An Improved Cuckoo Search Clustering Method for Line Loss Data of Transformer District with DGs

Autor:	Aiqing Yu, Wu Dongwen, Hu Zhiqiang, Lingang Yu, Wang Jun, Zhu Liang
Rok vydání:	2021
Předmět:	History Computer science Data mining Line loss Cluster analysis Cuckoo search computer.software_genre computer Computer Science Applications Education Transformer (machine learning model)
Zdroj:	Journal of Physics: Conference Series. 2093:012017
ISSN:	1742-6596 1742-6588
DOI:	10.1088/1742-6596/2093/1/012017
Popis:	For the low-voltage transformer district with distributed generations (DGs), the traditional theoretical calculation method of line loss is not applicable. This paper presents a novel clustering method for line loss data of transformer district with DGs, which combined an improved Cuckoo Search algorithm and K-Means clustering algorithm. Firstly, the influence factors of line loss are screened based on the maximum information coefficient, and the line loss index system is established. Secondly, an improved cuckoo search clustering algorithm is proposed to cluster the sample data set to reduce the dependence on the initial clustering center. Finally, the simulation results of 410 samples from a certain area with photovoltaic power supply show the accuracy and effectiveness of the proposed method. The simulation results show that the proposed method is accurate and effective.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::26565ea911a057de10bb06b16ad57bbe https://doi.org/10.1088/1742-6596/2093/1/012017 Zobrazit plný text záznamu