Calloquispe Huallpa, Ricardo
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Publication Optimized control of distribution switches using deep neural networks to balance a low cost dynamic photovoltaic microgrid in the DQ frame(2022-12-12) Calloquispe Huallpa, Ricardo; Aponte, Erick E.; College of Engineering; Darbali Zamora, Rachid; Arzuaga, Emmanuel; Department of Electrical and Computer Engineering; Ortiz Albino, Reyes M.This thesis presents an energy management strategy by opening and closing switches in a dynamic microgrid. This microgrid consists of several groups containing photovoltaic generation and load that can be connected and disconnected from the main microgrid. The optimization for power management seeks to minimize the power consumed by the grid with the constraint of continuously supplying power to a critical load. The algorithm in charge of energy management is deep neural network. The deep neural network receives as input data the parameters of the microgrid and, as a result, deliver the states of the switches of all the groups. For the deep neural network training process, a third algorithm was used to optimize the parameters of the microgrid. In this way, specific outputs are obtained for each type of input. In this implementation, the DNNs do not perform the optimization process, which takes a long time, what they do is learn a task that is already optimized, substantially reducing the time required to obtain results. Finally, to observe the dynamic behavior of the microgrid states caused by the opening and closing of switches, the microgrid was modeled in the direct-quadrature frame. For this, the average models of the inverters with voltage control for the grid forming group and with current control for the grid following groups were used. Simulation results showed that energy management can be performed in a microgrid through the opening and closing of its switches without losing system stability.