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首页> 外文会议>International Symposium on Discharges and Electrical Insulation in Vacuum >Research on fault diagnosis of operating mechanism for 12kV vacuum circuit breaker based on PSO-LSSVM
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Research on fault diagnosis of operating mechanism for 12kV vacuum circuit breaker based on PSO-LSSVM

机译：基于PSO-LSSVM的12kV真空断路器运行机构故障诊断研究

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The small fault samples and single characteristic parameters in vacuum circuit breakers (VCBs) would lower the accuracy and reliability of mechanical fault diagnosis. In this paper, the problem has been solved with applying a fault diagnosis method based on particle swarm optimization (PSO) and least square support vector machine (LSSVM). By analyzing the close coil current (CC) of VCB, the eigenvalues of time and current are extracted as input vectors. The paper uses PSO to optimize the model parameters of LSSVM, which are important to fault diagnosis, and to select the best subset of eigenvectors to obtain the optimal performance of LSSVM classifier. Then the improved support vector machine (SVM) is used to train and test the eigenvectors and different states of VCB are classified. Results show that the proposed method can detect whether VCB is normal or not. And the validity and accuracy is verified.
机译：真空断路器（VCB）中的小故障样本和单一特征参数会降低机械故障诊断的准确性和可靠性。本文采用基于粒子群算法（PSO）和最小二乘支持向量机（LSSVM）的故障诊断方法解决了该问题。通过分析VCB的闭合线圈电流（CC），提取时间和电流的特征值作为输入向量。本文使用粒子群优化算法优化了对故障诊断很重要的LSSVM模型参数，并选择特征向量的最佳子集以获得LSSVM分类器的最佳性能。然后，使用改进的支持向量机（SVM）训练和测试特征向量，并对VCB的不同状态进行分类。结果表明，该方法可以检测出VCB是否正常。并验证了有效性和准确性。

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