Self-Healing Multi-Agent Microgrids: Adaptive Formation Control with LLM-Guided Fault Recovery
Abstract
A novel framework combining multi-agent formation control and LLM-based reasoning for resilient microgrid management. The system uses distributed agents to maintain optimal power distribution while leveraging LLMs to diagnose faults, suggest recovery strategies, and adapt formation patterns for grid stability.
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Research Gap Analysis
Current microgrid control systems lack intelligent fault recovery mechanisms that can adapt formation patterns while maintaining system stability. The integration of LLM-based reasoning with distributed control has not been explored for power systems.
Self-Healing Multi-Agent Microgrids: Adaptive Formation Control with LLM-Guided Fault Recovery
Motivation
Modern microgrids face increasing complexity in managing distributed energy resources while maintaining stability under various fault conditions. Current approaches either focus on rigid control strategies or lack sophisticated fault recovery mechanisms. The integration of formation control with intelligent decision-making systems could revolutionize how microgrids respond to disturbances and maintain optimal operation.
Proposed Approach
1. Multi-Layer Control Architecture
- Base Layer: Distributed formation control for power converters and energy storage systems
- Coordination Layer: Event-triggered communication protocol for efficient resource allocation
- Intelligence Layer: LLM-based reasoning system for fault diagnosis and recovery planning
2. LLM-Enhanced Fault Recovery
- Continuous monitoring of system states and communication patterns
- Real-time analysis of fault signatures using specialized LLM prompts
- Generation of context-aware recovery strategies based on historical patterns
- Dynamic reformation of agent groups to isolate and mitigate faults
3. Adaptive Formation Patterns
- Time-varying formation control based on power demand and supply conditions
- Integration of H∞ tracking control for robustness against disturbances
- Neural observer-based state estimation for incomplete information scenarios
Expected Outcomes
- Reduced system downtime through proactive fault detection and recovery
- Improved power quality and stability during fault conditions
- Enhanced resource utilization through intelligent formation adaptation
- Scalable and flexible architecture for diverse microgrid configurations
Potential Applications
- Rural microgrids with limited maintenance access
- Industrial power systems requiring high reliability
- Renewable energy integration in smart cities
- Military installations with critical power requirements
Proposed Methodology
Develop a three-layer architecture combining distributed formation control, event-triggered communication, and LLM-based reasoning for fault diagnosis and recovery in microgrids.
Potential Impact
This research could significantly improve microgrid reliability and resilience, particularly in remote or critical infrastructure applications. The framework could reduce maintenance costs and system downtime while optimizing energy distribution.