Integrated Assessment Method for Transient Stability of Power System Under Sample Imbalance

被引：0
|
作者：
Li J. [1 ]
Yang H. [2 ]
Yan L. [1 ]
Liu D. [2 ]
Li Z. [2 ]
Xia Y. [1 ]
Zhao Y. [3 ]
机构：
[1] School of Automation, Beijing Institute of Technology, Beijing
[2] China Electric Power Research Institute, Beijing
[3] Electric Power Research Institute of State Grid Shandong Electric Power Company, Jinan
来源：
Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2021年 / 45卷 / 10期
关键词：
Convolution neural network; Integrated model; Power system; Sample imbalance; Transient stability assessment;
D O I：
10.7500/AEPS20200309001
中图分类号：
学科分类号：
摘要：
In order to quickly and accurately evaluate the stability of the power system after a transient fault occurs in the power system, and to solve the bias problem of the model caused by sample imbalance, an integrated transient stability assessment method for power systems based on the improved loss function is proposed. Firstly, based on the short-term measurement data after the fault clearing, a new integrated model that combines one-dimensional, two-dimensional single-channel and two-dimensional multi-channel convolutional neural networks is designed to realize the end-to-end abstract feature extraction and transient stability classification. Secondly, the loss function in the model training process is improved to enhance the fitting degree of unstable samples for increasing the weights of the misclassification samples. Thus, the global accuracy is improved, and the missing alarm rate of unstable samples is reduced. Moreover, the influence of the output threshold of the integrated model on the recall rate of instable samples is analyzed in this paper. Finally, the simulation results of IEEE 39-bus system and IEEE 145-bus system verify the effectiveness of the proposed algorithm. © 2021 Automation of Electric Power Systems Press.
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页码：34 / 41
页数：7
相关论文
共 28 条
  • [1] ZHANG Wenliang, LIU Zhuangzhi, WANG Mingjun, Et al., Research status and development trend of smart grid, Power System Technology, 33, 13, pp. 1-11, (2009)
  • [2] BIE Z H, LIN Y L, LI G F, Et al., Battling the extreme: a study on the power system resilience, Proceedings of the IEEE, 105, 7, pp. 1253-1266, (2017)
  • [3] LIU Daowei, ZHANG Dongxia, SUN Huadong, Et al., Construction of stability situation quantitative assessment and adaptive control system for large-scale power grid in the spatio-temporal big data environment, Proceedings of the CSEE, 35, 2, pp. 268-276, (2015)
  • [4] ZHU Shu, LIU Kaipei, QIN Liang, Et al., Analysis of transient stability of power electronics dominated power system: an overview, Proceedings of the CSEE, 37, 14, pp. 3948-3962, (2017)
  • [5] TANG Yi, CUI Han, LI Feng, Et al., Review on artificial intelligence in power system transient stability analysis, Proceedings of the CSEE, 39, 1, pp. 2-13, (2019)
  • [6] LAN G, YOSHUA B, AARON C., Deep learning, pp. 201-253, (2017)
  • [7] LECUN Y, BENGIO Y, HINTON G., Deep learning, Nature, 521, 7553, pp. 436-444, (2015)
  • [8] ZHOU Niancheng, LIAO Jianquan, WANG Qianggang, Et al., Analysis and prospect of deep learning application in smart grid, Automation of Electric Power Systems, 43, 4, pp. 180-191, (2019)
  • [9] ZHU Qiaomu, CHEN Jinfu, LI Hongyi, Et al., Transient stability assessment based on stacked autoencoder, Proceedings of the CSEE, 38, 10, pp. 2937-2946, (2018)
  • [10] WANG Huaiyuan, CHEN Qifan, A transient stability assessment method based on cost-sensitive stacked variational auto-encoder, Proceedings of the CSEE, 40, 7, pp. 2213-2220, (2020)
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