Prediction of Dissolved Gas in Power Transformer Oil Based on Random Forests Algorithm

被引:0
|
作者
Deng, Ke [1 ]
Xiong, Weihong [2 ]
Zhu, Liming [3 ]
Zhang, Hongzhi [3 ]
Li, Zhengtian [3 ]
机构
[1] State Grid Hubei Elect Power Co, Overhaul Branch, Wuhan 430050, Peoples R China
[2] Ctr China Grid Co Ltd, Wuhan 430077, Peoples R China
[3] HUST, Wuhan 430074, Peoples R China
来源
2015 5TH INTERNATIONAL CONFERENCE ON ELECTRIC UTILITY DEREGULATION AND RESTRUCTURING AND POWER TECHNOLOGIES (DRPT 2015) | 2015年
关键词
Power transformer; dissolved gas; condition based maintenance; prediction; random forests algorithm;
D O I
暂无
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
In order to adopt reasonable measures to anticipate and avoid possible internal failures in power transformers, accurate prediction of power transformer oil dissolved gas trends is useful. It is necessary to realize the condition based maintenance of power transformers, and the prediction of dissolved gas in the oil is solid foundation. The gas concentration is affected by many factors, so prediction model built by reasonable selection of the larger correlation factors will help to improve prediction accuracy. Random forests algorithm is used to predict the gas trends and the prediction performance evaluation is realized by appropriate evaluation indexes. By comparison with support vector machine prediction, the advantage of random forests method for power transformer oil dissolved gas prediction is proved.
引用
收藏
页码:1531 / 1534
页数:4
相关论文
共 50 条
  • [21] Prediction of Dissolved Gas Concentration in Transformer Oil Based on PSO-LSTM Model
    Liu K.
    Gou J.
    Luo Z.
    Wang K.
    Xu X.
    Zhao Y.
    Dianwang Jishu/Power System Technology, 2020, 44 (07): : 2778 - 2784
  • [22] A TIME SERIES ANALYSIS BASED DATA TENDENCY PREDICTION METHOD FOR DISSOLVED GAS PRODUCTION MONITORING IN POWER TRANSFORMER OIL
    Zhang Wei
    Wu Rongrong
    Pu Jinyu
    Deng Yurong
    UNIVERSITY POLITEHNICA OF BUCHAREST SCIENTIFIC BULLETIN SERIES C-ELECTRICAL ENGINEERING AND COMPUTER SCIENCE, 2019, 81 (01): : 99 - 108
  • [23] A time series analysis based data tendency prediction method for dissolved gas production monitoring in power transformer oil
    Wei, Zhang
    Rongrong, Wu
    Jinyu, Pu
    Yurong, Deng
    UPB Scientific Bulletin, Series C: Electrical Engineering and Computer Science, 2019, 81 (01): : 99 - 108
  • [24] A method for online monitoring the dissolved gas in power transformer oil based on temporal characteristics
    Kong, Yinghui
    Yuan, Jinsha
    Zhang, Tiefeng
    Zhao, Zhenbing
    2007 CONFERENCE PROCEEDINGS IPEC, VOLS 1-3, 2007, : 344 - 348
  • [25] Wind Power Prediction based on Random Forests
    Zhou, Zehong
    Li, Xiaohui
    Wu, Huaren
    PROCEEDINGS OF THE 2016 4TH INTERNATIONAL CONFERENCE ON ELECTRICAL & ELECTRONICS ENGINEERING AND COMPUTER SCIENCE (ICEEECS 2016), 2016, 50 : 352 - 356
  • [26] Prediction of Dissolved Gas Content in Transformer Oil Based on SSA-BiGRU-Attention Model
    Liu Z.
    Wang S.
    Tang B.
    Gaodianya Jishu/High Voltage Engineering, 2022, 48 (08): : 2972 - 2981
  • [27] Prediction of Dissolved Gas Content in Transformer Oil Based on SMA-VMD-GRU Model
    Yang T.
    Hu D.
    Tang C.
    Fang Y.
    Xie J.
    Diangong Jishu Xuebao/Transactions of China Electrotechnical Society, 2023, 38 (01): : 117 - 130
  • [29] Prediction of Dissolved Gas Concentrations in Transformer Oil Based on the KPCA-FFOA-GRNN Model
    Lin, Jun
    Sheng, Gehao
    Yan, Yingjie
    Dai, Jiejie
    Jiang, Xiuchen
    ENERGIES, 2018, 11 (01):
  • [30] Prediction Method of Dissolved Gas Concentration in Transformer Oil Based on CNN-BiLSTM Model
    Li, Xiaoping
    Bai, Chao
    Shi, Sen
    Tiedao Xuebao/Journal of the China Railway Society, 2022, 44 (05): : 42 - 48