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PMU-Based FOPID Controller of Large-Scale Wind-PV Farms for LFO Damping in Smart Grid

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  • معلومة اضافية
    • الموضوع:
      2021
    • Collection:
      Istituto Nazionale di Fisica Nucleare (INFN): Open Access Repository
    • نبذة مختصرة :
      Due to global warming problems and increasing environmental pollution, there is a strong tendency to install and apply renewable energy power plants (REPPs) around the world. On the other hand, with the increasing development of information and communication technology (ICT) infrastructures, power systems are using these infrastructures to act as smart grids. In fact, future modern power systems should be considered as smart grids with many small and large scale REPPs. One of the main problems and challenges of the REPPs is uncertainty and fluctuation of electrical power generation. Accordingly, a suitable solution can be combination of different types of REPPs. So, the penetration rate of large-scale wind-PV farms (LWPF) is expected to increase sharply in the coming years. Given that the LWPFs are added to the grid or will replace fossil fuel power plants, they should be able to play the important roles of synchronous generators such as power low-frequency oscillation (LFO) damping. In this paper, an LFO damping system is suggested for a LWPF, based on a phasor measurement unit (PMU)-based fractional-order proportional–integral–derivative (FOPID) controller with wide range of stability area and proper robustness to many power system uncertainties. Finally, the performance of the proposed method is evaluated under different operating conditions in a benchmark smart system.
    • Relation:
      url:https://www.openaccessrepository.it/communities/itmirror; https://www.openaccessrepository.it/record/142859
    • الرقم المعرف:
      10.1109/access.2021.3094170
    • الدخول الالكتروني :
      https://doi.org/10.1109/access.2021.3094170
      https://www.openaccessrepository.it/record/142859
    • Rights:
      info:eu-repo/semantics/openAccess ; https://creativecommons.org/licenses/by/4.0/
    • الرقم المعرف:
      edsbas.E1834482