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Adaptive robust PSS to enhance stabilization of interconnected power systems with high renewable energy penetration

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  • Cuk Supriyadi, A.N.
  • Takano, H.
  • Murata, J.
  • Goda, T.

Abstract

Today, the penetration of renewable energies in power system significantly increases. However, the intermittent power of renewable energies may cause severe problems of low frequency oscillations in interconnected power systems due to insufficient system damping. Moreover, the operating condition of power system may change any time. This paper proposes a design of adaptive robust power system stabilizer (PSS) to damp low frequency oscillations in an interconnected power system with high renewable energy (RE) penetration. System identification is used to construct an estimated model, and the estimated model will be updated whenever the estimated mismatch exceeds predetermined bound. Based on the estimated model, the PSS controller of each generator will be re-tuned using a genetic algorithm. The optimization problem is formulated to guarantee the robustness of PSS and to increase the damping ratio of the dominant mode. In this work, we also install a memory so that several models and corresponding PSS parameter sets can be stored in the memory and re-used in the similar situations. Moreover, the PSSs on generators are able to control without communications among them; it will reduce investment cost significantly. The performance, effectiveness and robustness of the proposed method have been investigated in an interconnected power system in comparison with a conventional PSS (CPSS). Simulation results confirm that the damping effects of the proposed PSS are much better than that of the CPSS against various operating conditions and system uncertainties.

Suggested Citation

  • Cuk Supriyadi, A.N. & Takano, H. & Murata, J. & Goda, T., 2014. "Adaptive robust PSS to enhance stabilization of interconnected power systems with high renewable energy penetration," Renewable Energy, Elsevier, vol. 63(C), pages 767-774.
    • Handle: RePEc:eee:renene:v:63:y:2014:i:c:p:767-774
    DOI: 10.1016/j.renene.2013.09.044
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    Cited by:

    1. Zhang, Guozhou & Hu, Weihao & Cao, Di & Huang, Qi & Chen, Zhe & Blaabjerg, Frede, 2021. "A novel deep reinforcement learning enabled sparsity promoting adaptive control method to improve the stability of power systems with wind energy penetration," Renewable Energy, Elsevier, vol. 178(C), pages 363-376.
    2. Assi Obaid, Zeyad & Cipcigan, L.M. & Muhssin, Mazin T., 2017. "Power system oscillations and control: Classifications and PSSs’ design methods: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 839-849.

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