使用卷积神经网络对电力系统惯性进行持续估计.
Daniele Linaro1, Federico Bizzarri2,3, Davide Del Giudice2
1DEIB, Politecnico di Milano, P.zza Leonardo da Vinci 32, Milano, 20133, Italy. daniele.linaro@polimi.it.
Nature communications
|July 24, 2023
概括
估计电力系统惯性对于增加可再生能源至关重要. 本研究介绍了用于持续惯性估计的AI框架,为运营商改进了电网稳定性分析.
科学领域:
- 电气工程 电气工程
- 电力系统分析 分析 分析
- 人工智能的人工智能
背景情况:
- 传统的电网依靠同步发电机来实现稳定的惯性.
- 增加可再生能源的整合减少了系统惯性,造成稳定性风险.
- 准确的惯性估计对于管理现代电网至关重要.
研究的目的:
- 开发一个框架,用于在动力系统中持续惯性估计.
- 研究人工智能 (AI) 在惯性估计中的应用.
- 了解基于AI的惯性估计所需的输入特征.
主要方法:
- 使用最先进的人工智能技术.
- 进行了功率光谱分析和输入-输出相关性分析.
- 在异质电网上验证了该方法.
主要成果:
- 开发了一种用于持续惯性估计的新框架.
- 确定了人工智能驱动的惯性估计的关键输入特征.
- 证明了不同电力系统组件的不同光谱足迹.
结论:
- 人工智能框架使可靠的连续惯性估计成为可能.
- 了解光谱足迹对于输电系统运营商来说至关重要.
- 这种方法可以增强在线网络稳定性分析,用于各种发电源的电网.
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