Fault Diagnosis of High Voltage Circuit Breaker Based on Multi-classification Relevance Vector Machine
Fault Diagnosis of High Voltage Circuit Breaker Based on Multi-classification Relevance Vector Machine
复制标题
基于多分类相关向量机的高压断路器故障诊断
DOI：
10.1007/s42835-019-00199-6
复制
发表时间：
2020-01-01
影响因子：
1.9
通讯作者：
Zhang, Ying
中科院分区：
文献类型：
--
作者：
Zhang, Yingjie;Jiang, Yuan;Zhang, Ying
高压断路器故障作为电力系统中电气接触故障的一种重要形式，在小故障数据集的情况下诊断极为困难。提出了一种基于多分类关联向量机的高压断路器故障诊断方法。为弥补高压断路器故障特征分类样本数据的不足，在“一对一”多分类模型的基础上，设计了多分类关联向量机算法，并通过公开数据集进行测试，验证了该算法在小样本数据集上的良好生成性能。然后，从高压断路器合闸线圈电流信息中提取时间和电流特征，形成故障特征向量。因此，许多两类相关向量机模型进行了训练，然后测试所获得的参数的最优性。结果表明，该算法能有效识别断路器多种故障，在小样本数据条件下，分类精度优于BP神经网络和支持向量机。
The high voltage circuit breaker's fault as an important form of electrical contact fault in the power system, which is extremely difficult to diagnose under the condition of small fault dataset. This paper proposes a fault diagnosis method based on multi-classification relevance vector machine for high voltage circuit breakers. To make up with the scarcity of the sample fault data in classifying the features of the high voltage circuit breakers, a multi-classification relevance vector machine algorithm is designed on the basis of "One-Against-One" multi-classification model, and tested by public data-sets to verify the good generating performance of this algorithm on small sample data-sets. Then, the time and the currents features are extracted from the closing coil current information of the high voltage circuit breaker to form fault eigenvector. Consequently, numerous two-classification relevance vector machine models were trained and then tested for the optimality of the acquired parameters. The results show that the proposed algorithm can effectively identify many faults of circuit breaker and has better classification accuracy than BP neural network and Support Vector Machine under conditions of small sample data.