基于Sage-Husa自适应的卡尔曼滤波负荷信号降噪研究

Noise Reduction of Load Signal Based on Sage-Husa Adaptive Kalman Filter

在对工业污水处理站运行设备进行负荷识别时,由于其所处的复杂工作环境,采集设备得到的信号中经常包含背景中的强噪声干扰,使得在对关键电力参量波形的识别和处理时出现较大困难。本文基于Sage-Husa自适应算法与卡尔曼滤波法相结合,在卡尔曼滤波基础上,加入遗忘因子,陌生参量指标,对工业污水站设备采集信号波形进行处理。采用公司智能电表采集平台数据进行实验验证,本文算法对比传统卡尔曼滤波算法,误差平均值降低42.91%,误差方差降低48.68%,获得更好的消除背景噪声效果。

In the load identification of the operating equipment of the industrial sewage treatment station,due to the complex working environment,the signal obtained by the acquisition equipment often contains strong noise interference in the background,which makes it difficult to identify and process the waveform of the key power parameters.This paper is based on the combination of Sage-Husa adaptive algorithm and Kalman filtering method.On the basis of Kalman filtering,forgetting factor and unfamiliar parameter index are added to process the signal waveform collected by equipment of industrial sewage station.The data of the company's smart meter acquisition platform was used for experimental verification.Compared with the traditional Kalman flter algorithm,the average error of the proposed algorithm is reduced by 42.91%,and the error variance is reduced by 48.68%,achieving better background noise elimination effect.