电力建设2018，Vol.39Issue(2)：103-108,6.DOI:10.3969/j.issn.1000-7229.2018.02.013
基于深度学习的电力系统暂态稳定评估方法
Transient Stability Assessment of Power System Based on Deep Learning Technology
摘要
Abstract
In the field of machine learning,transient stability assessment can be considered as a two-class problem of estimating the stability boundary through large number of fault samples.This paper proposes a method of deep learning to solve this problem.The method consists of four stages:firstly,using samples to construct the original input feature for describing the dynamic characteristics of the power system;secondly,variational auto-encoders (VAE) is used to perform unsupervised learning on the original input feature to obtain high-order features;thirdly,the supervised training of convolution neural network (CNN) is carried out to obtain the relationship between high order characteristic and transient stability of power system;finally,the model is applied to the transient stability assessment of power system.Simulation on the New England 39-bus test system shows that the proposed approach has high accuracy,rare misclassification of unstable sample and excellent robustness with noise for transient stability assessment (TSA).Therefore,it is suitable for quasi-real-time online transient stability assessment based on wide-area measurement information.关键词
深度学习/变分自动编码器(VAE)/高阶特征/卷积神经网络(CNN)/暂态稳定评估(TSA)/机器学习/无监督学习Key words
deep learning/variational auto-encoders(VAE)/high-order features/convolution neural network(CNN)/transient stability assessment(TSA)/machine learning/unsupervised learning分类
信息技术与安全科学引用本文复制引用
周悦,谭本东,李淼,杨旋,周强明,张振兴,谭敏,杨军..基于深度学习的电力系统暂态稳定评估方法[J].电力建设,2018,39(2):103-108,6.基金项目
国家自然科学基金项目(51277135) （51277135）
国家电网公司科技项目(521500160011)Project supported by National Natural Science Foundation of China (51277135) （521500160011）
Science and Technology Project of State Grid Corporation of China(521500160011) （521500160011）
