Litcius/Paper detail

An Inrush Current Suppression Strategy for UHV Converter Transformer Based on Simulation of Magnetic Bias

Pingping Han, Qingyang Tong, Yan Wang, Zhong Chen, Yang Wei, Di Hu, Hongbin Wu, Jingjing Zhang

2022IEEE Transactions on Power Delivery20 citationsDOI

Abstract

The Ultra High Voltage (UHV) converter transformer may generate serious inrush current during closing. It will cause commutation voltage distortion and easily result in commutation failure of converters on the same commutation bus. Due to the change of flux amplitude and phase caused by closing resistance in the Ultra High Voltage Direct Current (UHVDC) system, it is difficult to determine the closing time of the converter transformer when considering the influence of residual flux. Therefore, this paper first analyses the existing closing resistance strategy, and constructs the magnetic bias function according to the influence characteristics of closing resistance on the flux and phase. Then an inrush current suppression strategy based on magnetic bias simulation is proposed for star connection and delta connection of UHV converter transformers, which can accurately calculate the ideal closing time of mutual cancellation between the residual flux and bias flux during closing. Finally, the no-load closing model of the UHV converter transformer is established by PSCAD/EMTDC for simulation analysis of the feasibility, real-time verification is carried out using the RTDS closed-loop experimental platform. The results show that the proposed inrush current suppression strategy can effectively suppress the inrush current under different residual flux conditions.

Topics & Concepts

Inrush currentCommutationTransformerEngineeringControl theory (sociology)ConvertersMagnetic fluxVoltageElectrical engineeringDC biasElectronic engineeringComputer sciencePhysicsMagnetic fieldQuantum mechanicsArtificial intelligenceControl (management)Magnetic Properties and ApplicationsHigh-Voltage Power Transmission SystemsHVDC Systems and Fault Protection