ISSN (Print) : 2320 – 3765 ISSN (Online): 2278 – 8875 International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering (An ISO 3297: 2007 Certified Organization) Vol. 2, Special Issue 1, December 2013 Copyright to IJAREEIE www.ijareeie.com 1 Mathematical Transforms Clubbed With Artificial Intelligence for Power Quality Disturbance Classification Neenu Raphael, Ancy Sara Varghese M.Tech Student, Dept. of EEE, Saintgits College of Engineering, Pathamuttom, Kerala, India 1 Asst. Prof., Dept. of EEE, Saintgits College of Engineering, Pathamuttom, Kerala, India 2 Abstract: Efficient transmission of electric power is of atmost importance in the current scenario. Power Quality is an important concern for utility as well as consumers. Faults occurring in a transmission line is another fact of concern for power engineers, and it turns out to be a major problem once unrecognized. These faults also can lead to major quality issues in a system.This paper presents an efficient and easily adaptable method for power quality disturbance identification. Strong and efficient features are identified, which can efficiently discriminate between various disturbances. Wavelet and Fourier transforms are combinedly used for evaluating these features. These features are used for training an Artificial Neural network which is finally tested for checking the efficiency and authenticity of the method. Keywords: Artificial Neural Network, Backpropogation Algorithm, Fourier Transform, Wavelet Transform . I. INTRODUCTION Electricity is the most revolutionary development in the human history. The contribution of electric energy in day-to- day affairs is immense. Industrialisation has led to a current scenario that, no electricity means no useful output. And all emerging technologies rely upon an electric supply. Virtually it can be said that all equipments depend upon electrical energy in one form or the another. With the advent of new technologies the equipments that are in use is increasing daily. More sensitive electronic equipments have led to another technological failure- depletion of power quality. Power quality disturbances deforms the purity of an electric signal and moreover it results in failures to other sensitive equipments in the system. Power quality disturbance ranges from fast and furious transients to short and long outages. They include noisy flickers to momentary sags and swells. Repeated occurance of sags and swells can result in damage of electronic equipments. In some cases it can pose harm to the equipment performance and its reliability. These power quality disturbances are unpredictable and hence their identification as soon as they occur in a system defines the efficiency and stability of a transmission and distribution system. Here comes the need for an automatic, fast and easy method of power quality assessment. Power monitors are available at various nodes along a transmission system with diversified functionalities, to gather and store electrical events and conditions involved. But they are not intelligent enough to identify and classify the power quality disturbance occurring in a system. This again justifies the need for a fully automatic and intelligent off- line method for power quality disturbance classification. During signal analysis, to identify between various disturbance conditions certain efficient features should be defined. Efficient features which aids in better classification of power quality disturbance are identified, later these are used for training an artificial neural network. Three level testing is done to check the efficieny and applicability of the method for power quality classification. The potential of this method can be well clarified by extending the method for fault classification and faulty phase identification in a prototype system. II. MATHEMATICAL TRANSFORMS USED FOR FEATURE EXTRACTION The most critical stage of any signal processing or pattern recognition is the extraction of distinctive features from th raw data. The characteristics should be chosen such that they have very low computational time and distinct features to separate the various disturbances. Signal processing deals with the extraction of the typical informations from the raw signal. A number of signal processing tools have been developed which leads to easy and efficient classification of various disturbance events within a signal. Fourier Transform, Wavelet Transform, Short time Fourier Transform, Chirp Z Transform, Hilbert‟s transform etc. are some of them.