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Development of Low Voltage__ Network Template Part I- Load Profile Clustering and Substation Classification:substation clustering and classification


Reference:

Li, R., Gu, C., Li, F., Shaddick, G. and Dale, M., 2015. Development of Low Voltage__ Network Template Part I- Load Profile Clustering and Substation Classification:substation clustering and classification. IEEE Transactions on Power Systems, 30 (6), 6981990.

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Official URL:

http://dx.doi.org/10.1109/TPWRS.2014.2371474

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Abstract

In order to improve low voltage (LV) network visibility without extensive monitoring and integrate low carbon technologies (LCTs) in a cost-effective manner, this paper proposes a novel three-stage network load profiling method. It uses real-time information monitored from selective representative areas to develop network templates. The three stages are: clustering, classification and scaling. It can be used to identify the loading conditions of unmonitored LV systems with similar fixed data to those monitored LV substations. In the clustering stage, hierarchical clustering and K-means are used to cluster substations into groups based on the shape of the monitored load profiles. The classification tool designed with multinomial logistic regression maps an unmonitored LV substation into the most probable templates by using routinely available fixed data. Finally, clusterwise weighted constrained regression is employed to estimate peak for individual LV substations and the developed templates. The three-stage profiling is demonstrated on a practical system in the U.K. under the umbrella of a smart grid trail project. Ten LV templates are developed by using the metered data from 800 monitored LV substations. A comprehensive comparison between the estimated peaks using the three-stage process and the metered peaks suggests that the methodology can achieve superior accuracy. This is part I of the paper, introducing clustering and classification. The scaling (peak estimation) process will be introduced in part II of the paper.

Details

Item Type Articles
CreatorsLi, R., Gu, C., Li, F., Shaddick, G. and Dale, M.
DOI10.1109/TPWRS.2014.2371474
Related URLs
URLURL Type
http://www.scopus.com/inward/record.url?scp=84919427369&partnerID=8YFLogxKUNSPECIFIED
DepartmentsFaculty of Engineering & Design > Electronic & Electrical Engineering
Faculty of Science > Mathematical Sciences
Research CentresCentre for Sustainable Power Distribution
EPSRC Centre for Doctoral Training in Statistical Mathematics (SAMBa)
RefereedYes
StatusPublished
ID Code46832

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