Self-Adaptive Microgrids with LLM-Powered Predictive Maintenance and Dynamic Resource Allocation
Abstract
A novel framework integrating large language models with microgrid control systems for intelligent predictive maintenance and resource optimization. The system combines natural language processing of maintenance logs, sensor data analysis, and reinforcement learning to create self-healing power networks that can anticipate failures and automatically adjust resource allocation.
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Research Gap Analysis
Current systems lack integration between natural language understanding of maintenance data and control systems, missing opportunities for knowledge-driven optimization and automated adaptation
Self-Adaptive Microgrids with LLM-Powered Predictive Maintenance and Dynamic Resource Allocation
Motivation
Current microgrid systems face significant challenges in maintenance optimization and resource allocation, particularly when dealing with renewable energy integration and equipment failures. While existing solutions employ traditional predictive maintenance approaches, they often fail to leverage the wealth of unstructured data in maintenance logs, operator reports, and historical incident records. Additionally, current systems lack the ability to autonomously adapt their control strategies based on learned patterns and natural language feedback from operators.
Proposed Approach
The research proposes a three-tier architecture that combines the power of large language models with advanced control systems:
1. Natural Language Understanding Layer
- Implement specialized LLMs trained on maintenance logs, technical documentation, and operator reports
- Extract actionable insights from unstructured text data using self-certainty metrics
- Create knowledge graphs linking equipment states, maintenance actions, and outcomes
2. Predictive Analytics Layer
- Combine traditional sensor data analysis with LLM-extracted insights
- Develop hybrid neural networks that process both structured and unstructured data
- Implement reinforcement learning for optimal maintenance scheduling
3. Adaptive Control Layer
- Design dynamic resource allocation algorithms that respond to predicted maintenance needs
- Implement fault-tolerant control strategies using distributed consensus mechanisms
- Develop real-time optimization for power flow considering maintenance schedules
Expected Outcomes
- Reduction in unexpected equipment failures by 40-60%
- Improved resource utilization efficiency by 15-25%
- Enhanced system reliability through proactive maintenance
- Reduced operational costs through optimized scheduling
- Creation of transferable knowledge bases for similar systems
Potential Applications
- Smart city power distribution networks
- Industrial microgrids with critical reliability requirements
- Renewable energy integration systems
- Remote community power systems
- Military base power infrastructure
Proposed Methodology
Three-tier architecture combining LLMs for maintenance log analysis, hybrid predictive analytics, and adaptive control systems with reinforcement learning
Potential Impact
Significant reduction in maintenance costs, improved system reliability, and creation of self-healing power networks that can serve as templates for next-generation smart grid systems