Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1 -- 2049 2022-09-26 10:09:42 |
2 Format correction Meta information modification 2049 2022-09-27 02:49:12 |

Video Upload Options

Do you have a full video?


Are you sure to Delete?
If you have any further questions, please contact Encyclopedia Editorial Office.
Khan, P.W.;  Byun, Y. Fault Detection and Classification of Wind Turbines. Encyclopedia. Available online: (accessed on 21 April 2024).
Khan PW,  Byun Y. Fault Detection and Classification of Wind Turbines. Encyclopedia. Available at: Accessed April 21, 2024.
Khan, Prince Waqas, Yung-Cheol Byun. "Fault Detection and Classification of Wind Turbines" Encyclopedia, (accessed April 21, 2024).
Khan, P.W., & Byun, Y. (2022, September 26). Fault Detection and Classification of Wind Turbines. In Encyclopedia.
Khan, Prince Waqas and Yung-Cheol Byun. "Fault Detection and Classification of Wind Turbines." Encyclopedia. Web. 26 September, 2022.
Fault Detection and Classification of Wind Turbines

Wind turbines are widely used worldwide to generate clean, renewable energy. The biggest issue with a wind turbine is reducing failures and downtime, which lowers costs associated with operations and maintenance. Wind turbines’ consistency and timely maintenance can enhance their performance and dependability.

wind turbines fault detection stacking ensemble classifier

1. Introduction

The global energy system is recognized as a significant contributor to greenhouse gas emissions. Sustainable development of low carbon emissions is required for decarbonization. The need for modern energy has taken an essential step toward renewable energy. Due to its positive benefits, wind power is attracting a significant amount of interest from investors in renewable energy sources. Wind energy is a clean, pollution-free renewable energy source with excellent growth potential [1]. Wind’s kinetic energy does not produce carbon dioxide and provides clean, reliable electricity. Over the last few years, the installed capacity of wind power has grown exponentially. However, most wind farms are located in remote areas with challenging environments such as mountains, deserts, or oceans. Many components of wind turbines, such as fans, bearings, gears, and generators, are at risk of failure, leading to higher maintenance and operating costs for wind turbines. Continuous health monitoring and maintenance can improve the reliability of wind turbines and prevent catastrophic accidents. Therefore, monitoring the condition of wind turbines and identifying faults is necessary and valuable for maintenance and planning, which can reduce economic losses and promote the development of the wind power industry.
Due to high-quality complex structures, high maintenance costs, and an increase in wind turbine installations worldwide, wind turbine operation and maintenance (O&M) technologies are now required [2]. In addition, offshore wind power sources, especially floating wind turbines, are rapidly evolving, significantly increasing the hassle and cost of O&M. Operating and maintenance costs make up a large portion of the total annual cost of wind turbines. For a new turbine, the cost of O&M can easily be as high as 20–25 percent of the total cost of an aircraft producing kWh during the turbine’s life. If the turbine is relatively new, the component may be only 10–15 percent, but it may increase by at least 20–35 percent during the turbine’s life. As a result, more attention is paid to O&M as manufacturers try to significantly reduce costs by designing new turbines that require less service testing and shorter turbine downtime. The consistency and timely maintenance of wind turbines can enhance their reliability and performance [3]. Much research has focused on monitoring control and early detection of faults through Supervisory Control and Data Acquisition (SCADA). Detecting the condition or malfunction of wind turbines is an inexpensive solution to reduce maintenance costs and revenue loss due to component malfunction. The design of debugging deployment turbines has a more significant impact on life expectancy. Older turbine gearboxes did not take advantage of advances in engineering and were still at risk of decay and wear and tear. Several artificial intelligence solutions have been proposed that allow technicians to predict and identify turbine faults, assist in diagnosis, and determine when safety precautions should be taken. Machine learning solutions have become popular in many fields, and their use in wind turbines has yielded excellent results.

2. Fault Detection and Classification of Wind Turbines

Wind power is an essential renewable energy source due to technological advances and cheapness. Wind turbines (WT) are essential for wind power generation systems [4][5]. Defects in various parts of wind turbines should be eliminated for better performance. Vibration testing is an integral part of monitoring the condition of wind turbine components [6]. In the article by Bodla et al. [7], a test was performed using average and incorrect WT vibration data to monitor low position. The purpose is to monitor the condition of the wind turbine for immediate fault predictions so that the turbine can be configured for immediate better performance and longer service life. Quality monitoring and evaluation are essential for proper functioning wind power generation systems. In the study by Malik et al. [8], health conditions were assessed for three different fault types using K-nearest neighbors (KNN) algorithms. To decompose the raw signal, they used a separate estimation of the wave function called the Meyer wavelet function. The wind generator used 21 full-featured templates to classify imbalance errors. They compared the proposed method to a multilayer perceptron (MLP). The proposed approach results and various comparisons can serve as an essential tool for WTG error detection.
The approaches used in the study by Lima et al. [9] enable information mining from easily accessible SCADA data in order to identify potential abnormalities when wind turbines run. With this system monitoring the turbine parts, it is feasible to see problems before they arise and take appropriate action, minimizing downtime and maintenance expenses. A customized deep learning model was suggested by Chatterjee et al. [10] for anomaly prediction and transparent decision-making. It was extended to transfer learning from one domain to another, eliminating the need for training in the new domain.
The converter can quickly fail as a vital component of the transmission system. In order to isolate vulnerable sums during the integration process, the best model parameters for each SVM can be found by repeating BSA optimization in the article by Zheng et al. [11]. To improve fault detection accuracy for wind turbines, a method of correction of fault correlation with wave change and compression detection theory has been proposed. It also uses the AdaBoost-SVM wind turbine converter for diagnostics. Based on the compression observation theory, the estimated coefficient of the wave is quantitatively reduced to obtain the measured signal. The target of the orthogonal adjustment is the algorithm tracking vector and then the error multiplier vector. AdaBoost-SVM’s advanced classification for error detection has introduced error detection vectors.
The study by Wu et al. [12] presented a wind turbine diagnostic method based on the XGBoost and ReliefF algorithms that used SCADA data to improve the accuracy of wind turbine diagnostics. This article provides solutions to wind turbine malfunction. The advantage of high accuracy in the algorithm was verified by comparison with the state-of-the-art models.
Wind energy is converted into electrical energy by a rotating blade attached to a generator [13]. Due to environmental conditions and extensive construction, the blades suffer from many defects and lack of productivity [14]. The time of inactivity can be reduced if their condition is checked periodically using monitoring techniques. Feature extraction, feature selection, and error classification have been considered machine learning issues. The study by Joshua et al. [15] provided an explanation based on the algorithm of vibration signals for analyzing wind turbine aircraft conditions. A model was created using the data formatting technique from the vibration data obtained. A logistic model tree (LMT) algorithm was used to learn and classify different aircraft locations. Samples were tested ten times under cross-verification, and 90.33 percent accurate classification samples were found. The error rate was relatively low and can be considered blade debugging. Therefore, the tree logistics model is available mainly for monitoring wind turbine blades to reduce idle time and provide continuous wind power. Renewable energy sources such as wind power are plentiful. The reliability of wind turbines is essential to maximize wind power. Vibration signals on the rotating side of wind turbines were average, not Gaussian, and instability and malfunction patterns were generally minimal. With these issues in mind, The article by Wenyi et al. [16] designed and proposed a method for detecting wind turbine faults based on the grouping of the diagonal spectrum and binary tree support-vector machine (SVM). First, the input properties of the diagonal spectrum vibration machine were considered vectors. Second, a self-regulating neural network mapping feature was introduced to create error feature group sampling and a cluster binary tree. Numerous error classifications were made for practice and sample testing. The diagnostic analysis results confirm that this method is effective and efficient. Impact classification effects were RBF neural network methods, and higher accuracy could be achieved in better and fewer sample cases than for traditional SVM methods.
The study by Jiang et al. [17] focused on a survey of the proposed architecture and the multifaceted properties of low-turn vibration signals for troubleshooting WT gearboxes under different operating conditions. Unlike traditional methods, when the emission and classification of features are individually designed, the design’s purpose was to automatically determine the characteristics of the desired error of the vibration signal. The proposed multiscale convolutional neural network system was evaluated by testing the test machine on the WT gearbox. Experimental results and extensive comparative analysis of traditional CNN and traditional multidimensional extractors demonstrated the effectiveness of the proposed method. The accuracy could be enhanced by increasing the features [18]. They verified the scale of their proposed MSCNN with an accurate mass WT gearbox. In addition, they further explored analytical methods based on unbalanced multidimensional presentations to significantly reduce the impact of the distribution of diagonal data between standard and inaccurate data, leading to learning algorithms for error diagnosis to significantly improve performance.
Modern debugging and rating systems have become necessary to achieve wind turbines’ desired reliability and efficiency [19]. In the work by Vidal et al. [20], sampling frequency was increased, and database multi-error detection monitoring and current sensor classification (in all commercial wind turbines) monitoring controls and data acquisition (SCADA) systems were monitored without the use of special equipment. A high-quality wind turbine was used. They understood that there were several defects in wind turbine actuators and sensors. First, the SCADA measurement groups were preset by a feature change based on analyzing multiple key components and sample base openings. Then, classification based on 10-fold cross-verification SVM was applied. The result was a uniquely trained classification that can solve all the errors analyzed by calculating only a set of features from the data for evaluation. As a result, their proposed approach is better than the other methods.
Time series data on the monitoring and debugging of wind turbines and other power systems are widely used [21], where long-term reliability is essential for developing classification features. Lei et al. [22] identified errors in time series signals based on long shorts. They introduced a new method using the long short-term memory (LSTM) model to learn features directly from multiple variable time series data and gain long-term possibilities through recurring behaviors and LSTM gate mechanisms. Errors can be efficiently classified by one or more sensors using raw time-series signals, and the performance of modern technologies is more efficient. Further, the sustainability of the proposed structure can be verified by testing it on smaller datasets with limited data. This could be enhanced by using a CNN as part of preparations to acquire local features to enhance the functionality of the proposed structure.
Classification of multi-fault detection is a challenging task due to weak faults [23], especially in wind turbine gearboxes with different gears and bearings [24]. The study by Teng et al. [25] analyzed the vibration signals coming from a real multi-fault wind turbine gearbox with catastrophic failure. The complex waveform can provide a multiscale enveloping spectrogram for simultaneous decomposition and distortion of signals. Using this method, testing multiscale enveloping spectrogram disks on different scales can quickly determine the faulty characteristics of the mounted holder under compression force. Unplanned or unresponsive maintenance of wind turbines due to component failure can lose a significant amount of time and revenue [26]. For this purpose, it is necessary to maintain it before it is needed. By constantly monitoring the health of the turbine, it is possible to eliminate the need for periodic inspections, identify underlying defects, and adjust maintenance schedules as needed. Efforts have been made to develop a condition monitoring system (CMS) based on detecting expensive vibration [27][28] or oil analysis sensors [29][30] in turbines. Instead, critical analyses of existing data from the turbine’s SCADA system can provide essential insights into turbine performance at a low cost. The study by Leahy et al. [31] examines a new method for classifying and predicting turbine faults based on SCADA data. The data were taken from a SCADA system in southeastern Ireland. Error classification works on three levels: distinguishing between error/error-free operations and classifying a specific error. Error and warning data were analyzed using simple and forced curves to identify the duration of filtering and error activation. The results were good and showed that perfect memory and accuracy could differentiate between error and error-free operations, but the F1 score dropped due to poor accuracy.


  1. Xiang, L.; Yang, X.; Hu, A.; Su, H.; Wang, P. Condition monitoring and anomaly detection of wind turbine based on cascaded and bidirectional deep learning networks. Appl. Energy 2022, 305, 117925.
  2. Zhu, Y.; Zhu, C.; Tan, J.; Tan, Y.; Rao, L. Anomaly detection and condition monitoring of wind turbine gearbox based on LSTM-FS and transfer learning. Renew. Energy 2022, 189, 90–103.
  3. Zhang, Y.; Zheng, H.; Liu, J.; Zhao, J.; Sun, P. An anomaly identification model for wind turbine state parameters. J. Clean. Prod. 2018, 195, 1214–1227.
  4. Li, H.; Chen, Z. Overview of different wind generator systems and their comparisons. IET Renew. Power Gener. 2008, 2, 123–138.
  5. Wang, Y.; Ma, X.; Qian, P. Wind turbine fault detection and identification through PCA-based optimal variable selection. IEEE Trans. Sustain. Energy 2018, 9, 1627–1635.
  6. Chen, X.; Yang, Y.; Cui, Z.; Shen, J. Vibration fault diagnosis of wind turbines based on variational mode decomposition and energy entropy. Energy 2019, 174, 1100–1109.
  7. Bodla, M.K.; Malik, S.M.; Rasheed, M.T.; Numan, M.; Ali, M.Z.; Brima, J.B. Logistic regression and feature extraction based fault diagnosis of main bearing of wind turbines. In Proceedings of the 2016 IEEE 11th Conference on Industrial Electronics and Applications (ICIEA), Hefei, China, 5–7 June 2016; pp. 1628–1633.
  8. Malik, H. Wavelet and Hilbert Huang transform based wind turbine imbalance fault classification model using k-nearest neighbour algorithm. Int. J. Renew. Energy Technol. 2018, 9, 66–83.
  9. Lima, L.; Blatt, A.; Fujise, J. Wind turbine failure prediction using SCADA data. In Proceedings of the Journal of Physics: Conference Series; IOP Publishing: Bristol, UK, 2020; Volume 1618, p. 022017.
  10. Chatterjee, J.; Dethlefs, N. Deep learning with knowledge transfer for explainable anomaly prediction in wind turbines. Wind Energy 2020, 23, 1693–1710.
  11. Zheng, X.X.; Peng, P. Fault diagnosis of wind power converters based on compressed sensing theory and weight constrained Adaboost-SVM. J. Power Electron. 2019, 19, 443–453.
  12. Wu, Z.; Wang, X.; Jiang, B. Fault diagnosis for wind turbines based on ReliefF and eXtreme gradient boosting. Appl. Sci. 2020, 10, 3258.
  13. Downey, A.; Ubertini, F.; Laflamme, S. Algorithm for damage detection in wind turbine blades using a hybrid dense sensor network with feature level data fusion. J. Wind. Eng. Ind. Aerodyn. 2017, 168, 288–296.
  14. Kushwah, K.; Sahoo, S.; Joshuva, A. Health Monitoring of Wind Turbine Blades Through Vibration Signal Using Machine Learning Techniques. In Proceedings of the International Conference on Computing and Communication Systems, Vellore, India, 9–11 December 2021; Springer: Berlin/Heidelberg, Germany, 2021; pp. 239–247.
  15. Joshuva, A.; Deenadayalan, G.; Sivakumar, S.; Sathishkumar, R.; Vishnuvardhan, R. Logistic model tree classifier for condition monitoring of wind turbine blades. Int. J. Recent Technol. Eng. 2019, 8, 202–209.
  16. Wenyi, L.; Zhenfeng, W.; Jiguang, H.; Guangfeng, W. Wind turbine fault diagnosis method based on diagonal spectrum and clustering binary tree SVM. Renew. Energy 2013, 50, 1–6.
  17. Jiang, G.; He, H.; Yan, J.; Xie, P. Multiscale convolutional neural networks for fault diagnosis of wind turbine gearbox. IEEE Trans. Ind. Electron. 2018, 66, 3196–3207.
  18. Rahimilarki, R.; Gao, Z.; Jin, N.; Zhang, A. Convolutional neural network fault classification based on time-series analysis for benchmark wind turbine machine. Renew. Energy 2022, 185, 916–931.
  19. Li, Y.; Huang, X.; Tee, K.F.; Li, Q.; Wu, X.P. Comparative study of onshore and offshore wind characteristics and wind energy potentials: A case study for southeast coastal region of China. Sustain. Energy Technol. Assess. 2020, 39, 100711.
  20. Vidal, Y.; Pozo, F.; Tutivén, C. Wind turbine multi-fault detection and classification based on SCADA data. Energies 2018, 11, 3018.
  21. Miele, E.S.; Bonacina, F.; Corsini, A. Deep anomaly detection in horizontal axis wind turbines using Graph Convolutional Autoencoders for Multivariate Time series. Energy AI 2022, 8, 100145.
  22. Lei, J.; Liu, C.; Jiang, D. Fault diagnosis of wind turbine based on Long Short-term memory networks. Renew. Energy 2019, 133, 422–432.
  23. He, X.; Zhou, X.; Yu, W.; Hou, Y.; Mechefske, C.K. Adaptive variational mode decomposition and its application to multi-fault detection using mechanical vibration signals. ISA Trans. 2021, 111, 360–375.
  24. Joshuva, A.; Kumar, R.S.; Sivakumar, S.; Deenadayalan, G.; Vishnuvardhan, R. An insight on VMD for diagnosing wind turbine blade faults using C4. 5 as feature selection and discriminating through multilayer perceptron. Alex. Eng. J. 2020, 59, 3863–3879.
  25. Teng, W.; Ding, X.; Zhang, X.; Liu, Y.; Ma, Z. Multi-fault detection and failure analysis of wind turbine gearbox using complex wavelet transform. Renew. Energy 2016, 93, 591–598.
  26. Mishnaevsky, L., Jr.; Thomsen, K. Costs of repair of wind turbine blades: Influence of technology aspects. Wind Energy 2020, 23, 2247–2255.
  27. Ou, Y.; Tatsis, K.E.; Dertimanis, V.K.; Spiridonakos, M.D.; Chatzi, E.N. Vibration-based monitoring of a small-scale wind turbine blade under varying climate conditions. Part I: An experimental benchmark. Struct. Control. Health Monit. 2021, 28, e2660.
  28. Zhang, Y.; Hutchinson, P.; Lieven, N.A.; Nunez-Yanez, J. Adaptive event-triggered anomaly detection in compressed vibration data. Mech. Syst. Signal Process. 2019, 122, 480–501.
  29. Coronado, D.; Wenske, J. Monitoring the oil of wind-turbine gearboxes: Main degradation indicators and detection methods. Machines 2018, 6, 25.
  30. Bie, Y.; Liu, X.; Xu, T.; Zhu, Z.; Li, Z. A review of the application of oil analysis in condition monitoring and life prediction of wind turbine gearboxes. Insight-Non-Destr. Test. Cond. Monit. 2021, 63, 289–301.
  31. Leahy, K.; Hu, R.L.; Konstantakopoulos, I.C.; Spanos, C.J.; Agogino, A.M. Diagnosing wind turbine faults using machine learning techniques applied to operational data. In Proceedings of the 2016 IEEE International Conference on Prognostics and Health Management (Icphm), Ottawa, ON, Canada, 20–22 June 2016; pp. 1–8.
Subjects: Energy & Fuels
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to : ,
View Times: 709
Revisions: 2 times (View History)
Update Date: 27 Sep 2022