Effectiveness of Wavelet Scalogram on Partial Discharge Pattern Classification of XLPE Cable Insulation

• Published in: IEEE transactions on instrumentation and measurement Vol. 73; pp. 1 - 10
• Main Authors: Sahoo, Rakesh, Karmakar, Subrata
• Format: Journal Article
• Language: English
• Published: New York IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Artificial neural networks; Classification; Continuous wavelet transform; Continuous wavelet transforms; Convolutional neural network (CNN); Deep learning; Discharge; Effectiveness; Feature extraction; Insulation; Kurtosis; Machine learning; partial discharge (PD); Partial discharges; Pattern classification; scalogram image; Signal resolution; Spectrogram; Time-frequency analysis; transfer learning; wavelet transform; Wavelet transforms
• DOI: 10.1109/TIM.2024.3363790
• ISSN: 0018-9456

Detection and classification of partial discharge (PD) signal in XLPE cable is very important to find out the root cause of insulation failure. The purpose of this work is to improve the accuracy and effectiveness in classifying various types of PD signals including corona, surface, and internal discharges in a noisy environment using machine learning (ML) and deep learning (DL) techniques. For ML models, statistical features such as skewness, kurtosis, standard deviation, and variance are extracted from the PD signals and used for training. In contrast, the DL model focuses on continuous wavelet transforms (CWTs) based scalogram images which highlight the resolution of the signal's energy and are used for feature extraction in the classification study. Instead of using traditional phase-resolved PD (PRPD) plots, this work introduces a novel approach to converting PD signals to scalogram images using CWT. These scalogram images provide a visual representation of the frequency components of different PD types and how they change over time. The wavelet-based scalogram images are used as input features for the proposed convolutional neural network (CNN) and state-of-the-art (SOTA) DL models, along with shallow ML classifiers for model training, validation, and testing. Along with this, k-fold cross-validation and hyperparameter tuning are also employed to enhance the overall performance of the model. The classification results demonstrate that the proposed CNN model achieves up to 97.33% recognition accuracy and less computational complexity as compared to various ML and DL models.