Comparative analysis of machine learning and deep learning techniques on classification of artificially created partial discharge signal

• Published in: Measurement : journal of the International Measurement Confederation Vol. 235; p. 114947
• Main Authors: Sahoo, Rakesh, Karmakar, Subrata
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.08.2024
• Subjects: Convolutional neural network; Machine learning; Partial discharge; Scalogram image; Wavelet transform; Scalogram image; Partial discharge; Wavelet transform; Convolutional neural network; Machine learning
• ISSN: 0263-2241
• DOI: 10.1016/j.measurement.2024.114947

[Display omitted] •Three types of PD signals (corona, internal, and surface) are collected experimentally and denoised using Symlet4 before extracting input features for classification study.•ML approaches incorporate statistical characteristics, while the CNN model emphasizes CWT-based scalogram images.•Enhancing the PD classification with the help of CWT-based scalogram images using customize CNN model with an appropriate hyperparameter setting.•Classification performance is also analyzed using ML models (ANN, SVM, KNN, and RF) with the proposed CNN model. The purpose of this work is to classify different types of partial discharge (PD) signals including corona, surface, and internal discharges. The collected PD signals are de-noised using symlet4 mother wavelet and then subjected to classification using machine learning (ML) and convolutional neural network (CNN) architecture. Statistical features (skewness, kurtosis, standard deviation, variance, etc.) are extracted which are used to train the ML models, whereas the CNN model is emphasized automated feature extraction from a novel approach based on scalogram images. From the denoised signals, continuous wavelet transforms (CWT) based scalogram images are obtained which highlight the resolution of the signal’s energy and are used for feature extraction in the classification study. In this work, PD signals are used for both ML and CNN model training, validation, and testing. Classification results demonstrate that the proposed CNN model achieves a recognition accuracy of up to 100% compared with support vector machines (SVM), artificial neural network (ANN), k- nearest neighbors (KNN), and random forest (RF) based ML classifiers which are 94.26%, 94.26%, 91%, and 95.1% respectively.