Intelligent power equipment identification model based on grid topology analysis

Shi Su,Jun Yang,Xiaowei Ma,Siping Luan,Chunhui Zhang,S Trukhanov,C.-W Chow

doi:10.1051/e3sconf/202126002001

Abstract

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.

Highlights

How to quickly and accurately know equipment-related information during the overhaul and maintenance of power distribution network equipment is an important part of efficient and safe operation and maintenance
2.1 Faster-RCNN In order to speed up the detection, after R-convolutional neural networks (CNN) [5] and Fast-RCNN [6], Faster-RCNN [7] is proposed
Faster-RCNN is mainly composed of Conv layers, RoI pooling, Region Proposal Network, classification and regression layers

Summary

Introduction

How to quickly and accurately know equipment-related information during the overhaul and maintenance of power distribution network equipment is an important part of efficient and safe operation and maintenance. The mainstream is to use target detection to identify. Target detection and identification equipment cannot achieve intelligent identification of electrical equipment, because it cannot uniquely identify the equipment. This paper proposed a new power equipment prediction algorithm. It included a selfattention target detection network (SA-TDN) that combines Faster Region-based Convolutional Neural Networks (Faster-RCNN) and Attention mechanism, and a topology. The entire recognition model is divided into target detection (SA-TDN) and device prediction. According to the topology of the power grid, the target recognition part predicts and outputs the identification of equipment. This paper introduces the current research status of electrical equipment identification, and puts forward our research scheme. The second part introduces the related theory of the identification direction of electrical equipment.

Related theories

Convolutional block attention module

Intelligent identification model

Experimental analysis and comparison

Conclusion