考虑大规模风电接入的快速抗差状态估计研究

Research of fast and robust state estimation considering large-scale wind power integration

提出了精细化抗差最小二乘状态估计方法,用于解决大规模风电接入对状态估计带来的残差污染问题。该方法一方面在权函数中引入量测类型基准值,用于区分不同类型量测坏数据,提高了抗差状态估计的坏数据检测能力。另一方面,利用状态估计量测预校验信息,对SCADA量测进行预处理,形成坏数据参考因子,消除参数误差引起的坏数据误判,从而提高大规模风电接入电网的状态估计计算精度。同时使用Givens变换并行算法提升软件计算速度,提高抗差状态估计数据断面的实时性,实现精细化的快速抗差状态估计,以适应风电的大规模接入电网给分析控制类在线应用带来的影响。最后对某地区电网进行测试验证,证明该方法能够有效识别风电场遥测坏数据,消除其造成的残差污染,提高了估计计算速度和精度。

This paper presents a fine and robust least squares state estimation method for solving residual contamination problem caused by large-scale wind power integration. On the one hand, it introduces the reference value of measurement type into the weight function to distinguish different types of measurement bad data, which improves the bad data detection capability of robust state estimation; on the other hand, it uses the pre-check information of state estimation measurement to do SCADA measurement pretreatment, and then forms the bad data reference factor to eliminate bad data misjudgment caused by parameter errors, thereby improving the state estimation accuracy of large-scale wind power integration grid. In order to improve the software computing speed and the data section real-time performance of robust state estimation, parallel algorithms are used to do Givens transformation, so as to achieve the fine and rapid robust state estimation and accommodate the influence to the analysis and control class online applications caused by the large-scale wind power integration grid. Finally, the simulation tests of a regional power grid prove that the proposed method can effectively identify telemetry bad data of wind farms eliminate residual pollution caused by it, which improve the speed and accuracy of the state estimation.