A swarm-based soft computing approach for harvesting maximum power in practical photovoltaic system

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Chinese explanation / 中文解读

中文解读待补充：本站会优先为光伏效率、钙钛矿太阳能电池、储能技术、太阳能热利用、BIPV、并网技术等高价值论文补充中文说明。

Original abstract

In order to satisfy the depletion of fossil fuels and the increasing demand, researchers are concentrating on renewable energy sources such as photovoltaic (PV) systems. To guarantee the effective operation of PV systems, a robust Maximum Power Point (MPP) Tracking technique is required to address nonlinear and multimodal challenges, such as MPP Tracking under uniformly distributed irradiance and partially shaded situations. In this context, this work proposes a Zone Segregated Adaptive Particle Swarm Optimization (ZSAPSO) technique for addressing the MPP Tracking (MPPT) problem in partially shaded and uniformly distributed irradiance situations. The proposed ZSAPSO technique basically uses basic structure of the APSO algorithm but, in APSO technique, the only one local best particle is considered throughout the execution whereas, in ZSAPSO, three local best particles are considered from three zones of search space to incur well exploration virtue of algorithm during starting its execution. Moreover, the sensitivity analysis is carried out in search for better performance of the ZSAPSO. Thereafter, the performance based on Settling Time (ST), steady state error (ESS), efficiency of MPP tracking of proposed ZSAPSO is compared with other cutting-edge techniques in MPPT application. All the simulated works are executed through MATLAB/SIMULINK and the same are authenticated with the help of practical setup. Every outcome shows how effectively the ZSAPSO manages MPPT problem. Specifically, under uniform irradiance, ZSAPSO achieved a tracking efficiency of 99.987% with the lowest steady-state error (0.037%) and improved settling time compared to conventional PSO variants. Under partial shading, it attained 99.977% efficiency with the minimum ESS (0.025%) and faster convergence than other swarm-based techniques.

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