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Real-Time Fault Detection and Identification for MMC Using 1-D Convolutional Neural Networks.
- Source :
- IEEE Transactions on Industrial Electronics; Nov2019, Vol. 66 Issue 11, p8760-8771, 12p
- Publication Year :
- 2019
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 02780046
- Volume :
- 66
- Issue :
- 11
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Industrial Electronics
- Publication Type :
- Academic Journal
- Accession number :
- 137379944
- Full Text :
- https://doi.org/10.1109/TIE.2018.2833045