IST-MSCNN-OXGBoost: An integrated model for accurate classification of complex power quality disturbances

Abstract

Accurate classification of power quality disturbances (PQDs) is essential for improving the stability of power systems, ensuring reliable integration of renewable energy sources and advancing smart grid technologies. To address the challenges posed by complex PQDs, this study introduces a novel integrated model, IST-MSCNN-OXGBoost, which combines advanced signal processing, deep learning-based feature extraction, and an optimized classifier. The improved S-transform (IST) enables adaptive time–frequency resolution, facilitating precise detection and localization of transient events and signal variations across different frequency ranges. The multi-scale convolutional neural network (MSCNN) employs pyramid convolution operations to extract multi-scale features from time–frequency representations, effectively capturing intricate patterns and complex relationships within the data. Classification accuracy is further enhanced by optimized XGBoost (OXGBoost), which utilizes the duck swarm algorithm for automated hyperparameter tuning, ensuring robust and efficient performance. Comprehensive evaluations underscore the contributions of each component. IST delivers superior time–frequency analysis and improves classification accuracy by 3.33% compared with the conventional ST when integrated with MSCNN-OXGBoost. MSCNN excels in automated and multi-scale feature extraction, and OXGBoost achieves high classification accuracy with improved generalization. The final IST-MSCNN-OXGBoost achieves a classification accuracy of 99.86% and maintains robust performance under adverse noise conditions, preserving an accuracy of 96.67% at a signal-to-noise ratio of 20 dB. Additional analyses across varying dataset sizes, training ratios, image resolutions, noise levels, parameter configurations, and computational loads further validate its suitability for real-time industrial applications. These findings confirm the potential of IST-MSCNN-OXGBoost as robust and reliable solution for the accurate classification of complex PQDs, paving the way for smarter and more resilient power systems.