人工蜂群优化支持向量机的电气故障诊断方法研究 |
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引用本文:吴国诚,范良忠.人工蜂群优化支持向量机的电气故障诊断方法研究[J].电网与清洁能源,2016,32(7):88~91 |
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基金项目:基金项目:国家自然科学基金资助项目(31302231);浙江省教育厅科研项目(Y201226043); 宁波市自然科学基金资助项目(2012A610110)。 |
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中文摘要:摘要: 电气故障诊断具有重要的实际应用价值,针对电气故障诊断中的支持向量机(SVM)参数选择问题,提出了人工蜂群优化SVM的电气故障诊断模型。首先采用小波分析去除信号中的噪声,并提取特征,然后采用人工蜂群优化算法确定SVM的最优参数,建立电气故障诊断模型,最后通过与其他电气故障诊断模型进行对比实验。结果表明,WA-ABC-SVM可以描述电气设备状态与特征间的变化关系,提高了电气故障的诊断正确率,诊断结果要高于对比模型。 |
中文关键词:关键词: 电气设备 小波分析 故障诊断模型 噪声干扰 提取特征 人工蜂群优化算法 |
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Research on Electrical Fault Diagnosis Method Using WA-ABC-SVM |
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Abstract:ABSTRACT: Electrical fault diagnosis has important practical application value. In this paper, an electrical fault diagnosis model based on artificial bee colony algorithm (ABC) optimiz-ing support vector machine(SVM) is proposed to solve para-meter selection problem of SVM in electrical fault diagnosis. Firstly, the noise in the signal is removed by wavelet analysis, and features are extracted, and secondly, ABC algorithm is used to determine the optimal parameters of SVM, and elec-trical fault diagnosis model is established, finally, experiments are carried out by comparing with other models. The results show that the proposed model can describe relationship between state and features of electrical equipment, the correct rate of diagnosis is improved, and the result is better than the contrast models. |
keywords:KEY WORDS:electrical equipment wavelet analysis fault diagnosis model noise interference extraction feature arti-ficial bee colony optimization algorithm |
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