Model-Free Wind Farm Power Production Optimization using Multi-Resolution Optimized Relative Step Size Random Search

References

• GWEC, “Global Wind Report 2016,” Wind energy Technol., p. 76, 2017.
   Google Scholar
• G. Mosetti, C. Poloni, and B. Diviacco, “Optimization of wind turbine positioning in large windfarms by means of a genetic algorithm,” J. Wind Eng. Ind. Aerodyn., Vol. 51, no. 1, pp. 105–116, 1994. doi: 10.1016/0167-6105(94)90080-9
   Web of Science ®Google Scholar
• V. Spudic, “Hierarchical wind farm control for power/load optimization,” Sci. Mak. Torque from Wind, 1–3, 2010.
   Google Scholar
• J. R. Marden, S. D. Ruben, and L. Y. Pao. “Surveying game theoretic approaches for wind farm optimization,” 50th AIAA Aerosp. Sci. Meet. Incl. New Horizons Forum Aerosp. Expo., 2012, pp. 1–10.
   Google Scholar
• P. M. O. Gebraad, F. C. van Dam, and J. W. van Wingerden. “A model-free distributed approach for wind plant control,” 2013 American Control Conference (Acc). 2013, pp. 628–633.
   Google Scholar
• O. N. Granichin and A. T. Vakhitov, “Accuracy for the SPSA algorithm with two measurements,” WSEAS Trans. Syst., Vol. 5, no. 5, pp. 953–957, 2006.
   Google Scholar
• J. R. Marden, S. D. Ruben, and L. Y. Pao, “A model-free approach to wind farm control using game theoretic methods,” IEEE Trans. Control Syst. Technol., Vol. 21, no. 4, pp. 1207–1214, 2013. doi: 10.1109/TCST.2013.2257780
   Web of Science ®Google Scholar
• J. Park, S. Kwon, and K. H. Law, “Wind farm power maximization based on a cooperative static game approach,” SPIE Smart Struct. Conf., Vol. 8688, pp. 1–15, 2013.
   Google Scholar
• C. Kim, Y. Gui, and C. C. Chung, “Maximum power point tracking of a wind power plant with predictive gradient ascent method,” IEEE T Sustain Energ., Vol. 8, no. 2, pp. 685–694, 2016. doi: 10.1109/TSTE.2016.2615315
   Web of Science ®Google Scholar
• M. A. Ahmad, S. Azuma, and T. Sugie, “A model-free approach for maximizing power production of wind farm using multi-resolution simultaneous perturbation stochastic approximation,” Energies., Vol. 7, no. 9, pp. 5624–5646, 2014.
   Google Scholar
• J. Park, and K. H. Law, “A data-driven, cooperative wind farm control to maximize the total power production,” Appl. Energy, Vol. 165, pp. 151–165, Mar. 2016. doi: 10.1016/j.apenergy.2015.11.064
   Web of Science ®Google Scholar
• M. R. Hao, R. M. T. R. Ismail, and M. A. Ahmad, “Using spiral dynamic algorithm for maximizing power production of wind farm,” in 2017 International Conference on Applied System Innovation (ICASI), 2017, pp. 1706–1709.
   Google Scholar
• U. Ciri, M. A. Rotea, and S. Leonardi, “Model-free control of wind farms: A comparative study between individual and coordinated extremum seeking,” Renew. Energy, Vol. 113, pp. 1033–1045, Dec. 2017. doi: 10.1016/j.renene.2017.06.065
   Web of Science ®Google Scholar
• M. A. Ahmad, M. R. Hao, R. M. T. R. Ismail, and A. N. K. Nasir, “Model-free wind farm control based on random search,” in 2016 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS), 2016, pp. 131–134.
   Google Scholar
• M. R. Hao, M. A. Ahmad, R. M. T. Raja Ismail, and A. N. K. Nasir. “Performance evaluation of random search based methods on model-free wind farm control BT – intelligent manufacturing & mechatronics,” 2018, pp. 657–670.
   Google Scholar
• Z. Zabinsky. “Random search algorithms,” Wiley Encycl. Oper. Res. Manag. Sci., pp. 1–16, 2011.
   Google Scholar
• L. A. Rastrigin, “Convergence of random search method in extremal control of multi-parameter systems,” Avtom. i Telemekhanika, Vol. 24, no. 11, pp. 1467–14731473, 1963.
   Google Scholar
• M. A. Schumer and K. Steiglitz, “Adaptive step size random search,” IEEE Trans. Automat. Contr., Vol. 13, no. 3, pp. 270–276, 1968. doi: 10.1109/TAC.1968.1098903
   Google Scholar
• G. Schrack and M. Choit, “Optimized relative step size random searches,” Math. Program., Vol. 10, no. 1, pp. 230–244, 1976. doi: 10.1007/BF01580669
   Google Scholar
• F. D. Bianchi, H. De Battista, and R. J. Mantz. Wind Turbine Control Systems Principles, Modelling and Gain Scheduling Design. 2011.
   Google Scholar
• J. C. Spall, S. D. Hill, and D. R. Stark, “Theoretical comparisons of evolutionary computation and other optimization approaches,” in Proceedings of the 1999 Congress on Evolutionary Computation, CEC 1999, 1999, Vol. 2, pp. 1398–1405.
   Google Scholar
• A. K. Scholbrock. “Optimizing wind farm control strategies to minimize wake loss effects.” 2011.
   Google Scholar
• F. Porté-Agel, Y.-T. Wu, and C.-H. Chen, “A numerical study of the effects of wind direction on turbine wakes and power losses in a large wind farm,” Energies, Vol. 6, no. 10, pp. 5297–5313, 2013. doi: 10.3390/en6105297
   Web of Science ®Google Scholar
• M. J. Churchfield. Review of Wind Turbine Wake Models and Future Directions (Presentation). Golden, CO: National Renewable Energy Laboratory (NREL), 2013.
   Google Scholar