Research on A Methodology of AI-Driven Substation Layout Design Based on Large Language Model

In the design stage of substation projects, there is an enduring task for designing an optimal layout for equipment under various constraints from functional, geometric, and economic aspects. In traditional practices, professionals need to recursively adjust the positions of involved objects in the specialized working spaces to meet the requirements of different projects and to comply with design codes. It is highly dependent on professional skills and understanding of regulatory documents. To streamline this process, we propose an AI-driven substation layout design approach using large language model (LLM). This approach exploits the capacity of large language model by converting the task of generating the full layout plan into generating sequences of the positions of involved equipment. We have finetuned two models based on a base model Llama3.1-7B with two auto-generated datasets under professional guidance. One dataset consists of the input requirements and output layout scheme, while the other is augmented through chain-of-thought (CoT) to elicit the underlying language model to retrieve optimal capacity with the consideration of specific design constraints. To implement the output scheme, an automated procedure is further developed to translate the scheme into the corresponding layout plan and information models.