Comparative analysis of reinforcement learning and artificial neural networks for inverter control in improving the performance of grid-connected photovoltaic systems.
Journal:
Scientific reports
Published Date:
Jul 8, 2025
Abstract
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. The goal of the project is to investigate the potential applications of RL algorithms for achieving maximum power point tracking (MPPT) and managing PV system maintenance and operation. According to the results, control of the inverter by RL yields better results than the ANN controller in all cases. Globally, increasing the use of PV systems for energy generation is a top goal to satisfy rising energy demands sustainably. By improving efficiency and dependability, AI control of PV systems helps to meet this challenge and further efforts in environmental sustainability and energy security. In terms of efficiency, reliability, and overall system performance, the research findings demonstrate that RL-based control of inverters outperforms ANN controllers. This comparison highlights how well RL works to control PV systems adaptively and efficiently in various environmental conditions. Total Harmonic Distortion (THD) for both current and voltage is compared and evaluated under ramp and random conditions. The results show that by consistently achieving reduced THD values, the RL controller outperforms the ANN controller in both dynamic and uncertain scenarios. This study reveals that RL exhibits superior adaptability and achieves lower THD compared to ANN, particularly under varying operational conditions. This comparative analysis fills a significant research gap, as comprehensive evaluations of this nature have not been adequately addressed in previous works. These results highlight how RL approaches may increase the dependability and efficiency of PV systems, advancing sustainable energy technology.
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