Parameter Identification of Distribution Line Based on Noise Adaptive Kalman Filtering

Algorithm for three-phase line parameter identification based on noise adaptive Kalman filtering is proposed to address the problem of large deviation between measurement data and true values due to factors such as equipment aging and line interference, which affects the accuracy of parameter identification based on measurement data. Firstly, a state equation and a measurement equation for noise adaptive Kalman filtering are established based on the three-phase line model of the distribution system considering the coupled impedance. Then, an adaptive correction method for noise is introduced to continuously update the covariance matrix of measurement noise based on the residuals (i.e., the difference between the measured values and the predicted values), reducing the interference caused by the error in the covariance matrix of measurement noise and improving the accuracy of parameter identification results. The effectiveness of the proposed method is verified through case analysis results. Compared to traditional Kalman filtering methods, the proposed method in this paper achieves higher identification accuracy.