Self-Healing Wireless Power Networks using Multi-Agent LLM Controllers
Abstract
A novel system that combines LLM-based multi-agent systems with wireless power transfer networks to create resilient, self-optimizing power distribution systems. The system continuously adapts to failures, misalignments, and changing power demands while maintaining optimal efficiency through intelligent agent coordination.
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Research Gap Analysis
Current WPT systems lack intelligent, adaptive control mechanisms that can respond to complex real-world conditions. While multi-agent systems and LLMs have advanced significantly, their application to power system optimization remains unexplored.
Self-Healing Wireless Power Networks using Multi-Agent LLM Controllers
Motivation
Wireless power transfer (WPT) systems face significant challenges in maintaining optimal efficiency under real-world conditions, including misalignments, component failures, and varying load demands. Current solutions rely on fixed control strategies that cannot adapt dynamically to complex, changing environments. Meanwhile, Large Language Model (LLM) based multi-agent systems have shown remarkable capabilities in complex decision-making and coordination tasks, but haven't been applied to power systems optimization.
Proposed Approach
The research proposes integrating LLM-based multi-agent systems with WPT networks to create an intelligent, self-healing power distribution system. Each power transfer node (transmitter/receiver pair) would be controlled by an LLM agent that can:
- Monitor real-time performance metrics (efficiency, coupling, temperature)
- Communicate with neighboring nodes to coordinate power flow
- Predict potential failures using pattern recognition
- Generate adaptive control strategies based on system state
- Learn from past experiences to improve future decisions
The system would employ a hierarchical agent structure:
- Local agents managing individual WPT nodes
- Regional agents coordinating groups of nodes
- Global agents optimizing overall network performance
To ensure reliability, the system would incorporate:
- Redundant communication channels
- Fallback control mechanisms
- Security measures against potential attacks (like CORBA)
- Distributed decision-making to prevent single points of failure
Expected Outcomes
- 15-20% improvement in overall system efficiency
- 50% reduction in power transfer interruptions
- Near-instant adaptation to component failures
- Predictive maintenance capabilities
- Scalable architecture for large WPT networks
Potential Applications
- Electric vehicle charging networks
- Industrial wireless power systems
- Smart city infrastructure
- Medical device networks
- Autonomous robot charging stations
The system would be particularly valuable in critical infrastructure where power reliability is essential, such as hospitals, data centers, and emergency response systems.
Proposed Methodology
Develop a hierarchical multi-agent system using LLMs to control and optimize WPT networks, incorporating real-time monitoring, predictive analytics, and distributed decision-making.
Potential Impact
This research could revolutionize wireless power distribution by creating self-healing, highly efficient networks that adapt to changing conditions. Applications range from EV charging infrastructure to medical devices, potentially reducing power waste and improving system reliability.