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粒子群优化的KFCM及SVM诊断模型在断路器故障诊断中的应用

, PP. 134-141

Keywords: 模糊核聚类,粒子群,支持向量机,断路器,故障诊断

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Abstract:

为了利用相对较少的故障数据样本对断路器主要故障类型进行较为准确的在线判断,提出了一种基于融合粒子群的模糊核聚类(particleswarmfusedkernelfuzzyC-means,P-KFCM)与支持向量机(supportvectormachine,SVM)的故障诊断方法。通过对断路器分合闸电流信号的分析,找出与断路器主要故障类型相对应的特征量;据此对采样信号进行处理,建立故障特征样本空间;利用P-KFCM算法对故障训练样本进行预分类,并以此为基础建立多SVM故障预测模型。P-KFCM算法将粒子群(particleswarmoptimization,PSO)的全局搜索能力融入KFCM中,有效的解决了局部最优问题,在一定程度上提升了诊断结果的可靠性。实验结果表明,该方法在诊断断路器主要机械故障方面能够取得较好的效果。

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