Abstract
State estimators are vitally important in energy control centers. The measurements that come from control system are generally analysed by a state estimator. Since there can always be bad measurements in the system, estimated value and the true value of the state estimator can be far from each other. In this paper, by using an artificial neural network (ANN), a bad data detection, identification and then elimination preestimation filter is outlined.
Highlights
The electrical energy, from generation to consumption, is measured, protected and controlled at various times
By using the measurement value that come from the SCADA system and other data, state estimation and state variables of the network can be detected
If the square of the difference between the measured and estimated values of a measurement variable is greater than a given threshold, this value is flagged as bad measurement
Summary
Abstract-State estimators are vitally important in energy control centers. The measurements that come from control system are generally analysed by a state estimator. Since there can al\V
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