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Frequency Control in Microgrid Communities Using Neural Networks
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DOI：10.1109/naps46351.2019.9000219.png)
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Abstract
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This paper proposes a controller design for battery energy storage systems (BESS) and plug-in hybrid electric vehicles (PHEV) integration for frequency control in microgrid communities (MG) with solar photovoltaics (PV's) and wind turbines as distributed generators (DG). These DG's are intermittent power sources and are the causes of possible severe frequency fluctuations in MG's. The proposed control is a PID controller, while the design is based on neural networks. To obtain the appropriate input parameters of the proposed PID controller, a multilayer feedforward neural network is configured and implemented using MATLAB's Deep Learning Toolbox with random values as input. Results demonstrate the effectiveness of the proposed design method. A comparison of the results from the proposed approach with those from particle swarm optimization method proves proposed method is better and more effective.
Keywords:
Microgrids
frequency control
feedforward neural networks
PID controller
transfer function
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