Bad Data Processing in Electrical Power System using Binary Particle Swarm Optimization

As the measurements received from RTUs to the Control Center are transmitted via a transmission medium e.g. telephone, fibre optics, wireless medium, it is not possible that the data transmitted is 100% error free always. Other reasons for receiving bad measurements at control centers may be due to wrong reading of the meter. There can be a number of reasons for measurement’s value to be recorded as wrong, e.g. outage of meter, drift in meter and bias in the meter.Therefore the measurements received may be erroneous sometimes, due to which the state estimation results may be misleading, and consequently can cause problem in monitoring and control of power system. Thus it is necessary to remove bad data from the measurement set or establish some robust state estimation techniques which can remove the effects of bad data on the estimated states.In this paper the problem of multiple bad measurements detection and identification is defined as a binary variables optimization problem and it’s solutions are obtained by using Binary Particle Swarm Optimization (BPSO). It is observed that this method can be used to identify multiple interacting erroneous measurements.