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

Yutong Li, Lili Wang

TL;DR

The paper tackles frequency synchronization and active power sharing in islanded microgrids subject to high-density misbehaving agents. It introduces a distributed resilient secondary control method (DACC) that uses discounted attention-based confidence weights with softmax normalization to downweight misbehaving neighbors based on historical state differences. The authors derive necessary and sufficient conditions for Leader-Follower Resilient Uniformly Ultimately Bounded (LRUUB) stability, and propose a one-time parameter design algorithm to guarantee resilience under HDMA with sparse connectivity. Simulations on a modified IEEE 33-bus microgrid demonstrate that DACC achieves an extremely small ultimate bound ($b\le 2\times 10^{-6}$) and robust convergence where MSR-type and hidden-layer methods fail, highlighting its practical impact for secure MG operation.

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.

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

TL;DR

The paper tackles frequency synchronization and active power sharing in islanded microgrids subject to high-density misbehaving agents. It introduces a distributed resilient secondary control method (DACC) that uses discounted attention-based confidence weights with softmax normalization to downweight misbehaving neighbors based on historical state differences. The authors derive necessary and sufficient conditions for Leader-Follower Resilient Uniformly Ultimately Bounded (LRUUB) stability, and propose a one-time parameter design algorithm to guarantee resilience under HDMA with sparse connectivity. Simulations on a modified IEEE 33-bus microgrid demonstrate that DACC achieves an extremely small ultimate bound () and robust convergence where MSR-type and hidden-layer methods fail, highlighting its practical impact for secure MG operation.

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.
Paper Structure (18 sections, 9 theorems, 59 equations, 9 figures, 1 algorithm)

This paper contains 18 sections, 9 theorems, 59 equations, 9 figures, 1 algorithm.

Table of Contents

  1. introduction
  2. Preliminaries and Problem Formulation
  3. Notation
  4. Graph theory
  5. Modelling of MG system
  6. Modeling of misbehaving agents
  7. Assumption on topology
  8. Assumption on misbehaving states
  9. Problem formulation
  10. Resilient Protocol Design
  11. Weight design
  12. Parameter design
  13. Main Results
  14. Convergence without misbehaving agents
  15. Necessary condition for LRUUB
  16. ...and 3 more sections

Key Result

Theorem 1

Suppose that Assumption as:topology hold with $f=0$. For any $c_n \in [0,\infty)$, $\sigma \in \mathbb{R}_+$ and $\eta \in (0,1]$, the system (dynamic) with foll_al and eq:aij is asymptotically stable at the reference values when there is no misbehaving agents in the network.

Figures (9)

  • Figure 1: An example MG system.
  • Figure 2: Influence function $F_i(\theta_{ij})$.
  • Figure 3: The misbehaving errors of DES 22.
  • Figure 4: The performance of DESs using DACC with $\{ \sigma=0.9,\ \eta=0.4 \}$.
  • Figure 5: Comparison results of DACC with different parameters.
  • ...and 4 more figures

Theorems & Definitions (19)

  • Definition 1
  • Definition 2
  • Remark 1
  • Definition 3
  • Remark 2
  • Remark 3
  • Definition 4
  • Remark 4
  • Remark 5
  • Theorem 1
  • ...and 9 more