Abstract
Most power transformer faults are caused by iron core and winding faults. At present, the method that is most widely used for transformer iron core and winding faults identification is the vibration analysis method. The vibration analysis method generally determines the degree of fault by analyzing the energy spectrum of the transformer vibration signal. However, the noise reduction step in this method is complicated and costly, and the effect of denoising needs to be further improved to make the fault identification results more accurate. In addition, it is difficult to perform an accurate determination of the early mild failure of the transformer due to the effect of noise on the results. This paper presents a novel mathematical statistics method based on the vibration signal to optimize the vibration analysis method for the short-circuit failure of the transformer winding. The proposed method was used for linear analysis of the transformer vibration signal with different degrees of short-circuit failure of the transformer winding. By comparing the slope value of the transformer vibration signal cumulative probability distribution curve and analyzing the energy spectrum of the signal, the degree of short-circuit failure of the transformer winding was identified quickly and accurately. This method also simplified the signal denoising process in transformer fault detection, improved the accuracy of fault detection, reduced the time of fault detection, and provided good predictability for early mild faults of the transformer, thereby reducing the hidden hazards of operating the power transformer. The proposed optimization procedure offers a new research idea in transformer fault identification.
Highlights
After a transformer has been used for a long time, various losses cause the transformer to malfunction due to its complicated internal structure
The conventional vibration method requires the determination of the energy threshold of each frequency band of the transformer vibration signal with different fault degrees, after which the frequency band of the transformer vibration signal with different fault degrees, after which the transformer fault degree is identified by analyzing the energy variation of the signal
Distribution Characteristics of Transformer Vibration Signals separation result is not good, the vibration signal will be mixed with more high-frequency signals, Assuming that therecognition vibration signal the transformer and the distribution of the vibration resulting in incorrect results.ofTherefore, the method must be optimized to achieve the signal at different signal intervals are random makes it difficult to analyze the vibration signal trend
Summary
After a transformer has been used for a long time, various losses cause the transformer to malfunction due to its complicated internal structure. Traditional methods for identifying transformer winding and iron core faults are low voltage pulse method and frequency response analysis method. When the vibration method is used to monitor transformer operation, an energy spectrum analysis of the collected vibration signals should be performed In this process, the signal must be noise-reduced, and the effective signal can be separated and analyzed. If noise reduction is unsatisfactory, an energy spectrum analysis of the signal using the vibration method cannot quickly and accurately detect the faulty degree of the transformer. Reference [14] provided a blind source separation method for transformer winding and iron core vibration signal based on subband decomposition independent component analysis (SDICA). The method can directly separate the winding and iron core vibration signals, which can determine the phase of the transformer where the fault occurs. The study’s results are of considerable importance for the development of transformer fault detection techniques
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