Power Systems Dynamic State Estimation With the Two-Step Fault Tolerant Extended Kalman Filtering

Bad data may lead to performance degradation or even instability of a power system, which can be caused by various factors: unintentional PMU abnormalities, topology error, malicious cyber-attacks, electromagnetic interference, temporary loss communication links, external disturbances, extraneous noise biases, etc. In order develop more resilient and reliable state estimation technique, this manuscript presents novel two-step fault tolerant extended Kalman filter framework for discrete-time stochastic systems, under bad data, failures, noise, bounded observer-gain perturbation conditions. The failure mechanisms multiple phasor measurement units are assumed independent each other with malfunction rates. benchmark IEEE standard test systems utilized as demonstrative example carry out computer simulation studies examine different algorithms. Experimental results demonstrates that the proposed second-order provides accurate results, in comparison traditional first- filter, unscented filter. fault-tolerant serve powerful alternative existing dynamic system techniques.