Jorge Pleite Guerra

Bio: Jorge Pleite Guerra is an academic researcher from Charles III University of Madrid. The author has contributed to research in topics: Transformer & Frequency response. The author has an hindex of 6, co-authored 8 publications receiving 188 citations. Previous affiliations of Jorge Pleite Guerra include Carlos III Health Institute.

Current Status and Future Trends in Frequency-Response Analysis With a Transformer in Service

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: injection and excitation signal measurement; recording, filtering and processing of measured signals; and curve analysis and interpretation. This work presents an overview of the online FRA technique, useful for subsequent research in this area.

Detection of Transformer Faults Using Frequency-Response Traces in the Low-Frequency Bandwidth

Abstract: This paper describes a new procedure which facilitates the frequency-response curve interpretation in the low-frequency bandwidth. The procedure is able to estimate the magnetization inductance and winding capacitance, for each transformer phase by only using data from its frequency response. The described calculation procedure uses an R-L-C electrical equivalent circuit developed to simulate the impedance measured in the frequency-response test. The procedure takes into account the magnetic coupling among different phases and allows their analysis separately, enabling the identification of a possible failure. This paper describes the procedure to obtain the parameters and its application for interpreting the low-frequency results of frequency-response analysis measurement cases.

Application of Wavelet Transform to Obtain the Frequency Response of a Transformer From Transient Signals—Part 1: Theoretical Analysis

Abstract: This paper introduces a new theoretical method to obtain the frequency-response curve with the transformer in service (online) from the transient signals analysis by applying the continuous wavelet transform. To validate the method, the transient signals were simulated in the Alternative Transient Program for two cases: 1) under injection of pulses over the 60-Hz wave and 2) under switching operations, respectively. To simulate the transformer, a suitable wide-bandwidth model was employed. This study proposes a new setup of the Morlet wavelet, denominated Morlet-Modified, which showed the best results obtained for our application purpose (FRA On-line). This paper is a contribution for signal processing for the online frequency-response analysis technique on transformers.

Application of Wavelet Transform to Obtain the Frequency Response of a Transformer From Transient Signals—Part II: Practical Assessment and Validation

Abstract: This paper presents a practical assessment and validation for the new proposed approach based on the continuous wavelet transform (CWT) to obtain the frequency response from online transient signals for an actual transformer. Apart from the mathematical procedure, an electronic system was also designed and implemented in order to inject superimposed controlled pulses to the power system (50/60 Hz) wave. The results show improved performance of the wavelet transform compared to the Fourier transform, for transient signal analysis to be applied on a nonintrusive transformer monitoring approach. Particularly, this new approach enables overcoming some problems related to the signal filtering and the signal processing in an online frequency-response analysis transformer diagnosis. This paper is presented in three parts. Part I showed the main theoretical basis of this approach. The current Part II shows a practical assessment based on tests performed in a three-phase transformer of 1150/345 V-5 kVA and in a single-phase transformer of 13200/240 V-15 kVA. Part III shows the performance of this new approach on transformers being diagnosed under real conditions.

Multiple Cell Upsets Inside Aircrafts. New Fault-Tolerant Architecture

Abstract: Multiple cell upsets (MCU) is an issue that has to be dealt with when designing electronics for working in a radiated environment Furthermore, the constant evolution of ICs integration density causes an increment in the MCUs span These issues are typical in aviation applications, where, additionally, fault-tolerant (FT) performance is required FT systems are typically based on a redundancy concept for storing and retrieving healthy information, for example, with a triple modular redundancy (TMR) scheme The main issue with redundancy is design oversizing On the other hand, reconfiguration-based techniques allow error scrubbing with a limited overhead The main drawback here is overhead vulnerability to radiation, which is invalid for FT requirements This paper proposes a new hybrid architecture that takes advantage of the optimized performance of reconfiguration-based techniques supported on extremely compressed redundant information nonvulnerable to radiation, referred to in this paper as hardwired seed bits (HSB) It also includes different known techniques, such as interleaving, error detection and correction (EDAC) algorithms, etc, for optimizing the final architecture as much as possible As a result, the proposed approach meets FT requirements thanks to nonvulnerable tiny redundant information combined with an optimized performance through EDAC-based implementation

Optimal dissolved gas ratios selected by genetic algorithm for power transformer fault diagnosis based on support vector machine

Abstract: Dissolved gas analysis (DGA) of oil is used to detect the incipient fault of power transformers. This paper presents a new approach for transformer fault diagnosis based on selected gas ratios concentrated in oil and support vector machine (SVM). Firstly, based on IEC TC 10 database, the optimal dissolved gas ratios (ODGR) are obtained by genetic algorithm (GA) that is designed for simultaneous DGA ratios selection and SVM parameters optimization. Three traditional methods, namely, DGA data with SVM and back propagation neural network (BPNN), IEC criteria, and IEC three-key gas ratios with SVM and BPNN are employed for effectiveness comparison. The fault diagnosis results of IEC TC 10 database show that the proposed ODGR with SVM may be used as an alternative tool for transformer fault diagnosis. In addition, the robustness and generalization ability of ODGR is confirmed by the diagnosis accuracy of 87.18% of China DGA samples. The obtained results illustrate that it is preferable to apply the proposed ODGR to transformer fault diagnosis with the assistance of SVM.

Diagnostic Measurements for Power Transformers

Abstract: With the increasing age of the primary equipment of the electrical grids there exists also an increasing need to know its internal condition. For this purpose, off- and online diagnostic methods and systems for power transformers have been developed in recent years. Online monitoring is used continuously during operation and offers possibilities to record the relevant stresses which can affect the lifetime. The evaluation of these data offers the possibility of detecting oncoming faults early. In comparison to this, offline methods require disconnecting the transformer from the electrical grid and are used during planned inspections or when the transformer is already failure suspicious. This contribution presents the status and current trends of different diagnostic techniques of power transformers. It provides significant tutorial elements, backed up by case studies, results and some analysis. The broadness and improvements of the presented diagnostic techniques show that the power transformer is not anymore a black box that does not allow a view into its internal condition. Reliable and accurate condition assessment is possible leading to more efficient maintenance strategies.

Fault Diagnosis of On-Load Tap-Changer in Converter Transformer Based on Time–Frequency Vibration Analysis

Abstract: The suitable condition of an on-load tap-changer (OLTC) is essential for the operation of converter transformer due to its frequent switch for the voltage regulation of power system. This paper describes a methodology to obtain the OLTC vibration characteristics in time–frequency domain. Considering the possible aliasing effect in vibration signal processing, an improved empirical mode decomposition (EMD) is proposed with masking signals of multiple frequencies added, which has obvious superiority in aliasing reduction compared with conventional methods. Then, an effective fault-diagnosis strategy based on energy distributions variations of OLTC vibration signals according to Lorentz information measure is brought up. The calculated results under normal and typical fault conditions of model and real OLTC have shown that the energy spectrums of different conditions vary significantly so that the similarity index can measure the difference degree of energy distribution. Meanwhile, the index of contact looseness is higher than the insulated panel looseness which indicates that the contact looseness fault has greater influence on switch-over process of OLTC.

Classifying Transformer Winding Deformation Fault Types and Degrees Using FRA Based on Support Vector Machine

Abstract: As an important part of power system, power transformer plays an irreplaceable role in the process of power transmission. Diagnosis of transformer’s failure is of significance to maintain its safe and stable operation. Frequency response analysis (FRA) has been widely accepted as an effective tool for winding deformation fault diagnosis, which is one of the common failures for power transformers. However, there is no standard and reliable code for FRA interpretation as so far. In this paper, support vector machine (SVM) is combined with FRA to diagnose transformer faults. Furthermore, advanced optimization algorithms are also applied to improve the performance of models. A series of winding fault emulating experiments were carried out on an actual model transformer, the key features are extracted from measured FRA data, and the diagnostic model is trained and obtained, to arrive at an outcome for classifying the fault types and degrees of winding deformation faults with satisfactory accuracy. The diagnostic results indicate that this method has potential to be an intelligent, standardized, accurate and powerful tool.