Distribution line parameter estimation considering dynamic operating states with a probabilistic graphical model

Abstract Accurate distribution line parameters are critical for power system control, operation and management. Adjustment of the operating modes makes the estimation of real-time distribution line parameters dynamic and difficult. To calibrate the distribution line parameters from collected operating data, this paper proposes a Discrete Dynamic Bayesian Network (DDBN)-based distribution line parameter estimation model. The maximum likelihood estimation algorithm is utilized to train the DDBN model. The belief propagation (BP) algorithm is used to infer distribution line parameters with the DDBN model. The accuracy of the proposed model and the inference algorithm is investigated by carrying out experiments in a real-world medium voltage distribution network state estimation environment. The experimental results demonstrate that the proposed DDBN model performs better than existing approaches in providing accurate real-time line parameters.

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