Deep Domain-Invariant Long Short-Term Memory Network for Partial Discharge Localization in Gas-Insulated Switchgear

Abstract

Localization based on differences in the timing of an ultrahigh-frequency signal is difficult to apply in actual settings because of the strict requirement for accurate calculation of the difference in arrival time between discharge pulses. With the development of digital twinning, the operational state of equipment is mapped onto a digital virtual model, and large-scale simulation data can be obtained to assist in localization of partial discharge (PD) in gas-insulated switchgear (GIS). With this aim, this paper proposes a novel deep domain-invariant long short-term memory network (LSTM) for GIS PD localization. Firstly, an attention residual LSTM is constructed to automatically extract discriminative features and effectively obtain the temporal and spatial dependences between features. Collaborative domain adaptation is proposed to transfer the diagnostic knowledge learned from large-scale simulation data to GIS PD localization. Via maximum mean discrepancy-based domain adaptation and domain adversarial training, discrepancies between samples in the two domains are reduced so as to achieve a more perfect match. The domain-invariant LSTM achieved an average localization accuracy of less than 11 cm, and the standard deviation of the error reached 8.15. The domain-invariant LSTM thus provides a novel solution for the high-precision and robust localization of PD in GIS.