Robust distribution state estimation for active networks

A heuristic optimization algorithm based on the Dynamic Programming theory is proposed to find the optimal placement of measurement devices, i.e. to determine their number and position. The optimization procedure explicitly considers network reconfigurations (caused by random faults or by the active management of the network), so that the final measurement system allows the distribution state estimation to provide an accurate estimate of the system status in all the possible practical conditions. The branch currents are taken as state variables for improving the quality of the solution of the state estimator that exploits field measurements and load pseudo-measurements. The uncertainties introduced by the measurement chain are simulated with a Monte Carlo algorithm. Variations of both load demand and network parameters are also modeled in the Monte Carlo algorithm. The provided examples show the effectiveness of the optimization process.