Rough Set Theory Applied to Pattern Recognition of Partial Discharge in Noise Affected Cable Data
Rough Set Theory Applied to Pattern Recognition of Partial Discharge in Noise Affected Cable Data
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DOI：
10.1109/tdei.2016.006060
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发表时间：
2017-02-01
影响因子：
3.1
通讯作者：
Siew, W. H.
中科院分区：
文献类型：
--
作者：
Peng, Xiaosheng;Wen, Jinyu;Siew, W. H.
提出了一种有效的基于粗糙集(RS)的模式识别方法，用于抑制干扰信号和识别不同来源的局部放电信号。首先，从信息系统、上下近似、信号离散化、属性约简等方面介绍了粗糙集理论，并给出了基于粗糙集的模式识别方法的流程图。其次，对乙丙橡胶(EPR)电缆中的五种人工缺陷进行了局部放电检测，并采用数据预处理和特征提取的方法对局部放电和干扰信号进行分离。最后，将基于RS的局部放电信号识别方法应用于4000个样本，实验结果表明该方法具有99%的准确率。第四，将基于RS的局部放电识别方法应用于五种不同来源的信号，将信号离散化和属性约简相结合，识别准确率达到93%以上。最后，对反向传播神经网络(BPNN)和支持向量机(SVM)方法进行了研究，并与改进的方法进行了比较。实验证明，该方法比支持向量机和BP神经网络具有更高的精度，可用于电缆系统局部放电的在线监测。
This paper presents an effective, Rough Set (RS) based, pattern recognition method for rejecting interference signals and recognising Partial Discharge (PD) signals from different sources. Firstly, RS theory is presented in terms of Information System, Lower and Upper Approximation, Signal Discretisation, Attribute Reduction and a flowchart of the RS based pattern recognition method. Secondly, PD testing of five types of artificial defect in ethylene-propylene rubber (EPR) cable is carried out and data pre-processing and feature extraction are employed to separate PD and interference signals. Thirdly, the RS based PD signal recognition method is applied to 4000 samples and is proven to have 99% accuracy. Fourthly, the RS based PD recognition method is applied to signals from five different sources and an accuracy of more than 93% is attained when a combination of signal discretisation and attribute reduction methods are applied. Finally, Back-propagation Neural Network (BPNN) and Support Vector Machine (SVM) methods are studied and compared with the developed method. The proposed RS method is proven to have higher accuracy than SVM and BPNN and can be applied for on-line PD monitoring of cable systems after training with valid sample data.