Contribution of intelligent Control of Hybrid Renewable Energy Sources (Wind/ Photovoltaic) Connected to Grid

Abstract

The increasing depletion of fossil fuel resources and rising environmental concerns have accelerated the global transition toward renewable energy systems. Among available alternatives, hybrid solar–wind energy systems offer enhanced reliability by mitigating the inherent intermittency of individual renewable sources. However, their large-scale deployment faces significant challenges related to power variability, system efficiency, and grid integration. This thesis investigates advanced intelligent control strategies to optimize energy generation and ensure stable operation of hybrid solar–wind systems. Emphasis is placed on artificial intelligence–based techniques, including neural networks, fuzzy logic,and data-driven algorithms, for improved maximum power point tracking and energy management. Advanced power electronics and control methods, such as voltage source converters and direct power control, are employed to enhance power quality and grid stability. The proposed approaches demonstrate superior adaptability under rapidly changing environmental conditions. Simulation and analytical results validate the effectiveness of the developed control strategies. The findings contribute to improved efficiency, reliability, and sustainability of hybrid renewable energy systems. This research supports the development of smart, resilient, and environmentally friendly power systems for future energy networks.