Data-Driven Parameter Inversion for DC Fault Current Analytical Solution of Modular Multilevel Converter-Based High Voltage DC Grid

Abstract

In a multi-terminal modular multilevel converter-based high voltage direct current (MMC-HVDC) grid, due to the coupling between converter stations, it is difficult to obtain an accurate analytical solution on the DC fault current through the traditional equivalent resistance-inductance-capacitance circuit model (E-RLCM). In this paper, a data-driven parameter inversion method is proposed to derive the accurate equivalent parameters in the E-RLCM by combining the electromagnetic transient simulation data with the backpropagation neural network, and the polynomial regression. In this way, the accurate analytic calculation expression of the DC fault current for a multi-terminal MMC-HVDC grid with a pole-to-pole fault (PTPF) is obtained. To verify the effectiveness of the proposed method, simulations are performed for a four-terminal MMC-HVDC grid with a PTPF by PSCAD. The results show that the average calculation error of the DC fault current using the inversion parameters is significantly reduced from over 10% to 2.84%.