Development and hardware implementation of a fault transients recognition system

Summary form only given. This paper presents the development and hardware implementation of a classification scheme to distinguish the transients originated by faults from the other types of transients. In the proposed scheme, a set of Hidden Markov Model (HMM) based classifiers are employed to recognize the fault transients. Input features for the classifiers are the energy contained in wavelet coefficients of the measured current waveforms. A laboratory prototype of the fault recognition system was implemented on a floating point Digital Signal Processor (DSP) based hardware platform. The classification system was tested using the transient signals generated by a real-time waveform playback unit. The test waveforms were generated by simulating an actual extra high voltage (EHV) transmission system on an electromagnetic transient (EMT) simulation program. Operation of the classification system was further verified using waveforms obtained from an actual fault recorder. The performance of the classifier was investigated under different practical scenarios such as current transformer saturation, measurement noise and lightning faults.