基于无迹粒子滤波算法的发电机动态状态估计

发布时间：2018-03-10 10:20

本文选题：无迹粒子滤波　切入点：相量测量单元　出处：《电力系统自动化》2017年14期 　论文类型：期刊论文

【摘要】：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。
[Abstract]:The synchronous phasor measurement unit (PMU) can directly obtain the power angle isometric data in the dynamic process of the generator. Because of the random noise in the actual measurement data, in order to obtain more accurate generator state information, It is necessary to filter the measured data. A new method of generator dynamic state estimation based on unscented particle filter (UPF) is proposed. Firstly, the generator dynamic state estimation model is established based on the fourth order dynamic equation of generator. In the framework of particle filter (PFV), the unscented Kalman filter UKF is used to solve the importance density function of PF, and the latest measurement information is used in the process of generating predicted particles. The particle distribution is closer to the posteriori probability distribution of the real state. Finally, a numerical example of the WSCC 3-bus system and a real power grid system is given. The proposed algorithm is compared with the performance of UKF and PF. The simulation results show that the proposed algorithm is superior to PF and UKF in estimation accuracy and robustness to noise.
【作者单位】：河海大学能源与电气学院;国网江苏省电力公司电力科学研究院;
【基金】：国家自然科学基金资助项目(51277052) 国家电网公司科技项目(521001160038)~~
【分类号】：TM31

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