Partición de una Red Eléctrica de Distribución Aplicando Algoritmos de Agrupamiento K-means y DBSCAN
Contenido principal del artículo
Resumen
En este artículo se propone la metodología para realizar la partición eléctrica de una red de distribución utilizando algoritmos de agrupamiento de datos como K-means y DBSCAN. Los datos se obtienen generando variaciones en los parámetros de la red y simulando el perfil de voltaje con el software OpenDSS. La metodología propuesta se implementa en redes de distribución estándar de prueba IEEE de 34 y 123 barras. Los resultados obtenidos son comparados con métodos obtenidos de la literatura.
Descargas
Detalles del artículo
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial 4.0.
Aviso de Derechos de Autor
La Revista Técnica "energía" está bajo licencia internacional Creative Commons Reconocimiento-NoComercial 4.0.
Citas
“Community-detection-based approach to distribution network partition,” CSEE Journal of Power and Energy Systems, 2022, doi: 10.17775/cseejpes.2020.04150.
Y. Chai, L. Guo, C. Wang, Z. Zhao, X. Du, and J. Pan, “Network Partition and Voltage Coordination Control for Distribution Networks With High Penetration of Distributed PV Units,” IEEE Transactions on Power Systems, vol. 33, no. 3, pp. 3396–3407, May 2018, doi: 10.1109/TPWRS.2018.2813400.
B. Zhao, Z. Xu, C. Xu, C. Wang, and F. Lin, “Network Partition-Based Zonal Voltage Control for Distribution Networks with Distributed PV Systems,” IEEE Trans Smart Grid, vol. 9, no. 5, pp. 4087–4098, 2018, doi: 10.1109/TSG.2017.2648779.
Y. Chen, M. G. Fadda, and A. Benigni, “Decentralized Load Estimation for Distribution Systems Using Artificial Neural Networks,” IEEE Trans Instrum Meas, vol. 68, no. 5, pp. 1333–1342, 2019, doi: 10.1109/TIM.2018.2890052.
M. Bahramipanah, M. Nick, R. Cherkaoui, and M. Paolone, “Network clustering for voltage control in active distribution network including energy storage systems,” in 2015 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), IEEE, Feb. 2015, pp. 1–5. doi: 10.1109/ISGT.2015.7131916.
M. Mao, Z. Wu, D. Xu, and J. Xu, “Community-Detection-Based Approach to Distribution Network Partition,” pp. 1–11, 2015, doi: 10.17775/CSEEJPES.2020.04150.
Z. M. Ali, A. M. Galal, S. Alkhalaf, and I. Khan, “An Optimized Algorithm for Renewable Energy Forecasting Based on Machine Learning,” Intelligent Automation & Soft Computing, vol. 35, no. 1, pp. 755–767, 2022, doi: 10.32604/iasc.2023.027568.
Z. Ernesto Jaramillo, J. R. Castro, T. Castillo, and R. Reategui, “Data Mining in Electrical Distribution Networks: Optimal Location of Pilot Bus,” in 2021 IEEE Fifth Ecuador Technical Chapters Meeting (ETCM), IEEE, Oct. 2021, pp. 1–5. doi: 10.1109/ETCM53643.2021.9590646.
K. P. Schneider, “IEEE PES Test Feeders,” IEEE Transactions on Power Systems, p. 99, 2017, [Online]. Available: https://cmte.ieee.org/pes-testfeeders/resources/
A. Marot, S. Tazi, B. Donnot, and P. Panciatici, “Guided Machine Learning for Power Grid Segmentation,” Proceedings - 2018 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2018, Dec. 2018, doi: 10.1109/ISGTEurope.2018.8571843.
H. Gu, X. Chu, and Y. Liu, “Partitioning Active Distribution Networks by Using Spectral Clustering,” iSPEC 2020 - Proceedings: IEEE Sustainable Power and Energy Conference: Energy Transition and Energy Internet, no. 202008150000004, pp. 510–515, 2020, doi: 10.1109/iSPEC50848.2020.9351132.
Y. Zou and H. Li, “Study on Power Grid Partition and Attack Strategies Based on Complex Networks,” Front Phys, vol. 9, no. January, pp. 1–8, 2022, doi: 10.3389/fphy.2021.790218.
F. Liu, B. Gu, S. Qin, K. Zhang, L. Cui, and G. Xie, “Power grid partition with improved biogeography-based optimization algorithm,” Sustainable Energy Technologies and Assessments, vol. 46, Aug. 2021, doi: 10.1016/j.seta.2021.101267.
Y. Wang, Y. G. Li, H. Xie, B. Y. Wu, and Y. N. Yang, “Cluster division in wind farm through ensemble modelling,” IET Renewable Power Generation, vol. 16, no. 7, pp. 1299–1315, 2022, doi: 10.1049/rpg2.12276.