Radio-Frequency Localization of Multiple Partial Discharges Sources with Two Receivers

Department of Electrical Engineering, Universidad Carlos III de Madrid, Avda, Universidad, 30, Leganés, 28911 Madrid, Spain
*Author to whom correspondence should be addressed.
Robles, G.; Fresno, J.M.; Martínez-Tarifa, J.M. Radio-Frequency Localization of Multiple Partial Discharges Sources with Two Receivers. Sensors 2018, 18, 1410.
  • 2017 Impact Factor: 2.475
  • 16/62 (Q2) in ‘Instruments & Instrumentation’
  • Journal Impact Factor Percentile: 74.59

This paper is Open Access and can be downloaded here.

https://doi.org/10.3390/s18051410

http://www.mdpi.com/1424-8220/18/5/1410

Abstract—Spatial localization of emitting sources is especially interesting in different fields of application. The focus of an earthquake, the determination of cracks in solid structures, or the position of bones inside a body are some examples of the use of multilateration techniques applied to acoustic and vibratory signals. Radar, GPS and wireless sensors networks location are based on radiofrequency emissions and the techniques are the same as in the case of acoustic emissions. This paper is focused on the determination of the position of sources of partial discharges in electrical insulation for maintenance based on the condition of the electrical equipment. The use of this phenomenon is a mere example of the capabilities of the proposed method but it is very representative because the emission can be electromagnetic in the VHF and UHF ranges or acoustic. This paper presents a method to locate more than one source in space with only two receivers, one of them in a fixed position and the other describing a circumference around the first one. The signals arriving from the different sources to the antennas are first separated using a classification technique based on their spectral components. Then, the individualized time differences of arrival (TDOA) from the sources collected at different angles describe a function, angle versus TDOA, that has all the geometric information needed to locate the source. The paper will show how to derive these functions for any source analytically with the position of the source as unknown parameters. Then, it will be demonstrated that it is possible to fit the curve with experimental measurements of the TDOA to obtain the parameters of the position of each source. Finally, the technique is extended to the localization of the emitter in three dimensions.

Partial discharge spectral characterization in HF, VHF and UHF bands using particle swarm optimization

1Department of Electrical Engineering, Universidad Carlos III de Madrid, Leganés, 28911 Madrid, Spain
2Department of Electrical Engineering, Universidad Técnica Federico Santa María, 8940000 Santiago de Chile, Chile
3Department of Signal Processing and Communications, Universidad Carlos III de Madrid, Leganés, 28911 Madrid, Spain
*Author to whom correspondence should be addressed.

Robles, G.; Fresno, J.M.; Martínez-Tarifa, J.M.; Ardila-Rey, J.A.; Parrado-Hernández, E. Partial Discharge Spectral Characterization in HF, VHF and UHF Bands Using Particle Swarm Optimization. Sensors 2018, 18, 746.

  • 2017 Impact Factor: 2.475
  • 16/62 (Q2) in ‘Instruments & Instrumentation’
  • Journal Impact Factor Percentile: 74.59

This paper is Open Access and can be downloaded here.

https://doi.org/10.3390/s18030746

http://www.mdpi.com/1424-8220/18/3/746

Abstract— 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 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 HF/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 partial discharge monitoring system. Therefore, the dimensionality reduction technique should find out 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 partial discharges with low signal-to-noise ratio and separating different types of partial discharges measured in the UHF and HF/VHF bands.

Survey on the Performance of Source Localization Algorithms

1 Department of Electrical Engineering, Universidad Carlos III de Madrid, Avda. Universidad, 30, Leganés, 28911 Madrid, Spain
2 Department of Electronic and Electrical Engineering, University of Strathclyde, 204 George Street, Glasgow G1 1XW, UK

Fresno, J.M.; Robles, G.; Martínez-Tarifa, J.M.; Stewart, B.G. Survey on the Performance of Source Localization Algorithms. Sensors 2017, 17(11), 2666.

  • 2017 Impact Factor: 2.475
  • 16/62 (Q2) in ‘Instruments & Instrumentation’
  • Journal Impact Factor Percentile: 74.59

This paper is Open Access and can be downloaded here.

http://dx.doi.org/10.3390/s17112666

http://www.mdpi.com/1424-8220/17/11/2666

Abstract— The localization of emitters using an array of sensors or antennas is a prevalent issue approached in several applications. There exist different techniques for source localization, which can be classified into multilateration, received signal strength (RSS) and proximity methods. The performance of multilateration techniques relies on measured time variables: the time of flight (ToF) of the emission from the emitter to the sensor, the time differences of arrival (TDoA) of the emission between sensors and the pseudo-time of flight (pToF) of the emission to the sensors. The multilateration algorithms presented and compared in this paper can be classified as iterative and non-iterative methods. Both standard least squares (SLS) and hyperbolic least squares (HLS) are iterative and based on the Newton–Raphson technique to solve the non-linear equation system. The metaheuristic technique particle swarm optimization (PSO) used for source localisation is also studied. This optimization technique estimates the source position as the optimum of an objective function based on HLS and is also iterative in nature. Three non-iterative algorithms, namely the hyperbolic positioning algorithms (HPA), the maximum likelihood estimator (MLE) and Bancroft algorithm, are also presented. A non-iterative combined algorithm, MLE-HLS, based on MLE and HLS, is further proposed in this paper. The performance of all algorithms is analysed and compared in terms of accuracy in the localization of the position of the emitter and in terms of computational time. The analysis is also undertaken with three different sensor layouts since the positions of the sensors affect the localization; several source positions are also evaluated to make the comparison more robust. The analysis is carried out using theoretical time differences, as well as including errors due to the effect of digital sampling of the time variables. It is shown that the most balanced algorithm, yielding better results than the other algorithms in terms of accuracy and short computational time, is the combined MLE-HLS algorithm.

Detection of Partial Discharge Sources Using UHF Sensors and Blind Signal Separation

This paper is open access and can be dowloaded here.

Boya, C.; Robles, G.; Parrado-Hernández, E.; Ruiz-Llata, M. Detection of Partial Discharge Sources Using UHF Sensors and Blind Signal Separation. Sensors 2017, 17(11), 2625.

  • 2017 Impact Factor: 2.475
  • 16/62 (Q2) in ‘Instruments & Instrumentation’
  • Journal Impact Factor Percentile: 74.59

http://dx.doi.org/10.3390/s17112625

http://www.mdpi.com/1424-8220/17/11/2625

Abstract— The measurement of the emitted electromagnetic energy in the UHF region of the spectrum allows the detection of partial discharges and, thus, the on-line monitoring of the condition of the insulation of electrical equipment. Unfortunately, determining the affected asset is difficult when there are several simultaneous insulation defects. This paper proposes the use of an independent component analysis (ICA) algorithm to separate the signals coming from different partial discharge (PD) sources. The performance of the algorithm has been tested using UHF signals generated by test objects. The results are validated by two automatic classification techniques: support vector machines and similarity with class mean. Both methods corroborate the suitability of the algorithm to separate the signals emitted by each PD source even when they are generated by the same type of insulation defect.
Keywords— blind source separation; electric insulation; partial discharges; UHF detection

Antenna Deployment for the Localization of Partial Discharges in Open-Air Substations

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Open access: http://dx.doi.org/10.3390/s16040541

G. Robles, J.M. Fresno, M. Sánchez-Fernández, J.M. Martínez-Tarifa, Antenna Deployment for the Localization of Partial Discharges in Open-Air Substations. Sensors, 2016, vol. 16, no 4, p. 541.

  • 2016 Impact Factor: 2.677
  • 10/58 (Q1) in ‘Instruments & Instrumentation’
  • Journal Impact Factor Percentile: 83.62

Abstract – Partial discharges are ionization processes inside or on the surface of dielectrics that can unveil insulation problems in electrical equipment. The charge accumulated is released under certain environmental and voltage conditions attacking the insulation both physically and chemically. The~final consequence of a continuous occurrence of these events is the breakdown of the dielectric. The electron avalanche provokes a derivative of the electric field  with respect to time, creating an~electromagnetic impulse that can be detected with antennas. The localization of the source helps in the identification of the piece of equipment that has to be decommissioned. This can be done by deploying antennas and calculating the time difference of arrival (TDOA) of the electromagnetic~pulses. However, small errors in this parameter can lead to great displacements of the calculated position of the source. Usually, four antennas are used to find the source but the array  geometry has to be correctly deployed to have minimal errors in the localization. This paper demonstrates, by  an~analysis based on simulation and  also experimentally, that the most common layouts are not always the best options and proposes a simple antenna layout to reduce the systematic error in the TDOA calculation due to the positions of the antennas in the array.

Keywords – antennas; radio-frequency localization; partial discharges; particle swarm optimization.

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

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Open Access: http://dx.doi.org/10.3390/s150509882

G. Robles, J.M. Fresno, J.M. Martínez-Tarifa Separation of radio-frequency sources and localization of partial discharges in noisy environments. Sensors 2015, 15(5), 9882-9898 (ISSN 1424-8220).

  • 2015 Impact Factor: 2.033
  • 12/56 (Q1) in ‘Instruments & Instrumentation’
  • Journal Impact Factor Percentile: 79.46

Abstract – 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.

Keywords – Partial Discharges; Spectral Power; Condition Monitoring; RF Measurements; RF Localization; Particle Swarm Optimization.

Development of a moisture-in-solid-insulation sensor for power transformers

22

Open Access: http://dx.doi.org/10.3390/s150203610

B. García, D. García, G. Robles Development of a moisture-in-solid-insulation sensor for power transformers. Sensors 2015, 15(2), 3610-3624 (ISSN 1424-8220).

  • 2015 Impact Factor: 2.033
  • 12/56 (Q1) in ‘Instruments & Instrumentation’
  • Journal Impact Factor Percentile: 79.46

Abstract – Moisture is an important variable that must be kept under control to guarantee a safe operation of power transformers. Because of the hydrophilic character of cellulose, water mainly remains in the solid insulation while just a few parts per million are dissolved in oil. The distribution of moisture between paper and oil is not static but varies depending on the insulation temperature and thus, water migration processes take place continuously during transformers operation. In this work, a sensor is presented that allows the determination of the moisture content of the transformer solid insulation in steady state and during moisture migration processes. The main objective of the design is that the electrodes of the sensor should not obstruct the movement of water from the solid insulation to the oil, so the proposed prototype uses a metallic-mesh electrode to do the measurements. The measurement setup is based on the characterization of the insulation dielectric response by means of the Frequency Dielectric Spectroscopy (FDS) method. The sensitivity of the proposed sensor has been tested on samples with a moisture content within 1 to 5% demonstrating a good sensitivity and repeatability of the measurements.

Keywords – Dielectric response, Power transformer; Mositure; Solid insulation; Moisture sensor; FDS; Moisture monitoring.

Inductive sensor performance in partial discharges and noise separation by means of spectral power ratios

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Open Access: http://www.mdpi.com/1424-8220/14/2/3408

http://dx.doi.org/10.3390/s140203408

J.A. Ardila-Rey, M.V. Rojas-Moreno, J.M. Martínez-Tarifa, G. Robles Inductive sensor performance in partial discharges and noise separation by means of spectral power ratios. Sensors 2014, 14(2), 3408-3427 (ISSN 1424-8220).

  • 2014 Impact Factor: 2.245
  • 10/56 (Q1) in ‘Instruments & Instrumentation’
  • Journal Impact Factor Percentile: 83.04

Abstract – Partial discharge (PD) detection is a standardized technique to qualify electrical insulation in machines and power cables. Several techniques that analyze the waveform of the pulses have been proposed to discriminate noise from PD activity. Among them, spectral power ratio representation shows great flexibility in the separation of the sources of PD. Mapping spectral power ratios in two-dimensional plots leads to clusters of points which group pulses with similar characteristics. The position in the map depends on the nature of the partial discharge, the setup and the frequency response of the sensors. If these clusters are clearly separated, the subsequent task of identifying the source of the discharge is straightforward so the distance between clusters can be a figure of merit to suggest the best option for PD recognition. In this paper, two inductive sensors with different frequency responses to pulsed signals, a high frequency current transformer and an inductive loop sensor, are analyzed to test their performance in detecting and separating the sources of partial discharges.

Keywords – partial discharges; noise separation; spectral power ratios; high frequency current transformers; inductive sensors.