| Issue |
E3S Web Conf.
Volume 260, 2021
2021 International Conference on Advanced Energy, Power and Electrical Engineering (AEPEE2021)
|
|
|---|---|---|
| Article Number | 02001 | |
| Number of page(s) | 8 | |
| Section | Power Electronics Technology and Application | |
| DOI | https://doi.org/10.1051/e3sconf/202126002001 | |
| Published online | 19 May 2021 | |
Intelligent power equipment identification model based on grid topology analysis
1 Electric Power Research Institute of Yunnan Power Grid Co., Ltd, Kunming, China
2 Yuanjiang Power Supply Bureau of Yunnan Power Grid Co., Ltd, Yuxi, China
3 Yuxi Power Supply Bureau of Yunnan Power Grid Co., Ltd, Yuxi, China
* Corresponding author: luansiping17516@163.com
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.
© The Authors, published by EDP Sciences, 2021
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