基于形态小波和支持向量机的高压断路器状态监测和故障诊断

针对现场运行的断路器故障数据难以获取的现状,搭建了一个断路器故障模拟试验平台对部分常见故障进行了故障模拟。传统的基于单一特征量的状态监测难以有效判断断路器的故障,提出了一种多维映射的思路:研发了一种形态开闭加权复合滤波算法对断路器合分闸线圈电流信号进行滤波,从故障波形和正常波形的对比中充分挖掘其差异特征量,并利用形态小波分解包含丰富机械状态信息的振动信号,将不同频段的能量值作为判断断路器状态的特征量,利用支持向量机将断路器合分闸线圈电流信号的各差异特征量与振动信号特征量相结合形成多维映射,对断路器的状态进行诊断。本研究进行了大量的实验,实验分析结果验证了该思路和算法的有效性。

Condition Monitoring and Fault Diagnosis of Circuit Breaker Based on Morphology Wavelet and Support Vector Machine

It is generally difficult to obtain the data of circuit breaker failures.Considering this situation,an experiment platform for circuit breaker failures is built to obtain the failure data.In order to overcome the deficiency of traditional monitoring,which is based on single feature and thus is hard to judge the statues of the circuit breaker,this paper proposes the method of multi-dimensional mapping.An algorithm of opening and closing weighted filter based on mathematical morphology is proposed to filter tripping coil current signals of circuit breaker,and the feature values reflecting the waveform differences are extracted.For vibration signals morphological wavelet transform is used to extract their feature values,i.e.the energy of various frequency bands.Support vector machine is used for combining the feature values of current signals and vibration signals to form multi-dimensional mapping and to diagnose the status of circuit breakers.A great number of experiments are conducted and the results prove the effectiveness of the proposed method.