A novel fault diagnostic method in power converters for wind power generation system

Liang, Jinping; Zhang, Ke; Al‐Durra, Ahmed; Zhou, Daming

doi:10.1016/j.apenergy.2020.114851

Cited by 39 publications

(20 citation statements)

References 37 publications

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“…Although WT [24] can achieve good time-frequency analysis, it only decomposes the low-frequency components of the signal and ignores its high-frequency components. As a natural extension of WT, WPT can further decompose the unprocessed high-frequency signal of WT, so as to realize more refined signal analysis.…”

Section: Fault Diagnosis Algorithmmentioning

confidence: 99%

Fault Diagnosis with Wavelet Packet Transform and Principal Component Analysis for Multi-terminal Hybrid HVDC Network

Chen

2021

Journal of Modern Power Systems and Clean Energy

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In view of the fact that the wavelet packet transform (WPT) can only weakly detect the occurrence of fault, this paper applies a fault diagnosis algorithm including wavelet packet transform and principal component analysis (PCA) to the inverter-side fault diagnosis of multi-terminal hybrid highvoltage direct current (HVDC) network, which can significantly improve the speed and accuracy of fault diagnosis. Firstly, current amplitude and current slope are used to sample the data, and the WPT is used to extract the energy spectrum of the signal. Secondly, an energy matrix is constructed, and the PCA method is used to calculate whether the squared prediction error (SPE) statistics of various signals that can reflect the degree of deviation of the measured value from the principal component model at a certain time exceed the limit to judge the occurrence of the fault. Further, its maximum value is compared to determine the fault types. Finally, based on a large number of MATLAB/Simulink simulation results, it is shown that the PCA method using the current slope as the sampled data can detect the occurrence of a ground fault with small transition resistance within 2 ms, and identify the fault types within 10 ms, without being affected by the sampling frequency. Index Terms--Fault diagnosis, hybrid high-voltage direct current (HVDC), wavelet packet transform (WPT), principal component analysis (PCA).

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Section: Fault Diagnosis Algorithmmentioning

confidence: 99%

Fault Diagnosis with Wavelet Packet Transform and Principal Component Analysis for Multi-terminal Hybrid HVDC Network

Chen

2021

Journal of Modern Power Systems and Clean Energy

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“…The parameter drift under conditions is used for fault diagnosis in the early period of the WT converter. Liang et al (2020) proposed a fault diagnosis method based on WT converters. A series of inherent mode functions are obtained through the overall empirical mode decomposition processing of the measured output voltage.…”

Section: Fault Diagnosis Of Wind Turbinementioning

confidence: 99%

Review and Perspectives of Machine Learning Methods for Wind Turbine Fault Diagnosis

Tang

Zhao

et al. 2021

Front. Energy Res.

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Wind turbines (WTs) generally comprise several complex and interconnected systems, such as hub, converter, gearbox, generator, yaw system, pitch system, hydraulic system control system,integration control system, and auxiliary system. Moreover, fault diagnosis plays an important role in ensuring WT safety. In the past decades, machine learning (ML) has showed a powerful capability in fault detection and diagnosis of WTs, thereby remarkably reducing equipment downtime and minimizing financial losses. This study provides a comprehensive review of recent studies on ML methods and techniques for WT fault diagnosis. These studies are classified as supervised, unsupervised, and semi-supervised learning methods. Existing state-of-the-art methods are analyzed and characteristics are discussed. Perspectives on challenges and further directions are also provided.

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“…Since the current path of converter is formed by IGBTs and their respective antiparallel diodes, the fault phase current will lose its normal waveform, and the healthy phase current will be offset with the occurrence of IGBT OC fault of the converter [24]. According to this characteristic, the current signals can be…”

Section: Model Of Wind Power Converter Systemmentioning

confidence: 99%

Open-Circuit Faults Diagnosis in Direct-Drive PMSG Wind Turbine Converter

Zhang¹,

Ling²,

Zhao³

et al. 2021

Energy Engineering

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The condition monitoring and fault diagnosis have been identified as the key to achieving higher availabilities of wind turbines. Numerous studies show that the open-circuit fault is a significant contributor to the failures of wind turbine converter. However, the multiple faults combinations and the influence of wind speed changes abruptly, grid voltage sags and noise interference have brought great challenges to fault diagnosis. Accordingly, concerning the open-circuit fault of converters in direct-driven PMSG wind turbine, a diagnostic method for multiple open-circuit faults is proposed in this paper, which is divided into two tasks: The first one is the fault detection and the second one is the fault localization. The detection method is based on the relative current residuals after exponential transformation and on an adaptive threshold, and the localization method is based on the average values of fault phase currents. The scheduled diagnosis method is available to both the generator-side converter and the grid-side converter, allowing to detect and locate single and double open-circuit faults. For validating this, robustness test and multiple open-circuit faults diagnosis are presented in a 2-MW direct-driven PMSG wind turbine system, the results validate the reliability and effectiveness of the proposed method.

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A novel fault diagnostic method in power converters for wind power generation system

Cited by 39 publications

References 37 publications

Fault Diagnosis with Wavelet Packet Transform and Principal Component Analysis for Multi-terminal Hybrid HVDC Network

Fault Diagnosis with Wavelet Packet Transform and Principal Component Analysis for Multi-terminal Hybrid HVDC Network

Review and Perspectives of Machine Learning Methods for Wind Turbine Fault Diagnosis

Open-Circuit Faults Diagnosis in Direct-Drive PMSG Wind Turbine Converter

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