GMPPT Control of Photovoltaic Array System Based on BP-IWOA under Partial Shading

Solar photovoltaic power generation is an ideal alternative to traditional energy sources because it is naturally rich and environmentally friendly. Under the condition of partial shading, the PV array P-V characteristic curve presents multiple peaks, namely a global maximum power point (GMPP) and several local maximum power points (LMPP). Maximum power extraction of PV array plays a crucial role in improving the efficiency of solar power generation. Compared with the traditional tracking method, when tracking the maximum power point, the local maximum power point will fall into the local maximum power point. In this paper, under the condition of partial shading, a GMPPT control algorithm based on the combination of BP neural network and IWOA improved whale algorithm for 1x4 series photovoltaic array is proposed to track the global maximum power point. BP neural network generates approximate global maximum power point. The improved whale algorithm IWOA enhances the local search capability by adding inertia factor and modifying adaptive parameters so as to find the global maximum power point GMPP quickly and accurately. In this paper, the performance difference between BP-IWOA GMPPT control algorithm and traditional WOA GMPPT control algorithm under partial shading is compared in Matlab/Simulink. The results show that compared with the traditional WOA algorithm, BP-IWOA has better tracking performance and stronger anti-interference ability under the external environment change.