A Hybrid Meta-Heuristic Approach for Optimal Meter Allocation in Electric Power Distribution Systems

The number of nodes present in Electric Power Distribution Systems (EPDS) is a complicating factor for carrying out the State Estimation (SE) and the choice of allocation of available meters affects the quality of observability obtained by the SE. Thus, it is necessary to use optimization methods that evaluate the positions of meters in the system that can contribute to an optimal SE. Artificial Neural Networks (ANN) can perform SE, processing the information obtained by the available meters in an agile way. Meta-heuristics techniques apply to the optimal allocation problem but can be slow processing. Thus, the work seeks to evaluate the potential of a hybrid method that associates the meta-heuristic technique, Artificial Immune System (AIS), with ANNs for evaluating several allocation options in an agile way to find an optimal solution for the allocation of meters.