Distributed Resilient Secondary Control for Microgrids with Attention-based Weights against High-density Misbehaving Agents
<|reference_start|>Distributed Resilient Secondary Control for Microgrids with Attention-based Weights against High-density Misbehaving Agents: Microgrids (MGs) have been equipped with large-scale distributed energy sources (DESs), and become more vulnerable due to the low inertia characteristic. In particular, high-density misbehaving DESs caused by cascading faults bring a great challenge to frequency synchronization and active power sharing among DESs. To tackle the problem, we propose a fully distributed resilient consensus protocol, which utilizes confidence weights to evaluate the level of trust among agents with a first-order filter and a softmax-type function. We pioneer the analysis of this nonlinear control system from the system operating range and the graph structure perspectives. Both necessary and sufficient conditions are provided to ensure DACC to be uniformly ultimately bounded, even in a robust network with low connectivity. Simulations on a modified IEEE33-bus microgrid testbed with 17 DESs validate that DACC outperforms existing methods in the presence of 8 misbehaving DESs.<|reference_end|>
@article{li2024distributed,
    title={Distributed Resilient Secondary Control for Microgrids with
  Attention-based Weights against High-density Misbehaving Agents},
    author={Yutong Li and Lili Wang},
    journal={arXiv preprint arXiv:2409.11812},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.11812},
    primaryClass={eess.SY cs.SY}
}