基于多参量的高压断路器分/合闸线圈的故障诊断

Fault Diagnosis of Tripping/Closing Coil of High-voltage Circuit Breaker Based on Multiple Parameters

由于分/合闸线圈电流信号和振动信号的变化均可以反映操动机构的运行状态,因此文中阐述了根据多参量来诊断高压断路器分/合闸线圈故障的一种新方法,以提高高压断路器故障诊断的准确率。文中首先介绍了操动机构电磁铁的动作状态,并分析操动机构电流信号与运行状态的关系,其次设计了一套以NI数据采集卡和实时控制器为核心的硬件电路,最后运用多层小波包分解与重构算法对信号进行滤波,结合极值法对信号进行特征值提取,并采用粒子群优化算法与支持向量机相结合的方法进行状态分类。实验结果表明,文中提出的算法能够及时发现高压断路器运行过程中存在的安全隐患,有效提高高压断路器的运行可靠性。

Because the change of tripping/closing coil current(CC)and the vibration signal can reflect the run-ning state of an operating mechanism,a new method based on multiple parameters is presented for fault diagno-sis of tripping/closing coil of the high-voltage circuit breakers(HVCBs)and for improving the accuracy of the fault diagnosis. First,the operation state of the actuator electromagnet is introduced,and the relationship be-tween the signals and the operating states of the actuator is analyzed. Second, a hardware circuit taking the NI data acquisition card and the real-time controller as the core is designed. Third, the multi-layer wavelet packet(WP)decomposition and reconstruction algorithm is used to filter the signals,and the extreme value method is used to extract the eigen-values. Finally,the particle swarm optimization(PSO)method is combined with the support vector machine(SVM)to classify the states of HVCBs. Experimental results show that the present algo-rithm can detect the hidden failure of HVCBs in operation and improve their reliability.