Revealing gross errors in critical measurements and sets via forecasting-aided state estimators

State estimators are important monitoring tools which process real-time dat a in power system control centers. The capability of detecting and identify ing bad data depends on the redundancy level of the information to be proce ssed. Network changes or a temporary malfunction of the data acquisition sy stem reduce data redundancy for state estimation. Measurement redundancy de terioration can be characterized by the presence of critical measurements a nd sets. For the vast majority of data validation algorithms, it is impossi ble to process gross errors in critical measurements and sets. This paper p roposes an algorithm for detecting, identifying and removing bad data in cr itical measurements and sets through forecasting-aided state estimators. Us ing the IEEE-14 bus test system, the performance of the proposed algorithm is evaluated and discussed through the simulation of different levels of da ta redundancy degradation. (C) 2001 Elsevier Science S.A. All rights reserv ed.