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This article proposes an online scheme for state estimation of a generic class of nonlinear dynamical systems in the presence of abnormal measurement data from sensors. We thoroughly illustrate why the performance of standard recursive Bayesian inference degrades in the presence of any measurement distortion. After demonstrating how different abnormalities can be accommodated explicitly inside a generic state–space model, we propose a robust mechanism to perform recursive Bayesian inference on the presented model to not only detect but also mitigate the effect of corrupted measurements in the final state estimates. Using simulations and experimentation, we demonstrate the success of the proposed framework in reducing the impact of different types of distortions in measurements. The ability to tackle different kinds of measurement abnormalities during online inference sets the proposed method apart from the existing techniques. [ABSTRACT FROM AUTHOR]

This paper presents a new numeric technique to estimate the operating power system frequency. The technique employs a recurrent scheme which consists of a tunable input FIR filter, frequency calculator and an averaging output filter. The recurrent structure ensures that power system frequency is efficiently tracked while minimizing signal distortions arising from harmonic and noise effects. The performance of the developed technique has been thoroughly investigated using computer simulations, and the results are provided. The effectiveness of the proposed technique is demonstrated by comparing it with three different methods reported in the literature. An experimental setup is successfully developed to evaluate the practical operation of the estimator corroborating the performance during simulations. It was confirmed that the proposed estimator performs well during static and dynamic conditions which makes it useful for the estimation of online power system frequency. [ABSTRACT FROM AUTHOR]