| Summary: | Rectifier transformers are key components of high-frequency power supplies; their waveforms play an important role in diagnosing faults of high-frequency power supplies. Typically, the integral value of the current waveform is analyzed to diagnose rectifier transformer faults. However, the waveforms of currents are greatly influenced by the load, which results in serious fault-coupling problems. Generally, conventional methods cannot accurately locate faults, and they have high error rates. In this study, primary and secondary waveform currents were analyzed to extract fault feature data. The extracted data were used to train least-square support vector machines to build fault classifiers, thus realizing the fault detection and isolating the rectifier transformer. The proposed method was applied to actual waveform data from the Baosteel power plant; it performed satisfactorily.
|
|---|
