基于小波理论对负荷预测中不良数据的处理  

Processing Fault Data Method in Load Forecasting Based on Wavelet Analysis

作　　者：张文哲[1] 陈刚[1] 吴迎霞[2] 

机构地区：[1]重庆大学电气工程学院,重庆400030 [2]重庆电力调度通信中心,重庆400014

出　　处：《重庆大学学报（自然科学版）》2004年第6期77-81,共5页Journal of Chongqing University

摘　　要：历史负荷数据的真实可靠是电力系统负荷预测的基础,而在电力系统运行中产生的冲击负荷,以及由SCADA系统采集数据时产生的随机干扰数据都会导致历史数据中含有不良数据。提出采用小波分析与局部奇异性理论通过对模极大值的调整和细节信号的软阈值处理可以达到检测并消除不良数据的目的,从而为负荷预测提供能反应其变化规律的真实历史信息。通过仿真算例验证了所提方法的有效性。The reliability and reality of load historical data is the foundation of load forecasting.But,the impact load in running power system,and the disturb data in collecting load data through the SCADA may cause much fault data in load historical data. Focusing on solving this problem, a method through adjusting amplitade of its wavele modulus maxima and processing the wavelet decomposed detail signal by soft threshold based on wavelet analysis and singularity theory, then fault date can be eliminated,so that,the real historical imformation and regulation data can be gained by load forecasting.

关 键 词：负荷预测 小波变换 奇异性 不良数据 

分 类 号：TM715[电气工程—电力系统及自动化]