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The transformer is one of the most important electrical machines in electrical systems. Its proper operation is fundamental for the distribution and transmission of electrical energy. During its service life, it is under continuous electrical and mechanical stresses that can produce diverse types of damage. Among them, short-circuited turns (SCTs) in the windings are one of the main causes of the transformer fault; therefore, their detection in an early stage can help to increase the transformer life and reduce the maintenance costs. In this regard, this paper proposes a signal processing-based methodology to detect early SCTs (i.e., damage of low severity) through the analysis of vibroacoustic signals in steady state under different load conditions, i.e., no load, linear load, nonlinear load, and both linear and nonlinear loads, where the transformer is adapted to emulate different conditions, i.e., healthy (0 SCTs) and with damage of low severity (1 and 2 SCTs). In the signal processing stage, the contrast index is analyzed as a fault indicator, where the Unser and Tamura definitions are tested. For the automatic classification of the obtained indices, an artificial neural network is used. It showed better results than the ones provided by a support vector machine. Results demonstrate that the contrast estimation is suitable as a fault indicator for all the load conditions since 89.78% of accuracy is obtained if the Unser definition is used. [ABSTRACT FROM AUTHOR]

Data mining is a technological and scientific field that, over the years, has been gaining more importance in many areas, attracting scientists, developers, and researchers around the world. The reason for this enthusiasm derives from the remarkable benefits of its usefulness, such as the exploitation of large databases and the use of the information extracted from them in an intelligent way through the analysis and discovery of knowledge. This document provides a review of the predictive data mining techniques used for the diagnosis and detection of faults in electric equipment, which constitutes the pillar of any industrialized country. Starting from the year 2000 to the present, a revision of the methods used in the tasks of classification and regression for the diagnosis of electric equipment is carried out. Current research on data mining techniques is also listed and discussed according to the results obtained by different authors. [ABSTRACT FROM AUTHOR]

The application of signal processing techniques is a fundamental step for fault diagnostic methodologies. The application of empirical mode decomposition (EMD)-based methods such as classic EMD, ensemble EMD (EEMD), and complete EEMD (CEEMD) is presented in this work for the analysis of inrush current signals. This analysis leads to the detection of short-circuited turns in transformers. Results show that CEEMD provides the best performance, as it readily extracts the information related to the fault, requiring of acceptable computational resources. Actual inrush current signals of a transformer with short-circuited turns are also considered. The number of short-circuited turns ranges from 5 to 40. Useful indices, such as the Shannon Entropy, Energy, and root-mean-square value, are obtained from the information provided by the CEEMD approach. These indices are analyzed for both the transient state and the steady state of the current signals, providing the proper quantification of the fault severity. [ABSTRACT FROM AUTHOR]

From voltage and current signals it is possible to obtain relevant information for solving some problems in several industrial and scientific applications as power quality (PQ) monitoring, monitoring and diagnosis of electrical machines, electric systems protection and control. At present, the PQ monitoring, measure through a set of PQ indices (PQI), is an important topic for the industrial sector since a poor PQ, characterised by the presence of harmonics in the power line, produces irregular or wrong operation of protection systems, excessive neutral currents in 3‐phase four‐wire systems, overheating of motors, transformers, capacitor banks and wiring in general. The PQI calculation is performed by many techniques proposed in the literature; however, they do not have either good performance for transient signals or the requirements for satisfying the power standards. This work proposes the assessment of the PQI‐based in neural networks for transient or stationary signals in 3‐phase power systems without losing the power standard requirements. Besides, this work contributes to the industrial application field by allowing the continuous and online monitoring of the PQI thanks to the field programmable gate array implementation of the proposed methodology. [ABSTRACT FROM AUTHOR]

One of the most critical devices in an electrical system is the transformer. It is continuously under different electrical and mechanical stresses that can produce failures in its components and other electrical network devices. The short-circuited turns (SCTs) are a common winding failure. This type of fault has been widely studied in literature employing the vibration signals produced in the transformer. Although promising results have been obtained, it is not a trivial task if different severity levels and a common high-level noise are considered. This paper presents a methodology based on statistical time features (STFs) and support vector machines (SVM) to diagnose a transformer under several SCTs conditions. As STFs, 19 indicators from the transformer vibration signals are computed; then, the most discriminant features are selected using the Fisher score analysis, and the linear discriminant analysis is used for dimension reduction. Finally, a support vector machine classifier is employed to carry out the diagnosis in an automatic way. Once the methodology has been developed, it is implemented on a field-programmable gate array (FPGA) to provide a system-on-a-chip solution. A modified transformer capable of emulating different SCTs severities is employed to validate and test the methodology and its FPGA implementation. Results demonstrate the effectiveness of the proposal for diagnosing the transformer condition as an accuracy of 96.82% is obtained. [ABSTRACT FROM AUTHOR]

Transformers are vital and indispensable elements in electrical systems, and therefore, their correct operation is fundamental; despite being robust electrical machines, they are susceptible to present different types of faults during their service life. Although there are different faults, the fault of short-circuited turns (SCTs) has attracted the interest of many researchers around the world since the windings in a transformer are one of the most vulnerable parts. In this regard, several works in literature have analyzed the vibration signals that generate a transformer as a source of information to carry out fault diagnosis; however this analysis is not an easy task since the information associated with the fault is embedded in high level noise. This problem becomes more difficult when low levels of fault severity are considered. In this work, as the main contribution, the nonlinear mode decomposition (NMD) method is investigated as a potential signal processing technique to extract features from vibration signals, and thus, detect SCTs in transformers, even in early stages, i.e., low levels of fault severity. Also, the instantaneous root mean square (RMS) value computed using the Hilbert transform is proposed as a fault indicator, demonstrating to be sensitive to fault severity. Finally, a fuzzy logic system is developed for automatic fault diagnosis. To test the proposal, a modified transformer representing diverse levels of SCTs is used. These levels consist of 0 (healthy condition), 5, 10, 15, 20, and 25 SCTs. Results demonstrate the capability of the proposal to extract features from vibration signals and perform automatic fault diagnosis. [ABSTRACT FROM AUTHOR]

Motor current signals analysis (MCSA) is a widely used approach for fault diagnostics in induction motors (IMs). It consists of detecting a specific signature or pattern associated to a fault condition from current signals. In particular, the fault of broken rotor bars (BRBs) is featured by a V-shaped pattern in the time-frequency domain during the startup transient. Although many techniques and methodologies have been presented in literature, most of them have been focused on analyzing consolidated faults such as one- BRB and multiple BRBs; in contrast, the BRB incipient detection, such as half BRB, has been rarely investigated. Hence, a methodology based on a new technique named Tooth-fast Fourier transform (FFT) to detect both incipient and consolidated BRB conditions is presented in this work. It consists of two windows moving along the analyzed current signal, where the FFT is performed for each window. Next, the spectra are subtracted for minimizing the stationary frequencies and maximizing the moving-ones. The signature of the moving frequencies in the resulting spectrogram has a “teeth” shape, giving the name to the proposed technique. Next, a weight function and a classification stage employing four indicators are presented for automatic diagnostics. The proposal is validated and tested using both synthetic and real signals. For the latter, different levels of BRB, i.e., half BRB, one BRB, and two BRBs, are considered. Results demonstrate the effectiveness and usefulness of the proposal to detect both incipient and consolidated BRB faults in IMs. [ABSTRACT FROM AUTHOR]

• The online detection of short-circuited turns in transformers is presented. • The PMU scheme of the IEEE Std. C37.118.1 is extended to harmonic PMU (HPMU). • A fuzzy logic classifier is developed for automatic short-circuit fault detection. • HPMU provides magnitude, phase, and frequency of any harmonic component. Transformers in modern power systems often experience different types of faults due to electrical, mechanical, and thermal stresses during their service life. In this regard, online diagnosis is particularly important to ensure reliable and economic operation of power system by avoiding equipment damages and outages. In this work, a new methodology for online diagnosis of inter-turn faults in transformers is presented. In general, the proposal determines the fault condition through the differential current spectrum and a fuzzy logic (FL) system. For the spectrum estimation, a phasor measurement unit (PMU)-based scheme is expanded to a harmonic PMU (HPMU)-scheme to obtain the magnitude and phase of the harmonic content for the voltage and current signals in a synchronized way. The FL system diagnoses the transformer condition in a continuous and online way. As the proposed method is focused on the online diagnosis of fault, its performance is tested under different operating conditions, including no-load, linear loads, and non-linear loads. To increase the analysis, a simulated three-phase transformer and a real single-phase transformer that present different levels of fault severity for the inter-turn fault condition are analyzed. Results show the effectiveness and usefulness of the proposal. [ABSTRACT FROM AUTHOR]