User-Transformer Connectivity Relationship Identification Based on Knowledge-Driven Approaches

Abstract

Accurate user-transformer connectivity relationship (UTCR) plays a key role in fine management of low-voltage distribution network (LVDN) i.e., load expansion, line loss management, and electrical service restoration after outage. Limited data and low discriminability and noise in data increase the difficulty to identify UTCR for the existing data analytics methods. To overcome these hurdles, this paper proposes a novel UTCR algorithm which combining the data preprocessing with multi-dimensional priori knowledge based on voltage characteristics in LVDN. Firstly, the prior knowledge related to UTCR are refined on account of voltage correlation characteristics of users at different locations to provide theoretical foundation. Then, Z-score and principal component analysis are combined to standardize and extract features from the original voltage data to magnify the differences between data and reduce the impact of data noise. Further, on the basis of the prior knowledge of voltage correlation characteristics, a knowledge-driven identification model is proposed to identify users with wrong UTCR and their real UTCR. Finally, the performance of the proposed algorithm is verified on simulated LVNDs. The comparison analysis between the proposed method and other published methods and the impact of the number of principal components on the identification accuracy are also investigated. The results indicate that the proposed method achieves higher recognition accuracy than other published methods with low discriminability and noise in data.

Keywords

• User-transformer connectivity relationship identification
• low-voltage distribution network
• data pre-processing
• voltage correlation characteristics
• knowledge-driven approaches