A Deep-Learning Approach to Load Modeling in Modern Power Distribution System

Abstract

Modern Power Distribution Networks (MPDNs) are no longer passive because Distributed Generations (DGs) are integrated with them to enhance system reliability and power quality. For this reason, load modeling has to be updated to capture the new dynamics of active DNs. This paper presents a composite load modeling for a grid-connected photovoltaic (PV) distribution network using the Levenberg-Marquardt algorithm in the deep learning feed-forward neural network approach. Load modeling is constructing a relationship between input excitation(s) and output response(s); it can be used for simulation studies, stability analysis, and control/protection design. A grid-connected PV distribution network was modeled in Matlab/Simulink and generates data for training and model estimation. The estimated model was tested and validated using a laboratory experimental test bed. Results of the model exhibit a good fitness of 99.8% and 97.2% in active and reactive power models respectively during training. While 97.84% and 94.65% respectively were obtained during testing. The estimation errors were found to be 0.0025 and 0.0049 for active and reactive powers respectively with 0.0473 and 0.0701 corresponding errors in testing.