A Wavelet Energy Moment Based Classification and Recognition Method of Transient Signals in Power Transmission Lines

The distribution of signal energy in time domain and frequency domain can reveal essential differences among various signals in detail comparatively.The wavelet energy moment can reflect the distribution of signal energy in frequency domain and that in time domain indirectly as well.The authors apply the wavelet energy moment based method of signal feature extraction to distinguish fault transient signal from non-fault transient signal.At first,the wavelet energy moment based signal feature extraction method is applied to six types of transient signals from three-phase circuit breaker operation,single-phase earth fault,arcing fault in primary circuit,non-fault lightning stroke and fault lightning stroke,which are obtained by the simulation model of 500kV transmission line;then by use of wavelet transform the energy moments of these transient signals in various frequency bands are extracted to obtain statistical graphs of energy moments and the distribution characteristics of the wavelet energy moment of each transient signal is analyzed.On this basis the classification and recognition criteria for transient signals can be acquired.The transient signals extracted by wavelet energy moment possess evident features and are easy to classified and recognized.Simulation results verify the feasibility and effectiveness of the proposed method.