摘要
针对高压隔离开关故障诊断时特征库中故障类别不完备的问题,提出了基于多重支持向量域描述(Multi-SVDD)的故障诊断方法。首先通过主成分分析将正常和已知故障样本特征量按贡献度进行排序作为新的特征向量,并以特征量贡献度构造加权高斯核函数,提高对类间特征差异的辨识能力。然后利用粒子群算法对核参数进行优化,提高模型的推广能力和对样本类别识别的正确率。其次对正常和已知故障样本集进行训练,建立描述隔离开关不同工作状态的超球体作为预测模型。最后利用Multi-SVDD对样本空间进行划分并计算待测样本点至各超球体中心的距离,确定样本所属的种类。试验结果表明,该方法可以有效处理高压隔离开关故障诊断中故障类别不完备的问题,在诊断出已知故障的同时可对未知故障给出判断。
Focusing on the problem of incomplete fault types existing in high voltage isolating switch fault diagnosis, a fault diagnosis method based on multi-support vector domain description is proposed. Firstly, by principal component analysis to order the normal and known fault samples as a new eigenvector according to their contributions, and the weighted Gaussian kernel function was constructed with the contribution of features to improve the ability to identify inter type feature differences. Then the PSO was used to optimize the kernel parameters so that the model had a higher promotion ability and smaller false positive rate. Secondly, the normal and known fault samples were trained, the hypersphere describing the different working states of the isolating switch was established as the predictive model. Finally, the sample space was divided by multi support vector data description(Multi-SVDD) and the distance from the sample point to the center of the hypersphere was calculated to determine the type of sample. The experimental results showed that this method could effectively deal with the problem of incomplete fault samples in the fault diagnosis of high voltage isolating switch, and it could judge the unknown fault while diagnosing the known fault.
引文
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