Fault detection and reconfiguration of the 12-pulse thyristor rectifier

Nejad, Aref Zeynivand; Dastfan, Ali

doi:10.1109/icredg58341.2023.10092138

2023 10th Iranian Conference on Renewable Energy &Amp; Distributed Generation (ICREDG) 2023

DOI: 10.1109/icredg58341.2023.10092138

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Fault detection and reconfiguration of the 12-pulse thyristor rectifier

Aref Zeynivand Nejad

Ali Dastfan

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2024

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Fault diagnosis of power electronic circuits using optimized BP neural networks

Jiang,

Huang,

Guo

2024

MATEC Web of Conf.

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You should leave 8 mm of space above the abstract and 10 mm after the abstract. The heading Abstract should be typed in bold 9-point Arial. The body of the abstract should be typed in normal 9-point Times in a single paragraph, immediately following the heading. The text should be set to 1 line spacing. The abstract should be centred across the page, indented 17 mm from the left and right page margins and justified. It should not normally exceed 200 wordsThree-phase rectifiers have a wide range of applications in industrial production and daily life, and failure to diagnose their faults promptly may affect the reliability of the system operation, resulting in huge safety hazards and economic losses. Therefore, it is of great significance to conduct online fault diagnosis research on the power electronic circuits within three-phase rectifiers. An optimized BP neural network algorithm is proposed for diagnosing open-circuit faults of thyristors in three-phase rectifier circuits. The output voltage waveform characteristics of the circuit, when a fault occurs, are analyzed, and the corresponding output voltage peaks at the same cycle time when different tubes are damaged under the fifth fault type are used as fault feature vectors, and the fault information is input into the BP, optimized BP neural network for training, and the trained neural network is used for fault diagnosis. The fault diagnosis accuracy was obtained by comparing the network output with the desired output. The results of simulation experiments show that the optimized BP neural network can diagnose and analyze the faults of rectifier circuits more efficiently than directly applying the BP network for fault diagnosis.

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Fault diagnosis of power electronic circuits using optimized BP neural networks

Jiang,

Huang,

Guo

2024

MATEC Web of Conf.

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show abstract

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Fault detection and reconfiguration of the 12-pulse thyristor rectifier

Cited by 1 publication

References 26 publications

Fault diagnosis of power electronic circuits using optimized BP neural networks

Fault diagnosis of power electronic circuits using optimized BP neural networks

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