware has been implemented for PV MPPT verification of low power loss current measurement technique

V. MPPT TECHNIQUES

The earliest MPPT method published in 1960s.There are different types of MPPT algorithm have been discussed in literature. It is broadly classified into two types.

Conventional methods and

Soft computing methods

For conventional MPPT, the methods include incremental conductance, perturb and observe, hill combining, short circuit current, open circuit voltage, ripple correlation control, current sweep method. These methods are satisfied under uniform solar irradiance conditions. In normal condition, it is able to track efficiently, but continuous oscillation around MPP, loss of power in a steady state condition. These techniques are failing to track GMPP and cannot capable of handling partial shading conditions. In soft computing method, the methods such as artificial neural network, fuzzy logic controller, particle swarm optimization, Ant-colony optimization and differential evolution. Recent approachesin software computing methods are cuckoo search and firefly algorithm. Compare with conventional MPPT, soft computing method able to track the GMPP in multiple peaks.

VI. FUZZY LOGIC ALGORITHM

It is one of the most popular control algorithm methods which are known by its multimode based variable control algorithm. It provides more accurate for MPPT problems, but it is more complicated in implementations. It changes the duty cycle of the converter according to the voltage error input such that panel output voltage becomes equal to the voltage corresponding to maximum voltage. The fuzzy logic control algorithm is a Photovoltaic array dependent. It is based on the operator’s experience, because it is followed by certain rules that are given by the operator. It helps to improve the response of a Photovoltaic system. The main disadvantage of this method is that the efficiency of the whole system which depends on the operator's performance and the precision of the rules. Fuzzy logic control mainly consists of four stages, namely, Fuzzification, rule base, inference, defuzzification. First, we have to initialize the inputs to the Fuzzy logic controller. Fuzzy logic- based hill combining. Algorithm is introduced, where all the MPP’s values are periodically stored in advanced micro controller and fuzzy logic is later implemented to track the GMMP. The use of fuzzy logic also involves the complicated fuzzification and defuzzification. Compare with conventional nonlinear controllers, these methods will work with variable inputs, no need of an accurate mathematical model, handling nonlinearity and more robust. Compared with P&O algorithm, it is more complicated and possesses some advantages such as, better performance, good stability and fast response

VII. HARDWARE

Latching relay, dust cover removed, showing pawl and ratchet mechanism. The ratchet operates a cam, which raises and lowers the moving contact arm, seen edge-on just below it. The moving and fixed contacts are visible at the left side of the image. A latching relay has two relaxed states

bistable. These are also called "impulse", "keep", or "stay" relays. When the current is switched off, the relay remains in its last state. This is achieved with a solenoid operating a ratchet and cam mechanism, or by having two opposing coils with an over-center spring or permanent magnet to hold the armature and contacts in position while the coil is relaxed, or with a remanent core. In the ratchet and cam example, the first pulse to the coil turns the relay on and the second pulse turns it off. In the two coil example, a pulse to one coil turns the relay on and a pulse to the opposite coil turns the relay off. This type of relay has the advantage that it consumes power only for an instant, while it is being switched, and it retains its last setting across a power outage. A remanent core latching relay requires a current pulse of opposite polarity to make it change state.

Figure-3.Expremental model.

Microcontrollers are destined to play an increasingly important role in revolutionizing various industries and influencing our day to day life more strongly than one can imagine. Since its emergence in the early 1980's the microcontroller has been recognized as a general purpose building block for intelligent digital systems. It is finding using diverse area, starting from simple children's toys to highly complex spacecraft. Because of its versatility and many advantages, the application domain has spread in all conceivable directions, making it ubiquitous. As a consequence, it has generate a great deal of interest and enthusiasm among students, teachers and practicing engineers, creating an acute education need for imparting the knowledge of microcontroller based system design and development. It identifies the vital features responsible for their tremendous impact, the acute educational need created by them and provides a glimpse of the major application area. Liquid crystal displays (LCDs) have materials which combine the properties of both liquids and crystals. Rather than having a melting point, they have a temperature range within which the molecules are almost as mobile as they would be in a liquid, but are grouped together in an ordered form similar to a crystal.

An LCD consists of two glass panels, with the liquid crystal material sand witched in between them. The inner surface of the glass plates are coated with transparent electrodes which define the character, symbols or patterns to be displayed polymeric layers are present in between the electrodes and the liquid crystal, which makes the liquid One each polarisers are pasted outside the two glass panels. These polarisers would rotate the light rays passing through them to a definite angle, in a particular direction

When the LCD is in the off state, light rays are rotated by the two polarisers and the liquid crystal, such that the light rays come out of the LCD without any orientation, and hence the LCD appears transparent.

When sufficient voltage is applied to the electrodes, the liquid crystal molecules would be aligned in a specific direction. The light rays passing through the LCD would be rotated by the polarisers, which would result in activating / highlighting the desired characters.

The LCD’s are lightweight with only a few millimeters thickness. Since the LCD’s consume less power, they are compatible with low power electronic circuits, and can be powered for long durations.

The LCD’s don’t generate light and so light is needed to read the display. By using backlighting, reading is possible in the dark. The LCD’s have long life and a wide operating temperature range.

Changing the display size or the layout size is relatively simple which makes the LCD’s more customer friendly.crystal molecules to maintain a defined orientation angle

Figure-4.Circuit model.

uniquely decoded ‘E’ strobe pulse, active high, to accompany each module transaction. Address or control lines can be assigned to drive the RS and R/W inputs.

Utilize the Host’s extended timing mode, if available, when transacting with the module. Use instructions, which prolong the Read and Write or other appropriate data strobes, so as to realize the interface timing requirements. If a parallel port is used to drive the RS, R/W and ‘E’ control lines, setting the ‘E’ bit simultaneously with RS and R/W would violate the module’s set up time. A separate instruction should be used to achieve proper interfacing timing requirements.

VII. CONCLUSION

A new approach based on ABC has been proposed in this paper for GMPP tracking in a PV power generation system. Numerical simulations carried out on two different

configurations with varying shading patterns clearly demonstrate the improved performance of the proposed algorithm in comparison with the existing methods of PSO and EPO. The experimental results clearly demonstrate faster tracking characteristics of the proposed algorithm with reduced PV output power oscillations. The developed methodology is also shown to improve energy saving and increased revenue generation when compared with alternative schemes of MPPT.

REFERENCES[1] M. Z. S. EL-Dein, M. Kazerani, and M. M. A. Salama, “Optimal photovoltaicarray reconfiguration to reduce partial shading losses,” IEEETrans. Sustain. Energy, vol. 4, no. 1, pp. 145–153, Jan. 2013.[2] T. Esram and P. L. Chapman, “Comparison of photovoltaic array maximumpower point tracking techniques,” IEEE Trans. Energy Convers.,vol. 22, no. 2, pp. 439–449, Jun. 2007.[3] B. Subudhi and R. Pradhan, “A comparative study on maximum powerpoint tracking techniques for photovoltaic power systems,” IEEE Trans.Sustain. Energy, vol. 4, no. 1, pp. 89–98, Jan. 2013.[4] M. Aureliano, L. Galotto, Jr., L. P. Sampaio, G. de Azevedo e Melo,and C. Alberto Canesin, “Evaluation of the main MPPT techniques forphotovoltaic applications,” IEEE Trans. Ind. Electron., vol. 60, no. 3,pp. 1156–1166, Mar. 2013.[5] P. Sharma and V. Agarwal, “Exact maximum power point tracking ofgrid-connected partially shaded PV source using current compensationconcept,” IEEE Trans. Power Electron., vol. 29, no. 9, pp. 4684–4692,Sep. 2014.[6] K. Sundareswaran, S. Peddapati, and S. Palani, “MPPT of PV systemsunder partial shaded conditions through a colony of flashing fireflies,”IEEE Trans. Energy Convers., vol. 29, no. 2, pp. 463–472, Jun. 2014.[7] B. N. Alajmi, K. H. Ahmed, S. J. Finney, and B. W. Williams, “Amaximum power point tracking technique for partially shaded photovoltaicsystems in microgrids,” IEEE Trans. Ind. Electron., vol. 60, no. 4,pp. 1596–1606, Apr. 2013.[8] Y.-H. Liu, S.-C. Huang, J.-W. Huang, andW.-C. Liang, “A particle swarmoptimization-based maximum power point tracking algorithm for PV systemsoperating under partially shaded conditions,” IEEE Trans. EnergyConvers., vol. 27, no. 4, pp. 1027–1035, Dec. 2012.[9] M.Miyatake, M. Veerachary, F. Toriumi, N. Fujii, and H. Ko, “Maximumpower point tracking of multiple photovoltaic arrays: A PSO approach,”IEEE Trans. Aerosp. Electron. Syst., vol. 47, no. 1, pp. 367–380,Jan. 2011.[10] K. Ishaque, Z. Salam, M. Amjad, and S. Mekhilef, “An improved particleswarm optimization (PSO)-based MPPT for PV with reduced steady-stateoscillation,” IEEE Trans. Power Electron., vol. 27, no. 8, pp. 3627–3638,

Aug. 2012.[11] K. Ishaque and Z. Salam, “A deterministic particle swarm optimizationmaximum power point tracker for photovoltaic system under partial shadingcondition,” IEEE Trans. Ind. Electron., vol. 60, no. 8, pp. 3195–3206,Aug. 2013.[12] R. Eberhart and J. Kennedy, “A new optimizer using particle swarmtheory,” in Proc. 6th Int. Symp. MHS, 1995, pp. 39–43.[13] D. Karaboga and C. Ozturk, “A novel clustering approach: Artificial beecolony (ABC) algorithm,” Appl. Soft Comput., vol. 11, no. 1, pp. 652–657,Jan. 2011.[14] B. Babar and A. Cr˘aciunescu, “Comparison of artificial bee colony algorithmwith other algorithms used for tracking of maximum power point ofphotovoltaic arrays,” presented at the Int. Conf. Renew. Energies PowerQual. (ICREPQ’14), Cordoba, Spain, Apr. 8–10, 2014.[15] B. Bilal, “Implementation of artificial bee colony algorithm on maximumpower point tracking for PV modules,” in Proc. 8th Int. Symp.Adv. Topics Elect. Eng. (ATEE), Bucharest, Romania, May 23–25, 2013,pp. 1–4.[16] D. Karaboga and B. Akay, “A comparative study of artificial bee colonyalgorithm,” Appl. Math. Comput., vol. 214, no. 1, pp. 108–132, Aug.2009.[17] Q. Pan, L. Wang, K. Mao, J. Zhao, and M. Zhang, “An effective artificialbee colony algorithm for a real-world hybrid flowshop problemin steelmaking process,” IEEE Trans. Autom. Sci. Eng., vol. 10, no. 2,pp. 307–322, Apr. 2013.[18] F. S. Abu-Mouti and M. E. El-Hawary, “Optimal distributed generationallocation and sizing in distribution systems via artificial bee colony algorithm,”IEEE Trans. Power Del., vol. 26, no. 4, pp. 2091–2101, Oct.2011.[19] L. D. S. Coelho and P. Alotto, “Gaussian artificial bee colony algorithmapproach applied to Loney’s solenoid benchmark problem,” IEEE Trans.Magn., vol. 47, no. 5, pp. 1326–1329, May 2011.[20] M. Seyedmahmoudian, S. Mekhilef, R. Rahmani, R. Yusof, andE. T. Renani, “Analytical modeling of partially shaded photovoltaicsystems,” Energies, vol. 6, no. 1, pp. 128–144, Jan. 2013.[21] K. S. Tey and S. Mekhilef, “Modified incremental conductanceMPPT algorithm to mitigate inaccurate responses under fast-changingsolar irradiation level,” Solar Energy, vol. 101, no. 1, pp. 333–342,Mar. 2014.[22] H. Patel and V. Agarwal, “Maximum power point tracking scheme forPV systems operating under partially shaded conditions,” IEEE Trans.Ind. Electron., vol. 55, no. 4, pp. 1689–1698, Apr. 2008.[23] W. Gao, S. Liu, and L. Huang, “A novel artificial bee colony algorithmbased on modified search equation and orthogonal learning,” IEEE Trans.Cybern., vol. 43, no. 3, pp. 1011–1024, Jun. 2013.[24] N. Femia, G. Petrone, G. Spagnuolo, andM. Vitelli, “Optimization of perturband observe maximum power point tracking method,” IEEE Trans.

Power Electron., vol. 20, no. 4, pp. 963–973, Jul. 2005.

configurations with varying shading patterns clearly demonstrate the improved performance of the proposed algorithm in comparison with the existing methods of PSO and EPO. The experimental results clearly demonstrate faster tracking characteristics of the proposed algorithm with reduced PV output power oscillations. The developed methodology is also shown to improve energy saving and increased revenue generation when compared with alternative schemes of MPPT.

REFERENCES[1] M. Z. S. EL-Dein, M. Kazerani, and M. M. A. Salama, “Optimal photovoltaicarray reconfiguration to reduce partial shading losses,” IEEETrans. Sustain. Energy, vol. 4, no. 1, pp. 145–153, Jan. 2013.[2] T. Esram and P. L. Chapman, “Comparison of photovoltaic array maximumpower point tracking techniques,” IEEE Trans. Energy Convers.,vol. 22, no. 2, pp. 439–449, Jun. 2007.[3] B. Subudhi and R. Pradhan, “A comparative study on maximum powerpoint tracking techniques for photovoltaic power systems,” IEEE Trans.Sustain. Energy, vol. 4, no. 1, pp. 89–98, Jan. 2013.[4] M. Aureliano, L. Galotto, Jr., L. P. Sampaio, G. de Azevedo e Melo,and C. Alberto Canesin, “Evaluation of the main MPPT techniques forphotovoltaic applications,” IEEE Trans. Ind. Electron., vol. 60, no. 3,pp. 1156–1166, Mar. 2013.[5] P. Sharma and V. Agarwal, “Exact maximum power point tracking ofgrid-connected partially shaded PV source using current compensationconcept,” IEEE Trans. Power Electron., vol. 29, no. 9, pp. 4684–4692,Sep. 2014.[6] K. Sundareswaran, S. Peddapati, and S. Palani, “MPPT of PV systemsunder partial shaded conditions through a colony of flashing fireflies,”IEEE Trans. Energy Convers., vol. 29, no. 2, pp. 463–472, Jun. 2014.[7] B. N. Alajmi, K. H. Ahmed, S. J. Finney, and B. W. Williams, “Amaximum power point tracking technique for partially shaded photovoltaicsystems in microgrids,” IEEE Trans. Ind. Electron., vol. 60, no. 4,pp. 1596–1606, Apr. 2013.[8] Y.-H. Liu, S.-C. Huang, J.-W. Huang, andW.-C. Liang, “A particle swarmoptimization-based maximum power point tracking algorithm for PV systemsoperating under partially shaded conditions,” IEEE Trans. EnergyConvers., vol. 27, no. 4, pp. 1027–1035, Dec. 2012.[9] M.Miyatake, M. Veerachary, F. Toriumi, N. Fujii, and H. Ko, “Maximumpower point tracking of multiple photovoltaic arrays: A PSO approach,”IEEE Trans. Aerosp. Electron. Syst., vol. 47, no. 1, pp. 367–380,Jan. 2011.[10] K. Ishaque, Z. Salam, M. Amjad, and S. Mekhilef, “An improved particleswarm optimization (PSO)-based MPPT for PV with reduced steady-stateoscillation,” IEEE Trans. Power Electron., vol. 27, no. 8, pp. 3627–3638,

Aug. 2012.[11] K. Ishaque and Z. Salam, “A deterministic particle swarm optimizationmaximum power point tracker for photovoltaic system under partial shadingcondition,” IEEE Trans. Ind. Electron., vol. 60, no. 8, pp. 3195–3206,Aug. 2013.[12] R. Eberhart and J. Kennedy, “A new optimizer using particle swarmtheory,” in Proc. 6th Int. Symp. MHS, 1995, pp. 39–43.[13] D. Karaboga and C. Ozturk, “A novel clustering approach: Artificial beecolony (ABC) algorithm,” Appl. Soft Comput., vol. 11, no. 1, pp. 652–657,Jan. 2011.[14] B. Babar and A. Cr˘aciunescu, “Comparison of artificial bee colony algorithmwith other algorithms used for tracking of maximum power point ofphotovoltaic arrays,” presented at the Int. Conf. Renew. Energies PowerQual. (ICREPQ’14), Cordoba, Spain, Apr. 8–10, 2014.[15] B. Bilal, “Implementation of artificial bee colony algorithm on maximumpower point tracking for PV modules,” in Proc. 8th Int. Symp.Adv. Topics Elect. Eng. (ATEE), Bucharest, Romania, May 23–25, 2013,pp. 1–4.[16] D. Karaboga and B. Akay, “A comparative study of artificial bee colonyalgorithm,” Appl. Math. Comput., vol. 214, no. 1, pp. 108–132, Aug.2009.[17] Q. Pan, L. Wang, K. Mao, J. Zhao, and M. Zhang, “An effective artificialbee colony algorithm for a real-world hybrid flowshop problemin steelmaking process,” IEEE Trans. Autom. Sci. Eng., vol. 10, no. 2,pp. 307–322, Apr. 2013.[18] F. S. Abu-Mouti and M. E. El-Hawary, “Optimal distributed generationallocation and sizing in distribution systems via artificial bee colony algorithm,”IEEE Trans. Power Del., vol. 26, no. 4, pp. 2091–2101, Oct.2011.[19] L. D. S. Coelho and P. Alotto, “Gaussian artificial bee colony algorithmapproach applied to Loney’s solenoid benchmark problem,” IEEE Trans.Magn., vol. 47, no. 5, pp. 1326–1329, May 2011.[20] M. Seyedmahmoudian, S. Mekhilef, R. Rahmani, R. Yusof, andE. T. Renani, “Analytical modeling of partially shaded photovoltaicsystems,” Energies, vol. 6, no. 1, pp. 128–144, Jan. 2013.[21] K. S. Tey and S. Mekhilef, “Modified incremental conductanceMPPT algorithm to mitigate inaccurate responses under fast-changingsolar irradiation level,” Solar Energy, vol. 101, no. 1, pp. 333–342,Mar. 2014.[22] H. Patel and V. Agarwal, “Maximum power point tracking scheme forPV systems operating under partially shaded conditions,” IEEE Trans.Ind. Electron., vol. 55, no. 4, pp. 1689–1698, Apr. 2008.[23] W. Gao, S. Liu, and L. Huang, “A novel artificial bee colony algorithmbased on modified search equation and orthogonal learning,” IEEE Trans.Cybern., vol. 43, no. 3, pp. 1011–1024, Jun. 2013.[24] N. Femia, G. Petrone, G. Spagnuolo, andM. Vitelli, “Optimization of perturband observe maximum power point tracking method,” IEEE Trans.

Power Electron., vol. 20, no. 4, pp. 963–973, Jul. 2005.