Partial discharge (PD) classification is crucial for the condition monitoring of equipment with high power density. Recently, machine learning-based approaches have been developed for the detection and classification of PD signals. Most of these PD classification techniques are based on the availability of labeled training data. However, in practice, not all data may have labels. In this paper, we classify various types of PD signals using confidence-level-based semi-supervised learning. The algorithms investigated are Naïve Bayes, extreme gradient boosting (XGboost), and random forest. The performances of these algorithms are compared in terms of their PD source prediction accuracy. The results show that the performance of XGboost is much more superior than Naïve Bayes and random forest.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Confidence-Level-Based Semi-Supervised Machine Learning Approach for Partial Discharge Signal Classification


    Contributors:


    Publication date :

    2022-06-15


    Size :

    1676609 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Classification-based Reliability Assessment with Semi-Supervised Learning

    Patel, J. / Choi, S.-K. / American Institute of Aeronautics and Astronautics | British Library Conference Proceedings | 2011



    A Semi-Supervised Learning Approach for Pixel-Level Pavement Anomaly Detection

    Ren, Ruiqi / Shi, Peixin / Jia, Pengjiao et al. | IEEE | 2023


    The Semi-Supervised SVM Classification Algorithm Based on Two-Stage Learning

    Tao, X. / Cao, P. / Song, S. et al. | British Library Online Contents | 2012