A novel hybrid series salp particle Swarm optimization (SSPSO) for standalone battery charging applications

This paper presents a novel hybrid series salp particle swarm optimization (SSPSO) algorithm for tracking the global maximum power point (GMPP) of the standalone battery charging systems under partial shading conditions (PSC). The photovoltaic (PV) characteristics such as power current (P-I) and power voltage (P-V) have a global peak and various local peaks with complicated shapes under PSC. Some conventional techniques failed to track accurately the GMPP under shaded conditions, therefore, the proposed SSPSO with its accuracy and performance of tracking nearly the GMPP is used and compared with other existing algorithms. DC-DC buck boost converter is used to adjust the impedance and match the proposed model with the output of the system. The same PV shaded patterns are applied with other conventional algorithms and the simulations results show that the novel hybrid SSPSO can quickly track the GMPP within a minor time, with high efficiency and moreover presents a high quality of tracked power as compared to the existing selected techniques. The proposed SSPSO presents the average tracking efficiency of 99.99%, which is highly required to be applied in the suburb areas or remoted areas where the partial shading repeatedly occurs in PV array due to the environment effects. (c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Ain Shams University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/ by-nc-nd/4.0/).

Automated summary

This paper presents a novel hybrid SSPSO algorithm for accurately tracking the global maximum power point (GMPP) of standalone battery charging systems under partial shading conditions. The proposed algorithm outperforms existing techniques in terms of tracking efficiency and provides high-quality tracked power.