Artificial Neural Network Aided Cable Resistance Estimation in Droop-Controlled Islanded DC Microgrids

Abstract

Most of the existing methods used to estimate the cable resistance require the use of many hardware devices and the injection of perturbations to the system. Therefore, they are time-consuming, costly and prone to errors. In addition, the injection of perturbations has the potential of degrading the power quality of the system. In this paper, a new artificial neural network (ANN) aided cable resistance estimation approach is proposed. The ANN model is trained by simulation data. The trained ANN model can quickly and effectively map the current sharing ratios between the converters to the droop coefficients of the converters. In this way, the optimal droop coefficient combination that will yield the desired accurate current sharing ratio between the converters can be predicted by the trained ANN model. Subsequently, the optimal droop coefficient combination can be used in the estimation of the corresponding subsystem cable resistance by solving an equation set. The estimated cable resistance is compared with the simulated cable resistance and an excellent match is obser