Juan Manuel Martínez-Tarifa

Bio: Juan Manuel Martínez-Tarifa is an academic researcher from Charles III University of Madrid. The author has contributed to research in topics: Partial discharge & Inductive sensor. The author has an hindex of 18, co-authored 56 publications receiving 803 citations. Previous affiliations of Juan Manuel Martínez-Tarifa include Carlos III Health Institute.

Antenna Parametrization for the Detection of Partial Discharges

Abstract: Partial discharge (PD) detection is a widely extended technique for electrical insulation diagnosis. Ultrahigh-frequency detection techniques appear as a feasible alternative to traditional methods owing to their inherent advantages such as the capability to detect PDs online and to locate the piece of equipment with insulation problems in substations and cables. In this paper, four antennas are thoroughly studied by means of their theoretical and experimental behavior when measuring electromagnetic pulses radiated by PD activity. The theoretic study of the band of frequencies in which the pulse emits and the measurement of the parameters $S_{11}$ are complemented with the frequency response and wavelet transform of a set of 500 time signals acquired by the antennas, and the results are analyzed in detail.

Partial discharge and noise separation by means of spectral-power clustering techniques

Abstract: Partial Discharges (PDs) are one of the most important classes of ageing processes that occur within electrical insulation. The measurement of PDs is useful in the diagnosis of electrical equipment because PDs activity is related to different ageing mechanisms. Classical Phase-Resolved Partial Discharge (PRPD) patterns are able to identify PD sources when they are related to a clear degradation process and when the noise level is low compared to the amplitudes of the PDs. However, real insulation systems usually exhibit several PD sources and the noise level is high, especially if measurements are performed on-line. High-frequency (HF) sensors and advanced signal processing techniques have been successfully applied to identify these phenomena in real insulation systems. In this paper, spectral power analyses of PD pulses and the spectral power ratios at different frequencies were calculated to classify PD sources and noise by means of a graphical representation in a plane. This technique is a flexible tool for noise identification and will be useful for pulse characterization.

Electro-acoustic detection, identification and location of partial discharge sources in oil-paper insulation systems

Abstract: The detection of Partial Discharges (PD) is a reliable technique to analyze the status of electrical insulation in power transformers. Phase resolved partial discharge patterns are being complemented with high frequency pulse waveform analysis, in order to identify discharge sources. In addition to this, acoustic techniques are being implemented trying to locate PD sites in large power transformers. In this work, an inductive loop sensor will be used to identify two different PD sources by means of the energy distribution of the detected waveforms. Additionally, these PD sources will be located by means of acoustic measurements with an electrical reference, through the analysis of the acoustic activity detected for each sensor individually.

Multiple partial discharge source discrimination with multiclass support vector machines

Abstract: Different types of partial discharges are created with test objects in laboratory.Their frequency content depends on the type of discharge and other external factors.An SVM extracts characteristics from the power spectral density of the pulses.Noise, corona, internal and surface discharges have different characteristics.The differences are used to classify discharges and separate them from noise. The costs of decommissioning high-voltage equipment due to insulation breakdown are associated to the substitution of the asset and to the interruption of service. They can reach millions of dollars in new equipment purchases, fines and civil lawsuits, aggravated by the negative perception of the grid utility. Thus, condition based maintenance techniques are widely applied to have information about the status of the machine or power cable readily available. Partial discharge (PD) measurements are an important tool in the diagnosis of power systems equipment. The presence of PD can accelerate the local degradation of insulation systems and generate premature failures. Conventionally, PD classification is carried out using the phase resolved partial discharge (PRPD) pattern of pulses. The PRPD is a two dimensional representation of pulses that enables visual inspection but lacks discriminative power in common scenarios found in industrial environments, such as many simultaneous PD sources and low magnitude events that can be hidden below noise. The literature shows several works that complement PRPD with machine learning detectors (neural networks and support vector machines) and with more sophisticated signal representations, like statistics captured in several modalities, wavelets and other transforms, etc. These methods improve the classification accuracy but obscure the interpretation of the results. In this paper, the use of a support vector machine (SVM) operating on the power spectrum density of signals is proposed to identify different pulses what could be used in an online tool in the maintenance decision-making of the utility. Particularly, the approach is based on an SVM endowed with a special kernel that operates in the frequency domain. The SVM is previously trained with pulses of different PD types (internal, surface and corona) and noise that are obtained with several test objects in the laboratory. The experimental results demonstrate that this technique is highly effective in identifying PD for cases where several sources are active or when the noise level is high. Thus, the early identification of critical events with this approach during normal operation of the equipment will help in the decision of decommissioning the asset with reduced costs and low impact to the grid reliability.

The Effect of Nanoparticle Shape on Polymer-Nanocomposite Rheology and Tensile Strength

Abstract: Nanoparticles can influence the properties of polymer materials by a variety of mechanisms. With fullerene, carbon nanotube, and clay or graphene sheet nanocom- posites in mind, we investigate how particle shape influences the melt shear viscosity ! and the tensile strength " , which we determine via molecular dynamics simula- tions. Our simulations of compact (icosahedral), tube or rod-like, and sheet-like model nanoparticles, all at a volume fraction # ! 0.05, indicate an order of magnitude increase in the viscosity ! relative to the pure melt. This finding evidently can not be explained by continuum hydrodynamics and we provide evidence that the ! increase in our model nanocomposites has its origin in chain bridging between the nanopar- ticles. We find that this increase is the largest for the rod-like nanoparticles and least for the sheet-like nanoparticles. Curiously, the enhancements of ! and " exhibit opposite trends with increasing chain length N and with particle shape anisotropy. Evidently, the concept of bridging chains alone cannot account for the increase in " and we suggest that the deformability or flexibility of the sheet nanoparticles con- tributes to nanocomposite strength and toughness by reducing the relative value of the Poisson ratio of the composite. The molecular dynamics simulations in the present work focus on the reference case where the modification of the melt structure associ- ated with glass-formation and entanglement interactions should not be an issue. Since many applications require good particle dispersion, we also focus on the case where the polymer-particle interactions favor nanoparticle dispersion. Our simulations point to a substantial contribution of nanoparticle shape to both mechanical and processing prop- erties of polymer nanocomposites. © 2007 Wiley Periodicals, Inc." J Polym Sci Part B: Polym

Application of HFCT and UHF Sensors in On-Line Partial Discharge Measurements for Insulation Diagnosis of High Voltage Equipment

Abstract: Partial discharge (PD) measurements provide valuable information for assessing the condition of high voltage (HV) insulation systems, contributing to their quality assurance. Different PD measuring techniques have been developed in the last years specially designed to perform on-line measurements. Non-conventional PD methods operating in high frequency bands are usually used when this type of tests are carried out. In PD measurements the signal acquisition, the subsequent signal processing and the capability to obtain an accurate diagnosis are conditioned by the selection of a suitable detection technique and by the implementation of effective signal processing tools. This paper proposes an optimized electromagnetic detection method based on the combined use of wideband PD sensors for measurements performed in the HF and UHF frequency ranges, together with the implementation of powerful processing tools. The effectiveness of the measuring techniques proposed is demonstrated through an example, where several PD sources are measured simultaneously in a HV installation consisting of a cable system connected by a plug-in terminal to a gas insulated substation (GIS) compartment.

Review of Electric Machines in More-/Hybrid-/Turbo-Electric Aircraft

Abstract: Aircraft electrification is currently the best alternative to address the rising demand for more air transportation and deal with anticipated economic and environmental impacts. Although the all-electric-aircraft (AEA) concept is not yet a feasible solution, the more-electric aircraft (MEA) is gaining significant attention. Electrical systems either partially or entirely replace the large and inefficient hydraulic, pneumatic, and mechanical conventional aircraft actuating systems. The upgrade could also encompass the propulsion system, as in hybrid- and turbo-electric aircraft. This upgrade reduces the aircraft weight, reduces the usage of pollutant fluids, increases fuel efficiency, reduces carbon emissions, and increases aircraft controllability and reliability. This article reviews various application areas of electric machines in electrified aircraft, such as actuation, taxiing, propulsion, and generation. Moreover, it reviews the main types of currently/to be utilized electric machines and the critically required specifications. Finally, a comparison between the different considered machines and potential future research is discussed.

Condition Monitoring Based on Partial Discharge Diagnostics Using Machine Learning Methods: A Comprehensive State-of-the-Art Review

Abstract: This paper presents a state-of-the-art review on machine learning (ML) based intelligent diagnostics that have been applied for partial discharge (PD) detection, localization, and pattern recognition. ML techniques, particularly those developed in the last five years, are examined and classified as conventional ML or deep learning (DL). Important features of each method, such as types of input signal, sampling rate, core methodology, and accuracy, are summarized and compared in detail. Advantages and disadvantages of different ML algorithms are discussed. Moreover, technical roadblocks preventing intelligent PD diagnostics from being applied to industry are identified, such as insufficient/imbalanced dataset, data inconsistency, and difficulties in cost-effective real-time deployment. Finally, potential solutions are proposed, and future research directions are suggested.