Advertisement

Robust Adaptive Supervisory Fractional Order Controller for Optimal Energy Management in Wind Turbine with Battery Storage

  • B. Meghni
  • D. Dib
  • Ahmad Taher Azar
  • S. Ghoudelbourk
  • A. Saadoun
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 688)

Abstract

To address the challenges of poor grid stability, intermittency of wind speed, lack of decision-making, and low economic benefits, many countries have set strict grid codes that wind power generators must accomplish. 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. A high performance controller can significantly increase the amount and quality of energy that can be captured from wind. 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 of WTs, the stability and intermittency problems of wind power generation. This Chapter proposes a new robust adaptive supervisory controller for the optimal management of a variable speed turbines (VST) and a battery energy storage system (BESS) in both regions (II and III) simultaneously under wind speed variation and grid demand changes. To this end, the second order sliding mode (SOSMC) with the adaptive gain super-twisting control law and fuzzy logic control (FLC) are used in the machine side, BESS side and grid side converters. The control objectives are fourfold:
  1. (i)

    Control of the rotor speed to track the optimal value;

     
  2. (ii)

    Maximum Power Point Tracking (MPPT) mode or power limit mode for adaptive control;

     
  3. (iii)

    Maintain the DC bus voltage close to its nominal value;

     
  4. (iv)

    Ensure: a smooth regulation of grid active and reactive power quantity, 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.

Keywords

Power management A high performance Supervisory control Wind turbine Fuzzy logic Second order sliding mode control, power limit 

References

  1. 1.
    Nikolova, S., Causevski, A., & Al-Salaymeh, A. (2013). Optimal operation of conventional power plants in power system with integrated renewable energy sources. Energy Conversion and Management, 65(2013), 697–703.CrossRefGoogle Scholar
  2. 2.
    Zou, Y., Elbuluk, M. E., & Sozer, Y. (2013). Simulation comparisons and implementation of induction generator wind power systems. IEEE Transactions on Industry Applications, 49(3), 1119–1128.CrossRefGoogle Scholar
  3. 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. Applied Energy, 103(2013), 522–538.CrossRefGoogle Scholar
  4. 4.
    Aissaoui, A. G., Tahour, A., Essounbouli, N., Nollet, F., Abid, M., & Chergui, M. I. (2013). A Fuzzy-PI control to extract an optimal power from wind turbine. Energy Conversion and Management, 65(2013), 688–696.CrossRefGoogle Scholar
  5. 5.
    Abdullah, M. A., Yatim, A. H. M., Tan, C. W., et al. (2012). A review of maximum power point tracking algorithms for wind energy systems. Renewable and Sustainable Energy Reviews, 16(5), 3220–3227.CrossRefGoogle Scholar
  6. 6.
    Jaramillo-Lopez, F., Kenne, G., & Lamnabhi-Lagarrigue, F. (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. Renewable Energy, 86(2016), 38–48.CrossRefGoogle Scholar
  7. 7.
    Syed, I. M., Venkatesh, B., Wu, B., & Nassif, A. B. (2012). Two-layer control scheme for a supercapacitor energy storage system coupled to a Doubly fed induction generator. Electric Power Systems Research, 86(2012), 76–83.CrossRefGoogle Scholar
  8. 8.
    Domínguez-García, J. L., Gomis-Bellmunt, O., Bianchi, F. D., & Sumper, A. (2012). Power oscillation damping supported by wind power: a review. Renewable and Sustainable Energy Reviews, 16(7), 4994–5006.CrossRefzbMATHGoogle Scholar
  9. 9.
    Zhao, H., Wu, Q., Hu, S., Xu, H., & Rasmussen, C. N. (2015). Review of energy storage system for wind power integration support. Applied Energy, 137(2015), 545–553.CrossRefGoogle Scholar
  10. 10.
    Azar, A. T., & Zhu, Q. (2015). Advances and Applications In Sliding Mode Control Systems. Studies in computational intelligence (vol. 576). Germany: Springer. ISBN: 978-3-319-11172-8.Google Scholar
  11. 11.
    Abdeddaim, S., & Betka, A. (2013). Optimal tracking and robust power control of the DFIG wind turbine. International Journal of Electrical Power & Energy Systems, 49(2013), 234–242.CrossRefGoogle Scholar
  12. 12.
    Gao, R., & Gao, Z. (2016). Pitch control for wind turbine systems using optimization, estimation and compensation. Renewable Energy, 91(2016), 501–515.CrossRefGoogle Scholar
  13. 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. International Journal of Electrical Power & Energy Systems, 82(2016), 409–419.CrossRefGoogle Scholar
  14. 14.
    Yin, X. X., Lin, Y. G., Li, W., Liu, H. W., & Gu, Y. J. (2015). Adaptive sliding mode back-stepping pitch angle control of a variable-displacement pump controlled pitch system for wind turbines. ISA Transactions, 58(2015), 629–634.CrossRefGoogle Scholar
  15. 15.
    Saravanakumar, R., & Jena, D. (2015). Validation of an integral sliding mode control for optimal control of a three blade variable speed variable pitch wind turbine. International Journal of Electrical Power & Energy Systems, 69(2015), 421–429.CrossRefGoogle Scholar
  16. 16.
    Kim, H., Son, J., & Lee, J. (2011). A high-speed sliding-mode observer for the sensorless speed control of a PMSM. IEEE Transactions on Industrial Electronics, 58(9), 4069–4077.CrossRefGoogle Scholar
  17. 17.
    Ramesh, T., Panda, A. K., & Kumar, S. S. (2015). Type-2 fuzzy logic control based MRAS speed estimator for speed sensorless direct torque and flux control of an induction motor drive. ISA Transactions, 57(2915), 262–275.CrossRefGoogle Scholar
  18. 18.
    Thirusakthimurugan, P., & Dananjayan, P. (2007). A novel robust speed controller scheme for PMBLDC motor. ISA Transactions, 46(4), 471–477.CrossRefGoogle Scholar
  19. 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(2013), 154–162.CrossRefGoogle Scholar
  20. 20.
    Uhlen, K., Foss, B. A., & Gjøsæter, O. B. (1994). Robust control and analysis of a wind-diesel hybrid power plant. IEEE Transactions on Energy Conversion, 9(4), 701–708.CrossRefzbMATHGoogle Scholar
  21. 21.
    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 Transactions on Energy Conversion, 28(3), 682–689.CrossRefGoogle Scholar
  22. 22.
    Billela, M., Dib, D., & Azar, A. T. (2016). A Second order sliding mode and fuzzy logic control to Optimal Energy Management in PMSG Wind Turbine with Battery Storage. In Neural Computing and Applications. Springer. doi: 10.1007/s00521-015-2161-z.
  23. 23.
    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. Renewable and Sustainable Energy Reviews, 51(2015), 1023–1037.CrossRefGoogle Scholar
  24. 24.
    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-PapersOnLine, 48(21), 263–270.CrossRefGoogle Scholar
  25. 25.
    Ata, R. (2015). Artificial neural networks applications in wind energy systems: a review. Renewable and Sustainable Energy Reviews, 49(2015), 534–562.CrossRefGoogle Scholar
  26. 26.
    Suganthi, L., Iniyan, S., & Samuel, A. A. (2015). Applications of fuzzy logic in renewable energy systems–A review. Renewable and Sustainable Energy Reviews, 48(2015), 585–607.CrossRefGoogle Scholar
  27. 27.
    Banerjee, A., Mukherjee, V., & Ghoshal, S. P. (2014). Intelligent fuzzy-based reactive power compensation of an isolated hybrid power system. International Journal of Electrical Power & Energy Systems, 57(2014), 164–177.CrossRefGoogle Scholar
  28. 28.
    Castillo, O., & Melin, P. (2014). A review on interval type-2 fuzzy logic applications in intelligent control. Information Sciences, 279(2014), 615–631.MathSciNetCrossRefzbMATHGoogle Scholar
  29. 29.
    Mérida, J., Aguilar, L. T., & Dávila, J. (2014). Analysis and synthesis of sliding mode control for large scale variable speed wind turbine for power optimization. Renewable Energy, 71(2014), 715–728.CrossRefGoogle Scholar
  30. 30.
    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(2014), 1626–1629.CrossRefGoogle Scholar
  31. 31.
    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 Transactions, 53(3), 827–833.CrossRefGoogle Scholar
  32. 32.
    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(2015), 145–151.CrossRefGoogle Scholar
  33. 33.
    Evangelista, C. A., Valenciaga, F., & Puleston, P. (2012). Multivariable 2-sliding mode control for a wind energy system based on a double fed induction generator. International Journal of Hydrogen Energy, 37(13), 10070–10075.CrossRefGoogle Scholar
  34. 34.
    Eltamaly, A. M., & Farh, H. M. (2013). Maximum power extraction from wind energy system based on fuzzy logic control. Electric Power Systems Research, 97(2013), 144–150.CrossRefGoogle Scholar
  35. 35.
    Meghni, B., Saadoun, A., Dib, D., & Amirat, Y. (2015). Effective MPPT technique and robust power control of the PMSG wind turbine. IEEJ Transactions on Electrical and Electronic Engineering, 10(6), 619–627.CrossRefGoogle Scholar
  36. 36.
    Sarrias, R., Fernández, L. M., García, C. A., & Jurado, F. (2012). Coordinate operation of power sources in a doubly-fed induction generator wind turbine/battery hybrid power system. Journal of Power Sources, 205(2012), 354–366.CrossRefGoogle Scholar
  37. 37.
    Sarrias-Mena, R., Fernández-Ramírez, L. M., García-Vázquez, C. A., & Jurado, F. (2014). Improving grid integration of wind turbines by using secondary batteries. Renewable and Sustainable Energy Reviews, 34(2014), 194–207.CrossRefGoogle Scholar
  38. 38.
    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. International Journal of Electrical Power & Energy Systems, 53(2013), 857–866.CrossRefGoogle Scholar
  39. 39.
    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 Conversion and Management, 10(2015), 738–748.CrossRefGoogle Scholar
  40. 40.
    Nasiri, M., Milimonfared, J., & Fathi, S. H. (2014). 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, 86(2014), 892–900.CrossRefGoogle Scholar
  41. 41.
    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 Conversion and Management, 97(2015), 298–306.CrossRefGoogle Scholar
  42. 42.
    Kortabarria, I., Andreu, J., de Alegría, I. M., Jiménez, J., Gárate, J. I., & Robles, E. (2014). A novel adaptative maximum power point tracking algorithm for small wind turbines. Renewable Energy, 63(2014), 785–796.CrossRefGoogle Scholar
  43. 43.
    Ghedamsi, K., & Aouzellag, D. (2010). Improvement of the performances for wind energy conversions systems. International Journal of Electrical Power & Energy Systems, 32(9), 936–945.CrossRefGoogle Scholar
  44. 44.
    Poitiers, F., Bouaouiche, T., & Machmoum, M. (2009). Advanced control of a doubly-fed induction generator for wind energy conversion. Electric Power Systems Research, 79(7), 1085–1096.CrossRefGoogle Scholar
  45. 45.
    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 Conversion and Management, 69(2013), 58–67.CrossRefGoogle Scholar
  46. 46.
    Zou, Y., Elbuluk, M., & Sozer, Y. (2013). Stability analysis of maximum power point tracking (MPPT) method in wind power systems. IEEE Transactions on Industry Applications, 49(3), 1129–1136.CrossRefGoogle Scholar
  47. 47.
    Narayana, M., Putrus, G. A., Jovanovic, M., Leung, P. S., & McDonald, S. (2012). Generic maximum power point tracking controller for small-scale wind turbines. Renewable Energy, 44(2012), 72–79.CrossRefGoogle Scholar
  48. 48.
    Yin, M., Li, G., Zhou, M., & Zhao, C. (2007). Modeling of the wind turbine with a permanent magnet synchronous generator for integration. In Power Engineering Society General Meeting, IEEE 2007, June 24-28, 2007, Tampa, FL, (pp. 1–6). doi: 10.1109/PES.2007.385982.
  49. 49.
    SimPowerSystems, T. M. (2010). Reference, Hydro-Qu{é}bec and the MathWorks. Inc., Natick, MA.Google Scholar
  50. 50.
    Jain, B., Jain, S., & Nema, R. K. (2015). Control strategies of grid interfaced wind energy conversion system: An overview. Renewable and Sustainable Energy Reviews, 47(2015), 983–996.CrossRefGoogle Scholar
  51. 51.
    Benelghali, S., El Hachemi Benbouzid, M., Charpentier, J. F., 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 Transactions on Industrial Electronics, 58(1), 118–126.CrossRefGoogle Scholar
  52. 52.
    Rafiq, M., Rehman, S., Rehman, F., Butt, Q. R., & Awan, I. (2012). A second order sliding mode control design of a switched reluctance motor using super twisting algorithm. Simulation Modelling Practice and Theory, 25(2012), 106–117.CrossRefGoogle Scholar
  53. 53.
    Soler, J., Daroqui, E., Gimeno, F.J., Seguí-Chilet, S., & Orts, S. (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 Sysemst, Benalmadena, Spain, June 15–17, 2005.Google Scholar
  54. 54.
    Gkavanoudis, S. I., & Demoulias, C. S. (2014). A combined fault ride-through and power smoothing control method for full-converter wind turbines employing Supercapacitor Energy Storage System. Electric Power Systems Research, 106(2014), 62–72.CrossRefGoogle Scholar
  55. 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 Transactions on Industrial Electronics, 55(1), 330–339.CrossRefGoogle Scholar
  56. 56.
    Tapia, G., Tapia, A., & Ostolaza, J. X. (2007). Proportional–integral regulator-based approach to wind farm reactive power management for secondary voltage control. IEEE Transactions on Energy Conversion, 22(2), 488–498.CrossRefGoogle Scholar
  57. 57.
    Azar, A. T. (2012). Overview of type-2 fuzzy logic systems. International Journal of Fuzzy System Applications, 2(4), 1–28.CrossRefGoogle Scholar
  58. 58.
    Azar, A. T. (2010). Fuzzy systems. Vienna: IN-TECH. ISBN 978-953-7619-92-3.Google Scholar
  59. 59.
    Azar, A.T., & 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.Google Scholar
  60. 60.
    Azar, A. T., & Vaidyanathan, S. (2015). Computational intelligence applications in modeling and control. Studies in computational intelligence (vol. 575). Germany: Springer. ISBN 978-3-31911016-5.Google Scholar
  61. 61.
    Azar, A. T., & Vaidyanathan, S. (2015). Chaos modeling and control systems design, studies in computational intelligence (Vol. 581). Germany: Springer. ISBN 978-3-319-13131-3.zbMATHGoogle Scholar
  62. 62.
    Zhu, Q., & Azar, A. T. (2015). Complex system modelling and control through intelligent soft computations. Studies in fuzziness and soft computing (vol. 319). Germany: Springer. ISBN: 978-3-31912882-5 123.Google Scholar
  63. 63.
    Azar, A.T., & Serrano, F.E. (2015). Design and modeling of anti wind up PID controllers. In Q. Zhu & A. T. Azar (Eds.), Complex system modelling and control through intelligent soft computations, Studies in Fuzziness and Soft Computing (vol. 319, pp. 1–44). Germany: Springer, Germany. doi: 10.1007/978-3-319-12883-2_1.
  64. 64.
    Azar, A. T., & Serrano, F. E. (2015). Adaptive sliding mode control of the furuta pendulum. In A. T. Azar & Q. Zhu, (Eds.) Advances and Applications in Sliding Mode Control systems, Studies in Computational Intelligence, (vol. 576, pp. 1–42). Berlin/Heidelberg: Springer GmbH. doi: 10.1007/978-3-319-11173-5_1.
  65. 65.
    Azar, A. T., & Serrano, F. E. (2015). Deadbeat control for multivariable systems with time varying delays. In A. T. Azar & S. Vaidyanathan (Eds.), Chaos modeling and control systems design, studies in computational intelligence (vol 581, pp 97–132). Berlin: Springer GmbH. doi: 10.1007/978-3-319-13132-0_6.
  66. 66.
    Mekki, H., Boukhetala, D., & Azar, A. T. (2015). Sliding modes for fault tolerant control. In A.T. Azar & Q Zhu (Eds.) Advances and applications in sliding mode control systems, studies in computational intelligence book Series (vol. 576, pp 407–433). Berlin: Springer GmbH. doi: 10.1007/978-3-319-11173-5_15.
  67. 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.MathSciNetCrossRefzbMATHGoogle Scholar
  68. 68.
    Ebrahimkhani, S. (2016). Robust fractional order sliding mode control of doubly-fed induction generator (dfig)-based wind turbines. ISA transactions, 2016. In Press.Google Scholar
  69. 69.
    Munteanu, I., Bacha, S., Bratcu, A. I., Guiraud, J., & Roye, D. (2008). Energy-reliability optimization of wind energy conversion systems by sliding mode control. IEEE Transactions on Energy Conversion, 23(3), 975–985.CrossRefGoogle Scholar
  70. 70.
    Beltran, B., Ahmed-Ali, T., & Benbouzid, M. E. H. (2008). Sliding mode power control of variable-speed wind energy conversion systems. IEEE Transactions on Energy Conversion, 23(2), 551–558.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • B. Meghni
    • 1
  • D. Dib
    • 2
  • Ahmad Taher Azar
    • 3
    • 4
  • S. Ghoudelbourk
    • 2
  • A. Saadoun
    • 5
  1. 1.Faculty of Applied Science, Department of Electrical EngineeringUniversity of KasdiMerbahOuarglaAlgeria
  2. 2.Department of Electrical EngineeringUniversity Larbi TebessiTébessaAlgeria
  3. 3.Faculty of Computers and InformationBenha UniversityBanhaEgypt
  4. 4.Nanoelectronics Integrated Systems Center (NISC)Nile UniversityCairoEgypt
  5. 5.Department of ElectronicsUniversity of Badji MokhtarAnnabaAlgeria

Personalised recommendations