Global Maximum Power Point Research Articles

An improved sliding mode control–based GMPPT algorithm for photovoltaic system

In this paper, an improved fixed-frequency-based integral sliding mode controller is designed and applied to a stand-alone photovoltaic (PV) system to obtain the desired reference signal for the maximum power point tracking. The proposed technique is designed with the modified sliding surface and sigmoidal function-based control law which improves the transient and steady-state performance of the PV system under uniform and partial shading conditions. Moreover, the cost of the proposed technique is further reduced by predicting the inductor current without employing a current sensor. In addition, the varying tuning factor strategy is used to further reduce the undershoot and overshoot issues during the tracking process. The desired voltage reference signal is provided by the online fractional open-circuit voltage algorithm. Furthermore, the proposed sliding mode control technique is integrated with the 0.8 Voc model-based algorithm to obtain the global maximum power point. To check the effectiveness and fair analysis, the proposed technique is tested in MATLAB/Simulink environment under partial shading conditions, and its results are compared with the recently developed double integral derivative sliding mode control technique. The suggested technique is also tested experimentally on a 245 W PV module, in conjunction with a boost converter. The simulation and experimental findings reveal that the proposed technique outperforms the double integral derivative sliding mode control technique in terms of steady-state and transient performance, tracking speed, and transient efficiency.

An improved genetic algorithm based fractional open circuit voltage MPPT for solar PV systems

To extract the maximum power from solar PV, maximum power point tracking (MPPT) controllers are needed to operate the PV arrays at their maximum power point under varying environmental conditions. Fractional Open Circuit Voltage (FOCV) is a simple, cost-effective, and easy to implement MPPT technique. However, it suffers from the discontinuous power supply and low tracking efficiency. To overcome these drawbacks, a new hybrid MPPT technique based on the Genetic Algorithm (GA) and FOCV is proposed. The proposed technique is based on a single decision variable, reducing the complexity and convergence time of the algorithm. MATLAB/Simulink is used to test the robustness of the proposed technique under uniform and non-uniform irradiance conditions. The performance is compared to the Perturb & Observe, Incremental Conductance, and other hybrid MPPT techniques. Furthermore, the efficacy of the proposed technique is also assessed against a commercial PV system’s power output over one day. The results demonstrate that the proposed GA-FOCV technique improves the efficiency of the conventional FOCV method by almost 3%, exhibiting an average tracking efficiency of 99.96% and tracking speed of around 0.07 s with minimal steady-state oscillations. Additionally, the proposed technique can also efficiently track the global MPP under partial shading conditions and offers faster tracking speed, higher efficiency, and fewer oscillations than other hybrid MPPT techniques.

Spotted Hyena-Bat Optimized Extreme Learning Machine for Solar Power Extraction

Artificial intelligence, machine learning and deep learning algorithms have been widely used for Maximum Power Point Tracking (MPPT) in solar systems. In the traditional MPPT strategies, following of worldwide Global Maximum Power Point (GMPP) under incomplete concealing conditions stay overwhelming assignment and tracks different nearby greatest power focuses under halfway concealing conditions. The advent of artificial intelligence in MPPT has guaranteed of accurate following of GMPP while expanding the significant performance and efficiency of MPPT under Partial Shading Conditions (PSC). Still the selection of an efficient learning based MPPT is complex because each model has its advantages and drawbacks. Recently, Meta-heuristic algorithm based Learning techniques have provided better tracking efficiency but still exhibit dull performances under PSC. This work represents an excellent optimization based on Spotted Hyena Enabled Reliable BAT (SHERB) learning models, SHERB-MPPT integrated with powerful extreme learning machines to identify the GMPP with fast convergence, low steady-state oscillations, and good tracking efficiency. Extensive testing using MATLAB-SIMULINK, with 50000 data combinations gathered under partial shade and normal settings. As a result of simulations, the proposed approach offers 99.7% tracking efficiency with a slower convergence speed. To demonstrate the predominance of the proposed system, we have compared the performance of the system with other hybrid MPPT learning models. Results proved that the proposed cross breed MPPT model had beaten different techniques in recognizing GMPP viably under fractional concealing conditions.

A novel intelligent hybrid algorithm for maximum power point tracking in PV system

ABSTRACT The position of MPP of a solar panel changes with changes in weather conditions such as radiation, temperature and partial Shadow. In such a situation, several local MPPs and one GMPP are created in the P-V curve, and the common methods of MPPT(P&O, IC,…) have converged due to the failure to recognise the local MPP. They cause a lot of losses to the photovoltaic system. Therefore, in this article, it has been tried to provide a new method of particle swarm optimisation with variable coefficients, which in combination with the perturb and observe method can reach the GMPP in the fastest possible time. In this proposed method, we first reach the convergence of the GMPP using the VCPSO method, and after converging to it, we go to the P&O method and use P&O until significant changes are made in the system, and if observed Variations To re-examine the GMPP, we return to the VCPSO algorithm. The results of the simulation of the efficiency of the proposed method show that this method was able to reach the GMPP in the shortest time, and the efficiency of the photovoltaic system increases and the effects of partial shading are reduced.

DPVM-SEC based methodology to reconfigure PV system and characterization during PSCs: Experimental study using data acquisition system

Partial shadowing produces significant hindrance to the efficient use of solar photovoltaic (PV) system. Partial shading conditions (PSCs) reduce PV system performance and show the highly non-linear behavior with respect to power-voltage (P-V) graphs.. A DPVM- SEC , 6 × 4 size array is suggested in the manuscript to reduce the impacts of shadowing on PV arrays. Also, the efficiency of the traditional arrangements, series–parallel (SP) and total-cross-tied (TCT) are analyzed and compared with the dynamic PV module with static electrical connections (DPVM- SEC ). Various performance indices such as power and voltage at global maximum power point (GMPP), power loss (PL) and fill factor (FF) are evaluated to demonstrate that the DPVM- SEC based method is the appropriate configuration for eliminating the shading impacts. Furthermore, an ATmega-328 microcontroller-based data acquisition system (DAS) is developed to measure the real-time electrical parameters. The experimental study demonstrates the feasibility to characterize the PV system with accuracy and less time taken processes. In this paper, the design of DAS is very useful for new learners and affirms the feasibility to use in further related PV system characterization study under shading conditions.

An Efficient Honey Badger Optimization Based Solar MPPT Under Partial Shading Conditions

Due to the enormous utilization of solar energy, the photovoltaic (PV) system is used. The PV system is functioned based on a maximum power point (MPP). Due to the climatic change, the Partial shading conditions have occurred under non-uniform irradiance conditions. In the PV system, the global maximum power point (GMPP) is complex to track in the P-V curve due to the Partial shading. Therefore, several tracking processes are performed using various methods like perturb and observe (P & O), hill climbing (HC), incremental conductance (INC), Fuzzy Logic, Whale Optimization Algorithm (WOA), Grey Wolf Optimization (GWO) and Flying Squirrel Search Optimization (FSSO) etc. Though, the MPPT is not so efficient when the partial shading is increased. To increase the efficiency and convergences in MMPT, the Honey Badger optimization (HBO) algorithm is presented. This HBO model is motivated by the excellent foraging behaviour of honey badgers. This HBO model is used to achieve the best solution in GMPP tracking and speed convergence. The HBO methodology is also compared with prior P&O, WOA and FSSO methods using MATLAB. Therefore, the experiment shows that the HBO method is performed a higher tracking than all prior methods.

Optimized Voltage Search Algorithm for Fast Global Maximum Power Point Tracking in Photovoltaic Systems

This paper introduces an improved maximum power point tracking (MPPT) algorithm, the optimized voltage search algorithm (OVS). The OVS is used to search for the global maximum power point (GMPP) under partial shading conditions (PSCs) for photovoltaic (PV) systems. The proposed algorithm aims to optimize the search areas of the power–voltage curve and can be implemented by determining different search areas during the GMPP tracking. After determining these search areas, the proposed algorithm optimally searches these areas with multiple forward and optional backward processes as required. These multiple forward processes can skip more unnecessary voltage intervals than other MPPT methods, thereby reducing voltage search intervals and GMPP tracking time. The performance of the proposed algorithm was evaluated against seven other MPPT algorithms: the search-skip-judge, section-dividing point (SDP), skipping adaptive perturbation and observation, modified maximum power trapezium (M-MPT), high-performance, voltage track optimizer, and plain hybrid M-MPT and SDP algorithms. These MPPT algorithms were validated on the basis of simulation and experimental results, which demonstrated that the proposed algorithm exhibited a shorter tracking time, fewer computational iterations, shorter voltage scanning intervals, and longer voltage skipping intervals than those of the other algorithms under PSCs.

Research on the MPPT of Photovoltaic Power Generation Based on the CSA-INC Algorithm

The existing Maximum Power Point Tracking (MPPT) method has low tracking efficiency and poor stability. It is easy to fall into the Local Maximum Power Point (LMPP) in Partial Shading Condition (PSC), resulting in the degradation of output power quality and efficiency. It was found that various bio-inspired MPPT based optimization algorithms employ different mechanisms, and their performance in tracking the Global Maximum Power Point (GMPP) varies. Thus, a Cuckoo search algorithm (CSA) combined with the Incremental conductance Algorithm (INC) is proposed (CSA-INC) is put forward for the MPPT method of photovoltaic power generation. The method can improve the tracking speed by more than 52% compared with the traditional Cuckoo Search Algorithm (CSA), and the results of the study using this algorithm are compared with the popular Particle Swarm Optimization (PSO) and the Gravitational Search Algorithm (GSA). CSA-INC has an average tracking efficiency of 99.99% and an average tracking time of 0.19 s when tracking the GMPP, which improves PV power generation’s efficiency and power quality.

ИНТЕЛЛЕКТУАЛЬНАЯ МОДЕЛЬ МАКСИМИЗАЦИИ ГЕНЕРИРУЕМОЙ МОЩНОСТИ РЕКОНФИГУРИРУЕМОЙ СОЛНЕЧНОЙ ЭЛЕКТРОСТАНЦИИ

The global maximum power point tracking of a solar power plant in partial shading demands a global optimization. Standard algorithms for tracking of maximum power point do not provide for a maximum global power of a solar power plant during real time mode due to low convergence. A model of maximizing the generated power of a reconfigurable solar power plant was developed as a modified fuzzy deep neural network based on the modified quantum-behaved particle swarm optimizer. This neural network consists of the following: convolutional units, recurrent neural networks, and fuzzy units. By processing the sensor signals and images of the solar array, the set modified fuzzy deep neural network generates a reference voltage and an electrical interconnection matrix of the parallel-serial solar array, maximizing its power under non-uniform insolation.

Performance Analysis of Three MPPT Controls Under Partial Shading: P&O, PSO and ANN

Photovoltaic energy has gained an important place among renewable energies. Given its weather-dependent efficiency, Maximum Power Point Tracking (MPPT) techniques are essential to maximize the energy extracted from the panels. Partial shading is a phenomenon that degrades the efficiency of photovoltaic panels, by distorting the power vs. voltage curve, which consequently has several peaks, one of which is a global maximum. The aim of this work is to evaluate the ability of MPPT methods to find the global maximum power point in partial shading conditions. To achieve this, the work focuses on simulating and discussing the performance of three MPPT methods, the Perturb and Observe (P&O) command, a command based on particle swarm optimization (PSO), and a command based on artificial neural networks (ANN). By comparing the results of simulations of the three controllers under different partial conditions, we can see that the P&O controller loses accuracy in partially shaded conditions, while the PSO controller is more accurate but with a very slow response time, and lastly, the ANN-based controller offers the best precision and speed performance.

Photovoltaic global maximum power point tracking method based on fuzzy PI control

At present, algorithms for finding the maximum power point of photovoltaic systems generally have some limitations, especially in cases of uneven light intensity, traditional algorithms often fall into local optima, with limited application space, and their tracking speed and stability are also not very good. To address this issue and improve tracking performance, the combination control of global scanning and fuzzy PI control is proposed. This combination control can avoid falling into local optima and achieve stable output of maximum power. This article first proves theoretically that the proposed method can achieve maximum power output; Then, an experimental platform was built for testing. Build control and sampling circuits and use a solar simulator to irradiate the photovoltaic array unevenly. In this environment, test the control effects of combination control, conductivity increment method, and disturbance observation method respectively. Through testing, it was found that compared to conductivity increment method and disturbance observation method, the proposed method in this article has faster tracking speed, higher output power, and better stability. The overall performance of combination control is better than other algorithms and has a relatively broad application space.

Flower pollination global peak search algorithm for partially shaded solar photovoltaic system

Photovoltaic (PV) systems are required to function at their maximum power point (MPP) to fully utilize solar energy. Solar PV panels are configured with maximum power point tracking (MPPT) systems to enhance the generation and supply of the maximum available power. However, owing to external environmental factors and partial shading conditions (PSCs), the tracking process becomes complex. Conventional MPPT methods cannot always achieve the global MPP. Thus, an advanced metaheuristic MPPT scheme for PV systems, the flower pollination algorithm (FPA), is implemented in this study to find the best global duty cycle to obtain the maximum power output, and a Levy flight is utilized to enhance FPA convergence. The FPA MPPT model was established, and its performance was compared with the conventional, and some meta-heuristic-based MPPT techniques; perturb and observe (PO), incremental conductance (INC), particle swarm optimization (PSO), cuckoo search (CS), grey wolf optimization (GWO), genetic algorithm (GA), double integral sliding mode control (DISMC), and hill-climbing (HC). Different scenarios were considered for the simulation: partial shading, complex partial shading, long-width, and long-length partial shading, and different temperature conditions employing series and series-parallel PV array configurations. In addition, real-world field atmospheric irradiance data for Daejeon city is used for the study. In terms of efficiency, the FPA attains at most 83%–96% of the expected GMP from the PV array as compared with the other MPPT techniques in all scenarios considered. The adopted FPA obtained the following root mean square error (RMSE), mean relative error (MRE), and mean absolute error (MAE) values respectively for each Scenario as follows: Scenario I: RMSE: 0.0061, MRE: 0.0463, MAE: 6.0745e-06, Scenario II: RMSE: 0.0116, MRE: 0.0430, MAE: 1.0738e-05, Scenario III: RMSE: 0.0640, MRE: 0.206, MAE: 4.8239e-05, Scenario IV (varying temperature conditions): RMSE: 0.0062–0.0074, MRE: 0.0460–0.0548, MAE: 6.1333e-06 – 7.4922e-06. A unique sensitivity performance comparison analysis is done employing the power spectral analysis to validate the effectiveness of the adopted FPA to the other MPPT techniques. The FPA performs better than all the techniques in all scenarios in comparing their maximum power tracked, convergence speed and accuracy, system efficiency, and optimum statistical results attained.

A novel MPPT design based on the seagull optimization algοrithm for phοtovοltaic systems operating under partial shading

The use of a maximum power point (MPP) tracking (MPPT) controller is required for photovoltaic (PV) systems to extract maximum power from PV panels. However, under partial shading conditions, the PV cells/panels do not receive uniform insolation due to several power maxima appear on the PV array's P–V characteristic, a global MPP (GMPP) and two or more local MPPs (LMPPs). In this scenerio, conventional MPPT methods, including pertub and observe (P&O) and incremental conductance (INC), fail to differentiate between a GMPP and a LMPP, as they converge on the MPP that makes contact first, which in most cases is one of the LMPPs. This results in considerable energy loss. To address this issue, this paper introduces a new MPPT method based on the Seagull Optimization Algorithm (SOA) to operate PV systems at GMPP with high efficiency. The SOA is a new member of the bio-inspired algorithms. When compared to other evolutionary techniques, it uses fewer operators and modification parameters, which is advantageous when considering the rapid design process. In this paper, the SOA-based MPPT scheme is first proposed and then implemented for an 80 W PV system using the MATLAB/SIMULINK environment. The effectiveness of the SOA based MPPT method is verified by comparing its performance with P& O and PSO (particle swarm optimization) based MPPT methods under different shading scenarios. The results demonstrated that the SOA based MPPT method performs better in terms of tracking accuracy and efficiency.

A Maximum Power Point Tracker Using the Bald Eagle Search Technique for Grid-Connected Photovoltaic Systems

Maximum power point tracker (MPPT) methods work to maximize the output power of a PV system under changes in meteorological conditions. The performance of these methods depends on the complexity of the algorithm and the number of used variable inputs for obtaining the MPP value. Moreover, they oscillate around the MPP in steady-state operations, causing a waste of power and power loss. Moreover, they do not work perfectly for a PV system running under partial shading conditions. Therefore, this paper proposes modifications to the global maximum power point bald eagle search-based (GMPP BES) method so that it runs as an MPPT as well. The modifications enable the GMPP BES method to detect minor changes in insolation and temperature by observing the changes in the PV array output voltage and, accordingly, trigger the search for the suitable MPP voltage. An experimental setup using a real-time digital simulator (RTDS) was utilized to evaluate the modified GMPP BES-based method under real changes in insolation and ambient temperature. The RTDS simulations confirm the capability of the modified method to accurately and efficiently locate the MPP values. Furthermore, the results demonstrate that the proposed method performs better than the perturb and observe (PO) method concerning its ability to respond to changes in insolation and ambient temperature and its ability to arrive at correct MPP values with nearly zero oscillation around the maximum power point. Thus, with these advantages, the proposed method can be considered a practical solution for solar farms that have to harvest large amounts of energy.

Extended search pigeon-inspired optimized MPPT controller for solar quadcopter

PurposeThe purpose of this paper is to propose a novel maximum power point track (MPPT) controller for a type of solar quad-copter to solve the problem of tracking the maximum power point (MPP) when it works in nonuniform environment conditions.Design/methodology/approachThe influence of uniform and nonuniform illumination and different temperatures results in the output characteristics of the solar array arising multiple local MPPs. To track the global MPP of the solar array on the designed solar quadcopter, a type of MPPT controller based on an improved pigeon-inspired optimization (PIO) algorithm is proposed.FindingsA novel type of MPPT controller based on extended search PIO (ESPIO) algorithm, called ESPIO–MPPT controller, is introduced emphatically, which is used to extend the solar quadcopter’s flight time. The simulation experiments show that the ESPIO–MPPT controller can find the global MPP (GMPP) with smaller amplitudes of oscillation and less time cost.Practical implicationsThe proposed solar quadcopter with ESPIO–MPPT controller has satisfactory flight performance which can greatly broaden its mission scope.Originality/valueA type of efficient MPPT algorithm based on ESPIO is proposed for GMPP tracking of solar quadcopters in nonuniform environment conditions.

Power reserve control strategy of PV system for active power reserve under dynamic shading patterns

The de-loading technique in photovoltaics is used to reserve some active power for frequency regulation purposes. However, the selection of the de-loading method depends upon the shading conditions of photovoltaics. Many de-loading methods have already been developed for uniform shading conditions. Whereas for partial shading conditions, few methods are available for static shading patterns, while, the area of dynamic shading is untouched. Under dynamic shading, the global maximum power point changes continuously. Therefore, in this paper, in order to develop a power reserve under dynamic shading patterns of partial shading conditions, a trained artificial neural network is used. The information about the local and global maximum points is obtained from this neural network, whereas, the system de-loading is achieved using a de-loading algorithm. The neural network is trained using different irradiance patterns which lead to two or three peaks on the power vs. voltage curve of photovoltaics. The control is easy to implement and can handle up to three peak system. The simulation and experimental results have been used to verify the effectiveness of the control.

A comparative study between deterministic and two meta-heuristic algorithms for solar PV MPPT control under partial shading conditions

• Under partial shading conditions, adequate tuning of firefly and particle swarm optimization maximum power point tracking algorithms converge to the global maximum power point irrespectively of the starting voltage value. • Under partial shading conditions, there is no guarantee that perturb and observe algorithm will converge to the global maximum power point even for different starting voltage values. • The sensitivities of the parameters of firefly algorithm in improving the performance of the algorithm are investigated to find the best possible control parameters. • Firefly algorithm based maximum power point tracking performed slightly well than particle swarm optimization based maximum power point tracking for most irradiance patterns investigated in this paper. During partial shading conditions (PSC), the PV modules in a solar PV array become reverse biased and act as a load, resulting in hotspot issues. This can significantly decrease the efficiency of the solar PV. The general way to deal with PSC is to connect bypass diodes across the non-shaded PV module. However, this will alter the uniform characteristics of the PV array, resulting in multiple power peaks i.e., a multi modal landscape. The conventional gradient-based Perturb and Observe (PnO) algorithm generally used for maximum power point tracking (MPPT) in solar PV is ineffective in finding the maximum power during PSC because it is prone to converge to local optimum due to its nature of searching in a multimodal landscape. However, in this article an attempt is made to show how the PnO behaves in a multimodal landscape considering different starting points. Robust stochastic algorithms that are based on a population of search agents are used to guarantee convergence to the global MPP. In this paper, the maximum power point under PSC was tracked using two meta-heuristic algorithms namely, particle swarm optimization (PSO) and the firefly algorithm (FA). The performances of these algorithms in tracking the maximum power point are evaluated. The efficiency, standard deviation (STD), and root mean square error (RMSE) of the PSO and FA algorithms are compared to those of the PnO algorithm. The PSO was found to have the lowest RMSE, and the FA had the lowest STD. The efficiency of the PSO and FA were relatively the same. Simulation results show that PSO and FA-based MPPT algorithms can efficiently track the global maximum power point (GMPP) irrespectively of the starting points. On the other hand, PnO is shown to be unreliable under PSC even with different starting points.

Maximum power point tracking based on improved spotted hyena optimizer for solar photovoltaic

<span lang="EN-US">The conventional maximum power point tracking (MPPT) method such as perturb and observe (P&O) under partial shading conditions with non-uniform irradiation, can get trapped on local maximum power point (LMPP) and cannot reach global maximum power point (GMPP). This study proposes a bio-inspired metaheuristic algorithm spotted hyena optimizer (SHO) and improved SHO as a new MPPT technique. The proposed SHO-MPPT and improved SHO-MPPT are used to extract GMPP from solar photovoltaic (PV) arrays operated under uniform irradiation and non-uniform irradiation. Simulation with Powersim (PSIM) and experimental with the emulated PV source were presented. Furthermore, to evaluate the performance of the proposed algorithm, SHO-MPPT is compared with P&O-MPPT and particle swarm optimization (PSO)-MPPT. The SHO-MPPT has an accuracy of 99% and has the good capability, but there are power fluctuations before reaching MPP. Therefore, improved SHO-MPPT was developed to get better results. The improved SHO-MPPT proved high accuracy of 99% and faster than SHO-MPPT and PSO-MPPT in tracking the maximum power point (MPP). Furthermore, there are minor power fluctuations.</span>

A Novel Algorithm Based on Voltage and Current Perturbation to Track Global Peak Under Partial Shading Conditions

Under partial shading conditions, photovoltaic (PV) systems exhibit multiple peaks in their power-voltage ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbf{P-V}$</tex-math></inline-formula> ) characteristics. It is essential to extract maximum energy from the PV system. The global maximum power point tracking (GMPPT) algorithms presented in the literature, track the global peak using different methods. It is imperative to have minimal convergence time for GMPPT process. This paper proposes a novel algorithm to track the global peak using voltage and current perturbation. The new GMPPT algorithm operates in a current perturbation or voltage perturbation mode, based on the value of a control variable. In either mode, the proposed technique generates reference current or reference voltage, for navigating the operating point to GMPP location. The proposed algorithm is compared with two GMPPT algorithms, namely, modified maximum power trapezium (M-MPT) and high-performance GMPPT algorithms. The simulation studies are performed in MATLAB and is validated using a laboratory prototype, with dSPACE 1202 MicroLabBox controller for implementing GMPPT methods. Simulation and experimental results show that the new technique exhibits superior performance in terms of tracking time. Also, the energy efficiency is improved by 40% while using the proposed GMPPT algorithm for the irradiance profiles considered in this paper compared to the other two techniques.

Comparative analysis of evolutionary-based maximum power point tracking for partial shaded photovoltaic

&lt;span lang="EN-US"&gt;The characteristics of the photovoltaic module are affected by the level of solar irradiation and the ambient temperature. These characteristics are depicted in a V-P curve. In the V-P curve, a line is drawn that shows the response of changes in output power to the level of solar irradiation and the response to changes in voltage to ambient temperature. Under partial shading conditions, photovoltaic (PV) modules experience non-uniform irradiation. This causes the V-P curve to have more than one maximum power point (MPP). The MPP with the highest value is called the global MPP, while the other MPP is the local MPP. The conventional MPP tracking technique cannot overcome this partial shading condition because it will be trapped in the local MPP. This article discusses the MPP tracking technique using an evolutionary algorithm (EA). The EAs analyzed in this article are genetic algorithm (GA), firefly algorithm (FA), and fruit fly optimization (FFO). The performance of MPP tracking is shown by comparing the value of the output power, accuracy, time, and tracking effectiveness. The performance analysis for the partial shading case was carried out on various populations and generations.&lt;/span&gt;