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DOI:10.35833/MPCE.2021.000404
Single-ended Fault Detection Scheme Using Support Vector Machine for Multi-terminal Direct Current Systems Based on Modular Multilevel Converter
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Author: Guangyang Zhou1,Xiahui Zhang1,Minxiao Han1,Shaahin Filizadeh2,Zhi Geng1

Author Affiliation: 1.the School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China;2.the Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, Canada

Foundation:

This work was supported by the 111 project (No. B08013).
Abstract: This paper proposes a single-ended fault detection scheme for long transmission lines using support vector machine (SVM) for multi-terminal direct current systems based on modular multilevel converter (MMC-MTDC). The scheme overcomes existing detection difficulties in the protection of long transmission lines resulting from high grounding resistance and attenuation, and also avoids the sophisticated process of threshold value selection. The high-frequency components in the measured voltage extracted by a wavelet transform and the amplitude of the zero-mode set of the positive-sequence voltage are the inputs to a trained SVM. The output of the SVM determines the fault type. A model of a four-terminal DC power grid with overhead transmission lines is built in PSCAD/EMTDC. Simulation results of EMTDC confirm that the proposed scheme achieves 100% accuracy in detecting short-circuit faults with high resistance on long transmission lines. The proposed scheme eliminates mal-operation of DC circuit breakers when faced with power order changes or AC-side faults. Its robustness and time delay are also assessed and shown to have no perceptible effect on the speed and accuracy of the detection scheme, thus ensuring its reliability and stability.

Keywords:

Fault detection ; short-circuit fault ; multi-terminal direct current systems based on modular multilevel converter ; support vector machine (SVM) ; wavelet transform
Received:June 24, 2021               Online Time:2023/05/23
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