Current Status and Future Trends in Frequency ... - IEEE Xplore

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IEEE TRANSACTIONS ON POWER DELIVERY, VOL. 28, NO. 2, APRIL 2013

Current Status and Future Trends in Frequency-Response Analysis With a Transformer in Service Eduardo Gomez-Luna, Student Member, IEEE, Guillermo Aponte Mayor, Carlos Gonzalez-Garcia, Member, IEEE, and Jorge Pleite Guerra, Senior Member, IEEE

Abstract—This paper presents the current status and future trends in the application of the frequency-response analysis (FRA) technique with the transformer in service (online) through bibliographic review and analysis. As a result, three basic stages of the online FRA test have been identified and defined: 1) injection and excitation signal measurement; 2) recording, filtering and processing of measured signals; and 3) curve analysis and interpretation. This work presents an overview of the online FRA technique, useful for subsequent research in this area. Index Terms—Frequency response, power transformer, remote monitoring, review.

I. INTRODUCTION

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HE frequency-response analysis (FRA) is a technique to assess a power transformer’s condition. It is currently accepted worldwide as a complementary support to other diagnostic techniques. It is especially appreciated for detecting potential mechanical problems, like displacements or deformations in the windings and the core sheets because these kinds of faults are very difficult to locate through other methods. The principles of the FRA method consist of measuring the transformer response in a wide frequency bandwidth. Currently, the test is usually performed on a no-load and de-energized transformer. Different types of input signals and various stages can be considered, as shown in Fig. 1. According to the input signal nature, two measurement methods exist: impulse frequency-response analysis (IFRA) and sweep frequency-response analysis (SFRA). The IFRA method uses a single nonperiodic signal as excitation or input, injected into any of the available transformer terminals. Its maximum value may reach hundreds of volts and the wide frequency content is suitably ensured. This excitation causes induced voltages in the remaining ends of the same transformer. These reflected signals depend on the Manuscript received June 07, 2012; revised September 25, 2012; accepted December 08, 2012. Date of publication January 15, 2013; date of current version March 21, 2013. This work was supported in part by the Colombian Department of Administrative Science, Technology and Innovation (COLCIENCIAS 494-2009) and in part by the Spanish Government (Ministry of Science and Innovation DPI2008-05890). Paper no. TPWRD-00574-2012. E. Gomez-Luna and G. Aponte Mayor are with the Universidad del Valle, Cali 25360, Colombia (e-mail: [email protected]; guillermo. [email protected]). C. Gonzalez-Garcia and J. Pleite Guerra are with the Department of Electronic Technology, Universidad Carlos III de Madrid, Madrid 28911, Spain (e-mail: [email protected]; [email protected]). Digital Object Identifier 10.1109/TPWRD.2012.2234141

transformer structure and, therefore, are measured as an interesting output to evaluate. The frequency spectrum of the injected signal (input) and the measured signal (output) are obtained through mathematical procedures, usually the fast Fourier transform (FFT). Finally, the ratio between the two frequency spectra is obtained. In the SFRA measuring method, the excitation signal or input is a sinusoidal signal with a low-voltage (LV) amplitude (in the 1–20 range), which is applied to a transformer terminal in a frequency sweep (in the hertz to millihertz range); again, a transfer function (TF) is obtained from the output/input ratio. So far, the frequency-response technique for transformers has two major limitations: 1) interpretation of measured responses to obtain a reliable diagnosis is not clear, and 2) it is necessary to disconnect the transformer from the system to test it (offline measurement). Regarding the first point, progress has been made to interpret curves obtained offline by using transformer models, which are not only able to reproduce the same frequency response of the actual measured system (transformer), but also represent the transformer’s actual electromagnetic phenomena [1]–[3]. Some studies also define different coefficients to quantify differences in FRA curves [4]–[9]. The second point refers to the need to disconnect the transformer to test it, generating high costs and decreasing supply reliability. This implies that the test is usually not scheduled as frequently as desired (predictive approach), but only when a failure is suspected (corrective approach). Consequently, online performance of the frequency-response technique offers substantial benefits for a scheduled-based diagnosis in nonstop service and, moreover, it could even lead to condition-based maintenance. This is the main reason why this approach has generated remarkable interest among utility companies and the scientific community [10]–[12]. Taking into account a former proposal [13] and a bibliographic review and analysis whose sources are summed up in Fig. 2, this paper presents some contributions and future trends in the application of FRA with the transformer in service (online FRA) to be a reference for future research in this field. II. CURRENT STATE OF THE ART THE ONLINE FRA TECHNIQUE

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The FRA technique with the transformer in service (online measurement) implies using either the IFRA or the SFRA

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GOMEZ-LUNA et al.: CURRENT STATUS AND FUTURE TRENDS IN FRA

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Fig. 1. Scheme for the measurement of offline/online FRAs in the transformers.

niques discussed ahead) that uniquely relates faults in the transformer to the variation in the curves measured. A. Current Problems to Consider in the Implementation of the Online FRA Technique

Fig. 2. Sources of the literature survey.

method with the transformer still connected to the power system. Conducting an online test would lead to a more productive exploitation of the power system, as proposed in [14], which expresses the importance of developing diagnostic tests on power transformers in service, improving compliance with the electricity market’s requirements, and ensuring high reliability and low-cost maintenance. Similarly, other authors [15] have stated that it is necessary to implement online monitoring tools that continuously assess the changing conditions of the transformer in the electricity network (due to atmospheric discharges, input/output burdens, random electrical noise, and/or system faults) to avoid unexpected failure and ensure power delivery. Different efforts and contributions to carry out the frequencyresponse test with the transformer in service have been made [16]–[43]. However, as almost unanimously mentioned by different authors [32], research results are still required to have a reliable online FRA diagnosis system. At least two prerequisites must be fulfilled by a feasible online FRA measurement system: First, a repeatable measure, not influenced by external factors is inherent in the operation of the transformer in the power system; second, it is necessary to provide and implement an analysis method (based on different tech-

Applying the online FRA technique mainly involves three drawbacks associated with the transformer connection to the power system: 1) The measurement is performed in the presence of sinusoidal high voltage, involving: — the measurement itself becomes much more complex considering the high-voltage levels, personnel, and equipment safety concerns, and electric noise.[34]; — a possible impact on the measured response, given the nonlinear effects of some transformers, particularly core magnetization; this effect could lead to different responses with no actual change inside the transformer, depending only on the trigger timing of the measurement equipment. 2) The transformer is electrically connected to the remaining elements in the power system (source, load, switches, etc.) and, therefore, the measurement is actually the response of the whole system (not just the transformer), complicating the interpretation of the results [30], [31]. In addition, accessibility to the bushings is limited with respect to offline measurements [21]. For these two reasons, the connection configurations for online FRA cannot be as freely implemented as is in the offline approach. 3) The possible inclusion of elements in the transformer for FRA measurement in the network could lead to vulnerabilities, which may affect the power system operation. These three problems lead to particular difficulties in the online technique feasibility, such as: How to control the wide frequency input signal injection into the 50/60-Hz power wave. What is the impact of using different input signals (shapes) in the obtained transfer function? What is the impact of the sampling frequency on the results? How to avoid damage of measurement equipment and ensure personnel safety due to high voltages in the electrical grid? Are there any noise problems

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and environmental effects affecting the online test? What is the most appropriate mathematical tool to analyze the online FRA measurement? How to consider the three-phase structure in the measurement implementation? Different research addresses these concerns and they are analyzed in the following paragraphs. The first conclusion to extract is that a definitive solution for the technique application is not yet available. B. Current Alternatives for the Online FRA Technique To carry out the online FRA test in the transformer, first, the most viable option (IFRA or SFRA) must be determined. Most investigations, so far, have opted for the first option [16]–[20], [25], [26], [28]–[35], [37]–[39], with much fewer online SFRA contributions [26], [40]–[43]. A summarized analysis of these contributions is presented as follows. 1) Online IFRA Method: Application of the online IFRA methodology follows the three stages shown in Fig. 1, where the transformer is connected to the grid, and the input signal is not aperiodic voltage. Stage one refers to the excitation signal injection (input) and to the measurement of its reflected signal (output). This work proposes the classification of input signals in terms of their controlling capability. Controlled signals: are those injected on purpose into the power system line from the measurement instrumentation to generate transformer excitation. The controlled signals have some advantages: on one hand, the trigger timing is controlled and, on the other hand, the shape may also be controlled so that a wide frequency content to obtain an adequate bandwidth for a subsequent suitable FRA curve interpretation (from Hz to MHz) can be granted. In contrast, complex instrumentation would be required. Currently, no references of such a signal injecting system were found in the literature reviewed. Uncontrolled signals: are those disturbances inherent to the normal operation of the electrical network, such as wave-type switching pulse (opening and closing switches) or those considered signal type, which arise from atmospheric events (lightning), where high-frequency components predominate. Both types have advantages, which is that an external injection system is not necessary, but both are under random occurrences (in quantitative and qualitative terms). What is more, the shape of the disturbance is not controlled; therefore, a sufficient spectral content is not ensured, as shown specifically in [16]–[20], [23]–[35], [37]–[39], and [41]. Considering the measurement process in the uncontrolled signals approach, some alternatives have been found: — Measuring the voltage line through the bushing tap coupler (BTC), which is also a natural voltage divider with a voltage ratio, remaining constant over a wide bandwidth—as presented in [35], [44], and [45]. In [26], an alternative is shown for when the transformer does not have any BTC, which consists of a noninvasive capacitive sensor (NICS) installed on the surface of the bushings. — Measuring the current through the coil is peforned by using wide bandwidth current transformers [21], [31], such as the Rogowski coils, which limit the problem


of high-voltage connection because it is a nonintrusive galvanic-isolated device and has good performance over a wide bandwidth. During the last two decades, measurement of uncontrolled transients was a procedure proposed to determine possible deformations of the windings, as presented in [16] and [17]. Automatic digital recorders were used to measure the transients produced by switching and lightning events. Using the same approach, the five-year experience results of in-service transformer diagnosis were also published [18]. Similar experiences were also presented in [19]–[21], where different transformer switching transients were compared in terms of their advantages and disadvantages. The references conclude that the frequency content of signals caused by atmospheric events (lightning) is concentrated in the high-frequency bandwidth, while maneuver operations (opening and closing switches) have the highest frequency content in the low- and medium-frequency bandwidth. Finally, the main advantage of these signals is that an external injection system is not necessary. Reference [29] shows the technique used to obtain an online FRA response by means of pulses created from an open/ close switching operation applied to the energized transformer. A similar approach was proposed in [25], [29], [30], and [37]. Finally, in [31], U.S. Patent No. 6 549 017 was presented, which describes how to obtain the frequency response of the transformer in service from the time-domain response obtained from switching operations and maneuvers. However, it highlights the need for further research, continued in [32], [36], [38], [39], and [46]. Once injecting and measuring input and output signals, respectively, are addressed, stage two comes up, which consists of recording, filtering, and mathematical processing of signals. The measurement record is commonly performed through an analog-to-digital converter (ADC). High resolution, high-speed sampling, and sufficient memory size are essential system features to obtain a suitably wide bandwidth [20], [25]. In [30], a detailed analysis of measurement influence of different recorder configurations was presented, showing the importance of choosing an appropriate sampling frequency to obtain proper results for an online FRA response. Not many contributions are available related to filtering electrical signals coming from the power system. Particularly, [31] presents a mathematical filtering technique based on the coherence function and [21] and [30] merely mention the need for eliminating the aliasing effect. Signal processing is a mathematical procedure applied to signals in the time domain to determine their frequency content. For the online IFRA method, the tool traditionally used has been the fast Fourier transform (FFT), as presented in [16]–[20], [25], [26], [28]–[35], [37]–[39], and [41]. However, its use is questioned in [47] because of its limitations. The FFT requires periodic and infinite signals, while transient signals are neither periodic nor infinite. A possible alternative could be the window (also called short time) Fourier transform (STFT), allowing simultaneous treatment of the low- and high-frequency components of an uncontrolled signal, improving FFT processing. However, even with


TABLE I CLASSIFICATION OF KEY PARAMETERS RELATED TO ONLINE FRA

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the STFT, results have imperfections that can be explained by Heisenberg’s uncertainty principle [48], which indicates that it is impossible to know the accurate time and frequency of one of the components of the processed signal [47]. The difficulties and the need for suitable signal processing have been treated in [25], where the low-voltage impulse (LVI) method was analyzed to detect deformations in transformer windings in service. Stage three focuses on curve analysis and interpretation in which the operator avails of having a suitable modeling tool. A useful interpretation of the online IFRA results requires overcoming two main difficulties inherent to the fact that the transformer is a grid-connected device as follows. • A random bias point exists when measuring the FRA response because of the triggering moment related to the high-voltage wave and to the load. As long as noninear effects are present in the transformer, this could lead to different responses for the same unit in the same state. In [12], [21], [30], and [33], some of these effects are identified, such as: magnetic core hysteresis, temperature dependence, or an onload tap changer position. • The online FRA response includes the transformer behavior as well as the rest of the power system to which it is connected. The diagnosis based on the online IFRA technique is supported in [19] and [22], describing the method and the system components; this work was extended in [28]. Some authors have particularized the use of the transfer function to identify shifts in the transformer windings in service [24], [27]. In [30], a comparative study between the offline and online measurements is presented, and the impact of this leading to the high-frequency response is also pointed out. In [38] and [46], the experiences obtained from an actual online FRA prototype, applying switching operations in a 345/ 140-kV, 448-MVA autotransformer are presented, showing the feasibility of the technique. In [49], a prototype measurement system is also presented, concluding the persistent need for further developments. The advantages of an online IFRA diagnosis have also been defended by various institutions, as highlighted in [36]. 2) Online SFRA Method: Obtaining online FRA responses through the SFRA method follows the same stages as the online IFRA case presented in Fig. 1, with the following peculiarities: • The injected signals must be exclusively controlled signals, defined in terms of a sinusoidal wave in a frequency sweep. In this case, it is necessary to filter the signal from the power grid voltage (50/60 Hz) [41]. Furthermore, uncertainty remains regarding possible interferences between the injected signals and the wide bandwidth communication signals transmitted by the same cables, as described in [13], which mentions that injected signal frequency can reach high kilohertz or even megahertz values that can interact with the control signals travelling along the power lines. • The required mathematical procedure is much lighter as long as the response is directly obtained in the frequency domain.


The first reference related to the online SFRA application [26] corresponds to a chirp-type signal (signal generated in a sweep frequency), which was injected to the high side of a 765-kV/66-kV/550-V transformer and the output was monitored at the lower side of the grid voltage. The second reference presents an online assessment of winding deformation by using optimized multi-sine excitation compared to a chirp signal [42]. In [40], a practical and preliminary online monitoring system is presented, where a high-frequency signal is injected to a 650-kV transformer through the capacitive bushing tap. In [43], noninvasive capacitive sensors (NICs) are installed on the surface of the bushings to measure line voltage, and Rogowski coils are also used to measure phase currents. Both devices are suitable to handle a wide bandwidth response. But again, problems are shown related to signal injection with the transformer in service. Analysis of each reference in this study shows the progress made so far and points to difficulties in the online FRA technique’s implementation at its different stages. For the sake of simplicity, Table I shows a relation to the main references studied and their content. III. FUTURE TRENDS AND PROSPECTS ONLINE FRA TECHNIQUE

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A. Online IFRA No significant contributions were found about a signal injecting system. Nevertheless, an IFRA-controlled signal system would significantly increase the measurement quality and, therefore, interesting work remains to be done on this issue. The signal injection process to excite the transformer is mainly proposed in terms of controlled signals, instead of using uncontrolled signals. This allows avoiding the randomness of power system events and ensures monitoring the transformer status when requested. Currently, suitable data-recording devices are available, which comply with the monitoring needs of high-frequency signals in terms of resolution, sampling, and memory length. Also, apart from the classical methods, new alternatives exist to explore signal filtering and processing, such as the wavelet transform (WT). Particularly, its multiresolution analysis (MRA) approach may be an interesting option, given that it works with filter banks applied to discrete signals in sub-band coding, which constitutes a natural filtering. This method allows simultaneously filtering the grid frequency (50/60 Hz) and the electrical background noise [50], [51], some of the unwanted effects in the online FRA test. The WT allows filtering the unwanted effects, as well as extracting the transformer information by means of signal processing, in a more versatile manner than the Fourier Transform approach. The WT allows a detailed study of time and frequency domains, by using small time windows for high frequencies and large windows for low frequencies. The WT has been tested to treat power system transients, partial discharge analysis, and study of harmonic signals, as presented in [52]–[56].


The TF considered for the transformer diagnosis, obtained after measuring and processing the signals, should contain only the transformer information in a unique response set. It should not depend on the measurement (offline or online, IFRA or SFRA) or on the operating point conditions. To achieve this, some interpretation tools must be considered, such as: artificial intelligence, statistical methods, or Transformer models, which could be particularly useful for online measurement curves.

B. Online SFRA Method The stages to follow for the online SFRA method are the same as those implemented for online IFRA (Fig. 1). Nonetheless, differences exist in how each stage is applied, as will be discussed: Some systems with a chirp signal injection through the BTC have been proposed by means of blocking filters [33], [35], [44], and [45]. These systems care not only about the measurement itself but also about preventing interferences from/to the grid. There is a common application of these filters in power-line communication (PLC), which is used for the grid’s remote-control system. The latest technologies and trends on PLCs are described in [57] and [58]. Furthermore, in [59], the role of the PLC in the smart grid concept was presented, showing the great advantages and future trends in communication and control systems for power grids because it supports a timely decisions approach based on monitoring the current state of the transformer in operation, as shown in [34]. The SFRA systems may avail of the PLC approach and adapt it to the FRA needs. From the diagnosis point of view, modeling may be a good approach [1], but other approaches are worth exploring like the wavelet transform, which may also be a good tool to interpret the results [60], [61].

IV. CONCLUSIONS The main conclusions regarding the current state of the art on the online FRA for transformer diagnosis are as follows: Firstly, different issues still need to be overcome, as pointed in this paper. So far, the online IFRA method seems to offer greater possibilities to obtain satisfactory results related to the SFRA approach or, at least, more contributions proposing the IFRA system are available. Three stages have been identified in this work to sort the main issues when implementing the online FRA measurement. The particularization of these stages to the SFRA and IFRA approaches has also been presented. On one hand, the signal injection process was discussed. The use of controlled signals is preferred to uncontrolled signals, given their greater controllability and repeatability; however, they are much more complex to implement. On the other hand, signal filtering and processing was also considered and the Wavelet Transform was identified as a useful tool with even more potential than the standardized Fourier Transform and its derivatives.

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[45] A. Setayeshmehr, A. Akbari, H. Borsi, and E. Gockenbach, “On-line monitoring and diagnoses of power transformer bushings,” IEEE Trans. Dielect. Elect. Insul., vol. 13, no. 3, pp. 608–615, Jun. 2006. [46] L. Coffeen, J. Mcbride, N. Woldemariam, J. Benach, and L. V. Zel, “A summary of NEETRAC on-line Frequency Response Analysis (FRA) and a new EPRI commercial prototype FRA installation at first energy,” presented at the NEETRAC and EPRI Substation Equipment Maint. Optim. Diagnos. Conf., San Antonio, TX, Mar. 2009. [47] D. C. Robertson, O. I. Camps, J. S. Mayer, and W. B. Gish, “Wavelets and electromagnetic power system transients,” IEEE Trans. Power Del., vol. 11, no. 2, pp. 1050–1058, Apr. 1996. [48] M. Doroslovacki, “Discrete-time signals and uncertainty relations involving ordinary second moments in time and frequency,” in Proc. Int. Symp. Time-Frequency Time-Scale Analysis, Oct. 1994, pp. 186–189. [49] Elect. Power Res. Inst. (EPRI), On-line frequency response analysis–report on commercialization of system, Tech Rep. 1012345, Dec. 12, 2006. [Online]. Available: http://www.epri.com/abstracts/Pages/ProductAbstract.aspx?ProductId=000000000001012345 [50] M. B. Figueiredo, A. D. Almeida, and B. Ribeiro, “Wavelet decomposition and singular spectrum analysis for electrical signal denoising,” in Proc. IEEE Int. Conf. Syst., Man, Cybern., 2011, pp. 3329–3334. [51] V. L. Pham and K. P. Wong, “Wavelet-transform-based algorithm for harmonic analysis of power system waveforms,” Proc. Inst. Elect. Eng., Gen., Transm. Distrib., vol. 146, no. 3, pp. 249–254, May 1999. [52] M. Karimi, H. Mokhtari, and M. R. Iravani, “Wavelet based on-line disturbance detection for power quality applications,” IEEE Trans. Power Del., vol. 15, no. 4, pp. 1212–1220, Oct. 2000. [53] P. Purkait and S. Chakravorti, “Pattern classification of impulse faults in transformers by wavelet analysis,” IEEE Trans. Dielect. Elect. Insul., vol. 9, no. 4, pp. 555–561, Aug. 2002. [54] K. L. Butler and M. Bagriyanik, “Characterization of transients in transformers using discrete wavelet transformer,” IEEE Trans. Power Syst., vol. 18, no. 2, pp. 648–656, May 2003. [55] E. Zheng, Z. Liu, and L. Ma, “Study on harmonic detection method based on FFT and wavelet transform,” in Proc. IEEE 2nd Int.Conf. Signal Process. Syst., 2010, pp. 1–4. [56] S. R. Paraskar, M. A. Beg, and G. M. Dhole, “Continuous wavelet transform for discrimination between inrush and fault current transients in transformer,” Int. J. Sci., Spirituality, Bus. Technol., vol. 1, no. 1, Mar. 2012. [57] S. Ramseier and H. Spiess, Feb. 2006, “El canto de las líneas eléctricas,” Revista ABB No. 2, Aug. 2006. Baden, Switzerland. [Online]. Available: http://www05.abb.com/global/scot/scot221. nsf/veritydisplay/cd6491bf4accb5cfc1257264004903aa/$file/review_s.indd_1.pdf [58] D. D. Giustina, L. Andersson, C. Casirati, S. Zanini, and L. Cremaschini, “Testing the broadband power line communication for the distribution grid management in a real operation environment,” in Proc. Int. Symp. Power Electron., Elect. Drives, Autom. Motion, 2012, pp. 785–789. [59] S. Galli, A. Scaglione, and Z. Wang, “For the grid and through the grid: The role of power line communications in the smart grid,” Proc. IEEE, vol. 99, no. 6, pp. 998–1027, Jun. 2011. [60] J. C. Gonzales, E. E Mombello, and G. D. Guidi, “Diagnosis of power transformers through Frequency Response Analysis applying multiresolution analysis and pattern classification,” in Proc. IEEE/Power Eng. Soc. Transm. Distrib. Conf. Expo.: Latin America, Nov. 8–10, 2010, pp. 270–277. [61] E. Gómez-Luna, D. Silva, G. Aponte, J. Pleite, and D. Hinestroza, “Obtaining the electrical impedance using wavelet transform from the time response,” IEEE Power Eng. Lett., Dec. 04, 2012, accepted for publication. Eduardo Gomez-Luna (S’11) was born in Pradera Valle, Colombia, in 1982. He is currently pursuing the Ph.D. degree in electrical engineering from the Universidad del Valle, Cali, Colombia. He is part of the Research Group on High Voltage (GRALTA), Universidad del Valle. His research interests focus on power transformer diagnosis, power electronics, applied mathematics, and signal processing.


Guillermo Aponte Mayor was born in Palmira, Colombia, in 1955. He received the B.Sc. and Ph.D. degrees in electrical engineering from Universidad del Valle, Cali, Colombia, in 1978 and 2011, respectively, and the M.Sc. degree in high-voltage engineering from the University of Manchester Institute of Science and Technology (UMIST), Manchester, U.K., in 1985. Currenlty, he is a Professor at Universidad del Valle and the Director of the Research Group on High Voltage GRALTA. His research areas are focus on power transformer diagnosis and substations.

Carlos Gonzalez-Garcia (S’06–M’12) was born in Madrid, Spain, in 1980. He received the B.Sc., M.Sc., and Ph.D. degrees in electrical engineering from Carlos III University of Madrid, Madrid, Spain, in 2004, 2009, and 2012, respectively. His research interests focus on power transformer diagnosis, power electronics, and modeling of magnetic devices. Currently, he is an Assistant Professor at Carlos III University of Madrid in the Electronics Technology Department, Power Electronics Group.

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Jorge Pleite Guerra (SM’12) is an Associate Professor at Carlos III University of Madrid, Madrid, Spain, where he is also the Head of the Master’s Program in Advanced Electronics Systems. His main research line is the modeling of electric and analog electronic devices and, particularly, their application in diagnosing power transformers.