DC Fault Detection in Meshed MTDC Systems Based on Transient Average Value of Current
DC Fault Detection in Meshed MTDC Systems Based on Transient Average Value of Current
复制标题
基于电流暂态平均值的网状 MTdc 系统直流故障检测
DOI:
10.1109/tie.2019.2907499
复制
发表时间:
2020
影响因子:
7.7
通讯作者:
Zhao Xu
中科院分区:
文献类型:
--
作者:
Yujun Li;Jiapeng Li;Liansong Xiong;Xian Zhang;Zhao Xu
It is of great significance for a voltage source converter (VSC)-based multiterminal dc (MTdc) grid to isolate the dc fault lines within several milliseconds after dc fault. Existing protection schemes and fault analysis methods are mainly based on the numerical simulations, which lack the theoretical analysis. In this paper, a high-frequency (HF) equivalent model of VSC-based MTdc grid that is utilized for initial dc fault current calculation is first proposed. In the proposed model, the parallel connected capacitors of VSCs are regarded as short-circuited, and the initial fault current calculation of the fault dc line and the healthy dc line can be reduced as a simplified RL and RLC circuit. Accordingly, a novel dc fault detection method for VSC-based MTdc system is further proposed. In the proposed approach, the primary detection utilizes the transient average value of line current and the fault dc line can be identified quickly with only one-end information. In addition, the errors between the transient fault current calculation based on the distributed parameter line model and the lumped parameter line model are also evaluated. Numerous simulation studies carried out in PSCAD/EMTdc have demonstrated that the proposed HF equivalent model can be utilized for initial dc fault analysis of VSC-MTdc system and the proposed protection scheme is effective under different fault locations and high fault resistances. Compared with the traditional protection schemes based on rate of change of current, the proposed one requires relatively low sampling frequency and low computation burden, and has high fault resistance tolerance ability and high robustness with respect to the missing data.