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Plug-in connector of gas insulated bus (GIB) could be subject to the wear and electrical contact resistance (ECR) deterioration processes under cyclic mechanical and current loads. A specific test platform is designed to simulate the current-carrying wear and ECR degradation process of Ag-plated GIB electrical contact spot inside SF6, N2 and Air insulation mediums under current loads. Worn surface morphology and chemical composition are observed and analyzed. Influence of insulation medium and current load on deterioration mechanism of GIB electrical contact are discussed. Raw ECR curve obtained from current-carrying experiment is smoothed by the exponentially weighted moving average (EWMA) method. A long short-term memory (LSTM) neural network is then proposed to realize both single-step and multi-step ECR and failure predictions of GIB electrical contact. Results show that adhesive wear on contact surface happened due to materials transfer. Deterioration of contact spot inside air medium is much severer than those in SF6 and N2 due to oxidation effect. Deterioration of contact spot inside SF6 medium under high current load is severer than those under low current load due to the change of wear behavior and chemical reactions. Compared with other methods, LSTM could achieve better performance in both single-step and multi-step failure prediction of GIB electrical contact.

Deterioration Behavior Analysis and LSTM-Based Failure Prediction of GIB Electrical Contact Inside Various Insulation Gases