The Cluster Method of Heterogeneous Distributed Units in a Low Voltage Distribution Network

• Main Authors: Li, H. (Author), Liu, H. (Author), Liu, M. (Author), Song, H. (Author), Wang, T. (Author)
• Format: Article
• Language: English
• Published: MDPI 2022
• Subjects: cluster; Cluster; Cluster method; Day-ahead; day-ahead output curve; Day-ahead output curve; Division of labor; Electric power transmission networks; Extraction; feature extraction; Feature extraction; Features extraction; K-mean algorithms; K-means algorithm; K-means clustering; logical aggregation; Logical aggregation; Low voltages; Output curve; Voltage distribution measurement
• Online Access: View Fulltext in Publisher

 With the large amounts of small capacity and heterogeneous distributed electricity units connected to the distribution power network, there exist increasingly complex management challenges. In this paper, a new management scheme that can classify and divide the distributed units according to their adjustable characteristics is proposed, which consequently forms an effective collection of fragmented adjustable ability and promotes the utilization of micropower resources. Inspired by the social division of labor in the biological community, the approach is based on a logical aggregation with the division of labor. A feature extraction method was acquired on the basis of the daily output curve, which reduces the data dimension and, subsequently, clusters the output feature points by the K-means algorithm. The simulation is performed by taking the measured output curve of low voltage distributed units on the low voltage side. The experimental results analyze the characteristics of seven classes of distributed units, allocate two main features, and reorganize them into a cluster; so, the “5-dimensional feature array” is reduced to “2-dimensional feature points”. The results demonstrate that the proposed cluster method can enable the power grid to identify and classify the distributed units automatically. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.