Electric Power System Text Information Mining and Analysis Algorithm Combined with Natural Language Processing

In EPS, the text data such as equipment fault records, operation and maintenance logs and maintenance reports contain abundant equipment status information. By using natural language processing (NLP) technology, a large number of text data in electric power system (EPS) can be automatically processed, thus extracting information related to the health status of equipment. The purpose of this article is to explore and design an EPS text information mining and analysis algorithm combined with NLP technology. Aiming at the limitations of traditional methods in EPS text information processing, this article proposes a novel algorithm to improve the accuracy of information extraction and processing efficiency. Methodologically, the algorithm integrates NLP, feature extraction and text classification, and realizes the deep mining of EPS text data. The results show that, compared with traditional methods, the proposed algorithm shows higher accuracy in text classification and information extraction, and significantly improves the efficiency of data processing. The algorithm provides strong technical support for EPS operation and maintenance management and fault prediction, and promotes the intelligent development of EPS.