Detection and classification of current interruptions and transients by using Wavelet Transform and neural network

The worldwide increasing applications of nonlinear loads, mostly consisting of power electronics devices, have made the power quality problems an important concern than ever before for utilities and consumers. Therefore, detection and classification of Power Quality disturbances is highly desirable. Most power quality disturbances are non-stationary and transitory and their detection and classification have proved to be much needed. Hence there is a requirement of advanced tools and techniques for the analysis of Power Quality disturbances. In this work detection and classification of various types of transients and interruptions, caused due to severe fault and reclosing of circuit breaker, is done by using Discrete Wavelet Transform and two-layer feed forward neural network. This work presents new approach aimed at automating the analysis of power quality disturbances including transients, interruption and normal waveform. The disturbance current waveform can be obtained from the disturbance generation model. The Discrete Wavelet Transform is chosen for feature extraction. Feature extraction outputs are the coefficients (detailed and approximate) of Discrete Wavelet Transform represents the power quality disturbance signal at the different levels in time and frequency domain.