Electricity security risk point identification method based on large language modeling and instruction fine-tuning

Abstract

In the rapid development of the electric power industry, the scale of the electric power system continues to expand, the structure is more and more intricate, electric power safety has become the core element to ensure the stable supply of electricity. The purpose of this paper is to explore the application of large language modeling and command fine-tuning technology in the field of electric power security risk point identification, in order to improve the accuracy and efficiency of identification. This paper elaborates the background of the research, and points out the importance of power security in the current situation. It analyzes the basis of the big language model and command fine-tuning technology, and systematically analyzes the electric power security risk points to reveal the limitations of the traditional identification methods. On this basis, the identification method based on big language model and instruction fine-tuning is constructed, including the overall framework design, data processing, model selection and fine-tuning strategy. The effectiveness of the method is verified through case studies in actual power scenarios, and its practical application is evaluated. It provides new ideas and methods for electric power safety risk point identification and helps the safety development of electric power industry.