Abstract
This paper presents an experimental and simulation study of a novel hybrid technique for maximum power point tracking (MPPT) based on a modified PSO algorithm utilizing an intelligent artificial neural network (IANN) for partially shaded PV systems. The technique leverages experimental voltage and current data from the PV system, with filtered instructor feedback training the IANN-based optimization method. MATLAB-Simulink was used for analyzing and interpreting simulation results, as well as demonstrating the performance of the algorithm. In this hybrid approach, the IANN significantly accelerates MPP tracking by providing the PSO algorithm with more accurate initial particle positions, enhancing efficiency and data collection speed during rapid weather changes. Several algorithms, including P&O, Cuckoo, IANN, PSO, and the hybrid IANN-PSO, were implemented using a dual-core DSP F28379D card. The performance of the proposed technique was examined and compared with various algorithms such as PSO, Cuckoo, and IANN controllers. Compared to recent work published in the literature, the proposed hybrid technique shows superior results in various performance metrics, achieving a maximum power efficiency of 99.99%, a relative error of 0.000001, and a minimum tracking acceleration of 0.01 seconds. Additionally, electronic circuits (PCB boards) were developed and implemented to demonstrate the efficiency of the proposed system in real-world applications.