Artificial Neural Network Classification of Power Quality Disturbances Using Time-Frequency Plane in Industries

The rapid growth of industrialization has increased use of power systems tremendously in process and software industries. Therefore, there is thrust on industries to improve power quality with power monitoring and protection system by classifying voltage and current disturbances. This paper presents a new approach for classifying the events that represent or lead to the degradation of power quality. A neural network with feed forward structure is chosen as the classifier with modified Fisherpsilas Discriminant Ratio Kernel for feature extraction. The potential of developing a more powerful fuzzy classification method based on this algorithm is also discussed. This novel combination of methods shows promise for further development of a fully automated power quality monitoring system.