Separation of radio-frequency sources and localization of partial discharges in noisy environments

The detection of partial discharges (PD) can help in early-warning detection systems to protect critical assets in power systems. The radio-frequency emission of these events can be measured with antennas even when the equipment is in service which reduces dramatically the maintenance costs and favours the implementation of condition-based monitoring systems. The drawback of these type of measurements is the difficulty of having a reference signal to study the events in a classical phase-resolved partial discharge pattern (PRPD). Therefore, in open-air substations and overhead lines where interferences from radio and TV broadcasting and mobile communications are important sources of noise and other pulsed interferences from rectifiers or inverters can be present, it is difficult to identify whether there is partial discharges activity or not. This paper proposes a robust method to separate the events captured with the antennas, identify which of them are partial discharges and localize the piece of equipment that is having problems. The separation is done with power ratio (PR) maps based on the spectral characteristics of the signal and the identification of the type of event is done localizing the source with an array of four antennas. Several classical methods to calculate the time differences of arrival (TDOA) of the emission to the antennas have been tested, and the localization is done using particle swarm optimization (PSO) to minimize a distance function.Tests were done in the High-Voltage Research and Test Laboratory (LINEALT) at Universidad Carlos III de Madrid

Typical Internal Defects of Gas-Insulated Switchgear and Partial Discharge Characteristics

Gas-insulated switchgear (GIS) is a common electrical equipment, which uses sulfur hexafluoride (SF6) as insulating medium instead of traditional air. It has good reliability and flexibility. However, GIS may have internal defects and partial discharge (PD) is then induced. PD will cause great harm to GIS and power system. Therefore, it is of great importance to study the intrinsic characteristics and detection of PD for online monitoring. In this chapter, typical internal defects of GIS and the PD characteristics are discussed. Several detection methods are also presented in this chapter including electromagnetic method, chemical method, and optical method

A Study of the Detection of Defects in Ceramic Insulators Based on Radio Frequency Signatures.

The presence of defects in outdoor insulators ultimately results in the initiation of partial discharge (PD) activity. Because insulation failure and the consequent breakdown of power equipment can occur due to the cumulative adverse effects of partial discharges, it is important to detect PD activity in its early stages. Current techniques used in PD off-line analyses are not suitable for detecting defective insulators in the field. The work presented in this thesis involved the investigation of a number of cases of insulator defects, with the goal of developing an online RF-based PD technique for monitoring ceramic disc insulators that exhibit a variety of defects. The first three classes examined were an intentionally cracked ceramic insulator disc; a disc with a hole through the cap, which creates internal discharges; and a completely broken insulator disc. The fourth class involved an external corona noise using a point-to-plane setup. The defective discs were considered individually and were also incorporated into strings of 2, 3, and 4 insulators as a means of capturing the radiated RF signatures under external high voltage AC power. The captured RF pulses were processed in order to extract statistical, spectral, and wavelet packet based features. Feature reduction and selection is carried out and classification results pertaining to each feature-set type were obtained. To classify the discharges arising from different types of defects, an artificial neural network (ANN) algorithm was applied to the extracted features, and recognition rates of more than 90% were reported for each class. In addition, the position of the defective insulator within the string was varied and high defect classification results exceeding 90% were reported regardless of the position

Dielectric Properties, Partial Discharge Properties, and Dissolved Gas Analysis of Ricinnus Oils as Biodegradable Liquid Insulating Materials (Archive)

In this paper partial discharge (PD) characteristics of ricinnus oil and dissolved gas analysis (DGA) due to PD are investigated. The examination showed that the properties of ricinnus oil as a dielectric material such as breakdown voltage, dielectric constant, loss factor, neutralization number, and flash point complied with IEC standard. The I I-q-n pattern of PD and the change of the pattern with applied voltage were observed. Dissolved gas analyses due to PD in oil were also performed. The results were compared with ones in mineral oil which have been using as insulating materials in high voltage apparatus. The results showed that the PDIV of ricinnus oil is higher than one of mineral oil. Types and pattern of dissolved gas due to PD in ricinnus oil were similar with ones in mineral oil. The total combustible gas in ricinnus oil is a little bit higher than one in mineral oil. The measurement of the cumulative charge during 5 minutes showed that PD activity in ricinus oil is higher than one in mineral oil. The results showed that the gassing tendency due to PD of ricinus oil is good with small amount of combustible gase

Classification and localization of electromagnetic and ultrasonic pulsed emitters

Mención Internacional en el título de doctorThe localization of radiative sources is very important in many fields of work such as: sonar, radar and underwater radar, indoor localization in wireless networks, earthquake epicenter localization, defective assets localization in electrical facilities and so forth. In the process of locating radiative sources exist many issues which can provoke errors in the localization. The signals acquired may belong to different sources or they can be mixed with environmental noise, then, their separation before using localization algorithms is of great interest to be efficient and accurate in the computational process. Furthermore, the geometry and radiation characteristics of the receivers, the nature of the signal or their measuring process may cause deviations in the signal onset calculus and therefore the source localization could be displaced from the actual position. In this thesis, there are three kinds of algorithms to undertake three steps in the emitter localization: signal separation, onset and time delay estimation of the signals and source localization. For each step, in order to reduce the error in the localization, several algorithms are analyzed and compared in each application, to choose the most reliable. As the first step, to separate different kinds of signals is of interest to facilitate further processing. In this thesis, different optimization techniques are presented over the power ratio (PR) maps method. The PR uses a selective spectral signal characterization to extract the features of the analyzed signals. The technique identifies automatically the most representative frequency bands which report a great separation of the different kinds of signals in the PR map. After separating and selecting the signals, it is of interest to compare the algorithms to calculate the onset and time delay of the pulsed signals to know their performance because the time variables are inputs to the most common triangulation algorithms to locate radiative and ultrasonic sources. An overview of the algorithms used to estimate the time of flight (ToF) and time differences of arrival (TDoA) of pulsed signals is done in this thesis. In the comparison, there is also a new algorithm based on statics of high order, which is proposed in this thesis. The survey of their performance is done applied to muscle deep estimation, localization in one dimension (1D), and for the localization of emitters in three dimensions (3D). The results show how the presented algorithm yields great results. As the last step in the radiative source localization, the formulation and principle of work of both iterative and non-iterative triangulation algorithms are presented. A new algorithm is presented as a combination of two already existing improving their performance when working alone. All the algorithms, the proposed and the previous which already exist, are compared in terms of accuracy and computational time. The proposed algorithm reports good results in terms of accuracy and it is one of the fastest in computational time. Once the localization is achieved, it is of great interest to understand how the errors in the determination of the onset of the signals are propagated in the emitter localization. The triangulation algorithms estimate the radiative source position using time variables as inputs: ToF, TDoA or pseudo time of flight (pToF) and the receiver positions. The propagation of the errors in the time variables to the radiative source localization is done in two dimensions (2D) and 3D. New spherical diagrams have been created to represent the directions where the localization is more or less sensible to the errors. This study and their sphere diagrams are presented for several antenna layouts. Finally, how the errors in the positioning of the receivers are propagated to the emitter localization is analyzed. In this study, the effect in the propagation of both the relative distance from the receivers to the emitter and the direction between them has been characterized. The propagation of the error considering the direction is also represented in spherical diagrams. For a preferred direction identified in the spheres, the propagated error in the source localization has been quantified regarding both the source distance and the magnitude of the errors in the receivers positioning.Programa Oficial de Doctorado en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Andrea Cavallini.- Secretario: José Antonio García Souto.- Vocal: Iliana Portugués Peter

On-line measurement of partial discharges in high voltage rotating machines.

The on-line condition monitoring of rotating machines is given paramount importance, particularly in Oils and Gas industries where the financial implications of machine shutdown is very high. This project work was directed towards the on-line condition monitoring of high voltage rotating machines by detection of partial discharges (PD) which are indicative of stator insulation degradation. Partial discharge manifests itself in various forms which can be detected using various electrical and non-electrical techniques. The electrical method of detecting small current pulses generated by PD using a Rogowski coil as a sensor has been investigated in this work. Dowding & Mills, who are commercially involved in the condition monitoring of rotating machines, currently use a system called StatorMonotor® for PD detection. The research is intended to develop a new partial discharge detection system that will replace the existing system which is getting obsolete. A three phase partial discharge detection unit was specified, designed and developed that is capable of filtering, amplifying and digitising the discharge signals. The associated data acquisition software was developed using LabVIEW software that was capable of acquiring, displaying and storing the discharge signals. Additional software programs were devised to investigate the removal of external noise. A data compression algorithm was developed to store the discharge data in an efficient manner; also ensuring the backward compatibility to the existing analysis software. Tests were performed in laboratory and on machines on-site and the results are presented. Finally, the data acquisition (DAQ) cards that used the PCMCIA bus was replaced with new USB based DAQ cards with the software modified accordingly. The three phase data acquisition unit developed as a result of this project has produced encouraging results and will be implemented in an industrial environment to evaluate and benchmark its performance with the existing system. Most importantly, a hardware data acquisition platform for the detection of PD pulses has been established within the company which is easily maintainable and expandable to suit any future requirements

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

Study on the Application of an Ultra-High-Frequency Fractal Antenna to Partial Discharge Detection in Switchgears

The ultra-high-frequency (UHF) method is used to analyze the insulation condition of electric equipment by detecting the UHF electromagnetic (EM) waves excited by partial discharge (PD). As part of the UHF detection system, the UHF sensor determines the detection system performance in signal extraction and recognition. In this paper, a UHF antenna sensor with the fractal structure for PD detection in switchgears was designed by means of modeling, simulation and optimization. This sensor, with a flat-plate structure, had two resonance frequencies of 583 MHz and 732 MHz. In the laboratory, four kinds of insulation defect models were positioned in the testing switchgear for typical PD tests. The results show that the sensor could reproduce the electromagnetic waves well. Furthermore, to optimize the installation position of the inner sensor for achieving best detection performance, the precise simulation model of switchgear was developed to study the propagation characteristics of UHF signals in switchgear by finite-difference time-domain (FDTD) method. According to the results of simulation and verification test, the sensor should be positioned at the right side of bottom plate in the front cabinet. This research established the foundation for the further study on the application of UHF technique in switchgear PD online detection

Reflections on the Fukushima Daiichi Nuclear Accident: Toward Social-Scientific Literacy and Engineering Resilience

Nuclear Engineering; Environmental Science and Engineering; Social Sciences, genera