Research on electromagnetic induction characteristics of HVTL based on improved XGboost integrated machine learning

With the expansion of transmission demand in the power system, high-voltage transmission technology has been widely applied in the field of transmission. Some high-voltage transmission lines have small spacing between circuits, especially double circuit high-voltage transmission lines on the same tower or in the same corridor. When one circuit is shut down and the other circuit is in operation, the induced voltage and current on the shutdown circuit are high, which affects the stable operation of the power system. This article first analyzes the electromagnetic induction characteristics of high-voltage transmission lines. Then, based on the actual engineering background, ATP-EMPT simulation software was applied to build a simulation model of a 500 kV double circuit transmission line on the same tower or in the same corridor, considering the effects of different line lengths, transmission currents, horizontal spacing between lines, and tower call heights on the induced voltage and current of the shutdown line. Based on this prediction, the induced voltage and current are analyzed, and then the XGboost algorithm optimized by the Guanhao Pig algorithm is used to calculate and analyze the induced voltage and current of unknown lines. The measured data and simulation results show that the prediction algorithm proposed in this paper has high accuracy, providing a certain reference basis for determining the operating parameters of transmission lines.