Hanyan Xiao

Spring-energy-storage circuit breakers are critical switching devices in power systems, and their operating reliability directly affects the safety and stability of the grid. In practical operations of transmission equipment, contacts may experience degradation such as poor contact, overheating, etc., due to multiple factors, including contact arcing erosion, mechanical wear, oxidation aging, and reduced contact pressure. Developing contact-point health monitoring and assessment enables prognostic maintenance, improves power supply reliability, and reduces operation and maintenance costs. This paper surveys the related research on health monitoring technologies for contact-point state in spring-energy-storage circuit breakers, systematically sorting out the operating principles and application characteristics, vibration and acoustic emissions monitoring, as well as electrical and mechanical parameter monitoring. It further analyzes the key bottlenecks faced by current monitoring technologies in online measurement accuracy, anti-interference capability, and engineering applicability, and finally discusses the future development trends of intelligent monitoring integrated with artificial intelligence, multi-source data fusion, and digital twin technologies. The research results provide theoretical reference and practical guidance for the upgrading of contact-point state monitoring technologies and the construction of intelligent operation and maintenance systems for spring-energy-storage circuit breakers.

To address the practical engineering challenges of limited fault samples for high-voltage circuit breaker spring operating mechanisms and the inability of single features to fully reflect equipment status, this paper proposes a small-sample fault diagnosis method based on multi-source feature fusion and Stacking ensemble learning. First, a multi-source sensing system containing MEMS (Micro-Electro-Mechanical System) pressure and travel, coil, and motor current was constructed to achieve comprehensive monitoring of the mechanical and electrical states of a 220 kV circuit breaker; in particular, the introduction of non-invasive MEMS sensors effectively solves the difficulty of capturing static spring fatigue characteristics inherent in traditional methods. Second, a high-dimensional feature space was constructed using Savitzky–Golay filtering and physical feature extraction techniques. To address the characteristics of small-sample data distribution, a two-layer Stacking ensemble learning model based on 5-fold cross-validation was designed. This model utilizes the SVM (Support Vector Machine), RF (Random Forest), and KNN (K-Nearest Neighbors) as base classifiers and Logistic Regression as the meta-learner, achieving an adaptive fusion of the advantages of heterogeneous algorithms. True-type experimental results show that the average diagnostic accuracy of this method under normal conditions and four typical fault conditions reaches 96.1%, which is superior to single base models (the RF was 94.2%). Feature importance analysis further confirms that closing and opening pressures are the most critical features for distinguishing mechanical faults. This study provides effective theoretical basis and technical support for condition-based maintenance of high-voltage circuit breakers under small-sample conditions.