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

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