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<p>This paper investigates intelligent load frequency control (LFC) of the interval type-2 (IT-2) fuzzy power system with an improved memory event-triggered scheme (IMETS). Firstly, IT-2 fuzzy logic is introduced to model environmental uncertainties and nonlinearities more effectively, enhancing system representation accuracy and providing a foundation for robust controller design. Secondly, an IMETS is proposed that utilizes historical data to reduce unnecessary triggering and enhance the precision of event activation. In addition, it adaptively adjusts triggering thresholds in real time according to system evolution, thereby improving data processing efficiency and reducing resource consumption. Furthermore, we introduce a genetic algorithm (GA) to optimize the system parameter configurations. The aim is to achieve a balanced trade-off between dynamic response performance and communication efficiency. Moreover, a semi-looped functional method is adopted to reduce the conservativeness of stability conditions, thereby ensuring the feasibility of linear matrix inequality (LMI)-based control design. Finally, simulation studies verify the effectiveness of the overall approach and its superiority in terms of control performance and communication efficiency.</p>

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Intelligent LFC of IT-2 Fuzzy Power Systems with Improved Memory Event-Triggered Scheme supervised by Genetic Algorithm

  • 期刊论文

  • 发表日期：

    2025年10月6日

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  • Xiangkun Wang,
  • Kaibo Shi,
  • Ming Zhu,
  • Huaicheng Yan,
  • Kangkang Sun,
  • Xiao Cai

摘要

This paper investigates intelligent load frequency control (LFC) of the interval type-2 (IT-2) fuzzy power system with an improved memory event-triggered scheme (IMETS). Firstly, IT-2 fuzzy logic is introduced to model environmental uncertainties and nonlinearities more effectively, enhancing system representation accuracy and providing a foundation for robust controller design. Secondly, an IMETS is proposed that utilizes historical data to reduce unnecessary triggering and enhance the precision of event activation. In addition, it adaptively adjusts triggering thresholds in real time according to system evolution, thereby improving data processing efficiency and reducing resource consumption. Furthermore, we introduce a genetic algorithm (GA) to optimize the system parameter configurations. The aim is to achieve a balanced trade-off between dynamic response performance and communication efficiency. Moreover, a semi-looped functional method is adopted to reduce the conservativeness of stability conditions, thereby ensuring the feasibility of linear matrix inequality (LMI)-based control design. Finally, simulation studies verify the effectiveness of the overall approach and its superiority in terms of control performance and communication efficiency.