آمار

تعداد نشریات26
تعداد شماره‌ها407
تعداد مقالات3,560
تعداد مشاهده مقاله5,537,650
تعداد دریافت فایل اصل مقاله3,783,956
Mosayebian, M. E.. (1402). A New Approach for Modeling Wind Power in Reliability Studies. سامانه مدیریت نشریات علمی, 11(2), 144-150. doi: 10.22098/joape.2023.10081.1713
M. E. Mosayebian. "A New Approach for Modeling Wind Power in Reliability Studies". سامانه مدیریت نشریات علمی, 11, 2, 1402, 144-150. doi: 10.22098/joape.2023.10081.1713
Mosayebian, M. E.. (1402). 'A New Approach for Modeling Wind Power in Reliability Studies', سامانه مدیریت نشریات علمی, 11(2), pp. 144-150. doi: 10.22098/joape.2023.10081.1713
Mosayebian, M. E.. A New Approach for Modeling Wind Power in Reliability Studies. سامانه مدیریت نشریات علمی, 1402; 11(2): 144-150. doi: 10.22098/joape.2023.10081.1713

A New Approach for Modeling Wind Power in Reliability Studies

Journal of Operation and Automation in Power Engineering
دوره 11، شماره 2، آبان 2023، صفحه 144-150    اصل مقاله (1.01 M)
نوع مقاله: Research paper
شناسه دیجیتال (DOI): 10.22098/joape.2023.10081.1713
نویسنده
M. E. Mosayebian*
Department of Electrical Engineering, Tuyserkan Branch, Islamic Azad University, Tuyserkan, Iran
چکیده
Tremendous growth of wind power worldwide in the past decade requires serious research in various fields. Because wind power is weather dependent, it is stochastic and varies over various time-scales. Therefore, accuracy in wind power modeling is recognized as a major contribution for reliable large-scale wind power integration. In this paper, a method for generating synthetic wind power is proposed. The proposed method combines the random nature of wind with the operational information of the wind turbines (i.e., failure and repair rates). It uses chronological or sequential Monte Carlo Simulation (MCS) instead of non-sequential one owing to its usefulness and flexibility in preserving statistical characteristics of the chronological processes. The validity of the synthetic values generated by the proposed method and the Auto Regressive Moving Average (ARMA) time series is compared with the measured data in terms of reliability indices. Finally, the effect of some network parameters, such as network dimensions, the average coefficient of wind speed on the reliability of the power system has been evaluated. In this regard, historical wind speed data of Manjil area located in the north of Iran is used.
کلیدواژه‌ها
Modeling wind power؛ Reliability؛ Markov Chain؛ Monte Carlo Simulation‎
مراجع
  1. Hong et al., “Reliability of a power system with high penetration of renewables: a scenario-based study”, IEEE Access, vol. 9, pp. 78050-59, 2021.
  2. Naderi et al., “MILP based optimal design of hybrid microgrid by considering statistical wind estimation and demand response”, J. Oper. Autom. Power Eng., vol. 10, pp. 54-65, 2022.
  3. Sheykhloei1 et al., “Stochastic optimal operation and risk assessment for integrated power and gas systems”, J. Oper. Autom. Power Eng., vol. 9, pp. 80-87, 2021.
  4. Billinton, D. Huang, “Incorporating wind power in generating capacity reliability evaluation using different models”, IEEE Trans. Power Syst., vol. 26, pp. 2509-2517, 2011.
  5. Wangdee, R. Billinton, “Reliability assessment of bulk electric systems containing large wind farms”, Electr. Power Energy Syst., vol.29, pp.759-66, 2007.
  6. Najafi et al., “Capacitor placement in distorted distribution network subject to wind and load uncertainty”, J. Oper. Autom. Power Eng., vol. 4, No. 2, pp.153-164, 2016.
  7. Chao et al., “A Sequential MCMC model for reliability evaluation of offshore wind farms considering severe weather conditions”, IEEE Access, vol. 7, pp. 132552-62, 2019.
  8. Chen et al., “Markov model of wind power time series using Bayesian inferenceof transition matrix”, 35th Annual Conf. IEEE Ind. Electron., 2009.
  9. Votsi and A. Brouste, “Confidence interval for the mean time to failure in semi-markov models: An application to wind energy production”, J. Appl. Statistics, vol. 46, pp. 1756-73, 2019.
  10. Votsi et al., “Earthquake statistical analysis through multi-state modeling”, Wiley-ISTE: London, UK, 2019.
  11. D’Amico, “Age-usage semi-markov models”, Appl. Math. Modelling, vol. 35, pp. 4354-66, 2011.
  12. D’Amico, F. Petroni, F. Prattico, “Wind speed and energy forecasting at different time scales: A nonparametric approach”, Physica A: Statistical Mech. Appl., vol. 406, pp.59-66, 2014.
  13. D’Amico et al., “Managing wind power generation via indexed semi-markov model and copula”,Energies, vol. 13, no. 16, p. 4246, 2020. 
  14. D׳Amico, F. Petroni, F.Prattico, “Reliability measures for indexed semi-Markov chains applied to wind energy production”, Reliab. Eng. Syst. Safety, pp. 170-7, 2015.
  15. Bakhtiari, J. Zhong, M. Alvarez , “Predicting the stochastic behavior of uncertainty sources in planning a stand-alone renewable energy-based microgrid using Metropolis–coupled Markov chain Monte Carlo simulation”, Appl. Energy, Vol. 290, 2021.
  16. IEEE Committee Report, A reliability test system, IEEE Trans. Power Apparatus Syst., vol. 4, pp.1238-44, 1989.
  17. Li, W. Wei, “Probabilistic evaluation of available power of a renewable generation system consisting of wind turbines and storage batteries: A Markov chain method”, J. Renew. Sustain. Energy, vol. 6, 2014.
  18. Yang, T. L. Ching, “Optimal operating reserve scheduling for power system with reliability and wind curtailment limits”, 3rd Int. Conf. Syst. Reliability Safety Eng., 2021.
  19. Zaman et al., “Wind speed forecasting using ARMA and neural network models”, IEEE Electr. Power Energy Conf., 2021.
  20. Negra et al., “Model of a synthetic wind speed time series generator”, Wind Energy, vol. 11, pp. 193-209, 2008.
  21. S. Eryilmaz, İ. Bulanık, Y. Devrim, “Reliability based modeling of hybrid solar/wind power system for long term performance assessment”, Reliability Eng. Syst,. Safety, vol. 209, 2021.
  22. Billinton R, Allan RN. Reliability evaluation of power systems. New York: Plenum; 1996.
  23. Billinton et al., “A reliability test system for educational purposes- basic data”, IEEE Trans. Power Syst., vol. 4, No. 4, pp. 1238-44, 1989.
آمار
تعداد مشاهده مقاله: 4,927
تعداد دریافت فایل اصل مقاله: 972


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب