A Prediction Model of Ice Thickness Based on Grey Support Vector Machine

被引:0
|
作者
Ma Xiao-min [1 ]
Gao Jian [2 ]
Wu Chi [1 ]
He Rui [2 ]
Gong Yi-yu [1 ]
Li Yi [2 ]
Wu Tian-bao [1 ]
机构
[1] State Grid Sichuan Elect Power Res Inst, Chengdu, Peoples R China
[2] State Grid Sichuan Elect Power Co, Chengdu, Peoples R China
来源
2016 IEEE INTERNATIONAL CONFERENCE ON HIGH VOLTAGE ENGINEERING AND APPLICATION (ICHVE) | 2016年
关键词
icing; transmission line; short-term prediction; grey model; support vector machine; on-line monitoring;
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 reduce the icing accidents of transmission lines, the prediction of icing thickness on transmission lines will be able to effectively guide the anti-icing work of power grid. In this paper, a short-term prediction model based on grey support vector machine for icing thickness of transmission lines is proposed, and the elimination of dirty data and the method of data preprocessing are analyzed. The accuracy and applicability of the proposed model are verified by the comparison between the model predictions and the measured data based on the predicted maximum ice thickness, it can provide guidance on monitoring icing condition, the early warning and AC/DC melting ice work. The proposed model is compared with support vector machine (SVM) and particle swarm optimization algorithm (PSO) prediction model, and the average error of the proposed model is 0.28mm, and the average absolute error is 4.33%, which is suitable for short-term prediction of icing thickness of transmission line. In the ice area, the application of the prediction model can guide the transmission line ice-resistant work.
引用
收藏
页数:4
相关论文
共 50 条
  • [41] Data fusion model based on support vector machine for traffic flow prediction
    Chen, Liang
    Liu, Weizheng
    Li, Qiaoru
    Wei, Lianyu
    Ma, Shoufeng
    PROCEEDINGS OF THE 2007 CONFERENCE ON SYSTEMS SCIENCE, MANAGEMENT SCIENCE AND SYSTEM DYNAMICS: SUSTAINABLE DEVELOPMENT AND COMPLEX SYSTEMS, VOLS 1-10, 2007, : 211 - 218
  • [42] Support vector machine prediction model based on slope displacement monitoring data
    Tan, Xiao-Long
    Yantu Gongcheng Xuebao/Chinese Journal of Geotechnical Engineering, 2009, 31 (05): : 750 - 755
  • [43] Bearing Degradation Process Prediction Based on the Support Vector Machine and Markov Model
    Dong, Shaojiang
    Yin, Shirong
    Tang, Baoping
    Chen, Lili
    Luo, Tianhong
    SHOCK AND VIBRATION, 2014, 2014
  • [44] Machine learning prediction model based on enhanced bat algorithm and support vector machine for slow employment prediction
    Wei, Yan
    Rao, Xili
    Fu, Yinjun
    Song, Li
    Chen, Huiling
    Li, Junhong
    PLOS ONE, 2023, 18 (11):
  • [45] Marketing risk prediction based on the support vector machine
    Zhang, Yunqi
    Li, Jun
    PROCEEDINGS OF THE 2008 INTERNATIONAL CONFERENCE ON E-RISK MANAGEMENT (ICERM 2008), 2008, : 362 - 367
  • [46] Prediction of heating parameters based on support vector machine
    Meiping, Wang
    Qi, Tian
    International Journal of Wireless and Mobile Computing, 2015, 8 (03) : 294 - 300
  • [47] Prediction of Tobacco Sales Based on Support Vector Machine
    Ding, Fuli
    Sun, Limin
    LISS 2014, 2015, : 891 - 896
  • [48] Prediction Based on Wavelet Transform and Support Vector Machine
    Liu, Xiaohong
    Zhu, Yanwei
    Zhang, Yongli
    Wang, Xinchun
    INFORMATION COMPUTING AND APPLICATIONS, PT I, 2011, 243 : 618 - +
  • [49] Marketing risk prediction based on the support vector machine
    Zhang Yunqi
    Wang Jiajun
    Li Tengfei
    ADVANCES IN MANAGEMENT OF TECHNOLOGY, PROCEEDINGS, 2007, : 135 - +
  • [50] Prediction of Data Classification Based on Support Vector Machine
    Wu, Xinghui
    Zhou, Yuping
    PROCEEDINGS OF THE 2016 4TH INTERNATIONAL CONFERENCE ON ELECTRICAL & ELECTRONICS ENGINEERING AND COMPUTER SCIENCE (ICEEECS 2016), 2016, 50 : 694 - 699