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Articles

Error-Based Wind Power Prediction Technique Based on Generalized Factors Analysis with Improved Power System Reliability

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  • M. Yang, X. Chen, J. Du, and Y. Cui, “Ultra-short-term multistep wind power prediction based on improved EMD and reconstruction method using run-length analysis,” IEEE. Access., Vol. 6, pp. 31908–17, 2018. doi: 10.1109/ACCESS.2018.2844278
  • B. Khorramdel, C. Y. Chung, N. Safari, and G. C. D. Price, “A fuzzy adaptive probabilistic wind power prediction framework using diffusion kernel density estimators,” IEEE Trans. Power Syst, Vol. 33, no. 6, pp. 7109–21, 2018. doi: 10.1109/TPWRS.2018.2848207
  • Y. Zhang, B. Chen, Y. Zhao, and G. Pan, “Wind speed prediction of IPSO-BP neural network based on lorenz disturbance,” IEEE. Access., Vol. 6, pp. 53168–79, 2018. doi: 10.1109/ACCESS.2018.2869981
  • H. Quan, D. Srinivasan, and A. Khosravi, “Incorporating wind power forecast uncertainties into stochastic unit commitment using neural network-based prediction intervals,” IEEE Trans. Neural Networks Learn. Syst, Vol. 26, no. 9, pp. 2123–35, 2015. doi: 10.1109/TNNLS.2014.2376696
  • A. Kusiak, and Z. Zhang, “Short-horizon prediction of wind power: A data-driven approach,” IEEE Trans. Energy Convers, Vol. 25, no. 4, pp. 1112–22, Dec. 2010. doi: 10.1109/TEC.2010.2043436
  • C. Wan, J. Wang, J. Lin, Y. Song, and Z. Y. Dong, “Nonparametric prediction intervals of wind power via linear programming,” IEEE Trans. Power Syst, Vol. 33, no. 1, pp. 1074–6, 2018. doi: 10.1109/TPWRS.2017.2716658
  • T. H. M. El-Fouly, E. F. El-Saadany, M. M. A. Salama, T. H. M. El-Fouly, E. F. El-Saadany, and M. M. A. Salama, “Grey predictor for wind energy conversion systems output power prediction,” IEEE Trans. Power Syst, Vol. 21, no. 3, pp. 1450–2, 2006. doi: 10.1109/TPWRS.2006.879246
  • N. Safari, C. Y. Chung, and G. C. D. Price, “A novel multi-step short-term wind power prediction framework based on chaotic time series analysis and singular spectrum analysis,” IEEE Trans. Power Syst, Vol. 33, no. 1, pp. 1–1, 2017.
  • Y. Liu, J. Shi, Y. Yang, and W. J. Lee, “Short-term wind-power prediction based on wavelet transform-support vector machine and statistic-characteristics analysis,” IEEE Trans. Ind. Appl, Vol. 48, no. 4, pp. 1136–41, 2012. doi: 10.1109/TIA.2012.2199449
  • J. Wang, X. Xiong, Z. Li, W. Wang, and J. Zhu, “Wind forecast-based probabilistic early warning method of wind swing discharge for OHTLs,” IEEE Trans. Power Deliv, Vol. 31, no. 5, pp. 2169–78, 2016. doi: 10.1109/TPWRD.2016.2519599
  • S. Buhan, Y. Özkazanç, and I. Çadirci, “Wind pattern recognition and reference wind mast data correlations With NWP for improved wind-electric power forecasts,” IEEE Trans. Ind. Informatics, Vol. 12, no. 3, pp. 991–1004, 2016. doi: 10.1109/TII.2016.2543004
  • M. B. Ozkan, and P. Karagoz, “A novel wind power forecast model: Statistical hybrid wind power forecast technique (SHWIP),” IEEE Trans. Ind. Informatics, Vol. 11, no. 2, pp. 375–87, 2015.
  • Q. Xu, et al., “A short-term wind power forecasting approach with adjustment of numerical weather prediction input by data mining,” IEEE Trans. Sustain. Energy, Vol. 6, no. 4, pp. 1283–91, Oct. 2015. doi: 10.1109/TSTE.2015.2429586
  • C. Huang, F. Li, and Z. Jin, “Maximum power point tracking strategy for large-scale wind generation systems considering wind turbine dynamics,” IEEE Trans. Ind. Electron, Vol. 62, no. 4, pp. 2530–9, 2015. doi: 10.1109/TIE.2015.2395384
  • N. Chen, Z. Qian, I. T. Nabney, and X. Meng, “Wind power forecasts using Gaussian processes and numerical weather prediction,” IEEE Trans. Power Syst, Vol. 29, no. 2, pp. 656–65, 2014. doi: 10.1109/TPWRS.2013.2282366
  • C. Wan, Z. Xu, P. Pinson, Z. Y. Dong, and K. P. Wong, “Optimal prediction intervals of wind power generation,” IEEE Trans. Power Syst, Vol. 29, no. 3, pp. 1166–74, May 2014. doi: 10.1109/TPWRS.2013.2288100
  • Q. Hu, P. Su, D. Yu, and J. Liu, ““Pattern-based wind speed prediction based on generalized principal component analysis,” Sustain. Energy, IEEE Trans, Vol. 5, no. 3, pp. 866–74, 2014. doi: 10.1109/TSTE.2013.2295402
  • W. Lucas, and M. Greenway, “Discussions and closures,” J. Prof. Issues Eng. Educ. Pract, Vol. 29, no. 1, pp. 89–90, 2012.
  • M. Khalid, and A. V. Savkin, “A method for short-term wind power prediction with multiple observation points,” IEEE Trans. Power Syst, Vol. 27, no. 2, pp. 579–86, 2012. doi: 10.1109/TPWRS.2011.2160295
  • R. Karki, S. Thapa, and R. B. Billinton, “A simplified risk-based method for short-term wind power commitment,” IEEE Trans. Sustain. Energy, Vol. 3, no. 3, pp. 498–505, 2012. doi: 10.1109/TSTE.2012.2190999
  • I. Akhtar, S. Kirmani, and M. Jamil, “Analysis and design of a sustainable microgrid primarily powered by renewable energy sources with dynamic performance improvement,” IET Renew. Power Gener., Vol. 13, no. 8, pp. 1024–36, 2019. doi: 10.1049/iet-rpg.2018.5117
  • H. Oh, “Optimal planning to include storage devices in power systems,” IEEE Trans. Power Syst, Vol. 26, no. 3, pp. 1118–28, Aug. 2011. doi: 10.1109/TPWRS.2010.2091515
  • A. P. Tyagi. Solar Radiant Eenergy Over India. New Delhi: India Meteorogical department, Ministry of Earth Sciences, 2009.

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