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Adaptive Damping Control Strategy of Wind Integrated Power System

Author

Listed:
  • Jun Deng

    (State Grid Shaanxi Electric Power Research Institute, Xi’an 710100, China)

  • Jun Suo

    (State Grid Shaanxi Electric Power Research Institute, Xi’an 710100, China)

  • Jing Yang

    (School of Electrical & Electronic Engineering, North China Electric Power University, Changping District, Beijing 102206, China)

  • Shutao Peng

    (State Grid Shaanxi Electric Power Research Institute, Xi’an 710100, China)

  • Fangde Chi

    (State Grid Shaanxi Electric Power Company, Xi’an 710048, China)

  • Tong Wang

    (School of Electrical & Electronic Engineering, North China Electric Power University, Changping District, Beijing 102206, China)

Abstract

Random variation of grid-connected wind power can cause stochastic variation of the power system operating point. This paper proposes a new scheme to design an adaptive damping controller by tracking the variation of system operating points and updating the controller’s functions to achieve a robust damping control effect. Firstly, the operating space is classified into different modes according to the classification of wind power outputs. Multiple power system stabilizers (PSSs) are then designed. Secondly, the method of optimal classification and regression decision tree (CART) is utilized for classifying subspaces of system operating point and it is proposed that the on-line measurements from wide area measurement system (WAMS) are used for tracking the dynamic behaviors of stochastic drifting point and thus guide the updating of appropriate PSSs be switched on adaptively. A 16-generator-68-bus power system integrated with wind power is presented as a test system to demonstrate that the adaptive control scheme by use of the CART can damp multi-mode oscillations effectively when the wind power output changes.

Suggested Citation

  • Jun Deng & Jun Suo & Jing Yang & Shutao Peng & Fangde Chi & Tong Wang, 2019. "Adaptive Damping Control Strategy of Wind Integrated Power System," Energies, MDPI, vol. 12(1), pages 1-18, January.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:1:p:135-:d:194233
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    References listed on IDEAS

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    Cited by:

    1. Anastasios Dounis, 2019. "Special Issue “Intelligent Control in Energy Systems”," Energies, MDPI, vol. 12(15), pages 1-9, August.
    2. Xue Lin & Lixia Sun & Ping Ju & Hongyu Li, 2019. "Stochastic Control for Intra-Region Probability Maximization of Multi-Machine Power Systems Based on the Quasi-Generalized Hamiltonian Theory," Energies, MDPI, vol. 13(1), pages 1-16, December.

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