Review of fault diagnosis technology for new power systems (Part I): Mechanism modeling and signal analysis

The fault diagnosis technology of power systems is a core means to ensure the safe and stable operation of new power systems. As these systems evolve toward a high proportion of renewable energy, multi-energy coupling, and power electronicization, fault diagnosis technology faces challenges such as complex dynamic characteristics, diversified data dimensions, and increased uncertainty. Therefore, a comprehensive analysis of fault diagnosis technologies and their adaptation to new power systems is urgently needed.

Currently, fault analysis methods based on mechanism modeling usually analyze the operating characteristics of system mechanisms, construct corresponding physical equations, and infer fault states. These methods focus on building physically driven mathematical models to achieve fault localization through dynamic system analysis. They offer clear physical interpretability but face challenges such as high modeling complexity and limited adaptability in strongly nonlinear or multistage scenarios. Conversely, diagnostic methods based on signal analysis focus on extracting features from monitoring data, using time-domain or frequency-domain techniques to analyze fault signals, and combining these with feature engineering to complete fault classification. These methods employ machine learning algorithms to establish warning models for trend analysis and fault prediction. The application of big data technology further enhances the coupling between historical data and machine learning models, improving fault recognition rates and prediction robustness. However, these data-driven methods depend heavily on data quality and the completeness of feature engineering, and their generalization to new types of faults is limited. Mechanism modeling and signal analysis methods represent the traditional mainstream approaches, constructing diagnostic systems from complementary perspectives.

This article systematically reviews research progress in both approaches, analyzes their principles, advantages, and limitations, and summarizes the challenges and potential research directions based on the requirements of fault diagnosis in new power systems. In particular, future research should focus on the deep integration of the two approaches: (1) incorporating prior knowledge from mechanistic models to optimize the design of signal analysis–based methods and reduce dependence on annotated data, and (2) applying signal analysis techniques to address unmodeled dynamics in mechanistic models. These complementary strategies lay a solid technical foundation for constructing effective fault diagnosis methods.

By clarifying the characteristics and limitations of mechanism-based and signal analysis–based methods, this study provides valuable guidance for developing more robust and effective fault diagnosis strategies, ultimately supporting reliable analysis and control of faults in new power systems.