Optimal metering systems for monitoring power networks under multiple topological scenarios

Summary form only given. This work presents a methodology for designing optimal metering systems for real-time power system monitoring, taking into account different topologies that the network may experiment. Genetic algorithms are employed to achieve a trade-off between investment costs and reliability of the state estimation process under many different topology scenarios. This is done by formulating a fitness function where the cost of the metering system is minimized, while no critical measurements and/or critical sets are allowed in the optimal solution. An efficient algorithm for the identification of critical measurements and sets (irrespective of state estimation runs) is employed during the evaluation of the fitness function. Simulation results illustrate the performance of the proposed method.