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Centralized control of voltage magnitude and reactive power (Volt-VAr) is a highly complex combinatorial problem that seeks to determine the optimal adjustment of a set of control variables such as active and reactive power generation of distributed generators (DGs), modules in operation of capacitor banks, and voltage regulator taps; these with the purpose of ensuring an optimal operation of distribution systems. Looking for tools that allow real-time automation of this type of control, this study applies different intelligent system (ISs) techniques, such as decision trees, artificial neural networks, and support vector machines. Voltage magnitudes at nodes, current flow magnitudes in the circuits, and active and reactive power injections at the nodes at different grid points were used as input data. Training was performed from available measurements and actions recorded at the system control center. The tests were performed in a 42-bus distribution test system demonstrating the efficiency and robustness of the proposed solution techniques when compared with the results of a conventional mathematical model.

Application of Intelligent Systems in Volt-VAr Centralized Control in Modern Distribution Systems of Electrical Energy