SubstationAI: Multimodal Large Model-Based Approaches for Analyzing Substation Equipment Faults
This addresses the problem of manual expertise limitations in power system reliability for domain specialists, though it appears incremental as an application of existing MLLM techniques to a new domain.
The paper tackled substation equipment fault analysis by proposing SubstationAI, a multimodal large language model-based approach that outperformed existing models like GPT-4 in accuracy and practicality for fault cause analysis, repair suggestions, and preventive measures.
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.