Quantitative criteria of considering AC infeed in DC fault assessment of modular multilevel converters

• Published in: International journal of electrical power & energy systems 2022-10, Vol.141
• Main Authors: Xu, Yi, Zhang, Yi, Qin, Liang, Liu, Kaipei, Blaabjerg, Frede
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Computer Science; Electric power; Electronics; Engineering Sciences; Information Theory; Machine Learning
• ISSN: 0142-0615; 1879-3517
• DOI: 10.1016/j.ijepes.2021.000000

An accurate and efficient DC fault current assessment of modular multilevel converters (MMCs) is critical for designs and protection. The state-of-the-art often ignores the impact of the AC infeed in the DC fault assessment without MMC blocking. However, whether the AC infeed needs to be considered or not is a controversial problem. This paper proposes quantitative criteria to determine the necessity of considering the AC infeed in the fault current assessment. The method is based on the premise that the MMC blocking has not occurred. Based on the average model of the MMC, the fault current contribution from the AC side is quantified under different control schemes. The criteria are established by a contributing ratio of the AC infeed over the total fault current. The obtained criteria only rely on the system and control parameters, which enable designers to identify the impact by ignoring the AC infeed in the DC fault assessment. The effectiveness of the proposed criteria is validated by two case studies and two different control schemes. The results reveal that the submodule (SM) capacitance and the number of SMs have a large impact on the AC infeed contribution.