Load frequency control with an event-triggered mechanism for nonlinear power systems based on the improved grey wolf optimization algorithm

Yan Chen; Xingyue Liu; Fengying Zeng; Kaibo Shi; Fanglu Yang; Yan Chen; Xingyue Liu; Fengying Zeng; Kaibo Shi; Fanglu Yang

doi:10.3934/math.2025844

AIMS Mathematics

2025, Volume 10, Issue 8: 18887-18912. doi: 10.3934/math.2025844

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Load frequency control with an event-triggered mechanism for nonlinear power systems based on the improved grey wolf optimization algorithm

1.
School of Electronic Information and Electrical Engineering, Chengdu University, Chengdu 610106, Sichuan, China
2.
AECC Sichuan Gas Turbine Research Institute, Mianyang 621000, Sichuan, China

Received: 04 July 2025 Revised: 03 August 2025 Accepted: 12 August 2025 Published: 20 August 2025
MSC : 93-XX

This study focused on the load frequency control problem of power systems by integrating the event-triggered mechanism (ETM) and the improved grey wolf optimization (IGWO) algorithm. First, a nonlinear power system (NPS) model incorporating an energy storage system (ESS) and renewable energy sources (RESs) was constructed. The model considers the nonlinear characteristics of governors and turbines, and the takagi-sugeno (T-S) fuzzy theory is introduced to handle the nonlinear terms in the model. Subsequently, during the sampling process, an ETM is introduced, and the improved grey wolf algorithm is used to find the optimal event-triggered parameters to reduce unnecessary information transmission. Second, based on the Lyapunov functional, the stability criterion of the system under disturbance conditions was derived, a controller was designed, and the control gain was determined by solving linear matrix inequalities (LMIs). Finally, the performance differences between the traditional ETM and the ETM combined with IGWO were compared. The results show that the optimization method can further reduce the bandwidth resource consumption while ensuring system stability and control effectiveness.
- nonlinear power system,
- T-S fuzzy theory,
- load frequency control,
- event-triggered mechanism,
- improved grey wolf optimization algorithm
Citation: Yan Chen, Xingyue Liu, Fengying Zeng, Kaibo Shi, Fanglu Yang. Load frequency control with an event-triggered mechanism for nonlinear power systems based on the improved grey wolf optimization algorithm[J]. AIMS Mathematics, 2025, 10(8): 18887-18912. doi: 10.3934/math.2025844

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Abstract

This study focused on the load frequency control problem of power systems by integrating the event-triggered mechanism (ETM) and the improved grey wolf optimization (IGWO) algorithm. First, a nonlinear power system (NPS) model incorporating an energy storage system (ESS) and renewable energy sources (RESs) was constructed. The model considers the nonlinear characteristics of governors and turbines, and the takagi-sugeno (T-S) fuzzy theory is introduced to handle the nonlinear terms in the model. Subsequently, during the sampling process, an ETM is introduced, and the improved grey wolf algorithm is used to find the optimal event-triggered parameters to reduce unnecessary information transmission. Second, based on the Lyapunov functional, the stability criterion of the system under disturbance conditions was derived, a controller was designed, and the control gain was determined by solving linear matrix inequalities (LMIs). Finally, the performance differences between the traditional ETM and the ETM combined with IGWO were compared. The results show that the optimization method can further reduce the bandwidth resource consumption while ensuring system stability and control effectiveness.

References

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