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The paper studies the framework for the application of computational intelligence methods used for estimations in the distribution power system when a decreased number of measured data is present. Due to the lack of all measured data, the estimation of the distribution power system state is very challenging. The paper studies the application of the artificial neural network and metaheuristic optimization in synergy to solve the voltage phasors estimation problem. The proposed method uses a metaheuristic optimization technique to find virtual input data for the physical model of the network. The presented framework is based on the usage of different computational tools in co-simulation configuration. The research output is the proposed co-simulation setup for the estimation in the distribution power system using a decreased and limited number of available measured data. The estimation procedure was applied on four test distribution networks to validate the presented approach. The maximal estimation errors in voltage magnitudes and angles, using the proposed setup, are below 1.75% and 1∘, respectively, without considering the measurement errors. When the measurement errors are taken into account, the proposed procedure estimates voltage magnitudes and angles with errors below 2.5% and 1.4∘, respectively. In the scenario considering the consumers’ load shape, including the uncertainty range of 20%, the maximal estimation errors are below 1% for magnitude and 0.45∘ for the angle taking the measurement errors in the range of 2% into account.

electronics

Co-Simulation and Data-Driven Based Procedure for Estimation of Nodal Voltage Phasors in Power Distribution Networks Using a Limited Number of Measured Data