ANN-Based STATCOM Tuning for Performance Enhancement of Combined Wind Farms

References

• M. Laouer, A. Mekkaoui, and M. Younes, “STATCOM and capacitor banks in a fixed-speed wind farm,” Energy Proc., vol. 50, pp. 882–892, 2014.
   Google Scholar
• H. Heydari-Doostabad, M. R. Khalghani, and M. H. Khooban, “A novel control system design to improve LVRT capability of fixed speed wind turbines using STATCOM in presence of voltage fault,” Int. J. Electr. Power Energy Syst., vol. 77, pp. 280–286, 2016.
   Web of Science ®Google Scholar
• B. Boubekeur, A. Gherbi, and H. Mellah, “Application of STATCOM to increase transient stability of wind farm,” Am. J. Electr. Power Energy Syst., vol. 2, no. 2, pp. 50–56, 2013.
   Google Scholar
• C. Wessels, N. Hoffmann, M. Molinas, and F. W. Fuchs, “StatCom control at wind farms with fixed-speed induction generators under asymmetrical grid faults,” IEEE Trans. Ind. Electr., vol. 60, no. 7, pp. 2864–2873, 2013.
   Web of Science ®Google Scholar
• R. M. M. Pereira, C. M. M. Ferreira, and F. M. Barbosa, “Comparative study of STATCOM and SVC performance on dynamic voltage collapse of an electric power system with wind generation,” IEEE Latin Am. Trans., vol. 12, no. 2, pp. 138–145, 2014.
   Web of Science ®Google Scholar
• H. A. Mohammadpour, A. Ghaderi, H. Mohammadpour, and M. H. Ali, “Low voltage ride-through enhancement of fixed-speed wind farms using series FACTS controllers,” Sustain. Energy Technol. Assess., vol. 9, pp. 12–21, 2015.
   Web of Science ®Google Scholar
• D. Ananth, and G. N. Kumar, “Fault ride-through enhancement using an enhanced field oriented control technique for converters of grid connected DFIG and STATCOM for different types of faults,” ISA Trans., vol. 62, pp. 2–18, 2016.
   PubMed Web of Science ®Google Scholar
• D. Ananth, and G. N. Kumar, “Mitigation of voltage dip and power system oscillations damping using dual STATCOM for grid connected DFIG,” Ain Shams Eng. J., 2015.
   Web of Science ®Google Scholar
• L. Wang, and D.-N. Truong, “Stability enhancement of a power system with a PMSG-based and a DFIG-based offshore wind farm using a SVC with an adaptive-network-based fuzzy inference system,” IEEE Trans. Ind. Electr., vol. 60, no. 7, pp. 2799–2807, 2013.
   Web of Science ®Google Scholar
• L. Wang, and D.-N. Truong, “Stability enhancement of DFIG-based offshore wind farm fed to a multi-machine system using a STATCOM,” IEEE Trans. Power Syst., vol. 28, no. 3, pp. 2882–2889, 2013.
   Web of Science ®Google Scholar
• B. Mehta, P. Bhatt, and V. Pandya, “Small signal stability enhancement of DFIG based wind power system using optimized controllers parameters,” Int. J. Electr. Power Energy Syst., vol. 70, pp. 70–82, 2015.
   Web of Science ®Google Scholar
• S. S. Dhillon, J. Lather, and S. Marwaha, “Multi objective load frequency control using hybrid bacterial foraging and particle swarm optimized PI controller,” Int. J. Electr. Power Energy Syst., vol. 79, pp. 196–209, 2016.
   Web of Science ®Google Scholar
• S. Kahla, Y. Soufi, M. Sedraoui, and M. Bechouat, “On-Off control based particle swarm optimization for maximum power point tracking of wind turbine equipped by DFIG connected to the grid with energy storage,” Int. J. Hydrogen Energy, vol. 40, no. 39, pp. 13749–13758, 2015.
   Web of Science ®Google Scholar
• H. Li, C. Yang, B. Zhao, H. Wang, and Z. Chen, “Aggregated models and transient performances of a mixed wind farm with different wind turbine generator systems,” Electr. Power Syst. Res., vol. 92, pp. 1–10, 2012.
   Web of Science ®Google Scholar
• L. Fernandez, C. Garcia, J. Saenz, and F. Jurado, “Equivalent models of wind farms by using aggregated wind turbines and equivalent winds,” Energy Convers. Manage., vol. 50, pp. 691–704, 2009.
   Web of Science ®Google Scholar
• L. Wang, C.-H. Chang, and A. V. Prokhorov, “Stability improvement of a two-area power system connected with an integrated onshore and offshore wind farm using a STATCOM,” in Presented at the Industry Applications Society Annual Meeting, IEEE, 2016, pp. 1–9.
   Google Scholar
• Y. Pipelzadeh, N. R. Chaudhuri, B. Chaudhuri, and T. C. Green, “Coordinated control of offshore wind farm and onshore HVDC converter for effective power oscillation damping,” IEEE Trans. Power Syst.
   Web of Science ®Google Scholar
• A. Rashad, S. Kamel, and F. J. Melguizo, “Impact of static var compensator (SVC) ON wind farms based in different types of induction generators during grid faults,” Electr. Technol. Eng., vol. 6, pp. 12, 2017.
   Google Scholar
• A. M. Rashad, and S. Kamel, “Enhancement of Hybrid Wind Farm performance using tuned SSSC based on multiobjective genetic algorithm,” in Presented at the Power Systems Conference (MEPCON), 2016 Eighteenth International Middle East, IEEE; 2016, pp. 786–791.
   Google Scholar
• A. Rashad, S. Kamel, and F. Jurado, “Stability improvement of power systems connected with developed wind farms using SSSC controller,” Ain Shams Eng. J., 2017.
   Web of Science ®Google Scholar
• T. Eswaran, and V. S. Kumar, “Particle swarm optimization (PSO)-based tuning technique for PI controller for management of a distributed static synchronous compensator (DSTATCOM) for improved dynamic response and power quality,” J. Appl. Res. Technol., vol. 15, no. 2, pp. 173–189, 2017.
   Google Scholar
• G. Reynoso-Meza, J. Sanchis, X. Blasco, and R. Z. Freire, “Evolutionary MultiObjective optimisation with preferences for multivariable PI controller tuning,” Exp. Syst. Appl., vol. 51, pp. 120–133, 2016.
   Web of Science ®Google Scholar
• E. de Vargas Fortes, P. B. de Araujo, and L. H. Macedo, “Coordinated tuning of the parameters of PI, PSS and POD controllers using a specialized chu–Beasley's genetic algorithm,” Electr. Power Syst. Res., vol. 140, pp. 708–721, 2016.
   Web of Science ®Google Scholar
• M. G. Jolfaei, A. M. Sharaf, S. M. Shariatmadar, and M. B. Poudeh, “A hybrid PSS–SSSC GA-stabilization scheme for damping power system small signal oscillations,” Int. J. Electr. Power Energy Syst., vol. 75, pp. 337–344, 2016.
   Web of Science ®Google Scholar
• M. I. Mosaad, and F. Salem, “LFC based adaptive PID controller using ANN and ANFIS techniques,” J. Electr. Syst. Inf. Technol., vol. 1, no. 3, pp. 212–222, 2014.
   Google Scholar
• N. K. Saxena, and A. Kumar, “Reactive power control in decentralized hybrid power system with STATCOM using GA, ANN and ANFIS methods,” Int. J. Electr. Power Energy Syst., vol. 83, pp. 175–187, 2016.
   Web of Science ®Google Scholar
• S. Muyeen, H. M. Hasanien, and A. Al-Durra, “Transient stability enhancement of wind farms connected to a multi-machine power system by using an adaptive ANN-controlled SMES,” Energy Convers. Manage., vol. 78, pp. 412–420, 2014.
   Web of Science ®Google Scholar
• D. Kriesel, A Brief Introduction on Neural Networks, 2007.
   Google Scholar
• K. Gurney, An Introduction to Neural Networks. Boca Raton, FL: CRC, 2014.
   Google Scholar
• H. V. H. Ayala, and L. dos Santos Coelho, “Tuning of PID controller based on a multiobjective genetic algorithm applied to a robotic manipulator,” Exp. Syst. Appl., vol. 39, pp. 8968–8974, 2012.
   Web of Science ®Google Scholar
• S. Sivanandam, and S. Deepa, “Genetic algorithm optimization problems,” in Introduction to Genetic Algorithms. Berlin, Germany: Springer, 2008, pp. 165–209
   Google Scholar
• S. Mirjalili, and A. Lewis, “The whale optimization algorithm,” Adv. Eng. Softw., vol. 95, pp. 51–67, 2016.
   Web of Science ®Google Scholar
• P. D. P. Reddy, V. V. Reddy, and T. G. Manohar, “Whale optimization algorithm for optimal sizing of renewable resources for loss reduction in distribution systems,” Renew. Wind Water Solar, vol. 4, pp. 3, 2017.
   Google Scholar
• B. Wu, Y. Lang, N. Zargari, and S. Kouro, Power Conversion and Control of Wind Energy Systems. Hoboken, NJ: John Wiley & Sons, 2011.
   Google Scholar
• M. G. Simoes, and F. A. Farret, Alternative Energy Systems: design and Analysis with Induction Generators. Boca Raton, FL: CRC, 2011.
   Google Scholar
• A. Rashad, S. Kamel, and F. Jurado, “Chapter 2 – The basic principles of wind farms,” in Distributed Generation Systems: Design, Operation and Grid Integration – Book, 1st ed., G. B. Gharehpetian and S. M. M. Agah, Eds. Butterworth (trade): Sciencedirect, May 2017, pp. 21–67 http://www.sciencedirect.com/science/book/9780128042083.
   Google Scholar
• O. Noureldeen, and A. Rashad, “Modeling and investigation of Gulf El-Zayt wind farm for stability studying during extreme gust wind occurrence,” Ain Shams Eng. J., vol. 5, no. 1, pp. 137–148, 2014.
   Google Scholar
• W. T. I. Generator, Matlab/Simulink Help. SimPower-Systems Blocks.
   Google Scholar
• V. Gass, F. Strauss, J. Schmidt, and E. Schmid, “Assessing the effect of wind power uncertainty on profitability,” Renew. Sustain. Energy Rev., vol. 15, no. 6, pp. 2677–2683, 2011.
   Web of Science ®Google Scholar
• Commission IE. Wind turbine generator systems—Part 1: Safety requirements. International Standard IEC 61400, vol. 1, 1998.
   Google Scholar
• G. L. Johnson, Wind Energy Systems, 2006.
   Google Scholar
• BTM Consult ApS, International Wind Energy Development: World Market Update 2011, Forecast 2012–2016. København, Denmark: BTM Consult ApS, 2012.
   Google Scholar
• A. S. Ahmed, “Analysis of electrical power form the wind farm sitting on the nile river of Aswan, Egypt,” Renew. Sustain. Energy Rev., vol. 15, no. 3, pp. 1637–1645, 2011.
   Web of Science ®Google Scholar
• G. D’Amico, F. Petroni, and F. Prattico, “Economic performance indicators of wind energy based on wind speed stochastic modeling,” Appl. Energy, vol. 154, pp. 290–297, 2015.
   Web of Science ®Google Scholar
• E. Acha, C. R. Fuerte-Esquivel, H. Ambriz-Perez, and C. Angeles-Camacho, FACTS: Modelling and Simulation in Power Networks. Hoboken, NJ: John Wiley & Sons, 2004.
   Google Scholar
• P. Kundur, N. J. Balu, and M. G. Lauby, Power System Stability and Control. New York, NY: McGraw-Hill, 1994.
   Google Scholar