Prediction Model for Icing Thickness of Power Transmission Line Based on Grey Support Vector Machine

doi:10.11930/j.issn.1004-9649.2016.11.046.05

Abstract

Abstract: In order to reduce the impact of icing accidents on transmission lines, prediction of icing thickness on transmission lines is very effective in anti-icing work of power grid. A short-term prediction model based on grey support vector machine for icing thickness of transmission lines is proposed. The methods of dirty data elimination and data preprocessing are analyzed. The accuracy and applicability of proposed model are verified by comparison between model predictions and measured data. Predicted maximum ice thickness can provide guidance on monitoring icing condition, early warning and AC/DC ice melting work. Compared with support vector machine (SVM) and generalized regression neural network prediction model, the proposed method has average error of 0.325 mm, and average absolute error of 2.61%, which is suitable for short-term prediction of icing thickness of transmission line. The application of the prediction model can guide the transmission line ice-resistant work in the ice area.

Key words: icing, transmission line, short-term prediction, grey model, support vector machine, on-line monitoring

CLC Number:

TM752⁺.5

MA Xiaomin, GAO Jian, WU Chi, HE Rui, GONG Yiyu, LI Yi, WU Tianbao. Prediction Model for Icing Thickness of Power Transmission Line Based on Grey Support Vector Machine[J]. Electric Power, 2016, 49(11): 46-50.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.2016.11.046.05

https://www.electricpower.com.cn/EN/Y2016/V49/I11/46

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