Open AccessClustering methods for the efficient voltage regulation in smart grids
Author(s) -
Abegaz Brook W.
Publication year - 2022
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/tje2.12111
Subject(s) - cluster analysis , computer science , smart grid , voltage , stability (learning theory) , hierarchical clustering , controller (irrigation) , control theory (sociology) , correlation clustering , reliability (semiconductor) , data mining , power (physics) , artificial intelligence , control (management) , engineering , machine learning , agronomy , electrical engineering , biology , physics , quantum mechanics
In this paper, clustering methods are presented to enhance the stability of automatic voltage regulators using the efficient adjustment of their respective gains. The results show that implementations of some of the clustering algorithms provide better reliability and stability for the feedback‐based voltage regulators as compared to the other methods, namely, a model predictive controller (MPC), a gaussian mixture model (GMM), a self‐organizing mapping (SOM) and hierarchical clustering (HC) methods. Specifically, the K‐Means clustering approach (KM) provided superior stability but a slower rise time of the output voltage of the voltage regulators as compared to the other methods. Furthermore, coordination of the clustering methods is tested for a 10 machine, 39 bus power grid system. The results show that the clustering approach could be applied to improve the efficiency of voltage regulation methods in smart grids and related cyber‐physical systems.