@article{Liu2025, 
author = {Guobao Liu and Aobo Zhang and Xiaoming Yan and Shicheng Huo and Huai Liu},
title = {Robust data-driven load frequency control of interconnected power systems with Markovian jump parameters},
year = {2025},
journal = {Electronic Research Archive},
volume = {33},
number = {10},
pages = {6538-6557},
keywords = {data-driven control, LFC, unknown noise, MJPS, data-based LMIs},
url = {https://www.sciopen.com/article/10.3934/era.2025289},
doi = {10.3934/era.2025289},
abstract = {This paper focuses on the robust data-driven load frequency control (LFC) of an interconnected power system involving Markovian jump parameters from damaged data by unknown noise. Firstly, due to the changes in system structure, the LFC model for an interconnected power system involving Markovian jump parameters is described. The vehicle-to-grid technique is also applied to regulate the power system frequency by employing the electric vehicles as a new frequency regulation loop in this model. Secondly, a data-driven control method is used to stabilize the Markovian jump power system (MJPS). Drawing on the damaged data corrupted by unknown noise, two robust stability conditions for the MJPS are formulated, corresponding to the energy-bound approach and the instantaneous-bound approach, respectively. Then, the control gains of the system are obtained by the data-based linear matrix inequalities (LMIs). Finally, the effectiveness of the proposed methods is verified by applying them to a two-area interconnected power system with the participation of electric vehicles.}
}