Scholarlink

<p>System dynamics uncertainties and constraint communication channels pose significant challenges to load frequency control in power systems. This article investigates a data-driven dual-channel event-triggered load-frequency control strategy for interconnected multi-area power systems with quantized input and output data. Firstly, an enhanced model-free adaptive control framework is established by incorporating proportional, differential, and quadratic difference terms to improve the control performance. Moreover, semi-independent event-triggering mechanisms are designed for both the input and output channels to optimize resource utilization. Furthermore, a rigorous theoretical analysis is given. It demonstrates that the designed control scheme ensures the tracking error converges to a bound that is dependent on the quantization density. Finally, extensive simulation results further validate the effectiveness of the proposed method.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Data-driven dual-channel event-triggered load frequency control for interconnected multi-area power systems with quantized inputs and outputs

  • 期刊论文

  • 发表日期：

    2026年3月31日

查看全文
  • Longquan Ma,
  • Huarong Zhao,
  • Yuhao Chen,
  • Linbo Xie,
  • Hongnian Yu

摘要

System dynamics uncertainties and constraint communication channels pose significant challenges to load frequency control in power systems. This article investigates a data-driven dual-channel event-triggered load-frequency control strategy for interconnected multi-area power systems with quantized input and output data. Firstly, an enhanced model-free adaptive control framework is established by incorporating proportional, differential, and quadratic difference terms to improve the control performance. Moreover, semi-independent event-triggering mechanisms are designed for both the input and output channels to optimize resource utilization. Furthermore, a rigorous theoretical analysis is given. It demonstrates that the designed control scheme ensures the tracking error converges to a bound that is dependent on the quantization density. Finally, extensive simulation results further validate the effectiveness of the proposed method.