主成分在输电线路可听噪声预测中的应用研究  

Applied research of PCA method to transmission line audible noise prediction

作　　者：李静雅[1,2] 曹洁[1] 姜梅[3] 

机构地区：[1]兰州理工大学计算机与通信学院,兰州730050 [2]长治学院计算机系,山西长治046011 [3]甘肃电力科学研究院,兰州730050

出　　处：《计算机工程与应用》2011年第14期233-236,241,共5页Computer Engineering and Applications

基　　金：甘肃电力科学研究院项目(NoH14200906)

摘　　要：针对超高压输电线路可听噪声BP网络预测模型影响因素多的问题,运用主成分分析算法(PCA)对影响可听噪声的环境因素、地理参数、导线结构参数等14个因素进行简化,建立PCA-BP网络预测模型。选取甘肃省内多条750kV、330kV输电线路的可听噪声的实测资料为样本集,采用Matlab神经网络工具箱进行模型训练与预测,并与BP网络模型预测结果比较。结果表明:主成分分析方法在可听噪声影响因素的简化中不适用,预测结果没有BP网络模型预测结果理想。分析了主成分在可听噪声影响因素简化中不适用的原因。Aimed at solving the multiple-influence-factor problem in the audible noise BP network predictive model on EHVtransmission line,14 factors,which affect the value of audible noise,such as environment,geographical parameter and conduc-tor structure are simplified using Principal Component Analysis（PCA）and a PCA-BP network predictive model is established.Based on measured data of audible noise from 330 kV and 750 kV EHV transmission lines in Gansu province,the princi-pal-component-based model is trained and predicted with Matlab neural network toolbox,and compared with the predictive re-sult of BP neural network model.Results show that PCA is not suitable for the simplification of audible noise influence fac-tors and its predictive ability is worse than that of the BP network,and possible reasons of the PCA failure are pointed out

关 键 词：主成分分析(PCA) BP神经网络 超高压输电线路 可听噪声预测 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] TM723[自动化与计算机技术—计算机科学与技术]