Fault Diagnosis and Reconfiguration for Multilevel Inverter Drive Using AI-Based Techniques

Khomfoi, Surin; Tolbert, Leon M.

doi:10.1109/tie.2007.906994

Cited by 247 publications

(98 citation statements)

References 21 publications

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“…A.2.4.3.9 Fault Diagnosis and Reconfiguration for Multilevel Inverter Drive Using AIBased Techniques [46] Summary: This paper proposes a fault diagnostic and reconfiguration method for a cascaded H-bridge multilevel inverter drive (MLID) using artificial-intelligence-based techniques. Diagnostic signals in the form of output phase voltages of the MILD are used to detect fault and their locations.…”

Section: Issuesmentioning

confidence: 99%

Fault Locating, Prediction and Protection (FLPPS)

Yinger¹,

Venkata²,

Centeno³

2010

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Section: Issuesmentioning

confidence: 99%

Fault Locating, Prediction and Protection (FLPPS)

Yinger¹,

Venkata²,

Centeno³

2010

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“…Liu [12] proposed the Wavelet Transform (WT) to detect short-circuit faults in MMC sub-modules, but not open-circuit faults. The artificial intelligence methods such as Fast Fourier Transform (FFT) [13,14] Discrete Fourier Transform (DFT) [15] and Neural Network (NN) [16] can also be used to detect the open-circuit faults and short-circuit faults in MMCs and the only problem then is the long training time. To avoid the mentioned disadvantages, in this paper, a fault detection and localization method based on a nonlinear adaptive observer for both open-circuit faults and short-circuit faults is proposed for an MMC to improve the reliability.…”

Section: Introductionmentioning

confidence: 99%

Online Fault Identification Based on an Adaptive Observer for Modular Multilevel Converters Applied to Wind Power Generation Systems

Liu

Loh

et al. 2015

Energies

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Due to the possibility of putting a large number of modules consisting of switches and capacitors connected in series, the modular multilevel converter (MMC) can easily be scaled to high power and high voltage power conversion, which is an attractive feature for filter-less and transformer-less design and helpful to achieve high efficiency. However, a significantly increased amount of sub-modules in a MMC may increase the requirements for sensors and also increase the risk of failures. As a result, fault detection and diagnosis of MMC sub-modules are of great importance for continuous operation and post-fault maintenance. Therefore, in this paper, an effective fault diagnosis technique for real-time diagnosis of the switching device faults covering both the open-circuit faults and the short-circuit faults in MMC sub-modules is proposed, in which the faulty phase and the fault type is detected by analyzing the difference among the three output load currents, while the localization of the faulty switches is achieved by comparing the estimation results by the adaptive observer. In contrast to other methods that use additional sensors or devices, the presented technique uses the measured phase currents only, which are already available for MMC control. In additional, its operation, effectiveness and robustness are confirmed by simulation results under different operating conditions and load conditions.

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“…In industrial applications, where safe and reliable operation is always expected, it is important to monitor the condition of power electronic switches in inverters. As the number of level increases, number of power electronic switches also increases which leads to increase in probability of failure of any switch and hence any such fault should be detected at the earliest in order to avoid the operation of drive and motor under abnormal conditions [4][5][6][7][8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…Some researchers used the inverter current [8][9] and inverter output voltage [11][12][13][14][15] to develop the fault diagnostic system. Surin Khomfoi et al [13], developed an open-switch fault diagnostic system of a multilevel inverter using the output voltage FFT pattern and five parallel neural networks with 40 input neurons per network.…”

Section: Introductionmentioning

confidence: 99%

Identification of Open-Switch and Short-Switch Failure of Multilevel Inverters through DWT and ANN Approach using LabVIEW

Parimalasundar¹,

Vanitha²

2015

Journal of Electrical Engineering and Technology

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-In recent times, multilevel inverters are given high priority in many large industrial drive applications. However, the reliability of multilevel inverters are mainly affected by the failure of power electronic switches. In this paper, open-switch and short-switch failure of multilevel inverters and its identification using a high performance diagnostic system is discussed. Experimental and simulation studies were carried out on five level cascaded H-Bridge multilevel inverter and its output voltage waveforms were analyzed at different switch fault cases and at different modulation index values. Salient frequency domain features of the output voltage signal were extracted using the discrete wavelet transform multi resolution signal decomposition technique. Real time application of the proposed fault diagnostic system was implemented through the LabVIEW software. Artificial neural network was trained offline using the Matlab software and the resultant network parameters were transferred to LabVIEW real time system. In the proposed system, it is possible to precisely identify the individual faulty switch (may be due to open-switch (or) short-switch failure) of multilevel inverters.

show abstract

Fault Diagnosis and Reconfiguration for Multilevel Inverter Drive Using AI-Based Techniques

Cited by 247 publications

References 21 publications

Fault Locating, Prediction and Protection (FLPPS)

Fault Locating, Prediction and Protection (FLPPS)

Online Fault Identification Based on an Adaptive Observer for Modular Multilevel Converters Applied to Wind Power Generation Systems

Identification of Open-Switch and Short-Switch Failure of Multilevel Inverters through DWT and ANN Approach using LabVIEW

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