Research on Intelligent Matching Algorithm of Substation Naming Duplicate Checking System

The purpose of this study is to develop an efficient substation naming duplicate checking system to solve the management problems caused by repeated or similar substation naming in power system. In the feature extraction stage, TF-IDF (term frequency-inverse document frequency) method is used to extract text features, and electric power industry-related terms and rules are introduced as auxiliary features, so as to build a professional terminology dictionary and allocate additional weights. In the model construction stage, the Transformer model is used to capture the sequence information, and a binary classification model is constructed to judge whether the names of two substations are duplicate or similar. Transformer model is based on self-attention mechanism, which can effectively deal with the dependencies in sequence data, and realize binary classification by adding position coding and full connection layer. The experimental results show that the binary classification algorithm based on Transformer model performs well in judging the repeatability of substation names, with an accuracy rate of 0.92, a recall rate of 0.88 and a F1 value of 0.90. The algorithm can effectively identify potential naming conflicts, and has high accuracy and robustness. By combining natural language processing and machine learning technology, this study provides a new idea and method for the development of substation naming duplicate checking system. The proposed intelligent matching algorithm not only improves the management level of power system, but also reduces the operational errors and security risks caused by improper naming.