Application of artificial neural networks for transistor open‐circuit fault diagnosis in three‐phase rectifiers

Sobanski, Piotr; Kaminski, Marcin

doi:10.1049/iet-pel.2018.5330

Cited by 38 publications

(30 citation statements)

References 51 publications

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“…The number of neurons in the first layer of RBM is set to 14, and the number of neurons in the second layer of RBM is set to 5. The output of Softmax classifier is a 22 dimensions probability vectors 22) denots the probability of the j-th fault. Table 8 shows the probability output results of Softmax classifier and corresponding failure coding number.…”

Section: Dbn Training and Test Recults Analysismentioning

confidence: 99%

“…In order to improve the efficiency and accuracy of fault diagnosis, a combination of qualitative fault diagnosis and quantitative fault diagnosis is usually used. As stated in [22], the zero current periods are registered in each converter phase circuits. The open-circuit faults are identified calculating the average values of differences between predicted and measured phase currents.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, the input of DBN under each open-circuit failure state is a 24 dimensions trend feature vectorsI x = I x_1 I x_2 • • • I x_24 , x = 1, 2, • • •,22,where x denotes the failure coding number. The DBN consists of double-layer RBM and Softmax classifier.…”

mentioning

confidence: 99%

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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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Section: Dbn Training and Test Recults Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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show abstract

“…As shown in the figure, when the improved BP neural network data fusion algorithm based on weight adjustment is used, the sample converges faster in the training process and the training time of the sample can be shortened [12]. Although the average error of the samples of the BP neural network data fusion algorithm before and after the weight adjustment and improvement is the same at the beginning of the training, the average error value of the improved sample after 15 training sessions is close to 0.7, less than 0.8 before the algorithm improvement, indicating that the sample fusion accuracy under the improved algorithm is higher [13]. Figure 5 shows the average error comparison curve of the BP neural network and the BP neural network after weight adjustment and improvement in the training process.…”

Section: Total Time 3hourmentioning

confidence: 99%

The Analysis of Electronic Circuit Fault Diagnosis Based on Neural Network Data Fusion Algorithm

Wang

2020

Symmetry

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Symmetries play very important roles in the dynamics of electrical systems. The relevant electronic circuits with fault diagnostics, including the optimized neural network algorithm model, are designed on the basis of symmetry principles. In order to improve the efficiency of the circuit pressure test, a circuit pressure function equivalent compression test method based on the parallel neural network algorithm is proposed. For the implementation stage of the circuit pressure test, the improved modified node algorithm (MNA) is used to build an optimization model, and the circuit network is converted into an ordinary differential equation for the circuit pressure function equivalent compression test. The test aims to minimize flux. Then, backpropagation (BP) neural network algorithm data fusion is introduced to optimize the minimum flux model of the cyclic pressure functional equivalent compression test. Finally, a simulation experiment is carried out to verify the effectiveness of the algorithm in the accuracy and efficiency of the pressure test. The results show that the improved BP neural network improves the data fusion accuracy and shortens the sample training time; compared with the uncompressed algorithm, the running time of the proposed algorithm is greatly reduced and the execution efficiency is high; compared with the vascular pressure test method, there is no significant difference in the convergence accuracy and it is at a level of 10−5. Since the parallel computing problem is not considered in either of the two-pulse tube pressure test methods, the convergence time of the algorithm increases exponentially with the increase in the number of parallel threads. However, the algorithm in this research considers the problem of parallel execution and uses a quad-core processor, with no significant change in computing time and high computing efficiency. Therefore, BP neural network data fusion can be used for the fault diagnosis of electronic circuits, with a high operating efficiency and good development prospects.

show abstract

“…In order to improve the efficiency and accuracy of fault diagnosis, a combination of qualitative fault diagnosis and quantitative fault diagnosis is usually used. As stated in [14], the zero current periods are registered in each converter phase circuits. The open-circuit faults are identified calculating the average values of differences between predicted and measured phase currents.…”

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

Application of artificial neural networks for transistor open‐circuit fault diagnosis in three‐phase rectifiers

Cited by 38 publications

References 51 publications

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

The Analysis of Electronic Circuit Fault Diagnosis Based on Neural Network Data Fusion Algorithm

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

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