Artificial Rabbit Optimization based MPPT Technique for PV System Performance Investigation

Nirmal Kumar Pandey, Rupendra Kumar Pachauri, Sushabhan Choudhary, Sudhakar babu Thanikanti, Sumit Kumar Maitra

Abstract


Comprising partial shadowing (PS) conditions, the photovoltaic cells in the array of solar panels get reversed biased and function as a load, resulting in hotspot concerns that can dramatically decrease the photovoltaic's efficiency. The Perturb & Observe (MPPT) technique is typically used in solar systems, but it is highly challenging to recognize the actual maxima when both local maxima and global maxima are present during the PS conditions. The solar PV system's efficiency may be significantly decreased as a result of an innovative approach used in this research that employs artificial rabbit optimization (ARO). In the present research, the GMPP was tracked under PS conditions utilizing the proposed ARO technique in conjunction with additional meta-heuristic algorithms such as PSO, GWO, FPA, and CS.. We assess how well different strategies perform in terms of GMPP tracking. The effectiveness, standard deviation (STD), rise time, settling time, mean, root mean square error (RMSE), and median of metaheuristic procedures are evaluated and compared to those of the traditional P&O methodology. The constraints of conventional P&O algorithms are eliminated by a suggested algorithm, which can adapt to quickly changing conditions by collecting numerous peaks. The proposed algorithm's advantage over the traditional P&O technique and most poluar metaherustic algorithms such as PSO, GWO, and FPA is shown through comparison with the latter in terms of accuracy, efficiency, and decreased oscillations. The proposed model reaches an efficiency of roughly 99.98%. Additionally, it has a rising time of about 387.38 ms , a settling time of 2.77s, and has best standard deviation of 13.15.


Keywords


Artificial rabbit optimization, Partial shading, PV systems, Metaheuristic algorithms, Global maximum power point.

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References


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DOI (PDF): https://doi.org/10.20508/ijrer.v16i1.14797.g9166

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