一种基于共享特征提取的自适应动力系统暂时稳定性评估方法
Jiexiang Hu1,2, Le Zheng2, Wei Ai3
1Changsha Power Supply Branch, State Grid Hunan Electric Power Co., Ltd., Changsha 410035, China.
iScience
|April 14, 2025
概括
本研究介绍了一种使用共享特征提取的自适应电力系统暂时稳定性评估 (TSA) 方法. 尽管数据丢失和情景发生变化,它仍然保持高准确度,提高了稳定性和通用性.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 使用机器学习的电力系统暂时稳定性评估 (TSA) 在动态操作条件下与性能恶化作斗争.
- 现有的方法通常需要大量的标记数据,并且缺乏适应不断变化的系统场景的适应性.
研究的目的:
- 为电力系统开发一个强大且可转移的自适应式TSA方法.
- 为了提高机器学习模型的可通用性和可持续性,在不同的运营环境中为TSA.
主要方法:
- 使用域对抗对齐网络来训练共享特征提取器.
- 从操作变化之前和之后对齐的动力系统数据,以捕获关键稳定性特征.
- 实施了用于持续学习的同时数据和模型知识传输.
主要成果:
- 在IEEE 39总线和2179节点系统上实现了超过96%的预测准确度,数据损失为30%.
- 在不断变化的场景中保持97.99%的准确性,优于传统方法.
- 证明了对新情景的广泛标记数据的依赖减少.
结论:
- 拟议的自适应式TSA方法为现实世界电力系统应用提供了增强的通用性和稳定性.
- 这种方法可以实现可持续的学习能力,有效地适应系统变化和数据稀缺.
- 这种方法为可靠和高效的电力系统稳定性监测带来了重大进步.
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