SubstationAI: Multimodal Large Model-Based Approaches for Analyzing Substation Equipment Faults

TL;DR

SubstationAI leverages a multimodal large language model and a comprehensive database to improve accuracy and efficiency in analyzing substation equipment faults, surpassing existing models.

Contribution

This paper introduces SubstationAI, the first dedicated multimodal large model for substation fault analysis, with a large database and knowledge enhancement techniques.

Findings

SubstationAI outperforms GPT-4 in fault analysis accuracy.

The database contains 40,000 multimodal entries.

Data augmentation improves model robustness.

Abstract

The reliability of substation equipment is crucial to the stability of power systems, but traditional fault analysis methods heavily rely on manual expertise, limiting their effectiveness in handling complex and large-scale data. This paper proposes a substation equipment fault analysis method based on a multimodal large language model (MLLM). We developed a database containing 40,000 entries, including images, defect labels, and analysis reports, and used an image-to-video generation model for data augmentation. Detailed fault analysis reports were generated using GPT-4. Based on this database, we developed SubstationAI, the first model dedicated to substation fault analysis, and designed a fault diagnosis knowledge base along with knowledge enhancement methods. Experimental results show that SubstationAI significantly outperforms existing models, such as GPT-4, across various evaluation metrics, demonstrating higher accuracy and practicality in fault cause analysis, repair suggestions, and preventive measures, providing a more advanced solution for substation equipment fault analysis.