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A novel hybrid model for the estimation of energy conversion in a wind farm combining wake effects and stochastic dependability

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  • Famoso, Fabio
  • Brusca, Sebastian
  • D'Urso, Diego
  • Galvagno, Antonio
  • Chiacchio, Ferdinando

Abstract

The contribution of wind power systems to the reduction of the impact of fossil fuels sources has increased more and more during the last decades leading to a greater attention to the estimation of the performances of renewable power plants. However, forecast methods of productivity of onshore/offshore wind farms still suffer, in terms of accuracy, the innate variability of the energy resources and the effect of components failures. This paper proposes a novel “hybrid” approach for the estimation of the energy conversion of onshore wind farms. The model combines the Jensen wake mathematical theory with a stochastic dependability model, a Fault Tree, to better forecast the energy production. A new key index was conceived to optimize the preventive maintenance of wind turbines. This model was tested on a real case study, a wind farm (25.5 MWp) located in the south of Italy. Results were promising because the model achieved a twofold objective to improve the accuracy of the energy conversion forecast and to provide a support decision system for the activities of maintenance planning.

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  • Famoso, Fabio & Brusca, Sebastian & D'Urso, Diego & Galvagno, Antonio & Chiacchio, Ferdinando, 2020. "A novel hybrid model for the estimation of energy conversion in a wind farm combining wake effects and stochastic dependability," Applied Energy, Elsevier, vol. 280(C).
    • Handle: RePEc:eee:appene:v:280:y:2020:i:c:s0306261920314197
    DOI: 10.1016/j.apenergy.2020.115967
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    as
    1. Rafiee, Koosha & Feng, Qianmei & Coit, David W., 2017. "Reliability assessment of competing risks with generalized mixed shock models," Reliability Engineering and System Safety, Elsevier, vol. 159(C), pages 1-11.
    2. Ewing, Fraser J. & Thies, Philipp R. & Shek, Jonathan & Ferreira, Claudio Bittencourt, 2020. "Probabilistic failure rate model of a tidal turbine pitch system," Renewable Energy, Elsevier, vol. 160(C), pages 987-997.
    3. Kavasseri, Rajesh G. & Seetharaman, Krithika, 2009. "Day-ahead wind speed forecasting using f-ARIMA models," Renewable Energy, Elsevier, vol. 34(5), pages 1388-1393.
    4. Wang, Hongzhou, 2002. "A survey of maintenance policies of deteriorating systems," European Journal of Operational Research, Elsevier, vol. 139(3), pages 469-489, June.
    5. Seim, Fredrik & Gravdahl, Arne R. & Adaramola, Muyiwa S., 2017. "Validation of kinematic wind turbine wake models in complex terrain using actual windfarm production data," Energy, Elsevier, vol. 123(C), pages 742-753.
    6. Enrico Zio, 2013. "The Monte Carlo Simulation Method for System Reliability and Risk Analysis," Springer Series in Reliability Engineering, Springer, edition 127, number 978-1-4471-4588-2, February.
    7. Ferdinando Chiacchio & Fabio Famoso & Diego D’Urso & Luca Cedola, 2019. "Performance and Economic Assessment of a Grid-Connected Photovoltaic Power Plant with a Storage System: A Comparison between the North and the South of Italy," Energies, MDPI, vol. 12(12), pages 1-25, June.
    8. Tsoutsos, T. & Tsitoura, I. & Kokologos, D. & Kalaitzakis, K., 2015. "Sustainable siting process in large wind farms case study in Crete," Renewable Energy, Elsevier, vol. 75(C), pages 474-480.
    9. Enrico Zio, 2013. "System Reliability and Risk Analysis by Monte Carlo Simulation," Springer Series in Reliability Engineering, in: The Monte Carlo Simulation Method for System Reliability and Risk Analysis, edition 127, chapter 0, pages 59-81, Springer.
    10. Chiacchio, Ferdinando & D’Urso, Diego & Famoso, Fabio & Brusca, Sebastian & Aizpurua, Jose Ignacio & Catterson, Victoria M., 2018. "On the use of dynamic reliability for an accurate modelling of renewable power plants," Energy, Elsevier, vol. 151(C), pages 605-621.
    11. Yan, Jie & Liu, Yongqian & Han, Shuang & Wang, Yimei & Feng, Shuanglei, 2015. "Reviews on uncertainty analysis of wind power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1322-1330.
    12. Archer, Cristina L. & Vasel-Be-Hagh, Ahmadreza & Yan, Chi & Wu, Sicheng & Pan, Yang & Brodie, Joseph F. & Maguire, A. Eoghan, 2018. "Review and evaluation of wake loss models for wind energy applications," Applied Energy, Elsevier, vol. 226(C), pages 1187-1207.
    13. Chang, G.W. & Lu, H.J. & Chang, Y.R. & Lee, Y.D., 2017. "An improved neural network-based approach for short-term wind speed and power forecast," Renewable Energy, Elsevier, vol. 105(C), pages 301-311.
    14. Giwhyun Lee & Yu Ding & Marc G. Genton & Le Xie, 2015. "Power Curve Estimation With Multivariate Environmental Factors for Inland and Offshore Wind Farms," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 56-67, March.
    15. Jha, Sunil Kr. & Bilalovic, Jasmin & Jha, Anju & Patel, Nilesh & Zhang, Han, 2017. "Renewable energy: Present research and future scope of Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 297-317.
    16. Aziz Ezzat, Ahmed, 2020. "Turbine-specific short-term wind speed forecasting considering within-farm wind field dependencies and fluctuations," Applied Energy, Elsevier, vol. 269(C).
    17. Wang, Jianzhou & Qin, Shanshan & Zhou, Qingping & Jiang, Haiyan, 2015. "Medium-term wind speeds forecasting utilizing hybrid models for three different sites in Xinjiang, China," Renewable Energy, Elsevier, vol. 76(C), pages 91-101.
    18. Fernando Porté-Agel & Yu-Ting Wu & Chang-Hung Chen, 2013. "A Numerical Study of the Effects of Wind Direction on Turbine Wakes and Power Losses in a Large Wind Farm," Energies, MDPI, vol. 6(10), pages 1-17, October.
    19. Enrico Zio, 2013. "Monte Carlo Simulation: The Method," Springer Series in Reliability Engineering, in: The Monte Carlo Simulation Method for System Reliability and Risk Analysis, edition 127, chapter 0, pages 19-58, Springer.
    20. Sebastian Pfaffel & Stefan Faulstich & Kurt Rohrig, 2017. "Performance and Reliability of Wind Turbines: A Review," Energies, MDPI, vol. 10(11), pages 1-27, November.
    21. Kang, Jichuan & Sun, Liping & Guedes Soares, C., 2019. "Fault Tree Analysis of floating offshore wind turbines," Renewable Energy, Elsevier, vol. 133(C), pages 1455-1467.
    22. Ulku, I. & Alabas-Uslu, C., 2019. "A new mathematical programming approach to wind farm layout problem under multiple wake effects," Renewable Energy, Elsevier, vol. 136(C), pages 1190-1201.
    23. Enrico Zio, 2013. "System Reliability and Risk Analysis," Springer Series in Reliability Engineering, in: The Monte Carlo Simulation Method for System Reliability and Risk Analysis, edition 127, chapter 0, pages 7-17, Springer.
    24. Han, Woobeom & Kim, Jonghwa & Kim, Bumsuk, 2018. "Effects of contamination and erosion at the leading edge of blade tip airfoils on the annual energy production of wind turbines," Renewable Energy, Elsevier, vol. 115(C), pages 817-823.
    25. Chiacchio, Ferdinando & Iacono, Alessandra & Compagno, Lucio & D'Urso, Diego, 2020. "A general framework for dependability modelling coupling discrete-event and time-driven simulation," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
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    Cited by:

    1. Dong, Hongyang & Zhang, Jincheng & Zhao, Xiaowei, 2021. "Intelligent wind farm control via deep reinforcement learning and high-fidelity simulations," Applied Energy, Elsevier, vol. 292(C).
    2. Zouheyr, Dekali & Lotfi, Baghli & Abdelmadjid, Boumediene, 2021. "Improved hardware implementation of a TSR based MPPT algorithm for a low cost connected wind turbine emulator under unbalanced wind speeds," Energy, Elsevier, vol. 232(C).
    3. Shu, Tong & Song, Dongran & Hoon Joo, Young, 2022. "Decentralised optimisation for large offshore wind farms using a sparsified wake directed graph," Applied Energy, Elsevier, vol. 306(PA).
    4. Fabio Famoso & Ludovica Maria Oliveri & Sebastian Brusca & Ferdinando Chiacchio, 2024. "A Dependability Neural Network Approach for Short-Term Production Estimation of a Wind Power Plant," Energies, MDPI, vol. 17(7), pages 1-24, March.
    5. D'Urso, Diego & Chiacchio, Ferdinando & Cavalieri, Salvatore & Gambadoro, Salvatore & Khodayee, Soheyl Moheb, 2024. "Predictive maintenance of standalone steel industrial components powered by a dynamic reliability digital twin model with artificial intelligence," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    6. Firouzi, Mohsen & Samimi, Abouzar & Salami, Abolfazl, 2022. "Reliability evaluation of a composite power system in the presence of renewable generations," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    7. Luis Lopez & Ingrid Oliveros & Luis Torres & Lacides Ripoll & Jose Soto & Giovanny Salazar & Santiago Cantillo, 2020. "Prediction of Wind Speed Using Hybrid Techniques," Energies, MDPI, vol. 13(23), pages 1-13, November.
    8. Vladimir Simankov & Pavel Buchatskiy & Semen Teploukhov & Stefan Onishchenko & Anatoliy Kazak & Petr Chetyrbok, 2023. "Review of Estimating and Predicting Models of the Wind Energy Amount," Energies, MDPI, vol. 16(16), pages 1-24, August.
    9. Joseph, Lionel P. & Deo, Ravinesh C. & Prasad, Ramendra & Salcedo-Sanz, Sancho & Raj, Nawin & Soar, Jeffrey, 2023. "Near real-time wind speed forecast model with bidirectional LSTM networks," Renewable Energy, Elsevier, vol. 204(C), pages 39-58.
    10. Meysam Asadi & Kazem Pourhossein & Younes Noorollahi & Mousa Marzband & Gregorio Iglesias, 2023. "A New Decision Framework for Hybrid Solar and Wind Power Plant Site Selection Using Linear Regression Modeling Based on GIS-AHP," Sustainability, MDPI, vol. 15(10), pages 1-24, May.

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