电力系统自动化2019，Vol.43Issue(1)：149-157,9.DOI:10.7500/AEPS20180522004
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
摘要
Abstract
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Key words
power system/transient stability assessment/data augment/conditional generative adversarial network (CGAN)/G-mean value引用本文复制引用
谭本东,杨军,赖秋频,谢培元,李军,徐箭..基于改进CGAN的电力系统暂态稳定评估样本增强方法[J].电力系统自动化,2019,43(1):149-157,9.基金项目
国家重点研发计划资助项目(2017YFB0902900) This work is supported by National Key R&D Program of China (No . 2017YFB0902900). （2017YFB0902900）
