A Robust Transmission Line Parameters Identification Based on RBF Neural Network and Modified SCADA Data

Abstract

Transmission line (TL) parameters are the basis of power system calculation. In recent years, artificial intelligence has been widely applied and plays a great role in the power system. However, artificial intelligence is rarely applied to transmission line parameter identification. Therefore, from the perspective of line model and artificial intelligence, combined with median estimation and modified Supervisory Control And Data Acquisition (SCADA) data based on TL model, this paper proposes a robust method for parameter identification of TL based on Radial Basis Function (RBF) neural network and modified SCADA data. Firstly, the line parameter identification method based on RBF neural network is proposed. Then, the training set is established through the π-equivalent model in consideration of different operating conditions and different circuit parameters. Furthermore, the input data of RBF neural network is construed by modifying the SCADA data based on TL model. In addition, the median estimation robust method, which can estimate the final result and reduce the noise interference, is introduced. Finally, the validity and practicability of the proposed method are verified by simulation data and measured SCADA data respectively.