An Intelligent Tool for ANN Based Power Factor Correction

This paper describes a case study of a power factor correction technique based on the Artificial Neural Network (ANN). In order to accelerate the training process of ANNs, four learning algorithm, Incremental Back Propagation (IBP), Batch Back Propagation (BBP), Resilient Back Propagation (RBP), and Quick Back Propagation (QBP), were modeled and software that has a graphic user interface was developed. Using the developed software, the training actions of ANNs can be performed according to the inputs. Results show that the developed software can be used as a visual educational tool for training power factor correction using a synchronous motor.