Advancements in AI-Driven Approaches for Grid Stability Monitoring
Said Ćosić, I. Vokony · 2025
In the age marked by growing penetration of intermittent renewable energy sources, distributed energy resources, demand-side management and power electronics-based generation, alongside the trend of grid smartification, power system stability monitoring has become more complex and challenging than ever before. Traditional stability monitoring methods, based on conventional measurements, analytical modeling and rule-based control, have demonstrated limited real-time capabilities, inability to efficiently handle the massive influx of data, rigidity to adapt to rapidly changing grid dynamics, as well as high implementation and maintenance costs. As a response, artificial intelligence (AI) has emerged as a powerful solution for enhancing grid stability monitoring by enabling real-time analysis, predictive modeling, and automated decision-making. This paper provides a comprehensive overview of the state-of-the-art AI-based approaches that are utilized in voltage, frequency and transient stability assessment, as well as in fault detection and classification. The AI techniques such as machine learning, deep learning, fuzzy logic, reinforcement learning and hybrid AI models, have demonstrated advanced capabilities to anticipate grid disturbances, improve disturbance detection and enhance decision-making processes. Besides the great potential of AI-driven solutions, the paper outlines major challenges such as algorithmic limitations, data quality and availability concerns, model generalization, cybersecurity threats, computational complexity, model robustness, integration and interpretability, that must be addressed for a successful integration of AI-based solutions into grid monitoring systems. Ultimately, continuous advancement of AI-driven grid stability solutions will be instrumental for ensuring resilience and efficiency in the constantly evolving power system landscape.