基于改进CGAN的电力系统暂态稳定评估样本增强方法  

Data Augment Method for Power System Transient Stability Assessment Based on Improved Conditional Generative Adversarial Network

作　　者：谭本东 杨军[1] 赖秋频 谢培元 李军 徐箭[1] TAN Bendong;YANG Jun;LAI Qiupin;XIE Peiyuan;LI Jun;XU Jian(School of Electrical Engineering,Wuhan University,Wuhan 430072,China;State Grid Hunan Electric Power Company,Changsha 410000,China)

机构地区：[1]武汉大学电气工程学院,湖北省武汉市430072 [2]国网湖南省电力有限公司,湖南省长沙市410000

出　　处：《电力系统自动化》2019年第1期149-157,共9页Automation of Electric Power Systems

基　　金：国家重点研发计划资助项目(2017YFB0902900)~~

摘　　要：基于数据驱动的暂态稳定评估方法已成为电网安全领域研究的重点,由于实际电力系统中暂态失稳情况极少,给通过数据挖掘方法判断失稳情况带来了极大困难。针对这个问题,提出了一种用于暂态稳定评估中失稳样本合成的数据增强方法,对条件生成对抗神经网络(CGAN)训练方法的适应性进行改进以提高其学习稳定性,在离线训练时利用改进CGAN交替训练生成器和判别器,学习电力系统暂态数据的分布特性,然后采用极限学习机(ELM)分类器筛选出改进CGAN所生成的多组样本中G-mean值最高的生成样本,将其中失稳样本对原始失稳样本进行增强,最后用增强后的原始样本训练分类器,实现在线暂态稳定评估。仿真结果表明,所提出的样本数据增强方法通过改进CGAN实现对原始数据分布特征的有效学习,进而提升暂态稳定评估的正确率,具有抗噪声干扰性强、对高维数据鲁棒性好的优点,能够有效平衡电力系统失稳数据。Data-driven transient stability assessment method has become the focus of research in the field of power network security.However,transient unstable situation in the actual power system is very rare,which brings great difficulties to the data acquisition method for judging the instability.This paper proposes a data augment method for the synthesis of unstable samples in the transient stability assessment.It enhances the adaptability of training methods for conditional generative adversarial network(CGAN)to improve their learning stability and uses the improved CGAN training generators and discriminators during offline training to learn the distribution characteristics of raw data.Then,the extreme learning machine(ELM)classifier is used to filter out the generated samples with the highest G-mean value among the multiple sets of samples generated by the improved CGAN.The unstable samples are used to augment the original unstable samples,and the augmented original samples are used to train the classifier to achieve online transient stability assessment.The simulation results show that the proposed method can effectively learn the distribution characteristics of the original data by the improved CGANs.The method has the advantages of strong anti-noise interference and good robustness to high-dimensional data,and it can effectively balance the unstable data of power system.

关 键 词：电力系统 暂态稳定评估 数据增强 条件生成对抗神经网络 G-mean值 

分 类 号：TM712[电气工程—电力系统及自动化]