PSO-Based Model Predictive Control for Load Frequency Regulation with Wind Turbines

Wei Fan, Zhijian Hu and Veerapandiyan Veerasamy
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Wei Fan: School of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, China
Zhijian Hu: School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore
Veerapandiyan Veerasamy: School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore

Energies, 2022, vol. 15, issue 21, 1-15

Abstract: With the high penetration of wind turbines, many issues need to be addressed in relation to load frequency control (LFC) to ensure the stable operation of power grids. The particle swarm optimization-based model predictive control (PSO-MPC) approach is presented to address this issue in the context of LFC with the participation of wind turbines. The classical MPC model was modified to incorporate the particle swarm optimization algorithm for the power generation model to regulate the system frequency. In addition to addressing the unpredictability of wind turbine generation, the presented PSO-MPC strategy not only addresses the randomness of wind turbine generation, but also reduces the computation burden of traditional MPC. The simulation results validate the effectiveness and feasibility of the PSO-MPC approach as compared with other state-of-the-art strategies.

Keywords: wind turbines; load frequency control; particle swarm optimization; model predictive control (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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