A Self-Adaptive Evolutionary Programming Approach for Power System State Estimation

This paper presents the application of the Self-Adaptive Evolutionary Programming optimization technique in the Power System State Estimation problem. State Estimation is a statistical-based optimization process used in power systems to obtain the complex bus voltages of the system under study. The number of decision variables in the State Estimation problem grows in proportion to the size of the power system being considered. Therefore, it would be desirable to have robust state estimation procedures, with the ability to overcome premature stagnation or convergence problems. The Self-Adaptive Evolutionary Programming method is an enhanced version of the canonical Evolutionary Programming algorithm, which has been used successfully for solving high-dimensional continuous optimization problems. In order to validate the effectiveness of the proposed method, two case studies were considered. In the first one, the State Estimation problem was stated according with the Weighted Least Squares formulation and the results obtained on a 6-bus system were compared to those obtained via Newton´s method. In the second case study, which was based on a 30-bus system, the results given by the Weighed Least Squares and Weighed Least Absolute Value formulations were compared to each other. The results from both case studies demonstrate the applicability of the proposed method for State Estimation purposes.