使用预训练有素,精心调整的注意力驱动神经操作员,对故障后电压轨迹进行符合规范的预测
Amirhossein Mollaali1, Gabriel Zufferey2, Gonzalo Constante-Flores3
1School of Mechanical Engineering, Purdue University, West Lafayette, IN, 47906, USA.
概括
这项研究引入了一种用于预测电力系统中故障后电压行为的新方法. 它使用量子注意力-里埃深度操作员网络 (QAF-DeepONet) 进行可靠的间隔预测,确保系统稳定性.
科学领域:
- 电力系统工程 电力系统工程
- 数据驱动的预测建模.
- 控制系统的机器学习
背景情况:
- 准确预测故障后的电压轨迹对于电力系统的稳定性和运行安全至关重要.
- 现有的方法经常与电压行为的复杂,非线性动态以及量化预测不确定性作斗争.
- 数据隐私问题可能会限制集中式机器学习模型在电网中的应用.
研究的目的:
- 开发一种数据驱动的方法来预测电力系统中故障后电压轨迹的间隔.
- 提出一个量子注意力-里埃深度运算器网络 (QAF-DeepONet),能够在没有分布假设的情况下估计轨迹量子.
- 为了确保可靠的不确定性量化用于故障后电压预测.
主要方法:
- 开发了一种量子注意力-里埃深度操作员网络 (QAF-DeepONet) 用于电压轨迹的操作员回归.
- 采用预培训和微调策略来处理有限的数据可用性.
- 利用联合学习进行隐私保护的预培训和符合预测,以保证间隔覆盖范围.
主要成果:
- QAF-DeepONet有效地捕获复杂的电压动态,并估计断裂后轨迹的量子.
- 联合学习使得隐私保护的学习能够从邻近的公共汽车中学习潜在的电压动态.
- 合规预测确保了预测电压间隔的可靠覆盖率保证,在新英格兰39巴士系统上进行了验证.
结论:
- 拟议的数据驱动方法提供了实用和可靠的不确定性量化,用于预测故障后电压轨迹间隔.
- 联合学习和合规预测的整合解决了数据隐私和覆盖范围保证的挑战.
- 该方法在预测电压间隔方面表现出强的表现,这对于提高电力系统稳定性和弹性至关重要.
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