Intelligent Operation of Small-Scale Interconnected DC Grids via Measurement Redundancy

Interconnected dc grids are studied in this paper, which comprise resistive and constant-power loads (CPLs) fed by photovoltaic (PV) units. All the sources and CPLs are connected to the grid via dc-dc converters. Nonlinear behavior of PV units in addition to the effect of negative-resistance CPLs can destabilize the dc grid. Thus, the decentralized nonlinear model and intelligent control are proposed using adaptive output-feedback controller to stabilize the grid. The use of the output-feedback control makes possible the utilization of other available signals, in case of loss of main signal, at the converter location and creates measurement redundancy that improves reliability of the dc network. The switching between measured signals of different types are performed through using the neural network (NN) controllers without the need to further tuning. The stability of the entire network is assured through Lyapunov stability method while each converter employs only local measurements. The adaptive NNs are utilized to overcome the unknown dynamics of the dc-dc converters at distributed energy resources and CPLs and those of the interconnected network imposed on the converters. Simulation and experimental results are provided on a small-scale dc grid to show the effectiveness of the developed model and the proposed controller.