The Weighted Support Vector Machine Based on Hybrid Swarm Intelligence Optimization for Icing Prediction of Transmission Line

Not only can the icing coat on transmission line cause the electrical fault of gap discharge and icing flashover but also it will lead to the mechanical failure of tower, conductor, insulators, and others. It will bring great harm to the people’s daily life and work. Thus, accurate prediction of ice thickness has important significance for power department to control the ice disaster effectively. Based on the analysis of standard support vector machine, this paper presents a weighted support vector machine regression model based on the similarity (WSVR). According to the different importance of samples, this paper introduces the weighted support vector machine and optimizes its parameters by hybrid swarm intelligence optimization algorithm with the particle swarm and ant colony (PSO-ACO), which improves the generalization ability of the model. In the case study, the actual data of ice thickness and climate in a certain area of Hunan province have been used to predict the icing thickness of the area, which verifies the validity and applicability of this proposed method. The predicted results show that the intelligent model proposed in this paper has higher precision and stronger generalization ability.

[1]  Dongxiao Niu,et al.  Middle-long power load forecasting based on particle swarm optimization , 2009, Comput. Math. Appl..

[2]  Ertuğrul Çam,et al.  Forecasting electricity consumption: A comparison of regression analysis, neural networks and least squares support vector machines , 2015 .

[3]  Jinjin Liang,et al.  Smooth Diagonal Weighted Newton Support Vector Machine , 2013 .

[4]  Kin Keung Lai,et al.  A Fuzzy Group Forecasting Model Based on Least Squares Support Vector Machine (LS-SVM) for Short-Term Wind Power , 2012 .

[5]  F. Tay,et al.  Application of support vector machines in financial time series forecasting , 2001 .

[6]  Pierre McComber,et al.  A comparison of neural network and multiple regression transmission line icing models , 1998 .

[7]  LeiLei Shi.  Assessment of Forest Damage caused by Ice Storm based on MODIS Data- A Case Study of Jiangxi Province, China , 2013 .

[8]  Qian Ye,et al.  Integrated risk governance in the Yungui Plateau, China: The 2008 ice-snow storm disaster , 2012 .

[9]  Jianjun Wang,et al.  An annual load forecasting model based on support vector regression with differential evolution algorithm , 2012 .

[10]  M. Farzaneh,et al.  Statistical analysis of field data for precipitation icing accretion on overhead power lines , 2005, IEEE Transactions on Power Delivery.

[11]  Dongxiao Niu,et al.  Short-term wind speed forecasting using wavelet transform and support vector machines optimized by genetic algorithm , 2014 .

[12]  Sungwan Bang,et al.  Weighted Support Vector Machine Using k-Means Clustering , 2014, Commun. Stat. Simul. Comput..

[13]  Jiang Zhenglong.  Analysis of Hunan Power Grid Ice Disaster Accident in 2008 , 2008 .

[14]  Qiang Xu,et al.  Electronic design engineering , 2015 .

[15]  Jingcheng Liu,et al.  Forecast model for inner corrosion rate of oil pipeline based on PSO-SVM , 2012, Int. J. Simul. Process. Model..