A study on the application of wavelet analysis to power quality

The quality of electric power is a major concern, since poor electric power quality results in malfunctions, instabilities and shorter lifetime of the load. The poor quality of electric power can be attributed to power line disturbances such as waveshape faults, overvoltages, capacitor switching transients, harmonic distortion and impulse transients. For diagnosing power quality problems, the causes of the disturbances should be understood before appropriate actions can be taken. Monitoring the voltages and currents is the first requirement in this process. In order to determine the causes, one should be able to classify the type of disturbances from the monitored data. Several approaches such as point-to-point comparison of adjacent systems, neural networks and expert systems have been proposed and implemented in the past. Wavelet analysis is gaining greater importance for the recognition of disturbances. A study about the performance of wavelets in detecting and classifying the power quality disturbances is reported in this paper