Morales F. , García-Torres M. , Velázquez G. , Daumas-Ladouce F. , Gardel-Sotomayor P.E. , Gómez-Vela F. , Divina F. , Vázquez Noguera J.L. , Sauer Ayala C. , Pinto-Roa D.P. , Mello-Román J.C. , BECERRA ALONSO, DAVID
No
Electronics
Article
Científica
2.9
0.628
14/01/2022
000757670800001
2-s2.0-85122880481
Correctly defining and grouping electrical feeders is of great importance for electrical system operators. In this paper, we compare two different clustering techniques, K-means and hierarchical agglomerative clustering, applied to real data from the east region of Paraguay. The raw data were pre-processed, resulting in four data sets, namely, (i) a weekly feeder demand, (ii) a monthly feeder demand, (iii) a statistical feature set extracted from the original data and (iv) a seasonal and daily consumption feature set obtained considering the characteristics of the Paraguayan load curve. Considering the four data sets, two clustering algorithms, two distance metrics and five linkage criteria a total of 36 models with the Silhouette, Davies–Bouldin and Calinski–Harabasz index scores was assessed. The K-means algorithms with the seasonal feature data sets showed the best performance considering the Silhouette, Calinski–Harabasz and Davies–Bouldin validation index scores with a configuration of six clusters. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
Clustering; Distribution network; Energy; Feeder