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

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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.

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References

  • 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 2:41–47

    Article  Google Scholar 

  • 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):159–172

    Article  Google Scholar 

  • 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):40–454

    Article  MATH  Google Scholar 

  • Čepin M (2011) Assessment of power system reliability: methods and applications. Springer, New York

    Google Scholar 

  • Colli Alessandra (2015) Failure mode and effect analysis for photovoltaic systems renewable and sustainable. Energy Rev 50:804–809

    Google Scholar 

  • 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):83–88

    Article  Google Scholar 

  • 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

  • Ferdous R et al (2006) Fault and event tree analyses for process systems risk analysis: uncertainty handling formulations. Risk Anal 31(1):86–107

    Article  Google Scholar 

  • Gupta S, Bhattacharya J (2007) Reliability analysis of a conveyor system using hybrid data. Qual Reliab Eng Int 23(7):867

    Article  Google Scholar 

  • Lall P (1996) Tutorial. Temperature as an input to microelectronics-reliability models. IEEE Trans 45:3–9

    Google Scholar 

  • 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 93:75–88

    Article  Google Scholar 

  • Lee WS, Grosh DL, Tillman FA, Lie CH (1985) Fault tree analysis, methods, and applications—a review. IEEE Reliab Trans 4:194–203

    Article  MATH  Google Scholar 

  • Liang G, Wang JM (1993) Fuzzy fault tree analysis using failure possibility. Microelectr Reliab 33(4):583–597

    Article  Google Scholar 

  • 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):19–32

    Article  Google Scholar 

  • Markowski AS, Mannan MS (2008) Fuzzy risk matrix. J Hazard Mater 159(1):152–157

    Article  Google Scholar 

  • 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):29–42

    Google Scholar 

  • Modarres M (2006) Risk analysis in engineering: techniques, tools, and trends. Taylor & Francis, Routledge

    MATH  Google Scholar 

  • Onisawa T (1988) An approach to human reliability in man-machine systems using error possibility. Fuzzy Sets Syst 27(2):87–103

    Article  MathSciNet  MATH  Google Scholar 

  • Onisawa T (1990) An application of fuzzy concepts to the modeling of reliability analysis. Fuzzy Sets Syst 37(3):267–286

    Article  MathSciNet  Google Scholar 

  • 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):96–100

  • Pan H, Yun YW (1997) Fault tree analysis with fuzzy gates. Comput Ind Eng 33(3–4):569–572

    Article  Google Scholar 

  • Petrone G, Spagnuolo G, Teodorescu R, Veerachary M, Vitelli M (2008) Reliability issues in photovoltaic power processing systems. IEEE Trans Ind Electr 55(7):2569–2580

    Article  Google Scholar 

  • Pillay A, Wang J (2003) Modified failure mode and effects analysis using approximate reasoning. Reliab Eng Syst Safety 79:69–85

    Article  Google Scholar 

  • Prugh RW (1992) Computer-aided HAZOP and fault tree analysis. J Loss Prev Process Ind 5(1):3–9

    Article  Google Scholar 

  • Purba JH (2014a) Fuzzy probability on reliability study of nuclear power plant probabilistic safety assessment: a review. Prog Nucl Energy 76:73–80

    Article  Google Scholar 

  • Purba JH (2014b) A fuzzy reliability assessment of basic events of fault trees through qualitative data processing. Fuzzy Sets Syst 243:50–69

    Article  MathSciNet  MATH  Google Scholar 

  • Purba JH, Tjahyani DTS, Ekariansyah AS, Tjahjono H (2015) Fuzzy probability based fault tree analysis to propagate and quantify epistemic uncertainty. Ann Nucl Energy 85:1189–1199

    Article  Google Scholar 

  • 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 110:234–243

    Article  Google Scholar 

  • Ross JT (2004) Fuzzy logic with engineering applications. Wiley, West Sussex

    MATH  Google Scholar 

  • 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):287–304

    Article  Google Scholar 

  • Suresh PV, Babar AK, Raj VV (1996) Uncertainty in fault tree analysis: a fuzzy approach. Fuzzy Sets Syst 83(2):135–141

    Article  Google Scholar 

  • Tanaka H, Fan LT, Lai FS, Toguchi K (1983) Fault-tree analysis by fuzzy probability. IEEE Trans Reliab 32(5):150–163

    MATH  Google Scholar 

  • Wang LXA (1997) Course in fuzzy system and control. Prentice Hall PTR, Upper Saddle River

    Google Scholar 

  • Wang YH (1999) Safety system engineering. Tianjin University Press, Tianjin

    Google Scholar 

  • 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):1390–1398

    Article  Google Scholar 

  • Wei CP, Qiu WH, Wang XZ (2001) A new approach to group decision making under fuzzy preference. Syst Eng Theory Pract 7:81–86

    Google Scholar 

  • Wolkenhauer O (2001) Fuzzy mathematics. In: Data engineering: fuzzy mathematics in systems theory and data analysis. John, USA, pp 197–212

  • 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

    Article  Google Scholar 

  • Zadeh LA (1976) Fuzzy sets. Inf Control 8(3):338–353

    Article  MATH  Google Scholar 

  • 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 104:822–833

    Article  Google Scholar 

  • Zini Gabriele, Mangeant Christophe, Merten Jens (2011) Reliability of large-scale grid-connected photovoltaic systems. Renew Energy 36:2334–2340

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Electrical Engineering, Jamia Millia Islamia, New Delhi, 110025, India

    Sana Perveen & Haroon Ashfaq

  2. Department of Mechanical Engineering, Jamia Millia Islamia, New Delhi, 110025, India

    Mohammad Asjad

Authors
  1. Sana Perveen
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  2. Haroon Ashfaq
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  3. Mohammad Asjad
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Correspondence to Sana Perveen.

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Perveen, S., Ashfaq, H. & Asjad, M. Reliability assessment of solar photovoltaic systems based on fuzzy fault tree analysis. Life Cycle Reliab Saf Eng 8, 129–139 (2019). https://doi.org/10.1007/s41872-018-0068-2

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  • DOI: https://doi.org/10.1007/s41872-018-0068-2

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