Power grid inspection based on multimodal foundation models - Hao, Jingbo; Tao, Yang (EAI Endorsed Transactions on Energy Web, 2025)
INTRODUCTION: With the development of large foundation models, power grid inspection is transmitting from traditional deep learning to multimodal foundation models.OBJECTIVES: This paper aims to boost the application of multimodal foundation models for power grid inspection.METHODS: Current research on foundation models and multimodal large language models (LLMs) is introduced respectively. Three application forms of multimodal foundation models in power grid inspection are explored. The reliability of these models is discussed as well.RESULTS: These techniques can significantly reduce the time and cost of inspection by automating the analysis of large amounts of sensor data. They can also improve the accuracy and reliability of inspection by leveraging the understanding and reasoning abilities of LLMs.CONCLUSION: These advanced techniques have shown great application potential in power grid inspection. But it is important to note that they should not entirely replace human inspectors who can validate automatic findings and address possible issues not captured by these models alone.
Keywords: power grid inspection; foundation model; large language model; multimodal application