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Since the last decade, partial shading conditions (PSCs) and its adverse influences on photovoltaic (PV) system performance have received due attention. It motivates researchers to explore methods to diminish/disperse the shading effects and/or novel PV array configurations to sustain under PSCs. To diminish the effects of PSCs, this paper presents a comprehensive review of various PV array configuration models for PV systems and metaheuristic approaches for shade dispersion effectively. Different PV array modeling approaches are identified, emphasizing their benefits, inadequacies and categorized according to vital features such as shade dispersion and improved performance in terms of efficiency; fill factor (FF), and maxima power, minimized power losses (PL) primarily. Besides these various PV array configurations such as hybrid, reconfigured, mathematical/game puzzle based advanced configurations are uniquely discussed with the existing configurations. In the current scenario, the metaheuristic algorithms are explored and widely accepted by researchers due to the long wire length requirement for PV array reconfiguration. This paper discusses and deliberates recent developments in methods of solar PV performance enhancement that deserves further study. Overall, the present study is helpful for academicians and researchers in the committed solar power installation area. INDEX TERMS Photovoltaic system, partial shading condition (PSC), game puzzle, total cross-tied (TCT), honey comb, bridge link, metaheuristic algorithm.

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Parameter estimation of photovoltaic modules is an essential step to observe, analyze, and optimize the performance of solar power systems. An efficient optimization approach is needed to obtain the finest value of unknown parameters. Herewith, this article proposes a novel opposition-based tunicate swarm algorithm for parameter estimation. The proposed algorithm is developed based on the exploration and exploitation components of the tunicate swarm algorithm. The opposition-based learning mechanism is employed to improve the diversification of the search space to provide a precise solution. The parameters of three types of photovoltaic modules (two polycrystalline and one monocrystalline) are estimated using the proposed algorithm. The estimated parameters show good agreement with the measured data for three modules at different irradiance levels. Performance of the developed opposition-based tunicate swarm algorithm is compared with other predefined algorithms in terms of robustness, statistical, and convergence analysis. The root mean square error values are minimum (6.83×10 -4 , 2.06×10 -4 , and 4.48×10 -6 ) compared to the tunicate swarm algorithm and other predefined algorithms. Proposed algorithm decreases the function cost by 30.11%, 97.65%, and 99.80% for the SS2018 module, SolarexMSX-60 module, and Leibold solar module, respectively, as compared to the basic tunicate swarm algorithm. The statistical results and convergence speed depicts the outstanding performance of the anticipated approach. Furthermore, the Friedman ranking tests confirm the competence and reliability of the developed approach.

Parameter extraction of the photovoltaic cell is a highly nonlinear complex optimization problem. This article proposes a new hybrid version of whale optimization and particle swarm optimization algorithm to optimize the photovoltaic cell parameters. The exploitation ability of particle swarm optimization with adaptive weight function is implemented in the pipeline mode with a whale optimization algorithm to improve its exploitation capability and convergence speed. The performance of the proposed hybrid algorithm is compared with six different optimization algorithms in terms of root mean square error and rate of convergence. The simulation result shows that the proposed hybrid algorithm produces not only optimized parameters at different irradiation levels (i.e., 1000 W/m2, 870 W/m2, 720 W/m2, and 630 W/m2) but also estimates minimum root mean square error even at a low level of irradiations. Furthermore, the statistical analysis validates that the average accuracy and robustness of the proposed algorithm are better than other algorithms. The best values of root mean square error generated by the proposed algorithm are 7.1700×10−4 and 9.8412×10−4 for single-diode and double-diode models. It is observed that the estimated parameters based on the optimization process are highly consistent with the experimental data.