Revealing gross errors in critical measurements and sets via forecasting-aided state estimators |
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| Affiliation: | 1. The Shanghai Key Lab of Modern Optical System, Department of Control Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China;2. College of Science, University of Shanghai for Science and Technology, Shanghai 200093, China;1. The Interuniversity Institute for Marine Sciences in Eilat, POB 469, 88103 Eilat, Israel;2. Dept. of Ecology, Evolution and Behavior – Alexander Silberman Institute of Life Sciences,Hebrew University of Jerusalem, Jerusalem 91904, Israel;3. Dept. of Earth Sciences, University College London, Gower St., London WC1E 6BT, UK |
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| Abstract: | State estimators are important monitoring tools which process real-time data in power system control centers. The capability of detecting and identifying bad data depends on the redundancy level of the information to be processed. Network changes or a temporary malfunction of the data acquisition system reduce data redundancy for state estimation. Measurement redundancy deterioration can be characterized by the presence of critical measurements and sets. For the vast majority of data validation algorithms, it is impossible to process gross errors in critical measurements and sets. This paper proposes an algorithm for detecting, identifying and removing bad data in critical measurements and sets through forecasting-aided state estimators. Using the IEEE-14 bus test system, the performance of the proposed algorithm is evaluated and discussed through the simulation of different levels of data redundancy degradation. |
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