No abstract

No abstract

A novel method is proposed in this paper for the classification of power quality disturbances using a Probabilistic Neural Network with a Parzen kernel. An attempt has been made to solve the problem as a pattern classification problem in which there is a normal class and a series of abnormal classes. The traditional parametric techniques of pattern classification can't be employed due to unknown parameters of the density functions displayed by the extracted features of the signal. Hence non-parametric pattern classification method was to be adopted and Parzen kernel being used. Parzen kernel is one of the most famous non-parametric techniques and has been a good choice for this purpose with its ease of implementation and good accuracy level. The time varying nature of the probability densities are adaptively identified by Parzen windows. Experimental results have been presented for establishing the efficacy of the method as a tool to automate the Power Quality Classification problem. Various kinds of signals such as Sag, Swell, Momentary flicker, Harmonics were generated and subjected to the above classification scheme. A detailed study on the accuracy and performance of the proposed algorithm has been made with variations in parameters such as the number of training samples and the variance of the Gaussian kernel used.

No abstract

No abstract