Power Calculation Using RBF Neural Networks to Improve Power Sharing of Hierarchical Control Scheme in Multi-DER Microgrids

Abstract

All control methods for the decentralized control of distributed energy resources (DERs) in the microgrid need to calculate power to decide whether the power produced will be able to stabilize the system. Unlike the previous research that is limited to the primary and secondary control levels, the presented decentralized droop-based control scheme includes detailed modeling for three hierarchical control levels for either grid-connected or autonomous modes. A new complementary control loop that is added to the hierarchical droop-based control scheme determines and controls the reactive power reference by a novel application of radial basis function neural networks (RBFNNs) for a fast, authentic, and accurate calculation of power to improve power sharing and enhance microgrid stability margins in facing with small and large signal disturbances. This method suppresses the low-pass filter that is normally used to determine high-frequency components of power and replaces it by the power flow nonlinear equation set that is solved by a novel application of RBFNNs, and consequently, power sharing to loads and network is done sufficiently. The simulation studies that have been performed on a microgrid consisting of four DERs and local loads using MATLAB/SIMULINK software demonstrate the effectiveness of the proposed control scheme.