Abstract
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.
Highlights
Within the smart grids context, one of the problems in electrical distribution systems (EDS) is the control of voltage magnitudes and reactive power (Volt-VAr), in which the objective is to determine the optimum adjustment of a set of control variables to ensure the proper operation of the EDS [1]
The development of this work was performed considering a distribution network composed of 42 buses, with 5 capacitor banks (CBs), 2 distributed generators (DGs), and 4 voltage regulators (VRs)
This database considers a random variation of 5% demand relative to a daily demand curve divided into one-hour intervals and is used to perform the training of decision trees (DTs), support vector machines (SVMs), and artificial neural networks (ANNs)
Summary
Within the smart grids context, one of the problems in electrical distribution systems (EDS) is the control of voltage magnitudes and reactive power (Volt-VAr), in which the objective is to determine the optimum adjustment of a set of control variables to ensure the proper operation of the EDS [1]. Among the main control variables, there are the active and reactive power generation of distributed generators (DGs), number of modules in operation of capacitor banks (CBs), and number of tap steps for on-load tap changer (OLTC) transformers as well as voltage regulators (VRs) [2]. According to the smart grids philosophy, there is great interest in developing mechanisms that allow this control to be carried out automatically [3,4]
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