Abstract
This paper develops statistically robust particle filters that exhibit high statistical efficiency and good robustness to outliers. Three types of outliers, namely, observation outliers, structural outliers and innovation outliers, are considered. Two robust particle filters are developed and their performance are assessed. Firstly, a weight reconstruction particle filter is proposed that makes use of the projection statistics to derive non-zero particle weights in response to observation outliers. Secondly, a fitting-based particle filter is developed that generalizes the former filter to the case of structural and innovation outliers. This filter approximates the next estimated values using both the projection statistics and the linear fitting technique. The designed filters are applied to the state estimation of decentralized machines' internal state estimation and local bus frequency estimation in power systems. Simulation results performed on a four-machine two-area power system and the 39-bus <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">New England</i> power system demonstrate the excellent performance of the proposed robust particle filters in dealing with various types of outliers.
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