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中文摘要：
以＂光传感器输变电设备盐密在线监测系统＂提供的数据为依据,建立了一种基于最小二乘支持向量机的智能预测模型,该模型以温度（T）、相对湿度（H）、风速（W_V）、气压（P）、雨量（R）等5个变量为输入参数,等值附盐密度为输出参数,用二次损失函数取代支持向量机中的不敏感损失函数,将不等式约束条件变为等式约束,从而将二次规划问题转变为线性方程组的求解,用最小二乘法实现了支持向量机算法。用网格搜索法对最小二乘支持向量机最优参数进行自动选取,提高了预测的快速性和准确性。仿真结果表明,与BP神经网络预测的结果相比,该模型预测的等值附盐密度更接近实测结果。本文的方法为电网污区分布图的计算提供了一条新的思路。
英文摘要：
According to the data provided by Optical Sensor System for the ESDD Monitoring of Transmission Equipment,an intellectual prediction model based on least squares support vector machines（LS-SVM） is built,whose input variables are temperature （T）,humidity（H）,wind velocity（WV）,air pressure（P） and rainfall（R）,and output variable is equal salt deposit density（ESDD）.In this model,the non-sensitive loss function is replaced by quadratic loss function and the inequality constraints are replaced by equality constraints.Consequently,quadratic programming problem is simplified as the problem of solving linear equation groups,and the SVM algorithm is realized by least squares method.Through Grid Search Method,the optimal parameters of LS-SVM are selected automatically,which has improved the speed and accuracy of the forecasting.Compared to the BP simulated results,the predicted ESDD of the model are closer to the on-line measured ones.Therefore,the model presented provides a doable thought for the computerization of pollution area map of power network.
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