Reliable Communication in Distributed Photovoltaic Sensor Networks: A Large Language Model-Driven Approach

Area of Science:

• Energy Systems Engineering
• Artificial Intelligence in Industrial IoT
• Network Optimization

Background:

• Distributed photovoltaic (DPV) systems offer sustainable industrial energy but face monitoring challenges.
• Current monitoring systems struggle with data transmission constraints and timely fault detection.

Purpose of the Study:

• To develop a hierarchical optimization framework for reliable DPV system monitoring.
• To minimize latency and downtime in DPV systems through intelligent data management and diagnostics.

Main Methods:

• Integration of hysteresis-based traffic shaping at the network layer.
• Application of Large Language Model (LLM)-driven diagnostics with physics-informed prompt engineering for zero-shot root cause analysis.
• Priority-based scheduling for critical fault data transmission.

Main Results:

• Achieved a 46.08% to 49.87% reduction in P50 latency under a 10 Mbps gateway bandwidth.
• Demonstrated effective zero-shot root cause analysis without extensive training data.
• Enabled precise fault diagnosis through detailed LLM-powered assessments.

Conclusions:

• The proposed framework enhances the reliability and efficiency of DPV system monitoring.
• LLM-driven diagnostics offer a powerful, data-efficient approach to fault analysis in industrial IoT.
• This integrated solution addresses key technological constraints in DPV system management.