Novel adaptive MPPT technique for enhanced performance of grid integrated solar photovoltaic system

In order to extract the maximum photovoltaic (PV) power under stochastic weather conditions, a maximum power point tracking (MPPT) technique is required. PV modules behave nonlinearly in unpredictable weather conditions, and the effectiveness of many control strategies under such conditions especially conventional ones-decreases significantly. It is essential to maximize the use of PV power in the system; thus, MPPT algorithms have been developed to ensure that grid integrated PV systems work best at the maximum power point regardless of weather conditions. This research presents a novel HLO-ANN (Horned Lizard optimized artificial neural network) MPPT technique for grid-integrated solar photovoltaic systems which is the improved version of the ANN-MPPT, developed using a novel HLO technique, which effectively optimizes weights of the neural network. The irradiance and temperature datasets from the NASA website have been utilized to train the neural network. The proposed methods have been evaluated by comparing their simulation results to other ANN optimized MPPT techniques: The results showed that the proposed method outperforms the other methods in terms of robustness, DC bus voltage regulation, tracking speed, convergence to the minimum error value, and tracking efficiency in maximizing the harvested power from the PV systems. Furthermore, the EN50530 MPPT efficiency test was carried out during both fast and slow-varying irradiance levels to examine the effectiveness of proposed algorithms.