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Elektrik İletim Hatlarının Güvenirliği için Uygun Olasılık Dağılım Seçimi ve Analizi

Yıl 2021, Cilt: 9 Sayı: 1, 108 - 121, 25.03.2021
https://doi.org/10.29109/gujsc.868923

Öz

Güvenirlik, bir sistemin veya sistem bileşenlerinin belirlenen süre içerisinde, belirtilen koşullarda işlevini yerine getirme yeteneğidir. Elektrik iletim sistemlerinin en önemli kriterlerinden birisi sistem üzerindeki enerjiyi limitler dâhilinde sürekli tutabilmek ve mümkün olan en az süre kesintiye gitmektir. Güvenirlik çalışması, sistemin uygun çalışma aralığının tespit edilip gerekli müdahalenin yapılması gibi önemli avantajlar sunmaktadır. Bu çalışmada, elektrik iletim hatlarının bakım ve işletim faaliyetlerinin Güvenirlik Merkezli Bakım (Reliability Centered Maintance-RCM) ön planda tutularak yapıldığında, iletim hatlarının belirlenen limitler içerisinde en az süre kesintiye gidebileceğini gösterebilme hedefi ile iletim hattı arıza verileri kullanarak güvenirlik analizi yapılmıştır. Çalışmada, iletim hattı arıza verilerinin hangi istatistiksel dağılımdan geldiğini belirlemek için Anderson-Darling uyum iyiliği testi yapılmıştır. Uyum iyiliği ile belirlenmiş olan Log-normal dağılım ile iletim hattının güvenirlik değerlendirilmesi yapılmıştır. Güvenirlik değerlendirilmesi sonucunda iletim hattının güvenirliğinin düşük olduğu görülmüştür. İletim hatlarının bakım ve işletim faaliyetlerinin güvenirlik merkezli yapıldığında sağlayabileceği faydalar sunulmuştur.

Kaynakça

  • Yssaad, B., M. Khiat, and A. Chaker, Reliability centered maintenance optimization for power distribution systems. International Journal of Electrical Power & Energy Systems, 2014. 55: p. 108-115.
  • Geraci, A., et al., IEEE standard computer dictionary: Compilation of IEEE standard computer glossaries. 1991: IEEE Press.
  • Verma, A.K., S. Ajit, and D.R. Karanki, Reliability of Complex Systems, in Reliability and Safety Engineering. 2016, Springer. p. 123-159.
  • Nayak, P., A.N. Padmasali, and S.G. Kini. Life estimation of high power LED using distribution based reliability analysis. in 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT). 2017. IEEE.
  • Modarres, M., M.P. Kaminskiy, and V. Krivtsov, Reliability engineering and risk analysis: a practical guide. 2016: CRC press.
  • Transmission and D. Committee, IEEE Guide for Electric Power Distribution Reliability Indices. IEEE Std, 2003. 1366: p. 2003.
  • Shakhatreh, M.K., A.J. Lemonte, and G.M. Arenas, The log-normal modified Weibull distribution and its reliability implications. Reliability Engineering & System Safety, 2019. 188: p. 6-22.
  • Pham, H., Springer handbook of engineering statistics. 2006: Springer Science & Business Media.
  • Krit, M., Goodness-of-fit tests in reliability: Weibull distribution and imperfect maintenance models. 2014, Université de Grenoble.
  • Alptekin, D., Deneysel Dağılım Fonksiyonuna Dayalı Yeni Uyum İyiliği Testleri. 2018.
  • Gorjian Jolfaei, N., et al., Reliability modelling with redundancy—A case study of power generation engines in a wastewater treatment plant. Quality and Reliability Engineering International, 2020. 36(2): p. 784-796.
  • Kumar, A.R. and V. Krishnan, A Study on System Reliability in Weibull Distribution. Methods, 2017. 5(3).
  • Li, C., et al. Imprecise reliability of lifetime data based on three-parameter generalized inverse Weibull distribution. in 2019 IEEE Innovative Smart Grid Technologies-Asia (ISGT Asia). 2019. IEEE.
  • Volkanovski, A., M. Čepin, and B. Mavko, Application of the fault tree analysis for assessment of power system reliability. Reliability Engineering & System Safety, 2009. 94(6): p. 1116-1127.
  • Tur, M.R., Reliability Assessment of Distribution Power System When Considering Energy Storage Configuration Technique. IEEE Access, 2020. 8: p. 77962-77971.
  • Deng, J., Y. Liu, and H. Li. Reliability Analysis of Rotating Equipment Based on Weibull Distribution. in 2019 Chinese Control And Decision Conference (CCDC). 2019. IEEE.
  • Roshan, S., et al. Model selection among log-normal, Weibull, Gamma and generalized exponential distributions. in 2017 6th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions)(ICRITO). 2017. IEEE.
  • Gupta, R.C., M. Ghitany, and D. Al-Mutairi, Estimation of reliability from a bivariate log-normal data. Journal of Statistical Computation and Simulation, 2013. 83(6): p. 1068-1081.
  • Raqab, M.Z., S.A. Al-Awadhi, and D. Kundu, Discriminating among Weibull, log-normal, and log-logistic distributions. Communications in Statistics-Simulation and Computation, 2018. 47(5): p. 1397-1419.
  • Dey, A.K. and D. Kundu, Discriminating among the log-normal, Weibull, and generalized exponential distributions. IEEE Transactions on reliability, 2009. 58(3): p. 416-424.
  • Fiondella, L. and L. Xing, Discrete and continuous reliability models for systems with identically distributed correlated components. Reliability Engineering & System Safety, 2015. 133: p. 1-10.
  • Ross, S.M., Introduction to probability and statistics for engineers and scientists (Associated Press). 2009.
  • Montgomery, D.C., G.C. Runger, and N.F. Hubele, Engineering statistics. 2009. Hoboken, NJ: Weily. 4.
  • Dufour, J.M., et al., Simulation‐based finite sample normality tests in linear regressions. The Econometrics Journal, 1998. 1(1): p. 154-173.
  • Han, D., X. Tan, and P. Shi. Clutter distribution identification based on anderson-darling test. in 2017 3rd IEEE International Conference on Computer and Communications (ICCC). 2017. IEEE.
  • Giles, D.E., A saddlepoint approximation to the distribution function of the Anderson-Darling test statistic. Communications in Statistics-Simulation and Computation, 2001. 30(4): p. 899-905.
  • Tolikas, K. and S. Heravi, The Anderson–Darling goodness-of-fit test statistic for the three-parameter lognormal distribution. Communications in Statistics—Theory and Methods, 2008. 37(19): p. 3135-3143.
  • Jäntschi, L. and S.D. Bolboacă, Computation of probability associated with Anderson–Darling statistic. Mathematics, 2018. 6(6): p. 88.
  • Parchami, A., S.M. Taheri, and M. Mashinchi, Testing fuzzy hypotheses based on vague observations: a p-value approach. Statistical Papers, 2012. 53(2): p. 469-484.
  • Hair, J.F., et al., Multivariate Data Analysis. Always learning. 2013, Pearson Education Limited.
  • Tabachnick, B.G. and L.S. Fidell, Using Multivariate Statistics, Always Learning. 2013, Boston, MA: Pearson.

Selecting and Analyzing Appropriate Probability Distributions for Reliability of Electrical Transmission Lines

Yıl 2021, Cilt: 9 Sayı: 1, 108 - 121, 25.03.2021
https://doi.org/10.29109/gujsc.868923

Öz

Reliability is the ability of a system or system components to function within a specified time under specified conditions. One of the most important criteria of electricity transmission systems is to be able to keep the energy on the system continuously within the limits and to be interrupted for the least possible time. The reliability study offers important advantages such as determining the appropriate operating range of the system and making the necessary intervention. In this study, it is aimed to show that when the maintenance and operation activities of electricity transmission lines are carried out with Reliability Centered Maintenance (RCM) in the foreground, the transmission lines can be interrupted for the least amount of time within the specified limits. In this direction, reliability analysis has been conducted using transmission line fault data. In the study, the Anderson-Darling goodness of fit test was performed to determine from which statistical distribution the transmission line fault data came from. The reliability of the transmission line was evaluated with the Log-normal distribution, which was determined by the goodness of fit. As a result of the reliability assessment, the reliability of the transmission line was found to be low. The advantages that transmission lines can provide when maintenance and operating activities are conducted based on reliability are presented.

Kaynakça

  • Yssaad, B., M. Khiat, and A. Chaker, Reliability centered maintenance optimization for power distribution systems. International Journal of Electrical Power & Energy Systems, 2014. 55: p. 108-115.
  • Geraci, A., et al., IEEE standard computer dictionary: Compilation of IEEE standard computer glossaries. 1991: IEEE Press.
  • Verma, A.K., S. Ajit, and D.R. Karanki, Reliability of Complex Systems, in Reliability and Safety Engineering. 2016, Springer. p. 123-159.
  • Nayak, P., A.N. Padmasali, and S.G. Kini. Life estimation of high power LED using distribution based reliability analysis. in 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT). 2017. IEEE.
  • Modarres, M., M.P. Kaminskiy, and V. Krivtsov, Reliability engineering and risk analysis: a practical guide. 2016: CRC press.
  • Transmission and D. Committee, IEEE Guide for Electric Power Distribution Reliability Indices. IEEE Std, 2003. 1366: p. 2003.
  • Shakhatreh, M.K., A.J. Lemonte, and G.M. Arenas, The log-normal modified Weibull distribution and its reliability implications. Reliability Engineering & System Safety, 2019. 188: p. 6-22.
  • Pham, H., Springer handbook of engineering statistics. 2006: Springer Science & Business Media.
  • Krit, M., Goodness-of-fit tests in reliability: Weibull distribution and imperfect maintenance models. 2014, Université de Grenoble.
  • Alptekin, D., Deneysel Dağılım Fonksiyonuna Dayalı Yeni Uyum İyiliği Testleri. 2018.
  • Gorjian Jolfaei, N., et al., Reliability modelling with redundancy—A case study of power generation engines in a wastewater treatment plant. Quality and Reliability Engineering International, 2020. 36(2): p. 784-796.
  • Kumar, A.R. and V. Krishnan, A Study on System Reliability in Weibull Distribution. Methods, 2017. 5(3).
  • Li, C., et al. Imprecise reliability of lifetime data based on three-parameter generalized inverse Weibull distribution. in 2019 IEEE Innovative Smart Grid Technologies-Asia (ISGT Asia). 2019. IEEE.
  • Volkanovski, A., M. Čepin, and B. Mavko, Application of the fault tree analysis for assessment of power system reliability. Reliability Engineering & System Safety, 2009. 94(6): p. 1116-1127.
  • Tur, M.R., Reliability Assessment of Distribution Power System When Considering Energy Storage Configuration Technique. IEEE Access, 2020. 8: p. 77962-77971.
  • Deng, J., Y. Liu, and H. Li. Reliability Analysis of Rotating Equipment Based on Weibull Distribution. in 2019 Chinese Control And Decision Conference (CCDC). 2019. IEEE.
  • Roshan, S., et al. Model selection among log-normal, Weibull, Gamma and generalized exponential distributions. in 2017 6th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions)(ICRITO). 2017. IEEE.
  • Gupta, R.C., M. Ghitany, and D. Al-Mutairi, Estimation of reliability from a bivariate log-normal data. Journal of Statistical Computation and Simulation, 2013. 83(6): p. 1068-1081.
  • Raqab, M.Z., S.A. Al-Awadhi, and D. Kundu, Discriminating among Weibull, log-normal, and log-logistic distributions. Communications in Statistics-Simulation and Computation, 2018. 47(5): p. 1397-1419.
  • Dey, A.K. and D. Kundu, Discriminating among the log-normal, Weibull, and generalized exponential distributions. IEEE Transactions on reliability, 2009. 58(3): p. 416-424.
  • Fiondella, L. and L. Xing, Discrete and continuous reliability models for systems with identically distributed correlated components. Reliability Engineering & System Safety, 2015. 133: p. 1-10.
  • Ross, S.M., Introduction to probability and statistics for engineers and scientists (Associated Press). 2009.
  • Montgomery, D.C., G.C. Runger, and N.F. Hubele, Engineering statistics. 2009. Hoboken, NJ: Weily. 4.
  • Dufour, J.M., et al., Simulation‐based finite sample normality tests in linear regressions. The Econometrics Journal, 1998. 1(1): p. 154-173.
  • Han, D., X. Tan, and P. Shi. Clutter distribution identification based on anderson-darling test. in 2017 3rd IEEE International Conference on Computer and Communications (ICCC). 2017. IEEE.
  • Giles, D.E., A saddlepoint approximation to the distribution function of the Anderson-Darling test statistic. Communications in Statistics-Simulation and Computation, 2001. 30(4): p. 899-905.
  • Tolikas, K. and S. Heravi, The Anderson–Darling goodness-of-fit test statistic for the three-parameter lognormal distribution. Communications in Statistics—Theory and Methods, 2008. 37(19): p. 3135-3143.
  • Jäntschi, L. and S.D. Bolboacă, Computation of probability associated with Anderson–Darling statistic. Mathematics, 2018. 6(6): p. 88.
  • Parchami, A., S.M. Taheri, and M. Mashinchi, Testing fuzzy hypotheses based on vague observations: a p-value approach. Statistical Papers, 2012. 53(2): p. 469-484.
  • Hair, J.F., et al., Multivariate Data Analysis. Always learning. 2013, Pearson Education Limited.
  • Tabachnick, B.G. and L.S. Fidell, Using Multivariate Statistics, Always Learning. 2013, Boston, MA: Pearson.
Toplam 31 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Tasarım ve Teknoloji
Yazarlar

Yunus Kara 0000-0002-0582-7117

Mehmet Rahmi Canal 0000-0002-9942-3841

İbrahim Sefa 0000-0002-2093-683X

Fatih Emre Boran 0000-0001-8404-3814

Yayımlanma Tarihi 25 Mart 2021
Gönderilme Tarihi 30 Ocak 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 9 Sayı: 1

Kaynak Göster

APA Kara, Y., Canal, M. R., Sefa, İ., Boran, F. E. (2021). Selecting and Analyzing Appropriate Probability Distributions for Reliability of Electrical Transmission Lines. Gazi University Journal of Science Part C: Design and Technology, 9(1), 108-121. https://doi.org/10.29109/gujsc.868923

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