Abstract：Multi-terminal flexible DC grids are a significant development trend for future smart grids, but they have significant line protection problems. Control systems must coordinate existing flexible DC line protection, complicated threshold settings and imperfect protection functions. This paper shows that the waveform characteristics of the reverse traveling wave contain abundant fault location information that can be used to develop a deep learning protection scheme. The reverse traveling wave is calculated based on a polar mode transformation and the line frequency parameters. Then, a normalized wave is used as the input to a deep learning model that adaptively mines the waveform characteristics. This then gives a bipolar fault diagnosis and single pole fault selection in the protected area. Simulations show that this scheme can provide a fault diagnosis within 2 ms and can withstand a 200 Ω transition resistance.
和敬涵, 张可欣, 李猛, 聂铭, 宋元伟. 基于深度学习的柔性直流线路单端量波形特征保护[J]. 清华大学学报（自然科学版）, 2021, 61(5): 478-486.
HE Jinghan, ZHANG Kexin, LI Meng, NIE Ming, SONG Yuanwei. Single terminal waveform characteristic protection of flexible DC lines based on deep learning. Journal of Tsinghua University(Science and Technology), 2021, 61(5): 478-486.
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