Skip to main content

Advertisement

SpringerLink
Log in

A Comprehensive Review of Artificial Intelligence and Wind Energy

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

Abstract

Support of artificial intelligence, renewable energy and sustainability is currently increasing through the main policies of developed countries, e.g., the White Paper of the European Union. Wind energy is one of the most important renewable sources, growing in both onshore and offshore types. This paper studies the most remarkable artificial intelligence techniques employed in wind turbines monitoring systems. The principal techniques are analysed individually and together: Artificial Neural Networks; Fuzzy Logic; Genetic Algorithms; Particle Swarm Optimization; Decision Making Techniques; and Statistical Methods. The main applications for wind turbines maintenance management are also analysed, e.g., economic, farm location, non-destructive testing, environmental conditions, schedules, operator decisions, power production, remaining useful life, etc. Finally, the paper discusses the main findings of the literature in the conclusions.

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

Source: Scopus)

Fig. 2

Source: Scopus, 1970–2019)

Fig. 3

Source: Scopus, 1970–2019)

Fig. 4

Source: Scopus, 1970–2019)

Fig. 5

Source: Scopus, 1970–2019)

Fig. 6

Source: Scopus, 1970–2019)

Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Abbreviations

AI:

Artificial intelligence

WT:

Wind turbine

ANN:

Artificial neural network

SCADA:

Supervisory control and data acquisition

CNN:

Convolutional neural network

PCA:

Principal component analysis

GA:

Genetic algorithm

PSO:

Particle swarm optimization

O&M:

Operation and maintenance

SVM:

Support vector machine

FEM:

Finite element modelling

NDT:

Non-destructive testing

ANFIS:

Adaptive neuro-fuzzy inference system

NPV:

Net present value

EKF:

Extended Kalman filter

AHP:

Analytic hierarchy process

MILP:

Mixed integer linear programming

CAD:

Computer aided design

RCAM:

Reliability-centred asset maintenance

NA:

Not Available, unknown

References

  1. COM (2018)_237 (2018) Artificial intelligence for Europe. European commission, communication from the commission to the European parliament, The European Council, The council, the European economic and social committee and the committee of the regions

  2. ANSI_INCITS_172–2002_(R2007) (2007) Information technology—American national standard dictionary of information technology. International Committee for Information Technology Standards [INCITS] (Revision and Redesignation Of ANSI X3.172–1996)

  3. ISO/IEC_3WD_22989, Information technology—artificial intelligence—artificial intelligence concepts and terminology. International Organization for Standardization, Now under development.

  4. Federation, OotPotR (2019) Decree of the President of the Russian federation on the development of artificial intelligence in the russian federation. Office of the President of the Russian Federation

  5. Society TCtAN (2017) Draft AI R&D GUIDELINES for international discussions. The conference toward ai network society

  6. Tobin S et al. (2019) A brief historical overview of artificial intelligence research. Information Services & Use, pp. 1–6

  7. Webster G et al. (2017) Full Translation: China’s ‘new generation artificial intelligence development plan’(2017). DigiChina, vol. 1

  8. Smolka U, Chen PW (2013) On the design of measurement campaigns for fatigue life monitoring of offshore wind turbines. In: Proceedings of the international offshore and polar engineering conference

  9. Raghav MS, Sharma RB (2021) A review on fault diagnosis and condition monitoring of gearboxes by using AE technique. Archiv Comput Methods Eng 28(4):2845–2859

    Google Scholar 

  10. Reddy A et al (2019) Detection of Cracks and damage in wind turbine blades using artificial intelligence-based image analytics. Measure J Int Measure Confed 147:106823

    Google Scholar 

  11. Xu D, Wen C, Liu J (2019) Wind turbine blade surface inspection based on deep learning and UAV-taken images. J Renew Sustain Energy 11(5):053305

    Google Scholar 

  12. 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

    Google Scholar 

  13. Martinez I, Viles E, Cabrejas I (2018) Labelling drifts in a fault detection system for wind turbine maintenance. International symposium on intelligent and distributed computing. Springer, Cham, pp 145–156

    Google Scholar 

  14. 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(11):1622–1635

    Google Scholar 

  15. Barszcz T, Bielecki A, Wójcik M (2010) ART-type artificial neural networks applications for classification of operational states in wind turbines. International conference on artificial intelligence and soft computing. Springer, Berlin, pp 11–18

    Google Scholar 

  16. Márquez FPG, Chacón AMP (2020) A review of non-destructive testing on wind turbines blades. Renew Energy. https://doi.org/10.1016/j.renene.2020.07.145

    Article  Google Scholar 

  17. Chacón AMP, Ramírez IS, Márquez FPG (2020) False alarms analysis of wind turbine bearing system. Sustainability 12(19):7867

    Google Scholar 

  18. Butt AH et al (2020) Development of a linear acoustic array for aero-acoustic quantification of camber-bladed vertical axis wind turbine. Sensors 20(20):5954

    Google Scholar 

  19. Cui Y, Bangalore P, Tjernberg (2018) LB An anomaly detection approach based on machine learning and scada data for condition monitoring of wind turbines. In: 2018 international conference on probabilistic methods applied to power systems, PMAPS 2018—Proceedings

  20. Brandão RM, Carvalho JB, Barbosa FM (2015) Intelligent system for fault detection in wind turbines gearbox. 2015 IEEE Eindhoven PowerTech. https://doi.org/10.1109/PTC.2015.7232407

    Article  Google Scholar 

  21. Brandão RM, Carvalho JB, Barbosa FM (2012) Forecast of faults in a wind turbine gearbox. In: 2012 ELEKTRO. IEEE

  22. Koukoura, S. (2018) Failure and remaining useful life prediction of wind turbine gearboxes. In: Proceedings of the annual conference of the prognostics and health management society, PHM

  23. Peng Y. et al. (2017) Fault prognosis of drivetrain gearbox based on a recurrent neural network. In: IEEE international conference on electro information technology

  24. Koukoura S, Carroll J, McDonald AM (2019) On the use of AI based vibration condition monitoring of wind turbine gearboxes. In: Journal of physics: conference series

  25. Shaul Hameed S, Vaithiyanathan M, Kesavan M (2019) Fault detection in single stage helical planetary gearbox using artificial neural networks (ANN) and decision tree with histogram features. SAE Technical papers

  26. Huang P (2016) Fault diagnosis system of wind turbine gearbox based on GRNN and fault tree analysis. Acta Technica CSAV (Ceskoslovensk Akademie Ved) 61(4):109–122

    Google Scholar 

  27. Hu W, Chang H, Gu X (2019) A novel fault diagnosis technique for wind turbine gearbox. Appl Soft Comput J 82:105556

    Google Scholar 

  28. Peng G, Linlin L, Dengchang M (2012) Wind turbine gearbox condition monitoring with IPSO-BP. Acta Energiae Solaris Sinica 33(3):439–445

    Google Scholar 

  29. Qian P, Ma X, Cross P (2017) Integrated data-driven model-based approach to condition monitoring of the wind turbine gearbox. IET Renew Power Gener 11(9):1177–1185

    Google Scholar 

  30. Jiang R. et al. (2015) Failure prediction method of gearbox based on BP neural network with genetic optimization algorithm. In: IET conference publications

  31. Bangalore P et al (2017) An artificial neural network-based condition monitoring method for wind turbines, with application to the monitoring of the gearbox. Wind Energy 20(8):1421–1438

    Google Scholar 

  32. Godwin JL, Matthews PC, Chen B (2014) Prediction of wind turbine gearbox condition based on hybrid prognostic techniques with robust multivariate statistics and artificial neural networks. In: European wind energy association conference and exhibition 2014, EWEA 2014

  33. Zhong JH et al (2019) Multi-fault rapid diagnosis for wind turbine gearbox using sparse Bayesian extreme learning machine. IEEE Access 7:773–781

    Google Scholar 

  34. Elasha F et al (2019) Prognosis of a wind turbine gearbox bearing using supervised machine learning. Sensors (Switzerland) 19(14):3092

    Google Scholar 

  35. Qian P, Ma X, Wang Y (2015) Condition monitoring of wind turbines based on extreme learning machine. In: 2015 21st international conference on automation and computing: automation, computing and manufacturing for new economic growth, ICAC 2015

  36. Jiménez AA et al (2020) Maintenance management based on Machine Learning and nonlinear features in wind turbines. Renew Energy 146:316–328

    Google Scholar 

  37. Lin J (2018) Condition monitoring of wind turbine gearboxes using vibration-based methods. In: Proceedings of 2018 international computers, signals and systems conference, ICOMSSC 2018

  38. Lin J (2017) Condition monitoring of wind turbine gearboxes using vibration-based methods. In: Proceedings—2017 international conference on computer technology, electronics and communication, ICCTEC 2017

  39. Kral Z, Horn W, Steck J (2009) Composite panel damage detection using ultrasonic testing and neural networks.

  40. Nithya M, Nagarajan S, Navaseelan P (2017) Fault detection of wind turbine system using neural networks. In: Proceedings—2017 IEEE technological innovations in ICT for agriculture and rural development, TIAR 2017

  41. Sun D, Xue X, Wang Q (2019) Lamb wave and GA-BP neural network based damage identification for wind turbine blade. In: Proceedings of 2018 5th IEEE international conference on cloud computing and intelligence systems, CCIS 2018

  42. Aloraini A, Sayed-Mouchaweh M (2014) Graphical model based approach for fault diagnosis of wind turbines. In: Proceedings—2014 13th international conference on machine learning and applications, ICMLA 2014

  43. Hoell S, Omenzetter P (2016) Neuro-fuzzy computing for vibration-based damage localization and severity estimation in an experimental wind turbine blade with superimposed operational effects. In: Proceedings of SPIE—the international society for optical engineering

  44. Márquez FPG, Muñoz JMC (2012) A pattern recognition and data analysis method for maintenance management. Int J Syst Sci 43(6):1014–1028

    MATH  Google Scholar 

  45. Peng C et al (2019) Icing prediction of fan blade based on a hybrid model. Int J Performabil Eng 15(11):2882–2890

    Google Scholar 

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

    Google Scholar 

  47. Cao L et al (2018) Prediction of remaining useful life of wind turbine bearings under non-stationary operating conditions. Energies 11(12):3318

    Google Scholar 

  48. Durbhaka GK, Selvaraj B (2016) Predictive maintenance for wind turbine diagnostics using vibration signal analysis based on collaborative recommendation approach. In: 2016 International conference on advances in computing, communications and informatics, ICACCI 2016

  49. Yan Y et al (2015) A wind turbine anomaly detection method based on information entropy and combination model. Dianwang Jishu/Power Syst Technol 39(3):737–743

    Google Scholar 

  50. Guo P, Fu J, Yang X (2018) Condition monitoring and fault diagnosis ofwind turbines gearbox bearing temperature based on kolmogorov-smirnov test and convolutional neural network model. Energies 11(9):2248

    Google Scholar 

  51. Rodríguez-López MA et al (2018) Methodology for detecting malfunctions and evaluating the maintenance effectiveness in wind turbine generator bearings using generic versus specific models from SCADA data. Energies 11(4):746

    Google Scholar 

  52. Purarjomandlangrudi A et al. (2014) Application of anomaly technique in wind turbine bearing fault detection. In: IECON Proceedings (industrial electronics conference)

  53. Herp J, Pedersen NL, Nadimi ES (2019) Assessment of early stopping through statistical health prognostic models for empirical rul estimation in wind turbine main bearing failure monitoring. Energies 13(1):83

    Google Scholar 

  54. Li J et al. (2019) Fault diagnosis of wind turbine drive train using time-frequency estimation and CNN. In: 2019 prognostics and system health management conference, PHM-Qingdao 2019

  55. Murray C et al. (2014) Wind turbine drivetrain health assessment using discrete wavelet transforms and an artificial neural network. In: IET conference publications

  56. Tautz-Weinert J, Watson SJ (2016) Comparison of different modelling approaches of drive train temperature for the purposes of wind turbine failure detection. In: Journal of physics: conference series

  57. Liu YQ et al (2015) Fault identification of wind turbine drivetrain using BP neural network based on gravitational search algorithm. Zhendong yu Chongji/J Vib Shock 34(2):134–137

    Google Scholar 

  58. Brandão RFM, Carvalho JAB, Barbosa FPM (2012) Condition monitoring of the wind turbine generator slip ring. In: Proceedings of the universities power engineering conference

  59. Bi R, Zhou C, Hepburn DM (2017) Detection and classification of faults in pitch-regulated wind turbine generators using normal behaviour models based on performance curves. Renew Energy 105:674–688

    Google Scholar 

  60. Oyaga JV et al. (2017) Damage localization methodology using pattern recognition and machine learning approaches. In: Structural health monitoring 2017: real-time material state awareness and data-driven safety assurance—Proceedings of the 11th international workshop on structural health monitoring, IWSHM 2017

  61. Eickmeyer J et al. (2015) Data driven modeling for system-level condition monitoring on wind power plants. In: CEUR workshop proceedings

  62. Zhou S et al (2018) Prediction of wind turbine key load based on SCADA data. Nongye Gongcheng Xuebao/Trans Chinese Soc Agric Eng 34(2):219–225

    Google Scholar 

  63. Jiménez 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

    Google Scholar 

  64. Bangalore P, Tjernberg LB (2014) Self evolving neural network based algorithm for fault prognosis in wind turbines: a case study. In: 2014 international conference on probabilistic methods applied to power systems, PMAPS 2014—conference proceedings

  65. Bangalore P, Tjernberg LB (2013) An approach for self evolving neural network based algorithm for fault prognosis in wind turbine. In: 2013 IEEE Grenoble Conference PowerTech, POWERTECH 2013

  66. Kim SY, Ra IH, Kim SH (2012) Design of wind turbine fault detection system based on performance curve. In: 6th international conference on soft computing and intelligent systems, and 13th international symposium on advanced intelligence systems, SCIS/ISIS 2012

  67. Bangalore P, Patriksson M (2018) Analysis of SCADA data for early fault detection, with application to the maintenance management of wind turbines. Renew Energy 115:521–532

    Google Scholar 

  68. Bangalore P, Tjernberg LB (2015) An artificial neural network approach for early fault detection of gearbox bearings. IEEE Trans Smart Grid 6(2):980–987

    Google Scholar 

  69. Mazidi P, Bertling Tjernberg L, Sanz Bobi MA (2017) Wind turbine prognostics and maintenance management based on a hybrid approach of neural networks and a proportional hazards model. Proc Inst Mech Eng, Part O: J Risk Reliab 231(2):121–129

    Google Scholar 

  70. Cross P, Ma X (2015) Model-based and fuzzy logic approaches to condition monitoring of operational wind turbines. Int J Autom Comput 12(1):25–34

    Google Scholar 

  71. Obdam T, Rademakers L, Braam H (2009) Flight leader concept for wind farm load counting and performance assessment. In: European wind energy conference. Citeseer.

  72. Lonchampt J et al. (2019) An integrated asset management model for offshore wind turbines. In: Proceedings of the international offshore and polar engineering conference

  73. Cross P, Ma X (2013) Model-based condition monitoring for wind turbines. In: ICAC 2013—Proceedings of the 19th international conference on automation and computing: future energy and automation

  74. Tian Z, Jin T (2011) Maintenance of wind turbine systems under continuous monitoring. In: 2011 Proceedings-annual reliability and maintainability symposium IEEE

  75. Herp J, Ramezani MH (2015) State transition of wind turbines based on empirical inference on model residuals. In: Proceeding of the 2015 research in adaptive and convergent systems, RACS 2015

  76. Marugán AP, Márquez FP, Papaelias M (2017) Multivariable analysis for advanced analytics of wind turbine management. In: Proceedings of the tenth international conference on management science and engineering management

  77. Campbell P, Adamson K, Ford-Hutchinson R (2006) Towards DSS: enhancing domain knowledge through knowledge discovery. In: 2006 Innovations in information technology. IEEE

  78. Hur SH (2019) Estimation of useful variables in wind turbines and farms using neural networks and extended Kalman filter. IEEE Access 7:24017–24028

    Google Scholar 

  79. Perry M et al (2017) Wireless concrete strength monitoring of wind turbine foundations. Sensors (Switzerland) 17(12):2928

    Google Scholar 

  80. Mazidi P et al (2017) A health condition model for wind turbine monitoring through neural networks and proportional hazard models. Proc Inst Mech Eng, Part O: J Risk Reliabil 231(5):481–494

    Google Scholar 

  81. Zhan J et al (2019) Health assessment methods for wind turbines based on power prediction and mahalanobis distance. Int J Pattern Recogn Artif Intell 33(2):1951001

    Google Scholar 

  82. Izquierdo J et al (2020) On the importance of assessing the operational context impact on maintenance management for life cycle cost of wind energy projects. Renew Energy 153:1100–1110

    Google Scholar 

  83. Lu Y et al (2018) Condition based maintenance optimization for offshore wind turbine considering opportunities based on neural network approach. Appl Ocean Res 74:69–79

    Google Scholar 

  84. Stetco A et al. (2019) Wind Turbine operational state prediction: towards featureless, end-to-end predictive maintenance. In: Proceedings—2019 IEEE international conference on big data, big data 2019

  85. Vogt S, Otterson S, Berkhout V (2018) Multi-task distribution learning approach to anomaly detection of operational states of wind turbines. In: Journal of Physics: Conference Series

  86. Storti BA et al (2019) Improving the efficiency of a Savonius wind turbine by designing a set of deflector plates with a metamodel-based optimization approach. Energy 186:115814

    Google Scholar 

  87. Bermejo JF et al (2019) A review of the use of artificial neural network models for energy and reliability prediction. A study of the solar PV, hydraulic and wind energy sources. Appl Sci (Switzerland) 9(9):1844

    Google Scholar 

  88. Baptista D, Morgado-Dias F (2017) Energy production forecasting for a wind farm composed of turbines with different features. In: Energy and sustainability in small developing economies, ES2DE 2017—Proceedings

  89. Singh S, Bhatti T, Kothari D (2007) Wind power estimation using artificial neural network. J Energy Eng 133(1):46–52

    Google Scholar 

  90. Li S et al (2001) Using neural networks to estimate wind turbine power generation. IEEE Trans Energy Convers 16(3):276–282

    Google Scholar 

  91. Chan HC, Tsai JF (2016) Study on the application of neural network in the prognostic and health management of wind turbine. J Taiwan Soc Naval Archit Marine Eng 35(2):93–101

    Google Scholar 

  92. Wilson G, McMillan D, Ault G (2012) Modelling the effects of the environment on wind turbine failure modes using neural networks. In: IET conference publications

  93. Castellani F et al (2014) Wind energy forecast in complex sites with a hybrid neural network and CFD based method. Energy Procedia 45:188–197

    Google Scholar 

  94. Esmaeili MA, Twomey J (2012) Self-organizing map (SOM) in wind speed forecasting: a new approach in computational intelligence (CI) forecasting methods. In: ASME/ISCIE 2012 international symposium on flexible automation. American society of mechanical engineers digital collection

  95. Balluff S, Bendfeld J, Krauter S (2015) Short term wind and energy prediction for offshore wind farms using neural networks. In: 2015 International conference on renewable energy research and applications, ICRERA 2015

  96. Du J et al (2019) Ensemble interpolation of missing wind turbine nacelle wind speed data in wind farms based on robust particle swarm optimized generalized regression neural network. Int J Green Energy 16(14):1210–1219

    Google Scholar 

  97. Rodriguez H et al. (2017) Wind speed time series reconstruction using a hybrid neural genetic approach. In: IOP Conference series: earth and environmental science

  98. Brandão RM, Carvalh JB, Barbosa FM (2011) Application of neural networks for failure detection on wind turbines. In: 2011 IEEE Trondheim PowerTech. IEEE

  99. Brandäo RM, Carvalho JB, Barbosa FM (2011) Neural networks applications for fault detection on wind turbines. In: International conference on renewable energy and power quality (ICREPQ'11)

  100. Brandão RM, Carvalho JB, Barbosa FM (2010) Neural networks for condition monitoring of wind turbines. In: 2010 Modern electric power systems. IEEE

  101. Tafazzoli A, Mayo AN (2019) Smart wind turbine: artificial intelligence based condition monitoring system. In: SMARTGREENS 2019—proceedings of the 8th international conference on smart cities and green ICT systems

  102. Simani S, Castaldi P (2019) Intelligent fault diagnosis techniques applied to an offshore wind turbine system. Appl Sci (Switzerland) 9(4):783

    Google Scholar 

  103. Alves MM et al (2017) Damage prediction for wind turbines using wireless sensor and actuator networks. J Netw Comput Appl 80:123–140

    Google Scholar 

  104. Elijorde FI et al (2013) Wind turbine performance monitoring based on hybrid clustering method. Lecture notes in electrical engineering. Springer, Netherlands, pp 317–325

    Google Scholar 

  105. Melo Junior FEDA et al (2019) Unbalance evaluation of a scaled wind turbine under different rotational regimes via detrended fluctuation analysis of vibration signals combined with pattern recognition techniques. Energy 171:556–565

    Google Scholar 

  106. Carlos S et al (2014) Dynamic prediction of failures: a comparison of methodologies for a wind turbine. In: mathematical modeling in social sciences and engineering, pp. 51–58

  107. Cross P, Ma X, Wang Y (2013) Feature selection for artificial neural network model-based condition monitoring of wind turbines. In: 10th International conference on condition monitoring and machinery failure prevention technologies 2013, CM 2013 and MFPT 2013

  108. Jiang Z et al (2013) Trend prediction method of running stability deterioration faced to wind turbine group. Yi Qi Yi Biao Xue Bao/Chinese J Sci Instr 34(6 SUPPL.):124–127

    Google Scholar 

  109. Azimoh L et al. (2010) Minimization of power systems downtime after perturbations

  110. Roy K (2019) Analysis of power management and cost minimization in MG—a hybrid GOAPSNN technique. Int J Numer Model: Electr Netw, Dev Fields 32(5):e2624

    Google Scholar 

  111. Du M et al (2018) Research on the application of neural networks on wind turbine SCADA data analysis. Dianwang Jishu/Power Syst Technol 42(7):2200–2205

    Google Scholar 

  112. Liu X et al (2016) A predictive fault diagnose method of wind turbine based on K-means clustering and neural networks. J Internet Technol 17(7):1521–1528

    Google Scholar 

  113. Santospirito SP et al. (2013) Artificial intelligence and adaptive learning systems for condition monitoring of wind turbine blades and other complex ageing assets. In: 10th international conference on condition monitoring and machinery failure prevention technologies 2013, CM 2013 and MFPT 2013

  114. Sinha Y, Steel JA (2015) Failure prognostic schemes and database design of a software tool for efficient management of wind turbine maintenance. Wind Eng 39(4):453–477

    Google Scholar 

  115. De Souza DARMA et al. (2019) Evaluation of data based normal behavior models for fault detection in wind turbines. In: Proceedings—2019 Brazilian conference on intelligent systems, BRACIS 2019

  116. Souza DA et al (2018) Data prediction of a wind turbine using ANNs. Advances in intelligent systems and computing. Springer, Cham, pp 746–754

    Google Scholar 

  117. Wang M-H, Chen H-C (2012) Application of enn-1 for fault diagnosis of wind power systems. Math Probl Eng 2012:1–12

    MathSciNet  MATH  Google Scholar 

  118. Ferreira JC, Rolim LGB (2015) Wind turbine emulator using an MPPT controller based on neural networks. In: 2015 IEEE 13th Brazilian power electronics conference and 1st southern power electronics conference, COBEP/SPEC 2016

  119. Cross P, Ma X (2013) State dependent parameter model-based condition monitoring for wind turbines. In: 10th International conference on condition monitoring and machinery failure prevention technologies 2013, CM 2013 and MFPT 2013

  120. Su LX, Ma XX (2014) Monitor method of offshore wind turbine based on memory-like and neural network expert system. In: Applied mechanics and materials. pp. 687–693.

  121. Biswal S, Sabareesh GR (2015) Design and development of a wind turbine test rig for condition monitoring studies. In: 2015 International conference on industrial instrumentation and control, ICIC 2015

  122. Bielecki A et al (2014) Hybrid system of ART and RBF neural networks for classification of vibration signals and operational states of wind turbines. Lecture notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer, Cham, pp 3–11

    Google Scholar 

  123. Barszcz T et al (2013) Art-2 artificial neural networks applications for classification of vibration signals and opera-tional states of wind turbines for intelligent monitoring. Diagnostyka 14(4):21–26

    Google Scholar 

  124. Trstanova Z et al. (2015) Transferability of operational status classification models among different wind turbine types. In: Journal of physics: conference series

  125. Bunte A, Li P, Niggemann O (2018) Mapping data sets to concepts using machine learning and a knowledge based approach. In: ICAART 2018—proceedings of the 10th international conference on agents and artificial intelligence

  126. Yeh CH et al (2019) Machine learning for long cycle maintenance prediction of wind turbine. Sensors (Switzerland) 19(7):1671

    Google Scholar 

  127. Manrique RF, Giraldo FA, Esmeral JS (2012) Fault detection and diagnosis for wind turbines using data-driven approach. In: 2012 7th Colombian computing congress, CCC 2012—conference proceedings

  128. Zhang Z (2014) Comparison of data-driven and model-based methodologies of wind turbine fault detection with SCADA data. In: European wind energy association conference and exhibition 2014, EWEA 2014

  129. Shourangiz-Haghighi A et al (2020) State of the art in the optimisation of wind turbine performance using CFD. Archiv Comput Methods Eng 27(2):413–431

    Google Scholar 

  130. Kunakote T et al (2021) Comparative performance of twelve metaheuristics for wind farm layout optimisation. Archiv Comput Methods Eng. https://doi.org/10.1007/s11831-021-09586-7

    Article  MathSciNet  Google Scholar 

  131. Ghorbani N et al (2018) Optimizing a hybrid wind-PV-battery system using GA-PSO and MOPSO for reducing cost and increasing reliability. Energy 154:581–591

    Google Scholar 

  132. Younes M, Kherfane RL (2014) A new hybrid method for multi-objective economic power/emission dispatch in wind energy based power system. Int J Syst Assur Eng Manag 5(4):577–590

    Google Scholar 

  133. Das G, Das DC (2019) Demand side management for active power control of autonomous hybrid power system. In: 2nd International conference on energy, power and environment: towards smart technology, ICEPE 2018

  134. Ziegler L, Rhomberg M, Muskulus M (2018) Design optimization with genetic algorithms: How does steel mass increase if offshore wind monopiles are designed for a longer service life? In: Journal of physics: conference series

  135. Thejaswini R, Phani Raju HB (2018) Optimizing wind turbine-generator design using genetic algorithm. In: Proceedings of 2018 2nd International conference on advances in electronics, computers and communications, ICAECC 2018

  136. Tahir A et al. (2017) Evaluating the value of the excitation capacitance of a wind turbine/induction generator system using genetic algorithms. In: 2017 8th International renewable energy congress, IREC 2017.

  137. Young AC et al (2017) Methodology for optimizing composite towers for use on floating wind turbines. J Renew Sustain Energy 9(3):033305

    Google Scholar 

  138. Yan J et al (2015) Unit commitment in wind farms based on a glowworm metaphor algorithm. Electric Power Syst Res 129:94–104

    Google Scholar 

  139. Mohamed FA, Koivo HN (2012) Online management genetic algorithms of microgrid for residential application. Energy Convers Manag 64:562–568

    Google Scholar 

  140. Ferdoues MS, Ebrahimi S, Vijayaraghavan K (2017) Multi-objective optimization of the design and operating point of a new external axis wind turbine. Energy 125:643–653

    Google Scholar 

  141. Fonseca I, Farinha JT, Barbosa FM (2014) Maintenance planning in wind farms with allocation of teams using genetic algorithms. IEEE Lat Am Trans 12(6):1062–1070

    Google Scholar 

  142. Dey B, García Márquez FP, Basak SK (2020) Smart energy management of residential microgrid system by a novel hybrid mgwoscacsa algorithm. Energies 13(13):3500

    Google Scholar 

  143. Xu C et al (2017) Study of integrated optimization design of wind farm in complex terrain. Taiyangneng Xuebao/Acta Energiae Solaris Sinica 38(12):3368–3375

    Google Scholar 

  144. Al-Shamma’a AA, Addoweesh KE (2014) Techno-economic optimization of hybrid power system using genetic algorithm. Int J Energy Res 38(12):1608–1623

    Google Scholar 

  145. Dobrić G, Durišić Z (2012) Multi-criteria optimization of wind farm layout for WAsP application. In: European wind energy conference and exhibition EWEC 2012

  146. Suresh M, Meenakumari R (2019) An improved genetic algorithm-based optimal sizing of solar photovoltaic/wind turbine generator/diesel generator/battery connected hybrid energy systems for standalone applications. Int J Amb Energy 42:1136–1143

    Google Scholar 

  147. Suryoatmojo H, Robandi I, Ashari M (2012) Optimal design of fuel-cell, wind and micro-hydro hybrid system using genetic algorithm. Telkomnika 10(4):695

    Google Scholar 

  148. Attemene NS et al (2020) Optimal sizing of a wind, fuel cell, electrolyzer, battery and supercapacitor system for off-grid applications. Int J Hydrogen Energy 45(8):5512–5525

    Google Scholar 

  149. Chen L, Cheng R (2019) Optimization and analysis of microgrid based on different operational strategies. In: 2019 IEEE international conference on power, intelligent computing and systems, ICPICS 2019

  150. Ranjbar MR, Kouhi S (2015) Sources’ Response for supplying energy of a residential load in the form of on-grid hybrid systems. Int J Electr Power Energy Syst 64:635–645

    Google Scholar 

  151. Shahirinia A et al. (2005) Optimal sizing of hybrid power system using genetic algorithm. In: 2005 International conference on future power systems. IEEE

  152. Hameed Z, Vatn J (2011) Reliability of offshore wind turbines by grouping suitable inspection regimes. In: Proceedings of the EWEA conference

  153. Khattara A et al. (2013) Optimal number of DFIG wind turbines in farm using Pareto genetic algorithm to minimize cost and turbines fault effect. In: IECON Proceedings (industrial electronics conference)

  154. Gupta R, Kumar R, Bansal AK (2012) Economic analysis and design of stand-alone wind/photovoltaic hybrid energy system using Genetic algorithm. In: 2012 International conference on computing, communication and applications. IEEE

  155. Mao W, Dai N, Li H (2019) Economic dispatch of microgrid considering fuzzy control based storage battery charging and discharging. J Electr Syst 15(3):417–428

    Google Scholar 

  156. Singh SS, Fernandez E, Bharathikoppaka (2018) Impact of wind turbine generator for on the reliability and economics of a remote WTG system. In: IEEE International conference on power, control, signals and instrumentation engineering, ICPCSI 2017

  157. Rinaldi G et al (2019) Multi-objective optimization of the operation and maintenance assets of an offshore wind farm using genetic algorithms. Wind Eng 44:390–409

    Google Scholar 

  158. Meena NK et al (2019) Optimal planning of hybrid energy conversion systems for annual energy cost minimization in Indian residential buildings. Energy Procedia 158:2979–2985

    Google Scholar 

  159. Wang J, Zhang X, Zeng J (2019) Optimal maintenance decision for wind turbines based on imperfect maintenance model. Jisuanji Jicheng Zhizao Xitong/Comput Integr Manuf Syst, CIMS 25(5):1151–1160

    Google Scholar 

  160. Su Y et al (2017) A coordinative optimization method of active power and fatigue distribution in onshore wind farms. Int Trans Electr Energy Syst 27(10):e2392

    Google Scholar 

  161. Feng J et al (2019) An intelligent system for offshore wind farm maintenance scheduling optimization considering turbine production loss. J Intell Fuzzy Syst 37(5):6911–6923

    Google Scholar 

  162. Su C, Li T (2019) Joint optimization of condition-based maintenance policy and spare parts inventory for wind turbines. In: Proceedings of international conference on computers and industrial engineering, CIE

  163. Xiang H et al. (2016) Generating units maintenance scheduling considering peak regulation pressure with large-scale wind farms. In: China international conference on electricity distribution, CICED

  164. Miao JD et al. (2019) The bio-objective long term maintenance scheduling for wind turbines considering weather conditions. In: Proceedings—2018 prognostics and system health management conference, PHM-Chongqing 2018

  165. Liu L et al. (2019) Large-scale maintenance scheduling of wind turbines. In: 2019 Prognostics and system health management conference, PHM-Qingdao 2019

  166. Ioannou A, Wang L, Brennan F (2017) Design Implications towards Inspection Reduction of Large Scale Structures. Procedia CIRP 60:434–439

    Google Scholar 

  167. Srinivasulu G, Subramanyam B, Kalavathi MS (2016) Market based transmission expansion planning for tamilnadu test system. Int J Simul Syst Sci Technol 17(32)

  168. Stock-Williams C, Swamy SK (2019) Automated daily maintenance planning for offshore wind farms. Renew Energy 133:1393–1403

    Google Scholar 

  169. Kennedy K et al. (2016) Genetic optimisation for a stochastic model for opportunistic maintenance planning of offshore wind farms. In: 4th International symposium on environment friendly energies and applications, EFEA 2016

  170. Xie K, Billinton R (2010) Determination of the optimum capacity and type of wind turbine generators in a power system considering reliability and cost. IEEE Trans Energy Convers 26(1):227–234

    Google Scholar 

  171. Dobrić G, Durišić Z (2014) Double-stage genetic algorithm for wind farm layout optimization on complex terrains. J Renew Sustain Energy 6(3):033127

    Google Scholar 

  172. Alnatsha W et al. (2019) Techno-economic feasibility analysis of solar-wind energy conversion system utilizing genetic algorithm. In: Proceedings—2019 6th international conference on electrical and electronics engineering, ICEEE 2019

  173. Shahirinia A, Hajizadeh A, Moghaddamjoo A (2008) Genetic-based size optimization of renewable energy sources. Int J Power Energy Syst. https://doi.org/10.2316/Journal.203.2008.3.203-3992

    Article  Google Scholar 

  174. Gonzalez JS, Payan MB, Santos JR (2013) A new and efficient method for optimal design of large offshore wind power plants. IEEE Trans Power Syst 28(3):3075–3084

    Google Scholar 

  175. Zhong S et al (2019) A reliability-and-cost-based fuzzy approach to optimize preventive maintenance scheduling for offshore wind farms. Mech Syst Signal Process 124:643–663

    Google Scholar 

  176. Liu Y et al (2019) Bi-level fuzzy stochastic expectation modelling and optimization for energy storage systems planning in virtual power plants. J Renew Sustain Energy 11(1):014101

    Google Scholar 

  177. Hanish Nithin A, Omenzetter P (2017) Scheduling structural health monitoring activities for optimizing life-cycle costs and reliability of wind turbines. In: Proceedings of SPIE—the international society for optical engineering

  178. Hong YY et al (2015) Optimizing capacities of distributed generation and energy storage in a small autonomous power system considering uncertainty in renewables. Energies 8(4):2473–2492

    Google Scholar 

  179. Su C, Hu ZY, Liu Y (2020) Multi-component opportunistic maintenance optimization for wind turbines with consideration of seasonal factor. J Central South Univ 27(2):490–499

    Google Scholar 

  180. Zhang X, Su C (2019) Multi-objective optimization design of warranty strategy considering warranty cost and availability. In: Proceedings of 2019 IEEE 4th advanced information technology, electronic and automation control conference, IAEAC 2019

  181. Asaduz-Zaman M, Chowdhury AH (2015) Optimum economic dispatch of interconnected microgrid with energy storage system. In: 2nd International conference on electrical engineering and information and communication technology, iCEEiCT 2015

  182. Jiménez AA et al (2019) Linear and nonlinear features and machine learning for wind turbine blade ice detection and diagnosis. Renew Energy 132:1034–1048

    Google Scholar 

  183. Bekakra Y, Attous DB (2014) Optimal tuning of PI controller using PSO optimization for indirect power control for DFIG based wind turbine with MPPT. Int J Syst Assur Eng Manag 5(3):219–229

    Google Scholar 

  184. Bansal AK, Gupta R, Kumar R (2011) Optimization of hybrid PV/wind energy system using meta particle swarm optimization (MPSO). In: India international conference on power electronics 2010 (IICPE2010)

  185. Maleki A et al (2017) Optimization of a grid-connected hybrid solar-wind-hydrogen CHP system for residential applications by efficient metaheuristic approaches. Appl Therm Eng 123:1263–1277

    Google Scholar 

  186. Kaviani AK, Baghaee HR, Riahy GH (2009) Optimal sizing of a stand-alone wind/photovoltaic generation unit using particle swarm optimization. Simulation 85(2):89–99

    Google Scholar 

  187. Han Z et al (2019) Maintenance decision method for wind turbine components based on interval number fuzzy. Taiyangneng Xuebao/Acta Energiae Solaris Sinica 40(5):1394–1400

    Google Scholar 

  188. Bai X et al (2017) Health assessment and management of wind turbine blade based on the fatigue test data. In: Proceedings of 2016 prognostics and system health management conference, PHM-Chengdu 2016

  189. Shayeghi H, Hashemi Y (2015) Application of fuzzy decision-making based on INSGA-II to designing PV-wind hybrid system. Eng Appl Artif Intell 45:1–17

    Google Scholar 

  190. Shafiee M (2015) A fuzzy analytic network process model to mitigate the risks associated with offshore wind farms. Expert Syst Appl 42(4):2143–2152

    Google Scholar 

  191. Schlechtingen M, Santos IF, Achiche S (2013) Wind turbine condition monitoring based on SCADA data using normal behavior models Part 1: System description. Appl Soft Comput 13(1):259–270

    Google Scholar 

  192. Schlechtingen M, Santos IF (2014) Wind turbine condition monitoring based on SCADA data using normal behavior models. Part 2: Application examples. Appl Soft Comput 14:447–460

    Google Scholar 

  193. Gómez C et al (2017) A heuristic method for detecting and locating faults employing electromagnetic acoustic transducers. Eksploatacja i Niezawodność 19:493–500

    Google Scholar 

  194. Gomez CQ et al (2017) Calculus of the defect severity with EMATs by analysing the attenuation curves of the guided waves. Smart Struct Syst 19(2):195–202

    Google Scholar 

  195. Memarzadeh M, Pozzi M, Zico Kolter J (2015) Optimal planning and learning in uncertain environments for the management of wind farms. J Comput Civil Eng 29(5):04014076

    Google Scholar 

  196. Yang Y, Sørensen JD (2019) Cost-optimal maintenance planning for defects on wind turbine blades. Energies 12(6):998

    Google Scholar 

  197. Mensah AF, Dueñas-Osorio L (2012) A closed-form technique for the reliability and risk assessment of wind turbine systems. Energies 5(6):1734–1750

    Google Scholar 

  198. Munoz J et al (2009) Risk assessment of wind power generation project investments based on real options. In: 2009 IEEE Bucharest PowerTech

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

    Google Scholar 

  200. García Marquez FP, Gómez Muñoz CQ (2020) A new approach for fault detection, location and diagnosis by ultrasonic testing. Energies 13(5):1192

    Google Scholar 

  201. Flah M et al (2021) Machine learning algorithms in civil structural health monitoring: a systematic review. Archiv Comput Methods Eng 28(4):2621–2643

    Google Scholar 

  202. Herp J, Ramezani MH, Nadimi ES (2017) Dependency in state transitions of wind turbines-inference on model residuals for state abstractions. IEEE Trans Industr Electron 64(6):4836–4845

    Google Scholar 

  203. Márquez FPG, Schmid F, Collado JC (2003) Wear assessment employing remote condition monitoring: a case study. Wear 255(7–12):1209–1220

    Google Scholar 

  204. Ashrafi M, Davoudpour H (2019) A hierarchical Bayesian network to compare maintenance strategies based on cost and reliability: a case of onshore wind turbines. Int J Ind EngTheory Appl Pract 26(3):361–375

    Google Scholar 

  205. Sørensen JD (2009) Framework for risk-based planning of operation and maintenance for offshore wind turbines. Wind Energy: Int J Progr Appl Wind Power Convers Technol 12(5):493–506

    Google Scholar 

  206. Lazakis I, Kougioumtzoglou MA (2019) Assessing offshore wind turbine reliability and availability. Proc Inst Mech Eng Part M: J Eng Maritime Environ 233(1):267–282

    Google Scholar 

  207. Rastayesh S et al (2019) Risk assessment and value of action analysis for icing conditions of wind turbines close to highways. Energies 12(14):2653

    Google Scholar 

  208. Nielsen JJ, Sørensen JD (2011) On risk-based operation and maintenance of offshore wind turbine components. Reliab Eng Syst Saf 96(1):218–229

    Google Scholar 

  209. Leontaris GO, Morales-Nápoles, Wolfert ARM (2018) Probabilistic decision support for offshore wind operations: a Bayesian network approach to include the dependence of the installation activities. In: PSAM 2018—probabilistic safety assessment and management

  210. Nielsen JS, Tcherniak D, Ulriksen MD (2020) A case study on risk-based maintenance of wind turbine blades with structural health monitoring. Structure and Infrastructure Engineering

  211. Byon E (2013) Wind turbine operations and maintenance: a tractable approximation of dynamic decision making. IIE Trans (Inst Ind Eng) 45(11):1188–1201

    Google Scholar 

  212. Byon E, Ding Y (2010) Season-dependent condition-based maintenance for a wind turbine using a partially observed Markov decision process. IEEE Trans Power Syst 25(4):1823–1834

    Google Scholar 

  213. Mazidi P et al. (2016) A performance and maintenance evaluation framework for wind turbines. In: 2016 International conference on probabilistic methods applied to power systems, PMAPS 2016—proceedings

  214. Ambühl S, Sørensen JD (2017) Sensitivity of risk-based maintenance planning of offshore wind turbine farms. Energies 10(4):505

    Google Scholar 

  215. García Márquez FP, García-Pardo IP (2010) Principal component analysis applied to filtered signals for maintenance management. Qual Reliab Eng Int 26(6):523–527

    Google Scholar 

  216. Rezamand M et al (2020) A new hybrid fault detection method for wind turbine blades using recursive PCA and wavelet-based PDF. IEEE Sens J 20(4):2023–2033

    Google Scholar 

  217. De Paiva DF, Santos N, Bortoni EC, Yamachita RA (2019) Decision making on generator for wind turbines using the AHP methodology. In: 2019 IEEE Milan PowerTech

  218. Lee HC, Chang CT (2018) Comparative analysis of MCDM methods for ranking renewable energy sources in Taiwan. Renew Sustain Energy Rev 92:883–896

    Google Scholar 

  219. Hwangbo H, Johnson A, Ding Y (2017) A production economics analysis for quantifying the efficiency of wind turbines. Wind Energy 20(9):1501–1513

    Google Scholar 

  220. Yang Z, Lin, M (2019) Intelligent fan system based on big data and artificial intelligence. In: 2019 6th International conference on systems and informatics, ICSAI 2019

  221. Dawid R, McMillan D, Revie M (2018) Decision support tool for offshorewind farm vessel routing under uncertainty. Energies 11(9):2190

    Google Scholar 

  222. Rafik K et al. (2013) Enhancing the performance of wind turbine's O&M by employing multi-agent-systems. In: European wind energy conference and exhibition, EWEC 2013

  223. Zhang F et al (2019) An operating condition recognition method of wind turbine based on SCADA parameter relations. Jixie Gongcheng Xuebao/J Mech Eng 55(4):1–9

    Google Scholar 

  224. García Márquez FP et al (2020) Reliability dynamic analysis by fault trees and binary decision diagrams. Information 11(6):324

    Google Scholar 

  225. 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(9):1753

    Google Scholar 

  226. Mustafa AM, Barabadi A, Markeset T (2019) Risk assessment of wind farm development in ice proven area. In: Proceedings of the international conference on port and ocean engineering under arctic conditions, POAC

  227. Bharadwaj UR, Speck JB, Ablitt CJ (2007) A practical approach to risk based assessment and maintenance optimisation of offshore wind farms. In: ASME 2007 26th international conference on offshore mechanics and arctic engineering. American society of mechanical engineers digital collection.

  228. Nachimuthu S, Zuo MJ, Ding Y (2019) A decision-making model for corrective maintenance of offshore wind turbines considering uncertainties. Energies 12(8):1408

    Google Scholar 

  229. Bharadwaj UR, Silberschmidt VV, Wintle JB (2012) A risk based approach to asset integrity management. J Qual Maint Eng 18:417–431

    Google Scholar 

  230. Shafiee M, Kolios A (2015) A multi-criteria decision model to mitigate the operational risks of offshore wind infrastructures. In: safety and reliability: methodology and applications—proceedings of the European safety and reliability conference, ESREL 2014

  231. Oelker S et al. (2019) Simulation-based economic evaluation of the operational phase of offshore wind turbines. In: Proceedings of the international offshore and polar engineering conference

  232. Papatzimos AK, Dawood T, Thies PR (2018) Operational data to maintenance optimization: closing the loop in offshore wind O&M. In: ASME 2018 1st international offshore wind technical conference, IOWTC 2018

  233. Kennedy K, Scully T, Mastaglio T (2015) Using simulation to support management of offshore renewable energy facilities. In: 3rd International workshop on simulation for energy, sustainable development and environment, SESDE 2015

  234. Jin W et al. (2017) CPS-enabled worry-free industrial applications. In: 2017 Prognostics and system health management conference, PHM-Harbin 2017—proceedings

  235. Tamilselvan P, Wang Y, Wang P (2012) Prognosis informed design framework for operation and maintenance of wind turbines. In: Proceedings of the ASME design engineering technical conference

  236. Tamilselvan P, Wang Y, Wang P (2012) Optimization of wind turbines operation and maintenance using failure prognosis. In: 2012 ieee conference on prognostics and health management

  237. Tamilselvan P et al. (2012) Prognosis informed stochastic decision making framework for operation and maintenance of wind turbines. In: ASME/ISCIE 2012 international symposium on flexible automation. American Society of Mechanical Engineers Digital Collection

  238. Li M et al (2019) A data-driven residual-based method for fault diagnosis and isolation in wind turbines. IEEE Trans Sustain Energy 10(2):895–904

    Google Scholar 

  239. Márquez FPG et al (2017) Optimal dynamic analysis of electrical/electronic components in wind turbines. Energies 10(8):1111

    Google Scholar 

  240. Richmond M et al (2018) Multi-criteria decision analysis for benchmarking human-free lifting solutions in the offshorewind energy environment. Energies 11(5):1175

    Google Scholar 

  241. Sobral J, Kang JC, Soares CG (2019) Weighting the influencing factors on offshore wind farms availability. In: Advances in renewable energies offshore—proceedings of the 3rd international conference on renewable energies offshore, RENEW 2018

  242. Meena NK et al. (2017) Wind power generation planning by utilizing the reactive power capabilities of generators. In: 2017 Asian conference on energy, power and transportation electrification, ACEPT 2017

  243. Ali L, Shahnia F (2017) Determination of an economically-suitable and sustainable standalone power system for an off-grid town in Western Australia. Renew Energy 106:243–254

    Google Scholar 

  244. Fathi Aghdam F, Liao H (2014) Prognostics-based two-operator competition in proactive replacement and service parts procurement. Eng Econ 59(4):282–306

    Google Scholar 

  245. Yürüşen NY et al (2020) Automated wind turbine maintenance scheduling. Reliab Eng Syst Saf 200:106965

    Google Scholar 

  246. Mostajeran F, Joschko P, Göbel J (2017) Heuristic optimisation and simulation as decision support for operation and maintenance of offshore wind farms. In: 19th international conference on harbor, maritime and multimodal logistics modeling and simulation, HMS 2017, held at the international multidisciplinary modeling and simulation multiconference, I3M 2017

  247. Zhu W, Castanier B, Bettayeb B (2019) A dynamic programming-based maintenance model of offshore wind turbine considering logistic delay and weather condition. Reliab Eng Syst Saf 190:106512

    Google Scholar 

  248. Erguido A et al (2017) A dynamic opportunistic maintenance model to maximize energy-based availability while reducing the life cycle cost of wind farms. Renew Energy 114:843–856

    Google Scholar 

  249. Nurpeissova HGB, Panyukova DV (2014) Autonomous power supply systems based on wind power plants for agroformations. In: International multidisciplinary scientific geoconference surveying geology and mining ecology management, SGEM

  250. Quintáns CV, Pestana G (2019) Design of a situation-awareness solution to support infrastructure inspections. Advances in intelligent systems and computing. Springer, Cham, pp 394–404

    Google Scholar 

  251. Jiménez AA, Gómez Muñoz CQ, García Márquez FP (2018) Machine learning for wind turbine blades maintenance management. Energies 11(1):13

    Google Scholar 

  252. Yildirim M, Gebraeel NZ, Sun XA (2017) Integrated predictive analytics and optimization for opportunistic maintenance and operations in wind farms. IEEE Trans Power Syst 32(6):4319–4328

    Google Scholar 

  253. Wang CC et al (2014) Research on preventive maintenance cycle decision of wind turbine. Adv Mater Res 860–868:383–388

    Google Scholar 

  254. McMillan D et al. (2014) Asset modelling challenges in the wind energy sector. In: CIGRE Session 45—45th international conference on large high voltage electric systems 2014

  255. Rinaldi G et al. (2016) A novel reliability-based simulation tool for offshore renewable technologies. In: Progress in renewable energies offshore—proceedings of 2nd international conference on renewable energies offshore, RENEW 2016

  256. Beccali M et al. (2015) Assessment of the technical and economic potential of offshore wind energy via a GIS application: a case study for the Sicily Region according to Italian laws and incentive frameworks. In: 2015 International conference on renewable energy research and applications, ICRERA 2015

  257. Khalid MY et al (2021) Developments in chemical treatments, manufacturing techniques and potential applications of natural-fibers-based biodegradable. Composites 11(3):293

    Google Scholar 

  258. Aghajani D et al (2019) Using Web-GIS technology as a smart tool for resiliency management to monitor wind farms performances (Ganjeh site, Iran). Int J Environ Sci Technol 16(9):5011–5022

    Google Scholar 

  259. Tautz-Weinert J et al (2019) Sensitivity study of a wind farm maintenance decision—a performance and revenue analysis. Renew Energy 132:93–105

    Google Scholar 

  260. Bettayeb B, Castanier B, Zhu W (2017) An adaptive condition-based maintenance planning approach: an offshore wind turbine case study. In safety and reliability–theory and applications—proceedings of the 27th European safety and reliability conference, ESREL 2017

  261. Marugán AP, García Márquez FP, Pinar Pérez JM (2016) Optimal maintenance management of offshore wind farms. Energies 9(1):46

    Google Scholar 

  262. Khalid MY et al (2021) Characterization of failure strain in fiber reinforced composites: under on-axis and off-axis loading. Curr Comput-Aided Drug Des 11(2):216

    MathSciNet  Google Scholar 

  263. Viharos ZJ, et al. (2010) AI supported maintenance and reliability system in wind energy production. In: European wind energy conference and exhibition (EWEC), Tuesday

  264. Geiss C, Guder S (2018) Reliability-centered asset management of wind turbines—a holistic approach for a sustainable and cost-optimal maintenance strategy. In: 2017 2nd international conference on system reliability and safety, ICSRS 2017

  265. Nabati EG, Thoben KD (2017) Data driven decision making in planning the maintenance activities of off-shore wind energy. Procedia CIRP 59:160–165

    Google Scholar 

  266. Gintautas T, Sørensen JD, Vatne SR (2016) Towards a Risk-based Decision Support for Offshore Wind Turbine Installation and Operation & Maintenance. In: Energy Procedia

  267. Lei X et al. (2015) PHM based predictive maintenance option model for offshore wind farm O&M optimization. In: Proceedings of the annual conference of the prognostics and health management society, PHM

  268. Lara C et al. (2017) MILP formulation and Nested decomposition algorithm for electric powerinfrastructure planning. In: Computing and systems technology division 2017—core programming area at the 2017 AIChE annual meeting

  269. Shafiee M (2016). A decision analysis model for maintenance policy selection in multi-unit systems. In: Proceedings—22nd ISSAT international conference on reliability and quality in design

  270. Li X et al (2016) A decision support system for strategic maintenance planning in offshore wind farms. Renew Energy 99:784–799

    Google Scholar 

  271. Hajej Z, Rezg N, Chelbi A (2016) Optimization of power generation and maintenance for a wind turbine under stochastic climatic conditions. In: Proceedings of 2015 international conference on industrial engineering and systems management, IEEE IESM 2015

  272. Piel JH et al. (2019) Lifetime extension, repowering or decommissioning? Decision support for operators of ageing wind turbines. In: Journal of physics: conference series

  273. Scheu M et al. (2016) Challenges in using risk assessments in offshore wind asset management In: Proceedings of the international offshore and polar engineering conference

  274. Animah I, Shafiee M (2018) A framework for assessment of technological readiness level (TRL) and commercial readiness index (CRI) of asset end-of-life strategies. in safety and reliabilit—safe societies in a changing world. In: Proceedings of the 28th international european safety and reliability conference, ESREL 2018

  275. Shen J et al (2019) Geographic information systems visualization of wind farm operational data to inform maintenance and planning discussions. Wind Eng 45:3–10

    Google Scholar 

  276. Kim CK, Jang S, Kim TY (2018) Site selection for offshore wind farms in the southwest coast of South Korea. Renew Energy 120:151–162

    Google Scholar 

  277. Heinbach K et al (2014) Renewable energies and their impact on local value added and employment. Energy, Sustain Soc. https://doi.org/10.1049/iet-rpg.2018.5909

    Article  Google Scholar 

  278. Rezamand M et al (2019) Aggregate reliability analysis of wind turbine generators. IET Renew Power Gener 13(11):1902–1910

    Google Scholar 

  279. Ghamlouch H, Fouladirad M, Grall A (2019) The use of real option in condition-based maintenance scheduling for wind turbines with production and deterioration uncertainties. Reliab Eng Syst Saf 188:614–623

    Google Scholar 

Download references

Funding

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).

Author information

Authors and Affiliations

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

    Fausto Pedro García Márquez

  2. School of Engineering, University of Birmingham, Birmingham, B15 2SA, UK

    Alfredo Peinado Gonzalo

Authors
  1. Fausto Pedro García Márquez
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alfredo Peinado Gonzalo
    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

García Márquez, F.P., Peinado Gonzalo, A. A Comprehensive Review of Artificial Intelligence and Wind Energy. Arch Computat Methods Eng 29, 2935–2958 (2022). https://doi.org/10.1007/s11831-021-09678-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11831-021-09678-4

Search

Navigation