基于分布式抗差最小二乘法的状态估计

State Estimation with the Least Robustness Square Method Based on Distribution

针对电力系统互联的必然趋势,在研究传统算法和分布式算法的基础上,提出了基于结构和电压等级分布的组合分布式状态估计算法,建立了相应的数学模型。抗差估计理论主要研究抗拒少量粗差对估值的影响。拓扑错误和坏数据可以分别看作带有粗差的网络参数和量测数据,因此可以将抗差最小二乘法用于存在拓扑错误和坏数据时的状态估计。算例结果表明,基于分布式的抗差最小二乘法具有良好的抗粗差能力和收敛可靠性,并且收敛速度快。

The interconnection of power systems is an inevitable trend with the development of future power system, correspondingly. Based on traditional arithmetic and distributing arithmetic, this thesis puts forward the combined distributed state estimation method based on the distribution of structure and voltage grades and establishes the corresponding math model. Robustness square estimation theory pays much attention to the influence of little outlier resistance to estimation. Since topology errors and bad data can be considered as network parameters with outlier and measured data separately, the least robustness square method can be used in state estimations with topology errors and bad data. As shown in the results of calculation examples, the least robustness square method has favorable outlier resistance, convergence reliability and high convergence speed.