Skip to main content

Advertisement

SpringerLink
Log in

State of the Art of Artificial Intelligence Applied for False Alarms in Wind Turbines

  • Review article
  • Published:
Archives of Computational Methods in Engineering Aims and scope Submit manuscript

Abstract

Operation and maintenance activities, considering condition monitoring systems, are necessary to ensure the reliability of wind turbines, but provide complex and large amounts of data and alarms. In some cases, data analysis generates false alarms that cause unnecessary and significant downtimes; and, therefore, high costs. Their reduction implies the improvement of wind turbine maintenance strategies and data analysis. This paper presents the first exhaustive review of the methodologies, algorithms and techniques used for false alarm detection and diagnosis. This review studies the current state of the art and discusses the future trends and challenges in false alarm detection according to different criteria employing artificial intelligence. In addition, statistical and hybrid methods are studied. An overall analysis is provided considering the most important references obtained in the analysis of current the state of the art.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Abbreviations

AE:

Acoustic emission

ANFIS:

Adaptative neuro-fuzzy interference system

ANNs :

Artificial neural networks

APK-ANFIS:

A priori knowledge adaptative neuro-fuzzy interference system

ART2:

Adaptative resonance theory 2

AUC:

Area under curve

BPA:

Basic probability assignment

CC:

Cluster centroids

CMS:

Control monitoring system

CNN:

Convolutional neural network

COK:

Combined observer and Kalman filter

CW:

Class weight

DAE:

Deep auto-encoder

DBN:

Deep belief network

DNNs:

Deep neural networks

D-S:

Dempster-Shafer

EB:

Estimation-based

EFT:

Extreme function theory

EMD-LDA-PNN-SFAM:

Empirical modes decomposition-linear discriminant analysis-probabilistic neural network and simplified fuzzy adaptive resonance theory map

ESN:

Echo state network

EWMA:

Exponentially Weighted Moving Average

FAR:

False alarm rate

FDD:

Fault detection and diagnosis

FDI:

Fault detection and isolation

FDR:

Fault detection rate

FP:

False positive

GFM:

General fault model

GKSV:

Gaussian kernel support vector machine solution

GLR:

Generalized likelihood ratio

GMM-L2:

Gaussian mixture model-L2 distance

GP:

Gaussian process

GRU:

Gated recurrent unit

GWMA:

Generally weighted moving average

HI:

Health index

LWL:

Local weighted learning

MBK-SMOTE:

Mini batch K-means synthetic minority over-sampling technique

MEWMA:

Multivariate weighted moving average

NARX:

Nonlinear auto-regressive with exogenous input

NN:

Neural network

NN-Residue:

Neural network with residue analysis

O&M:

Operation and maintenance

PC2:

Secondary principal component

PCA:

Principal component analysis

PMSG:

Permanent-magnet synchronous generators

RF:

Random forest

ROC:

Receiver operating characteristic

RUL:

Remaining useful life

RUS:

Random under sampling

RVM:

Relevance vector machine

SCADA:

Supervisory control and data acquisition

SHM:

Structural health monitoring

SMOTE:

Synthetic minority over-sampling technique

SOM-MQE:

Self-organizing maps-minimum quantization error

SVM:

Support vector machine

SVR:

Support vector regression

TNR:

True negative rate

TPR:

True positive rate

UDC:

Up-down counter

WT:

Wind turbine

XGBoost:

EXtreme gradient boosting

References

  1. Xia F, Song F (2017) Evaluating the economic impact of wind power development on local economies in China. Energy Policy 110:263–270

    Article  Google Scholar 

  2. Joyce Lee FZ (2021) Global Wind Report 2021. https://gwec.net/wp-content/uploads/2021/03/GWEC-Global-Wind-Report-2021.pdf. Accessed Aug 2021

  3. Junginger M, Louwen A, Gomez Tuya N, de Jager D, van Zuijlen E, Taylor M (2020) Chapter 7—Offshore wind energy. In: Junginger M, Louwen A (eds) Technological learning in the transition to a low-carbon energy system. Academic Press, pp 103–117

    Chapter  Google Scholar 

  4. Márquez FPG, Karyotakis A, Papaelias M (2018) Renewable energies: business outlook 2050. Springer

    Book  Google Scholar 

  5. Márquez FPG, Pinar Pérez JM (2020) Chapter 1—Wind turbines: a general reliability analysis. In: Papaelias M, Márquez FPG, Karyotakis A (eds) Non-destructive testing and condition monitoring techniques for renewable energy industrial assets. Butterworth-Heinemann, Boston, pp 1–18

    Google Scholar 

  6. Njiri JG, Beganovic N, Do MH, Söffker D (2019) Consideration of lifetime and fatigue load in wind turbine control. Renew Energy 131:818–828. https://doi.org/10.1016/j.renene.2018.07.109

    Article  Google Scholar 

  7. Pérez JMP, Márquez FPG, Hernández DR (2016) Economic viability analysis for icing blades detection in wind turbines. J Clean Prod 135:1150–1160

    Article  Google Scholar 

  8. Menezes EJN, Araújo AM, da Silva NSB (2018) A review on wind turbine control and its associated methods. J Clean Prod 174:945–953

    Article  Google Scholar 

  9. Pérez JMP, Márquez FPG, Tobias A, Papaelias M (2013) Wind turbine reliability analysis. Renew Sustain Energy Rev 23:463–472

    Article  Google Scholar 

  10. Castellani F, Astolfi D, Sdringola P, Proietti S, Terzi L (2017) Analyzing wind turbine directional behavior: SCADA data mining techniques for efficiency and power assessment. Appl Energy 185:1076–1086

    Article  Google Scholar 

  11. Pliego Marugán A, Garcia Marquez FP, Lev B (2017) Optimal decision-making via binary decision diagrams for investments under a risky environment. Int J Prod Res 55:5271–5286

    Article  Google Scholar 

  12. Márquez F, Papaelias J, Hermosa RR (2012) Wind turbines maintenance management based on FTA and BDD. In: Proceedings of the international conference on renewable energies and power quality (ICREPQ’12), pp 4–6

  13. Hameed Z, Hong YS, Cho YM, Ahn SH, Song CK (2009) Condition monitoring and fault detection of wind turbines and related algorithms: a review. Renew Sustain Energy Rev 13:1–39. https://doi.org/10.1016/j.rser.2007.05.008

    Article  Google Scholar 

  14. Sánchez PJB, Ramírez IS, Márquez FPG (2019) Sistema de Inspección Acústica con Drones para Gestión del Mantenimiento y Detección de Fallos en Turbinas Eólicas

  15. Moraleda VB, Marugán AP, Márquez FPG (2018) Acoustic maintenance management employing unmanned aerial vehicles in renewable energies. In: Proceedings of the international conference on management science and engineering management, pp 969–981

  16. Raghavan A (2007) Guided-wave structural health monitoring

  17. Muñoz CQG, Jiménez AA, Márquez FPG (2018) Wavelet transforms and pattern recognition on ultrasonic guides waves for frozen surface state diagnosis. Renew Energy 116:42–54

    Article  Google Scholar 

  18. García Márquez FP, Gómez Muñoz CQ, Segovia Ramírez I (2016) A condition monitoring system for blades of wind turbine maintenance management

  19. Muñoz CQG, Márquez FPG, Tomás JMS (2016) Ice detection using thermal infrared radiometry on wind turbine blades. Measurement 93:157–163

    Article  Google Scholar 

  20. Garcia Marquez FP, Pliego Marugan A, Pinar Perez JM, Hillmansen S, Papaelias M (2017) Optimal dynamic analysis of electrical/electronic components in wind turbines. Energies 10:1111

    Article  Google Scholar 

  21. Wang Y, Ma X, Qian P (2018) Wind turbine fault detection and identification through PCA-based optimal variable selection. IEEE Trans Sustain Energy 9:1627

    Article  Google Scholar 

  22. Bazilevs Y, Yan J, Deng X, Korobenko A (2019) Computer modeling of wind turbines: 2. Free-surface FSI and fatigue-damage. Arch Comput Methods Eng 26:1101–1115

    Article  MathSciNet  Google Scholar 

  23. García Márquez FP, Segovia Ramírez I, Mohammadi-Ivatloo B, Marugán AP (2020) Reliability dynamic analysis by fault trees and binary decision diagrams. Information 11:324. https://doi.org/10.3390/info11060324

    Article  Google Scholar 

  24. García Márquez FP, Segovia Ramírez I, Pliego Marugán A (2019) Decision making using logical decision tree and binary decision diagrams: a real case study of wind turbine manufacturing. Energies 12:1753

    Article  Google Scholar 

  25. Pedro F, Marquez G (2008) Binary decision diagrams applied to fault tree analysis

  26. Zhang Y, Zheng H, Liu J, Zhao J, Sun P (2018) An anomaly identification model for wind turbine state parameters. J Clean Prod 195:1214–1227. https://doi.org/10.1016/j.jclepro.2018.05.126

    Article  Google Scholar 

  27. Leite GDNP, Araújo AM, Rosas PAC (2018) Prognostic techniques applied to maintenance of wind turbines: a concise and specific review. Renew Sustain Energy Rev 81:1917–1925. https://doi.org/10.1016/j.rser.2017.06.002

    Article  Google Scholar 

  28. Yang W, Tavner PJ, Crabtree CJ, Feng Y, Qiu Y (2014) Wind turbine condition monitoring: technical and commercial challenges. Wind Energy 17:673–693

    Article  Google Scholar 

  29. Marquez FG, Singh V, Papaelias M (2011) A review of wind turbine maintenance management procedures. In: Proceedings of the the eighth international conference on condition monitoring and machinery failure prevention technologies, pp 1–14

  30. Qiu Y, Feng Y, Tavner P, Richardson P, Erdos G, Chen B (2012) Wind turbine SCADA alarm analysis for improving reliability. Wind Energy 15:951–966

    Article  Google Scholar 

  31. Chacón AMP, Márquez FPG (2019) False alarms management by data science. Data science and digital business. Springer, pp 301–316

    Chapter  Google Scholar 

  32. Pliego Marugán A, García Márquez FP (2019) Advanced analytics for detection and diagnosis of false alarms and faults: a real case study. Wind Energy 22:1622–1635

    Article  Google Scholar 

  33. Márquez FPG, Chacón AMP (2020) A review of non-destructive testing on wind turbines blades. Renew Energy 161:998

    Article  Google Scholar 

  34. Feng Y, Qiu Y, Crabtree CJ, Long H, Tavner PJ (2013) Monitoring wind turbine gearboxes. Wind Energy 16:728–740

    Article  Google Scholar 

  35. Yoon JT, Youn BD, Yoo M, Kim Y, Kim S (2019) Life-cycle maintenance cost analysis framework considering time-dependent false and missed alarms for fault diagnosis. Reliab Eng Syst Saf 184:181–192. https://doi.org/10.1016/j.ress.2018.06.006

    Article  Google Scholar 

  36. Khan MJ, Mathew L (2020) Comparative study of optimization techniques for renewable energy system. Arch Comput Methods Eng 27:351–360

    Article  Google Scholar 

  37. Gill S, Stephen B, Galloway S (2011) Wind turbine condition assessment through power curve copula modeling. IEEE Trans Sustain Energy 3:94–101

    Article  Google Scholar 

  38. Inc., D.S.R.S.D. Publications of false alarms in wind turbines. https://app.dimensions.ai/discover/publication?search_mode=content&search_text=false%20alarms%20wind%20turbine&search_type=kws&search_field=full_search&or_facet_for=2209&or_facet_for=2208&or_facet_for=2746&or_facet_for=2867&or_facet_for=2790. Accessed Jan 2020

  39. Tchakoua P, Wamkeue R, Ouhrouche M, Slaoui-Hasnaoui F, Tameghe TA, Ekemb G (2014) Wind turbine condition monitoring: state-of-the-art review, new trends, and future challenges. Energies 7:2595–2630

    Article  Google Scholar 

  40. García Márquez FP, Tobias AM, Pinar Pérez JM, Papaelias M (2012) Condition monitoring of wind turbines: techniques and methods. Renew Energy 46:169–178. https://doi.org/10.1016/j.renene.2012.03.003

    Article  Google Scholar 

  41. Raghav MS, Sharma RB (2021) A review on fault diagnosis and condition monitoring of gearboxes by using AE technique. Arch Comput Methods Eng 28:2845–2859

    Article  Google Scholar 

  42. Tautz-Weinert J, Watson SJ (2016) Using SCADA data for wind turbine condition monitoring–a review. IET Renew Power Gener 11:382–394

    Article  Google Scholar 

  43. Fischer K, Coronado D (2015) Condition monitoring of wind turbines: state of the art, user experience and recommendations. In: Fraunhofer-IWES, Bremerhaven

  44. Goyal D, Pabla B, Dhami S (2017) Condition monitoring parameters for fault diagnosis of fixed axis gearbox: a review. Arch Comput Methods Eng 24:543–556

    Article  MathSciNet  MATH  Google Scholar 

  45. Qiao W, Lu D (2015) A survey on wind turbine condition monitoring and fault diagnosis—part II: signals and signal processing methods. IEEE Trans Ind Electron 62:6546–6557

    Article  Google Scholar 

  46. Stetco A, Dinmohammadi F, Zhao X, Robu V, Flynn D, Barnes M, Keane J, Nenadic G (2019) Machine learning methods for wind turbine condition monitoring: a review. Renew Energy 133:620–635

    Article  Google Scholar 

  47. Marugán AP, Chacón AMP, Márquez FPG (2019) Reliability analysis of detecting false alarms that employ neural networks: a real case study on wind turbines. Reliab Eng Syst Saf 191:106574

    Article  Google Scholar 

  48. Ozdemir AA, Seiler P, Balas GJ (2011) Wind turbine fault detection using counter-based residual thresholding. IFAC Proc Volumes 44:8289–8294

    Article  Google Scholar 

  49. Yoon JT, Youn BD, Yoo M, Kim Y, Kim S (2018) Life-cycle maintenance cost analysis framework considering time-dependent false and missed alarms for fault diagnosis. Reliab Eng Syst Saf. https://doi.org/10.1016/j.ress.2018.06.006

    Article  Google Scholar 

  50. Diez P (2018) Smart wheelchairs and brain-computer interfaces: mobile assistive technologies. Academic Press

    Google Scholar 

  51. Chicco D, Jurman G (2020) The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics 21:6

    Article  Google Scholar 

  52. Wang H, Ma C, Zhou L (2009) A brief review of machine learning and its application. In: Proceedings of the 2009 international conference on information engineering and computer science, pp 1–4

  53. Jiménez AA, Zhang L, Muñoz CQG, Márquez FPG (2020) Maintenance management based on Machine Learning and nonlinear features in wind turbines. Renew Energy 146:316–328

    Article  Google Scholar 

  54. Jimenez AA, Muñoz CQG, Márquez FPG (2019) Dirt and mud detection and diagnosis on a wind turbine blade employing guided waves and supervised learning classifiers. Reliab Eng Syst Saf 184:2–12

    Article  Google Scholar 

  55. Jiménez AA, Márquez FPG, Moraleda VB, Muñoz CQG (2019) Linear and nonlinear features and machine learning for wind turbine blade ice detection and diagnosis. Renew Energy 132:1034–1048

    Article  Google Scholar 

  56. Herraiz ÁH, Marugán AP, Márquez FPG (2020) Photovoltaic plant condition monitoring using thermal images analysis by convolutional neural network-based structure. Renew Energy 153:334–348

    Article  Google Scholar 

  57. Alpaydin E (2020) Introduction to machine learning. MIT Press

    MATH  Google Scholar 

  58. Arcos Jiménez A, Gómez Muñoz CQ, García Márquez FP (2018) Machine learning for wind turbine blades maintenance management. Energies 11:13

    Article  Google Scholar 

  59. Garcia Marquez FP, Gomez Munoz CQ (2020) A new approach for fault detection, location and diagnosis by ultrasonic testing. Energies 13:1192

    Article  Google Scholar 

  60. Abraham A (2005) Artificial neural networks. Handbook of measuring system design. Wiley

    Google Scholar 

  61. Marugán AP, Márquez FPG, Perez JMP, Ruiz-Hernández D (2018) A survey of artificial neural network in wind energy systems. Appl Energy 228:1822–1836. https://doi.org/10.1016/j.apenergy.2018.07.084

    Article  Google Scholar 

  62. Ramirez IS, Marquez FPG (2020) Supervisory control and data acquisition analysis for wind turbine maintenance management. In: Proceedings of the international conference on management science and engineering management, pp 470–480

  63. Pliego Marugán A, Peco Chacón AM, García Márquez FP (2019) Reliability analysis of detecting false alarms that employ neural networks: a real case study on wind turbines. Reliab Eng Syst Saf 191:106574. https://doi.org/10.1016/j.ress.2019.106574

    Article  Google Scholar 

  64. Simani S, Turhan C (2018) Fault diagnosis of a wind turbine simulated model via neural networks. IFAC-PapersOnLine 51:381–388. https://doi.org/10.1016/j.ifacol.2018.09.605 (Invited paper for the special session on “Industrial Fault Diagnosis and Fault-tolerant Control” organised by Christophe Aubrun and Vicenc Puig)

    Article  Google Scholar 

  65. Odgaard PF, Stoustrup J, Kinnaert M (2013) Fault-tolerant control of wind turbines: a Benchmark model. IEEE Trans Control Syst Technol 21:1168–1182. https://doi.org/10.1109/TCST.2013.2259235

    Article  Google Scholar 

  66. Bangalore P, Letzgus S, Karlsson D, Patriksson M (2017) An artificial neural network-based condition monitoring method for wind turbines, with application to the monitoring of the gearbox. Wind Energy 20:1421

    Article  Google Scholar 

  67. Cui Y, Bangalore P, Tjernberg LB (2018) An anomaly detection approach using wavelet transform and artificial neural networks for condition monitoring of wind turbines' gearboxes. In: Proceedings of the 2018 power systems computation conference (PSCC), pp 1–7

  68. Adouni A, Chariag D, Diallo D, Ben Hamed M, Sbita L (2016) FDI based on artificial neural network for low-voltage-ride-through in DFIG-based wind turbine. ISA Trans 64:353–364. https://doi.org/10.1016/j.isatra.2016.05.009

    Article  Google Scholar 

  69. Schlechtingen M, Ferreira Santos I (2011) Comparative analysis of neural network and regression based condition monitoring approaches for wind turbine fault detection. Mech Syst Signal Process 25:1849–1875. https://doi.org/10.1016/j.ymssp.2010.12.007

    Article  Google Scholar 

  70. Wang L, Zhang Z, Long H, Xu J, Liu R (2017) Wind turbine gearbox failure identification with deep neural networks. IEEE Trans Ind Inf 13:1360–1368. https://doi.org/10.1109/TII.2016.2607179

    Article  Google Scholar 

  71. Zhao H, Liu H, Hu W, Yan X (2018) Anomaly detection and fault analysis of wind turbine components based on deep learning network. Renew Energy 127:825–834. https://doi.org/10.1016/j.renene.2018.05.024

    Article  Google Scholar 

  72. Jiang G, Xie P, He H, Yan J (2017) Wind turbine fault detection using a denoising autoencoder with temporal information. IEEE/ASME Trans Mechatron 23:89–100

    Article  Google Scholar 

  73. Kong Z, Tang B, Deng L, Liu W, Han Y (2020) Condition monitoring of wind turbines based on spatio-temporal fusion of SCADA data by convolutional neural networks and gated recurrent units. Renew Energy 146:760–768. https://doi.org/10.1016/j.renene.2019.07.033

    Article  Google Scholar 

  74. Liu Z, Xiao C, Zhang T, Zhang X (2020) Research on fault detection for three types of wind turbine subsystems using machine learning. Energies 13:460

    Article  Google Scholar 

  75. Yu D, Chen ZM, Xiahou KS, Li MS, Ji TY, Wu QH (2018) A radically data-driven method for fault detection and diagnosis in wind turbines. Int J Electr Power Energy Syst 99:577–584. https://doi.org/10.1016/j.ijepes.2018.01.009

    Article  Google Scholar 

  76. Ben Ali J, Saidi L, Harrath S, Bechhoefer E, Benbouzid M (2018) Online automatic diagnosis of wind turbine bearings progressive degradations under real experimental conditions based on unsupervised machine learning. Appl Acoust 132:167–181. https://doi.org/10.1016/j.apacoust.2017.11.021

    Article  Google Scholar 

  77. Ben Ali J, Saidi L, Mouelhi A, Chebel-Morello B, Fnaiech F (2015) Linear feature selection and classification using PNN and SFAM neural networks for a nearly online diagnosis of bearing naturally progressing degradations. Eng Appl Artif Intell 42:67–81. https://doi.org/10.1016/j.engappai.2015.03.013

    Article  Google Scholar 

  78. Wu X, Wang H, Jiang G, Xie P, Li X (2019) Monitoring wind turbine gearbox with echo state network modeling and dynamic threshold using SCADA vibration data. Energies 12:982

    Article  Google Scholar 

  79. Cambron P, Lepvrier R, Masson C, Tahan A, Pelletier F (2016) Power curve monitoring using weighted moving average control charts. Renew Energy 94:126–135

    Article  Google Scholar 

  80. Tcherniak D, Mølgaard LL (2017) Active vibration-based structural health monitoring system for wind turbine blade: demonstration on an operating Vestas V27 wind turbine. Struct Health Monit 16:536–550. https://doi.org/10.1177/1475921717722725

    Article  Google Scholar 

  81. Joshuva A, Sugumaran V (2020) A lazy learning approach for condition monitoring of wind turbine blade using vibration signals and histogram features. Measurement 152:107295. https://doi.org/10.1016/j.measurement.2019.107295

    Article  Google Scholar 

  82. Benmessaoud T, Marugán AP, Mohammedi K, Márquez FPG (2017) Fuzzy logic applied to SCADA systems. In: Proceedings of the international conference on management science and engineering management, pp 749–757

  83. Qu F, Liu J, Zhu H, Zhou B (2020) Wind turbine fault detection based on expanded linguistic terms and rules using non-singleton fuzzy logic. Appl Energy 262:114469. https://doi.org/10.1016/j.apenergy.2019.114469

    Article  Google Scholar 

  84. Zhao Y, Li D, Dong A, Kang D, Lv Q, Shang L (2017) Fault prediction and diagnosis of wind turbine generators using SCADA data. Energies 10:1210

    Article  Google Scholar 

  85. Leahy K, Hu RL, Konstantakopoulos IC, Spanos CJ, Agogino AM, O’Sullivan DT (2018) Diagnosing and predicting wind turbine faults from SCADA data using support vector machines. Int J Progn Health Manag 9:1–11

    Google Scholar 

  86. Hu RL, Leahy K, Konstantakopoulos IC, Auslander DM, Spanos CJ, Agogino AM (2016) Using domain knowledge features for wind turbine diagnostics. In: Proceedings of the 2016 15th IEEE international conference on machine learning and applications (ICMLA), pp 300–307

  87. Zhou Q, Xiong T, Wang M, Xiang C, Xu Q (2017) Diagnosis and early warning of wind turbine faults based on cluster analysis theory and modified ANFIS. Energies 10:898

    Article  Google Scholar 

  88. Morshedizadeh M, Kordestani M, Carriveau R, Ting DSK, Saif M (2017) Application of imputation techniques and Adaptive Neuro-Fuzzy Inference System to predict wind turbine power production. Energy 138:394–404. https://doi.org/10.1016/j.energy.2017.07.034

    Article  Google Scholar 

  89. Chen B, Matthews PC, Tavner PJ (2013) Wind turbine pitch faults prognosis using a-priori knowledge-based ANFIS. Expert Syst Appl 40:6863–6876. https://doi.org/10.1016/j.eswa.2013.06.018

    Article  Google Scholar 

  90. Wei L, Qian Z, Zareipour H (2019) Wind turbine pitch system condition monitoring and fault detection based on optimized relevance vector machine regression. IEEE Trans Sustain Energy 11:2326

    Article  Google Scholar 

  91. Gonzalez E, Stephen B, Infield D, Melero J (2017) On the use of high-frequency SCADA data for improved wind turbine performance monitoring. Proc J Phys Conf Ser 296:012009

    Article  Google Scholar 

  92. Canizo M, Onieva E, Conde A, Charramendieta S, Trujillo S (2017) Real-time predictive maintenance for wind turbines using Big Data frameworks. In: Proceedings of the 2017 IEEE international conference on prognostics and health management (ICPHM), 19–21 June 2017, pp 70–77

  93. Gómez CQ, Villegas MA, García FP, Pedregal DJ (2016) Big data and web intelligence for condition monitoring: A case study on wind turbines. In Big Data: concepts, methodologies, tools, and applications; IGI global, pp 1295–1308

  94. Pliego A, de la Hermosa RR, Marquez FPG (2018) Big data and wind turbines maintenance management. Renewable energies. Springer, pp 111–125

    Chapter  Google Scholar 

  95. Ge Y, Yue D, Chen L (2017) Prediction of wind turbine blades icing based on MBK-SMOTE and random forest in imbalanced data set. In: Proceedings of the 2017 IEEE conference on energy internet and energy system integration (EI2), 26–28 Nov 2017, pp 1–6

  96. Pandit RK, Infield D (2018) SCADA based wind turbine anomaly detection using Gaussian Process (GP) models for wind turbine condition monitoring purposes. IET Renew Power Gener 12:1249

    Article  Google Scholar 

  97. Colone L, Dimitrov N, Straub D (2019) Predictive repair scheduling of wind turbine drive-train components based on machine learning. Wind Energy. https://doi.org/10.1002/we.2352

    Article  Google Scholar 

  98. Gómez Muñoz CQ, García Márquez FP, Hernández Crespo B, Makaya K (2019) Structural health monitoring for delamination detection and location in wind turbine blades employing guided waves. Wind Energy 22:698–711

    Article  Google Scholar 

  99. Marquez FG (2006) An approach to remote condition monitoring systems management

  100. Lim DW, Mantell SC, Seiler PJ (2017) Wireless monitoring algorithm for wind turbine blades using Piezo-electric energy harvesters. Wind Energy 20:551–565

    Article  Google Scholar 

  101. Gomez CQ, Garcia FP, Arcos A, Cheng L, Kogia M, Papelias M (2017) Calculus of the defect severity with EMATs by analysing the attenuation curves of the guided waves. Smart Struct Syst 19:195–202

    Article  Google Scholar 

  102. Koltsidopoulos Papatzimos A, Thies PR, Dawood T (2019) Offshore wind turbine fault alarm prediction. Wind Energy 22:1779

    Article  Google Scholar 

  103. Yin S, Wang G, Karimi HR (2014) Data-driven design of robust fault detection system for wind turbines. Mechatronics 24:298–306. https://doi.org/10.1016/j.mechatronics.2013.11.009

    Article  Google Scholar 

  104. Bakdi A, Kouadri A, Mekhilef S (2019) A data-driven algorithm for online detection of component and system faults in modern wind turbines at different operating zones. Renew Sustain Energy Rev 103:546–555

    Article  Google Scholar 

  105. Márquez FPG (2010) A new method for maintenance management employing principal component analysis. Struct Durab Health Monit 6:89

    Google Scholar 

  106. Jlassi I, Estima JO, Khil SKE, Bellaaj NM, Cardoso AJM (2015) Multiple open-circuit faults diagnosis in back-to-back converters of PMSG drives for wind turbine systems. IEEE Trans Power Electron 30:2689–2702. https://doi.org/10.1109/TPEL.2014.2342506

    Article  Google Scholar 

  107. Simani S, Farsoni S, Castaldi P (2014) Fault diagnosis of a wind turbine benchmark via identified fuzzy models. IEEE Trans Ind Electron 62:3775–3782

    Article  MATH  Google Scholar 

  108. Pisu P, Ayalew B (2011) Robust fault diagnosis for a horizontal axis wind turbine. In: Proceedings of the 18th IFAC world congress, pp 7055–7060

  109. Papatheou E, Dervilis N, Maguire AE, Campos C, Antoniadou I, Worden K (2017) Performance monitoring of a wind turbine using extreme function theory. Renew Energy 113:1490–1502. https://doi.org/10.1016/j.renene.2017.07.013

    Article  Google Scholar 

  110. Rasmussen CE (2003) Gaussian processes in machine learning. In: Proceedings of the summer school on machine learning, pp 63–71

  111. Aghenta LO, Iqbal MT (2019) Development of an IoT based open source SCADA system for PV system monitoring. In: Proceedings of the 2019 IEEE Canadian conference of electrical and computer engineering (CCECE), pp 1–4

  112. Wang S, Huang Y, Li L, Liu C (2016) Wind turbines abnormality detection through analysis of wind farm power curves. Measurement 93:178–188

    Article  Google Scholar 

  113. de la Hermosa González-Carrato RR (2018) Wind farm monitoring using Mahalanobis distance and fuzzy clustering. Renew Energy 123:526–540. https://doi.org/10.1016/j.renene.2018.02.097

    Article  Google Scholar 

  114. Pashazadeh V, Salmasi FR, Araabi BN (2017) Data driven sensor and actuator fault detection and isolation in wind turbine using classifier fusion. Renew Energy 116:99

    Article  Google Scholar 

  115. Chen W, Ding SX, Haghani A, Naik A, Khan AQ, Yin S (2011) Observer-based FDI Schemes for Wind Turbine Benchmark. IFAC Proceedings Volumes 44:7073–7078. https://doi.org/10.3182/20110828-6-IT-1002.03469

    Article  Google Scholar 

  116. Odgaard PF, Stoustrup J (2009) Unknown input observer based scheme for detecting faults in a wind turbine converter. IFAC Proc Volumes 42:161–166

    Article  Google Scholar 

  117. Nazir M, Khan AQ, Mustafa G, Abid M (2017) Robust fault detection for wind turbines using reference model-based approach. J King Saud Univ Eng Sci 29:244–252. https://doi.org/10.1016/j.jksues.2015.10.003

    Article  Google Scholar 

  118. Kevin L, Colm G, Ken B, Peter OD, Dominic TJOS (2017) Automatically identifying and predicting unplanned wind turbine stoppages using SCADA and alarms system data: case study and results. J Phys Conf Ser 926:012011

    Article  Google Scholar 

  119. Leahy K, Gallagher C, O’Donovan P, Bruton K, O’Sullivan D (2018) A robust prescriptive framework and performance metric for diagnosing and predicting wind turbine faults based on SCADA and alarms data with case study. Energies 11:1738

    Article  Google Scholar 

  120. Ghane M, Rasekhi Nejad A, Blanke M, Gao Z, Moan T (2018) Condition monitoring of spar-type floating wind turbine drivetrain using statistical fault diagnosis. Wind Energy 21:575

    Article  Google Scholar 

  121. Orozco R, Sheng S, Phillips C (2018) Diagnostic models for wind turbine gearbox components using SCADA time series data. In: Proceedings of the 2018 IEEE international conference on prognostics and health management (ICPHM), pp 1–9

  122. Ferguson D, McDonald A, Carroll J, Lee H (2018) Standardisation of wind turbine SCADA data for gearbox fault detection. IET Renew Power Gener 2019:5147

    Google Scholar 

  123. Yang C, Liu J, Zeng Y, Xie G (2019) Real-time condition monitoring and fault detection of components based on machine-learning reconstruction model. Renew Energy 133:433–441. https://doi.org/10.1016/j.renene.2018.10.062

    Article  Google Scholar 

  124. Qiu Y, Feng Y, Infield D (2020) Fault diagnosis of wind turbine with SCADA alarms based multidimensional information processing method. Renew Energy 145:1923–1931

    Article  Google Scholar 

  125. Helin L, Junyong T, Guanghan B, Chengzhi Z (2017) A practical method for fault diagnosis of wind turbine gearbox using multi-source information fusion. In: Proceedings of the prognostics and system health management conference (PHM-Harbin), 2017, pp 1–6

  126. Helbing G, Ritter M (2020) Improving wind turbine power curve monitoring with standardisation. Renew Energy 145:1040–1048. https://doi.org/10.1016/j.renene.2019.06.112

    Article  Google Scholar 

  127. Wilkinson M, Darnell B, van Delft T, Harman K (2014) Comparison of methods for wind turbine condition monitoring with SCADA data. IET Renew Power Gener 8:390–397

    Article  Google Scholar 

  128. Chatterjee J, Dethlefs N (2020) Deep learning with knowledge transfer for explainable anomaly prediction in wind turbines. Wind Energy 23:1693

    Article  Google Scholar 

  129. Jia X, Jin C, Buzza M, Wang W, Lee J (2016) Wind turbine performance degradation assessment based on a novel similarity metric for machine performance curves. Renew Energy 99:1191–1201

    Article  Google Scholar 

  130. Fernandez-Canti RM, Blesa J, Tornil-Sin S, Puig V (2015) Fault detection and isolation for a wind turbine benchmark using a mixed Bayesian/Set-membership approach. Annu Rev Control 40:59–69. https://doi.org/10.1016/j.arcontrol.2015.08.002

    Article  Google Scholar 

  131. Freire NM, Estima JO, Cardoso AJM (2013) Open-circuit fault diagnosis in PMSG drives for wind turbine applications. IEEE Trans Ind Electron 60:3957–3967

    Article  Google Scholar 

  132. Zhao H, Cheng L (2017) Open-circuit faults diagnosis in back-to-back converters of DF wind turbine. IET Renew Power Gener 11:417–424

    Article  Google Scholar 

  133. Hu W, Barthelmie RJ, Letson F, Pryor SC (2019) A new seismic-based monitoring approach for wind turbines. Wind Energy 22:473–486

    Article  Google Scholar 

  134. Soua S, Van Lieshout P, Perera A, Gan T-H, Bridge B (2013) Determination of the combined vibrational and acoustic emission signature of a wind turbine gearbox and generator shaft in service as a pre-requisite for effective condition monitoring. Renew Energy 51:175–181

    Article  Google Scholar 

  135. Tang J, Soua S, Mares C, Gan T-H (2016) An experimental study of acoustic emission methodology for in service condition monitoring of wind turbine blades. Renew Energy 99:170–179. https://doi.org/10.1016/j.renene.2016.06.048

    Article  Google Scholar 

  136. Kandukuri ST, Klausen A, Karimi HR, Robbersmyr KG (2016) A review of diagnostics and prognostics of low-speed machinery towards wind turbine farm-level health management. Renew Sustain Energy Rev 53:697–708

    Article  Google Scholar 

  137. Yang H-H, Huang M-L, Lai C-M, Jin J-R (2018) An approach combining data mining and control charts-based model for fault detection in wind turbines. Renew Energy 115:808–816. https://doi.org/10.1016/j.renene.2017.09.003

    Article  Google Scholar 

  138. Koltsidopoulos Papatzimos A, Thies PR, Dawood T (2019) Offshore wind turbine fault alarm prediction. Wind Energy 22:1779–1788

    Article  Google Scholar 

  139. Gonzalez E, Stephen B, Infield D, Melero J (2017) On the use of high-frequency SCADA data for improved wind turbine performance monitoring. Proc J Phys Conf Ser 926:012009

    Article  Google Scholar 

Download references

Acknowledgements

The work reported herewith has been financially by the Dirección General de Universidades, Investigación e Innovación of Castilla-La Mancha, under Research Grant ProSeaWind project (Ref.: SBPLY/19/180501/000102). The paper has been carefully corrected by Alfredo Peinado (Birmingham University, UK).

Author information

Authors and Affiliations

  1. Ingenium Research Group, Universidad Castilla-La Mancha, 13071, Ciudad Real, Spain

    Ana María Peco Chacón, Isaac Segovia Ramírez & Fausto Pedro García Márquez

Authors
  1. Ana María Peco Chacón
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Isaac Segovia Ramírez
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fausto Pedro García Márquez
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fausto Pedro García Márquez.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Peco Chacón, A.M., Segovia Ramírez, I. & García Márquez, F.P. State of the Art of Artificial Intelligence Applied for False Alarms in Wind Turbines. Arch Computat Methods Eng 29, 2659–2683 (2022). https://doi.org/10.1007/s11831-021-09671-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11831-021-09671-x

Search

Navigation