Robust super-twisting algorithm-based single-phase sliding mode frequency controller in power systems integrating wind turbines and energy storage systems
Huynh V.V. Naqvi S. Nguyen B.L.-H. Tran A.-T. Shim J.W. Do T.D.
December 2025Nature Research
Scientific Reports
2025#15Issue 1
Frequency regulation in multi-area power systems (MPSs) faces increasing challenges due to the integration of renewable energy sources, such as wind power, and the dynamic behavior of energy storage systems (ESSs). These challenges are further compounded by disturbances from tie-line power exchanges, wind power fluctuations, and variations in battery and flywheel storage. To address this, this paper proposes a robust sliding mode control (SMC) strategy based on a proportional-derivative sliding surface (PD-SS) structure for load frequency control (LFC), leveraging a single-phase approach enhanced by an improved super-twisting algorithm (ISTA). A reduced-order LFC model is introduced to effectively characterize the frequency dynamics. The proposed model explicitly considers lumped disturbances including tie-line power exchanges, wind power fluctuations, and power variations in ESSs of battery and flywheel. A novel SMC scheme is therefore designed based on the simplified model, where the PD-SS structure and single-phase approach eliminate reaching time, ensure immediate trajectory convergence and improve transient performance. An improved super-twisting control law is developed to further enhance robustness by effectively mitigating chattering and oscillation in system dynamics under uncertainties. The global stability of the proposed control strategy is mathematically verified via Lyapunov stability theory. Simulation results under step and stochastic load variations show that the proposed method achieves up to 56% and 84.5% reduction in overshoot compared to PD and PI SMC schemes, respectively, along with a 54.5% improvement in settling time over the PI SMC scheme, thereby confirming its enhanced performance and robustness relative to existing control strategies.
Load frequency control , Multiarea power systems , Sliding mode control
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Modeling Evolutionary Algorithms Simulation and Artificial Intelligence, Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Viet Nam
Department of Energy Operations, Stanford University, Stanford, 94305, CA, United States
Institute of Research and Development, Duy Tan University, Da Nang, 550000, Viet Nam
Department of Electrical Engineering, Sangmyung University, Seoul, 03016, South Korea
Department of Robotics and Mechatronics, School of Engineering and Digital Sciences (SEDS), Nazarbayev University, Astana, 010000, Kazakhstan
Modeling Evolutionary Algorithms Simulation and Artificial Intelligence
Department of Energy Operations
Institute of Research and Development
Department of Electrical Engineering
Department of Robotics and Mechatronics
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