An Enhanced Controller for Improving Network Performance Amid Renewable Energy Integration Challenges | ||
| Sohag Engineering Journal | ||
| Articles in Press, Accepted Manuscript, Available Online from 21 May 2025 | ||
| Document Type: Original Article | ||
| DOI: 10.21608/sej.2025.366415.1075 | ||
| Authors | ||
| Mohamed Ahmed Mohamed* 1; Ahmed Kassem1; Ahmed M. Montaser2; Fatma Ahmed Mohamed2 | ||
| 1Department of Electrical Engineering, Faculty of Engineering, Sohag University, 82524 Sohag, Egypt | ||
| 2Department of Electrical Engineering, Faculty of Technology and Education, Sohag University, 82524 Sohag, Egypt | ||
| Abstract | ||
| Integrating renewable energy sources (RESs) into traditional power grids provides a pathway to achieving sustainable and resilient energy systems. However, the inherent variability of RESs poses significant challenges to grid frequency stability, necessitating advanced frequency regulation solutions. This research proposes a novel Iλ(1+T)DμN controller in the auxiliary control loop to address these challenges and enhance grid stability. The Iλ(1+T)DμN controller represents a significant advancement in fractional-order control by incorporating multiple input signals and advanced feedback-feedforward strategies. Optimized using the advanced Fata Morgana Algorithm (FATA), the proposed controller effectively manages frequency deviations, achieving a 93% improvement in system stability over previous studies and a 71% performance boost compared to conventional controllers. Key results demonstrate reduced overshoot, faster stabilization, and minimized energy losses, ensuring robust operation even under high-RESs penetration and dynamic conditions. This work underscores the potential of combining fractional-order controllers with cutting-edge optimization techniques to address the evolving challenges of modern power systems. | ||
| Keywords | ||
| Feedforward-Feedback controllers (FFC); Fractional-Order Controllers (FOC); Load Frequency Control (LFC); Renewable Energy Sources (RESs); Fata Morgana Algorithm (FATA) | ||
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