Obtaining observable measurement plans for state estimation studies

Over the last few decades, several methods have been proposed to correct problems that affect the reliability of the State Estimation (EE) function, most of them focusing on ensuring observability and handling gross errors. It is important that the effectiveness of the proposed methods is evaluated for different measurement planes, as the redundancy and location of the measurements can have a significant influence on the performance of such methods and the EE function itself. In this work, the set of measurement planes is obtained through a systematic method based on the Genetic Algorithm (GA). Different approaches are evaluated in the tuning of the parameters of the process of obtaining plans. Finally, methods capable of extracting a diverse basis are proposed, in addition to proposing a halting criterion, which uses Bayesian inference to estimate the number of observable planes. Results and evaluations are obtained with the IEEE-14 network bars.