Planning of Measurement Systems Using Information Theory

State estimation plays an important role in the operation of electrical systems in real time, being responsible for obtaining the best estimate of the state of an electrical network from a set of measurements containing uncertainties and eventually Gross Errors. The quality of the observation performed by the state estimator depends on the degree of redundancy of the measurement system, which in turn is limited by financial issues. Consequently, the design of the measurement plan must establish the best compromise between its cost and its observation capacity. This work presents a methodology for planning measurement systems using criteria based on Information Theory. A genetic algorithm is employed to find the measurement plane that has the greatest amount of information possible subject to a specific amount of measurements. Simulations with IEEE 6- and 14-bar networks illustrate the proposed method.