深圳大学学报理工版

提出一种基于无迹粒子滤波(unscented particle filter, UPF)算法的电网动态谐波估计方法.通过无迹卡尔曼滤波算法得到电网动态谐波状态量的估计值和协方差,运用这些结果改进传统粒子滤波算法的重要密度函数,采用粒子滤波算法得到电网动态谐波的最优估计值.该方法克服了无迹卡尔曼滤波算法(unscented Kalman filter, UKF)对噪声要求为高斯分布的限制和传统粒子滤波(particle filter,PF)算法易退化的缺点,保留了UKF对非线性问题的较好处理和PF强抗干扰性能力.仿真结果表明,在高斯噪声和非高斯噪声情况下,UPF算法得到的电网动态谐波幅值、相位的估计值都更接近真实值.

This paper proposes a new method for estimating dynamic harmonics in power systems based on the unscented particle filter(UPF)algorithm. The UPF algorithm is used to estimate the values and covariance of the state variables of the dynamic harmonics in power systems. These estimated values are used to generate the importance density function of particle filter(PF)algorithm. The optimal estimation of dynamic harmonics in the power system is achieved by using the derived PF algorithm. The proposed method overcomes not only the restriction of Gaussian distribution noise required in unscented Kalman filters(UKF)but also the drawback of easy degeneration for conventional PF. In addition, it retains the high performance of UKF in processing nonlinearity and the strong anti-interference capability of PF.Experiments show that the estimates of dynamic harmonic amplitude and phase by the proposed method are closer to the true values under both Gaussian noise and non-Gaussian noise situations.

引言
1 谐波分析模型
2 无迹粒子滤波
3 基于无迹粒子滤波的谐波估计
4 仿真分析
5 结语

图1 5次谐波幅值估计及MSE值<br/>Fig.1 Amplitude estimation and MSE for the 5th harmonic signal

图1 5次谐波幅值估计及MSE值
Fig.1 Amplitude estimation and MSE for the 5th harmonic signal

图2 5次谐波相位估计及MSE值 <br/>Fig.2 Phase estimation and MSE for the 5th Harmonic signal

图2 5次谐波相位估计及MSE值
Fig.2 Phase estimation and MSE for the 5th Harmonic signal

表1 t=0.05 s时采用KF、UKF和UPF的静态谐波幅值与相位估计<br/>Table 1 Comparison of harmonic amplitude and phase estimation at t=0.05 s for KF, UKF and UPF

表1 t=0.05 s时采用KF、UKF和UPF的静态谐波幅值与相位估计
Table 1 Comparison of harmonic amplitude and phase estimation at t=0.05 s for KF, UKF and UPF

图3 基波幅值估计及MSE值<br/>Fig.3 Amplitude estimation and MSE for fundamental harmonic

图3 基波幅值估计及MSE值
Fig.3 Amplitude estimation and MSE for fundamental harmonic

图4 3次谐波幅值估计及MSE值<br/>Fig.4 Amplitude estimation and MSE for the 3rd harmonic

图4 3次谐波幅值估计及MSE值
Fig.4 Amplitude estimation and MSE for the 3rd harmonic

图5 5次谐波幅值估计及MSE值<br/>Fig.5 Amplitude estimation and MSE for the 5th harmonic

图5 5次谐波幅值估计及MSE值
Fig.5 Amplitude estimation and MSE for the 5th harmonic

表2 t=0.05 s时动态幅值估计<br/>Table 2 Amplitude estimation for dynamic harmonic at t=0.05 s

表2 t=0.05 s时动态幅值估计
Table 2 Amplitude estimation for dynamic harmonic at t=0.05 s

表3 t=0.05 s时非高斯噪声下幅值估计<br/>Table 3 Amplitude estimation for non-Gaussian noise at t=0.05 s

表3 t=0.05 s时非高斯噪声下幅值估计
Table 3 Amplitude estimation for non-Gaussian noise at t=0.05 s

表4 t=0.05 s时动态相位估计<br/>Table 4 Phase estimation for dynamic harmonic at t=0.05 s

表4 t=0.05 s时动态相位估计
Table 4 Phase estimation for dynamic harmonic at t=0.05 s

图6 基波相位估计及MSE值<br/>Fig.6 Phase estimation and MSE for fundamental harmonic

图6 基波相位估计及MSE值
Fig.6 Phase estimation and MSE for fundamental harmonic

图7 3次谐波相位估计及MSE值<br/>Fig.7 Phase estimation and MSE for the 3rd harmonic

图7 3次谐波相位估计及MSE值
Fig.7 Phase estimation and MSE for the 3rd harmonic

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备注

引言

1 谐波分析模型

2 无迹粒子滤波

3 基于无迹粒子滤波的谐波估计

4 仿真分析

5 结语

期刊信息

备注