A fault diagnosis method for three-phase rectifiers

Wang, Rongjie; Zhan, Yong; Zhou, Haifeng; Bo-wen, Cui

doi:10.1016/j.ijepes.2013.03.029

Cited by 24 publications

(7 citation statements)

References 7 publications

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“…Current similarity analysis‐based open‐circuit fault diagnosis for one or two switches is presented in [20]. The machine learning methods for fault diagnosis have also been investigated in [21–24]. An application of combined knowledge‐driven and data‐driven‐based approaches for OC fault diagnosis in an inverter is presented in [25].…”

Section: Introductionmentioning

confidence: 99%

Spectral analysis‐based fault diagnosis algorithm for 3‐phase passive rectifiers in renewable energy systems

Sharan

Jain

2020

IET power electron.

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Section: Introductionmentioning

confidence: 99%

Spectral analysis‐based fault diagnosis algorithm for 3‐phase passive rectifiers in renewable energy systems

Sharan

Jain

2020

IET power electron.

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“…The fault classification process is used to construct a classifier model to realize the automatic diagnostic. Artificial Neural Network (ANN) [21], [22] and Support Vector Machine (SVM) [23], [24] are the two most commonly used classifier models in the fault diagnostic applications of VSR. In addition, some new algorithms and technologies have been employed in this field.…”

Section: Introductionmentioning

confidence: 99%

Open Switch Fault Diagnosis Method for PWM Voltage Source Rectifier Based on Deep Learning Approach

Shi

Wang

et al. 2019

IEEE Access

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With the development of machine learning technology, numerous studies have been proposed to diagnose the open circuit (OC) faults in the pulse width modulation (PWM) voltage source rectifier (VSR) systems. However, most methods require system signals of more than one current period, which show poor real-time performance. Aiming at this problem, this paper presents an improved diagnosis system based on deep belief networks (DBN) and least square support vector machine (LSSVM). First, the double chain quantum genetic algorithm (DCQGA) is employed to obtain the proper length of measured signals and DBN structure parameters. Then, the fault features are extracted from the signals through DBN. Finally, these features are used to train the LSSVM fault classifier to construct the diagnosis model. The experimental results show that the proposed method can achieve the fault diagnosis including six kinds of single switch faults and 15 kinds of different double switches faults correctly. Besides, the proposed method also shows the superior anti-interference performance and high robustness on abrupt load transient conditions, unbalanced, and/or distorted grid voltage conditions, as well as, different power factor conditions. Furthermore, the average diagnostic time of this method is only 2.57 ms.

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“…There are many electronic circuits in some large detector arrays. Learning algorithms have been studied for fault diagnosis in electric power [9], machinery [10], electronics [11][12][13][14], etc. The application of intelligent fault diagnosis would greatly liberate manpower and make the detectors more effectively supervised.…”

Section: Introductionmentioning

confidence: 99%

A direct method of nuclear pulse shape discrimination based on principal component analysis and support vector machine

Zhang

Fan

et al. 2019

J. Inst.

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Fault diagnosis and particle discrimination can be fundamentally solved as a case of pulse shape discrimination (PSD). The classical methods of PSD are inconvenient or not effective when more than two pulse shapes need to be discriminated or the pulse shapes have only small differences. A direct method to discriminate nuclear pulse shapes based on principal component analysis (PCA) and support vector machine (SVM) is reported in this paper. The training and testing accuracies of SVM classifiers with different kernels were not the same, and the algorithms were shown to have great noise immunity. Though the samples in the Group A and Group C cannot be discriminated with the naked eye, the accuracies are all above 94.7% if suitable SVM kernels are selected. There is no evidence showing that the Gaussian kernel is superior. The lower sampling frequency of the analog-to-digital converter and the information loss caused by dimension reduction were also considered. K: Pattern recognition, cluster finding, calibration and fitting methods; Digital signal processing (DSP); Ion identification systems; Particle identification methods 1Corresponding author.

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A fault diagnosis method for three-phase rectifiers

Cited by 24 publications

References 7 publications

Spectral analysis‐based fault diagnosis algorithm for 3‐phase passive rectifiers in renewable energy systems

Spectral analysis‐based fault diagnosis algorithm for 3‐phase passive rectifiers in renewable energy systems

Open Switch Fault Diagnosis Method for PWM Voltage Source Rectifier Based on Deep Learning Approach

A direct method of nuclear pulse shape discrimination based on principal component analysis and support vector machine

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