An Explainable Physics-Informed Dual-Input Deep Learning Framework for Partial Discharge Detection in Noisy Environments
Partial discharge (PD) detection is essential for assessing insulation health in high-voltage equipment but is hindered by noise and non-stationary signals. This paper proposes an explainable, physics-informed hybrid framework for PD detection under synthetic noisy conditions. The method integrates wavelet-based denoising with time-frequency feature extraction using short-time Fourier transform (STFT) and continuous wavelet transform (CWT). A dual-input deep learning architecture combining convolutional layers, bidirectional long short-term memory (BiLSTM), and multi-head attention captures spatial-temporal dependencies from raw and engineered features. Explainable artificial intelligence (XAI) techniques, including attention visualization and Integrated Gradients, are incorporated to improve interpretability by identifying signal regions that drive model decisions. On a controlled synthetic dataset, the proposed framework achieves 89.38% accuracy, demonstrating promising discrimination between PD and noise signals. These results establish a proof of concept; validation on real-world PD datasets remains an important direction for future work.