主成分在输电线路可听噪声预测中的应用研究

摘要

Aimed at solving the multiple-influence-factor problem in the audible noise BP network predictive model on EHV transmission line, 14 factors,which affect the value of audible noise,such as environment,geographical parameter and conductor 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 principal-component-based model is trained and predicted with Matlab neural network toolbox,and compared with the predictive result of BP neural network model.Results show that PCA is not suitable for the simplification of audible noise influence factors and its predictive ability is worse than that of the BP network,and possible reasons of the PCA failure are pointed out.%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.