Diagnosis and location of the open-circuit fault in modular multilevel converters: An improved machine learning method

Cui, Li; Liu, Zhenxing; Zhang, Yong; Chai, Li; Xu, Bo

doi:10.1016/j.neucom.2018.09.041

Cited by 32 publications

(17 citation statements)

References 34 publications

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“…In the future work, we will pay more attentions on the machine learning based fault diagnosis of MMC system (Li et al, 2019;Song, Xu, & Xu, 2019;Wang, Xu, Han, Elbouchikhi, & Benbuzid, 2015), as well as the hybrid approach of model and machine learning on the fault diagnosis of power electronic systems (Cui & Liu, 2017;Xu et al, 2018;Zhang, Song, & Zhao, 2017).…”

Section: Resultsmentioning

confidence: 99%

“…Thus the software-based fault detection method is more prevalent in the fault diagnosis of MMC sub-module, including model-based method, signal processing-based method and knowledge-based method, among others. For example, a mixed kernel support tensor machine (MKSTM) has been proposed for the diagnosis and location of open-circuit fault of MMC sub-module in Li, Liu, Zhang, Chai, Xu (2019), where MKSTM is employed to train and classify the data samples extracted from AC current and internal circulation current in normal and open-circuit fault cases. In Yin, Duan, Shen, and Qu (2018), a novel method has been presented to diagnose the open-circuit fault of modular five-level inverter, where stacked sparse auto-encoder is applied to extract the fault feature from MMC.…”

Section: Introductionmentioning

confidence: 99%

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A new approach to open-circuit fault diagnosis of MMC sub-module

Song

2020

Systems Science & Control Engineering

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This paper is concerned with the open-circuit fault diagnosis of MMC sub-module. To estimate the arm current accurately in normal and fault cases, a sliding mode observer is designed based on Lyapunov theory. The residual between the estimation and measurement of the arm current will increase rapidly in fault cases, which can facilitate not only the open-circuit fault detection of the arm but also the identification of the fault type. Based on the open-circuit fault analysis of MMC sub-module, the fault feature is defined and calculated for the fault location of sub-module, which can remarkably promote the efficiency of fault diagnosis. To test the performance of the proposed approach, some simulation experiments are carried out and can confirm the effectiveness of the proposed approach for the open-circuit fault diagnosis of MMC sub-module.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A new approach to open-circuit fault diagnosis of MMC sub-module

Song

2020

Systems Science & Control Engineering

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“…Over the past decade, a number of studies have been made concerning open-circuit fault diagnosis of the IGBT module in the power converter [16][17][18][19][20][21], which can be divided into qualitative fault diagnosis and quantitative fault diagnosis. Qualitative fault diagnosis includes fault tree analysis method and expert system method, whose basic idea is to build a knowledge base by using the effective experience and expertise accumulated by experts in the case of open-circuit fault of the power converter, and to determine the diagnosis results and fault causes according to certain logic reasoning for the fault status.…”

Section: Introductionmentioning

confidence: 99%

Open-Circuit Fault Diagnosis of Wind Power Converter Using Variational Mode Decomposition, Trend Feature Analysis and Deep Belief Network

Zhang

Sun

et al. 2020

Applied Sciences

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The power converter is a significant device in a wind power system. The wind turbine will be shut down and off grid immediately with the occurrence of the insulated gate bipolar transistor (IGBT) module open-circuit fault of the power converter, which will seriously impact the stability of grid and even threaten personal safety. However, in the existing diagnosis strategies for the power converter there are few single and double IGBT module open-circuit fault diagnosis methods producing negative results, including erroneous judgment, omissive judgment and low accuracy. In this paper, a novel method to diagnose the single and double IGBT modules open-circuit faults of the permanent magnet synchronous generator (PMSG) wind turbine grid-side converter (GSC) is proposed: Primarily, by collecting the three-phase current varying with a wind speed of 22 states, including a normal state and 21 failure states of PMSG wind turbine GSC as the original signal data. Afterward, the original signal data are decomposed by using variational mode decomposition (VMD) to obtain the mode coefficient series, which are analyzed by the proposed method base on fault trend feature for extracting the trend feature vectors. Finally, the trend feature vectors are utilized as the input of the deep belief network (DBN) for decision-making and obtaining the classification results. The simulation and experimental results show that the proposed method can diagnose the single and double IGBT modules open-circuit faults of GSC, and the accuracy is higher than the benchmark models.

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“…Over the past decade, a number of studies have been made concerning open-circuit fault diagnosis of IGBT module in power converter [8][9][10][11][12][13], which can be divided into qualitative fault diagnosis and quantitative fault diagnosis. Qualitative fault diagnosis includes fault tree analysis method and expert system method, whose basic idea is to build a knowledge base by using the effective experience and expertise accumulated by experts in the case of open-circuit fault of power converter, and to determine the diagnosis results and fault causes according to certain logic reasoning for the fault status.…”

Section: Introductionmentioning

confidence: 99%

Open-Circuit Fault Diagnosis of Wind Power Converter Using Variational Mode Decomposition, Trend Feature Analysis and Deep Belief Network

Zhang¹,

Sun²,

Sun³

et al. 2020

Preprint

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The power converter is the significant device in a wind power system. Wind turbine will be shut down and off grid immediately with the occurrence of the IGBT module open-circuit fault of power converter, which will seriously impact the stability of grid and even threaten personal safety. However, in the existing diagnosis strategies of power converter, there are few single and double IGBT modules open-circuit fault diagnosis methods producing negative results including erroneous judgment, omissive judgment and low accuracy. In this paper, a novel method to diagnose the single and double IGBT modules open-circuit faults of the permanent magnet synchronous generator (PMSG) wind turbine grid-side converter (GSC) is proposed. Above all, collecting the three-phase current varying with wind speed of 22 failure states including a normal state of PMSG wind turbine GSC as the original signal data. Afterward, the original signal data are decomposed by using variational mode decomposition (VMD) to obtain the mode coefficient series, which are analyzed by the proposed method base on fault trend feature for extracting the trend feature vectors. Finally, the trend feature vectors are used as the input of deep belief network (DBN) for decision-making and obtaining the classification results. The simulation and experimental results show that the proposed method can diagnose the single and double IGBT modules open-circuit faults of GSC, and the accuracy is higher than the benchmark models.

show abstract

Diagnosis and location of the open-circuit fault in modular multilevel converters: An improved machine learning method

Cited by 32 publications

References 34 publications

A new approach to open-circuit fault diagnosis of MMC sub-module

A new approach to open-circuit fault diagnosis of MMC sub-module

Open-Circuit Fault Diagnosis of Wind Power Converter Using Variational Mode Decomposition, Trend Feature Analysis and Deep Belief Network

Open-Circuit Fault Diagnosis of Wind Power Converter Using Variational Mode Decomposition, Trend Feature Analysis and Deep Belief Network

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