Authors

Zhongxian Wang, Yuning Shao, Tengfei Ye, Shuli Sun

Abstract

In order to address the issue of controller stability reduction caused by the fixed damping injection method, this paper provides a variable damping injection for the passive control strategy optimization method in the superconducting magnetic energy storage (SMES) system based on the capacitor-inductor-inductor-capacitor (CLLC) topology structure. First, the paper analyzes the CLLC-SMES system’s charging and discharging principle and gives a port-controlled Hamiltonian-with-dissipation (PCHD) model for the passive controller. Then, the range of damping values is clarified, and the attention is paid to understanding how changes in damping parameters affect system stability on both the alternating current (AC) and direct current (DC) sides. After that, using the self-learning methodology of the back propagation (BP) neural network, a variety of damping parameters and optimization goals are added, and the BP neural network controllers on the AC and DC side are created to provide the variable damping injection for the passive system. Finally, the active and reactive power, the AC side and DC side voltage, and the superconducting magnet discharge current are computed and implemented in a simulation model of the CLLC-SMES passive control system based on the BP neural network. According to the simulation results, the system with the variable damping injection reaches the steady state earlier and has less overshoot than the system with the fixed damping injection. Additionally, the optimization method of the passive control strategy with the variable damping injection based on the BP neural network is capable of improving the power quality and system stability.

Keywords

bp neural network, cllc-smes, fixed damping injection, passive control strategy, smes system, variable damping injection

Citation

  • Journal: Journal of Energy Storage
  • Year: 2024
  • Volume: 86
  • Issue:
  • Pages: 111175
  • Publisher: Elsevier BV
  • DOI: 10.1016/j.est.2024.111175

BibTeX

@article{Wang_2024,
  title={{Research on optimization method for passive control strategy in CLLC-SMES system based on BP neural network}},
  volume={86},
  ISSN={2352-152X},
  DOI={10.1016/j.est.2024.111175},
  journal={Journal of Energy Storage},
  publisher={Elsevier BV},
  author={Wang, Zhongxian and Shao, Yuning and Ye, Tengfei and Sun, Shuli},
  year={2024},
  pages={111175}
}

Download the bib file

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