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The results obtained for the measurements of acoustic emission (AE) signal parameters emitted in three dielectric liquids are presented in this paper. In particular, the velocity of AE wave was calculated based on the time of arrival of the AE signal. A frequency modulated signal was generated by a piezoelectric transducer and measured at various distances by a hydrophone. The changes in velocity values at particular distances and for different frequencies were investigated. The analyses include the determination of the dependency of the velocity values from the distance between the communicating devices. A nonlinear regression model was calculated, and the differences between AE velocities propagating in the considered dielectrics were determined. Similarly, the influence of modulation frequency on the AE velocity was determined using nonlinear regression. Based on the calculation data, it can clearly be stated that the velocity of AE wave depends significantly on the frequency and distance at which it is registered. These two factors may have an important influence on the localization of partial discharges (PD) occurring in these types of dielectric liquids. [ABSTRACT FROM AUTHOR]

Results of a comparative analysis of two non-dimensional coefficients, calculated based on signals emitted by partial discharges (PD) are presented in the paper. The PD were generated using setups for modeling of three basic types of PD sources, which can occur in paper-oil insulation in power transformers. The following measuring methods were considered: electrical, ultra-high frequency, acoustic emission, and optical spectrophotometry. All measurements, for each of the PD-source configuration using the various methods, were conducted simultaneously in a laboratory under same measurement conditions. Based on the gathered results it was stated that it is possible to recognize the partial discharges form based on the non-dimensional coefficients, which differ in value for all of the considered methods except for the acoustic emission. The main contribution of the research performed lies in the simplicity of the applied signal processing procedure, which can be adopted to a device or system for diagnosis of partial discharges occurring in paper-oil insulation. [ABSTRACT FROM AUTHOR]

They also focus on developing a method that is robust to disturbances and can reduce the error evaluation of material defect depth by 10%, even when the signal-to-noise ratio reaches 10 dB. In [[7]], the authors aim to improve the diagnosis of on-load tap changers (OLTC) using the acoustic emission method. The authors of [[5]] consider a combination of piezocomposite transducers and pulse-compression-processing methods to study the propagation of ultrasonic signals through steel pipes insulated with different materials. The authors applied time, frequency, and wavelet transformation methods to determine the features for the purpose of classification using various machine learning methods. [Extracted from the article]

The principal objective of this study is to improve the diagnostics of power transformers, which are the key element of supplying electricity to consumers. On Load Tap Changer (OLTC), which is the object of research, the results of which are presented in this article, is one of the most important elements of these devices. The applied diagnostic method is the acoustic emission (AE) method, which has the main advantage over others, that it is considered as a non-destructive testing method. At present, there are many measuring devices and sensors used in the AE method, there are also some international standards, according to which, measurements should be performed. In the presented work, AE signals were measured in laboratory conditions with various OLTC defects being simulated. Five types of sensors were used for the measurement. The recorded signals were analyzed in the time and frequency domain and using discrete wavelet transformation. Based on the results obtained, sets of indicators were determined, which were used as features for an autonomous classification of the type of defect. Several types of learning algorithms from the group of supervised machine learning were considered in the research. The performance of individual classifiers was determined by several quality evaluation measures. As a result of the analyses, the type and characteristics of the most optimal algorithm to be used in the process of classification of the OLTC fault type were indicated, depending on the type of sensor with which AE signals were recorded. [ABSTRACT FROM AUTHOR]

Effective, accurate and adequately early detection of any potential defects in power transformers is still a challenging issue. As the acoustic method is known as one of the noninvasive and nondestructive testing methods, this paper proposes a new approach of the classification method for defect identification in power transformers based on the acoustic measurements. Typical application of acoustic emission (AE) method is the detection of partial discharges (PD); however, during PD measurements other defects may also be identified in the transformer. In this research, a database of various signal sources recorded during acoustic PD measurements in real-life power transformers over several years was gathered. Furthermore, all of the signals are divided into two groups (PD/other) and in the second step into eight classes of various defects. Based on these, selected classification models including machine learning algorithms were applied to training and validation. Energy patterns based on the discrete wavelet transform (DWT) were used as model inputs. As a result, the presented method allows one to identify with high accuracy, not only the selected kind of PD (1st step), but other kinds of faults or anomalies within the transformer being tested (2nd step). The proposed two-step classification method may be applied as a supplementary part of a technical condition assessment system or decision support system for management of power transformers. [ABSTRACT FROM AUTHOR]