A reduced model for bad data processing in state estimation

A reduced model theory for bad data processing is proposed which utilizes the concept of error residual spread areas. Based on the reduced model theory, statistical indices can be defined for each error residual spread area, and therefore existing detection and identification techniques can be applied separately for each error residual spread area. In this way, errors can be isolated in smaller regions of the system, making it possible to avoid the search for bad data in the global system. Results from several test cases on power systems show the effectiveness and robustness of the method.<
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