Real-Time Fault Detection and Identification for MMC Using 1-D Convolutional Neural Networks
Author | Kiranyaz S. |
Author | Gastli A. |
Author | Ben-Brahim L. |
Author | Al-Emadi N. |
Author | Gabbouj M. |
Available date | 2020-04-01T09:45:58Z |
Publication Date | 2019 |
Publication Name | IEEE Transactions on Industrial Electronics |
Resource | Scopus |
ISSN | 2780046 |
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. - 2018 IEEE. |
Sponsor | Manuscript received October 23, 2017; revised February 11, 2018 and March 27, 2018; accepted April 16, 2018. Date of publication May 3, 2018; date of current version June 28, 2019. This work was supported by the National Priorities Research Program award (NPRP10-1203-160008) from the Qatar National Research Fund, a member of the Qatar Foundation. (Corresponding author: Serkan Kiranyaz.) S. Kiranyaz, A. Gastli, L. Ben-Brahim, and N. Al-Emadi are with the Department of Electrical Engineering, Qatar University, Doha 2713, Qatar (e-mail: mkiranyaz@qu.edu.qa; adel.gastli@qu.edu.qa; brahim@qu.edu.qa; alemadin@qu.edu.qa). |
Language | en |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Subject | Convolutional neural network (CNN) fault detection fault identification modular multilevel converter (MMC) |
Type | Article |
Pagination | 8760-8771 |
Issue Number | 11 |
Volume Number | 66 |
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Electrical Engineering [2616 items ]