Online diagnostic method of open‐switch faults in PWM voltage source rectifier based on instantaneous AC current distortion

Shi, Tiancheng; He, Yigang; Deng, Fang−Ming; Jin, Tong; Wang, Tao; Shi, Luqiang

doi:10.1049/iet-epa.2017.0438

Cited by 26 publications

(24 citation statements)

References 33 publications

(41 reference statements)

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“…In practice, a power switch comprises two main parts: an insulated-gate bipolar transistor (IGBT) and a parallel freewheel diode (PFD). Power switch faults caused by gate drive failures easily lead to OCFs in the motor side converter (MSC) [22]. In this case, the PFD remains connected to the MSC, even though its parallel connected switch is open.…”

Section: B Fault Conditionsmentioning

confidence: 99%

Fault Detection Method for Permanent Magnet Synchronous Generator Wind Energy Converters Using Correlation Features Among Three-phase Currents

Tan

Zhang

Zhou

2020

Journal of Modern Power Systems and Clean Energy

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In a permanent magnet synchronous generator (PMSG) system, conversion systems are major points of failure that create expensive and time-consuming problems. Fault detection is usually used to achieve a steady system. This paper presents a full analysis of a PMSG system for wind turbines (WT) and proposes a fault detection method using correlation features. The proposed method is motivated by the balance among the three-phase currents both before and after an opencircuit fault occurs in a converter of the PMSG system. It is unnecessary to analyze the output waveforms of a converter during fault detection. In this study, two correlation features of stator currents, the mean and covariation, are extracted to train an artificial neural network (ANN), thereby enhancing the performance of the proposed method under different wind speed conditions. Moreover, additional sensors and the collection of a massive amount of data are not required. Model simulations of an ideal inverter and a PMSG system are conducted using PSCAD software. The simulation results show that the proposed method can detect the locations of faulty switches with a diagnostic rate greater than 99.4% for the ideal inverter, and the PMSG drives settings at different wind speeds. Index Terms-Permanent magnet synchronous generator (PMSG), artificial neural network (ANN), mixed logical dynamical (MLD) theory, conversion system.

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Section: B Fault Conditionsmentioning

confidence: 99%

Fault Detection Method for Permanent Magnet Synchronous Generator Wind Energy Converters Using Correlation Features Among Three-phase Currents

Tan

Zhang

Zhou

2020

Journal of Modern Power Systems and Clean Energy

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“…The detection and location technique based on the Park approach combined with polar coordinates. 20 The fault angle is calculated from Equations 16-18 as follows. 20…”

Section: Detection and Location The Open-circuit Fault Each Igbt Swmentioning

confidence: 99%

“…The fault current vector of each IGBT, expressed by the following equation 20 : According to Figure 5 it is possible to calculate the intervals of the faulty angles of each leg, the intervals of the faulty angles of each IGBT and the exact faulty angles of each IGBT.…”

Section: Detection and Location The Open-circuit Fault Each Igbt Swmentioning

confidence: 99%

Indirect vector controlled of an induction motor using H ^∞ current controller for IGBT open circuit fault compensation

Chérif

Djerioui

Zeghlache

et al. 2020

Int Trans Electr Energ Syst

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The purpose of this paper is to design the robust fault-tolerant control FTC open-circuit fault IGBT's, first of all. The modeling and control of the induction motor in the healthy inverter and in the faulty inverter (open-circuit fault at the IGBT switch) are proposed. Furthermore, the technique for detection and location the open-circuit fault an IGBT based on the Park vector combined with the polar coordinate. In order to ensure the service continuity of the system, two methods of tolerance are developed: the first method, the indirect vector control with H ∞ controller of the induction motor fed by a three-phase inverter based on the fault compensation. The second method, the indirect vector control of the induction motor fed by a three-phase inverter with the redundant leg. Finally, comparative study between the two techniques of tolerance is carried out. The performance of each technique is confirmed by experimental results.

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“…Once fault occurs, the system runs under abnormal conditions or causes substantial economic losses. Hence, an efficient and accurate fault diagnosis approach is of the utmost to ensure the reliability and security of the PWM rectifiers [3].…”

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

Self Cite

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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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Online diagnostic method of open‐switch faults in PWM voltage source rectifier based on instantaneous AC current distortion

Cited by 26 publications

References 33 publications

Fault Detection Method for Permanent Magnet Synchronous Generator Wind Energy Converters Using Correlation Features Among Three-phase Currents

Fault Detection Method for Permanent Magnet Synchronous Generator Wind Energy Converters Using Correlation Features Among Three-phase Currents

Indirect vector controlled of an induction motor using H ^∞ current controller for IGBT open circuit fault compensation

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

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Online diagnostic method of open‐switch faults in PWM voltage source rectifier based on instantaneous AC current distortion

Cited by 26 publications

References 33 publications

Fault Detection Method for Permanent Magnet Synchronous Generator Wind Energy Converters Using Correlation Features Among Three-phase Currents

Fault Detection Method for Permanent Magnet Synchronous Generator Wind Energy Converters Using Correlation Features Among Three-phase Currents

Indirect vector controlled of an induction motor using H ∞ current controller for IGBT open circuit fault compensation

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

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Indirect vector controlled of an induction motor using H ^∞ current controller for IGBT open circuit fault compensation