Identificação Inteligente de Dados Críticos para a Estimação de Estado em Sistemas de Potência
Keywords: Power System State Estimation, Observability, Criticality, Artificial Intelligence
AbstractState estimation plays an important role in the real-time operation of electrical systems, being responsible for obtaining the best estimate of the state of an electrical network considering measurement uncertainties and possibly Bad Data. Critical Data groups or criticalities present in the database made available to the estimation process constitute limitations and risks to a successful realization of the state estimation. The identification of these criticalities is a problem of combinatorial nature and difficult solution, for which one can build intelligent solution strategies that explores the properties that these criticalities exhibit. This work aims to investigate the application of Artificial Intelligence search algorithms in the identification of accurate data for state estimation in power systems. Simulations with the IEEE 30-bus system will illustrate the proposed methodology of intelligent identification of criticalities.