Open AccessStructure optimization based on phase‐locked loop and controller parameters optimization of Y‐connected modular multi‐level converter for fractional frequency offshore wind power system under weak grid
Author(s) -
Meng Yongqing,
Jia Feng,
Wu Kang,
Duan Ziyue,
Wang Xiuli,
Yang Yong,
Wang Xifan
Publication year - 2022
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/gtd2.12538
Subject(s) - control theory (sociology) , phase locked loop , decoupling (probability) , particle swarm optimization , small signal model , controller (irrigation) , modular design , transmission system , matlab , grid , electric power system , computer science , engineering , transmission (telecommunications) , power (physics) , control engineering , electronic engineering , mathematics , jitter , algorithm , voltage , artificial intelligence , biology , operating system , geometry , control (management) , quantum mechanics , agronomy , physics , electrical engineering , telecommunications
Based on a practical long‐distance large‐capacity transmission engineering case, the small‐signal stability of fractional frequency transmission system (FFTS) with Y‐connected modular multi‐level converter (Y‐MMC) is mainly researched in this paper. Different from the previous control strategies of Y‐MMC, this paper establishes a frequency decoupling mathematical model and proposes the decoupling control strategies. Then, the small‐signal model of the Y‐MMC system is obtained. Considering the existence of phase locked loop (PLL) will deteriorate the stability of the system under weak grid, this paper applies the short‐circuit ratio (SCR) for analysis and proposed a new structure of PLL, called phase‐shifted PLL (PS‐PLL), to improve the small‐signal stability of the system. In addition, for better stability and dynamic response performance of the closed‐loop control system, this paper proposes a new parameter optimization objective function for parameters adjustment. Controller parameters optimization is conducted by using the particle swarm optimization (PSO) algorithm and stability evaluation of the system is conducted based on the eigenvalue distribution. Finally, the feasibility and superiority of the proposed strategies and optimization are verified in MATLAB/ Simulink.