期刊
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
卷 66, 期 11, 页码 8760-8771出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIE.2018.2833045
关键词
Convolutional neural network (CNN); fault detection; fault identification; modular multilevel converter (MMC)
资金
- National Priorities Research Program award from the Qatar National Research Fund, Qatar Foundation [NPRP10-1203-160008]
Automated early detection and identification of switch faults are essential in high-voltage applications. Modular multilevel converter (MMC) is a new and promising topology for such applications. MMC is composed of many identical controlled voltage sources called modules or cells. Each cell may have one or more switches and a switch failure may occur in anyone of these cells. The steady-state normal and fault behavior of a cell voltage will also significantly vary according to the changes in the load current and the fault timing. This makes it a challenging problem to detect and identify such faults as soon as they occur. In this paper, we propose a real-time and highly accurate MMC circuit monitoring system for early fault detection and identification using adaptive one-dimensional convolutional neural networks. The proposed approach is directly applicable to the raw voltage and current data and thus eliminates the need for any feature extraction algorithm, resulting in a highly efficient and reliable system. Simulation results obtained using a four-cell, eight-switch MMC topology demonstrate that the proposed system has a high reliability to avoid any false alarm and achieves a detection probability of 0.989, and average identification probability of 0.997 in less than 100 ms.
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