| 基于粒子群优化PCA-LSSVM算法的高压断路器机械故障诊断研究 |
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| 引用本文：孔敏儒1,陈怡2,李文慧3,高健3,侯喆3.基于粒子群优化PCA-LSSVM算法的高压断路器机械故障诊断研究[J].电网与清洁能源,2019,35(10):68~74 |
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| 基金项目:国家自然科学基金青年科学基金项目（51707141） |
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| 中文摘要:为了对高压断路器操作机构进行故障诊断，提出了基于粒子群优化的PCA-LSSVM算法模型（PCA-PSO-LSSVM），该模型的输入为高压断路器操作机构分合闸线圈电流曲线上的5组特征点，输出为1—5的故障类别。对某台高压断路器进行故障模拟，建立了PCA-PSO-LSSVM算法模型，对测试的断路器操作机构进行故障分类。结果表明，基于粒子群优化的PCA-LSSVM算法能够准确地对高压断路器操作机构进行故障分类。将PCA-PSO-LSSVM算法和多种基于SVM的故障诊断算法进行比较，比较结果表明：在综合考虑了算法准确率和运算时间的基础上，PCA-PSO-LSSVM算法是几种算法中最优的。 |
| 中文关键词:高压断路器 故障诊断 PCA-LSSVM算法 粒子群优化算法 |
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| Research on Mechanical Fault Diagnosis of High Voltage Circuit Breakers Based on Particle Swarm Optimization PCA-LSSVM |
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| Abstract:In order to diagnose the fault of the operating mechanism of the high voltage circuit breaker， a PCA-LSSVM algorithm model based on particle swarm optimization （PCA-PSO-LSSVM) is proposed in this paper. The inputs of the model are the five sets of characteristic points on the current curve of the opening/closing coil of the operating mechanism and the output is a fault category of 1-5. the fault of a high voltage circuit breake is simulated and the PCA-PSO-LSSVM algorithm model is established to classify the fault of the operating mechanism of the tested circuit breaker. The results show that the PCA-LSSVM algorithm based on particle swarm optimization can accurately classify the faults of the operating mechanism of the high voltage circuit breaker. The PCA-PSO-LSSVM algorithm is compared with various SVM-based fault diagnosis algorithms. The comparison results show that the PCA-PSO-LSSVM algorithm is the best among several algorithms considering the accuracy and operation time of the algorithm. |
| keywords:high voltage circuit breaker fault diagnosis PCA-LSSVM algorithms particle swarm optimization |
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