Novel power capture optimization based sensorless maximum power point tracking strategy and internal model controller for wind turbines systems driven SCIG

Abstract

Under the trends to using renewable energy sources as alternatives to the traditional ones, it is important to contribute to the fast growing development of these sources by using powerful soft computing methods. In this context, this paper introduces a novel structure to optimize and control the energy produced from a variable speed wind turbine which is based on a squirrel cage induction generator (SCIG) and connected to the grid. The optimization strategy of the harvested power from the wind is realized by a maximum power point tracking (MPPT) algorithm based on fuzzy logic, and the control strategy of the generator is implemented by means of an internal model (IM) controller. Three IM controllers are incorporated in the vector control technique, as an alternative to the proportional integral (PI) controller, to implement the proposed optimization strategy. The MPPT in conjunction with the IM controller is proposed as an alternative to the traditional tip speed ratio (TSR) technique, to avoid any disturbance such as wind speed measurement and wind turbine (WT) characteristic uncertainties. Based on the simulation results of a six KW-WECS model in Matlab/Simulink, the presented control system topology is reliable and keeps the system operation around the desired response.

Key words： power optimization; wind energy conversion system; maximum power point tracking (MPPT); fuzzy logic; internal model (IM) controller

References

1 The Ministry of Energy. Mines, Water and Environment of Morocco (MEMWE) (2016). Press Release. 2016, available at the website of men.gov.ma

2 Munteanu I, Bratcu A I, Ceangǎ E. Wind turbulence used as searching signal for MPPT in variable-speed wind energy conversion systems. Renewable Energy, 2009, 34(1): 322–327

3 Narayana M, Putrus G A, Jovanovic M, Leung P S, McDonald S. Generic maximum power point tracking controller for small-scale wind turbines. Renewable Energy, 2012, 44: 72–79

4 Eltamaly A M, Farh H M. Maximum power extraction from wind energy system based on fuzzy logic control. Electric Power Systems Research, 2013, 97: 144–150

5 Zhao H, Wu Q, Rasmussen C N, Blanke M. Adaptive speed control of a small wind energy conversion system for maximum power point tracking. IEEE Transactions on Energy Conversion, 2014, 29(3): 576–584

6 Yaakoubi A E, Asselman A, Djebli A, Aroudam E H. A MPPT strategy based on fuzzy control for a wind energy conversion system. Procedia Technology, 2016, 22: 697–704

7 Senjyu T, Ochi Y, Kikunaga Y, Tokudome M, Yona A, Muhando E B, Urasaki N, Funabashi T. Sensor-less maximum power point tracking control for wind generation system with squirrel cage induction generator. Renewable Energy, 2009, 34(4): 994–999

8 Meharrar A, Tioursi M, Hatti M, Boudghène Stambouli A. A variable speed wind generator maximum power tracking based on adaptative neuro-fuzzy inference system. Expert Systems with Applications, 2011, 38(6): 7659–7664

9 Hong C M, Chen C H, Tu C S. Maximum power point tracking-based control algorithm for PMSG wind generation system without mechanical sensors. Energy Conversion and Management, 2013, 69: 58–67

10 Kim K H, Van T L, Lee D C, Song S H, Kim E H. Maximum output power tracking control in variable-speed wind turbine systems considering rotor inertial power. IEEE Transactions on Industrial Electronics, 2013, 60(8): 3207–3217

11 Elnaggar M, Abdel Fattah H A, Elshafei A L. Maximum power tracking in WECS (Wind energy conversion systems) via numerical and stochastic approaches. Energy, 2014, 74: 651–661

12 Belmokhtar K, Doumbia M L, Agbossou K. Novel fuzzy logic based sensorless maximum power point tracking strategy for wind turbine systems driven DFIG (doubly-fed induction generator). Energy, 2014, 76: 679–693

13 Liu P, Yang W T, Yang C E, Hsu C L. Sensorless wind energy conversion system maximum power point tracking using Takagi–Sugeno fuzzy cerebellar model articulation control. Applied Soft Computing, 2015, 29: 450–460

14 Huang C, Li F, Jin Z. Maximum power point tracking strategy for large-scale wind generation systems considering wind turbine dynamics. IEEE Transactions on Industrial Electronics, 2015, 62(4): 2530–2539

15 Abdullah M A, Yatim A H M, Tan C W, Saidur R. A review of maximum power point tracking algorithms for wind energy systems. Renewable & Sustainable Energy Reviews, 2012, 16(5): 3220–3227

16 Eltamaly A M, Alolah A I, Farh H M. Maximum power extraction from utility-interfaced wind turbines. 2016-06, available at the website of intechopen.com

17 Nasiri M, Milimonfared J, Fathi S H. Modeling, analysis and comparison of TSR and OTC methods for MPPT and power smoothing in permanent magnet synchronous generator-based wind turbines. Energy Conversion and Management, 2014, 86: 892–900

18 Daili Y, Gaubert J P, Rahmani L. Implementation of a new maximum power point tracking control strategy for small wind energy conversion systems without mechanical sensors. Energy Conversion and Management, 2015, 97: 298–306

19 Dalala Z M, Zahid Z H, Lai J S. New overall control strategy for small-scale WECS in MPPT and stall regions with mode transfer control. IEEE Transactions on Energy Conversion, 2013, 28(4): 1082–1092

20 Uddin M N, Patel N. Maximum power point tracking control of IPMSG incorporating loss minimization and speed sensorless schemes for wind energy system. IEEE Transactions on Industry Applications, 2016, 52(2): 1902–1912

21 Hussain J, Mishra M K. Adaptive maximum power point tracking control algorithm for wind energy conversion systems. IEEE Transactions on Energy Conversion, 2016, 31(2): 697–705

22 Zeng Q, Chang L, Shao R. Fuzzy-logic-based maximum power point tracking strategy for Pmsg variable-speed wind turbine generation systems. In: Canadian Conference on Electrical and Computer Engineering, 2008

23 Chekkal S, Lahaçani N A, Aouzellag D, Ghedamsi K. Fuzzy logic control strategy of wind generator based on the dual-stator induction generator. Electrical Power and Energy Systems, 2014, 59: 166–175

24 Belmokhtar K, Doumbia M L, Agbossou K. Novel fuzzy logic based sensorless maximum power point tracking strategy for wind turbine systems driven DFIG (doubly-fed induction generator). Energy, 2014, 76: 679–693

25 Lee J, Kim Y S. Sensorless fuzzy-logic-based maximum power point tracking control for a small-scale wind power generation systems with a switched-mode rectifier. IET Renewable Power Generation, 2016, 10(2): 194–202

26 Domínguez-García J L, Gomis-Bellmunt O, Trilla-Romero L, Junyent-Ferré A. Indirect vector control of a squirrel cage induction generator wind turbine. Computers & Mathematics with Applications (Oxford, England), 2012, 64(2): 102–114

27 Boukhezzar B, Siguerdidjane H. Nonlinear control with wind estimation of a DFIG variable speed wind turbine for power capture optimization. Energy Conversion and Management, 2009, 50(4): 885–892

28 Chen Z, Guerrero J M, Blaabjerg F. A review of the state of the art of power electronics for wind turbines. IEEE Transactions on Power Electronics, 2009, 24(8): 1859–1875

29 Tamaarat A, Benakcha A. Performance of PI controller for control of active and reactive power in DFIG operating in a grid-connected variable speed wind energy conversion system. Frontiers in Energy, 2014, 8(3): 371–378

30 Duong M Q, Grimaccia F, Leva S, Mussetta M, Ogliari E. Pitch angle control using hybrid controller for all operating regions of SCIG wind turbine system. Renewable Energy, 2014, 70: 197–203

31 Duong M Q, Grimaccia F, Leva S, Mussetta M, Le K H. Improving transient stability in a grid-connected squirrel-cage induction generator wind turbine system using a fuzzy logic controller. Energies, 2015, 8(7): 6328–6349

32 Zadeh L A. Fuzzy sets. Information and Control, 1965, 8(3): 338–353

33 Anaya-Lara O, Campos-Gaona D, Moreno-Goytia E, Adam G. Offshore Wind Energy Generation: Control, Protrction, and Integration to Electrical Systems. Hoboke: Wiley-Blackwell, 2014