A Novel Hybrid Adaptive Multi-Resolution Feature Extraction Method for Power Quality Disturbance Detection

Monitoring power quality (PQ) and classifying disturbances are essential for guaranteeing the reliable operation of contemporary electrical systems. Nonetheless, deriving discriminative features from PQ signals poses difficulties due to the complexity and non-stationary characteristics of disturbances. Therefore, this research introduces a novel Hybrid Adaptive Multi-Resolution Feature Extraction (HAMRFE) approach for classifying power quality disturbances (PQDs). The proposed HAMRFE framework incorporates six synergistic techniques: adaptive signal decomposition, multi-resolution wavelet analysis, time–frequency analysis, morphological feature extraction, entropy-based feature extraction, and feature selection optimization. Experiments are performed on a dataset consisting of fifteen types of PQDs with differing noise levels. In addition, the performance of five classification algorithms is assessed, including Support Vector Machine (SVM), Artificial Neural Networks, Random Forest, Extreme Gradient Boosting, and K-nearest neighbor. The results indicate the exceptional efficacy of SVM utilizing HAMRFE features, with classification accuracies of 99.86% for noiseless signals, 99.85% at 40 dB, 99.82% at 30 dB, 99.74% at 20 dB, and 97.92% at 10 dB noise levels. Additionally, an analysis of different feature set sizes reveals that the set comprising 125 features is optimal at all noise levels, achieving a balance between computational efficiency and classification accuracy. Finally, the proposed HAMRFE approach exhibits remarkable resilience to noise and offers a thorough framework for classifying PQDs in practical applications.