基于行程信息的断路器弹簧故障程度诊断

Circuit breaker opening/closing springs have different degrees of fatigue for a long time running, during the power system failure, it is possible to have a major accident if not cut off the line and the failure timely. In order to detect the types of the fault degree of high voltage circuit breaker, quantify the fault degree of the circuit breaker spring and gives the measures of whether circuit breaker can continue to use. This paper analyzes the circuit breaker travel signal in mechanical vibration signal and proposes a method to process the travel signal. Processing the travel signal to obtain velocity and the variance of volatility distribution and form a feature vector P=[v,σ]. Using the serial decision to support vector machine (support vector machine, SVM) to classify the degree of fault of spring and define the fitting function with variance of the spring as the independent variable and the variance of the distribution to express the spring fault diagnosis concretely, it turns out this method can diagnose high-voltage circuit breaker spring failure effectively. And the fitting function based on wave variance can judge the state of the spring fully. It is of practical significance to prevent circuit breaker failure.