Wind turbines are made of several electrical and mechanical components that are subject to several types of failures. Thus, the proper assessment of different failure modes and the selection of proper corrective actions will ensure the continuous and reliable functionality of wind turbines. In this research, the authors introduce a combined hybrid “Failure Modes and Effects Analysis” and “Analytic Hierarchy Process” (FMEA-AHP) method. This hybrid approach will be used to identify and analyze failure risk factors of wind turbine components. Firstly, FMEA is used to assess the impact of each component failure. Secondly, AHP is used to prioritize the severity of failures and the best measures aiming to reduce the risk of individual failures. The proposed measures, in this article, will enhance reliability and reduce operational costs of power generation using a wind turbine.
AbabnehMKakishWMoharebOA, et al. (2009) Investigation of wind energy in jordan. In: International conference and exhibition on green energy & sustainability for arid regions & Mediterranean countries, ICEGES 2009, Le Royal Hotel Amman, Jordan, 15–17 June 2009.
2.
AlkhalidiAAl-MousaYZubeidyM (2017) Wind turbine coupled with perpetual motion. In: Proceedings of 2017 international renewable and sustainable energy conference, IRSEC 2017, 4–7 December 2017, Tangier, Morocco. New York: Institute of Electrical and Electronics Engineers Inc.
3.
AlkhalidiAFraihatAMasaeedK (2019) Micro-smart wind collecting technology: Micro power generation. In: Proceedings of 2019 7th international renewable and sustainable energy conference, IRSEC 2019, Agadir, Morocco, 27–30 November. New York: Institute of Electrical and Electronics Engineers Inc.
4.
AlkhalidiATahatSSmadiM, et al. (2020) Risk assessment using the analytic hierarchy process while planning and prior to constructing wind projects in Jordan. Wind Engineering44(3): 282–293.
5.
AmanoRSMalloyRJAlkhalidiA (2010) Improvement of wind turbine blade aerodaynamics. In: 2010 1st international nuclear and renewable energy conference, INREC’10, Amman, Jordan, 21–24 March 2010.
6.
Arabian-HoseynabadiHOraeeHTavnerPJ (2010) Failure modes and effects analysis (FMEA) for wind turbines. International Journal of Electrical Power & Energy Systems32(7): 817–824.
7.
BiRZhouCHepburnDM (2017) Detection and classification of faults in pitch-regulated wind turbine generators using normal behaviour models based on performance curves. Renewable Energy105: 674–688.
8.
BlaabjergFMaK. (2013) Future on power electronics for wind turbine systems. IEEE Journal of Emerging and Selected Topics in Power Electronics1(3): 139–152.
9.
BrouwerSRAl-JibouriSHSCárdenasIC, et al. (2018) Towards analysing risks to public safety from wind turbines. Reliability Engineering & System Safety180: 77–87.
10.
CarterKJRitcheyNPCastroF, et al. (1999) Analysis of three decision-making methods: A breast cancer patient as a model. Medical Decision Making19(1): 49–57.
11.
CastroFCaccamoLPCarterKJ, et al. (1996) Sequential test selection in the analysis of abdominal pain. Medical Decision Making16(2): 178–183.
12.
ChanDMoJ (2017) Life cycle reliability and maintenance analyses of wind turbines. Energy Procedia110: 328–333.
13.
EmrouznejadAMarraM (2017) The state of the art development of AHP (1979–2017): A literature review with a social network analysis. International Journal of Production Research55(22): 6653–6675.
14.
HübnerPSchlosserHPuschG, et al. (2007) Load history effects in ductile cast iron for wind turbine components. International Journal of Fatigue29(9–11): 1788–1796.
JavalgiRGArmacostRLHosseiniJC (1989) Using the analytic hierarchy process for bank management: Analysis of consumer bank selection decisions. Journal of Business Research19(1): 33–49.
17.
JohnsonRASrinivasanVBolsterPJ (1990) Sovereign debt ratings: A judgmental model based on the Analytic Hierarchy Process. Journal of International Business Studies21(1): 95–117.
18.
KangJSunLSunH, et al. (2017) Risk assessment of floating offshore wind turbine based on correlation-FMEA. Ocean Engineering129(154): 382–388.
19.
KaylaniHAlmuhtadyAAtiehAM (2016) Novel approach to enhance the performance of production systems using lean tools. Jordan Journal of Mechanical & Industrial Engineering10(3): 215–229.
20.
LeimeisterMKoliosA (2018) A review of reliability-based methods for risk analysis and their application in the offshore wind industry. Renewable and Sustainable Energy Reviews91: 1065–1076.
21.
LinYTuLLiuH, et al. (2016) Fault analysis of wind turbines in China. Renewable and Sustainable Energy Reviews55: 482–490.
22.
LiuXLiuZLiangZ, et al. (2019a) PSO-BP neural network-based strain prediction of wind turbine blades. Materials (Basel)12(12): 1889.
23.
LiuZZhangL (2020) A review of failure modes, condition monitoring and fault diagnosis methods for large-scale wind turbine bearings. Measurement149: 107002.
24.
LiuZLiuXWangK, et al. (2019b) GA-BP neural network-based strain prediction in full-scale static testing of wind turbine blades. Energies12(6): 1–15.
25.
MadiEPopeKHuangW, et al. (2019) A review of integrating ice detection and mitigation for wind turbine blades. Renewable and Sustainable Energy Reviews103: 269–281.
26.
MengDHuZWuP, et al. (2020) Reliability-based optimisation for offshore structures using saddlepoint approximation. Proceedings of the Institution of Civil Engineers - Maritime Engineering173(2): 33–42.
27.
MerizaldeYHernández-CallejoLDuque-PerezO, et al. (2019) Maintenance models applied to wind turbines. A comprehensive overview. Energies12(2): 225.
28.
RamanathanRGaneshLS (1994) A multi-objective evaluation of decentralized electricity generation options available to urban households. Energy Conversion and Management35(8): 661–670.
29.
SaatyTLRamanujamV (1983) An objective approach to faculty promotion and tenure by the analytic hierarchy process. Research in Higher Education18(3): 311–331.
30.
ShipurkarUMaKPolinderH, et al. (2015) A review of failure mechanisms in wind turbine generator systems. In: 2015 17th European conference on power electronics and applications (EPE’15 ECCE-Europe), Geneva, Switzerland, 8–10 September 2015. New York: IEEE.
31.
ShohagMASHammelECOlawaleDO, et al. (2017) Damage mitigation techniques in wind turbine blades: A review. Wind Engineering41(3): 185–210.
32.
SpreaficoCRussoDRizziC (2017) A state-of-the-art review of FMEA/FMECA including patents. Computer Science Review25: 19–28.
33.
SrinivasanVBolsterPJ (1990) An industrial bond rating model based on the Analytic Hierarchy Process. European Journal of Operational Research48(1): 105–119.
34.
TengWDingXChengH, et al. (2019) Compound faults diagnosis and analysis for a wind turbine gearbox via a novel vibration model and empirical wavelet transform. Renewable Energy136: 393–402.
35.
WangLZhangZLongH, et al. (2017) Wind turbine gearbox failure identification with deep neural networks. IEEE Transactions on Industrial Informatics13(3): 1360–1368.
WindYSaatyTL (1980) Marketing applications of the analytic hierarchy process. Management Science26(7): 641–658.
38.
ZhouPYinPT (2019) An opportunistic condition-based maintenance strategy for offshore wind farm based on predictive analytics. Renewable and Sustainable Energy Reviews109: 1–9.
