Submodule Voltage Similarity-Based Open-Circuit Fault Diagnosis for Modular Multilevel Converters

Zhou, Dehong; Qiu, Huan; Yang, Shunfeng; Tang, Yi

doi:10.1109/tpel.2018.2883989

Cited by 81 publications

(25 citation statements)

References 30 publications

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“…A SM capacitor voltage correlation coefficient-based method is proposed in [29] to determine the faulty SMs. After the faulty SM detection mode is activated, the correlation coefficients between the SM capacitor voltages in each arm are calculated.…”

Section: A Diagnosis Of Oc Switching Faultmentioning

confidence: 99%

See 1 more Smart Citation

On-line fault diagnosis and fault-tolerant operation of modular multilevel converters — A comprehensive review

Yang

Wang

2020

Trans. Electr. Mach. Syst.

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Section: A Diagnosis Of Oc Switching Faultmentioning

confidence: 99%

“…If all the correlation coefficients related to a certain SM is significantly less than 1, the SMs will be marked as faulty ones. The faulty phase leg detection method in [29] is based on the error between measured arm voltages and the predicted arm voltages.…”

Section: A Diagnosis Of Oc Switching Faultmentioning

confidence: 99%

On-line fault diagnosis and fault-tolerant operation of modular multilevel converters — A comprehensive review

Yang

Wang

2020

Trans. Electr. Mach. Syst.

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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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“…When the equipment state changes, the corresponding relations will also change [21]. Correlation analysis, as one kind of mathematical method to analyze the changes of the correlation between different variables, can describe the overall change rules of equipment states, and thus it could applied to the field of fault signal analysis [22][23][24][25] and the multisensor fault diagnosis [26][27][28][29]. Kang et al [21] correlated the different battery voltages with different sensors and used the recursive correlation coefficient to diagnose the fault signals and accurately identified the locations and types of faults.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis Method Research of Mechanical Equipment Based on Sensor Correlation Analysis and Deep Learning

Bai

Yang

Duan

et al. 2020

Shock and Vibration

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Large-scale mechanical equipment monitoring involves various kinds and quantities of information, and the present research on multisensor information fusion may face problems of information conflicts and modeling complexity. This paper proposes an analysis method combining correlation analysis and deep learning. According to the characteristics of monitoring data, three types of correlation coefficients between sensors in different states are obtained, and a new composite correlation analytical matrix is established to fuse the multisource heterogeneous data. The matrix represents fault feature information of different equipment states and helps further image generation. Meanwhile, a convolutional neural network-based deep learning method is developed to process the matrix and to discover the relationship between results and equipment states for fault diagnosis. To verify the method of this paper, experimental and field case studies are performed. The results show that it can accurately identify fault states and has higher diagnostic efficiency and accuracy than traditional methods.

show abstract

Submodule Voltage Similarity-Based Open-Circuit Fault Diagnosis for Modular Multilevel Converters

Cited by 81 publications

References 30 publications

On-line fault diagnosis and fault-tolerant operation of modular multilevel converters — A comprehensive review

On-line fault diagnosis and fault-tolerant operation of modular multilevel converters — A comprehensive review

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

Fault Diagnosis Method Research of Mechanical Equipment Based on Sensor Correlation Analysis and Deep Learning

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