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Author: search.filters.author.Tolbert, Leon M.Subject: search.filters.subject.Fault diagnosis

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    Fault diagnosis and reconfiguration for multilevel inverter drive using AI-based techniques
    (2007-12-01)
    Khomfoi, Surin
    ;
    Tolbert, Leon M.
    A fault diagnostic and reconfiguration method for a cascaded H-bridge multilevel inverter drive (MLID) using artificial-intelligence-based techniques is proposed in this paper. Output phase voltages of the MLID are used as diagnostic signals to detect faults and their locations. It is difficult to diagnose an MLID system using a mathematical model because MLID systems consist of many switching devices and their system complexity has a nonlinear factor. Therefore, a neural network (NN) classification is applied to the fault diagnosis of an MLID system. Multilayer perceptron networks are used to identify the type and location of occurring faults. The principal component analysis is utilized in the feature extraction process to reduce the NN input size. A lower dimensional input space will also usually reduce the time necessary to train an NN, and the reduced noise can improve the mapping performance. The genetic algorithm is also applied to select the valuable principal components. The proposed network is evaluated with simulation test set and experimental test set. The overall classification performance of the proposed network is more than 95%. A reconfiguration technique is also proposed. The proposed fault diagnostic system requires about six cycles to clear an open-circuit or short-circuit fault. The experimental results show that the proposed system performs satisfactorily to detect the fault type, fault location, and reconfiguration. © 2007 IEEE.
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    Fault detection and reconfiguration technique for cascaded H-bridge 11-level inverter drives operating under faulty condition
    (2007-12-01)
    Khomfoi, Surin
    ;
    Tolbert, Leon M.
    A fault detection and reconfiguration technique for a cascaded H-bridge 11-level inverter drives during faulty condition is proposed in this paper. The ability of cascaded H-bridge multilevel inverter drives (MLID) to operate under faulty condition is also discussed. Output phase voltages of a MLID can be used as a diagnostic signal to detect faults and their locations. AI-based techniques are used to perform the fault classification. A neural network (NN) classification is applied to the fault diagnosis of a MLID system. Multilayer perceptron (MLP) networks are used to identify the type and location of occurring faults. The principal component analysis (PCA) is utilized in the feature extraction process to reduce the NN input size. The genetic algorithm (GA) is also applied to select the valuable principal components to train the NN. A reconfiguration technique is also developed. The developed system is validated with simulation and experimental results. The developed fault diagnostic system requires about 6 cycles (∼100 ms at 60 Hz) to clear an open circuit and about 9 cycles (∼150 ms at 60 Hz) to clear a short circuit fault. The experiment and simulation results are in good agreement with each other, and the results show that the developed system performs satisfactorily to detect the fault type, fault location, and reconfiguration. © 2007 IEEE.