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Single-Submodule Open-Circuit Fault Diagnosis for a Modular Multi-level Converter Using Arti cial Intelligence-based Techniques

Ke, Ziwei
http://orcid.org/0000-0001-7244-1495
http://rave.ohiolink.edu/etdc/view?acc_num=osu156262961593976

Abstract Details

2019, Doctor of Philosophy, Ohio State University, Electrical and Computer Engineering.
Due to the increased attention in electronic power conversion for high-power applications, the development of new converter topologies with new semiconductor technology has been increased. In fact, multi-level converters have been under research and development for more than three decades and have successfully made their way into many industries. They are commercialized in standard or customized products within a wide range of applications including energy and power systems such as High Voltage Direct Current (HVDC) transmission systems, industry such as conveyors and compressors, transportation such as ship propulsion and high-speed train traction, and renewable energy such as wind energy conversion. Modular multilevel converters (MMCs) are one of the promising topologies in recent years for medium or high voltage applications. They are considered as the next generation AC/DC, DC/AC converters for medium/high voltage (MV/HV) motor drive applications due to their transform-less structures, high efficiency, and modularity. In addition, MMCs provide better quality of output voltages and eliminate additional capacitors on the dc link, as the storage is distributed among the capacitors in the submodules of the converters. Reliability is one of the most important challenges in MMCs, since a large number of power switching devices are used and each of these devices can be considered as a potential failure point. These switching devices are the most vulnerable components in power converters. Therefore, it is significant to detect and locate the fault correctly in a short time after the fault occurrence. Failures of switching devices can be classified into short-circuit faults and open-circuit faults. Since the short-circuit fault is normally protected in device level, only single-submodule open-circuit faults are considered and investigated in this paper. This paper studies and investigates the behaviors of both failure and healthy submodules, and uses artificial neural network (ANN) classification algorithms for single-submodule open-circuit fault diagnosis. In addition, the fault tolerance and reconfiguration control of the MMC is also investigated. Individual submodule capacitor voltages of the MMC are used as diagnostic signals to detect faults and their locations (which device of which submodule). An ANN classification is applied to the fault diagnosis of the MMC drive system. Multilayer structure networks are applied to identify the fault type and location based on the characteristics of the capacitor voltage waveforms. Simulation and experimental results of the proposed fault diagnosis for a single-module open-circuit fault are presented in this paper. The ANN algorithm is implemented in field programmable gate array (FPGA) and the ANN parameters and weights training is completed offline in Matlab by using the experimental data. In summary, the overall single-submodule open-circuit fault diagnosis and reconfiguration control include 1) a modeling method for detecting the fault, 2) a neural network method for classifying the type of the fault and locating the failure device, 3) reconfiguration control.
Julia Zhang (Advisor)
Longya Xu (Committee Member)
Jin Wang (Committee Member)
Elaine Richardson (Committee Member)
144 p.
Electrical Engineering
power converter; multi-level converter; modular multi-level converter; artificial intelligence; fault diagnosis

Recommended Citations

Citations

  • Ke, Z. (2019). Single-Submodule Open-Circuit Fault Diagnosis for a Modular Multi-level Converter Using Arti cial Intelligence-based Techniques [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu156262961593976

    APA Style (7th edition)

  • Ke, Ziwei. Single-Submodule Open-Circuit Fault Diagnosis for a Modular Multi-level Converter Using Arti cial Intelligence-based Techniques. 2019. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu156262961593976.

    MLA Style (8th edition)

  • Ke, Ziwei. "Single-Submodule Open-Circuit Fault Diagnosis for a Modular Multi-level Converter Using Arti cial Intelligence-based Techniques." Doctoral dissertation, Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu156262961593976

    Chicago Manual of Style (17th edition)

osu156262961593976
562
© 2019, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.