scholarly journals A Survey of Condition Monitoring and Fault Diagnosis toward Integrated O&M for Wind Turbines

Energies ◽  
2019 ◽  
Vol 12 (14) ◽  
pp. 2801 ◽  
Author(s):  
Pinjia Zhang ◽  
Delong Lu
Keyword(s):  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Information Flow ◽  
Wind Turbines ◽  
Energy Flow ◽  
Global Climate ◽  
Rapid Development ◽  
Wind Farms ◽  
Flow Information ◽  
Comprehensive Survey

Wind power, as a renewable energy for coping with global climate change challenge, has achieved rapid development in recent years. The breakdown of wind turbines (WTs) not only leads to high repair expenses but also may threaten the stability of the whole power grid. How to reduce the operation and the maintenance (O&M) cost of wind farms is an obstacle to its further promotion and application. To provide reliable condition monitoring and fault diagnosis (CMFD) for WTs, this paper presents a comprehensive survey of the existing CMFD methods in the following three aspects: energy flow, information flow, and integrated O&M system. Energy flow mainly analyzes the characteristics of each component from the angle of energy conversion of WTs. Information flow is the carrier of fault and control information of WT. At the end of this paper, an integrated WT O&M system based on electrical signals is proposed.

Condition Monitoring of Wind Turbines Based on the Scattering Transform of Vibration Data

2021 ◽  
Author(s):  
Junyu Qi ◽  
Alexandre Mauricio ◽  
Konstantinos Gryllias
Keyword(s):  
Condition Monitoring ◽  
Wind Turbines ◽  
Wind Farms ◽  
Operating Conditions ◽  
Vibration Data ◽  
Bearing Diagnostics ◽  
Scattering Transform ◽  
The One ◽  
Envelope Spectrum ◽  
High Effort

Abstract As a renewable, unlimited and free resource, wind energy has been intensively deployed in the past to generate electricity. However, the maintenance of Wind Turbines (WTs) can be challengeable. On the one hand, most wind farms operate in remote areas and on the other hand, the dimension of WTs’ tip/hub/rotor are usually enormous. In order to prevent abrupt breakdowns of WTs, a number of Condition Monitoring (CM) methods have been proposed. Focusing on bearing diagnostics, Squared Envelope Spectrum is one of the most common techniques. Moreover in order to identify the optimum demodulation frequency band, fast Kurtogram, Infogram and Sparsogram are nowadays popular tools evaluating respectively the Kurtosis, the Negentropy and the Sparsity. The analysis of WTs usually requires high effort due to the complexity of the drivetrain and the varying operating conditions and therefore there is still need for research on effective and reliable CM techniques for WT monitoring. Thus the purpose of this paper is to investigate a blind and effective CM approach based on the Scattering Transform. Through the comparison with state of the art techniques, the proposed methodology is found more powerful to detect a fault on six validated WT datasets.

scholarly journals Condition Monitoring and Fault Diagnosis of Wind Turbines Gearbox Bearing Temperature Based on Kolmogorov-Smirnov Test and Convolutional Neural Network Model

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2248 ◽  
Author(s):  
Peng Guo ◽  
Jian Fu ◽  
XiYun Yang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Condition Monitoring ◽  
Large Scale ◽  
Wind Farm ◽  
Wind Farms ◽  
Main Trend ◽  
Maintenance Cost ◽  
Monitoring Method

Wind turbine condition-monitoring and fault diagnosis have important practical value for wind farms to reduce maintenance cost and improve operating level. Due to the special distribution law of the operating parameters of similar turbines, this paper compares the instantaneous operation parameters of four 1.5 MW turbines with strong correlation of a wind farm. The temperature-power distribution of the gearbox bearings is analyzed to find out the main trend of the turbines and the deviations of individual turbine parameters. At the same time, for the huge amount of data caused by the increase of turbines number and monitoring parameters, this paper uses the huge neural network and multi-hidden layer of a convolutional neural network to model historical data. Finally, the rapid warning and judgment of gearbox bearing over-temperature faults proves that the monitoring method is of great significance for large-scale wind farms.

scholarly journals Condition monitoring and vibration analysis of wind turbine

2020 ◽  
Vol 12 (3) ◽  
pp. 168781402091378 ◽  
Author(s):  
Feng Xiao ◽  
Chen Tian ◽  
Isaac Wait ◽  
Zhaohui (Joey) Yang ◽  
Benjamin Still ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Condition Monitoring ◽  
Wind Turbines ◽  
Mode Coupling ◽  
Health Condition ◽  
Wind Turbine System ◽  
Spectral Models ◽  
Non Gaussian ◽  
Stochastic Properties

Health condition monitoring through comprehensive monitoring, incipient fault diagnosis, and the prediction of impending faults allows for the promotion of the long-term performance of wind turbines, particularly those in harsh environments such as cold regions. The condition monitoring of wind turbines is characterized by the difficulties associated with the lack of measured data and the nonstationary, stochastic, and complicated nature of vibration responses. This article presents a characterization of the vibrations of an operational wind turbine by spectrogram, scalogram, and bi-spectrum analyses. The results reveal varied nonstationary stochastic properties and mode-coupling instability in the vibrations of the tested wind turbine tower. The analysis illustrates that the wind turbine system vibrations exhibit certain non-Gaussian stochastic properties. An analytical model is used to evaluate the nonstationary, stochastic phenomena and mode-coupling phenomena observed in the experimental results. These results are of significance for the fault diagnosis of wind turbine system in operation as well as for improving fatigue designs beyond the wind turbulence spectral models recommended in the standards.

Algorithmic performance constraints for wind turbine condition monitoring via convolutional sparse coding with dictionary learning

2021 ◽  
pp. 1748006X2098426
Author(s):  
Sergio Martin-del-Campo ◽  
Fredrik Sandin ◽  
Stephan Schnabel
Keyword(s):  
Wind Turbine ◽  
Condition Monitoring ◽  
Wind Turbines ◽  
Dictionary Learning ◽  
Population Distribution ◽  
Wind Farms ◽  
Data Availability ◽  
Rolling Element Bearing ◽  
Data Sampling ◽  
Selection Of

We analyze vibration signals from wind turbines with dictionary learning and investigate the relation between dictionary distances and faults occurring in a wind turbine output shaft rolling element bearing and gearbox under different data and compute constraints. Dictionary learning is an unsupervised machine learning method for signal processing, which permits learning a set of signal-specific features that have been used to monitor the condition of rotating machines, including wind turbines. Dictionary distance is one such feature, and its effectiveness depends on an adequate selection of the dictionary learning hyperparameters and the data availability, which typically is constrained in condition monitoring systems for remotely located wind farms. Here we evaluate the characteristics of the dictionary distance feature under healthy and faulty conditions of the wind turbines using different options for the selection of the pretrained dictionary, the sparsity of the signal model which determines the compute requirements, and the interval between data samples. Furthermore, we compare the dictionary distance feature to the typical time-domain features used in condition monitoring. We find that the dictionary distance based feature of a faulty wind turbine deviates by a factor of two or more from the population distribution several weeks before the gearbox bearing fault was reported, using a data sampling interval as long as 24 h and a model sparsity as low as 2.5%.

Predictive Condition Monitoring and Fault Diagnosis Techniques for Offshore Wind Turbines

2012 ◽  
Vol 608-609 ◽  
pp. 638-643
Author(s):  
Xiao Xia Zheng ◽  
Cong Jie Ye ◽  
Yang Fu ◽  
Dong Dong Li
Keyword(s):  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Wind Turbines ◽  
Offshore Wind ◽  
Special Focus ◽  
Offshore Wind Turbines ◽  
Wind Energy Conversion ◽  
Bearing Failures ◽  
Paper Review ◽  
Oil Monitoring

Wind energy conversion is the fastest-growing source of new electric generation in the world. The unforeseen damages of a component in a wind turbine have significant impact on the wind turbines, especially offshore ones. There has been a trend towards adoption of various forms of condition monitoring and fault diagnosis to know the deteriorating condition for both mechanical and electrial components, well in advance of a breakdown. This paper review the most recent advances in these areas with a special focus on offshore wind turbines. Different type of faults on offshore wind turbines are reviewed, including gearbox and bearing failures, generators failures, rotor failures and electrical failures. And then two widely-used methods(vibration analysis and oil monitoring) for predictive condition monitoring and fault diagnosis in offshore wind turbines are reviewed.

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