IGBT Open-Circuit Fault Diagnosis in a Quasi-Z-Source Inverter

Yaghoubi, M.; Moghani, Javad Shokrollahi; Noroozi, Negar; Zolghadri, Mohammad Reza

doi:10.1109/tie.2018.2847709

Cited by 56 publications

(19 citation statements)

References 37 publications

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“…It is an essential component in the control of motors, grid‐connected photovoltaics, wind turbines, rolling machines and so on. Diagnosis and fault tolerance of inverters have become crucial issues to maintain the reliability of systems [5–8]. Compared to the conventional two‐level inverter, the multilevel inverter has specific advantages [9, 10], including the characteristics of higher output voltage levels, higher‐quality output voltage and current with lower harmonic distortion.…”

Section: Introductionmentioning

confidence: 99%

Multi‐mode fault‐tolerant control strategy for cascaded H‐bridge multilevel inverters

Wang

Zhang

Wang

et al. 2020

IET power electron.

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Section: Introductionmentioning

confidence: 99%

Multi‐mode fault‐tolerant control strategy for cascaded H‐bridge multilevel inverters

Wang

Zhang

Wang

et al. 2020

IET power electron.

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“…The soft fault manifests that the parameter value of the component exceeds the tolerance range of the nominal value. Additionally, the hard faults of the power semiconductor devices are the most common in PWM rectifiers, which can be divided into short-circuit fault (SCF) and open-circuit fault (OCF) [7].…”

Section: Introductionmentioning

confidence: 99%

Open-Circuit Fault Diagnosis of Three-Phase PWM Rectifier Using Beetle Antennae Search Algorithm Optimized Deep Belief Network

Zhang

2020

Electronics

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Effective open-circuit fault diagnosis for a two-level three-phase pulse-width modulating (PWM) rectifier can reduce the failure rate and prevent unscheduled shutdown. Nevertheless, traditional signal-based feature extraction methods show poor distinguishability for insufficient fault features. Shallow learning diagnosis models are prone to fall into local extremum, slow convergence speed, and overfitting. In this paper, a novel fault diagnosis strategy based on modified ensemble empirical mode decomposition (MEEMD) and the beetle antennae search (BAS) algorithm optimized deep belief network (DBN) is proposed to cope with these problems. Initially, MEEMD is applied to extract useful fault features from each intrinsic mode function (IMF) component. Meanwhile, to remove features with redundancy and interference, fault features are selected by calculating the importance of each feature based on the extremely randomized trees (ERT) algorithm, and the dimension of fault feature vectors is reduced by principal component analysis. Additionally, the DBN stacked with two layers of a restricted Boltzmann machine (RBM) is selected as the classifier, and the BAS algorithm is used as the optimizer to determine the optimal number of units in the hidden layers of the DBN. The proposed method combined with feature extraction, feature selection, optimization, and fault classification algorithms significantly improves the diagnosis accuracy.

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“…Open‐circuit faults and short‐circuit faults are the most common faults of the three‐phase PWM rectifier. Since short‐circuit faults in insulated‐gate bipolar transistors (IGBTs) are destructive and easy to cause over‐current, but the protection of short‐circuit fault is also completed by a standard protection circuit [7, 8]. A method of turning short‐circuit fault into an open‐circuit fault by fast fuse was put forward in [9], thereby reducing the impact of faults.…”

Section: Introductionmentioning

confidence: 99%

Data‐driven design of fault diagnosis for three‐phase PWM rectifier using random forests technique with transient synthetic features

Kou

Liu

Cai

et al. 2020

IET power electron.

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A three-phase pulse-width modulation (PWM) rectifier can usually maintain operation when open-circuit faults occur in insulated-gate bipolar transistors (IGBTs), which will lead the system to be unstable and unsafe. Aiming at this problem, based on random forests with transient synthetic features, a data-driven online fault diagnosis method is proposed to locate the opencircuit faults of IGBTs timely and effectively in this study. Firstly, by analysing the open-circuit fault features of IGBTs in the three-phase PWM rectifier, it is found that the occurrence of the fault features is related to the fault location and time, and the fault features do not always appear immediately with the occurrence of the fault. Secondly, different data-driven fault diagnosis methods are compared and evaluated, the performance of random forests algorithm is better than that of support vector machine or artificial neural networks. Meanwhile, the accuracy of fault diagnosis classifier trained by transient synthetic features is higher than that trained by original features. Also, the random forests fault diagnosis classifier trained by multiplicative features is the best with fault diagnosis accuracy can reach 98.32%. Finally, the online fault diagnosis experiments are carried out and the results demonstrate the effectiveness of the proposed method, which can accurately locate the open-circuit faults in IGBTs while ensuring system safety.

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IGBT Open-Circuit Fault Diagnosis in a Quasi-Z-Source Inverter

Cited by 56 publications

References 37 publications

Multi‐mode fault‐tolerant control strategy for cascaded H‐bridge multilevel inverters

Multi‐mode fault‐tolerant control strategy for cascaded H‐bridge multilevel inverters

Open-Circuit Fault Diagnosis of Three-Phase PWM Rectifier Using Beetle Antennae Search Algorithm Optimized Deep Belief Network

Data‐driven design of fault diagnosis for three‐phase PWM rectifier using random forests technique with transient synthetic features

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