通过使用遗传算法优化自动发电控制,在现实世界的负载变化下优化电力系统的弹性
Muhammad Ayaz1, Dur-E-Zehra Baig2, Syed Muhammad Hur Rizvi3
1Pak-Austria Fachhochschule Institute of Applied Sciences and Technology, Haripur, 21090, Pakistan.
Scientific reports
|July 2, 2025
概括
基因算法优化PID (GA-PID) 显著提高了面临广泛负载变化的电力系统的自动发电控制 (AGC) 稳定性. GA-PID的性能优于传统方法,确保更快的稳定性和在负载变化期间更高的准确性.
科学领域:
- 电气工程 电气工程
- 控制系统 控制系统
- 电力系统 电力系统
背景情况:
- 现代电力系统需要强大的自动发电控制 (AGC),以保持对突然负载波动的稳定性.
- 在多样化和显著的负载变化下评估AGC性能对于可靠的电网运行至关重要.
研究的目的:
- 综合评估三种AGC控制策略在各种负载变化 (100-300MW增量/减量) 下的性能.
- 为了比较传统的AGC (CAGC),连线偏差 (TLB) 控制和基因算法优化PID (GA-PID) 在两区域互连的电力系统中.
主要方法:
- 模拟了一个双区域互连的电力系统,经过12个不同的负载变化场景 (100-300兆瓦).
- 在所有场景中评估了CAGC,TLB和GA-PID控制策略,共进行了360次测试.
- 性能指标包括超额冲击,低额冲击,结算时间和两个系统区域的稳定状态精度.
主要成果:
- 与CAGC和TLB相比,GA-PID在最小化短暂偏差和确保更快的稳定方面表现优异.
- 在负载增加时,GA-PID减少了高达90%的超支,并在几个情况下消除了超支.
- CAGC和TLB表现出较大的干扰的弱点,导致长时间的振荡和显著的偏差.
结论:
- GA-PID是现代电力系统的高效和灵活的控制策略,需要适应不可预测的负载变化.
- 这些发现强调了GA-PID等先进控制方法对于保持电力系统稳定性和可靠性的重要性.
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