An Online Identification Method for Health State Parameters of Thyristor Modules in HVdc Converter Valve

Abstract

Online identification on the health state parameters of thyristor modules in the HVdc converter is important to ensure the safe operation of the equipment. This article constructs a digital twin model of six-pulse converter by analyzing its working principle, the specific topology of thyristor modules in the valve assembly, and the available system information in practical engineering. The Runge–Kutta (RK) method is used to discretize the state equations of the six-pulse converter and to deduce the output data of digital twin model. The simulation results from a MATLAB/SIMULINK model of the six-pulse converter are utilized instead of the data from the actual physical model. According to the data from the simulated physical model and the digital twin model, the optimization objective function for the parameter identification is determined. Then, the equivalent insulation resistance and the damping capacitance parameters of each thyristor module are identified with the particle swarm optimization (PSO) algorithm. The results indicate that the identified equivalent insulation resistance and damping capacitance parameters have a maximum deviation of 7% from the true values. The identification errors of the damping capacitances are less than that of the equivalent insulation resistance, which is consistent with the trajectory sensitivity analysis. The contrast results show that the method in this article has better identification accuracy and noise immunity than those of the previous study. The proposed method provides a convenient solution for the intelligent maintenance of HVdc converter valves, without a large number of external sensors.