Using AI Techniques to Improve the Power Quality of Standalone Hybrid Renewable Energy Systems

Abstract

As a developed and environmentally benign alternative for energy generation, renewable energy sources (RES) have attracted a lot of attention. The issue of energy quality, however, is one of the most difficult integration problems for RES. Voltage and frequency violations caused by waveform instability in the energy production from RES can harm equipment and lower the reliability of the power supply. In recent years, renewable energy sources like wind and solar power have drawn a lot of attention as environmentally friendly substitutes for traditional energy sources. Systems with artificial intelligence (AI) are becoming more and more common for use in automation, automated automation, and data analysis. In this research, the authors present a unique theory for an AI system that combines solar and wind energy to power AI applications. In order to improve the effectiveness and efficiency of AI systems, the suggested system makes use of the complementary properties of solar and wind energy, optimizing their generation and utilization.