Optimizing Power Quality in Hybrid Renewable Energy Systems through Advanced Intelligent Control Techniques

The ability of hybrid renewable energy systems (HRES) to combine the advantages of several renewable energy sources has attracted a lot of attention. The intermittent nature of renewable energy sources, such wind and solar power, can make it difficult to keep the grid's power quality constant. Advanced intelligent control strategies are presented in this work with the goal of improving power quality in HRES. In order to reduce power quality difficulties, the research suggests a multidimensional approach that combines the capabilities of advanced control algorithms, intelligent decision-making, and predictive analytics. The main goal is to solve typical issues in HRES, such as harmonic distortions, voltage fluctuations and frequency variations. Proactive system management is made possible by the control approach, which forecasts renewable energy generation trends using machine learning techniques. In addition, real-time monitoring and control systems are included to enable prompt responses to modifications in the power generating mix. The HRES guarantees smooth integration and interaction by utilizing sophisticated hardware and software components to achieve these control mechanisms. This paper presents the results of an extensive simulation research that shows how well the suggested intelligent control solutions mitigate problems with power quality. The results show that the grid's frequency regulation, harmonic distortions, and voltage stability have all significantly improved.