Classification of power quality disturbances using time-frequency ambiguity plane and neural networks

Identification and classification of voltage and current disturbances in power systems is an important task in power system monitoring and protection. This paper presents a new approach for classifying the events that represent or lead to the degradation of power quality. The concept of ambiguity plane together with modified Fisher's Discriminant Ratio Kernel is used for feature extraction. A neural network with feedforward structure is chosen as the classifier. The results of extensive simulations confirm the feasibility of the proposed algorithm. This novel combination of methods shows promise for further development of a fully automated power quality monitoring system. The potential of developing a more powerful fuzzy classification method based on this algorithm is also discussed.