Rotor-Current-Based Fault Diagnosis for DFIG Wind Turbine Drivetrain Gearboxes Using Frequency Analysis and a Deep Classifier

Cheng, Fangzhou; Zhu, Zhongkui; Qu, Liyan; Wang, Qiao

doi:10.1109/tia.2017.2773426

Cited by 91 publications

(46 citation statements)

References 37 publications

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“…According to the E total , the mean e i and the covariance V ij are described by Equations (15) and (16), respectively.…”

Section: Selecting Appropriate Ensemble Membersmentioning

confidence: 99%

“…Such discrepancies will reflect whether the machine is in normal or failure mode, which requires a classifier to judge. Recently, some Artificial Intelligent (AI) classifiers, such as neural networks [7][8][9][10][11][12], machine learning methods [13][14][15], and deep learning methods [16,17], have been widely applied in classifying the incipient faults of wind turbines. These methods are really very effective for some faults within a certain working state, but it seems impossible for them to diagnose other faults under other working states.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Fault Monitoring of Pitch System in Wind Turbines using Selective Ensemble Small-World Neural Networks

Wang

2019

Energies

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Pitch system failures occur primarily because wind turbines typically work in dynamic and variable environments. Conventional monitoring strategies show limitations of continuously identifying faults in most cases, especially when rapidly changing winds occur. A novel selective-ensemble monitoring strategy is presented to diagnose the most pitch failures using Supervisory Control and Data Acquisition (SCADA) data. The proposed strategy consists of five steps. During the first step, the SCADA data are partitioned according to the turbine’s four working states. Correlation Information Entropy (CIE) and 10 indicators are used to select correlation signals and extract features of the partition data, respectively. During the second step, multiple Small-World Neural Networks (SWNNs) are established as the ensemble members. Regarding the third step, all the features are randomly sampled to train the SWNN members. The fourth step involves using an improved global correlation method to select appropriate ensemble members while in the fifth step, the selected members are fused to obtain the final classification result based on the weighted integration approach. Compared with the conventional methods, the proposed ensemble strategy shows an effective accuracy rate of over 93.8% within a short delay time.

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“…According to the E total , the mean e i and the covariance V ij are described by Equations (15) and (16), respectively.…”

Section: Selecting Appropriate Ensemble Membersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic Fault Monitoring of Pitch System in Wind Turbines using Selective Ensemble Small-World Neural Networks

Wang

2019

Energies

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“…However, the stator current will be directly affected when an inter-turn short circuit fault occurs in the stator windings. References [14][15][16] analysed the stator current before and after the fault and claimed that there are harmonics in stator current during the inter-turn short circuit fault. By monitoring the variance of the harmonics' amplitude, early fault detection can be realised.…”

Section: Introductionmentioning

confidence: 99%

Extended Park's vector method in early inter‐turn short circuit fault detection for the stator windings of offshore wind doubly‐fed induction generators

Wei

Zhang

Xu³

et al. 2020

IET Generation, Transmission & Distribution

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“…In addition, a technician needs knowledge and a good amount of experience to correctly use such sensors [78][79][80][81][82][83][84]. However, the ESA reported being able to reveal a large number of relations between the machine parameters [85][86][87][88]. Therefore, the ML techniques are highly suitable to support the processing of such extracted information.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Rotating Electrical Machines Using Multi-Label Classification

et al. 2019

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Fault Detection and Diagnosis of electrical machine and drive systems are of utmost importance in modern industrial automation. The widespread use of Machine Learning techniques has made it possible to replace traditional motor fault detection techniques with more efficient solutions that are capable of early fault recognition by using large amounts of sensory data. However, the detection of concurrent failures is still a challenge in the presence of disturbing noises or when the multiple faults cause overlapping features. Multi-label classification has recently gained popularity in various application domains as an efficient method for fault detection and monitoring of systems with promising results. The contribution of this work is to propose a novel methodology for multi-label classification for simultaneously diagnosing multiple faults and evaluating the fault severity under noisy conditions. In this research, the Electrical Signature Analysis as well as traditional vibration data have been considered for modeling. Furthermore, the performance of various multi-label classification models is compared. Current and vibration signals are acquired under normal and fault conditions. The applicability of the proposed method is experimentally validated under diverse fault conditions such as unbalance and misalignment.

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Rotor-Current-Based Fault Diagnosis for DFIG Wind Turbine Drivetrain Gearboxes Using Frequency Analysis and a Deep Classifier

Cited by 91 publications

References 37 publications

Dynamic Fault Monitoring of Pitch System in Wind Turbines using Selective Ensemble Small-World Neural Networks

Dynamic Fault Monitoring of Pitch System in Wind Turbines using Selective Ensemble Small-World Neural Networks

Extended Park's vector method in early inter‐turn short circuit fault detection for the stator windings of offshore wind doubly‐fed induction generators

Fault Diagnosis of Rotating Electrical Machines Using Multi-Label Classification

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