A novel method based on tuna swarm algorithm under complex partial shading conditions in PV system

Under partial shading conditions (PSCs), the output characteristic curve of PV system fluctuates, hence contains multiple peaks. The higher efficiency of the Global Maximum Power Point(GMPP) tracking technique becomes a must to the Photovoltaic(PV) system, therefore, an algorithm based on Tuna Swarm Optimization (TSO) is proposed in this study. Then, it is applied to track the global MPPT of PV system under partial shading conditions, six shading scenarios have been considered. This method is on a PV system consisting of five panels that are formed in a MATLAB/Simulink platform. And then Particle Swarm Optimization(PSO) algorithm, Black Widow Spider(BWO), Squirrel Search Algorithm(SSA) and TSO are compared, the outcomes are analyzed in detail. The proposed algorithm reached maximum power of over 97% under the six PSCs. And the response time in all six scenarios were between 0.2s and 0.4s. This value is good compared to the other three algorithms. Finally, hardware verification demonstrate the performance and feasibility of the proposed MPPT. The proposed method reached maximum power over 91% in the Hardware-in-loop(HIL) and Rapid Control Prototype(RCP) platform.