Overhead transmission line condition assessment based on intention classification and slot filling using optimized BERT model

Abstract

As the key equipment in power system, the running state of overhead transmission lines is affected by various complex and random factors, and the maintenance workload of the line is too heavy to overhaul regularly. Therefore, it is very necessary to build a refined condition assessment system to improve the diagnosis accuracy and maintenance efficiency of overhead transmission lines. Recent years, State Grid Corporation of China (SGCC) has recorded mass of monitoring reports of transmission lines. The natural language processing (NLP) with deep learning model provides an effective way to extract the key defect information from the monitoring reports. In this paper, the joint model of intention classification and slot filling (ICSF) based on bidirectional encoder representation from transformers (BERT) is introduced. To improve the precision of defect information extraction, two optimization models of BERT are presented. The results show that Robustly Optimized BERT Pre-Training Approach (RoBERTa) has achieved better effects on ICSF with the extraction accuracy of 92.22%. Then, the hierarchical weighted scoring method is introduced to score the status of overhead transmission line based on the results of ICSF-RoBERTa. And the assessment results of the overhead transmission line state and the corresponding maintenance strategies are provided according to the scores. Finally, the feasibility of the proposed method is validated by practical cases of line inspection reports.