Parameter estimation of distribution system based on radial basis neural network
Huang Rui1, Guo Moufa1, Chen Yongwang2
1. College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350116; 2. State Grid Fujian Jinjiang County Electric Power Supply Co., Ltd, Jinjiang, Fujian 362200
Abstract：The effect of temperature, surrounding environment, skin effect and many other factors can contribute to the difference of distribution lines. A big error happened in distribution system analysis when distribution line parameter for calculation is much different from actual parameter data. To address parameter issue, this paper introduces a novel parameter estimation method based on radial basis function neural network (RBFNN) for three-phase unbalanced distribution line to map the nonlinear relation between measured values of branch power and node voltage and line parameter which is deduced from accurate equivalent distribution line three-phase unbalanced model. For a well trained RBFNN, accurate distribution line parameter can be obtained directly from measurement at two end of distribution line which can decrease the error caused by ill-conditioned matrix in parameter estimation.
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2019, Vol. 20 
