Scale Modular Test Platform for the Characterization of PD Measuring Systems Using HFCT Sensors

Today, online partial discharge (PD) measurements are common practice to assess the condition status of dielectrics in high-voltage (HV) electrical grids. However, when online PD measurements are carried out in electrical facilities, several disadvantages must be considered. Among the most important are high levels of changing electrical noise and interferences, signal phase couplings (cross-talk phenomena), and the simultaneous presence of various defects and difficulties in localizing and identifying them. In the last few decades, various PD-measuring systems have been developed to deal with these inconveniences and try to achieve the adequate supervision of electrical installations. In the state of the art, one of the main problems that electrical companies and technology developers face is the difficulty in characterizing the measuring system's functionalities in laboratory setups or in real-world facilities, where simulated or real defects must be detected. This is mainly due to the complexity and costs that the laboratory setups entail and the fact that the facilities are permanently in service. Furthermore, in the latter scenario, owners cannot assign facilities to carry out the tests, which could cause irreversible damage. Additionally, with the aforementioned installations, a comparison of results over time in various locations is not possible, and noise conditions cannot be controlled to perform the characterizations in a correct way. To deal with the problems indicated, in this article, an affordable scale modular test platform that simulates an HV installation is presented, where real on-site PD measuring conditions are simulated and controlled. In this first development, the HV installation comprises a cable system connected at both ends to a gas-insulated substation (GIS). As the most common acquisition technique in online applications is based on the placement of high-frequency current transformer (HFCT) sensors in the grounding cables of facilities, the test platform is mainly adapted to carry out measurements with this type of sensor. The designed and developed test platform was validated to assess its features and the degree of convergence with a real installation, showing the convenience of its use for the appropriate and standardized characterization of PD-measuring systems.

Characterization of Partial Discharges in Dielectric Oils Using High-Resolution CMOS Image Sensor and Convolutional Neural Networks

In this work, an exhaustive analysis of the partial discharges that originate in the bubbles present in dielectric mineral oils is carried out. To achieve this, a low-cost, high-resolution CMOS image sensor is used. Partial discharge measurements using that image sensor are validated by a standard electrical detection system that uses a discharge capacitor. In order to accurately identify the images corresponding to partial discharges, a convolutional neural network is trained using a large set of images captured by the image sensor. An image classification model is also developed using deep learning with a convolutional network based on a TensorFlow and Keras model. The classification results of the experiments show that the accuracy achieved by our model is around 95% on the validation set and 82% on the test set. As a result of this work, a non-destructive diagnosis method has been developed that is based on the use of an image sensor and the design of a convolutional neural network. This approach allows us to obtain information about the state of mineral oils before breakdown occurs, providing a valuable tool for the evaluation and maintenance of these dielectric oils.

The Design, Fabrication, and Evaluation of a Phase-Resolved Partial Discharge Sensor Embedded in a MV-Class Bushing

This paper proposes a novel phase-resolved partial discharge (PRPD) sensor embedded in a MV-class bushing for high-accuracy insulation analysis. The design, fabrication, and evaluation of a PRPD sensor embedded in a MV-class bushing aimed to achieve the detection of partial discharge (PD) pulses that are phase-synchronized with the applied primary HV signal. A prototype PRPD sensor was composed of a flexible printed circuit board (PCB) with dual-sensing electrodes, utilizing a capacitive voltage divider (CVD) for voltage measurement, the D-dot principle for PD detection, and a signal transducer with passive elements. A PD simulator was prepared to emulate typical PD defects, i.e., a metal protrusion. The voltage measurement precision of the prototype PRPD sensor was satisfied with the accuracy class of 0.2 specified in IEC 61869-11, as the maximum corrected voltage error ratios and corrected phase errors in 80%, 100%, and 120% of the rated voltage (13.2 kilovolts (kV)) were less than 0.2% and 10 min, respectively. In addition, the prototype PRPD sensor had good linearity and high sensitivity for PD detection compared with a conventional electrical detection method. According to performance evaluation tests, the prototype PRPD sensor embedded in the MV-class bushing can measure PRPD patterns phase-synchronized with the primary voltage without any additional synchronization equipment or system. Therefore, the prototype PRPD sensor holds potential as a substitute for conventional commercial PD sensors. Consequently, this advancement could lead to the enhancement of power system monitoring and maintenance, contributing to the digitalization and minimization of power apparatus.

Metrological Qualification of PD Analysers for Insulation Diagnosis of HVDC and HVAC Grids

On-site partial discharge (PD) measurements have turned out to be a very efficient technique for determining the insulation condition in high-voltage electrical grids (AIS, cable systems, GIS, HVDC converters, etc.); however, there is not any standardised procedure for determining the performances of PD measuring systems. In on-line and on-site PD measurements, high-frequency current transformers (HFCTs) are commonly used as sensors as they allow for monitoring over long distances in high-voltage installations. To ensure the required performances, a metrological qualification of the PD analysers by applying an evaluation procedure is necessary. A novel evaluation procedure was established to specify the quantities to be measured (electrical charge and PD repetition rate) and to describe the evaluation tests considering the measured influence parameters: noise, charge amplitude, pulse width and time interval between consecutive pulses. This procedure was applied to different types of PD analysers used for off-line measurements, sporadic on-line measurements and continuous PD monitoring. The procedure was validated in a round-robin test involving two metrological institutes (RISE from Sweden and FFII from Spain) and three universities (TUDelft from the Netherlands, TAU from Finland and UPM from Spain). With this round-robin test, the effectiveness of the proposed qualification procedure for discriminating between efficient and inappropriate PD analysers was demonstrated. Furthermore, it was shown that the PD charge quantity can be properly determined for on-line measurements and continuous monitoring by integrating the pulse signals acquired with HFCT sensors. In this case, these sensors must have a flat frequency spectrum in the range between several tens of kHz and at least two tens of MHz, where the frequency pulse content is more significant. The proposed qualification procedure can be useful for improving the future versions of the technical specification TS IEC 62478 and the standard IEC 60270.

Identification of Electrical Tree Aging State in Epoxy Resin Using Partial Discharge Waveforms Compared to Traditional Analysis

Electrical treeing is one of the main degradation mechanisms in high-voltage polymeric insulation. Epoxy resin is used as insulating material in power equipment such as rotating machines, power transformers, gas-insulated switchgears, and insulators, among others. Electrical trees grow under the effect of partial discharges (PDs) that progressively degrade the polymer until the tree crosses the bulk insulation, then causing the failure of power equipment and the outage of the energy supply. This work studies electrical trees in epoxy resin through different PD analysis techniques, evaluating and comparing their ability to identify tree bulk-insulation crossing, the precursor of failure. Two PD measurement systems were used simultaneously-one to capture the sequence of PD pulses and another to acquire PD pulse waveforms-and four PD analysis techniques were deployed. Phase-resolved PD (PRPD) and pulse sequence analysis (PSA) identified tree crossing; however, they were more sensible to the AC excitation voltage amplitude and frequency. Nonlinear time series analysis (NLTSA) characteristics were evaluated through the correlation dimension, showing a reduction from pre- to post-crossing, and thus representing a change to a less complex dynamical system. The PD pulse waveform parameters had the best performance; they could identify tree crossing in epoxy resin material independently of the applied AC voltage amplitude and frequency, making them more robust for a broader range of situations, and thus, they can be exploited as a diagnostic tool for the asset management of high-voltage polymeric insulation.

Locating Partial Discharges in Power Transformers with Convolutional Iterative Filtering

The most common source of transformer failure is in the insulation, and the most prevalent warning signal for insulation weakness is partial discharge (PD). Locating the positions of these partial discharges would help repair the transformer to prevent failures. This work investigates algorithms that could be deployed to locate the position of a PD event using data from ultra-high frequency (UHF) sensors inside the transformer. These algorithms typically proceed in two steps: first determining the signal arrival time, and then locating the position based on time differences. This paper reviews available methods for each task and then propose new algorithms: a convolutional iterative filter with thresholding (CIFT) to determine the signal arrival time and a reference table of travel times to resolve the source location. The effectiveness of these algorithms are tested with a set of laboratory-triggered PD events and two sets of simulated PD events inside transformers in production use. Tests show the new approach provides more accurate locations than the best-known data analysis algorithms, and the difference is particularly large, 3.7X, when the signal sources are far from sensors.

PCB-Based Planar Inductive Loops for Partial Discharges Detection in Power Cables

Partial discharge (PD) diagnosis tests, including detecting, locating, and identifying, are used to trace defects or faults and assess the degree of aging in order to monitor the insulation condition of medium- and high-voltage power cables. In this context, an experimental evaluation of three different printed circuit board (PCB)-based inductive sensor topologies, with spiral, non-spiral, and meander shapes, is performed. The aim is to assess their capabilities for PD detection along a transmission power cable. First, simulation and experimental characterization are carried out to determine the equivalent electrical circuit and the quality factor of the three sensors. PD activity was studied in the lab on a 10-m-long defective MVAC cable. The three PCB-based sensors were tested in three different positions: directly on the defective cable (P1), at a separation distance of 10 cm to 3 m (P2), and on the ground line (P3). For the three positions, all sensors' outputs present a damped sine wave signal with similar frequencies and durations. Experimental results showed that the best sensitivity was given by the non-spiral inductor, with a peak voltage of around 500 mV in P1, 428 mV in P2, and 45 mV in P3, while the meander sensor had the lowest values, which were approximately 80 mV in P1. The frequency spectrum bandwidth of all sensors was between 10 MHz and 45 MHz. The high sensitivity of the non-spiral inductor could be associated with its interesting properties in terms of quality factor and SFR, which are due to its very low resistivity. To benchmark the performance of the designed three-loop sensors, a comparison with a commercial high-frequency current transformer (HFCT) is also made.

Use of DSLR and Sonic Cameras to Detect and Locate High-Voltage Corona Discharges

Corona discharges are a concern in high-voltage applications. It is of utmost importance to detect and locate the discharges at an early stage using simple methods for this purpose. This paper evaluates and compares the sensitivity of two methods for detecting and locating the source of discharges, which are based on a digital single-lens reflex (DSLR) camera and a portable wideband sonic camera incorporating a matrix of micro-electromechanical systems (MEMS) microphones. Both cameras can generate an image of the studied area where the discharge sites are identified. The study is carried out with different electrode geometries, 50 Hz alternating current (ac) and positive and negative direct current (dc) supplies, and the effect of the distance between the sensor and the discharge sites is also analyzed. The presented results show that the sonic camera enables fast, simple, and sensitive detection and localization of the source of corona discharges even at a very early stage in daylight conditions, regardless of the type of power supply, that is, ac or positive/negative dc, and at distance of several meters from the discharge source.

Effect of Conducting, Semi-Conducting and Insulating Nanoparticles on AC Breakdown Voltage and Partial Discharge Activity of Synthetic Ester: A Statistical Analysis

This paper is aimed at studying the influence of conducting (Fe₃O₄), semi-conductive (ZnO), and insulating (ZrO₂, SiO₂, and Al₂O₃) nanoparticles (NPs) at various concentrations on the AC dielectric strength of MIDEL 7131 synthetic ester (SE) and partial discharges activity. First, a detailed and improved procedure for preparing nanofluids (NFs) in five concentrations ranging from 0.1 g/L to 0.5 g/L is presented, including high-speed agitation and ultrasonication. Then, the long-term stability is checked based on zeta potential analysis. After preparing and characterizing the NF samples, the following step is to measure their AC breakdown voltage (BDV). Due to the limitation of the high voltage supply (Baur system), the tests are performed according to IEC 60156 standard (2.5 mm gap distance) only with ZnO, ZrO₂, and SiO₂ NPs, and for comparison, tests are executed for all considered NPs with an electrodes gap of 2 mm. It is shown that the addition of Fe₃O₄ (20 nm), ZnO (25 nm), ZrO₂ (20–30 nm), SiO₂ (10–20 nm), Al₂O₃ (20–30 nm), and Al₂O₃ (50 nm) NPs improves the dielectric strength of synthetic ester upon an optimal concentration which gives the highest AC BDV. SiO₂ (10–20 nm) and Al₂O₃ (20–30 nm) manifest their best improvement at 0.3 g/L, while for the other NFs, the best improvement is observed at 0.4 g/L. Further, the Anderson–Darling goodness-of-fit test is performed on the experimental data to check their conformity with the Extreme value (EV), normal, and Weibull distributions; the normal and EV fit curves are plotted and used to evaluate the breakdown voltages at probabilities of 1%, 10%, and 50%. It is shown that the AC breakdown voltage outcomes for most investigated nanofluids mostly obey the three EV, normal, and Weibull distributions. Then, the best combinations (nature of NP and optimal concentration), namely Fe₃O₄ (20 nm, 0.4 g/L), Al₂O₃ (20–30 nm, 0.3 g/L), and Al₂O₃ (50 nm, 0.4 g/L) NPs, that highly enhance the AC BDV of SE are chosen for a partial discharge activity investigation and comparison with pure SE. It is shown that the addition of those NPs significantly reduces the activity of partial discharges compared to pure SE.

A Comparative Analysis Applied to the Partial Discharges Identification in Dry-Type Transformers by Hall and Acoustic Emission Sensors

Dry-type insulated transformers stand out for their higher applicability in substations, high-voltage instrumentation systems, and electrical installations. In this machine, the insulation system is constituted of dielectric materials such as epoxy resin and Nomex paper. Some critical issues in the operation of this equipment, such as overload, moisture, or heat, can induce a slow degradation of the physical-chemical properties of the dielectric materials, which can culminate in the total failure of the transformer. However, before the transformer's shutdown, it is common to detect discharge activity in the insulation system. Based on this issue, this work proposes an experimental and comparative analysis between acoustic emission and Hall-effect sensors, aiming at differentiating discharges in epoxy resin and Nomex paper, materials that constitute the insulation of the dry-type insulated transformers. Two signal processing techniques were studied: traditional frequency analysis and discrete wavelet transform. The objective is to develop signal processing techniques to differentiate each type of discharge since different discharges require different maintenance actions. The results obtained indicate that acoustic emission sensors and Hall sensors are promising in differentiating discharge in epoxy resin and Nomex paper. Furthermore, the pattern recognition tools presented by this work, which associated the wavelet levels energies and the energy of the full signals with the average band and the equivalent bandwidth, were effective to perform feature extraction of power transformer condition.

Designing the Insulation System for Motors in Electrified Aircraft: Optimization, Partial Discharge Issues and Use of Advanced Materials

Designing the insulation system for motors to be used in electrical aircraft requires efforts for maximizing specific power, but, in parallel, particular attention to achieve high reliability. As a major harm for organic insulation systems is partial discharges, design must be able to infer their likelihood during any operation stage and handle their potential inception. This paper proposes a new approach to carry out optimized or conservative insulation system designs which can provide the specified life at the chosen failure probability as well as look at the option of possibly reducing the risk of partial discharges to zero, at any altitude. Examples of designing turn, phase to ground and phase-to-phase insulation systems are reported, with cases where the design can be optimized and other cases where the optimized design does not pass IEC testing standard. Therefore, the limits for design feasibility as a function of the required level of safety and reliability are discussed, showing that the presence of partial discharges cannot be always avoided even through conservative design criteria. Therefore, the use of advanced, corona-resistant materials must be considered, in order to reach a higher, sometimes redundant, level of reliability.

Interpretable Detection of Partial Discharge in Power Lines with Deep Learning

Partial discharge (PD) is a common indication of faults in power systems, such as generators and cables. These PDs can eventually result in costly repairs and substantial power outages. PD detection traditionally relies on hand-crafted features and domain expertise to identify very specific pulses in the electrical current, and the performance declines in the presence of noise or of superposed pulses. In this paper, we propose a novel end-to-end framework based on convolutional neural networks. The framework has two contributions: First, it does not require any feature extraction and enables robust PD detection. Second, we devise the pulse activation map. It provides interpretability of the results for the domain experts with the identification of the pulses that led to the detection of the PDs. The performance is evaluated on a public dataset for the detection of damaged power lines. An ablation study demonstrates the benefits of each part of the proposed framework.

Modeling Capacitive Low-Power Voltage Transformer Behavior over Temperature and Frequency

The use of capacitive dividers (CDs) in medium-voltage (MV) networks started as simple voltage detectors and as rough voltage measurement instruments for protective purposes. Now, with the spread of intelligent electronic devices and renewable energy sources at the distribution level, capacitive dividers are designed and installed to perform accurate voltage measurements. Such a requirement is mandatory when the power quality has to be assessed. Therefore, CDs are currently being used either for power frequency or for high-frequency (supraharmonic- or partial-discharge-level) measurements. In this paper, typical off-the-shelf CDs are studied and modeled to understand how they behave in a wide range of frequencies and when the temperature varies. To this purpose, specific setups and tests have been developed and performed. From the results, it is clear that with proper modeling of CDs, it is possible to exploit them for measuring phenomena in a wide range of frequencies, including the effects due to temperature variations and self-resonances.

Experimental Results of Partial Discharge Localization in Bounded Domains

This work presents a novel diagnostic method to localize Partial Discharges (PDs) inside Medium Voltage (MV) and High Voltage (HV) equipment. The method is well suited for that equipment presenting a bounded domain with fixed Boundary Conditions (BCs) such as Oil-Filled Power Transformers (OFPTs), Air Insulated Switchgears (AISs), Gas Insulated Switchgears (GISs) or Gas Insulated Transmission Lines (GILs). It is based on Electromagnetic (EM) measurements which are used to reconstruct the EM field produced by the PD and localize the PD itself. The reconstruction and localization tasks are based on the eigenfunctions series expansion method which intrinsically accounts for the physical information of the propagation phenomenon. This fact makes the proposed diagnostic method very robust and accurate even in real and complex scenarios. The promising experimental results, obtained in two different test cases, confirmed the ability and powerfulness of the proposed PD localization method.

A Study on the Life Estimation and Cavity Surface Degradation Due to Partial Discharges in Spherical Cavities within Solid Polymeric Dielectrics Using a Simulation Based Approach

Partial Discharges (PD) in cavities are responsible for the greatest ageing rate in polymeric solid dielectrics due to chemical and physical deterioration mechanisms activated by the charge carriers, Ultra Violet (UV) radiation and local temperature rising during PDs activity. From the above, it is necessary to develop prognosis tools based on PDs measurements as diagnostic quantities in order to infer the time-to-breakdown, life, of solid dielectrics for improving the reliability of electrical assets, especially in current applications where they are subject to great electrical stresses in voltage frequency and magnitude. In this paper, the degradation in polymeric materials induced by PDs in cavities is briefly discussed from a phenomenological point of view, and then it is quantitatively evaluated using a simulation-based approach and a new proposed damage function. The time-to-breakdown calculated from simulations exhibits good agreement when compared with experimental measurements. Additionally, an analysis on the effect of the magnitude and frequency of the applied voltage on the degradation rate is also presented and the effectiveness of a degradation indicator, proposed by other authors, is evaluated under different stress conditions.

Forecast Model Update Based on a Real-Time Data Processing Lambda Architecture for Estimating Partial Discharges in Hydrogenerator

The prediction of partial discharges in hydrogenerators depends on data collected by sensors and prediction models based on artificial intelligence. However, forecasting models are trained with a set of historical data that is not automatically updated due to the high cost to collect sensors’ data and insufficient real-time data analysis. This article proposes a method to update the forecasting model, aiming to improve its accuracy. The method is based on a distributed data platform with the lambda architecture, which combines real-time and batch processing techniques. The results show that the proposed system enables real-time updates to be made to the forecasting model, allowing partial discharge forecasts to be improved with each update with increasing accuracy.

Insulation Failure Quantification Based on the Energy of Digital Images Using Low-Cost Imaging Sensors

Insulation faults in high-voltage applications often generate partial discharges (PDs) accompanied by corona activity, optical radiation mainly in the ultraviolet (UV) and visible bands. Recent developments in low-cost, small-size, and high-resolution visible imaging sensors, which are also partially sensitive to the UV spectral region, are gaining attention due to their many industrial applications. This paper proposes a method for early PD detection by using digital imaging sensors, which allows the severity of insulation faults to be assessed. The electrical power dissipated by the PDs is correlated to the energy of the acquired visible images, and thus, the severity of insulation faults is determined from the energy of the corona effect. A criterion to quantify the severity of insulation faults based on the energy of the corona images is proposed. To this end, the point-to-plane gap configuration is analyzed in a low-pressure chamber, where digital image photographs of the PDs are taken and evaluated under different pressure conditions ranging from 10 to 100 kPa, which cover the typical pressure range of aeronautic applications. The use of digital imaging sensors also allows an early detection, location and quantification of the PD activity, and thus assessing the severity of insulation faults to perform predictive maintenance tasks, while enabling the cost and complexity of the instrumentation to be reduced. Although the approach proposed in this paper has been applied to detect PDs in aeronautic applications, it can be applied to many other high-voltage applications susceptible of PD occurrence.

Surface potential decay and DC surface flashover characteristics of DBD plasma-treated silicone rubber

This paper presents an investigation on DC flashover voltage of silicone rubber (SiR) improved by dielectric barrier discharge (DBD) plasma treatments under ambient atmospheric pressure air. DC surface conductivity, surface potential decay (SPD), DC surface flashover voltage, partial discharge magnitude, Fourier transform infrared (FT-IR) spectrograms, and surface water contact angles are measured to analyze the influence of plasma treatment on the SiR. It is found that the speed of SPD increase consistently with the plasma modification time. The tendency of flashover voltage is increasing at first and then decreasing with the increased time of the plasma treatment. The magnitude and number of partial discharge pulses increase apparently with the increased plasma treatment time. Physicochemical measurements indicate that more amount of polar groups appear on surface after the DBD plasma modification, whereas the surface water contact angles decline continuously with the increased plasma modification time. However, the hydrophobicity is recovered after 30 d exposure in the air. It is demonstrated that the SPD is accelerated significantly due to the increased surface conductivities and density of shallow traps. However, the reduction of flashover voltage after longer time of the plasma treatment is attributed to the increased mobility of charge carriers on the sample surface.

Partial Discharges and Noise Discrimination Using Magnetic Antennas, the Cross Wavelet Transform and Support Vector Machines

This paper presents a wavelet analysis technique together with support vector machines (SVM) to discriminate partial discharges (PD) from external disturbances (electromagnetic noise) in a GIS PD measuring system based on magnetic antennas. The technique uses the Cross Wavelet Transform (XWT) to process the PD signals and the external disturbances coming from the magnetic antennas installed in the GIS compartments. The measurements were performed in a high voltage (HV) GIS containing a source of PD and common-mode external disturbances, where the external disturbances were created by an electric dipole radiator placed in the middle of the GIS. The PD were created by connecting a needle to the main conductor in one of the GIS compartments. The cross wavelet transform and its local relative phase were used for feature extraction from the PD and the external noise. The features extracted formed linearly separable clusters of PD and external disturbances. These clusters were automatically classified by a support vector machine (SVM) algorithm. The SVM presented an error rate of 0.33%, correctly classifying 99.66% of the signals. The technique is intended to reduce the PD false positive indications of the common-mode signals created by an electric dipole. The measuring system fundamentals, the XWT foundations, the features extraction, the data analysis, the classification algorithm, and the experimental results are presented.

Arc Tracking Control in Insulation Systems for Aeronautic Applications: Challenges, Opportunities, and Research Needs

Next generation aircrafts will use more electrical power to reduce weight, fuel consumption, system complexity and greenhouse gas emissions. However, new failure modes and challenges arise related to the required voltage increase and consequent rise of electrical stress on wiring insulation materials, thus increasing the risk of electrical arc appearance. This work performs a critical and comprehensive review concerning arc tracking effects in wiring insulation systems, underlying mechanisms, role of materials and possible mitigation strategies, with a special focus on aircraft applications. To this end an evaluation of the scientific and technological state of the art is carried out from the analysis of theses, research articles, technical reports, international standards and white papers. This review paper also reports the limitations of existing insulation materials, standard test methods and mitigation approaches, while identifying the research needs to comply with the future demands of the aircraft industry.

Separation of Partial Discharge Sources Measured in the High-Frequency Range with HFCT Sensors Using PRPD-teff Patterns

During the last two decades, on-line partial discharge (PD) measurements have been proven as a very efficient test to evaluate the insulation condition of high-voltage (HV) installations in service. Among the different PD-measuring techniques, the non-conventional electromagnetic methods are the most used due to their effectiveness and versatility. However, there are two main difficulties to overcome in on-line PD measurements when these methods are applied: the ambient electric noise and the simultaneous presence of various types of PD or pulse-shaped signals in the HV facility to be evaluated. A practical and effective method is presented to separate and identify PD sources acting simultaneously in HV systems under test. This method enables testers to carry out a first accurate diagnosis of the installation while performing the measurements in situ with non-invasive high-frequency current transformers (HFCT) used as sensors. The data acquisition in real-time reduces the time of postprocessing by an expert. This method was implemented in a Matlab application named PRPD-time tool, which consists of the analysis of the Phase-Resolved Partial Discharge (PRPD) pattern in combination with two types of interactive graphic representations. These graphical depictions are obtained including a feature parameter, effective time (t_eff), related to the duration of single measured pulses as a third axis incorporated in a classical PRPD representation, named the PRPD-t_eff pattern. The resulting interactive diagrams are complementary and allow the pulse source separation of pulses and clustering. The effectiveness of the proposed method and the developed Matlab application for separating PD sources is demonstrated with a practical laboratory experiment where various PD sources and pulse-type noise interferences were simultaneously measured.

Computer Simulation of Partial Discharges in Voids inside Epoxy Resins Using Three-Capacitance and Analytical Models

Epoxy resin is one of the most common polymers used as part of the insulation system in key electrical assets such as power transformers and hydrogenerators. Thus, it is necessary to know their main characteristics and to evaluate their condition when subjected to High Voltage (HV). A brief review of epoxy resins’ applications as insulating materials is made, their main characteristics as insulating media are given, the improvements with nano-fillers are summarized and the main electric properties required for Partial Discharges (PD) modelling are listed. In addition, the theoretical background and state-of-the-art of the three-capacitance and analytical models for simulating PD in solid dielectrics, such as epoxy resins, are reviewed in detail. Besides, their main advantages and disadvantages are presented, some critical arguments to the modelling procedure and assumptions are made and some improvements are proposed, taking into account conclusions made from other authors using models related to the PD development process. Finally, a case study was simulated using a modified three-capacitance model and the analytical model. The PD rate, q-φ-n diagrams and the minimum, mean and maximum PD electric charge are compared with measurements reported in the literature. Simulation results are in reasonable agreement with measured values. Capacitance models can be implemented in general purpose electric circuit simulation packages; however, its simulation is computationally expensive. Additional to this, although the modified three-capacitance model is not as accurate as finite elements or analytical models, results are also in agreement with real data.

A Classification Method for Select Defects in Power Transformers Based on the Acoustic Signals

Effective, accurate and adequately early detection of any potential defects in power transformers is still a challenging issue. As the acoustic method is known as one of the noninvasive and nondestructive testing methods, this paper proposes a new approach of the classification method for defect identification in power transformers based on the acoustic measurements. Typical application of acoustic emission (AE) method is the detection of partial discharges (PD); however, during PD measurements other defects may also be identified in the transformer. In this research, a database of various signal sources recorded during acoustic PD measurements in real-life power transformers over several years was gathered. Furthermore, all of the signals are divided into two groups (PD/other) and in the second step into eight classes of various defects. Based on these, selected classification models including machine learning algorithms were applied to training and validation. Energy patterns based on the discrete wavelet transform (DWT) were used as model inputs. As a result, the presented method allows one to identify with high accuracy, not only the selected kind of PD (1st step), but other kinds of faults or anomalies within the transformer being tested (2nd step). The proposed two-step classification method may be applied as a supplementary part of a technical condition assessment system or decision support system for management of power transformers.

Using a Scintillation Detector to Detect Partial Discharges

This article presents the possibility of using a scintillation detector to detect partial discharges (PD) and presents the results of multi-variant studies of high-energy ionizing generated by PD in air. Based on the achieved results, it was stated that despite a high sensitivity of the applied detector, the accompanying electromagnetic radiation from the visible light, UV, and high-energy ionizing radiation can be recorded by both spectroscopes and a system commonly used to detect radiation. It is also important that the scintillation detector identifies a specific location where dangerous electrical discharges and where the E-M radiation energy that accompanies PD are generated. This provides a quick and non-invasive way to detect damage in insulation at an early stage when it is not visible from the outside. In places where different radiation detectors are often used due to safety regulations, such as power plants or nuclear laboratories, it is also possible to use a scintillation detector to identify that the recorded radiation comes from damaged insulation and is not the result of a failure.

Experimental Characterization of Polymer Surfaces Subject to Corona Discharges in Controlled Atmospheres

Polymeric dielectrics are employed extensively in the power transmission industry, thanks to their excellent properties; however, under normal operating conditions these materials tend to degrade and fail. In this study, samples of low-density polyethylene, polypropylene, polymethyl methacrylate, and polytetrafluorethylene were subjected to corona discharges under nitrogen and air atmospheres. The discharges introduced structural modifications over the polymer surface. From a chemical perspective, the alterations are analogous among the non-fluorinated polymers (i.e., polyethylene (PE), polypropylene (PP), and polymethyl methacrylate (PMMA)). A simulation of the corona discharge allowed the identification of highly reactive species in the proximity of the surface. The results are consistent with the degradation of insulating polymers in high-voltage applications due to internal partial discharges that ultimately lead to the breakdown of the material.

Design and Application of a Metamaterial Superstrate on a Bio-Inspired Antenna for Partial Discharge Detection through Dielectric Windows

The adaptation of dielectric windows as metamaterial superstrate over a bio-inspired Printed Monopole Antenna (PMA) was evaluated in order to improve the detection sensitivity of Ultra High Frequency (UHF) sensors designed for Partial Discharge (PD) measurement. For this purpose, rectangular and circular Split Ring Resonators (SRR) structures were designed and evaluated aiming to achieve a metamaterial superstrate that improves the characteristics of the bio-inspired PMA as the gain, bandwidth, and radiation pattern. Measurements of the PMA with metamaterial superstrate were carried out in an anechoic chamber and compared to the simulations performed. The results show that the metamaterial superstrate insertion did not impact the original operating bandwidth, covering most of the characteristic frequency range of PD activity. Moreover, this insertion resulted in a mean gain enhancement of 0.7 dBi regarding the reference PMA, resulting in an antenna with better sensitivity for PD detection (mean gain of 3.61 dBi). The PMA-metamaterial set PD detection sensitivity was evaluated through laboratory tests with a point-to-plane PD generator setup and in field with measurements from a 230 kV current transformer. The developed PMA-metamaterial set was able to detect, successfully, the activity of PD for both tests, being classified as an optimized sensor for PD detection through dielectric windows.

Variability of the UHF Signals Generated by Partial Discharges in Mineral Oil

The paper presents the results of the analysis on the variability of the ultra-high frequency (UHF) signals generated by partial discharges (PD) under the long-term AC voltage. Surface PD (SD) are generated by model PD source (PDS) immersed in brand new mineral oil. Three scenarios are compared and investigated, where different solid dielectrics are applied: pressboard paper (PBP), polytetrafluoroethylene (PTFE) and glass-ceramic (GLS). The PDS is powered continuously by the AC voltage with its relative level of 1.3 of the inception voltage (U_i) within 168 h. UHF signals generated by the continuously occurred SD within 168 h are registered. Various indicators describing the variability of the UHF signals emitted by SD are assigned and analyzed in order to discover if there are any relevant trends presented. Furthermore, some long-term characteristics of the UHF signals emitted by the applied PDS are also announced. As a result, some relevant trends are discovered and related to the properties of the applied dielectric materials, thus the variability of the UHF signals emitted by SD is confirmed. The highest variability of the UHF signals is associated with PBP and the first 48 h after PD inception. Moreover achieved results may be potentially applied for modeling of the PD variability in time, which may be useful for works that concern the development of the UHF method.

A Novel Approach for Partial Discharge Measurements on GIS Using HFCT Sensors

This paper presents a novel measuring system for partial discharge (PD) measurements in Gas Insulated Systems (GIS) using high frequency current transformers (HFCT). The system is based on the measurement of the induced PD currents in the GIS enclosure. In opposition to the existing antenna technologies that measure the radiated energy in the very high frequency/ultra-high frequency (VHF/UHF) range, the proposed system measures the PD conducted currents in the high frequency (HF) range and below. The foundation of the measurements together with a detailed explanation of the sensor installed conveniently at the bolts of the GIS spacer are presented. An experimental study on the current distribution in the GIS enclosure is described to evaluate the impact of the sensor on the measurements. Laboratory experiments have been performed that show the suitability of this method to properly measure particle discharges caused by corona, surface and free moving particle discharges in SF₆. Discharges in the range of 1 to 4 pC have been properly measured. An analysis to evaluate the performance of the method is shown, in comparison to VHF/UHF antenna measurements. The potential benefits of this novel technique rely on the small attenuation of PD signals in the GIS components in the HF range and sample rate reductions. Finally, a discussion on the potential applicability of present cluster and charge calculation techniques to the proposed PD GIS measurement using HFCT is presented.

Novel Simulation Technique of Electromagnetic Wave Propagation in the Ultra High Frequency Range within Power Transformers

Diagnoses of power transformers by partial discharge (PD) measurement are effective to prevent dielectric failures of the apparatus. Ultra-high frequency (UHF) method has recently received attention due to its various advantages, such as the robustness against external noise and the capability of PD localization. However, electromagnetic (EM) waves radiated from PD tend to suffer attenuation before arriving at UHF sensors, because active part of the transformer disturbs the EM wave propagation. In some cases, that results in poor detection sensitivity. To understand propagation and attenuation characteristics of EM waves and to evaluate the detection sensitivity quantitatively, a computational approach to simulate the EM wave propagation is important. Although many previous researches have dealt with EM wave simulation for transformers, validations of those simulations by comparing with the experimental ones have seldom been reported. In this paper, cumulative energies, signal amplitudes and propagation times of EM waves were measured using a 630 kVA transformer. EM wave propagation was computed using the time-domain finite integration technique and the results were compared with the experimentally obtained ones. These simulation results showed good agreement with the experimental ones. The results can serve as guidelines to improve the efficiency of UHF PD detection and offer the possibility to achieve optimal placement of UHF sensors in power transformers.

Directional Sensitivity of a MEMS-Based Fiber-Optic Extrinsic Fabry⁻Perot Ultrasonic Sensor for Partial Discharge Detection

Extrinsic Fabry–Perot (FP) interferometric sensors are being intensively applied for partial discharge (PD) detection and localization. Previous research work has mainly focused on novel structures and materials to improve the sensitivity and linear response of these sensors. However, the directional response behavior of an FP ultrasonic sensor is also of particular importance in localizing the PD source, which is rarely considered. Here, the directional sensitivity of a microelectromechanical system (MEMS)-based FP ultrasonic sensor with a 5-μm-thick micromechanical vibrating diaphragm is experimentally investigated. Ultrasonic signals from a discharge source with varying incident angles and linear distances are measured and analyzed. The results show that the sensor has a 5.90 dB amplitude fluctuation over a ±60° incident range and an exciting capability to detect weak PD signals from 3 m away due to its high signal–noise ratio. The findings are expected to optimize the configuration of a sensor array and accurately localize the PD source.

Partial Discharge Ultrasound Detection Using the Sagnac Interferometer System

Partial discharge detection is crucial for electrical cable safety evaluation. The ultrasonic signals frequently generated in the partial discharge process contains important characteristic information. However, traditional ultrasonic transducers are easily subject to strong electromagnetic interference in environments with high voltages and strong magnetic fields. In order to overcome this problem, an optical fiber Sagnac interferometer system is proposed for partial discharge ultrasound detection. Optical fiber sensing and time-frequency analysis of the ultrasonic signals excited by the piezoelectric ultrasonic transducer is realized for the first time. The effective frequency band of the Sagnac interferometer system was up to 175 kHz with the help of a designed 10 kV partial discharge simulator device. Using the cumulative histogram method, the characteristic ultrasonic frequency band of the partial discharges was between 28.9 kHz and 57.6 kHz for this optical fiber partial discharge detection system. This new ultrasound sensor can be used as an ideal ultrasonic source for the intrinsically safe detection of partial discharges in an explosive environment.

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

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

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.

Diagnosis of Insulation Condition of MV Switchgears by Application of Different Partial Discharge Measuring Methods and Sensors

Partial discharges (PD) measurement provides valuable information for the condition assessment of the insulation status of high-voltage (HV) electrical installations. During the last three decades, several PD sensors and measuring techniques have been developed to perform accurate diagnostics when PD measurements are carried out on-site and on-line. For utilities, the most attractive characteristics of on-line measurements are that once the sensors are installed in the grid, the electrical service is uninterrupted and that electrical systems are tested in real operating conditions. In medium-voltage (MV) and HV installations, one of the critical points where an insulation defect can occur is inside metal-clad switchgears (including the cable terminals connected to them). Thus, this kind of equipment is increasingly being monitored to carry out proper maintenance based on their condition. This paper presents a study concerning the application of different electromagnetic measuring techniques (compliant with IEC 62478 and IEC 60270 standards), together with the use of suitable sensors, which enable the evaluation of the insulation condition mainly in MV switchgears. The main scope is to give a general overview about appropriate types of electromagnetic measuring methods and sensors to be applied, while considering the level of detail and accuracy in the diagnosis and the particular fail-save requirements of the electrical installations where the switchgears are located.

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

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.

A Novel Partial Discharge Localization Method in Substation Based on a Wireless UHF Sensor Array

Effective Partial Discharge (PD) localization can detect the insulation problems of the power equipment in a substation and improve the reliability of power systems. Typical Ultra-High Frequency (UHF) PD localization methods are mainly based on time difference information, which need a high sampling rate system. This paper proposes a novel PD localization method based on a received signal strength indicator (RSSI) fingerprint to quickly locate the power equipment with potential insulation defects. The proposed method consists of two stages. In the offline stage, the RSSI fingerprint data of the detection area is measured by a wireless UHF sensor array and processed by a clustering algorithm to reduce the PD interference and abnormal RSSI values. In the online stage, when PD happens, the RSSI fingerprint of PD is measured via the input of pattern recognition for PD localization. To achieve an accurate localization, the pattern recognition process is divided into two steps: a preliminary localization is implemented by cluster recognition to reduce the localization region, and the compressed sensing algorithm is used for accurate PD localization. A field test in a substation indicates that the mean localization error of the proposed method is 1.25 m, and 89.6% localization errors are less than 3 m.

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

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.

On the Use of Monopole Antennas for Determining the Effect of the Enclosure of a Power Transformer Tank in Partial Discharges Electromagnetic Propagation

A well-defined condition-monitoring for power transformers is key to implementing a correct condition-based maintenance (CBM). In this regard, partial discharges (PD) measurement and its analysis allows to carry out on-line maintenance following the standards IEC-60270 and IEC-60076. However, new PD measurements techniques, such as acoustics or electromagnetic (EM) acquisitions using ultra-high-frequency (UHF) sensors are being taken into account, IEC-62478. PD measurements with antennas and the effect of their EM propagation in power transformer tanks is an open research topic that is considered in this paper. In this sense, an empty tank model is studied as a rectangular cavity and their resonances are calculated and compared with their measurement with a network analyser. Besides, two low cost improved monopole antennas deployed inside and outside of the tank model capture background noise and PD pulses in three different test objects (Nomex, twisted pair and insulator). The average spectrum of them are compared and can be found that mainly, the antenna frequency response, the frequency content distribution depending on the PD source and the enclosure resonances modes are the main factors to be considered in PD acquisitions with these sensors. Finally, with this set-up, it is possible to measure PD activity inside the tank from outside.

A fiber optic PD sensor using a balanced Sagnac interferometer and an EDFA-based DOP tunable fiber ring laser

A novel fiber-optic acoustic sensor using an erbium-doped fiber amplifier (EDFA)-based fiber ring laser and a balanced Sagnac interferometer for acoustic sensing of the partial discharge (PD) in power transformers is proposed and demonstrated. As a technical background, an experimental investigation on how the variations of the fiber birefringence affect the sensor performances was carried out, and the results are discussed. The operation principles are described, and the relevant formulas are derived. The analytical results show that an EDFA-based fiber ring laser operating in chaotic mode can provide a degree of polarization (DOP) tunable light beam for effectively suppressing polarization fading noises. The balanced Sagnac interferometer can eliminate command intensity noises and enhance the signal-to-noise ratio (SNR). Furthermore, it inherently operates at the quadrature point of the response curve without any active stabilizations. Several experiments are conducted for evaluating the performances of the sensor system, as well as for investigating the ability of the detection of high-frequency acoustic emission signals. The experimental results demonstrate that the DOP of the laser beam can be continuously tuned from 0.2% to 100%, and the power fluctuation in the whole DOP tuning range is less than 0.05 dBm. A high-frequency response up to 300 kHz is reached, and the high sensing sensitivity for detections of weak corona discharges, as well as partial discharges also is verified.

Fiber optic sensor for acoustic detection of partial discharges in oil-paper insulated electrical systems

A fiber optic interferometric sensor with an intrinsic transducer along a length of the fiber is presented for ultrasound measurements of the acoustic emission from partial discharges inside oil-filled power apparatus. The sensor is designed for high sensitivity measurements in a harsh electromagnetic field environment, with wide temperature changes and immersion in oil. It allows enough sensitivity for the application, for which the acoustic pressure is in the range of units of Pa at a frequency of 150 kHz. In addition, the accessibility to the sensing region is guaranteed by immune fiber-optic cables and the optical phase sensor output. The sensor design is a compact and rugged coil of fiber. In addition to a complete calibration, the in-situ results show that two types of partial discharges are measured through their acoustic emissions with the sensor immersed in oil.

New Method for Measuring Statistical Distributions of Partial Discharge Pulses

A new digital detection system is described for measuring pulsating partial discharges (PDs). The PD detection system can continuously record all PD pulses that occur over extended periods of time, with a minimum inter-pulse time separation of 6 μs and a vertical amplitude resolution of 12 bits. Earlier PD detection systems detected PD pulse amplitude and time using complex custom-designed hardware while the present system continuously records the complete electrical waveform that carries the PD pulses using a commercial data acquisition board and extracts, in real time, the time and amplitude information of all PD pulses in software. The current approach considerably reduces the development and maintenance cost of the PD detection system, significantly increases the system portability, and may prove to be a crucial step for transferring the digital PD detection and analysis technology developed in laboratories to industry. The features of the new system are illustrated by the study of dc-excited PD pulses occurring in a point-to-plane gap in air. A new surface-mediated burst mode of PDs is discovered in which a PD pulse has a certain probability to induce another pulse. The probability is determined for several gap voltages and is found to vary strongly with the applied voltage.

System for Measuring Conditional Amplitude, Phase, or Time Distributions of Pulsating Phenomena

A detailed description is given of an electronic stochastic analyzer for use with direct "real-time" measurements of the conditional distributions needed for a complete stochastic characterization of pulsating phenomena that can be represented as random point processes. The measurement system described here is designed to reveal and quantify effects of pulse-to-pulse or phase-to-phase memory propagation. The unraveling of memory effects is required so that the physical basis for observed statistical properties of pulsating phenomena can be understood. The individual unique circuit components that comprise the system and the combinations of these components for various measurements, are thoroughly documented. The system has been applied to the measurement of pulsating partial discharges generated by applying alternating or constant voltage to a discharge gap. Examples are shown of data obtained for conditional and unconditional amplitude, time interval, and phase-of-occurrence distributions of partial-discharge pulses. The results unequivocally show the existence of significant memory effects as indicated, for example, by the observations that the most probable amplitudes and phases-of-occurrence of discharge pulses depend on the amplitudes and/or phases of the preceding pulses. Sources of error and fundamental limitations of the present measurement approach are analyzed. Possible extensions of the method are also discussed.