Phase Resolved Partial Discharge Research Articles

Time Synchronized Distributed Acoustic Sensing of Partial Discharge at the Oil-Pressboard Interface

Distributed acoustic sensing (DAS) is a well-established technology used across a variety of industries. Due to its inherently low sample rates at detection ranges of a couple of hundred meters or more, at face value, it appears ineffective for partial discharge (PD) detection and therefore has not been previously used. However, in this publication, we show that aliasing effects due to the DAS sampling methods successfully downsample the higher-frequency acoustic emissions (AEs) and can provide detection of PD above 120 pC under treeing discharge across an oil–pressboard interface. This is supported by comparisons between DAS, high-sample rate acoustic sensors, as well as industry-standard electrical measurements. Synchronization between the different measurement systems is achieved allowing for sample-to-sample comparison as well as a more statistical approach. We additionally show that phase-resolved PD (PRPD) analysis can be applied to the DAS results with an additional voltage zero-crossing synchronization signal, with a clear resemblance to electrical methods.

Electrical Tree Initiation and Growth in LDPE Under Negative HVDC Superimposed With AC Ripples

Understanding the role of power quality in the aging of HVdc cable systems is critical to the reliable connection of offshore renewable energy sources, and hence global ambitions to reduce carbon emissions. This work investigates electrical treeing with a needle-plane geometry in low-density polyethylene (LDPE) under a high negative dc voltage superimposed with ac ripples (−60 kV dc ± 7 kV ac). Tree initiation showed behavior similar to that widely reported under pure power frequencies. Subsequent tree growth, however, was observed to be confined in a smaller area with limited length and width than seen under pure ac voltages, even after long periods of voltage application. Traditional 2-D imaging showed overlapped tree channels which developed to cover the whole area within the tree outline. A distinguishing tree shape resulted, which we have named a “slim bouquet” shape. The volume rendering from X-ray computed tomography (XCT) showed the structure had a 3-D fractal dimension greater than 2, considerably larger than its 2-D representation. PD signals during the growth had wing-like phase-resolved partial discharge (PRPD) clusters and signal phase concentrations between 10°–45° appeared after hours of growth. There was a comb-like appearance in maximum partial discharge (PD) magnitude variation, which is typical in pure high negative dc fields. Consideration is given to both space charge distribution controlled by high dc fields and continuous degradation by ac fields to explain the slim bouquet tree formation.

Experimental investigation of partial discharges activities in palm kernel oil methyl ester and mineral oil by using a Rogowski coil

This paper deals with the influence of the characteristics of a Rogowski coil compared to a shunt sensor in the detection of partial discharges (PDs) in insulating oils. An experimental investigation is conducted on the activity of PDs and their inception voltage in mineral oil (MO) and palm kernel oil methyl ester (PKOME). This is done by using the analysis of the phase-resolved partial discharge patterns model and the Weibull distribution. The experimental set-up consists of a 220 V/50 kV high voltage transformer, a control box and a test cell containing four electrode configurations. The tests are conducted according to the IEC 61,294 for the determination of partial discharge inception voltage. The experimental results show that the characteristics of a Rogowski coil have a considerable effect on the waveform and the number of occurrences of detected discharges. The number of occurrences of discharges detected with the shunt sensor is reduced by almost 50% compared to those of a Rogowski sensor. The parameters obtained from the Weibull distribution show that PKOME has a higher resistance to discharge inception than MO.

GIS Partial Discharge Pattern Recognition Based on Multi-Feature Information Fusion of PRPD Image

Partial discharge (PD) pattern recognition is a critical indicator for evaluating the insulation state of gas-insulated switchgear (GIS). Aiming at the disadvantage of traditional PD pattern recognition methods, such as single feature extraction and low recognition accuracy, a pattern recognition method of PD based on multi-feature information fusion is proposed in this paper. Firstly, a recognition model based on quasi-Hausdorff distance is established according to the statistical characteristics of the phase-resolved partial discharge (PRPD) image, and then a modified convolutional neural network recognition model is established according to the image features of the PRPD image. Finally, Dempster–Shafer (D–S) evidence theory is used to fuse the two pattern recognition results and complement the advantages of the two approaches to improve the accuracy of partial discharge pattern recognition. The experimental results show that the total recognition accuracy rate of this method for four typical PD is more than 94.00%, and the recognition rate is significantly improved compared to support vector machine and normal convolution neural network. Maintaining stability in typical bipedal robots is challenging due to two main reasons.

Detection technology of partial discharge in transformer based on optical signal

The detection of partial discharge (PD) in transformers is of great significance for preventing insulation defects from developing into insulation failures. Transformer partial discharge detection technology includes pulse current method, radio frequency measurement method, UHF method, ultrasonic detection method, gas chromatography, and optical detection method. Among them, the optical detection method has the characteristics of strong anti-electromagnetic interference ability, and the signal can be transmitted and received without loss. The optical detection method has unique advantages. In this paper, an optical signal measuring device for partial discharge inside the transformer is designed and fabricated, the typical insulation defect inside the transformer is simulated, and the optical signal characteristics of typical partial discharge are obtained. Compared with the pulse current method, it is found that the magnitude of partial discharge can be characterized by the integral of the optical signal. The characteristic of the optical signal phase resolved partial discharge (PRPD) spectrum of typical insulation defects is obtained. The PRPD spectrum of the optical signals of different defects is obviously different. It is feasible to draw the PRPD spectrum and identify the defect types according to the optical signal, which can be further applied to the identification of internal faults in transformers.

Partial Discharge Characteristics of C3F7CN Gas Mixture Using the UHF Method

Manufacturing or assembly defects in gas-insulated equipment can introduce field enhancements that could lead to partial discharge (PD). This paper examines the PD characteristics of SF6 alternatives considered for potential application to retro-filling existing SF6-designed equipment. The PD performance of the C3F7CN/CO2 gas mixture and SF6 were characterised adopting the ultra-high frequency (UHF) method and investigated for different defect configurations, pressures, and gas mediums. Hemispherical rod-plane and plane-to-plane configurations with needle on the high-voltage (HV) and ground electrodes were used to mimic conductor and enclosure protrusion defects, respectively. The results demonstrate that with a needle length of 15 mm, the 20% C3F7CN/80% CO2 gas mixture had almost half the partial discharge inception and extinction voltages (PDIV/EV) of SF6. For less divergent fields, the 20% C3F7CN/80% CO2 gas mixture demonstrated a comparable PDIV/EV performance as SF6. The phase-resolved PD patterns of the 20% C3F7CN/80% CO2 gas mixture demonstrated a 3-stage transition phase that was not observed with SF6, which could be due to the discharge mechanism of the weakly attaching CO2 gas used within the mixture.

Defects Classification of Hydro Generators in Indonesia by Phase-Resolved Partial Discharge

This paper proposed a phase-resolved partial discharge (PRPD) shape method to classify types of defect generator units by using offline partial discharge (PD) measurement instruments. In this paper, the experimental measurement was applied to two generators in the Inalum hydropower plant, located in North Sumatera, Indonesia. The recorded PRPD using the instrument MPD600 can illustrate the PRPD patterns of generator defects. The proposed PRPD shape method is used to mark auxiliary lines on the PRPD patterns. Moreover, four types of defects refer to the IEC 60034-27 standard, which are microvoid (S1), delamination tape layer (S2), slot defect (S3), and internal delamination (S4) and are used to classify the defect types of the generators. The results show that the proposed method performs well to classify types of defect generator units.

Partial Discharge Detection in Gas-Insulated Switchgears Using Sensors Integrated With UHF and Optical Sensing Methods

Partial discharge (PD) detection from protrusion defects is performed in an actual gas-insulated switchgears (GISs) based on a built-in sensor integrated with fluorescent fiber and ultrahigh frequency (UHF) antenna. The characteristics of both optical and UHF signals are analyzed in terms of inception voltage, pulse distribution, and statistical characteristics including the phase-resolved partial discharge (PRPD) pattern and PD number. In the experiment, protrusions with different curvatures are adopted to investigate the reason for the differences in the characteristics of UHF and optical signals. The results show that UHF and optical methods have different detection sensitivity for different discharge types owing to the different energies of electromagnetic waves and optical signals radiated in different discharge types. The optical method can easily detect avalanche discharge, while the UHF method can easily detect streamer discharge. This difference leads to the difference of the PD characteristics obtained by the two partial methods. For example, for the PD of small curvature protrusions, the PD inception voltage (PDIV) of the optical method is lower than that of the UHF method, because the defect triggers avalanche discharge. Moreover, the optical and UHF signal pulses are not always simultaneous in the time domain because they detect different discharge types. The PRPD patterns obtained by the optical method have a wider phase distribution than the UHF method because the optical method can detect avalanche discharge under low voltage.

Multiple detections of insulation defects partial discharge in gas-insulated equipment

Gas-insulated equipment is an important part of the power system owing to its advantages of small footprint, large transmission capacity, and high reliability. Detection of insulation defects partial discharge (PD) is important for its operation and maintenance, and the combination of multiple detection methods can greatly improve the accuracy of PD detection. In this study, the three kinds of insulation defects, namely, floating defect, void defect, and surface defect, are set up on the PD simulation platform and detected using the ultra-high frequency (UHF) method, high-frequency current (HFCT) method, and acoustic emission (AE) method. The partial discharge inception voltage (PDIV) and phase-resolved partial discharge (PRPD) spectrum of defects measured by these three methods are recorded in the experiments. The experimental results show that the UHF method is effective for the detection of the three defects. The HFCT method is sensitive to floating defects but not to void and surface defects. Discharge signals of these two defects can be detected by HFCT when the discharge amplitude is large. The AE method can detect floating defects effectively but cannot detect void defects and surface defects.

Research on data diagnosis method of acoustic array sensor device based on spectrogram

Acoustic array sensor device for partial discharge detection is widely used in power equipment inspection with the advantages of non-contact and precise positioning compared with partial discharge detection methods such as ultrasonic method and pulse current method. However, due to the sensitivity of the acoustic array sensor and the influence of the equipment operation site interference, the acoustic array sensor device for partial discharge type diagnosis by phase resolved partial discharge (PRPD) map might occasionally presents incorrect results, thus affecting the power equipment operation and maintenance strategy. The acoustic array sensor detection device for power equipment developed in this paper applies the array design model of equal-area multi-arm spiral with machine learning fast fourier transform clean (FFT-CLEAN) sound source localization identification algorithm to avoid the interference factors in the noise acquisition system using a single microphone and conventional beam forming algorithm, improves the spatial resolution of the acoustic array sensor device, and proposes an acoustic array sensor device based on the acoustic spectrogram. The analysis and diagnosis method of discharge type of acoustic array sensor device can effectively reduce the system misjudgment caused by factors such as the resolution of the acoustic imaging device and the time domain pulse of the digital signal, and reduce the false alarm rate of the acoustic array sensor device. The proposed method is tested by selecting power cables as the object, and its effectiveness is proved by laboratory verification and field verification.

Impact of Magnetic Field on Corona Discharge Behavior of Mineral Oil Under AC Voltage

This article reports an experimental investigation of the impact of magnetic field on corona discharge activity in mineral oil, using both ultrahigh-frequency (UHF) and fluorescence techniques under ac voltage. The results show that corona inception voltage (CIV) increased under ac voltage with varying harmonic frequencies without considering any phase shift and reduced marginally under magnetic field. In addition, the impact of the magnetic field shifted the dominating frequency of the UHF signal (0.9 GHz) obtained under ac voltage toward lower frequencies (0.3 and 0.6 GHz). Under high magnetic fields, the parameters such as rise time, fall time, and pulsewidth of the fluorescence signal increased in its magnitude compared to its effect without a magnetic field. Also, using both the UHF sensor and the fluorescence sensor, there was no clear difference in the phase-resolved partial discharge (PRPD) pattern caused by corona discharge (with and without magnetic field). Furthermore, the number of discharges is observed to be high under the impact of harmonic ac voltages with third- and seventh-order containing total harmonic distortions (THDs) of 4% and 40%.

Partial Discharge Characteristics and Deterioration Mechanisms of Bubble-Containing Oil-Impregnated Paper

Bubbles, as typical defects of oil–paper insulation systems, pose a great threat to the insulation state. In this article, the characteristics and mechanisms of the partial discharge (PD) inception, evolution, and induced breakdown of bubbles embedded between oil-impregnated paper (OIP) layers are investigated. The results show that the PD inception has a significant statistical time delay due to the lack of free electrons. The PD inception time delay (PDTD) follows an exponential distribution and leads to the large dispersion of the PD inception voltage (PDIV), which follows a Weibull distribution. The changes in PD behavior [charge, repetition rate, pulse current waveform, and phase-resolved PD (PRPD)] over time all show two separable stages (stages 1 and 2). The continuous recording of bubble area and gas composition under PD reveals that the evolution of bubble PD is attributed to the change in gas composition rather than the change in surface conditions as believed by researchers. The OIP breakdown induced by bubble PD indicates two breakdown modes, fast and slow, which correspond to direct-induced breakdown in stage 1 and cumulative-damage breakdown in stage 2. The former occurs because of the severe distortion of the electric field by the space charge at the head of the discharge channel inside the bubble, whereas the latter occurs because of the gradual deterioration of the OIP due to the continuous PD action. This study increases our understanding of the mechanisms of bubble discharge and of oil–paper insulation deterioration, and it provides guidance for fault diagnoses.

Partial Discharge Behavior of Typical Defects in Power Equipment Under Multilevel Staircase Voltage

With the widespread application of power electronic switching technology, power equipment is facing new electrical stresses brought about by multilevel staircase voltages during testing and operation. Therefore, the partial discharge (PD) behavior of five typical defects in power equipment under the staircase waveform needs to be investigated. This article mainly analyses the phase-resolved PD (PRPD) pattern and pulse repetition rate (PRR) of five typical defects under sinusoidal voltage and multilevel staircase voltage with different number of levels. Also, the PD behavior under staircase voltage with different step responses are investigated. By analyzing the reasons behind the PD behavior between different cases, the PD characteristics and the transformation of PRPD patterns under different staircase voltages are obtained. Moreover, this research finds that the sensitivity of different defects to the staircase voltage is different. These results provide experimental and theoretical support for the testing and diagnosis of PD under new electrical stress present in the future flexible electric grid.

Use of Data-Driven Approaches for Defect Classification in Stator Winding Insulation

Partial discharges (PD) in the high voltage insulation systems are both a symptom and cause of terminal and impending failures. The use of data-driven methods based on PD measurements will enable predictive strategies to replace traditional maintenance strategies. This paper employs machine learningbased classification models to identify and characterizePD signals originating from lab-made artificial defects in epoxy-mica material samples. Three different PD sources were studied: surface discharges in air, corona discharges, and discharges caused by internal cavities/delaminations. To generate high-quality datasets for the training, validation, and testing of classification models, Phase-Resolved PD (PRPD) data for each test object was obtained at room temperature under 50 Hz AC excitation at 10 % above the PD inception voltage (PDIV) of each sample. Relevant statistical and deterministic features were extracted for each observation and were labeled based on the defect type (supervised learning). Finally, the trained and validated ML models were used to identify PD sources in the service-aged stator winding insulation. Support vector machines (SVM), ensemble, and k-nearest neighbor (kNN) algorithms achieved significantly high accuracy (≥ 95 %) of defect identification.

Influence of the Cable Accessories Installing Method on the Partial Discharge Activity in Medium Voltage Cables

This article proposes a method to modify the construction of a medium voltage (MV) heat shrinkable cable termination in cases of atypical damage to the shields of cross-linked polyethylene (XLPE) insulated cables. The proposed solutions include a modified method of assembling electric field control coating. An attempt was made to check the effect of such damage to the shields of MV cables with XLPE insulation on the level of occurrence of partial discharges within the cable termination. The investigations included testing the XRUHAKXS 1 × 240/25 cable type using the electric method (ME) and high frequency (HF) method with sinusoidal AC test voltage. As a result of the measurements, the values of total charges in the period and phase-resolved partial discharge (PRPD) patterns were obtained. The presented experimental results show the influence of the damage of the semiconducting coating surface on the occurrence of a defect in the cable termination without a modified method of control mantissa pinning. We suggest new methods of assembling MV cable accessories in the case of the presented coating damage in MV cable insulation.

Application of Pulse Sequence Partial Discharge Based Convolutional Neural Network in Pattern Recognition for Underground Cable Joints

In this study, a pulse sequence analysis (PSA) method is presented, which does not measure the voltage signal for the defect-type recognition of power cable joints based on partial discharges (PDs) using convolutional neural networks (CNNs). The PSA showed effective results in comparison to a conventional phase-resolved PD (PRPD) pattern, which is a combination of a PD and voltage signals. The optimal drawing formats of the PSA patterns, including the size, type, color, and marker size of the images, which influence the recognition accuracy, were evaluated. In total, 12 sets of PD data, consisting of three types of cable joint defects, were trained and tested using CNN-based models. The results show that the performance of the PSA-based CNN model is as good as that of a PRPD even when the voltage signal is lost, whereas PRPD mostly fails under such cases. The total recognition accuracy obtained from a PSA-based CNN was 95.3%, whereas that of the PRPD-based CNN was 90.6%. In addition, an advanced and useful application of a PSA pattern is discussed herein.

Evaluating the role of carbon quantum dots covered silica nanofillers on the partial discharge performance of transformer insulation

The article presents the experimental results on the role of carbon quantum dots (CQD) covered silica nanofillers on the partial discharge (PD) properties of transformer oil insulation. The improvement in PD performance of nanofiller blend oil is tested with increased voltage gradient and nanofiller concentration. PD of nanoblend oils for various concentrations of modified silica ranging from 0 to 0.1%wt was measured. PD activity of the test samples is simulated in the laboratory with needle, rod and plane electrode geometry combinations. The facets of PD signals such as PD magnitude, PD inception and time duration of PD extracted from phase-resolved partial discharge patterns were analyzed. Detailed time-frequency domain evaluation of PD pulse is carried out. The enclosure of CQD-SiO2 nanostructures has considerably improved the inception voltage up to 23% than base transformer oil and SiO2 nanofluid. Magnitude of discharge is noticeably reduced by the influence of CQD-treated SiO2 nanoparticles in mineral oil irrespective of the electrode geometry.

Diagnosis of Partial Discharge in Power Transformer using Convolutional Neural Network

In an electric power system, power transformers are essential. Transformer failures can degrade the quality of the power and create power outages. Partial Discharges (PD) are a condition that, if not adequately monitored, can cause power transformer failures. This project addresses the diagnosis of PD in power transformer using the Phase Amplitude (PA) response of PRPD (Phase-Resolved Partial Discharge) patterns recorded using PD Detectors. It is a widely used pattern for analysing Partial Discharge. A Convolutional Neural Network (CNN) is used to classify the type of PD defects. The PRPD patterns of 240 PA sample images have been taken from power transformer of rating 132/11 KV and 132/25 KV for training and testing the network. The feature extraction has also been done using CNN. In this work, the classification of PD faults is done using a supervised machine learning technique. The three different classes of PD faults such as Floating PD, Surface PD and Void PD are considered and predicted using Support Vector Machine (SVM) classifier. Simulation study is carried out using MATLAB. Based on the results obtained, it is found that CNN model has achieved a greater classification accuracy and thereby the life span of power transformer is enhanced.

Impacts of Harmonic Voltage Distortions on the Dynamic Behavior and the PRPD Patterns of Partial Discharges in an Air Cavity Inside a Solid Dielectric Material

The monitoring of partial discharges (PDs) is one of the main methods used worldwide for evaluation and diagnosis of the insulation conditions in equipment powered by medium and high voltages. The occurrence of PDs is usually an indication of the appearance of insulation defects, which over time can compromise the dielectric withstand of the material used, increasing the probability of complete breakdown. In general, laboratory tests for detecting and registering PDs are carried out using purely sinusoidal voltages. However, it is very common for an electrical asset to be subjected at some point in its operating life to voltages distorted by harmonic components. Some studies reported in the literature reveal that harmonic distortions can affect the PDs’ characteristics, nevertheless, the effects of individual harmonic components on PDs still need to be analyzed. In this context, this paper proposes to evaluate the impacts of harmonic voltage distortions on the dynamic behavior and the phase-resolved partial discharge (PRPD) patterns of PDs in an air cavity within a solid dielectric material. For this, a simulation model was implemented, which was used to analyze the effects of applying distorted voltages composed of different harmonic orders (third, fifth, and seventh) and distinct levels of distortion (1%, 3%, 5%, 10%, 15%, and 20%). In addition, the influence of the third harmonic phase angle on PDs is also analyzed. The results extracted from the simulations revealed that the harmonic distortions caused changes in the numbers of PDs per cycle, in the mean apparent charges of the PDs per cycle, and in the PRPD patterns’ characteristics. These changes were very significant for higher distortion levels, which in practice may impair the interpretation of PD measurement records for the diagnosis of the condition of the insulation system.

Measurements and Analysis of Partial Discharges at HVDC Voltage with AC Components

This paper presents the methodology for phase-resolved partial discharge measurements in HVDC systems with a DC voltage containing trace AC harmonics or a DC voltage ripple. The measurement result of partial discharges is an indicator of the current condition of the high-voltage power devices’ insulation system. The voltage waveforms in HVDC systems are not ideal DC, because different disturbances occurring naturally in these systems can affect the DC voltage. The AC harmonics related to the AC source voltage, and the voltage ripples provided by the power converter topology, can be found in the HVDC voltage. This paper proposes a novel approach to partial discharge measurement in DC networks. The synchronization to the particular AC harmonics appearing in DC voltage was applied to the PD measurements. The analyses were performed on the model sample containing a void inclusion, which was placed between electrodes fed by the DC voltages with the imposed chosen AC harmonics. Two scenarios were analyzed at a constant DC level: one with a variable AC magnitude and the second with a variable frequency of an AC source adjusted to the harmonics: 50, 150, 300, and 350 Hz. It was observed that the superimposed AC voltage component resulted in an intensification of PDs.