预测输电线路动态电流承载能力的方法,通过整合改进的VMD和时间变化的集成模型
1School of Electrical and Information Engineering, Yunnan Minzu University, Kunming, China.
PloS one
|February 17, 2026
概括
本研究引入了一种改进的方法,通过优化变量模式分解 (VMD) 超参数和使用时间变化的集成模型来预测电力线路的动态热评分 (DTR). 新方法提高了对安全电力线路容量增加的预测准确度.
科学领域:
- 电气工程 电气工程
- 计算智能是一种计算智能.
- 数据科学数据科学数据科学
背景情况:
- 准确的动态热等级 (DTR) 预测对于提高电力线路容量至关重要.
- 现有的方法在捕获多尺度DTR数据特征时,与变化模式分解 (VMD) 超参数选择和单模型限制作斗争.
- 单个模型的概括能力较差,妨碍了可靠的DTR预测.
研究的目的:
- 提出改进的输电线路DTR预测方法.
- 为了解决VMD超参数设置和单模型性能方面的局限性.
- 为了提高DTR预测的准确性和概括能力,以提高电力线路容量.
主要方法:
- 优化了VMD超参数选择,使用基于最小信封和粘液模具算法的健身函数.
- 开发了一个时间变化的集成模型,集成Elman和时间卷积网络 (TCN) 来进行多级特征提取.
- 实施了一个基于灰色关系系数的动态权重机制,用于集体模型中的自适应权重分配.
主要成果:
- 拟议的方法在各种数据集中实现了超过95%的预测准确度.
- 与现有的最先进的方法相比,其准确度提高了5.7%.
- 成功解决了手动超参数调整和单个模型不良概括的问题.
结论:
- 改进的VMD和时间变化的组合模型为准确的DTR预测提供了强大的解决方案.
- 该方法为安全的电力线路容量增加提供了重要的理论支持.
- 该方法对电力系统管理具有相当大的工程应用价值.
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