Nonparametric Kernel Density Estimation Model of Transformer Health Based on Dissolved Gases in Oil

被引：0
|
作者：
Li, Houying [1 ]
Wang, Youyuan [1 ]
Liang, Xuanhong [1 ]
He, Yigang [2 ]
Zhao, Yushun [2 ]
机构：
[1] Chongqing Univ, State Key Lab Power Transmiss Equipment & Syst Se, Chongqing, Peoples R China
[2] HeFei Univ Technol, Hefei, Peoples R China
来源：
2018 IEEE ELECTRICAL INSULATION CONFERENCE (EIC) | 2018年
关键词：
transformer; dissolved gas in oil; non-parametric kernel density estimation; Association rule; health state;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In this paper, a health status calculation method based on nonparametric kernel density estimation of dissolved gas in oil and association rules for transformer is proposed. Firstly, the online monitoring data of dissolved gas content which collected from multiple identical transformers are analyzed by nonparametric density estimation to obtain the health probability distribution function of various gases. Then, by mining the correlation between various gases and transformer fault conditions, an association rule method to calculate the weight coefficient of each gas is introduced. Finally, the weighted method is applied to calculate the health probability of transformer when the health probability and weight of each gas are obtained. The method introduced in this paper is validated by the state parameter data of transformer in a substation. The example shows that the health status of the transformer can be obtained in real time and this method is completely based on data driven, which is important to ensure the safety of grid.
引用
收藏
页码：236 / 239
页数：4
相关论文
共 50 条
  • [1] The Kernel Density Estimation of Nonparametric Model
    Nong, Jifu
    CCDC 2009: 21ST CHINESE CONTROL AND DECISION CONFERENCE, VOLS 1-6, PROCEEDINGS, 2009, : 1173 - 1177
  • [2] Prediction of dissolved gases content in power transformer oil using BASA-based mixed kernel RVR model
    Fei, Sheng-Wei
    He, Chuang-Xin
    INTERNATIONAL JOURNAL OF GREEN ENERGY, 2019, 16 (08) : 652 - 656
  • [3] Nonparametric density estimation based on beta prime kernel
    Ercelik, Elif
    Nadar, Mustafa
    COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, 2020, 49 (02) : 325 - 342
  • [4] Prediction Model for Dissolved Gases Content in Transformer Oil Based on Twice Dimensionality Reduction
    Tang Y.
    Xiong Y.
    Diangong Jishu Xuebao/Transactions of China Electrotechnical Society, 2017, 32 (21): : 194 - 202
  • [5] Method for predicting dissolved gases in transformer oil by multivariable grey model
    Gaodianya Jishu, 2007, 8 (98-101):
  • [6] Photoacoustic detection of dissolved gases in transformer oil
    Yun, Yuxin
    Chen, Weigen
    Wang, Youyuan
    Pan, Chong
    EUROPEAN TRANSACTIONS ON ELECTRICAL POWER, 2008, 18 (06): : 562 - 576
  • [7] Analysis and Interpretation of Dissolved Gases in Transformer Oil
    Hettiwatte, S. N.
    Fonseka, H. A.
    PROCEEDINGS OF 2012 IEEE INTERNATIONAL CONFERENCE ON CONDITION MONITORING AND DIAGNOSIS (IEEE CMD 2012), 2012, : 35 - 38
  • [8] Anomalous State Detection of Dissolved Gases in Transformer Oil Based on the Canopy Hyper Sphere Model
    Rong Z.
    Qi B.
    Zhang P.
    Li C.
    Yang Y.
    Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering, 2018, 38 (13): : 3987 - 3996
  • [9] Anomalous State Detection of Dissolved Gases in Transformer Oil Based on the Canopy Hyper Sphere Model
    Zhang, Peng
    Qi, Bo
    Rong, Zhihai
    Wang, Yiming
    Li, Chengrong
    Yang, Yi
    Zheng, Wenjie
    2018 IEEE ELECTRICAL INSULATION CONFERENCE (EIC), 2018, : 228 - 231
  • [10] Probability Density Estimation Based on Nonparametric Local Kernel Regression
    Han, Min
    Liang, Zhi-ping
    ADVANCES IN NEURAL NETWORKS - ISNN 2010, PT 1, PROCEEDINGS, 2010, 6063 : 465 - 472
12345