Mechanical status detection model of high-voltage circuit breaker based on multi-modal high-efficiency Transformer

QU Deyu; XIAO Baihui; REN Yijia; CONG Peijie; WU Qiong

doi:10.7688/j.issn.1000-1646.2025.06.08

Abstract：[Objective]High-voltage circuit breakers are key control and protection devices in the power system,and their reliable operation is crucial for the safety and stability of power grids.However,during long-term operation,high-voltage circuit breakers may trigger various faults due to mechanical wear,component aging,and other problems.Currently,the detection of high-voltage circuit breakers faces challenges such as diverse detection signals,great difficulty in fault detection,and low accuracy.Therefore,studying an efficient and accurate mechanical status detection method for high-voltage circuit breakers is of great significance for ensuring the safe and stable operation of the power system.[Methods]This study proposed a mechanical status detection model for high-voltage circuit breakers based on multi-modal efficient Transformer.In the data acquisition stage,vibration sensors,current sensors,and displacement sensors were comprehensively employed to synchronously acquire vibration signals,current signals,and displacement signals during the operation of high-voltage circuit breakers,thus constructing a multi-modal signal dataset.In the signal preprocessing stage,wavelet transform technology was adopted to process the acquired multi-modal signals and decompose the signals into different frequency scales,thus effectively removing noise components in the signals,enhancing fault feature signals,and significantly improving signal quality.In terms of model building,an efficient Transformer module was introduced.With its powerful self-attention mechanism,the module could effectively capture long-distance dependency relationships in signal sequences and dig deeply into complex features in multi-modal signals.Additionally,by classifying the operation status of high-voltage circuit breakers into six categories,including normal operation,failure to maintain closing,loose soft connection,single-phase contact wear,loose insulating tie rod,and opening spring fracture,accurate diagnosis of the mechanical status of circuit breakers was realized.[Results]In the simulation experiments,simulation models of different fault types of high-voltage circuit breakers were built to simulate various working conditions during actual operation and generate multi-modal signal data.Inputting the data into the proposed detection model for testing shows that the model can accurately identify different fault types.In the actual experiments,multiple high-voltage circuit breakers were selected as test objects,and multi-modal signal data were collected under their normal operation and different fault settings.The experimental results reveal that the proposed method significantly improves the detection accuracy compared with traditional detection methods while ensuring the detection speed.[Conclusions]The proposed mechanical status detection model for high-voltage circuit breakers based on multi-modal efficient Transformer effectively solves the problems of complex detection signals and severe noise interference.By leveraging the powerful feature extraction and classification capabilities of the efficient Transformer model,accurate identification of multiple mechanical faults in high-voltage circuit breakers is realized.Simulation analysis and experimental results fully demonstrate that this method performs well in both detection accuracy and speed,providing reliable technical support for the status monitoring and fault diagnosis of high-voltage circuit breakers in the power system.It helps to timely detect potential faults of the equipment,and holds application significance and broad promotion prospects for ensuring the safe and stable operation of the power system.