基于最大指数绝对值目标函数的抗差状态估计方法

A Robust State Estimation Approach Based on Objective Function of Maximum Exponential Absolute Value

为提高状态估计的抗差性,提出一种基于最大指数绝对值目标函数的状态估计(maximum exponential absolute value state estimation,MEAV)方法。首先给出了MEAV的基本模型,并介绍了其理论基础和数学性质。由于MEAV基本模型的目标函数并非处处可导,因而无法利用基于梯度的方法进行求解。为此,给出了MEAV基本模型的等价模型,并详细推导了基于原-对偶内点算法的MEAV等价模型的求解方法。算例分析表明,MEAV在估计过程中可自动抑制多个强相关不良数据,显示了良好的抗差性和较高的计算效率,因而具有良好的工程应用前景。

To improve the robustness of state estimation, a state estimation approach based on maximum exponential absolute value （MEAV） is proposed. Firstly, the basic model of MEAV is given and its theoretical base and mathematical properties are given. Since the objective function of the basic model of MEAV is not differentiable, it cannot be solved by the gradient based method. For this reason, an equivalent model of the basic model of MEAV is given and an original-dual interior point algorithm based solving method for the equivalent model of MEAV is derived in detail. Results of calculation example show that MEAV can automatically suppress a lot of strongly correlated bad data during the estimation process, so it shows up that the MEAV method possesses good robustness and high calculation efficiency, therefore it has a bright prospect in engineering practice.