Quantitative Evaluation of Wind Turbine Faults Under Variable Operational Conditions

Jin, Xiaohang; Wang, Qiao; Peng, Yayu; Cheng, Fangzhou; Qu, Liyan

doi:10.1109/tia.2016.2519412

Cited by 40 publications

(16 citation statements)

References 14 publications

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“…In 2015, upon examining the problem that internal faults in doubly fed induction generators are not easily diagnosed and identified, reference [10] proposed a method for fault diagnosis in and positioning of doubly fed wind generators based on electromagnetic and wavelet transforms from the perspective of electromagnetic changes. In 2016, the study in reference [11] used a synchronous sampling method to extract fault features from current signals of varying conditions and then used correlation dimension analysis to quantitatively analyze the different faults of wind turbines. In 2017, aimed at the nonstationary and nonlinear characteristics of wind turbine vibration signals, reference [12] proposed a novel fault diagnosis method based on integral extension load mean decomposition multiscale entropy and a least squares support vector machine.…”

Section: Electrical Signal Methodsmentioning

confidence: 99%

Fault Diagnosis Based on an Approach Combining a Spectrogram and a Convolutional Neural Network with Application to a Wind Turbine System

Huang

Weihong

2018

Energies

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To investigate problems involving wind turbines that easily occur but are hard to diagnose, this paper presents a wind turbine (WT) fault diagnosis algorithm based on a spectrogram and a convolutional neural network. First, the original data are sampled into a phonetic form. Then, the data are transformed into a spectrogram in the time-frequency domain. Finally, the data are sent into a convolutional neural network (CNN) model with batch regularization for training and testing. Experimental results show that the method is suitable for training a large number of samples and has good scalability. Compared with Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), Extreme Learning Machine (ELM), and other fault diagnosis methods, the average diagnostic correctness rate is higher; so, the method can provide more accurate reference information for wind turbine fault diagnosis.

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Section: Electrical Signal Methodsmentioning

confidence: 99%

Fault Diagnosis Based on an Approach Combining a Spectrogram and a Convolutional Neural Network with Application to a Wind Turbine System

Huang

Weihong

2018

Energies

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“…In which, the power m is fixed ∀i, r and #X i is the number of samples in the class C i . Then, the data vector is compared to the ideal vector and the similarities S(x, I) are determined using a generalized Lukasiewicz structure in Equation (13). Finally, the decision whether x belongs to class C k is made according to the highest similarity value, which is calculated using Equation (14).…”

Section: Artificial Machine Learning Classifiersmentioning

confidence: 99%

“…Vibration signal analysis, however, could increase the cost of wind farms because it requires the installation of vibration sensors on each turbine. Current-based monitoring techniques have been used to identify faults in wind turbines, and have proved to be effective in detecting gearbox faults [13]. Cheng et al used experimental stator and rotor current signals and analyzed them in the frequency domain.…”

Section: Introductionmentioning

confidence: 99%

Wind Farm Fault Detection by Monitoring Wind Speed in the Wake Region

Tran

Lan

et al. 2020

Energies

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A novel concept of wind farm fault detection by monitoring the wind speed in the wake region is proposed in this study. A wind energy dissipation model was coupled with a computational fluid dynamics solver to simulate the fluid field of a wind turbine array, and the wind velocity and direction in the simulation were exported for identifying wind turbine faults. The 3D steady Navier–Stokes equations were solved by using the cell center finite volume method with a second order upwind scheme and a k−ε turbulence model. In addition, the wind energy dissipation model, derived from energy balance and Betz’s law, was added to the Navier–Stokes equations’ source term. The simulation results indicate that the wind speed distribution in the wake region contains significant information regarding multiple wind turbine faults. A feature selection algorithm specifically designed for the analysis of wind flow was proposed to reduce the number of features. This algorithm proved to have better performance than fuzzy entropy measures and recursive feature elimination methods under a limited number of features. As a result, faults in the wind turbine array could be detected and identified by machine learning algorithms.

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“…Synchronous resampling technique (SRT) is used to handle non-stationary current signal in variable speed direct drive wind generator [67]. In fact, fault diagnosis in [67] is based on the machine current signal and its Fourier transform. The difference is that this method is used to transform the nonstationary current signal to a stationary one.…”

Section: Stator Current Resampled Fft Correlation Dimensions (Seventhmentioning

confidence: 99%

Eccentricity fault diagnosis indices for permanent magnet machines: state‐of‐the‐art

Faiz

Nejadi-Koti

2019

IET Electric Power Applications

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Eccentricity fault with 10% severity is probably the only existing fault in a brand new healthy electrical machine that is acceptable as a manufacturing tolerance. This fault can be developed due to a continuous pull between stator and rotor, even in a de-energized machine. So, it must be diagnosed,and its severity monitored. The authors introduced and criticized various indices for eccentricity fault diagnosis. Advantages, drawbacks, and ambiguous points of each index and potential ground for improvement of these techniques are addressed. Moreover, different eccentric PM machine modelling techniques are reviewed and environmental factors affecting the eccentricity fault detection process are discussed. This survey covers all direct current and alternating current PM machines. It provides information about the researches performed since 1986 and it is helpful to newcomers to this field, artisans, and protection system designers. Considering the given information, the readers achieve enough understanding of the weaknesses and strong points of each eccentricity fault diagnosis index. Then, these indices are evaluated through assigning weight coefficients under various scenarios. Interested individuals can modify these weight coefficients based on the specifics of the application and decide which index is the most appropriate.

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Quantitative Evaluation of Wind Turbine Faults Under Variable Operational Conditions

Cited by 40 publications

References 14 publications

Fault Diagnosis Based on an Approach Combining a Spectrogram and a Convolutional Neural Network with Application to a Wind Turbine System

Fault Diagnosis Based on an Approach Combining a Spectrogram and a Convolutional Neural Network with Application to a Wind Turbine System

Wind Farm Fault Detection by Monitoring Wind Speed in the Wake Region

Eccentricity fault diagnosis indices for permanent magnet machines: state‐of‐the‐art

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