基于容积卡尔曼滤波的发电机动态状态估计

发布时间：2018-09-08 11:39

【摘要】：同步相量测量单元(phasor measurement unit,PMU)能够对动态过程中同步发电机功角进行直接量测,但随机误差`PMU存在量测误差,从而对应用造成不良后果。提出一种机电暂态过程中发电机动态状态估计新方法。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;给出了过程噪声误差计算方法;由于动态状态估计模型为非线性,进而提出基于容积卡尔曼滤波(cubature Kalman filter,CKF)的动态状态估计方法,利用球面-径向规则生成Cubature点,通过发电机动态方程对Cubature点进行变换,得到发电机状态量预报值。仿真分析利用某实际大区域电网同时实现了基于CKF和无迹卡尔曼滤波(unscented Kalman filter,UKF)的动态状态估计。对这两种方法的估计性能指标进行对比分析,结果表明CKF法状态估计效果和计算效率均优于UKF法状态估计。
[Abstract]:Synchronous phasor measurement unit (phasor measurement unit,PMU) can measure the power angle of synchronous generator directly in the dynamic process, but the random error 'PMU' has measurement error, which has bad consequences for application. A new method of generator dynamic state estimation in electromechanical transient process is proposed. Based on the fourth order dynamic equation of generator, the dynamic state estimation model of generator is established, and the calculation method of process noise error is given. Then a dynamic state estimation method based on volumetric Kalman filter (cubature Kalman filter,CKF) is proposed. The Cubature point is generated by spherical radial rule and the Cubature point is transformed by the generator dynamic equation to obtain the prediction value of generator state quantity. The dynamic state estimation based on CKF and unscented Kalman filter (unscented Kalman filter,UKF is realized simultaneously by using a large area power network. The performance indexes of the two methods are compared and analyzed. The results show that the CKF method is better than the UKF method in the effect of state estimation and the computational efficiency.
【作者单位】：新能源电力系统国家重点实验室(华北电力大学);中国电力科学研究院;
【基金】：国家重点基础研究发展计划项目(973项目)(2012CB 215206) 国家自然科学基金项目(51222703) 高等学校博士学科点专项科研基金(20120036110009)~~
【分类号】：TM31

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