Wind power forecast optimization by integration of CFD and Kalman filtering

References

• Baró Pérez, A., C. Lynch, A. L. Ferrer Hernández, I. Borrajero Montejo, and A. Roque Rodríguez. 2019. Kalman Filter bank post-processor methodology for the Weather Research and Forecasting Model wind speed grid model output correction. International Journal of Sustainable Energy 38:511–25. doi:10.1080/14786451.2018.1432615.
   Web of Science ®Google Scholar
• Blaabjerg, F., and K. Ma. 2017. Wind energy systems. Proceedings of the IEEE 105:2116–31. doi:10.1109/JPROC.2017.2695485.
   Web of Science ®Google Scholar
• Castellani, F., M. Burlando, S. Taghizadeh, D. Astolfi, and E. Piccioni. 2014. Wind energy forecast in complex sites with a hybrid neural network and CFD based method. Energy Procedia 45:188–97. doi:10.1016/j.egypro.2014.01.021.
   Google Scholar
• Costa, A., A. Crespo, J. Navarro, G. Lizcano, H. Madsen, and E. Feitosa. 2008. A review on the young history of the wind power short-term prediction. Renewable and Sustainable Energy Reviews 12:1725–44. doi:10.1016/j.rser.2007.01.015.
   Web of Science ®Google Scholar
• Dao, P. B., W. J. Staszewski, T. Barszcz, and T. Uhl. 2018. Condition monitoring and fault detection in wind turbines based on cointegration analysis of SCADA data. Renewable Energy 116:107–22. doi:10.1016/j.renene.2017.06.089.
   Web of Science ®Google Scholar
• Defforge, C. L., B. Carissimo, M. Bocquet, R. Bresson, and P. Armand. 2019. Improving CFD atmospheric simulations at local scale for wind resource assessment using the iterative ensemble Kalman smoother. Journal of Wind Engineering and Industrial Aerodynamics 189:243–57. doi:10.1016/j.jweia.2019.03.030.
   Web of Science ®Google Scholar
• Doucoure, B., K. Agbossou, and A. Cardenas. 2016. Time series prediction using artificial wavelet neural network and multi-resolution analysis: Application to wind speed data. Renewable Energy 92:202–11. doi:10.1016/j.renene.2016.02.003.
   Web of Science ®Google Scholar
• Gallego-Castillo, C., A. Cuerva-Tejero, and O. Lopez-Garcia. 2015. A review on the recent history of wind power ramp forecasting. Renewable and Sustainable Energy Reviews 52:1148–57. doi:10.1016/j.rser.2015.07.154.
   Web of Science ®Google Scholar
• GAQS (General Administration of Quality Supervision). 2002. GB/T 18710-2002 Methodology of wind energy resource assessment for wind farm.
   Google Scholar
• Hua, S., S. Wang, S. Jin, S. Feng, and B. Wang. 2017. Wind speed optimisation method of numerical prediction for wind farm based on Kalman filter method. The Journal of Engineering 2017:1146–49. doi:10.1049/joe.2017.0508.
   Google Scholar
• Kumar, Y., J. Ringenberg, S. S. Depuru, V. K. Devabhaktuni, J. W. Lee, E. Nikolaidis, and A. Afjeh. 2016. Wind energy: Trends and enabling technologies. Renewable and Sustainable Energy Reviews 53:209–24. doi:10.1016/j.rser.2015.07.200.
   Web of Science ®Google Scholar
• Kusiak, A., and W. Li. 2011. The prediction and diagnosis of wind turbine faults. Renewable Energy 36:16–23. doi:10.1016/j.renene.2010.05.014.
   Web of Science ®Google Scholar
• Kusiak, A., and A. Verma. 2013. Monitoring wind farms with performance curves. IEEE Transactions on Sustainable Energy 4:192–99. doi:10.1109/TSTE.2012.2212470.
   Web of Science ®Google Scholar
• Landberg, L., and S. J. Watson. 1994. Short-term prediction of local wind conditions. Boundary-Layer Meteorology 70:171–95. doi:10.1007/BF00712528.
   Web of Science ®Google Scholar
• Launder, B. E., and D. B. Spalding. 1983. The numerical computation of turbulent flows. In Suhas V. Patankar, Andrew Pollard, Ashok K. Singhal, and S. Pratap Vanka (Eds.), Numerical prediction of flow, heat transfer, turbulence and combustion, 96–116. Pergamon.
   Google Scholar
• Lei, M., L. Shiyan, J. Chuanwen, L. Hongling, and Z. Yan. 2009. A review on the forecasting of wind speed and generated power. Renewable and Sustainable Energy Reviews 13 (4):915–20. doi:10.1016/j.rser.2008.02.002.
   Web of Science ®Google Scholar
• Li, C., H. Shi, Y. Cao, J. Wang, Y. Kuang, Y. Tan, and J. Wei. 2015. Comprehensive review of renewable energy curtailment and avoidance: A specific example in China. Renewable and Sustainable Energy Reviews 41:1067–79. doi:10.1016/j.rser.2014.09.009.
   Web of Science ®Google Scholar
• Liu, L. J., and Y. J. Liang. 2016. Quality Assessment of Low-level Wind Prediction Product by BJ-RUC Model–A Case Study of Akesai Wind Farm in Gansu Province. Journal of Arid Meteorology 344:743–51.
   Google Scholar
• Liu, L. J., P. Lv, and Y. J. Liang. 2013. Simulating Wind Environment of Valley City Based on CFD Technology: A case study of Chengguan District of Lanzhou, China. Journal of Desert Research 33 (6):1840–47.
   Google Scholar
• Liu, S., W. Pan, H. Zhang, X. Cheng, Z. Long, and Q. Chen. 2017. CFD simulations of wind distribution in an urban community with a full-scale geometrical model. Building and Environment 117:11–23. doi:10.1016/j.buildenv.2017.02.021.
   Web of Science ®Google Scholar
• Liu, Z. 2017. China’s strategy for the development of renewable energies. Energy Sources, Part B: Economics, Planning, and Policy 12 (11):971–75. doi:10.1080/15567249.2017.1336813.
   Web of Science ®Google Scholar
• Luo, G. L., Y. L. Li, W. J. Tang, and X. Wei. 2016. Wind curtailment of China׳ s wind power operation: Evolution, causes and solutions. Renewable and Sustainable Energy Reviews 53:1190–201. doi:10.1016/j.rser.2015.09.075.
   Web of Science ®Google Scholar
• NEA (National energy administration). 2014. NB/T 31046–2013 Function specification of wind power forecasting system.
   Google Scholar
• Noorollahi, Y., M. A. Jokar, and A. Kalhor. 2016. Using artificial neural networks for temporal and spatial wind speed forecasting in Iran. Energy Conversion and Management 115:17–25. doi:10.1016/j.enconman.2016.02.041.
   Web of Science ®Google Scholar
• Okumus, I., and A. Dinler. 2016. Current status of wind energy forecasting and a hybrid method for hourly predictions. Energy Conversion and Management 123:362–71. doi:10.1016/j.enconman.2016.06.053.
   Web of Science ®Google Scholar
• Pei, W., Y. Chen, K. Sheng, W. Deng, Y. Du, Z. Qi, and L. Kong. 2015. Temporal-spatial analysis and improvement measures of Chinese power system for wind power curtailment problem. Renewable and Sustainable Energy Reviews 49:148–68. doi:10.1016/j.rser.2015.04.106.
   Web of Science ®Google Scholar
• Ren, Y., P. N. Suganthan, and N. Srikanth. 2015. Ensemble methods for wind and solar power forecasting – A state-of-the-art review. Renewable and Sustainable Energy Reviews 50:82–91. doi:10.1016/j.rser.2015.04.081.
   Web of Science ®Google Scholar
• Rouhani, M., M. Mohammadi, and A. Kargarian. 2016. Parzen window density estimator-based probabilistic power flow with correlated uncertainties. IEEE Transactions on Sustainable Energy 7 (3):1170–81. doi:10.1109/TSTE.2016.2530049.
   Web of Science ®Google Scholar
• SBTS (State Bureau of Technology Supervision), and SEPA (State Environmental Protection Administration). 1992. GB/T 3840-1991 Technical methods for making local emission standards of air pollutants.
   Google Scholar
• Schlechtingen, M., I. F. Santos, and S. Achiche. 2013. Wind turbine condition monitoring based on SCADA data using normal behavior models. Part 1: System description. Applied Soft Computing 13 (1):259–70. doi:10.1016/j.asoc.2012.08.033.
   Web of Science ®Google Scholar
• Shen, J., and C. Luo. 2015. Overall review of renewable energy subsidy policies in China – Contradictions of intentions and effects. Renewable and Sustainable Energy Reviews 41:1478–88. doi:10.1016/j.rser.2014.09.007.
   Web of Science ®Google Scholar
• Song, M. X., K. Chen, X. Zhang, and J. Wang. 2015. The lazy greedy algorithm for power optimization of wind turbine positioning on complex terrain. Energy 80:567–74. doi:10.1016/j.energy.2014.12.012.
   Web of Science ®Google Scholar
• Surucu, B. 2006. Goodness-of-fit tests for multivariate distributions. Communications in Statistics – Theory and Methods 35 (7):1319–31. doi:10.1080/03610920600628999.
   Web of Science ®Google Scholar
• Surucu, B. 2008. A power comparison and simulation study of goodness-of-fit tests. Computers & Mathematics with Applications 56 (6):1617–25. doi:10.1016/j.camwa.2008.03.010.
   Web of Science ®Google Scholar
• Uchida, T. 2017. High-resolution micro-siting technique for large scale wind farm outside of japan using LES turbulence model. Energy and Power Engineering 9:802–13. doi:10.4236/epe.2017.912050.
   Google Scholar
• Yu, M., S. Miao, and Q. Li. 2017. Synoptic analysis and urban signatures of a heavy rainfall on 7 August 2015 in Beijing. Journal of Geophysical Research: Atmospheres 122:65–78.
   Web of Science ®Google Scholar