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

Partial discharges and noise separation using spectral power ratios and genetic algorithms

31

J.M. Fresno, J.A. Ardila-Rey, J.M. Martínez-Tarifa, G. Robles, Partial discharges and noise separation using spectral power ratios and genetic algorithms. IEEE Transactions on Dielectrics and Electrical Insulation Vol. 24, Issue 1, pp. 31-38, January 2017

  • 2017 Impact Factor: 1.774
  • 73/146 (Q2) in ‘Physics, Applied’
  • Journal Impact Factor Percentile: 50.342
Abstract – Accurate measurements of partial discharge (PD) activity is essential for the application of this technique to condition-based monitoring. Noise and PD source characterization is necessary to fulfil that goal, since the interpretation of classical phase-resolved partial discharge (PRPD) patterns is usually complex for the measurements done in field. A successful pulse source separation prior to the identification seems to be the best option. The authors proposed in a previous work a method based on spectral power ratios (PR) to separate pulse sources with quite good experimental results. This technique calculates the spectral power in two frequency bands to obtain two parameters which, represented in a 2-dimensional map (PR map), characterize each pulse source by a cluster of points. The main difficulty of this technique is the choice of the appropriate frequency intervals that give a good separation of clusters, which sometimes can be cumbersome by manual means. Thus, this paper presents an unsupervised technique to select the two frequency intervals that gives the best separation among several clusters. This will give a great support for the system user to separate PD and noise sources in real measurements. The authors used genetic algorithms (GAs) to select these frequencies, with good results in several real experiments.
Keywords – Partial discharges, genetic algorithms, electrical insulation, power ratio maps, clustering techniques, spectral power, statistical dispersion.

Study on the self-integration of a Rogowski coil used in the measurement of partial discharges pulses

33

http://dx.doi.org/10.1007/s00202-016-0456-4

M.V. Rojas-Moreno, G. Robles, R. Albarracín, J.A. Ardila-Rey, J.M. Martínez-Tarifa, Study on the self-integration of a Rogowski coil used in the measurement of partial discharges pulses. Electrical Engineering. , Volume 99, Issue 3, pp 817–826.

  • 2017 Impact Factor: 1.269
  • 181/260 (Q3) in ‘Engineering, Electrical & Electronic’
  • Journal Impact Factor Percentile: 30.577

Abstract – The maintenance of high-voltage power systems requires the determination of the amplitude and waveforms of fast current pulses that commonly occur in electric equipment. These high-frequency pulses may arise from different sources such as lightning strikes, electrical arcs and post-arc phenomena, switching operations of circuit breakers and gas-insulated switchgears, electromagnetic pulses and partial discharges. This paper is a step forward in the modelling of the Rogowski coils, which are commonly used to measure these pulses. This study was performed by means of a simplified model based on lumped electrical parameters. The model was simulated in Simulink and validated by measuring Partial Discharges (PD) in two different electrical insulation systems. The validation corroborates that the electrical model can be used to study the time and frequency responses of Rogowski coils with different number of turns and dimensions to obtain a configuration that fits the needs of the designer concerning the type of pulses that she/he wishes to measure.

Keywords – Rogowski coil, High-frequency pulses, Partial discharges, self-integration.

Ultrasonic bone localization algorithm based on time-series cumulative kurtosis

32

http://dx.doi.org/10.1016/j.isatra.2016.09.012

G. Robles, J.M. Fresno, R. Giannetti, Ultrasonic bone localization algorithm based on time-series cumulative kurtosis. ISA Transactions, Vol 66. January 2017, pp 469-475

  • 2017 Impact Factor: 3.370
  • 6/61 (Q1) in ‘Instruments & Instrumentation’
  • Journal Impact Factor Percentile: 90.984

Abstract – The design and optimization of protective equipment and devices such as exoskeletons and prosthetics have the potential to be enhanced by the ability of accurately measure the positions of the bones during movement. Existing technologies allow a quite precise measurement of motion — mainly by using coordinate video-cameras and skin-mounted markers — but fail in directly measuring the bone position. Alternative approaches, as fluoroscopy, are too invasive and not usable during extended lapses of time, either for cost or radiation exposure. An approach to solve the problem is to combine the skin-glued markers with ultrasound technology in order to obtain the bone position by measuring at the same time the marker coordinates in 3D space and the depth of the echo from the bone. Given the complex structure of the bones and the tissues, the echoes from the ultrasound transducer show a quite complex structure. To reach a good accuracy in determining the depth of the bones, it is of paramount importance the ability to measure the time-of-flight (TOF) of the pulse with a high level of confidence. In this paper, the performance of several methods for determining the TOF of the ultrasound pulse has been evaluated when they are applied to the problem of measuring the bone depth. Experiments have been made using both simple setups used for calibration purposes and in real human tissues to test the performance of the algorithms. The results show that the method used to process the data to evaluate the time-of-flight of the echo signal can significantly affect the value of the depth measurement, especially in the cases when the verticality of the sensor with respect to the surface causing the main echo cannot be guaranteed. Finally, after testing several methods and processing algorithms for both accuracy and repeatability, the cumulative kurtosis algorithm was found to be themost appropriate in the case of measuring bone depths in-vivo with ultrasound sensors at frequencies around 5 MHz.

Keywords – ultrasound, time of flight, biomedical transducers, ultrasonic transducers, localization.

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

30.png

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.

Multiple partial discharge source discrimination with multiclass support vector machines

29

http://dx.doi.org/10.1016/j.eswa.2016.02.014

G. Robles, E. Parrado-Hernández, J.A. Ardila-Rey, J.M. Martínez-Tarifa  Multiple partial discharge source discrimination with multiclass support vector machines. Expert Systems with Applications, vol 55, pp. 417-428, August 2016.

  • 2016 Impact Factor: 3.928
  • 3/83 (Q1) in ‘Operations Research & Management Science’
  • Journal Impact Factor Percentile: 96.988

Abstract – 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.

Keywords – Support Vector Machine, Partial Discharges, Electric Maintenance, Machine Learning, Condition Monitoring, Risk Assessment.

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

27

http://dx.doi.org/10.1016/j.isatra.2015.04.006

R. Albarracín, G. Robles, J.M. Martínez-Tarifa J.A. Ardila-Rey Separation of sources in radiofrequency measurements of partial discharges using time-power ratios maps. ISA Transactions, vol 58, pp. 389-397, 2015 (ISSN 0019-0578).

  • 2015 Impact Factor: 2.600
  • 6/56 (Q1) in ‘Instruments & Instrumentation’
  • Journal Impact Factor Percentile: 90.179

Abstract – 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 otherevents, quite possibly from PD activity.

Keywords – Partial discharges, dielectric materials, condition monitoring, RF measurements, VHF and UHF measurements, Vivaldi antennas, spectral power.

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

26.png

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.

Location of partial discharges sources by means of Blind Source Separation of UHF signals

25

https://doi.org/10.1109/TDEI.2015.004482

Boya, M.V. Rojas-Moreno, M. Ruiz-Llata, G. Robles Location of partial discharges sources by means of Blind Source Separation of UHF signals. IEEE Transactions on Dielectrics and Electrical Insulation Vol. 22, Issue 4, pp. 2302-2310, August 2015.

  • 2015 Impact Factor: 1.306
  • 124/257 (Q2) in ‘Engineering, Electrical & Electronic’
  • Journal Impact Factor Percentile: 51.946

Abstract – Partial discharges (PD) detection is a widely extended technique for the diagnosis of electrical equipment. Ultra-high frequency (UHF) detection techniques appear as the best choice if the goal is to detect PD online and to locate devices with insulation problems in substations and overhead lines. The location of PD is based on the determination of the difference of the time of arrival of electromagnetic pulses radiated by a source of PD to an array of antennas distributed around the monitored area. However, when measuring electromagnetic pulses radiated by PD activity many interfering signals, such as those coming from television (TV), global positioning system (GPS), wireless communication signals and others coming from electrical equipment distort the waveform detected by the sensors. Under these circumstances, the application of traditional techniques to estimate the time differences may fail. In this paper, the use Blind Source Separation (BSS) techniques applied to pairs of UHF sensors is proposed to extract the information of the difference of the time of arrival of the electromagnetic pulses radiated by a source of PD. The paper is focused on the application of the algorithm and the description of an experimental setup for controlled generation and detection of PD to verify the performance of the proposed technique.

Keywords – Condition monitoring, impulsive noise, partial discharges, UHF detection, blind source separation, wavelet transform.

Automatic selection of frequency bands for the power ratios separation technique in partial discharge measurements: Part II

24

https://doi.org/10.1109/TDEI.2015.004821

J.A. Ardila-Rey, J.M. Martínez-Tarifa, G. Robles Automatic selection of frequency bands for the power ratios separation technique in partial discharge measurements: Part II, PD source recognition and applications. IEEE Transactions on Dielectrics and Electrical Insulation Vol. 22, Issue 4, pp. 2284-2291, August 2015.

  • 2015 Impact Factor: 1.306
  • 124/257 (Q2) in ‘Engineering, Electrical & Electronic’
  • Journal Impact Factor Percentile: 51.946

Abstract – Partial discharge (PD) measurement is a widely extended technique for the diagnosis of electrical machines and power cables. Since PD and noise recognition is really important in industrial environments, the authors proposed the use of the spectral Power Ratios (PR) of the detected high-frequency (HF) signals, which show promising results. However, the frequency bands selected for their calculation have a clear influence on the position of the clusters in the classification maps. Following this research trend, a new algorithm for the automatic selection of the frequency bands which gives a proper separation between the clusters, has been proposed in part I of this paper. The first results presented in this paper revealed a good behavior of the technique for PD and noise separation in simple test objects. In this second part of the paper, more results from the application of this system to PD source recognition insimple test objects and also in real equipment are developed.

Keywords – partial discharges, electrical insulation, power ratio maps, clustering techniques, spectral power, statistical dispersion.