Reliability assessment for wind turbines considering the influence of wind speed using bayesian network

• Autorzy: Su Ch. , Fu Y.

Warianty tytułu

PL

Ocena niezawodności turbin wiatrowych za pomocą sieci Bayesa z uwzględnieniem wpływu prędkości wiatru

• Języki publikacji: EN

Abstrakty

EN

The reliability of wind turbine is of great importance for the availability and economical efficiency of wind power system. In this article, a reliability model for wind turbine is built with Bayesian network (BN), in which the influence of wind speed is considered. Causal logic method (CLM) is presented for qualitative modeling, which combines the merits of fault tree in handling technical aspects and the strength of BN in dealing with environmental factors and uncertainty. A novel adjustment method based on expectation is proposed for quantitative calculation, by which historical data and expert judgment are integrated to describe the uncertainty in the prior probability distributions. An approximate inference algorithm combining with dynamic discretization of continuous variables is adopted to obtain the reliability index of wind turbine and its elements. A case study is given to illustrate the proposed method, and the results indicate that wind speed is an important factor for the reliability of wind turbine.

PL

Niezawodność turbiny wiatrowej ma ogromne znaczenie dla gotowości i efektywności ekonomicznej instalacji wiatrowej. W niniejszym artykule zbudowano, w oparciu o sieci Bayesa (BN), model niezawodności turbiny wiatrowej uwzględniający wpływ prędkości wiatru. Przedstawiono Metodę Logiki Przyczynowości (Causal Logic Method, CLM), służącą do modelowania jakościowego, która łączy zalety drzewa błędów w odniesieniu do aspektów technicznych z atutami BN w odniesieniu do czynników środowiskowych i niepewności. Do kalkulacji ilościowych zaproponowano nową metodę dopasowania opartą na oczekiwaniach, w której dane z eksploatacji i opinie ekspertów łącznie pozwalają opisać niepewność rozkładów prawdopodobieństwa a priori. Wskaźnik niezawodności turbiny wiatrowej i jej elementów otrzymano posługując się algorytmem wnioskowania przybliżonego w połączeniu z dynamiczną dyskretyzacją zmiennych ciągłych. Dla zilustrowania proponowanej metody przedstawiono studium przypadku, którego wyniki wskazują, że prędkość wiatru jest ważnym czynnikiem niezawodności turbiny wiatrowej.

Słowa kluczowe

EN

dependent competing risks; copula function; simulated data; degradation; random shocks; maintenance optimization

PL

zależne ryzyka konkurujące; funkcja kopuły; dane symulowane; degradacja; zaburzenia losowe; optymalizacja eksploatacji

• Wydawca: Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne
• Czasopismo: Eksploatacja i Niezawodność
• Rocznik: 2014
• Tom: Vol. 16, no. 1
• Strony: 1--8
• Opis fizyczny: Bibliogr. 30 poz.

Twórcy

autor

Su Ch.

• Department of Industrial Engineering, School of Mechanical Engineering, Southeast University, Jiangning district, Nanjing 211189, Jiangsu Province, China

autor

Fu Y.

• Department of Industrial Engineering, School of Mechanical Engineering, Southeast University, Jiangning district, Nanjing 211189, Jiangsu Province, China

Bibliografia

• 1. Arabian-Hoseynabadi H, Tavner PJ, Oraee H. Reliability comparison of direct-drive and geared-drive wind turbine concepts. Wind energy 2010; 13(1): 62–73.
• 2. Bai YS, Jia XS, Cheng ZH. Group optimization models for multi-component system compound maintenance tasks. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2011; 49(1): 42–47.
• 3. Bobbio A, Portinale L, Minichino M, Ciancamerla E. Improving the analysis of dependable systems by mapping fault trees into Bayesian networks. Reliability Engineering & System Safety 2001; 71(3): 249–260.
• 4. Boudali H, Dugan JB. A continuous-time Bayesian Network reliability modeling, and analysis framework. IEEE Transactions on Reliability 2006; 55(1): 86–97.
• 5. Boudali H, Dugan JB. A discrete-time Bayesian network reliability modeling and analysis framework. Reliability Engineering & System Safety. 2005; 87(3): 337–349.
• 6. Boudali H, Dugan JB. A new Bayesian Network approach to solve dynamic fault trees. Proceedings of Reliability and Maintainability Symposium, Alexandria, Virginia, 2005.
• 7. Ding FF, Tian ZG. Opportunistic maintenance for wind farms considering multi-level imperfect maintenance thresholds. Renewable Energy 2012; 45:175–182.
• 8. Fazio AR Di, Russo M. Wind farm modelling for reliability assessment. IET Renewable Power Generation 2008; 2 (4): 239–248.
• 9. Gao Q, Liu C, Xie B, Cai X. Evaluation of the mainstream wind turbine concepts considering their reliabilities. IET Renewable Power Generation 2012; 6(5): 348–357.
• 10. Guo HT, Watson S, Tavner P, Xiang JP. Reliability analysis for wind turbines with incomplete failure data collected from after the date of initial installation. Reliability Engineering & System Safety 2009; 94(6): 1057–1063.
• 11. Guo JY, Sun YQ, Wang MY, Ding XB. System reliability synthesis of wind turbine based on computer simulation. Journal of Mechanical Engineering 2012; 48 (2): 2–8. (In Chinese)
• 12. Joshi DR, Jangamshetti SH. A novel method to estimate the O&M costs for the financial planning of the wind power projects based on wind speed – A case study. IEEE Transactions on Energy Conversion 2010; 25(2):1–7.
• 13. Kusiak A, Verma A. Analyzing bearing faults in wind turbines: A data-mining approach. Renewable Energy 2012; 48: 110–116.
• 14. Li X, Hubacek K, Siu YL. Wind power in China – Dream or reality? Energy 2012; 37 (1): 51–60.
• 15. Li YF, Huang HZ, Xiao NC, Li HQ. A new fault tree analysis method: fuzzy dynamic fault tree analysis. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2012; 14(3): 208–214.
• 16. Manco T, Testa A. A Markovian approach to model power availability of a wind turbine. In Power Tech, Lausanne, 2007, 1256–1261.
• 17. Marquez D, Neil M, Fenton N. Solving Dynamic Fault Trees using a New Hybrid Bayesian Network Inference Algorithm. In 16th Mediterranean Conference on Control and Automation Congress Centre, Ajaccio, France, 2008, 604–609.
• 18. Nadkarni S, Shenoy PP. A Bayesian network approach to making inferences in causal maps. European Journal of Operational Research 2001; 128 (3): 479–498.
• 19. Nechval KN, Nechval NA, Berzins G, Purgailis M. Planning inspections in service of fatigue-sensitive aircraft structure components for initial crack detection. Eksploatacja i Niezawodnosc- Maintenance and Reliability 2007; 3(35): 76–80.
• 20. Negra NB, Holmstrom O, Bak-Jensen B, Sorensen P. Aspects of relevance in offshore wind farm reliability assessment. IEEE Transactions on Energy Conversion 2007; 22 (1): 159–166.
• 21. Neil M, Tailor M, Marquez D. Inference in hybrid Bayesian networks using dynamic discretization. Statistics and Computing 2007; 17(3): 219–233.
• 22. Røed W, Mosleh A, Vinnemc JE, Aven T. On the use of the hybrid causal logic method in offshore risk analysis. Reliability Engineering & System Safety 2009; 94 (2): 445–455.
• 23. Sørensen J D. Framework for risk-based planning of operation and maintenance for offshore wind turbines. Wind Energy 2009; 12(5): 493–506.
• 24. Spinato F, Tavner PJ, Van Bussel GJW, Koutoulakos E. Reliability of wind turbine subassemblies. IET Renewable Power Generation. 2008; 3(4): 387–401.
• 25. Su C, Jin Q, Fu YQ. Correlation analysis for wind speed and failure rate of wind turbines using time series approach. Journal of Renewable and Sustainable Energy 2012; 4 (3): 1–13.
• 26. Tavner PJ, Edwards C, Brinkman A, Spinato F. Influence of Wind Speed on Wind Turbine Reliability. Wind Engineering 2006; 30(1): 55–72.
• 27. Tavner PJ, Xiang J, Spinato F. Reliability Analysis for Wind Turbines. Wind Energy 2007; 10(1): 1–18.
• 28. Weber P, Medina-Oliva G, Simon C, Iung B. Overview on Bayesian networks applications for dependability, risk analysis and maintenance areas. Engineering Applications of Artificial Intelligence 2012; 25(4): 671–682.
• 29. Weisser D. A wind energy analysis of Grenada: an estimation using the ‘Weibull’ density function. Renewable energy 2003; 28(11): 1803–1812.
• 30. Xiea KG, Billinton R. Considering wind speed correlation of WECS in reliability evaluation using the time-shifting technique. Electric Power Systems Research 2009; 79 (4): 687–693.