An Underdetermined Single-Channel Blind-Source-Separation for Multisource Acoustic-Emission-Based Partial Discharge Signals in Power Transformers

Condition monitoring of the power transformer is important in extending the lifetime of the transformer through timely maintenance. Acoustic-emission (AE)-based partial discharge (PD) monitoring in a transformer is a commonly used technique for assessing the PD levels inside the transformer. The classification of the type of PD or localization of PD generally involves using multiple acoustic sensors, and recently, deep learning techniques have been widely used with the acquired PD data for classification. In the present work, a single-channel synchrosqueezing transform (SST)-based FastIVA blind-source-separation (BSS) technique in the time-frequency (TF) domain is proposed for the separation of multiple PD signals. SST, along with singular value decomposition (SVD), is used to identify the number of sources. The SST-FastIVA-based method is used for PD signal separation for the first time and it performs better than other separation techniques in noisy conditions. An SVM-based machine learning model is developed with original unmixed source signals for a classification task. The separated signals are tested using the developed model, and it attains an F1 score of 78.70% on the separated signals. Furthermore, the proposed methodology is used with the experimental data, and the F1 score obtained with the separated signal is 84.44%. The proposed single-channel (sensor) technique with BSS is a cost-effective method that can be used for the classification of multiple PD sources.