Partial Discharge Defect Classification in MV Switchgear by Using CWT and Deep Learning Approach

Accurately identifying partial discharges (PDs) is crucial for ensuring the reliability of high-voltage switchgear components. This paper presents a novel framework that integrates advanced signal processing and deep learning techniques to enhance PD classification. Utilizing, logarithmic scaling and continuous wavelet transform (CWT), combined with an optimized convolutional neural network (CNN) for feature extraction, the framework outperforms traditional models in terms of classification accuracy and computational efficiency. Principal component analysis (PCA) is applied to further optimize the extracted features, reducing dimensionality and improving fault-specific feature selection. The proposed framework achieves a 100% classification accuracy, demonstrating superior performance when compared to existing models such as ResNet-50, DenseNet-201, MobileNetV2, and conventional deep CNNs. The framework not only achieves high accuracy but also significantly reduces training time, making it highly effective for real-time applications. With its ability to generalize across different PD types, the model provides a robust and computationally efficient solution for PD detection, offering significant advancements in the maintenance and reliability assessment of power system components.