Intelligent power equipment identification model based on grid topology analysis

• Published in: E3S Web of Conferences Vol. 260; p. 2001
• Main Authors: Su, Shi, Yang, Jun, Zhang, Chunhui, Ma, Xiaowei, Luan, Siping
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Les Ulis EDP Sciences 01.01.2021
• Subjects: Artificial neural networks; Electric power grids; Maintenance; Network topologies; Neural networks; Personnel; Predictions; Target detection; Target recognition; Topology; Workload; Workloads
• ISSN: 2267-1242; 2555-0403; 2267-1242
• DOI: 10.1051/e3sconf/202126002001

With the continuous development of the power grid, power equipment becomes more complex and diverse, which has increased the workload of power maintenance personnel. This paper proposes a method of intelligent identification of distribution network equipment to reduce the power maintenance personnel's workload. The model needs device photos, GPS coordinates, and device topology information of the entire power grid to infer the possible situation of the current device. The model is mainly divided into two parts: target recognition and equipment prediction. In target recognition, we propose a Self-attention target detection network (SA-TDN) that combines Faster-RCNN and Attention mechanism. In equipment prediction part, we use KD-Tree to analyse the grid topology to predict the real identification of the device. We compared this model with other convolutional neural networks (CNN) classification models. The results show that our model is ahead of current models in prediction accuracy.