Comparative analysis of reinforcement learning and artificial neural networks for inverter control in improving the performance of grid-connected photovoltaic systems.

This research aims to explore the potential applications of artificial intelligence (AI) methods, such as reinforcement learning (RL) and artificial neural networks (ANN), in controlling inverter systems and enhancing the performance of photovoltaic (PV) systems. PV systems are essential for producing sustainable energy, as they improve the reliability and efficiency of renewable power resources by utilizing AI to control inverters. This study examines the application of AI techniques to manage PV systems, given the increasing importance of energy generation through PV systems on a global scale.

Transformer fault diagnose intelligent system based on DGA methods.

Power transformers have great importance in power system networks. Any malfunctions in the power transformers cause a system disconnection, which leads to lost profits for the electricity utilities. Transformer malfunctions can result from various stresses like electrical, thermal, or mechanical pressures acting on the insulation system, typically composed of insulating oil and paper.