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

被引：0
|
作者：
Tan B. [1 ]
Yang J. [1 ]
Lai Q. [1 ]
Xie P. [2 ]
Li J. [2 ]
Xu J. [1 ]
机构：
[1] School of Electrical Engineering, Wuhan University, Wuhan
[2] State Grid Hunan Electric Power Company, Changsha
来源：
Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2019年 / 43卷 / 01期
关键词：
Conditional generative adversarial network (CGAN); Data augment; G-mean value; Power system; Transient stability assessment;
D O I：
10.7500/AEPS20180522004
中图分类号：
学科分类号：
摘要：
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. © 2019 Automation of Electric Power Systems Press.
引用
收藏
页码：149 / 157
页数：8
相关论文
共 40 条
  • [1] Kundur P., Paserba J., Ajjarapu V., Et al., Definition and classification of power system stability, IEEE Transactions on Power Systems, 19, 3, pp. 1387-1401, (2004)
  • [2] Zhang C., Li Y., Yu Z., Et al., A weighted random forest approach to improve predictive performance for power system transient stability assessment, Power and Energy Engineering Conference (APPEEC), pp. 1259-1263
  • [3] Kundur P., Balu N.J., Lauby M.G., Power System Stability and Control, pp. 848-859, (1994)
  • [4] Xue Y., Van Cutsem T., Ribbens-Pavella M., A simple direct method for fast transient stability assessment of large power systems, IEEE Transactions on Power Systems, 3, 2, pp. 400-412, (1988)
  • [5] Wang X., Analysis on Modern Power System, pp. 354-362, (2003)
  • [6] Zhang Y., Markham P., Xia T., Et al., Wide-area frequency monitoring network (FNET) architecture and applications, IEEE Transactions on Smart Grid, 1, 2, pp. 159-167, (2010)
  • [7] De La R.J., Centeno V., Thorp J.S., Et al., Synchronized phasor measurement applications in power systems, IEEE Transactions on Smart Grid, 1, 1, pp. 20-27, (2010)
  • [8] Yu Z., Huang Y., Lu G., Et al., A time series associative classification method for the operation rule extracting of transient stability, Proceedings of the CSEE, 35, 3, pp. 519-526, (2015)
  • [9] Huang T., Xue Y., Chen G., Et al., An efficient stable case screening algorithm for transient stability assessment, Automation of Electric Power Systems, 42, 8, pp. 83-91, (2018)
  • [10] Zhu Q., Chen J., Li H., Et al., Transient stability assessment based on stacked autoencoder, Proceedings of the CSEE, 38, 10, pp. 2937-2946, (2018)
1234