基于自适应容积卡尔曼滤波算法的电力系统动态谐波状态估计

Dynamic Harmonic State Estimation of Power System Based on Adaptive Volumetric Kalman Filter

由于传统的谐波状态估计的参数辨识算法要求噪声的协方差矩阵固定不变,而实际工程中噪声的协方差矩阵是随时间变化的,工程中存在错误的量测数据,导致传统参数辨识算法估计的谐波电流参数的准确度较低。因此,提出自适应容积卡尔曼滤波算法来提高辨识谐波电流参数的准确度。首先,针对时变噪声干扰,采用基于渐消记忆指数加权法的噪声估值器算法生成时变噪声的协方差矩阵;其次,针对错误的量测数据,采用开窗估计算法修正错误的量测数据;然后,将修正的噪声协方差矩阵和量测数据代入容积卡尔曼滤波算法中,对谐波电流参数进行估计;最后,搭建IEEE 13节点系统仿真模型,验证了自适应容积卡尔曼滤波算法在时变噪声干扰及量测数据错误情况下仍可准确地估计谐波电流参数,确保了动态谐波状态估计的准确性。

The traditional parameter identification algorithm is based on the fixed noise covariance matrix,however the noise covariance matrix changes over time in actual project.The measurement errors lead to low accuracy of parameters of harmonic current with traditional parameter identification algorithm.Therefore,the adaptive capacity of Kalman filtering algorithm is presented to solve those problems.Firstly,the algorithm of noise signal based on fading memory index weighting method is proposed to generate time-varying noise covariance matrix in view of the time-varying noise.Secondly,the window estimation algorithm is proposed to correct the error of measurement data.Then,the noise covariance matrix and the measurement data are put into the Kalman filter algorithm to estimate the harmonic current parameters.Finally,the IEEE13 node system simulation model is built to get simulation data,the simulation data are used to test that the adaptive volume Kalman filter algorithm could accurately estimate the harmonic current parameters under the time-varying noise and errors measurement data,the accuracy of dynamic harmonic state estimation is guaranteed.