Stacked auto-Encoder-Based Transients Recognition in VSC-HVDC

被引:0
|
作者
Luo G. [1 ]
Cheng M. [1 ]
Sun H. [1 ]
Li M. [1 ]
Tan Y. [1 ]
He J. [1 ]
Zhang H. [2 ]
机构
[1] School of Electrical Engineering, Beijing Jiaotong University, Beijing
[2] State Grid Beijing Maintenance Company, Beijing
基金
中国博士后科学基金; 中国国家自然科学基金;
关键词
Stacked auto-encoder; transient recognition; VSC-HVDC;
D O I
10.1109/aCCESS.2020.2966645
中图分类号
学科分类号
摘要
For overhead long-distance high voltage direct current (HVDC) transmission lines, transients are produced due to complicated field conditions and lightning activities. To ensure reliable operation of protection devices, accurate recognition of faults and disturbances is quite critical. The most popular recognition methods include threshold-based ones which require the proper setting of the threshold value, and classifier-based ones that need suitable feature extractions. These methods depend heavily on the experience of engineers or experts and are ineffective in dealing with the variation of system parameters. In this paper, a transient recognition method based on stack auto-encoder (SaE) is proposed to characterize different transients and to avoid human interferences. a symmetrical ±500kv HVDC system is modeled to illustrate the performance of the proposed method. The effect of some factors, such as noises and conductors, are discussed and compared. The simulation results demonstrate that the proposed SaE-based recognition has excellent potential in transient recognition of practical HVDC systems. © 2013 IEEE.
引用
收藏
页码:14223 / 14233
页数:10
相关论文
共 50 条
  • [21] Deep Multiple Auto-Encoder-Based Multi-view Clustering
    Du, Guowang
    Zhou, Lihua
    Yang, Yudi
    Lu, Kevin
    Wang, Lizhen
    DATA SCIENCE AND ENGINEERING, 2021, 6 (03) : 323 - 338
  • [22] Deep Multiple Auto-Encoder-Based Multi-view Clustering
    Guowang Du
    Lihua Zhou
    Yudi Yang
    Kevin Lü
    Lizhen Wang
    Data Science and Engineering, 2021, 6 : 323 - 338
  • [23] MAEC: MULTI-INSTANCE LEARNING WITH AN ADVERSARIAL AUTO-ENCODER-BASED CLASSIFIER FOR SPEECH EMOTION RECOGNITION
    Fu, Changzeng
    Liu, Chaoran
    Ishi, Carlos Toshinori
    Ishiguro, Hiroshi
    2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021), 2021, : 6299 - 6303
  • [24] An End-to-end Transient Recognition Method for VSC-HVDC Based on Deep Belief Network
    Guomin Luo
    Jiaxin Hei
    Changyuan Yao
    Jinghan He
    Meng Li
    Journal of Modern Power Systems and Clean Energy, 2020, 8 (06) : 1070 - 1079
  • [25] An End-to-end Transient Recognition Method for VSC-HVDC Based on Deep Belief Network
    Luo, Guomin
    Hei, Jiaxin
    Yao, Changyuan
    He, Jinghan
    Li, Meng
    JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY, 2020, 8 (06) : 1070 - 1079
  • [26] Features Masked Auto-Encoder-Based Anomaly Detection in Process Industry
    Hu, Junhao
    Jia, Mingwei
    Yang, Qinmin
    Liu, Yi
    2023 IEEE 12TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE, DDCLS, 2023, : 1075 - 1079
  • [27] An auto-reclosing scheme for DC circuit breaker in VSC-HVDC transmission system
    Wang, Yuan
    Wang, Yuhong
    Liu, Chengzhuo
    Yang, Liwen
    JOURNAL OF ENGINEERING-JOE, 2019, (16): : 2474 - 2479
  • [28] Auto-encoder-based generative models for data augmentation on regression problems
    Ohno, Hiroshi
    SOFT COMPUTING, 2020, 24 (11) : 7999 - 8009
  • [29] Auto-encoder-based generative models for data augmentation on regression problems
    Hiroshi Ohno
    Soft Computing, 2020, 24 : 7999 - 8009
  • [30] Choosing the Best Auto-Encoder-Based Bagging Classifier: An Empirical Study
    Nie, Yifan
    Rong, Wenge
    Shen, Yikang
    Li, Chao
    Xiong, Zhang
    NEURAL INFORMATION PROCESSING (ICONIP 2014), PT I, 2014, 8834 : 413 - 420