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

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Fractional Order Control and Synchronization of Chaotic Systems

Part of the book series: Studies in Computational Intelligence ((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.

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Authors and Affiliations

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

    B. Meghni

  2. Department of Electrical Engineering, University Larbi Tebessi, Tébessa, Algeria

    D. Dib & S. Ghoudelbourk

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

    Ahmad Taher Azar

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

    Ahmad Taher Azar

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

    A. Saadoun

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  1. B. Meghni
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  2. D. Dib
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  5. A. Saadoun
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Correspondence to Ahmad Taher Azar .

Editor information

Editors and Affiliations

  1. Faculty of Computers and Information, Benha University Faculty of Computers and Information, AND Nanoelectronics Integrated Systems Center (NISC), Nile University, Egypt. , Benha, Egypt

    Ahmad Taher Azar

  2. Research and Development Centre, Vel Tech University Research and Development Centre, Chennai, Tamil Nadu, India

    Sundarapandian Vaidyanathan

  3. Department of Mathematics and CS, University of Tebessa Department of Mathematics and CS, Tebessa, Algeria

    Adel Ouannas

Appendix A

Appendix A

Tables 3, 4 and 5.

Table 3 Nomenclatures and Abbreviations
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Table 4 PMSG parameters
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Table 5 Wind turbine parameters
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Meghni, B., Dib, D., Azar, A.T., Ghoudelbourk, S., Saadoun, A. (2017). Robust Adaptive Supervisory Fractional Order Controller for Optimal Energy Management in Wind Turbine with Battery Storage. In: Azar, A., Vaidyanathan, S., Ouannas, A. (eds) Fractional Order Control and Synchronization of Chaotic Systems. Studies in Computational Intelligence, vol 688. Springer, Cham. https://doi.org/10.1007/978-3-319-50249-6_6

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