Machine Learning-Based High-Voltage Circuit Breaker Defect Classification Utilizing Savitzky–Golay Filter

Abstract

High-voltage circuit breakers (HVCBs) are critical components in power systems to maintain reliable operation. Accurate condition monitoring of HVCBs is vital to reduce maintenance costs and consequently to enhance the grid reliability. However, achieving this with low-cost measurement devices, which often provide noisy signals, poses a significant challenge. In this article, a novel defect classification framework for HVCBs is proposed that uses the Savitzky–Golay filter to preprocess the most common condition monitoring signal, which is the trip/close coil current. This filter is well-known for denoising while preserving critical signal features. Following signal preprocessing, a robust defect detection and classification methodology is introduced, combining time-series similarity assessment techniques, such as Euclidean distance and dynamic time warping (DTW), with machine learning (ML) algorithms. Moreover, an experimental setup is designed to emulate the behavior of an HVCB’s coil mechanism. To further enhance the model transparency, Shapley additive explanations (SHAP) analysis is applied, providing interpretability into feature contributions toward model decisions. The obtained results validate the effectiveness of the proposed hybrid approach, demonstrating its potential to provide a cost-effective, accurate, and reliable solution for HVCB condition monitoring.