Integrated DWT–FFT approach for detection and classification of power quality disturbances

Abstract

The signals in the electrical power system always have some power quality disturbances and noise contents which is the biggest obstacle in detection and time localization. In this paper, an integrated rule based approach of discrete wavelet transform – fast Fourier transform is proposed. For the detection of power quality disturbance present in the input signal, the input waveform is processed by discrete wavelet transform. The discrete wavelet coefficients are used to calculate average energy entropy of squared detailed coefficients feature. The various power quality disturbances are initially detected and then classified into four main categories as disturbances related to sag, swell, interruption and harmonics using this feature. Further classification of each main category is done using fast Fourier transform features. The total twelve types of power quality disturbances including seven basic and five combinations which are very close to real situations, are considered for the classification which are generated by parametric equations. Also four another cases are considered by adding noise to four basic disturbances sag, swell, harmonics and flicker. All sixteen cases are simulated using Mathworks Matlab R2008b. The performance of classifier is tested for 150 test signals for various durations with different disturbances with and without noise. The developed classifier is able to achieve 99.043% accuracy. From the simulation results, it can be seen that the proposed approach is effective for the detection and classification of various power quality disturbances.