Comparative study of CNN and fused 2D CNN-LSTM with CWT and STFT for power quality disturbance classification

Abstract

The integration of solar and wind energy has increased electricity generation but also introduced power quality disturbances (PQDs) that threaten grid stability. This study examines the detection and classification of five PQD types—voltage sag, swell, interruption, harmonics, and normal conditions—across noisy environments (0, 10, 20, and 30 dB) signal-to-noise ratio (SNR). Traditional methods— support vector machine (SVM), random forest (RF), artificial neural networks (ANN), and 1D convolutional neural networks (1D CNN)—are evaluated on raw signal data, while advanced models—2D CNN and fused 2D CNN-LSTM—utilize time-frequency representations (continuous wavelet transform (CWT) and short-time Fourier transform (STFT)). Results show that deep learning (DL) models achieve high accuracy even in noisy environments, with the fused 2D CNNLSTM using CWT outperforming all other methods. Noise adversely affects feature extraction, with CWT consistently outperforming STFT under low SNR conditions. These findings demonstrate that combining DL models with robust time-frequency analysis and temporal modeling enhances PQD classification and supports dependable monitoring in smart grid environments.