Studies on denoising method of the stator current based on PCA Kalman filter and window processing

Zhai Kun, L. Feng, Du Wen-Xia, Shao Meng-Ya, Huang Zhan-ping
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Abstract

Aiming at noise interference problem in detecting stator current of induction motor, denoising method based on principal component analysis (PCA) Kalman filtering is proposed in this paper. Firstly, the state equation is obtained by building five order mathematical model, in order to reduce the correlation of data, data space is mapped to low dimensional subspaces via orthogonal transformations. Then, the Kalman prediction value is taken as the center of the observation signal which is processed by adding window processing, and the prediction value is gotten based on the PCA in the window. There are good stability and simple calculation in the algorithm. Simulation results demonstrate that the mean square error based on PCA) Kalman filtering is lower than that based on the traditional Kalman filtering algorithm, and the filtering effect is obviously improved.
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基于PCA卡尔曼滤波和窗口处理的定子电流去噪方法研究
针对感应电机定子电流检测中的噪声干扰问题,提出了基于主成分分析(PCA)卡尔曼滤波的去噪方法。首先,通过建立五阶数学模型得到状态方程,为了降低数据的相关性,通过正交变换将数据空间映射到低维子空间;然后,以卡尔曼预测值为观测信号的中心,对观测信号进行加窗处理,在窗内进行主成分分析得到预测值。该算法稳定性好,计算简单。仿真结果表明,基于PCA卡尔曼滤波的均方误差小于基于传统卡尔曼滤波算法的均方误差,滤波效果明显提高。
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