Optimization of maximum power point tracking in a wind-solar hybrid power plant by neural networks for reducing total harmonic distortion (THD)

The economic viability of distributed energy resources (DERs) like wind turbines and photovoltaic (PV) units is hampered by their low conversion efficiencies and ineffective energy management. This research aims to improve Maximum Power Point Tracking (MPPT) in a hybrid wind-PV power system to reduce voltage and current oscillations by using the properties of DC link behavior. The innovation in this study stems from creating an integrated MPPT supervisor that responds to both the partial shading of PV modules and the variability of wind simultaneously and without utilizing current or voltage sensors. An artificial neural network (ANN) is used for this purpose, and its parameters are tuned by the Particle Swarm Optimization (PSO) algorithm. The proposed strategy was implemented in simulations compared to dynamic P&O, standalone ANN, and hybrid PSO-ANN frameworks. Based on the simulations performed, the PSO-ANN controller outperforms other methods by achieving better efficiency in MPPT when using DC link voltage as input and lowering Total Harmonic Distortion (THD). Also, the controller reduces the DC link voltage ripple while attenuating current and voltage THD to 3% and 2%, respectively. Moreover, during islanded operation, the controller decreases the distortion by 1.27%, showing enhanced system stability without traditional feedback control.