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Real-Time Fault Detection and Identification for MMC Using 1-D Convolutional Neural Networks

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Original languageEnglish
Pages (from-to)8760-8771
Number of pages12
JournalIEEE Transactions on Industrial Electronics
Issue number11
Publication statusPublished - 1 Nov 2019
Publication typeA1 Journal article-refereed


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.


  • convolutional neural nets, fault diagnosis, feature extraction, power engineering computing, power generation faults, probability, switching convertors, voltage control, high-voltage applications, modular multilevel converter, identical controlled voltage sources, switch failure, steady-state, fault behavior, cell voltage, load current, fault timing, highly accurate MMC circuit monitoring system, early fault detection, one-dimensional convolutional neural networks, raw voltage, current data, highly efficient system, reliable system, detection probability, average identification probability, time fault detection, 1-d convolutional neural networks, automated early detection, switch faults, eight-switch MMC topology, Fault diagnosis, Circuit faults, Topology, Fault detection, Switches, Capacitors, Convolutional neural network (CNN), fault detection, fault identification, modular multilevel converter (MMC)

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