AI-Powered Wireless Energy Distribution Networks with Multi-Agent Fault Tolerance
Abstract
A novel framework combining wireless power transfer technology with multi-agent LLM systems to create self-healing, adaptive energy distribution networks. The system uses AI to optimize power transfer paths, predict equipment failures, and automatically reroute energy flow while maintaining system stability under various attack scenarios.
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Research Gap Analysis
Current WPT systems lack intelligent control mechanisms for complex scenarios, while existing multi-agent systems haven't been applied to physical power infrastructure. This research bridges the gap between AI-driven control systems and practical energy distribution.
AI-Powered Wireless Energy Distribution Networks with Multi-Agent Fault Tolerance
Motivation
Wireless power transfer (WPT) technologies show immense promise for next-generation energy distribution, but current systems lack the intelligence to handle dynamic environments, security threats, and complex failure scenarios. Meanwhile, recent advances in LLM-based multi-agent systems demonstrate remarkable capabilities in coordination and adaptive decision-making, yet haven't been applied to critical infrastructure like power systems. Combining these technologies could revolutionize energy distribution networks by creating self-managing, resilient systems.
Proposed Approach
The framework consists of three main components:
1. Intelligent Power Transfer Layer
- Implement distributed WPT nodes with real-time efficiency monitoring
- Deploy neural network-based predictive maintenance systems
- Integrate battery thermal management optimization
2. Multi-Agent Control System
- Deploy LLM-based agents to monitor and control WPT nodes
- Implement secure communication protocols between agents
- Design reflection mechanisms for continuous system improvement
3. Fault Tolerance & Security
- Develop attack detection algorithms for both physical and cyber threats
- Create dynamic power routing algorithms using reinforcement learning
- Implement distributed consensus mechanisms for system state verification
Expected Outcomes
- 15-20% improvement in overall system efficiency
- 40% reduction in downtime through predictive maintenance
- Near-instantaneous fault detection and power rerouting
- Robust operation under various attack scenarios
- Self-optimizing network behavior
Potential Applications
- Smart city power distribution
- Electric vehicle charging networks
- Industrial IoT power management
- Healthcare facility backup systems
- Military field operations
- Disaster response infrastructure
Proposed Methodology
Develop a hierarchical system combining WPT hardware with LLM-based agents for monitoring, control, and optimization. Implement secure communication protocols and fault-tolerant mechanisms using distributed consensus algorithms.
Potential Impact
This research could revolutionize energy distribution by creating self-healing, efficient networks that adapt to changing conditions and threats. Applications range from smart cities to disaster response, potentially reducing energy waste and improving infrastructure resilience.