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精确、快速的基波分量跟踪是对电网运行状态进行分析和评估的前提。提出一种基于移动窗口迭代修正策略的自适应无迹卡尔曼平滑算法(moving window based adaptive unscented Kalman smoother,MW_AUKS)。该方法兼顾稳态检测精度和动态检测速度,前者通过在前向无迹滤波过程中并行嵌入一个后向迭代修正的Rauch-Tung-Striebel平滑器实现,后者则先依据窗口内平均新息量在线判断是否有突变发生,再对状态估计协方差做自适应修正运算。利用建立的基波分量非线性状态估计模型对所提算法进行验证,结果表明所提算法可精确跟踪到基频、功率角、有功功率、视在功率等参数,并大幅提高初始收敛速度,同时准确判断和快速跟踪到状态突变。
Accurate and fast fundamental component tracking is the premise of analyzing and evaluating the operation status of power grid. This paper presents a moving window based adaptive unscented Kalman smoother (MW_AUKS) algorithm based on moving window iterative correction strategy. The method combines the steady-state detection accuracy and the dynamic detection speed. The former is realized by the parallel embedding of a backward-corrected Rauch-Tung-Striebel smoother in the forward unscented filter. The latter is based on the average amount of new information in the window Online determine whether there is a mutation occurs, and then make an adaptive correction of the state estimation covariance operation. The proposed algorithm is validated by using the established nonlinear state estimation model of fundamental components. The results show that the proposed algorithm can accurately track parameters such as fundamental frequency, power angle, active power and apparent power and greatly improve the initial convergence rate. Accurate judgment and rapid tracking of state mutations.