Temperature Rise Prediction of GIS Electrical Contact Using an Improved Kalman Filter

Effective fault diagnosis and parameter estimation is the precondition to ensure safe and reliable operation of Gas Insulated Switchgear (GIS). In this paper, aiming at GIS busbar electrical contact overheating fault, an Improved Kalman Filter (IKF) method is proposed, which can predict the temperature rise of GIS internal contacts accurately. In this method, the state equation and the measurement equation are obtained by the Auto-Regressive Integrated Moving Average (ARIMA) model in time series theory and the Back Propagation Neural Network (BPNN) model respectively, which reduces the accumulated error caused by one single prediction method. Genetic Algorithm (GA) is used to optimize the system noise and measurement noise, which reduces the interference of noise on the prediction result. The experiment and comparison results show that the proposed IKF prediction method can effectively estimate and predict the temperature rise of the contacts in the GIS busbar with high prediction accuracy and strong anti-interference ability.