A Hypothesis Method for T-Type Three-Level Inverters Open-Circuit Fault Diagnosis Based on Output Phase Voltage Model

Zhang, Weiwei; He, Yigang

doi:10.1109/tpel.2022.3151731

Cited by 15 publications

(3 citation statements)

References 40 publications

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“…These methods diagnose faults by measuring the phase voltage, line voltage, or voltage values at key points output by the inverter, as discussed in references [18][19][20][21][22]. After considering factors such as neutral point voltage imbalance and time-offset injection, references [23] proposed an assumption-based method for T-type three-level inverter OC fault diagnosis based on the output phase voltage model. The method established a phase voltage model and determined the fault location based on the amplitude and angle of the residual vector.…”

Section: Introductionmentioning

confidence: 99%

Online Fault Detection of Open-Circuit Faults in a DTP-PMSM Using Double DQ Current Prediction

Geng,

Du,

Jin

et al. 2024

WEVJ

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This research proposes a strategy to diagnose open-phase faults (OPF) and open-switching faults (OSF) in dual three-phase permanent magnet synchronous motor (DTP-PMSM) inverters. The method is based on the dual d–q predictive current model and involves establishing a mathematical model and utilizing the finite control set model predictive current extraction technique to predict the motor current. It then analyzes the characteristics of the switching-tube current under both normal and fault conditions. Finally, a fault predictive current model is introduced and the residual is calculated based on the predicted fault current value and the actual measured current value to diagnose the inverter fault. The proposed method effectively overcomes misjudgment issues encountered in traditional open-circuit fault diagnosis of inverters. It enhances the system’s response speed during dynamic processes and strengthens the robustness of diagnosis algorithm parameters. The experimental results demonstrate that the proposed method can rapidly, effectively, and accurately diagnose open-circuit faults presented in this paper fastest within one-fifth of a current cycle. It achieves a diagnostic accuracy rate of 97% in the dual three-phase permanent magnet synchronous motor drive system.

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Section: Introductionmentioning

confidence: 99%

Online Fault Detection of Open-Circuit Faults in a DTP-PMSM Using Double DQ Current Prediction

Geng,

Du,

Jin

et al. 2024

WEVJ

View full text Add to dashboard Cite

show abstract

“…Line voltage error-based method is proposed for open circuit faults; however, it requires higher sampling time. The open switch faults are detected using a Fourier series and a histogram of the trajectory of voltage [10].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Asymmetric Cascaded Multilevel Inverter using Ensemble Machine Learning

2024

Informacije MIDEM

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Cascaded Multi-Level Inverters (CMLI) are used in a wide range of high-power industrial drives and for integrating solar PV system. Asymmetric Cascaded Multilevel Inverter (ACMLI) produces an output voltage with reduced Total Harmonic Distortion (THD) when compared to Symmetric Cascaded Multilevel Inverter (SCMLI). ACMLI comprises of more semiconductor devices and thus reliability is a major concern. Efficient, high speed and precise fault detection is required for ACMLI to reduce failure rates and avoid unplanned shutdown. RMS voltage, mean voltage and THD under various single and double switch fault conditions are used as features for fault diagnosis. Fault diagnosis method for ACMLI based on probabilistic principal component analysis (PPCA) and Ensemble Machine Learning (EML) is presented. PPCA is used to optimize data and reduce the size of fault features. Finally, an EML classifier combining Support Vector Machine (SVM), K-Nearest Neighborhood (KNN) and Decision Tree (DT) is employed to diagnose the various open circuit faults. The proposed fault diagnosis method is validated using an experimental setup. The simulation and experimental result shows that EML technique diagnosis the fault with 99.32% accuracy.

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“…Similar to IGBT open-circuit faults, motor drive systems are also sensitive to sensor failures. Among them, current sensors are not only used for system control but also for diagnosing power device faults, once an opencircuit fault occurs, the consequences are even more severe [3].…”

Section: Introductionmentioning

confidence: 99%

An open-circuit fault diagnosis method for PMSM drive system based on multi-branch 1D-CNN

Zhang,

Zhang

2024

J. Phys.: Conf. Ser.

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In this paper, a fault diagnosis method based on multi-branch one-dimensional convolutional neural network (1D-CNN) is proposed for the open-circuit fault of the inverter IGBT and current sensor in permanent magnet synchronous motor (PMSM) drive system. Firstly, to avoid adding additional sensors, a three-phase current acts as a highly responsive signal for fault detection. Secondly, a multi-branch 1D-CNN diagnosis model is built to solve the complexity and uncertainty problems caused by traditional artificial feature extraction and selection. Then, the residual connection is introduced into the diagnostic model to alleviate the disappearance of the network gradient and accelerate the convergence speed. Finally, the three-phase current is directly input into the model for training and classification. The test results show that the method can identify the open-circuit fault of the IGBT and current sensor within the sample length of a fundamental cycle, and the accuracy rate is 99.84%. Compared with other traditional machine learning methods, it has been proven to have higher diagnostic accuracy and good anti-noise ability.

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A Hypothesis Method for T-Type Three-Level Inverters Open-Circuit Fault Diagnosis Based on Output Phase Voltage Model

Cited by 15 publications

References 40 publications

Online Fault Detection of Open-Circuit Faults in a DTP-PMSM Using Double DQ Current Prediction

Online Fault Detection of Open-Circuit Faults in a DTP-PMSM Using Double DQ Current Prediction

Fault Diagnosis of Asymmetric Cascaded Multilevel Inverter using Ensemble Machine Learning

An open-circuit fault diagnosis method for PMSM drive system based on multi-branch 1D-CNN

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