Achieving 100x Acceleration for N-1 Contingency Screening With Uncertain Scenarios Using Deep Convolutional Neural Network
Achieving 100x Acceleration for N-1 Contingency Screening With Uncertain Scenarios Using Deep Convolutional Neural Network
复制标题
DOI：
10.1109/tpwrs.2019.2914860
复制
发表时间：
2019-07-01
影响因子：
6.6
通讯作者：
Zheng, Tongxin
中科院分区：
文献类型：
--
作者：
Du, Yan;Li, Fangxing;Zheng, Tongxin
可再生能源渗透率的不断提高，使得传统的N-1应急筛查在大量不确定场景需要与应急筛查相结合时变得极具挑战性。在这封信中，提出了一种类似于图像处理技术的新型数据驱动方法，用于加速电力系统的 N-1 意外事件筛选，该方法基于深度卷积神经网络（CNN）方法来计算 N-1 意外事件和不确定场景下的交流潮流。一旦深度 CNN 得到良好的训练，它就具有高度泛化性，并且可以以几乎无需计算的方式处理未见过的实例，例如 N-1 情况中的拓扑变化和不确定的可再生场景。所提出的深度 CNN 在多个标准 IEEE 测试系统上实现，以验证其准确性和计算效率。这项研究有力地证明了数据驱动的深度 CNN 在未来在线应用中的巨大潜力。
The increasing penetration of renewable energy makes the traditional N-1 contingency screening highly challenging when a large number of uncertain scenarios need to he combined with contingency screening. In this letter, a novel data-driven method, similar to image-processing technique, is proposed for accelerating N-1 contingency screening of power systems based on the deep convolutional neural network (CNN) method for calculating AC power flows under N-1 contingency and uncertain scenarios. Once the deep CNN is well trained, it has high generalization and works in a nearly computation-free fashion for unseen instances, such as topological changes in the N-1 cases and uncertain renewable scenarios. The proposed deep CNN is implemented on several standard IEEE test systems to verify its accuracy and computational efficiency. The proposed study constitutes a solid demonstration of the considerable potential of the data-driven deep CNN in future online applications.