TY - GEN
T1 - Phase Identification of Power Distribution Systems using Hierarchical Clustering Methods
AU - Zaragoza, Nicholas
AU - Rao, Vittal
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - In order to maintain optimal load balancing of transformers in the power distribution systems, the hierarchical clustering methods are investigated for phase identification purposes. This method is based on one minus linear correlation and Euclidean pair-wise distance measures to cluster loads that are on the same phase. The phases are assigned to loads based on a majority vote of phase labels within each cluster. The hierarchical clustering methods are compared with other clustering methods such as k-means clustering for various loads connected to different phases. The Electric Power Research Institute's (EPRI) ckt5 test circuit is used to generate a synthetic dataset of single-phase load voltage magnitude measurements. Real power load profiles generated from uniform white noise is used with the OpenDSS software to generate the load voltage magnitude measurements. The accuracy and the running time of the proposed hierarchical clustering are comparable with the k-means algorithm.
AB - In order to maintain optimal load balancing of transformers in the power distribution systems, the hierarchical clustering methods are investigated for phase identification purposes. This method is based on one minus linear correlation and Euclidean pair-wise distance measures to cluster loads that are on the same phase. The phases are assigned to loads based on a majority vote of phase labels within each cluster. The hierarchical clustering methods are compared with other clustering methods such as k-means clustering for various loads connected to different phases. The Electric Power Research Institute's (EPRI) ckt5 test circuit is used to generate a synthetic dataset of single-phase load voltage magnitude measurements. Real power load profiles generated from uniform white noise is used with the OpenDSS software to generate the load voltage magnitude measurements. The accuracy and the running time of the proposed hierarchical clustering are comparable with the k-means algorithm.
UR - http://www.scopus.com/inward/record.url?scp=85124334946&partnerID=8YFLogxK
U2 - 10.1109/NAPS52732.2021.9654617
DO - 10.1109/NAPS52732.2021.9654617
M3 - Conference contribution
AN - SCOPUS:85124334946
T3 - 2021 North American Power Symposium, NAPS 2021
BT - 2021 North American Power Symposium, NAPS 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 North American Power Symposium, NAPS 2021
Y2 - 14 November 2021 through 16 November 2021
ER -