Artificial Intelligence in Power System Monitoring and Control
Keywords:
Deep Learning (DL), Fuzzy logic, Machine Learning (ML), Reinforcement learning (RL), sensorsAbstract
Artificial Intelligence (AI) has emerged as a game-changing tool for power system monitoring. Power systems must contend with issues including grid congestion, integrating renewable energy sources, and the requirement for predictive maintenance and real-time defect identification. AI techniques, including Machine Learning (ML), Deep Learning (DL), and optimization algorithms, are poised to address these challenges by enhancing the accuracy and responsiveness of power and control, offering advanced solutions to meet the increasing demands for efficiency, reliability, and sustainability in modern electrical grids. Traditional system operations like aaccurate load forecasting, anomaly detection, and energy consumption prediction are made possible by AI-based systems that allow the real-time analysis of vast volumes of data gathered from smart meters, sensors, and SCADA systems. Additionally, AI enhances power flow management, frequency control, and voltage regulation to maximize grid stability. In the context of renewable energy, AI supports better integration by predicting fluctuating power generation, enhancing storage management, and balancing demand with supply.
The potential of AI extends to autonomous control systems, where intelligent agents dynamically optimize grid operations without human intervention, contributing to self-healing smart grids. Despite its vast potential, the integration of AI in power systems faces challenges, such as data quality issues, model interpretability, scalability, and cybersecurity concerns. However, with ongoing advancements, AI stands as a critical enabler of the next-generation power grids, supporting sustainability and resilience in a rapidly evolving energy landscape. This paper explores the applications, benefits, challenges, and future directions of AI in power system monitoring and control.
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