基于最佳分段和改进半物理模型的特高压并联电抗器顶层油温预测  

Prediction of UHV Shunt Reactor Top Oil Temperature Based on Optimal Segmentation and Improved Semi-physical Model

作　　者：谭风雷 陈昊 何嘉弘 TAN Fenglei;CHEN Hao;HE Jiahong(Maintenance Branch Company,State Grid Jiangsu Electric Power Co.,Ltd.,Nanjing 211102,Jiangsu Province,China;School of Electrical Engineering,Southeast University,Nanjing 210096,Jiangsu Province,China)

机构地区：[1]国网江苏省电力有限公司检修分公司,江苏省南京市211102 [2]东南大学电气工程学院,江苏省南京市210096

出　　处：《电网技术》2021年第8期3314-3323,共10页Power System Technology

基　　金：国家自然科学基金项目(51807028);国网江苏省电力有限公司科技项目(J201814)。

摘　　要：为有效评估特高压并联电抗器内部热状态,提出一种基于最佳分段和改进半物理模型的特高压并联电抗器顶层油温预测方法。首先在充分研究特高压并联电抗器顶层油温曲线“正弦”变化趋势的基础上,采用K-means聚类法对顶层油温曲线进行了分段。然后基于分段内距离、分段间距离和分段重叠度,建立了有效性函数,进而实现了顶层油温曲线最佳分段数的选择。最后综合考虑变压器顶层油温半物理预测模型的离散化处理误差和特高压并联电抗器的主要影响因素,提出一种适用于特高压并联电抗器顶层油温预测的改进半物理模型,并利用Elman神经网络实现了华东地区某特高压并联电抗器顶层油温的预测。结果表明所提出方法的平均预测误差为1.00%,预测精度较高,能够满足现场实际应用的精度要求,从而验证了方法的有效性。A top oil temperature prediction method for UHV shunt reactor based on optimal segmentation and improved semi-physical model is proposed to effectively evaluate the internal thermal state of the UHV shunt reactor.Firstly,based on fully studying the"sinusoidal"change trend of the top oil temperature curve of the UHV Shunt reactor,the top oil temperature curve is segmented by the K-means clustering method.Then the validity function is established based on the intra-segment distance,the inter-segment distance and the overlap degree,and the selection of the optimal number of top oil temperature curves is realized.Finally,after comprehensively considering the discretization error of the semi-physical forecasting model of the transformer top oil temperature and the main influencing factors of the UHV shunt reactor,an improved semi-physical model suitable for the prediction of the UHV shunt reactor top oil temperature is proposed,and the Elman neural network is used to forecast the UHV shunt reactor top oil temperature in East China.The results show that the average forecasting error of the method proposed is relatively high with the forecasting accuracy of 1.00%,which can meet the accuracy requirements of the actual application in the field,thus verifying the effectiveness of the method.

关 键 词：特高压并联电抗器 顶层油温 预测 K-means聚类 有效性函数 半物理模型 ELMAN神经网络 

分 类 号：TM721[电气工程—电力系统及自动化]