Hassan Waqar, Hussain G Amjad, Wahid Abdul, Safdar Madia, Khalid Haris M, Jamil Mohamad Kamarol Mohd
School of Electrical & Electronics Engineering, Universiti Sains Malaysia, Pulau Pinang, Malaysia.
College of Engineering & IT, University of Dubai, Dubai, United Arab Emirates.
Sci Rep. 2024 Oct 8;14(1):23446. doi: 10.1038/s41598-024-73196-z.
Partial discharges (PD) are initiated in electrical equipment during various points of the equipment's lifecycle. The intensity of PD defects rises continuously with time, which can lead to insulation degradation and reduced operational life of the electrical equipment. The optimum feature selection of PD signals captured, from different insulation defects, can enhance the classification accuracy of PD defects and facilitate better visualization of PD parameters for electric motor (EM) insulation monitoring and diagnostics. This paper presents a hybrid approach, based on Maximize Relevancy and Minimize Redundancy (mRMR) and random forest (RF), for the optimum feature selection and classification of PD signals in EMs containing multiple defects. For this purpose, four PD defects are developed in the EMs insulation under laboratory conditions, and 800 PD signals are acquired using a conventional IEC-60,270 experimental platform. The severity of these defects is determined and investigated based on PD characteristic parameters. Several features of both PD sweep signals and conventional PD pulses are extracted. Consequently, the mRMR feature selection technique is implemented to select the significant features of the detected PD signals. To establish the plausibility of this technique, several other feature selection algorithms, including RefliefF, Gini Index (GI), and Information Gain (IG), are introduced for the same datasets. The performance of all these feature selection algorithms is validated using three commonly used classification techniques such as RF, support vector machines (SVM), and k-nearest neighbors (k-NN). In summary, the results show that the combination of mRMR and RF proves to be the most effective feature selection algorithm for the classification of insulation defects in EMs, achieving an accuracy of 99.875%. This accuracy is significantly better than other feature selection and classification techniques and indicates its potential for application to other power system components.
局部放电（PD）在电气设备生命周期的不同阶段产生。PD缺陷的强度随时间持续上升，这可能导致绝缘性能下降以及电气设备使用寿命缩短。从不同绝缘缺陷中捕获的PD信号进行最优特征选择，可以提高PD缺陷的分类准确率，并有助于更好地可视化电动机（EM）绝缘监测和诊断中的PD参数。本文提出了一种基于最大化相关性和最小化冗余（mRMR）以及随机森林（RF）的混合方法，用于对包含多种缺陷的EM中的PD信号进行最优特征选择和分类。为此，在实验室条件下在EM绝缘中产生了四种PD缺陷，并使用传统的IEC - 60270实验平台采集了800个PD信号。基于PD特征参数确定并研究了这些缺陷的严重程度。提取了PD扫描信号和传统PD脉冲的几个特征。随后，实施mRMR特征选择技术来选择检测到的PD信号的显著特征。为了验证该技术的合理性，针对相同数据集引入了其他几种特征选择算法，包括ReliefF、基尼指数（GI）和信息增益（IG）。使用三种常用的分类技术，如RF、支持向量机（SVM）和k近邻（k - NN），对所有这些特征选择算法的性能进行了验证。总之，结果表明，mRMR和RF的组合被证明是用于EM绝缘缺陷分类的最有效特征选择算法，准确率达到99.875%。该准确率明显优于其他特征选择和分类技术，表明其在应用于其他电力系统组件方面的潜力。
