Connectionist Models for Intelligent Reactive Power Control
Ajith Abraham, Baikunth Nath · 2000
In this paper, we demonstrate the usage of connecfionist 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.