电力负荷数据预处理的二维小波阈值去噪方法  

A Two-dimension Wavelet Threshold De-noising Method for Electric Load Data Pre-processing

作　　者：童述林[1] 文福拴[2] 陈亮[1,3] 

机构地区：[1]华南理工大学电力学院,广东省广州市510640 [2]浙江大学电气工程学院,浙江省杭州市310027 [3]广东省电力调度中心,广东省广州市510600

出　　处：《电力系统自动化》2012年第2期101-105,共5页Automation of Electric Power Systems

基　　金：教育部高等学校博士学科点专项科研基金资助项目(200805610020);广东电网公司科技项目~~

摘　　要：历史负荷数据中的噪声会影响以其为基础所进行的负荷预测的准确性,有必要对负荷数据进行去噪处理。考虑到负荷数据的横向连续性和纵向连续性,可以先把负荷数据按照日期排列成二维数据集,经归一化处理后形成灰度图像矩阵,然后用基于图像的二维小波阈值去噪方法进行去噪处理,最后通过反归一化得到去噪后的负荷数据。实例分析结果表明这种方法可行且有效。There are usually some noises in the historical load data, and the accuracy of load forecasting could then be impacted. Hence, it is necessary to de-noise the noises before the load data are used for load forecasting or power system analysis. Considering both horizontal and vertical continuities of electric loads, a new method for load de-noising is presented based on a two-dimension wavelet threshold de-noising method. First, the load data is transformed into a matrix of gray-scale images by normalization. Next, the images are processed by employing the two-dimension wavelet threshold de-noising method. Finally, the de-noised data are obtained after de-normalization. The feasibility and efficiency of the developed method are demonstrated by the improvement of the load forecasting accuracy of an actual example.

关 键 词：电力负荷 数据预处理 二维小波 阈值去噪 

分 类 号：TN911.4[电子电信—通信与信息系统] TM715[电子电信—信息与通信工程]