Failure analysis in power system by the discrete wavelet transform

Voltage variations are the most common power quality events that may result in corruption of different industrial processes. The electric power utility industry requires significant improvement in the quality of power provided to customers during faults or wide area system disturbance. Power system failure signals are monitored by using embedded systems. This paper presents an approach to provide the detection and location in time as well as the identification of power quality problems which are represented in both transient and fixed signals. The method was applied by using the discrete wavelet transform (DWT). The given signal is decomposed with wavelet transform and any change on the lower frequencies of the signal is detected at the finer wavelet transform resolution levels. The entropy contents of the signal are considered and a relationship between this entropy contents and the one of the corresponding component is accomplished. This paper shows wavelet transform can be used to reduce data set while covering original information. Because of the reduced data set embedded systems can work within a wider time range. Paper helps power system analysis and classifies power system failures