Power system static state estimation using JADE-adaptive differential evolution technique

Power system state estimation is the important module in monitoring the power system network. The state estimator provides states of the power system by processing the measurements placed optimally across the power system network. However, temporary un-observability may occur due to unexpected loss of measurement devices or communication links. In the present paper, the reliability of the static state estimation has been tested under three different scenarios, viz. over-determined, critically determined, and under-determined systems. The state estimation problem has been solved using JADE-adaptive differential evolution algorithm utilizing both the conventional and the synchronized phasor measurements. From the test results it has been determined that the proposed technique determines the solution even when the power system is partially observable and also detects the un-observable buses present in the network. The results thus obtained using JADE have been compared with conventional weighted least square and improved self-adaptive particle swarm optimization-based SE techniques. On the bases of various performance indices, the results show the effectiveness, reliability, and accuracy of the proposed algorithm when compared to other state estimation techniques.