Separación de fuentes de descargas parciales y ruido eléctrico mediante análisis de potencia espectral en alta frecuencia

En entornos industriales o incluso en ambientes controlados como laboratorios de alta tensión, los impulsos procedentes de múltiples fuentes de Descargas Parciales (PDs) y ruido eléctrico se pueden superponer llegando modificar y alterar los resultados de las mediciones de PDs conduciendo finalmente a interpretaciones erróneas. Asimismo, algunos tipos de descargas, como es el caso de las tipo corona o superficiales, no suelen influir algunas veces en la expectativa de vida en los sistemas aislantes, al contrario de lo que ocurre con las internas, que sí pueden llegar a causar la ruptura en un corto tiempo. Por estos motivos, la separación e identificación de fuentes de PDs y ruido se ha convertido en un requisito fundamental a la hora de obtener un diagnóstico efectivo del aislamiento y evitar así evaluaciones erróneas en equipos como máquinas eléctricas y cables aislados. El propósito de esta Tesis es la presentación de un nuevo método de separación y clasificación de fuentes de PDs y ruido basado en el análisis de la potencia espectral de los pulsos de PDs para determinadas bandas de frecuencia. Este método permite separar en un mapa 2D las diferentes fuentes que puedan estar presentes durante la adquisición, a través de nubes de puntos clusters que se ubican en diferentes partes del mapa de acuerdo a su naturaleza. Conjuntamente se presenta el desarrollo de un algoritmo que permite seleccionar de forma automática las bandas de frecuencia de mayor interés con el fin de mejorar la separación de los clusters en el mapa. Adicionalmente, una serie de experimentos realizados en varios objetos de ensayo y equipos reales son presentados, con el fin de validar el comportamiento del método de separación y del algoritmo de selección automática propuestos en este documento.Both at industrial environments and even controlled facilities as high voltage laboratories the onset and overlap of Partial Discharges (PDs) and electromagnetic noise is possible which may lead to disturbances on the measurements made and entailing misleading results. Furthermore, some specific types of discharges as corona and surface PDs do not usually affect the expected lifetime of insulation systems such as occur in the case of internal discharges which frequently leads to the breakdown of the insulation in a short period of time. On those grounds the accurate identification of the PDs and its differentiation from other signals like electric noise have become a basic requirement when an effective insulation diagnosis is required avoiding erroneous results and a wrong diagnosis of electrical machines and power cables. The purpose of this thesis is to present a new method for separating and classifying PDs and noise sources by means of the analysis of the spectral power from the detected pulses for certain frequency bands. This method allows the separation in a 2D map of different sources that may be present during acquisition, through clouds of clusters that are located in different parts of the map according to their nature. In addition it has been developed an algorithm for the automatic selection of the frequency bands of interest in order to improve the separation of the clusters on the map. Additionally, a series of experiments conducted on various test objects and real high voltage equipment are presented, in order to validate the performance of the separation method and the algorithm for the automatic selection of frequency intervals that is proposed in this document.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Andrea Cavallini.- Secretario: Javier Sanz Feito.- Vocal: Daniel Muñoz Fría

Separation of sources in radiofrequency measurements of partial discharges using time-power ratios maps

Partial discharges measurement is one of the most useful tools for condition monitoring of high-voltage (HV) equipment. These phenomena can be measured on-line with antennas provided that the signal to noise ratio is improved by reducing common radiofrequency (RF) emission. One approach to this problem is the use of specific sensors like Vivaldi antennas which reject FM radio and low-frequency TV broadcasting bands. Additionally, the application of advanced signal processing techniques is paramount to separate noise and interferences from the signals of interest. In this paper, the power ratios (PR), a technique based on the power distribution of the incoming signals in frequency bands, is used to characterize different sources of PD and electromagnetic noise (EMN). The calculation of the time length of the pulses is introduced to separate signals where the PR alone do not give a conclusive solution. Thus, if several EM sources could be isolated and previously calibrated, it is possible to detect pulses that correspond to other events, quite possibly from PD activity.Publicad

Electrical Properties of Different Polymeric Materials and their Applications: The Influence of Electric Field

In this chapter, a comprehensive study on the general structure of polymers, their properties and applications has been carried out. In particular, the application of polymers for insulating high‐voltage (HV) equipment has been reported, together with the effect of electric field when they are subjected to HV stress. Experimental results related to the effect of partial discharge (PD) on polymeric insulations have been reported and discussed. Practical implications of the results have been discussed, and recommendations are made for future improvement. It is important to obtain new information regarding novel polymeric materials such as nano‐polymers that can possibly outperform the currently used ones. It is also vital to investigate the right information for electrical equipment, i.e. by using the appropriate polymer as solid insulation, minimizing the presence of any metallic sharp object and any other conducting path during manufacture in order to avoid any type of internal or external PD

Multiple partial discharge source discrimination with multiclass support vector machines

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

Robust Condition Assessment of Electrical Equipment with One Class Support Vector Machines Based on the Measurement of Partial Discharges

This paper presents a system for the detection of partial discharges (PD) in industrial applications based on One Class Support Vector Machines (OCSVM). The study stresses the detection of Partial Discharges (PD) as they represent a major source of information related to degradation in the equipment. PD measurement is a widely extended technique for condition monitoring of electrical machines and power cables to avoid catastrophic failures and the consequent blackouts. One of the most important keystones in the interpretation of partial discharges is their separation from other signals considered as not-PD especially in low SNR measurements. In this sense, the OCSVM is an interesting alternative to binary SVMs since it does not need a training set with examples of all the output classes correctly labelled. On the contrary, the OCSVM learns a model of the signals acquired when the equipment is in PD-free mode, defined as a state where no degradation mechanism is active, so one only needs to make sure that the training signals were recorded under this setting. These default mode signals are easier to characterize and acquire in industrial environments than PD and lead to more robust detectors that practically do not need domain adaptation to perform in scenarios prone to different types of PD. In fact, the experimental results show that the performance of the OCSVM is comparable to that achieved by a binary SVM trained using both noise and PD pulses. Finally, the method is successfully applied to a more realistic scenario involving the detection of PD in a damaged distribution power cable.Tests were conducted at the High Voltage Research and Testing Laboratory (LINEALT) of Universidad Carlos III de Madrid. This work has been funded by the Spanish Government through project SI-DP (DPI2015-66478-C2-1 MINECO/FEDER, UE) and the Chilean Research Council (CONICYT), under the project Fondecyt 11160115

COMPARACIÓN DE MÉTODOS DE SEPARACION DE FUENTES DE DESCARGAS PARCIAL PARA LA CARACTERIZACIÓN DE FALLOS EN SISTEMAS DE AISLAMIENTO ELÉCTRICOS

En la medición y ensayos de descargas parciales (DP) en equipamiento de potencia en ambiente industrial, existen variadas fuentes de ruido que se superponen a la señal de DP, alterando los resultados obtenidos. Estas perturbaciones pueden llevar a erróneas interpretaciones de la fuente u origen de DP. El daño que sufre la aislación eléctrica del dispositivo depende de la fuente de DP, por lo tanto, la separación e identificación de las fuentes de DP y ruido eléctrico son cruciales para efectuar un diagnóstico más preciso. Para mejorar la identificación de fuentes de DP dos métodos son propuestos y comparados: mapas Power-Ratio (PR) y Time-Frequency (TF). Ambos crean mapas bi-dimensionales, donde cada punto representa una DP, y cada coordenada una ecuación matemática correspondiente a su tipo de mapa. Estos métodos son estudiados para la separación de múltiples fuentes de DP que ocurren simultáneamente

An ensemble-boosting algorithm for classifying partial discharge defects in electrical assets

This paper presents an ensemble-boosting algorithm (EBA) for classifying partial discharge (PD) patterns in the condition monitoring of insulation diagnosis applied for electrical assets. This approach presents an optimization technique for creating a sequence of artificial neural network (ANNs), where the training data for each constituent of the sequence is selected based on the performance of previous ANNs. Four different PD faults scenarios were manufactured in the high-voltage (HV) laboratory to simulate the PD faults of cylindrical voids in methacrylate, point-air-plane configuration, ceramic bushing with contaminated surface and a transformer affected by the internal PD. A PD dataset was collected, pre-processed and prepared for its use in the improved boosting algorithm using statistical techniques. In this paper, the EBA is extensively compared with the widely used single artificial neural network (SNN). Results show that the proposed approach can effectively improve the generalization capability of the PD patterns. The application of the proposed technique for both online and offline practical PD recognition is examined

Lightning Activity Over Chilean Territory

This work presents the spatial distribution and temporal variability of lightning activity over the continental territory of Chile by means of Thunderstorms days (Td), on the basis of 7 years (2012–2018) of lightning measurement from World Wide Lightning Location Network (WWLLN). Td are obtained separately for the 15 geopolitical regions of Chile, reporting the higher lightning activity in the northeastern region of the country with 85 thunderstorms days per year. These values are mainly located in the mountains between 2,000 and 5,000 m.a.s.l. where extensive mining activity is located and there are electrical facilities of great importance for Chile. The Td values obtained in this study update the information presented by the World Meteorological Organization (WMO) in 1953, so far the only one available for the entire Chilean territory. From the diurnal cycle analysis, there is a marked mono-modal behavior of lightning activity in the afternoon for latitudes between (Formula presented.) S and (Formula presented.) S (regions XV, I, and II) and a different behavior of lightning activity over the region between (Formula presented.) S and (Formula presented.) S (regions X, XI, and XII) known as Chilean Patagonia, due to special weather conditions in that area. Further more, the seasonal analysis showed that the highest lightning activity occurs in January and February and the lowest activity takes place between June and August. Once again, the Chilean Patagonia showed a different behavior because the highest activity is presented in May and August, and the lowest in September. The analysis and results presented here contribute to the knowledge of lightning activity in the region that has not been characterized before and can serve as a basis for future research to determine the behavior of this natural phenomenon.Fil: Montana, Johny. Universidad Tecnica Federico Santa Maria.; ChileFil: Rodriguez Morales, Carlos Augusto. Universidade do Sao Paulo. Instituto de Astronomia, Geofísica e Ciências Atmosféricas; BrasilFil: Nicora, Maria Gabriela. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Investigación y Desarrollo Estratégico para la Defensa. Ministerio de Defensa. Unidad de Investigación y Desarrollo Estratégico para la Defensa; Argentina. Ministerio de Defensa. Instituto de Investigaciones Científicas y Técnicas para la Defensa; ArgentinaFil: Rey Ardila, Jorge. Universidad Tecnica Federico Santa Maria.; ChileFil: Schurch, Roger. Universidad Tecnica Federico Santa Maria.; ChileFil: Aranguren, D.. Keraunos; Colombi

Partial Discharge Spectral Characterization in HF, VHF and UHF Bands Using Particle Swarm Optimization

The measurement of partial discharge (PD) signals in the radio frequency (RF) range has gained popularity among utilities and specialized monitoring companies in recent years. Unfortunately, in most of the occasions the data are hidden by noise and coupled interferences that hinder their interpretation and renders them useless especially in acquisition systems in the ultra high frequency (UHF) band where the signals of interest are weak. This paper is focused on a method that uses a selective spectral signal characterization to feature each signal, type of partial discharge or interferences/noise, with the power contained in the most representative frequency bands. The technique can be considered as a dimensionality reduction problem where all the energy information contained in the frequency components is condensed in a reduced number of UHF or high frequency (HF) and very high frequency (VHF) bands. In general, dimensionality reduction methods make the interpretation of results a difficult task because the inherent physical nature of the signal is lost in the process. The proposed selective spectral characterization is a preprocessing tool that facilitates further main processing. The starting point is a clustering of signals that could form the core of a PD monitoring system. Therefore, the dimensionality reduction technique should discover the best frequency bands to enhance the affinity between signals in the same cluster and the differences between signals in different clusters. This is done maximizing the minimum Mahalanobis distance between clusters using particle swarm optimization (PSO). The tool is tested with three sets of experimental signals to demonstrate its capabilities in separating noise and PDs with low signal-to-noise ratio and separating different types of partial discharges measured in the UHF and HF/VHF bands.The work done in this paper has been funded by the Spanish Government (MINECO) and the European Regional Development Fund (ERDF) under contract DPI2015-66478-C2-1-R (MINECO/FEDER, UE)