Abstract
This paper presents a fast state estimator and a corresponding bad data (BD) processing architecture aimed at improving computational efficiency and maintaining high estimation accuracy of existing state estimation (SE) algorithms, simultaneously. The conventional and phasor measurements are separately processed by a three-stage SE method and a linear estimator, respectively. Then, the derived estimates are combined using estimation fusion theory. To eliminate computational bottlenecks of the conventional BD processing scheme, BD identification is moved before the second stage of supervisory control and data acquisition based SE, and bad phasor measurements or bad conventional measurements in the phasor measurement units observable area are identified and processed all at once, which can dramatically reduce the implementation time, especially for large-scale networks with multiple BD. The proposed estimator is compared to existing methods in terms of estimation accuracy and computational effort through simulation studies conducted on standard IEEE test systems. Promising simulation results show that the proposed estimator could be an effective method to obtain system states in a fast and accurate manner.
Published Version
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