Methods of detecting inrush current in power transformers
Based on wavelet transform, correlation analysis and HMM
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Recently, wavelet transform has become widespread as a powerful mathematical tool for signal analysis. In this book, a new method for classifying transient phenomena in power transformers is presented. Detection between internal errors, external errors, inrush currents, and over-excitation conditions is performed using a combination of discrete wavelet transform (DWT) and fuzzy logic analyzes. First, the analysis data were extracted from unusual conditions such as external faults, external short circuits and inrush magnetization current using ATP-EMTP software. The signals are then analyzed by...
Recently, wavelet transform has become widespread as a powerful mathematical tool for signal analysis. In this book, a new method for classifying transient phenomena in power transformers is presented. Detection between internal errors, external errors, inrush currents, and over-excitation conditions is performed using a combination of discrete wavelet transform (DWT) and fuzzy logic analyzes. First, the analysis data were extracted from unusual conditions such as external faults, external short circuits and inrush magnetization current using ATP-EMTP software. The signals are then analyzed by discrete wavelet transform (DWT) and the detection method is based on direct detection of the DWT (high frequency) detail waveform. Finally, the results are given to the fuzzy logic system to detect transient phenomena in the power transformer. All code is programmed with Matlab 6.5 compiler. The results show that the waveform deviation in different cases is a good case for distinguishing between transient states in a power transformer.Advances in digital system protection technology have helped us find a new way to look at existing digital protection methods for power system components.
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