基于SVM增量学习算法的煤矿高压断路器故障模式识别方法

摘要

The accurate fault pattern identification for the high voltage circuit breaker (HVCB) plays an important role in the development of mine smart grids.Aiming at the inaccessible obtainment of fault data and the lack of fault samples,a method of fault recognition was proposed based on the incremental learning algorithm for SVM.Firstly,the state monitoring variables were determined by the current signal and voltage signal of control unit and the vibration signal of the switching for HVCB.Secondly,four common faults,including the spring loosening,the core jamming,the coil aging and the abnormal electrical power supply,were simulated.Then the fault features were extracted,and the fault data samples as well as the incremental learning data samples were established.After training fault data samples based on the incremental learning algorithm for SVM,the fault recognition model was acquired and its accuracy was validated through exerting the new fault data samples into the model.Finally,it is shown that the incremental learning algorithm for SVM can be used to recognize the above four common faults for HVCB effectively,and its recognition accuracy can be improved by continuous learning on new samples.%高压断路器故障模式的准确识别是矿井电网智能化发展过程中的重要支撑环节.针对高压断路器故障数据不易获取且故障样本较少的问题,提出了一种支持向量机与增量学习算法相结合的故障识别方法,确定了以断路器控制回路电流信号、电压信号以及分合闸振动信号为状态监测量,模拟了弹簧松动、铁芯卡涩、供电异常与线圈老化4种常见故障,提取了故障特征量并建立了故障数据样本与增量学习数据样本,采用支持向量机增量学习算法训练得到了故障识别模型,并利用新增数据样本对其进行了验证.结果表明:支持向量机增量学习算法可准确识别上述4种常见故障,并可以通过对新增样本的不断学习进一步提高识别精度.