A Structured Review of Artificial Intelligence Techniques for Ferroresonance Detection and Mitigation in Power Systems

Ferroresonance is a nonlinear phenomenon in power systems capable of producing irregular oscillations and severe overvoltages that threaten transformers, voltage transformers, cables, and associated equipment. This paper presents a structured comprehensive review of ferroresonance detection and mitigation techniques reported up to 2025, with particular emphasis on artificial intelligence (AI)-based approaches published during the last five years. A systematic literature search was conducted across IEEE Xplore, Scopus, Web of Science, and Google Scholar using predefined ferroresonance- and AI-related keywords. Eligible studies were screened using explicit inclusion criteria requiring demonstrated ferroresonance relevance. Numerical modeling approaches, electromagnetic transient tools, ferroresonance modes, and mitigation strategies are synthesized, followed by a critical evaluation of machine learning, deep learning, fuzzy logic, evolutionary algorithms, and hybrid intelligent frameworks. Particular emphasis is placed on signal preprocessing, data representation, real-time protection constraints, and cross-topology robustness. The review identifies key research gaps, including the scarcity of benchmark datasets, limited validation under realistic network variability, and the absence of standardized evaluation methodologies. While this work is presented as a structured comprehensive review, PRISMA-inspired screening principles were applied to enhance transparency and reproducibility. Current evidence indicates that hybrid approaches combining physics-informed preprocessing—particularly wavelet-based feature extraction—with lightweight neural classifiers offer the most practical pathway for relay-grade ferroresonance protection in modern smart grids.