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Practical state estimation model and algorithm based on kernel density theory
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DOI：10.1016/j.ijepes.2021.107442.png)
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Abstract
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This paper presented a practical state estimation model and algorithm based on the kernel density theory (PKDE), which owns better performance than the existing methods. First, a PKDE was presented, which transforms the calculation of the kernel bandwidth of each measurement to that of each state variable. Therefore, the number of kernel bandwidths required to be determined is greatly reduced as the number of state variables are much less than that of the measurements. The feasibility of the PKDE model is supported by a proposition which is strictly proved. Second, an approach for choosing the appropriate kernel bandwidth was presented through the integration of the optimal bandwidth method based on the rule of thumb, the initial bandwidth selection method and the correcting bandwidth selection of the state variables. Third, PKDE's robustness to bad data is further enhanced by a strategy to correct the measurements according to the hidden component of the measurement error. The simulation results finally verify the effectiveness of the proposed models and algorithms.
Keywords:
Adaptive
Kernel density estimation
Hidden component
Optimal bandwidth
State estimation
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