Real-Time Adaptive Emergency Control for Transient Stability in Power Grids: A Meta-Analysis Review
Keywords:
Adaptive control, power grid stability, transient events, emergency control, machine learning, grid resilience, smart gridAbstract
Modern power grids face unprecedented challenges due to increasing complexity, renewable energy integration, and unpredictable transient events that threaten system stability. This meta-analysis reviews the evolution and current state of adaptive emergency control systems designed to maintain power grid stability during transient disturbances. The study examines 150 research papers published between 2010-2024, analyzing various adaptive control methodologies including machine learning-based approaches, model predictive control, and hybrid intelligent systems. Key findings reveal that adaptive emergency control systems demonstrate superior performance compared to conventional static control methods, with average stability improvement of 35-45% and response time reduction of 60-75%. The analysis identifies machine learning algorithms, particularly deep reinforcement learning and neural networks, as dominant trends in recent implementations. Critical gaps include limited real-time validation, insufficient consideration of cybersecurity aspects, and challenges in multi-area coordination. The review synthesizes methodological approaches, performance metrics, and implementation challenges while providing recommendations for future research directions. Results indicate that adaptive control systems show promise for enhancing grid resilience, though standardization and practical deployment remain significant challenges requiring continued research focus.
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