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.
Ferroresonance is a nonlinear electrical phenomenon capable of producing severe and potentially damaging overvoltages. Ferroresonance can lead to severe voltage and current distortions that threaten power system reliability. It typically arises when a saturable inductance, such as a transformer, interacts with the system capacitance under specific conditions, including during switching operations or faults. The unpredictable behavior of ferroresonance produces complex system responses because its outcomes are difficult to control. The new analytical methods require complex modeling systems and detection technologies because they operate through distinct mechanisms from traditional methods.
The present-day power grid system is becoming increasingly susceptible to network failures because its evolving design introduces new points of vulnerability to ferroresonance. The implementation of Renewable Energy (RE) systems that use inverter-based distributed generation has led to different impedance patterns that modify power system and network resonance characteristics and has expanded the set of operating conditions under which ferroresonance may occur. Similarly, the proliferation of underground cables, lightly loaded lines, and capacitor-rich components has added operational difficulties for predicting and preventing ferroresonance events. Traditional analytical tools, though foundational, often fall short in capturing the nonlinear magnetization dynamics, hysteresis effects, and switching-induced transients that govern ferroresonant behavior. The system must operate effectively in both extensive power networks and in systems that undergo ongoing changes. In
[1], ferroresonance was investigated in a distribution system integrated with multiple Distributed Generation (DG) and proposed an
RLC shunt limiter as an effective mitigation technique.
The present Artificial Intelligence (AI) research has brought about fundamental changes, opening new possibilities for solving these problems. Machine Learning (ML), Deep Learning (DL), and hybrid intelligent systems have demonstrated strong potential in identifying early signatures of ferroresonance, extracting meaningful features from complex waveforms, and improving predictive capability under uncertainty. AI-enhanced methods enable users to monitor systems in real time while their protection systems adapt to changing system environments. The system uses advanced ML and data-driven classification methods, producing better results than conventional threshold-based and rule-based systems techniques. In addition, the deployment of AI-supported modeling frameworks improves nonlinear system modeling through their advanced capabilities. The system requires two operational functions to function properly: it must produce chaotic response patterns and exhibit transformer behavior to achieve optimal mitigation. Different mitigation strategies operate under different grid configurations. For instance, Ref.
[2] introduced an algorithm that demonstrates the ability to detect faults using estimated flux analysis of voltage transformers. The study investigates the occurrence of ferroresonance oscillations that affect medium-voltage networks.
Modeling ferroresonance requires precise methods to characterize its behavior and to develop effective mitigation strategies. Traditional models fail to adequately represent the nonlinear dynamics involved. Recent research has used chaos theory to develop models of ferroresonance under nominal conditions, providing deeper insights into its unpredictable nature. These models allow users to create virtual scenarios, which enable them to develop improved design solutions for robust protection schemes.
Mitigation techniques for ferroresonance have evolved alongside advancements in detection and modeling. The system uses passive methods, including damping resistors and ferroresonance suppression circuits, which have been traditionally used. However, their effectiveness can be limited in complex systems. The effectiveness of damping devices in auxiliary power systems of high-voltage substations was analyzed in
[3], emphasizing the importance of proper parameter coordination for successful mitigation. Active methods, including Static Var Compensators (SVCs), have also been explored for their ability to dynamically adjust system parameters to suppress ferroresonance.
The application of AI extends beyond detection to a mitigation phase. AI algorithms can predict which circumstances will produce specific results. The system will detect ferroresonance conditions and trigger automatic preventive measures to prevent these events. For example, morphological filtering techniques have been employed to remove high-frequency components from zero-sequence currents, thereby eliminating ferroresonance events.
The research shows promising results, but existing studies remain scattered across different fields. This includes diverse modeling methodologies, numerical simulation techniques, heuristic mitigation strategies, and a growing but heterogeneous body of AI-driven detection research. The evolution of power systems toward greater reliance on RE and automated control systems requires a new approach to power system protection.
This review addresses this gap by synthesizing recent advancements in ferroresonance detection and by focusing on emerging AI techniques during the last five years. The paper evaluates theoretical foundations, numerical approaches, electromagnetic transients (EMT) simulation tools, and contemporary mitigation strategies, while providing a comprehensive comparative analysis of AI methodologies, including neural networks, DL, fuzzy logic, evolutionary algorithms, and hybrid systems. By integrating insights from conventional engineering analysis and cutting-edge computational intelligence, this work aims to support researchers, utilities, and protection engineers in designing more resilient, adaptive, and predictive ferroresonance countermeasures suitable for next-generation smart grids.
This entry is adapted from the peer-reviewed paper 10.3390/encyclopedia6030058
