G. Robles, M. Shafiq and J. M. Martínez-Tarifa, “Multiple Partial Discharge Source Localization in Power Cables Through Power Spectral Separation and Time-Domain Reflectometry,” in IEEE Transactions on Instrumentation and Measurement. doi: 10.1109/TIM.2019.2896553
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Abstract— Insulated power cables are becoming increasingly popular in today’s developing distribution and transportion networks. However, due to aging, deterioration, and various operational and environmental stresses, insulation defects may appear and so the cable needs to be monitored in a timely manner to avoid unexpected failures. Many of these defects are responsible for partial discharge (PD) activity. The localization of the sources of these discharges is a highly decisive facet in the condition-based monitoring of power cables. The techniques for the localization of single-PD defects in insulated power cables are well presented in the current bibliography. However, when several simultaneous PD sources are active, the localization of the sources becomes quite complex. This paper develops an efficient technique for the separation and localization of multiple PD sources in a medium voltage cable. The experimental results are obtained with single-end-based measurements using a high-frequency current transformer in a laboratory environment. The data processing based on the spectral characteristics of the signals is carried out by using the power ratios technique in order to determine the presence of different types of PD. Once the signals are separated, the PD sources can be localized with an individualized analysis of each source through time-domain reflectometry. The proposed methodology can be very valuable to improve the location diagnostic capability of the condition-based monitoring solutions, especially for underground cables.
Keywords— Condition monitoring; partial discharges (PDs); particle swarm optimization (PSO); power cables; signal characterization; signal propagation; spectral power ratios (PRs); time-domain reflectometry (TDR).
M. Shafiq, K. Kauhaniemi, G. Robles, M. Isa, L. Kumpulainen, “Online condition monitoring of MV cable feeders using Rogowski coil sensors for PD measurements”, Electric Power Systems Research, Volume 167, February 2019, Pages 150-162, ISSN 0378-7796,
Abstract— Condition monitoring is a highly effective prognostic tool for incipient insulation degradation to avoid sudden failures of electrical components and to keep the power network in operation. Improved operational performance of the sensors and effective measurement techniques could enable the development of a robust monitoring system. This paper addresses two main aspects of condition monitoring: an enhanced design of an induction sensor that has the capability of measuring partial discharge (PD) signals emerging simultaneously from medium voltage cables and transformers, and an integrated monitoring system that enables the monitoring of a wider part of the cable feeder. Having described the conventional practices along with the authors’ own experiences and research on non-intrusive solutions, this paper proposes an optimum design of a Rogowski coil that can measure the PD signals from medium voltage cables, its accessories, and the distribution transformers. The proposed PD monitoring scheme is implemented using the directional sensitivity capability of Rogowski coils and a suitable sensor installation scheme that leads to the development of an integrated monitoring model for the components of a MV cable feeder. Furthermore, the paper presents forethought regarding huge amount of PD data from various sensors using a simplified and practical approach. In the perspective of today’s changing grid, the presented idea of integrated monitoring practices provide a concept towards automated condition monitoring.
Keywords—Condition monitoring; Rogowski coil; Dielectric insulation; Partial discharge; Medium voltage cable; Transformer.
M. Shafiq, K. Kauhaniemi, G. Robles, G. A. Hussain and L. Kumpulainen, “Partial discharge signal propagation in medium voltage branched cable feeder,” in IEEE Electrical Insulation Magazine, vol. 34, no. 6, pp. 18-29, November-December 2018.
Abstract— Rising global and regional electricity use is accelerating the need to upgrade networks. The adoption of sustainable ways of energy generation (renewables energy resources) is the top priority of today’s grid, and these resources are predominantly embedded within the distribution networks that are mostly connected by medium voltage (MV) cables. Driven by urbanization trends, negative land value impacts, public safety, environmental aesthetics, and network reliability, the resistance to overhead lines in distribution networks is gradually increasing in many countries. Either choosing the proactive path considering the operational superiority of underground cables compared with overhead lines or following the ongoing legislative policies, the use of cables has been increasing rapidly over the past 30 years. This trend is likely to accelerate.
Keywords— Power cables; Partial discharges; Power cable insulation; Cable shielding; Current measurement; Voltage measurement; Medium voltage; Condition monitoring; Cables; Branch; Joint; Diagnostic; Sensor},
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.
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.
1Department of Signal Processing and Communications, Universidad Carlos III de Madrid, Avda. Universidad, 30, Leganés, Madrid 28911, Spain
2Department of Electrical Engineering, Universidad Carlos III de Madrid, Avda. Universidad, 30, Leganés, Madrid 28911, Spain; Emails: firstname.lastname@example.org
3Department of Electrical Engineering, Federico Santa María Technical University, 8940000 Santiago de Chile, Chile
Parrado-Hernández, E.; Robles, G.; Ardila-Rey, J.A.; Martínez-Tarifa, J.M. Robust Condition Assessment of Electrical Equipment with One Class Support Vector Machines Based on the Measurement of Partial Discharges. Energies2018, 11, 486.
2017 Impact Factor: 2.676
48/97 (Q2) in ‘Energy & Fuels’
Journal Impact Factor Percentile: 51.031
This paper is Open Access and can be downloaded here.
Abstract— 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.
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. Sensors2017, 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.
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.
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
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.
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.