Power quality monitoring using an integrated Fourier linear combiner and fuzzy expert system
The paper presents a new approach for the classification of transient disturbance waveforms in a power system by using a Fourier linear combiner and a fuzzy expert system. The measured voltage or current waveforms at a distribution bus are passed through a Fourier linear combiner block to provide peak or root mean square (RMS) amplitude and phase of the fundamental component at every sampling instant. The peak or RMS amplitude and computed slope of the waveforms are then passed on to a diagnostic module that computes the truth value of the signal combination and determines the class to which the waveform belongs. Computer simulated tests are carried out using emtp programs to obtain the disturbance waveform classification with the help of a new hybrid approach which is much simpler than the recently postulated neural network and wavelet based techniques. The classification is found to be robust and yields accurate results in most cases with the least amount of computational burden.
fourier linear combiner fuzzy expert system disturbance waveform classification