Adversarial Partial Discharge Signal Reconstruction and Denoising With an Encoder-Decoder Network - Keyvan Firuzi (IEEE Access, 2024)
As the application of online partial discharge (PD) measurement increases the importance of denoising becomes increasingly obvious. Besides denoising a PD signal to detect and calculate discharge amplitude, pulse reconstructing is required to compute rise time, fall time and other features which play a crucial role in determining discharge severity and identifying defect types. In this paper, a deep learning method based on adversarial deep network by using encoder-decoder network as a generator is developed to perform a denoising and signal reconstruction task. The issue is to provide a database that contains both noisy and denoised data because every denoising method has its limitation, and it is not possible to train a network with real data and their perfect denoised pulses. Therefore, a synthetic database is developed, and signal deformation and noise added synthetically. The trained network’s performance is evaluated under actual conditions in two distinct laboratories on various days, with differing noise levels and waveforms. The proposed method outperforms the wavelet method in denoising synthetic data and shows an improvement on real data, while successfully reconstructing the PD pulses. Enhancing the network’s performance further, it underwent fine-tuning with actual noise, which led to a marked enhancement in its denoising ability and overall capabilities.