Power system state forecasting using regression analysis
Power system state forecasting using regression analysis
复制标题
使用回归分析进行电力系统状态预测
DOI：
10.1109/pesgm.2012.6345595
复制
发表时间：
2012
期刊：
影响因子：
--
通讯作者：
C. Evrenosoglu
中科院分区：
文献类型：
--
作者：
M. Hassanzadeh;C. Evrenosoglu
提出了一种基于回归分析的块对角线状态转移矩阵。利用状态转移矩阵预测系统状态，然后通过经典动态估计中的扩展卡尔曼滤波进行修正。当新的在线测量数据可用时，转换矩阵被更新。预测的准确性可以根据更新的频率进行权衡。在IEEE 14总线和30总线系统上的测试表明，与现有的状态预测方法相比，该方法在动态状态估计方面的预测精度有所提高。
This paper presents a block-diagonal state transition matrix based on regression analysis. The state transition matrix is used to forecast the system state, which is subsequently corrected through extended Kalman filter in classical dynamic state estimation (DSE). The transition matrix is updated when new online measurement data are available. The forecasting accuracy can be traded off according to the frequency of the updates. The tests on IEEE 14- and 30-bus system show improvement in the state forecasting accuracy when compared to the existing state forecasting methods in dynamic state estimation.