Abstract
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.