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Mathematical Transforms Clubbed With Artificial Intelligence for Power Quality Disturbance Classification

Neenu Raphael, Ancy Sara Varghese

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.