Depth Diagnosis of Spring Mechanical Faults of High Voltage Circuit Breakers Considering Wavelet Packet-Gray Level Co-occurrence Matrix Method
As an energy storage unit for the opening and closing operations of high-voltage circuit breakers(HVCBs),the reliability of the spring operating mechanism is of great significance to the safe operation of the power system.In this paper,the spring operating mechanism of the SF6 HVCB is the research object,the action mechanism of the opening and closing spring is analyzed,and the mechanical failure of the spring is simulated.The vibration and sound sensor equipment and acquisition parameters are introduced.Aiming at the shortcomings of wavelet packet time-frequency analysis,a feature extraction method based on wavelet packet-gray level co-occurrence matrix(GLCM)is proposed.Then,the four diagnostic models of support vector machine(SVM),decision tree(DT),naive Bayes,and K nearest neighbors(KNN)were compared in terms of diagnosis speed and diagnosis accuracy.The experimental results demonstrate that in the simulation actual application scenario,the KNN algorithm is selected to perform an in-depth diagnosis of the opening and closing spring faults,which can accurately determine the type and degree of the fault,and has practical application value for the safe and reliable operation of high-voltage circuit breakers.
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