一种电力系统量测噪声自适应抗差状态估计方法
An Adaptive Robust State Estimation Approach for Measurement Noise
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摘要: 为克服传统状态估计方法在处理量测噪声方面的局限性,文中首先提出一种基于最大相关熵准则的抗差状态估计一般模型,该模型可从理论上统一已有的几种抗差状态估计方法,并可导出新的抗差状态估计方法。在此基础上提出一种量测噪声自适应抗差状态估计方法(ARSE),ARSE能够通过统计学习获得量测噪声的分布规律,并与所提出的抗差状态估计一般模型进行在线匹配,从而实现对量测噪声类型的自适应,即在常见的量测噪声分布类型下,ARSE可得到更接近于状态变量真值的估计结果。最后通过仿真算例验证了所述方法的有效性。Abstract: To overcome the limitations of traditional state estimation in dealing with noise measurement,ageneral model of robust state estimation（RSE）is proposed based on maximum correntropy criterion（MCC）,which is able to unify several existing RSE models in theory and derive new RSE models.An adaptive robust state estimator（ARSE）for measuring noise is proposed,which makes it possible to obtain the distribution type of noise measurement through statistical learning.Then the corresponding optimal RSE model is selected online to adapt to the variation of noise measuring distribution type.ARSE is able to yield optimal estimation results closer to the true values of the state variables for common noise measuring distribution types.Simulation results show the effectiveness of the proposed ARSE.
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