Necessary Condition to Utilize Adaptive Control in Wind Turbine Systems to Improve Power System Stability
The global capacity of wind power has dramatically increased in recent years. Therefore, improving the technology of wind turbines to take different advantages of this enormous potential in the power grid, could be interesting subject for scientists. The doubly-fed induction generator (DFIG) wind turbine is a popular system due to its many advantages such as the improved power quality, high energy efficiency and controllability, etc. With an increase in wind power penetration in the network and with regard to the flexible control of wind turbines, the use of wind turbine systems to improve the dynamic stability of power systems has been of significance importance for researchers. Subsynchronous oscillations are one of the important issues in the stability of power systems. Damping subsynchronous oscillations by using wind turbines has been studied in various research efforts, mainly by adding an auxiliary control loop to the control structure of the wind turbine. In most of the studies, this control loop is composed of linear blocks. In this paper, simple adaptive control is used for this purpose. In order to use an adaptive controller, the convergence of the controller should be verified. Since adaptive control parameters tend to optimum values in order to obtain optimum control performance, using this controller will help the wind turbines to have positive contribution in damping the network subsynchronous oscillations at different wind speeds and system operating points. In this paper, the application of simple adaptive control in DFIG wind turbine systems to improve the dynamic stability of power systems is studied and the essential condition for using this controller is considered. It is also shown that this controller has an insignificant effect on the dynamic stability of the wind turbine, itself.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1131776Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 451
 Landau I. D, Lozano R., M'Saad M. Adaptive control: Springer Berlin; 1998.
 Åström K. J., Wittenmark B. Adaptive control: Courier Dover Publications; 2013.
 Zhang, S., &Luo, F. L. An improved simple adaptive control applied to power system stabilizer. Power Electronics, IEEE Transactions on. 2009; 24(2), 369-375.
 Irving, E., Barret, J., Charcossey, C., &Monville, J. Improving power network stability and unit stress with adaptive generator control. Automatica, 1979, 15(1), 31-46.
 Barkana I, "Simple adaptive control–a stable direct model reference adaptive control methodology–brief survey," International Journal of Adaptive Control and Signal Processing, 2014, vol. 28, pp. 567-603.
 Walker D. N., Bowler C. L., Jackson R. L., Hodges D. A. Results of subsynchronousresonance test at Mohave. IEEE Trans Power ApSyst 1975; 5:1878–89.
 Group, I. S. R. W., Terms, definitions and symbols for subsynchronous oscillations, IEEE Trans. power Appl. Syst., 1985, PAS-104, 6, pp. 1325-1334.
 Al Jowder F. A. R., Ooi B-T. Series compensation of radial power system by a combination of SSSC and dielectric capacitors. Power Delivery, IEEE Transactions on. 2005;20(1):458-65.
 Bongiorno M., Angquist L., Svensson J. A novel control strategy for subsynchronous resonance mitigation using SSSC. Power Delivery, IEEE Transactions on. 2008;23(2):1033-41.
 De Toledo P. F., Ängquist L., Nee H.-P. Frequency-domain modelling of subsynchronous torsional interaction of synchronous machines and a high voltage direct current transmission link with line-commutated converters. IET generation, transmission & distribution. 2010;4(3):418-31.
 Carlin P. W., Laxson A. X., Muljadi E. B. The history and state of the art of variablespeed wind turbine technology. Natl Renew Energ Lab Tech Rep NREL/TP-500- 28 607; 2001.
 Mokhtari, M., Khazaei, J., &Nazarpour, D. Subsynchronous resonance damping via doubly fed induction generator. International Journal of Electrical Power & Energy Systems, 2013, 53, 876-883.
 Zhu, C., Fan, L., & Hu, M. Control and analysis of DFIG-based wind turbines in a series compensated network for SSR damping. Paper presented at the Power and Energy Society General Meeting, 2010 IEEE.
 Fan L., Miao Z. Mitigating SSR using DFIG-based wind generation. IEEE Trans Sustainable Energy 2013; 3:349–58.
 First benchmark model for computer simulation of subsynchronous resonance, IEEE Trans. power Appl. Syst., 1977, PAS-96, 5, pp. 1565-1572.
 Gautam, D., Vittal, V., Harbour, T., Impact of increased penetration of DFIG-based wind turbine generators on transient and small signal stability of power systems, IEEE Trans. Power Syst., 2009, 24, 3, pp. 1426-1434.
 Fan L., Kavasseri R., Miao Z., Zhu Ch. Modelling of DFIG-based wind farms for SSRanalysis. IEEE Trans Power Deliver 2010; 25:2073–82.
 Pourbeik, P., Wind Farm Integration in British Columbia – Stages 1&2: Planning and Interconnection Criteria, ABB Report, 2005.
 Manwell J. F., McGowan J. G., Rogers A. L., Wind energy explained. Amherst, USA: Wiley, 2002.
 Yang L., Xu Z., Østergaard J., Dong Z. Y., and Wong K. P., "Advanced control strategy of DFIG wind turbines for power system fault ride through," IEEE Trans. Power Syst., 2012, vol. 27, pp. 713-722.
 Miao Z., Fan L. "The art of modelling and simulation of induction generator in wind generation applications using high-order model," Simul. Pract. Theory, 2008, vol. 16, pp. 1239–125.
 Bianchi F. D., De Battista H., and Mantz R. J., Wind turbine control systems: principles, modelling and gain scheduling design: Springer Science & Business Media, 2006.
 Yang L., Xu Z., Østergaard J., Dong Z. Y., Kit P. W., Ma X, Oscillatory stability and eigenvalue sensitivity analysis of A DFIG wind turbine system, IEEE Trans. Energy Convers., 2011, 26, 1, pp. 328-339.
 Pourbeik P. (convener), Modelling and dynamic behavior of wind generation as it relates to power system control and dynamic performance, International Council Large Electrical System (CIGRE), Technical Brochure WGC4.601, 2007.
 Mei F., Pal B., Modal analysis of grid-connected doubly fed induction generators, IEEE Trans. Energy Convers., 2007, 22, 3, pp. 728-736.
 Fan L., Zhu Ch., Miao Z., Kavasseri R., Hu M. Modal analysis of a DFIG-based windfarms influenced with a series compensated network. IEEE Trans EnergConvers 2011; 26:1010–20.
 Jafarian M, Ranjbar A. M. The impact of wind farms with doubly fed inductiongenerators onpower system electromechanical oscillations. Renew Energ 2012; 50:780–5.
 Fan, L., Yin, H., & Miao, Z. On active/reactive power modulation of DFIG-based wind generation for interarea oscillation damping. Energy Conversion, IEEE Transactions on. 2011; 26(2), 513-521.
 Farsangi M. M., Sung Y. H., Lee K. Y. Choice of FACTS device control inputs fordamping interarea oscillations. IEEE Trans Power Syst 2004; 19:1135–43.
 Magaji N., Mustafa M. W. Optimal location and signal selection of UPFC devicefor damping oscillation. Electr Power EnergSyst 2011; 33:1031–42.
 Varma R. K., Auddy S., Semsedini Y. Mitigation of subsynchronous resonance in a series-compensated wind farm using FACTS controllers. Power Delivery, IEEE Transactions on. 2008;23(3):1645-54.
 El-Moursi M. S., Bak-Jensen B., Abdel-Rahman M. H. Novel STATCOM controller for mitigating SSR and damping power system oscillations in a series compensated wind park. Power Electronics, IEEE Transactions on. 2010;25(2):429-41.
 El‐Moursi M. S. Mitigating subsynchronous resonance and damping power system oscillation in a series compensated wind park using a novel static synchronous series compensator control algorithm. Wind Energy. 2012;15(3):363-77.
 Kaufman H., Barkana I., Sobel K., Directadaptive control algorithms,Springer Verlag, New York,1994.