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基于受限搜索空间选择的优化方法,用于增强互连电力系统模型的减少顺序近似
Bala Bhaskar Duddeti1,2, Asim Kumar Naskar2, V P Meena3
1Department of Electrical and Electronics Engineering, SASI Institute of Technology and Engineering (A), Tadepalligudem, 534101, India.
Scientific reports
|March 7, 2025
概括
本研究引入了一个临时减少模型 (IRM),以改善电力系统模型的减少. 均衡残留方法 (BRM) 和几何平均优化 (GMO) 创建了一个集中搜索空间,增强模型的准确性和稳定性.
科学领域:
- 电气工程 电气工程
- 计算科学 计算科学
背景情况:
- 超启发式优化用于复杂的电力系统建模.
- 目前的方法遭受随机边界选择,导致不准确或不稳定的缩小模型.
- 改进的模型减速技术对于电力系统的稳定性和分析至关重要.
研究的目的:
- 引入一种新的方法来减少电力系统模型,使用临时减少模型 (IRM).
- 为了提高近似的电力系统模型的准确性和稳定性.
- 在元启发式优化中解决任意搜索空间选择的局限性.
主要方法:
- 均衡残留方法 (BRM) 用于获得临时缩小模型 (IRM).
- 几何平均值优化 (GMO) 算法使用IRM调整了减少的模型系数.
- IRM对转基因算法的解决方案空间进行了结构化,确保了有针对性的搜索.
主要成果:
- 拟议的方法确保了对可行解决方案的集中搜索,并改善了模型稳定性.
- 保持短暂增益可以减轻与BRM相关的高频谱误差.
- 在三个复杂的相互连接的电力系统上进行的验证表明,与现有方法相比,性能优越.
结论:
- 临时缩小模型 (IRM) 概念有效地缩小了优化算法的解决方案空间.
- 结合BRM-GMO方法提供了一个更准确,更稳定的方法来减少电力系统模型.
- 这种技术在复杂的电力系统中推进了模型订单减少 (MOR) 的最先进技术.
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