ANALYSIS OF HYBRID SOLAR - PV WIND STAND-ALONE SYSTEMS USING ARTIFICIAL INTELLIGENCE TECHNIQUES

作者：Sunil, Deepak Kumar Joshi, Nirma Kumari Sharma · 发表于：International Journal of Technical Research & Science · 年份：2024 · DOI：10.30780/ijtrs.v09.i04.002 · 被引用次数：1

This research proposes a solution to the challenges posed by the intermittent nature of renewable energy sources (RES) like wind and solar electricity through the utilization of a hybrid RES system, comprising a solar photovoltaic (PV) array and a wind turbine generator (WTG). The system's power delivery and quality are enhanced using AI technology, particularly neural networks employed in an innovative eye-based control technique. By integrating energy storage systems (ESS) and employing advanced AI-based control methods, the power quality of the hybrid RES system is significantly improved. This improvement, characterized by reduced voltage and frequency stress, enables the system to operate reliably across various weather conditions, thus ensuring consistent and dependable power supply. Standalone hybrid renewable energy systems, combining solar, wind, and energy storage devices, offer a viable solution for delivering safe and reliable power in rural areas. However, challenges such as voltage swings and harmonic distortion may arise due to the erratic nature of renewable energy sources. In this study, an AI-driven approach is proposed to address these challenges. The system predicts the output power of solar and wind systems using techniques like artificial neural networks (ANN) and fuzzy logic (FL), enabling the energy storage system to balance power generation and consumption based on anticipated values. This dynamic adjustment of output power to match load demand enhance...