Automatic Identification Method of HPLC Platform Topology Based on Characteristic Data Extraction

Abstract

The topological structure of distribution network system is complex, and the operation state changes frequently, so the obtained distribution network topological information has a high error. Therefore, this article proposes a clustering feature extraction method of load curve based on singular value decomposition. The load curve is given by the invariance of singular vectors to improve the generalization ability of feature processing. On the basis of considering the weight of load feature, the data integrity is ensured by singular value curve, and the clustering accuracy is high, which makes the load feature have practical physical significance. The experimental results show that this method can achieve good clustering effect, reduce the clustering time, improve the reliability of data transmission and communication coverage, and meet the communication access requirements of the power Internet of Things sensing layer.

Keywords:

• substation topology
• load data
• feature extraction
• data clustering
• singular value curves

Acknowledgement

The study was supported by Science and Technology Project of State Grid Sichuan Electric Power Company (No. 521997200033).

Data Availability

All datasets generated for this study are included within the article.

Additional information

Funding

The study was supported by Science and Technology Project of State Grid Sichuan Electric Power Company (No. 521997200033).

Notes on contributors

Chao Tang

Chao Tang, Master degree. He works as a senior engineer in State Grid Sichuan Electric Power Research Institute. His main research direction is electric power big data, artificial intelligence, new power grid digital technology.

Zhengwei Chang

Zhengwei Chang, Ph.D. He works as a senior engineer in State Grid Sichuan Electric Power Research Institute. His main research direction is electric power big data, artificial intelligence, new power grid digital technology.

Huihui Liang

Huihui Liang, Ph.D. He works as a senior engineer in State Grid Sichuan Electric Power Research Institute. His main research direction is electric power big data, artificial intelligence, new power grid digital technology.

Linghao Zhang

Linghao Zhang, Ph.D. He works as a senior engineer in State Grid Sichuan Electric Power Research Institute. His main research direction is electric power big data, artificial intelligence, new technology of power grid digitalization.

Bo Pang

Bo Pang, Master degree. He works as an assistant engineer in State Grid Sichuan Electric Power Research Institute. His main research direction is electric power big data, artificial intelligence, image recognition.