Novel Transformer Fault Identification Optimization Method Based on Mathematical Statistics

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.

A study on input noise second-order filtering and smoothing of linear stochastic discrete systems with packet dropouts

“…The second-order extended system according to the following equations is yielded by substituting (1) and 7into (5) and substituting (1) and (8) into (6):…”

“…During the last three decades, the estimation problem of input noise has become an active field in industry and has a wide range of applications in fault detection, petroleum prospecting, image restoration, speech processing, and so forth [1][2][3][4][5][6]. The Kalman deconvolution filter approach applied to the reflection coefficient sequence estimate in oil exploration was first proposed in [3].…”

Vibration Signal Processing-Based Detection of Short-Circuited Turns in Transformers: A Nonlinear Mode Decomposition Approach

“…To perform fault diagnosis in transformers, different methods have been proposed, e.g., dissolved gas analysis, power factor testing, thermography, frequency response analysis, insulating oil quality, energization current, and vibration analysis, among others [5][6][7][8]. Although promising results have been obtained, some issues still remain unsolved in several diagnostic methods.…”

“…For instance, the analysis of energization currents requires further research in order to properly differentiate between an energization current and a short-circuit fault current; on the other hand, temperature measurement-based diagnosis methods require long observation times. In recent years, vibration analysis has demonstrated, on one hand, to overcome some drawbacks of the aforementioned methods [5] and, on the other hand, to improve the diagnosis results through its combination with other methods [9], becoming a very attractive topic for many researchers around the world. Another advantage is that the vibration signals maintain a direct relation with the mechanical performance of the winding [4,5,10]; from this point of view, changes or alterations in the windings will modify the vibration patterns.…”

“…In recent years, vibration analysis has demonstrated, on one hand, to overcome some drawbacks of the aforementioned methods [5] and, on the other hand, to improve the diagnosis results through its combination with other methods [9], becoming a very attractive topic for many researchers around the world. Another advantage is that the vibration signals maintain a direct relation with the mechanical performance of the winding [4,5,10]; from this point of view, changes or alterations in the windings will modify the vibration patterns. In this regard, the diagnosis task relies on extracting and characterizing such patterns; yet, it is not an easy task since the vibration signals have nonstationary and nonlinear properties and are embedded in high level noise, compromising the performance of many signal processing techniques.…”