Skip to main content

Advertisement

SpringerLink
Log in

Effective supervisory controller to extend optimal energy management in hybrid wind turbine under energy and reliability constraints

  • Published:
International Journal of Dynamics and Control Aims and scope Submit manuscript

Abstract

One of the major factors that can increase the efficiency of wind turbines (WTs) is the simultaneous control of the different parts in several operating area. The main problem associated with control design in wind generator is the presence of asymmetric in the dynamic model of the system, which makes a generic supervisory control scheme for the power management of WT complicated. Consequently, supervisory controller can be utilized as the main building block of a wind farm controller (offshore), which meets the grid code requirements and can increased the efficiency and protection of WTs in (region II and III) at the same time. This paper proposes a new effective adaptive supervisory controller for the optimal management and protection simultaneously of a hybrid WT, in both regions (II and III). To this end, the second order sliding mode with the adaptive gain super-twisting control law and fuzzy logic control are used in the machine side, batteries side and grid side converters, to achieve four control objectives: (1) control of the rotor speed to track the optimal value; (2) adaptive control (commutative mode) in order to maximum power point tracking (MPPT) or power limit in various regions; (3)regulate the average DC link voltage near to its nominal value;(4) ensure: a smooth regulation with high quality of power supply injected into the grid, a satisfactory power factor correction and a high harmonic performance in relation to the AC source and eliminating the chattering effect. Results of extensive simulation studies prove that the proposed supervisory control system guarantees to track reference signals with a high harmonic performance despite external disturbance uncertainties.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20

Similar content being viewed by others

References

  1. Nikolova S, Causevski A, Al-Salaymeh A (2013) Optimal operation of conventional power plants in power system with integrated renewable energy sources. Energy Convers Manag 65:697–703

    Article  Google Scholar 

  2. Zou Y, Elbuluk ME, Sozer Y (2013) Simulation comparisons and implementation of induction generator wind power systems. IEEE Trans Ind Appl 49(3):1119–1128

    Article  Google Scholar 

  3. Carranza O, Figueres E, Garcerá G, Gonzalez-Medina R (2013) Analysis of the control structure of wind energy generation systems based on a permanent magnet synchronous generator. Appl Energy 103:522–538

    Article  Google Scholar 

  4. Aissaoui AG, Tahour A, Essounbouli N, Nollet F, Abid M, Chergui MI (2013) A fuzzy-PI control to extract an optimal power from wind turbine. Energy Convers Manag 65:688–696

    Article  Google Scholar 

  5. Abdullah MA, Yatim AHM, Tan CW et al (2012) A review of maximum power point tracking algorithms for wind energy systems. Renew Sustain Energy Rev 16(5):3220–3227

    Article  Google Scholar 

  6. Jaramillo-Lopez F, Kenne G, Francoise Lamnabhi-Lagarrigue (2016) A novel online training neural network-based algorithm for wind speed estimation and adaptive control of PMSG wind turbine system for maximum power extraction. Renew Energy 86:38–48

    Article  Google Scholar 

  7. Syed IM, Venkatesh B, Wu B, Nassif AB (2012) Two-layer control scheme for a supercapacitor energy storage system coupled to a doubly fed induction generator. Electr Power Syst Res 86:76–83

    Article  Google Scholar 

  8. Domínguez-García JL, Gomis-Bellmunt O, Bianchi FD, Sumper A (2012) Power oscillation damping supported by wind power: a review. Renew Sustain Energy Rev 16(7):4994–5006

    Article  MATH  Google Scholar 

  9. Zhao H, Wu Q, Hu S, Xu H, Rasmussen CN (2015) Review of energy storage system for wind power integration support. Appl Energy 137:545–553

    Article  Google Scholar 

  10. Azar AT, Zhu Q (2015) Advances and applications in sliding mode control systems. Springer, Berlin

    Book  MATH  Google Scholar 

  11. Abdeddaim S, Betka A (2013) Optimal tracking and robust power control of the DFIG wind turbine. Int J Electr Power Energy Syst 49:234–242

    Article  Google Scholar 

  12. Gao R, Gao Z (2016) Pitch control for wind turbine systems using optimization, estimation and compensation. Renew Energy 91:501–515

    Article  Google Scholar 

  13. Kumar A, Verma V (2016) Photovoltaic-grid hybrid power fed pump drive operation for curbing the intermittency in PV power generation with grid side limited power conditioning. Int J Electr Power Energy Syst 82:409–419

    Article  Google Scholar 

  14. Yin X, Lin Y, Li W et al (2015) Adaptive sliding mode back-stepping pitch angle control of a variable-displacement pump controlled pitch system for wind turbines. ISA Trans 58:629–634

    Article  Google Scholar 

  15. Saravanakumar R, Jena D (2015a) Validation of an integral sliding mode control for optimal control of a three blade variable speed variable pitch wind turbine. Int J Electr Power Energy Syst 69:421–429

    Article  Google Scholar 

  16. Kim H, Son J, Lee J (2011) A high-speed sliding-mode observer for the sensorless speed control of a PMSM. IEEE Trans Ind Electron 58(9):4069–4077

    Article  Google Scholar 

  17. Ramesh T, Panda AK, Kumar SS (2015) Type-2 fuzzy logic control based MRAS speed estimator for speed sensorless direct torque and flux control of an induction motor drive. ISA Trans 57:262–275

    Article  Google Scholar 

  18. Thirusakthimurugan P, Dananjayan P (2007) A novel robust speed controller scheme for PMBLDC motor. ISA Trans 46(4):471–477

    Article  Google Scholar 

  19. Pichan M, Rastegar H, Monfared M (2013) Two fuzzy-based direct power control strategies for doubly-fed induction generators in wind energy conversion systems. Energy 51:154–162

    Article  Google Scholar 

  20. Li H, Jiahui Wang, Lam H et al (2016) Adaptive sliding mode control for interval type-2 fuzzy systems. IEEE Trans Syst Man Cybern Syst 46(12):1654–1663

    Article  Google Scholar 

  21. Li H, Wang J, Shi P (2016) Output-feedback based sliding mode control for fuzzy systems with actuator saturation. IEEE Trans Fuzzy Syst 24(6):1282–1293

    Article  Google Scholar 

  22. Uhlen K, Foss BA, Gjøsæter OB (1994) Robust control and analysis of a wind-diesel hybrid power plant. IEEE Trans Energy Convers 9(4):701–708

    Article  MATH  Google Scholar 

  23. Evangelista C, Valenciaga F, Puleston P (2013) Active and reactive power control for wind turbine based on a MIMO 2-sliding mode algorithm with variable gains. IEEE Trans Energy Convers 28(3):682–689

    Article  Google Scholar 

  24. Li H, Gao H, Shi P et al (2014) Fault-tolerant control of Markovian jump stochastic systems via the augmented sliding mode observer approach. Automatica 50(7):1825–1834

    Article  MathSciNet  MATH  Google Scholar 

  25. Li H, Shi P, Yao D et al (2016) Observer-based adaptive sliding mode control for nonlinear Markovian jump systems. Automatica 64:133–142

    Article  MathSciNet  MATH  Google Scholar 

  26. Meghni B, Dib D, Azar AT (2016) A second-order sliding mode and fuzzy logic control to optimal energy management in wind turbine with battery storage. Neural Comput Appl 1–18. doi:10.1007/s00521-015-2161-z

  27. Assareh E, Biglari M (2015) A novel approach to capture the maximum power from variable speed wind turbines using PI controller, RBF neural network and GSA evolutionary algorithm. Renew Sustain Energy Rev 51:1023–1037

    Article  Google Scholar 

  28. Witczak P, Patan K, Witczak M, Puig V, Korbicz J (2015) A neural network-based robust unknown input observer design: application to wind turbine. IFAC Pap OnLine 48(21):263–270

    Article  Google Scholar 

  29. Ata R (2015) Artificial neural networks applications in wind energy systems: a review. Renew Sustain Energy Rev 49:534

    Article  Google Scholar 

  30. Suganthi L, Iniyan S, Samuel AA (2015) Applications of fuzzy logic in renewable energy systems—a review. Renew Sustain Energy Rev 48:585–607

    Article  Google Scholar 

  31. Banerjee A, Mukherjee V, Ghoshal SP (2014) Intelligent fuzzy-based reactive power compensation of an isolated hybrid power system. Int J Electr Power Energy Syst 57:164–177

    Article  Google Scholar 

  32. Castillo O, Melin P (2014) A review on interval type-2 fuzzy logic applications in intelligent control. Inf Sci 279:615–631

    Article  MathSciNet  MATH  Google Scholar 

  33. Mérida J, Aguilar LT, Dávila J (2014) Analysis and synthesis of sliding mode control for large scale variable speed wind turbine for power optimization. Renew Energy 71:715–728

    Article  Google Scholar 

  34. Hong C-M, Huang C-H, Cheng F-S (2014) Sliding mode control for variable-speed wind turbine generation systems using artificial neural network. Energy Procedia 61:1626–1629

    Article  Google Scholar 

  35. Benbouzid M, Beltran B, Amirat Y, Yao G, Han J, Mangel H (2014) Second-order sliding mode control for DFIG-based wind turbines fault ride-through capability enhancement. ISA Trans 53(3):827–833

    Article  Google Scholar 

  36. Liu J, Lin W, Alsaadi F, Hayat T (2015) Nonlinear observer design for PEM fuel cell power systems via second order sliding mode technique. Neurocomputing 168:145–151

    Article  Google Scholar 

  37. Evangelista CA, Valenciaga F, Puleston P (2012) Multivariable 2-sliding mode control for a wind energy system based on a double fed induction generator. Int J Hydrogen Energy 37(13):10070–10075

    Article  Google Scholar 

  38. Eltamaly AM, Farh HM (2013) Maximum power extraction from wind energy system based on fuzzy logic control. Electr Power Syst Res 97:144–150

    Article  Google Scholar 

  39. Meghni B, Saadoun A, Dib D, Amirat Y (2015) Effective MPPT technique and robust power control of the PMSG wind turbine. IEEJ Trans Electr Electron Eng 10(6):619–627

    Article  Google Scholar 

  40. Sarrias R, Fernández LM, García CA, Jurado F (2012) Coordinate operation of power sources in a doubly-fed induction generator wind turbine/battery hybrid power system. J Power Sources 205:354–366

    Article  Google Scholar 

  41. Sarrias-Mena R, Fernández-Ramírez LM, García-Vázquez CA, Jurado F (2014) Improving grid integration of wind turbines by using secondary batteries. Renew Sustain Energy Rev 34:194–207

    Article  Google Scholar 

  42. Sharma P, Sulkowski W, Hoff B (2013) Dynamic stability study of an isolated wind–diesel hybrid power system with wind power generation using IG, PMIG and PMSG: a comparison. Int J Electr Power Energy Syst 53:857–866

    Article  Google Scholar 

  43. Liu J, Meng H, Hu Y, Lin Z, Wang W (2015) A novel MPPT method for enhancing energy conversion efficiency taking power smoothing into account. Energy Convers Manag 101:738–748

    Article  Google Scholar 

  44. Nasiri M, Milimonfared J, Fathi SH (2014) Modeling, analysis and comparison of TSR and OTC methods for MPPT and power smoothing in permanent magnet synchronous generator-based wind turbines. Energy Convers Manag 86:892–900

    Article  Google Scholar 

  45. Daili Y, Gaubert J-P, Rahmani L (2015) Implementation of a new maximum power point tracking control strategy for small wind energy conversion systems without mechanical sensors. Energy Convers Manag 97:298–306

    Article  Google Scholar 

  46. Kortabarria I, Andreu J, de Alegría IM, Jiménez J, Gárate JI, Robles E (2014) A novel adaptative maximum power point tracking algorithm for small wind turbines. Renew Energy 63:785–796

    Article  Google Scholar 

  47. Hong C-M, Chen C-H, Tu C-S (2013) Maximum power point tracking-based control algorithm for PMSG wind generation system without mechanical sensors. Energy Convers Manag 69:58–67

    Article  Google Scholar 

  48. Narayana M, Putrus GA, Jovanovic M, Leung PS, McDonald S (2012) Generic maximum power point tracking controller for small-scale wind turbines. Renew Energy 44:72–79

    Article  Google Scholar 

  49. Yin M, Li G, Zhou M, Zhao C (2007) Modeling of the wind turbine with a permanent magnet synchronous generator for integration. In IEEE Power Engineering Society general meeting, pp 1–6

  50. Jain B, Jain S, Nema RK (2015) Control strategies of grid interfaced wind energy conversion system: an overview. Renew Sustain Energy Rev 47:983–996

    Article  Google Scholar 

  51. Benelghali S, El Hachemi Benbouzid M, Charpentier JF, Ahmed-Ali T, Munteanu I (2011) Experimental validation of a marine current turbine simulator: application to a permanent magnet synchronous generator-based system second-order sliding mode control. IEEE Trans Ind Electron 58(1):118–126

    Article  Google Scholar 

  52. Rafiq M, Rehman S, Rehman F, Butt QR, Awan I (2012) A second order sliding mode control design of a switched reluctance motor using super twisting algorithm. Simul Model Pract Theory 25:106–117

    Article  Google Scholar 

  53. Soler J et al (2005) Analog low cost maximum power point tracking PWM circuit for DC loads. In: Proceedings of the fifth IASTED international conference on power and energy systems, Benalmadena, Spain, June 15–17

  54. Gkavanoudis SI, Demoulias CS (2014) A combined fault ride-through and power smoothing control method for full-converter wind turbines employing supercapacitor energy storage system. Electr Power Syst Res 106:62–72

    Article  Google Scholar 

  55. Pena R, Cardenas R, Proboste J, Asher G, Clare J (2008) Sensorless control of doubly-fed induction generators using a rotor-current-based MRAS observer. IEEE Trans Ind Electron 55(1):330–339

    Article  Google Scholar 

  56. Tapia G, Tapia A, Ostolaza JX (2007) Proportional–integral regulator-based approach to wind farm reactive power management for secondary voltage control. IEEE Trans Energy Convers 22(2):488–498

    Article  Google Scholar 

  57. Azar AT (2012) Overview of type-2 fuzzy logic systems. Int J Fuzzy Syst Appl 2(4):1–28

    Article  Google Scholar 

  58. Azar AT (2010) Fuzzy systems. IN-TECH, Vienna. ISBN: 978-953-7619-92-3

  59. Azar AT, Vaidyanathan S (2015) Handbook of research on advanced intelligent control engineering and automation. In: Advances in computational intelligence and robotics (ACIR) book series. IGI Global, USA

  60. Azar AT, Vaidyanathan S (2015) Computational intelligence applications in modeling and control. In: Studies in computational intelligence, vol 575, Springer, Germany

  61. Azar AT, Vaidyanathan S (2015) Chaos modeling and control systems design, studies in computational intelligence, vol 581. Springer, Berlin

    MATH  Google Scholar 

  62. Zhu Q, Azar AT (2015) Complex system modelling and control through intelligent soft computations. In: Studies in fuzziness and soft computing, vol 319. Springer, Germany

  63. Azar AT, Serrano FE (2015) Design and modeling of anti wind up PID controllers. Complex system modelling and control through intelligent soft computations. Springer, Berlin, pp 1–44

    Google Scholar 

  64. Azar AT, Serrano FE (2015) Adaptive sliding mode control of the Furuta pendulum. In: Azar AT, Zhu Q (eds) Advances and applications in sliding mode control systems, studies in computational intelligence, vol 576. Springer, Berlin, pp 1–42. doi:10.1007/978-3-319-11173-5_1

    Google Scholar 

  65. Azar AT, Serrano FE (2015) Deadbeat control for multivariable systems with time varying delays. In: Azar AT, Vaidyanathan S (eds) Chaos modeling and control systems design, studies in computational intelligence, vol 581. Springer, Berlin, pp 97–132. doi:10.1007/978-3-319-13132-06

    Google Scholar 

  66. Mekki H, Boukhetala D, Azar AT (2015) Sliding modes for fault tolerant control. In: Azar AT, Zhu Q (eds) Advances and applications in sliding mode control systems, studies in computational intelligence book Series, vol 576. Springer, Berlin, pp 407–433. doi:10.1007/978-3-319-11173-5_15

    Google Scholar 

  67. Luo Y, Chen Y (2012) Stabilizing and robust fractional order PI controller synthesis for first order plus time delay systems. Automatica 48(9):2159–2167

    Article  MathSciNet  MATH  Google Scholar 

  68. Ebrahimkhani S (2016) Robust fractional order sliding mode control of doubly-fed induction generator (DFIG)-based wind turbines. ISA Trans 63:343–354

    Article  Google Scholar 

  69. Munteanu I, Bacha S, Bratcu AI, Guiraud J, Roye D (2008) Energy-reliability optimization of wind energy conversion systems by sliding mode control. IEEE Trans Energy Convers 23(3):975–985

    Article  Google Scholar 

  70. Beltran B, Ahmed-Ali T, Benbouzid MEH (2008) Sliding mode power control of variable-speed wind energy conversion systems. IEEE Trans Energy Convers 23(2):551–558

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Faculty of Applied Science, University of Kasdi Merbah, 3000, Ouargla, Algeria

    Billel Meghni

  2. Electrical Engineering Department, University LarbiTebessi, Tebessa, Algeria

    Djalel Dib

  3. Faculty of Computers and Information, Benha University, Banha, Al Qalyubiyah, Egypt

    Ahmad Taher Azar

  4. Department of Electronics, University of Badji Mokhtar, Annaba, Algeria

    Abdallah Saadoun

  5. Nanoelectronics Integrated Systems Center (NISC), Nile University, Cairo, Egypt

    Ahmad Taher Azar

Authors
  1. Billel Meghni
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Djalel Dib
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ahmad Taher Azar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Abdallah Saadoun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ahmad Taher Azar.

Appendix

Appendix

See Table 3 and 4.

Table 3 PMSG parameters
Full size table
Table 4 Wind turbine parameters
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Meghni, B., Dib, D., Azar, A.T. et al. Effective supervisory controller to extend optimal energy management in hybrid wind turbine under energy and reliability constraints. Int. J. Dynam. Control 6, 369–383 (2018). https://doi.org/10.1007/s40435-016-0296-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40435-016-0296-0

Keywords

Search

Navigation