基于无迹粒子滤波算法的发电机动态状态估计

同步相量测量单元(PMU)能够直接获取发电机动态过程中的功角等量测数据,由于实际的量测数据中含有随机噪声,为了得到更精确的发电机状态信息,有必要对量测数据进行滤波处理。提出一种基于无迹粒子滤波(UPF)的发电机动态状态估计新方法。首先,该方法基于发电机四阶动态方程建立了发电机动态状态估计模型,其次,在粒子滤波(PF)的框架下,该方法采用无迹卡尔曼滤波(UKF)求解PF的重要性密度函数,且在生成预测粒子的过程中使用了最新的量测信息,使得粒子的分布更加接近真实状态的后验概率分布。最后,通过美国西部系统协调委员会(WSCC)3机9节点系统和某实际电网系统的算例测试,将所提算法与UKF及PF的性能进行了对比。仿真结果表明,UPF在估计精度及对噪声的鲁棒性方面均优于PF与UKF。

Dynamic State Estimation for Generators Based on Unscented Particle Filtering algorithm

The phasor measurement unit(PMU)can directly obtain measurement data such as the rotor angle of generators in the dynamic process. However, considering random noises in the real-time measurement data, it is necessary to filter out the noises in the measurement data to get more accurate information on generator states. This paper presents a novel method based on the unscented particle filter(UPF)to dynamically estimate the states of synchronous generators. Firstly, dynamic state estimating models of the generators are developed based on the fourth-order dynamic equations. Secondly, in the framework of the particle filter(PF), the proposed method obtains the important density function of PF by using unscented Kalman filter(UKF). The proposed method takes the latest measurement information into consideration in the process of generating predictive particles, which makes the distribution of particles much closer to the posterior probability distribution of the true states. Finally, the performance of the proposed method is compared with UKF and PF both in American Western Systems Coordinating Council(WSCC)3-machine 9-bus system and an actual grid. Simulation results show that, compared with PF and UKF, the UPF performs better in estimation precision and robustness to measurement noise.