Distributed Resilient Secondary Control for Microgrids with Attention-based Weights against High-density Misbehaving Agents

TL;DR

This paper introduces a distributed resilient control protocol for microgrids that uses attention-based weights to identify trustworthy agents, improving frequency synchronization and power sharing despite high-density misbehaving agents.

Contribution

It proposes a novel fully distributed consensus protocol with confidence weights and analyzes its stability and robustness in low-connectivity networks.

Findings

Outperforms existing methods in simulations with misbehaving agents

Ensures bounded consensus under low connectivity conditions

Provides necessary and sufficient conditions for system stability

Abstract

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.