基于优化无迹Kalman滤波的电网动态谐波估计

Dynamic harmonic estimation based on optimized unscented Kalman filter model

提出一种基于粒子群优化的无迹卡尔曼滤波(particle swarm optimized unscented Kalman filter,PSOUKF)的电网动态谐波估计方法,利用包含种群分类与动态学习因子的改进粒子群优化算法,优化无迹卡尔曼滤波算法(unscented Kalman filter,UKF)的状态噪声协方差和观测噪声协方差,使系统噪声对电网动态谐波估计结果的影响得到充分考虑,克服了传统UKF算法将这两种方差视为常数导致的动态谐波估计精度低的缺陷.仿真结果表明,PSOKUF算法比卡尔曼滤波(Kalman filter,KF)算法和传统的UKF算法更有效,在没有增加计算复杂度的情况下,能够提高动态谐波估计精度.

We propose a particle swarm optimized unscented Kalman filter （ PSOUKF） method to estimate the power system dynamic harmonics. By using the improved particle swarm optimization algorithm with species classification and dynamic learning factor, we optimize the state noise covariance and the measurement noise covariance of the unscented Kalman filter （ UKF） so as to sufficiently take the impacts of power system noise on dynamic harmonic estimation into account. The proposed method overcomes the deficiency of low dynamic harmonic estimation accuracy in the traditional UKF method in which the above two kinds of covariance are taken as constants. Simulation results show that the proposed PSOUKF is more effective than Kalman filter （ KF） and UKF, and PSOUKF can improve the dynamic harmonic estimation accuracy without increasing the computational complexity.