Advertisement

Reliability assessment of solar photovoltaic systems based on fuzzy fault tree analysis

  • Sana Perveen
  • Haroon Ashfaq
  • Mohammad Asjad
Original Research
  • 4 Downloads

Abstract

Solar photovoltaic (SPV) systems are widely used as a renewable energy source to produce electricity to endusers. SPV system convert free and unlimited sunlight into electricity without carbon dioxide emission or any other air pollutants. The stochastic nature of SPV system may be responsible for degradation of performance in terms of availability, production, etc. Therefore, the assessment of the performance measures like reliability and quality of its components is vital concern that may open the new vista of research. There are various reliability assessment techniques used in conventional (thermal power plant, hydropower plant, nuclear power plant, etc.) and non-conventional power system (SPV power plant, wind power plant, etc.) like Fault Tree Analysis (FTA), Failure Mode and Effect Analysis (FMEA), etc. FTA is one of the powerful reliability assessment techniques, which gives modes of the failure and its consequences, that are demonstrated on SPV systems. FTA can further be broadly classified into two: qualitative and quantitative analysis. Qualitative FTA can be solved by determining a minimal cut set (MCS) and quantitative FTA can be solved by calculating the failure probability of each the basic event. In conventional FTA failure probability of each event must be known, to assess for quantitative FTA. However, to quantify probabilistic failure, data of all basic events seem to be inadequate. To overcome this problem, conventional FTA may be integrated with fuzzy linguistic scale method. In this research work, failure possibilities in terms of a linguistic variable of unknown failure data for all basic events, which lead the Top Events for SPV systems are assigned by the experts from academics, research, industries and practicing engineers. For Aggregated fuzzy number based on expert’s opinion, a consistency agreements method is used. Fussell-Vesely (F-V) importance measures is also implemented to rank the basic events and MCS to apportion the most critical event.

Keywords

SPV system Fuzzy Fault Tree Analysis (FFTA) Minimal cut set (MCS) Fussell–Vesely (F–V) importance measure 

References

  1. Ali AHH, Matsushita Y, Ookawara S (2011) Photovoltaic module thermal regulation:effect of the cells arrangement configurations on the performance. Int J Therm Environ Eng 2:41–47CrossRefGoogle Scholar
  2. Asjad M, Kulkarni MS, Gandhi OP (2013) A life cycle cost based approach of O&M support for mechanical systems. Int J Syst Assur Eng Manag 4(2):159–172CrossRefGoogle Scholar
  3. Bryson N, Mobolurin A (1994) An approach to using the analytic hierarchy process for solving multiple criteria decision making problems. Eur J Oper Res 76(3):40–454CrossRefGoogle Scholar
  4. Čepin M (2011) Assessment of power system reliability: methods and applications. Springer, New YorkCrossRefGoogle Scholar
  5. Colli Alessandra (2015) Failure mode and effect analysis for photovoltaic systems renewable and sustainable. Energy Rev 50:804–809Google Scholar
  6. Dong YH, Yu DT (2005) Estimation of failure probability of oil and gas transmission pipelines by fuzzy fault tree analysis. J Loss Prev Process Ind 18(2):83–88CrossRefGoogle Scholar
  7. Eikelboom JA, Jansen MJ (2000) Characterisation of PV modules of new generations. Results of tests and simulations. https://www.ecn.nl/docs/library/report/2000/c00067.pdf
  8. Ferdous R et al (2006) Fault and event tree analyses for process systems risk analysis: uncertainty handling formulations. Risk Anal 31(1):86–107CrossRefGoogle Scholar
  9. Gupta S, Bhattacharya J (2007) Reliability analysis of a conveyor system using hybrid data. Qual Reliab Eng Int 23(7):867CrossRefGoogle Scholar
  10. Lall P (1996) Tutorial. Temperature as an input to microelectronics-reliability models. IEEE Trans 45:3–9Google Scholar
  11. Lavasani SM, Zendegani A, Celik M (2015) An extension to fuzzy fault tree analysis (FFTA) application in a petrochemical process industry. Process Safety Environ Protect 93:75–88CrossRefGoogle Scholar
  12. Lee WS, Grosh DL, Tillman FA, Lie CH (1985) Fault tree analysis, methods, and applications—a review. IEEE Reliab Trans 4:194–203CrossRefGoogle Scholar
  13. Liang G, Wang JM (1993) Fuzzy fault tree analysis using failure possibility. Microelectr Reliab 33(4):583–597CrossRefGoogle Scholar
  14. Mahmood YA, Ahmadi A, Verma AK, Srividya A, Kumar U (2013) Fuzzy fault tree analysis: a review of concept and application. Int J Syst Assur Eng Manag 4(1):19–32CrossRefGoogle Scholar
  15. Markowski AS, Mannan MS (2008) Fuzzy risk matrix. J Hazard Mater 159(1):152–157CrossRefGoogle Scholar
  16. Miri Lavasani MR, Wang J, Yang Z, Finlay J (2011) Application of fuzzy fault tree analysis on oil and gas offshore pipelines. Int J Mater Sci Eng 1(1):29–42Google Scholar
  17. Modarres M (2006) Risk analysis in engineering: techniques, tools, and trends. Taylor & Francis, RoutledgezbMATHGoogle Scholar
  18. Onisawa T (1988) An approach to human reliability in man-machine systems using error possibility. Fuzzy Sets Syst 27(2):87–103MathSciNetCrossRefGoogle Scholar
  19. Onisawa T (1990) An application of fuzzy concepts to the modeling of reliability analysis. Fuzzy Sets Syst 37(3):267–286MathSciNetCrossRefGoogle Scholar
  20. Pan N, Wang H (2007) Assessing failure of bridge construction using fuzzy fault tree analysis. In: 4th International conference on fuzzy system and knowledge discovery 1(2):96–100Google Scholar
  21. Pan H, Yun YW (1997) Fault tree analysis with fuzzy gates. Comput Ind Eng 33(3–4):569–572CrossRefGoogle Scholar
  22. Petrone G, Spagnuolo G, Teodorescu R, Veerachary M, Vitelli M (2008) Reliability issues in photovoltaic power processing systems. IEEE Trans Ind Electr 55(7):2569–2580CrossRefGoogle Scholar
  23. Pillay A, Wang J (2003) Modified failure mode and effects analysis using approximate reasoning. Reliab Eng Syst Safety 79:69–85CrossRefGoogle Scholar
  24. Prugh RW (1992) Computer-aided HAZOP and fault tree analysis. J Loss Prev Process Ind 5(1):3–9CrossRefGoogle Scholar
  25. Purba JH (2014a) Fuzzy probability on reliability study of nuclear power plant probabilistic safety assessment: a review. Prog Nucl Energy 76:73–80CrossRefGoogle Scholar
  26. Purba JH (2014b) A fuzzy reliability assessment of basic events of fault trees through qualitative data processing. Fuzzy Sets Syst 243:50–69MathSciNetCrossRefGoogle Scholar
  27. Purba JH, Tjahyani DTS, Ekariansyah AS, Tjahjono H (2015) Fuzzy probability based fault tree analysis to propagate and quantify epistemic uncertainty. Ann Nucl Energy 85:1189–1199CrossRefGoogle Scholar
  28. Purba JH, Tjahyani DTS, Widodo S, Tjahjono H (2017) α-Cut method based importance measure for criticality analysis in fuzzy probability—based fault tree analysis. Ann Nucl Energy 110:234–243CrossRefGoogle Scholar
  29. Ross JT (2004) Fuzzy logic with engineering applications. Wiley, West SussexzbMATHGoogle Scholar
  30. Sadiq R, Saint-Martin E, Kleiner Y (2008) Predicting risk of water quality failures in distribution networks under uncertainties using fault-tree analysis. Urban Water J 5(4):287–304CrossRefGoogle Scholar
  31. Suresh PV, Babar AK, Raj VV (1996) Uncertainty in fault tree analysis: a fuzzy approach. Fuzzy Sets Syst 83(2):135–141CrossRefGoogle Scholar
  32. Tanaka H, Fan LT, Lai FS, Toguchi K (1983) Fault-tree analysis by fuzzy probability. IEEE Trans Reliab 32(5):150–163zbMATHGoogle Scholar
  33. Wang LXA (1997) Course in fuzzy system and control. Prentice Hall PTR, Upper Saddle RiverGoogle Scholar
  34. Wang YH (1999) Safety system engineering. Tianjin University Press, TianjinGoogle Scholar
  35. Wang D, Zhang P, Chen L (2013) Fuzzy fault tree analysis for fire and explosion of crude oil tanks. J Loss Prev Process Ind 26(6):1390–1398CrossRefGoogle Scholar
  36. Wei CP, Qiu WH, Wang XZ (2001) A new approach to group decision making under fuzzy preference. Syst Eng Theory Pract 7:81–86Google Scholar
  37. Wolkenhauer O (2001) Fuzzy mathematics. In: Data engineering: fuzzy mathematics in systems theory and data analysis. John, USA, pp 197–212Google Scholar
  38. Yazdi M, Nikfar F, Nasrabadi M (2017) Failure probability analysis by employing fuzzy fault tree analysis. Int J Syst Assur Eng Manag 8(2):S1177–S1193.  https://doi.org/10.1007/s13198-017-0583-y CrossRefGoogle Scholar
  39. Zadeh LA (1976) Fuzzy sets. Inf Control 8(3):338–353CrossRefGoogle Scholar
  40. Zhang P, Li W, Li S, Wang Y, Xiao W (2013) Reliability assessment of photovoltaic power systems: review of current status and future perspectives. Appl Energy 104:822–833CrossRefGoogle Scholar
  41. Zini Gabriele, Mangeant Christophe, Merten Jens (2011) Reliability of large-scale grid-connected photovoltaic systems. Renew Energy 36:2334–2340CrossRefGoogle Scholar

Copyright information

© Society for Reliability and Safety (SRESA) 2018

Authors and Affiliations

  1. 1.Department of Electrical EngineeringJamia Millia IslamiaNew DelhiIndia
  2. 2.Department of Mechanical EngineeringJamia Millia IslamiaNew DelhiIndia

Personalised recommendations