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This article proposes a hybrid method that combines the self-adaptive neural network (SANN) and incremental conductance (IC) algorithm for maximum power point tracking (MPPT) in a photovoltaic (PV) system. The main objective of the proposed algorithm is to extract the maximum power of solar panels quickly and faster than the recent artificial neural network (ANN)-IC MPPT algorithm. Compared to the previous ANN-IC algorithm, a minimum number of data sets were used to obtain the GMP without compromising accuracy. Furthermore, comparison demonstrates that the suggested SANN-IC provides high efficiency, reliability, rapid tracking, and less response time tracking of GMP than w.r.t. all state-of-the-art techniques and ANN-IC method. The simulation is done with the help of MATLAB/SIMULINK. This is the first article about the performance of the hybrid MPPT technique tested by a sun simulator. The benefit of this hybrid method is that it reduces the search space of ANN, and hence, convergence speed is significantly increased. Training time and computing power are optimized by altering the number of epochs and requiring less memory. The experimental result showed the best convergence time and less oscillation than the ANN-IC method. Hence, the overall performance of SANN-IC in a steady-state condition with a partial shading condition meets the motive of the work.

Maximum power point tracking based on self-adaptive neural network combined with incremental conductance