Connectionist Models for Intelligent Reactive Power Control

In this paper, we demonstrate the usage of connectionist networks for intelligent prediction of power factor and efficient utilization of power. We propose two neuro-fuzzy networks and artificial neural networks using various learning techniques. The proposed connectionist networks are trained with the plant load current and a highly fluctuated voltage for online prediction of power factor. For on-line control, voltage and current are fed into the network after preprocessing and standardization. The models are trained with a 24-hour load demand pattern and performance of the proposed method is evaluated by comparing the test results with the actual expected values. It is observed that neuro-fuzzy models perform better than neural networks.