对于电力系统的强大表示学习 短期电压稳定性评估 在各种数据丢失条件下
IEEE transactions on neural networks and learning systems
|October 26, 2023
概括
本研究引入了一种可靠的电网动态稳定性评估方法,即使缺少数据,也能提高性能. 该方法通过使用合体学习和时空图形网络来增强短期电压稳定性预测.
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:
- 电力系统的数据驱动动态稳定性评估 (DSA) 依赖于神经网络,但在实际数据丢失方面存在困难.
- 现有的DSA解决方案在有缺陷或缺失的输入数据的情况下会显著退化,从而限制了现实世界的适用性.
研究的目的:
- 为数据驱动动的动态稳定性评估 (DSA) 开发一种强大的表示学习方法,该方法能够抵御各种输入数据丢失条件.
- 为了提高电力系统中在线短期电压稳定性 (SVS) 评估的性能,尽管数据不完美.
主要方法:
- 设计了一个集体表示学习方案 (ERLS),结合了高效的数据掩盖技术,以便在培训期间统一处理缺失的数据.
- 时空图卷积网络 (STGCN) 用于创建各种基础学习者熟练的SVS特征提取和表示.
- 集群方法将SVS场景分组为组,通过STGCN启用的学习者通过多线性回归 (MLR) 组装为集群特定组合评估.
主要成果:
- 拟议的整体策略在各种严重数据丢失场景下显示了非常强大的SVS评估性能.
- 北欧测试系统的数值测试验证了数据损失耐受性方法的有效性.
结论:
- 开发的强大的表示学习方法在数据丢失的情况下显著提高了在线DSA的可靠性.
- 利用STGCN和MLR,ERLS为复杂电力系统中弹性短期电压稳定性评估提供了一个有希望的解决方案.
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