Analysis of partial discharge in air using emitted electromagnetic waves and the HFCT method

Radio frequency (RF) detection of partial discharges (PD) is a long-established principle, being described in the international standard for partial discharge measurement. The growing market for mobile communication devices in recent years has led to big reductions in the cost of electronic hardware operating in the VHF and UHF bands, making advanced measurements of PD in these bands increasingly affordable. Some 20 years ago, ultra-high frequency methods for PD detection were pioneered in gas-insulated substations (GIS) and have since become widely accepted. Locating PD in GIS is essentially a one-dimensional problem. More recent research into PD location in power transformers, also using the UHF method, has had to address the more challenging problem of PD location in three-dimensions. Both the GIS and transformer applications monitor enclosed volumes, where the PD is within a metalclad system that traps the signal and provides good screening of external RF interference. At the same time, other researchers have developed RF detection and location systems for open environments, such as complete air-insulated substations. When deployed in a fixed installation or a surveying vehicle equipped with GPS, this system is capable of monitoring insulation defects as they develop, and provides a location capability using sophisticated signal processing. This paper begins with an overview of some aspects of radiometric monitoring techniques and goes on to outline some research challenges which it is hoped will attract the attention of the many researchers who are continuing to advance the areas of radiometric PD detection, measurement and location technology.

Noise has been a major problem in Partial Discharge (PD) measurement, hampering its effectiveness as a means of assessing insulation condition and of diagnosing faults in power plant insulation. The problem has become more evident in recent years as efforts are made to adopt on-line PD-detection for practical plant insulation condition monitoring. The majority of PD detection systems use Ultra High Frequency (UHF), High Frequency Current Transformer (HFCT), Acoustic Emission (AE) sensors or IEC60270 standard electrical contact systems. Despite great advances which have been achieved in hardware instrumentation over the past years, various forms of noise still affect PD measurements, irrespective of the type of measurement system employed. This paper presents the experience which the authors have accumulated in recent years dealing with noise in PD measurement using the four, aforementioned, measurement systems. Following a brief comparison of the requirement for signal analysis of the four PD measurement systems, the paper addresses the source of noise that often appears in practical applications. Denoising techniques such as the traditional digital filters, matched filters, discrete wavelet transforms and the second generation wavelet transforms will then be introduced and contrasted in terms of their distinct features, advantages and disadvantages. The most effective technique will then be proposed for the four PD measurement systems discussed. Real world data will be used to support the conclusions of the paper.

Two signal classification methods have been examined to discover their suitability for the task of partial discharge (PD) identification. An experiment has been designed to artificially mimic signals produced by a range of PD sources that are known to occur within high voltage (HV) items of plant. The bushing tap point of a large Auto-transformer has been highlighted as a possible point on which to attach PD sensing equipment and is utilized in this experiment. Artificial PD signals are injected into the HV electrode of the bushing itself and a high frequency current transformer (HFCT) is used to monitor the current between the tap-point and earth. The experimentally produced data was analyzed using two different signal processing algorithms and their classification performance compared. The signals produced by four different artificial PD sources (surface discharge in air, corona discharge in air, floating discharge in oil and internal discharge in oil) have been processed, then classified using two machine learning techniques, namely the support vector machine (SVM) and probabilistic neural network (PNN). The feature extraction algorithms involve performing wavelet packet analysis on the PD signals recorded over a single power cycle. The dimensionality of the data has been reduced by finding the first four moments of the probability density function (Mean, Standard deviation, Skew and Kurtosis) of the wavelet packet coefficients to produce a suitable feature vector. Initial results indicate that very high identification rates are possible with the SVM able to classify PD signals with a slightly higher accuracy than a PNN.

Increasing need of cost effective identical sensors for partial discharge (PD) location prompted us to take up this research study. Four high-frequency current transformers (HFCT) were assembled and its performances with a used commercial sensor were compared with the known type of PD signals. In this paper, the responses due to surface, oil corona and air corona are analyzed. PD occurrence in 20 ms period and the shape of single PD with more sampled data points are characterized using seven techniques. The study indicates that HFCT with biflar winding is more sensitive in identifying different types of known PD. The number of turns and gauge of the used wire in HFCT play a role in increasing the sensitivity of PD detection.

High voltage equipment can be damaged when there is a failure of the insulation. Failure would be indicated by the Partial Discharge (PD) phenomenon during the pre-breakdown condition. PD detection methods commonly used, such as thermography and acoustic emission (AE), still have some weaknesses. Based on these conditions, this study discusses the detection method of PD containing Ultra High Frequency (UHF) in air insulation using an antenna that can capture electromagnetic waves. The electrodes used are needle to plane. The variables used are the shape and distance of the electrode. Characteristics analyzed during Partial Discharge Inception Voltage (PDIV). The data retrieved would be processed so that it would display Phase Resolved Partial Discharge (PRPD). The results obtained from the antenna would be compared with the leakage current method using the High Frequency Current Transformer (HFCT). PDIV pattern can be observed in the PRPD analysis, and each electrode has the characteristics of PD at a specific phase angle. The dominant PD point electrodes occur at an angle of 331.2 ° to 342 °, the dominant PD box electrodes occur at an angle of 334.8 ° to 356.4 °, and the dominant PD blunt electrode occurs at an angle of 306 ° to 313.2 °. So, it can be concluded that the loop antenna can detect and observe PDIV patterns in air insulation.

Partial discharge detection on power equipment using a magneto-resistive sensor

Maintenance of high voltage equipment is vital in order to detect an early partial discharge (PD) activity. PD based on IEC 60270 standard is defined as a localized electrical discharge that occurs between conductors and it only partially bridges the insulation. Nowadays, a commercial high-frequency current transformer (HFCT) has been used widely as a current transducer to detect and measure the PD current. However, commercial HFCT is very expensive in the market and quite heavy due to a large size. This paper presents a development of low-cost HFCT using ferrite core with a few benefits such as low cost, lightweight and also has a wide operating bandwidth which is suitable in detecting PD. Due to it is an iron-cored, it has also offered a higher sensitivity which makes the PD measurement more accurate. Two configurations of the developed HFCT with the different number of turns at the secondary side which are 7 turns and 12 turns were presented to analyze the higher sensitivity for PD detection. Based on the obtained results of PD measurement between two configurations of developed HFCT, the developed HFCT with the lower number of turn had more appropriate results as it provided higher sensitivity and wider bandwidth.

Partial discharge (PD) detection is essential for evaluating insulation health in overhead lines with covered conductors (CC), particularly within smart grid systems. However, since transient PD signals require high-sampling-rate equipment for capture and analysis, PD detection faces the challenge of efficiently extracting sparse PD features from long signals. To address these challenges, this paper proposes a novel LWPET framework for PD detection. Specifically, we design the Legendre multi-wavelet channel attention (LWCA) module and the Legendre multi-wavelet spatial attention (LWSA) module in the multi-wavelet multi-scale frequency domain to effectively extract PD features. This approach fully leverages the multi-regularity of LW, which closely matches the physical characteristics of PD faults, to enable in-depth PD pattern mining and also enhances the analytical capacity of the feature extraction model through attention mechanisms. Additionally, the Legendre multi-wavelet patch embedding (LWPE) module is introduced to address the difficulty of long-sequence representation learning. This module extracts key patch-level features using LWCA and LWSA, and then feeds the refined features into a Transformer Encoder for further PD feature extraction. Experiments on the VSB dataset demonstrate that LWPET achieves high accuracy (MCC 0.822, precision 0.866, recall 0.800) and efficiency, demonstrating its potential for real-time PD detection in smart grids.

Partial discharge (PD) based condition monitoring has been widely applied to power cables. However, difficulties in interpretation of measurement results (location and criticality) remain to be tackled. This paper aims to develop further knowledge in PD signal propagation in power cables and attenuation by the PD monitoring system devices to address the localization and criticality issues. As on-line or in-service PD monitoring sensors commonly comprise of a high frequency current transformer (HFCT) and a high-pass filter, the characteristics of detected PD pulses depend on the attenuation of the cable, the HFCT used and the filter applied. Simulation of pulse propagation in a cable and PD monitoring system are performed, based on analyses in the frequency domain using the concept of transfer functions. Results have been verified by laboratory experiments and using on-site PD measurements. The knowledge gained from the research on the change in pulse characteristics propagating in a cable and through a PD detection system can be very useful to PD denoising and for development of a PD localization technique.

Partial discharge (PD) classification in power cable accessories and high voltage equipment in general is essential in evaluating the severity of the damage in the insulation. In this article, the PD classification was realised as a two-fold process. Firstly, measurements taken from a high-frequency current transformer (HFCT) sensor were represented as features by means of a transformation to the classifier and secondly, the probabilistic neural network (PNN) classifier itself was capable of effectively recognising features coming from different types of discharges. The feature that was used as a fingerprint for PD characterisation was extracted from the moments of the probability density function (PDF) of the wavelet coefficients at various scales, obtained through the wavelet packets transformation. The PNN classifier was used to classify the PDs and assess the suitability of this feature vector in PD classification. Four types of artificial PDs were created in a high voltage laboratory, namely corona discharge in air, floating discharge in oil, internal discharge in oil and surface discharge in air, at different applied voltages, and were used to train the PNN algorithm. The results obtained here (97.49, 91.9, 100 and 99.8% for the corona, the floating, the internal and the surface discharges, respectively) are very encouraging for the use of PNN in PD classification with this particular feature vector. This article suggests a feature extraction and classification algorithm for PD classification, which when combined together reduced the dimensionality of the feature space to a manageable dimension, and achieved very high levels of classification.

Rapidly increasing growth and automation of power are given the opportunity to power engineers to incorporate new ideas in the field of online monitoring of power apparatus for reliable operation of electrical system. As a part of new innovation in power system, photonics play an important role for driving innovation toward the sensor development, optical data communication to imaging, lighting and displays. In any power apparatus, degradation of insulation occurs due to the accumulation of mechanical, thermal and electrical effects for a long period. As the insulation of high voltage (HV) power apparatus are deteriorated by partial discharges (PDs), detection and measurement of PDs are of utmost important for the quality assessment of HV power equipment. In this chapter, an attempt has been made to detect the PDs with the help of photonic technology which eliminates the electrical interference; a challenge is being faced by power engineers in recent decades. It is understood from the several studies, PDs of HV power apparatus can be detected by electrical, chemical and acoustic methods. Electrical detection focuses on capturing the electrical impulses created by the current streamer in the void and impurities. Electrical detection method has some limitations. The primary limitation is its susceptibility to noise. The high voltage transformer (HVT) environment contains high levels of electrical noise, both narrowband and broadband. Chemical detection has also some drawbacks. Firstly, this detection method does not give any information about the location of the PD and the insulation condition. Secondly, the main problem is that chemical tests cannot be performed continuously when the HV equipment are in working operation. To explore the detection of PDs through acoustic method, acoustic phenomenon of PDs has been studied, and it is found that it leads to the propagation of acoustic waves having frequencies typically between 30 and 1000 kHz inside the transformer. The propagation of acoustic wave modulates the refractive index of the medium, which modulates the intensity of the laser beam in sympathy with the acoustic waves. Acoustic methods have many advantages over other methods in that they are immune to electromagnetic interference (EMI), which can greatly reduce the sensitivity of electrical methods and they can be applied to find failure spot and location of discharges. Optical technique is also capable to couple PDs with the optical signal. To capture the acoustic waves, an optical method is demonstrated in this chapter for detection and localization of PD sources by using photo detector inside the HV power apparatus. In this work, a new concept such as direct optical partial discharge detection technique has been introduced, where the energy exchange mechanism of electrical to acoustic is used for online monitoring of PDs. The acoustic signals are collected directly through the photonic technology, which is free from electrical interference, and finally they are converted to electrical signals for detection of amplitude as well as location of PDs occurring inside the HV power apparatus. This is a cost-effective tool for online monitoring of PDs and can be used as a predictive maintenance tool for utilities.

Partial discharges (PDs) are believed to be one of the key factors accelerating high voltage (HV) equipment ageing. The chemical by-products of PDs actively contribute to the modification of insulations surface quality, especially in air medium due to the diversity of its constituents. Although nonthermal plasmas in air have been widely studied, their adaptation to HV systems is still highly complex and prone to controversies. In this work, a typical simulation of a fault leading to PD in air, through a Dielectric Barrier Discharge (DBD) point-to-plane configuration, is performed in order to measure ozone (O <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf> ), nitrogen oxide (NO) and dioxide (NO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> ) by-products gases. The evolution of these species regarding PD duration, PD power and humidity is analyzed. Ozone production rate and dynamics are found to predominate over NO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> and NO ones. The effect of humidity on gases evolution is found to be indirect, as it mainly affects PD power.

Partial discharge (PD) is a localized dielectric breakdown (which does not completely bridge the space between the two conductors) of a small portion of a solid or fluid electrical insulation system under high voltage stress. While a corona discharge is usually revealed by a relatively steady glow or brush discharge in air, partial discharges within solid insulation system are not visible. PD can occur in a gaseous, liquid or solid insulating medium. It often starts within gas voids, such as voids in solid epoxy insulation or bubbles in transformer oil. Protracted partial discharge can erode solid insulation and eventually lead to breakdown of insulation. One of the PD measurements in oil insulation is using UHF method, by measuring the waves generated by PD using antenna. One of antenna having good characteristics is UWB double layer printed antenna. In this paper the application of circular patch microstrip antenna for partial discharge detection is reported. The application of antenna on PD measurement in oil insulation, shows that the antenna is able to detect PD. The characteristics of PD: PDIV, PD waveform, PD phase pattern are measured using this antenna. The VNA testing of the antenna shows that the antenna bandwidth is 3.39 GHz.

We constructed an on-line partial discharge (PD) monitoring system (PDOM) with a CT sensor, an analogue-digital converter, and a tablet PC. A sampling frequency for data acquisition is 100 MHz, and we exchange on-site PD data through a cloud system. We successfully measured PDs in the early stage of insulation deterioration of a cross-linked polyethylene (XLPE) cable in a field site. Additionally, following PD detection by using a PDOM, we can locate a PD position by using a PD locator (PDL) system. By locating a PD, we can estimate the actual PD magnitude considering attenuation factor of a high frequency pulse. We show how the PDL system works on a XLPE cable network with time synchronization between two positions by using an injection current pulse. The locating accuracy of the PDL system is evaluated.

A synthetic partial discharge (PD) calibrator has been developed to qualify PD analyzers used for insulation diagnosis of HVAC and HVDC grids including cable systems, AIS, GIS, GIL, power transformers, and HVDC converters. PD analyzers that use high-frequency current transformers (HFCT) can be qualified by means of the metrological and diagnosis tests arranged in this calibrator. This synthetic PD calibrator can reproduce PD pulse trains of the same sequence as actual representative defects (cavity, surface, floating potential, corona, SF6 protrusion, SF6 jumping particles, bubbles in oil, etc.) acquired in HV equipment in service or by means of measurements made in HV laboratory test cells. The diagnostic capabilities and PD measurement errors of the PD analyzers using HFCT sensors can be determined. A new time parameter, "PD Time", associated with any arbitrary PD current pulse i(t) is introduced for calibration purposes. It is defined as the equivalent width of a rectangular PD pulse with the same charge value and amplitude as the actual PD current pulse. The synthetic PD calibrator consists of a pulse generator that operates on a current loop matched to 50 Ω impedance to avoid unwanted reflections. The injected current is measured by a reference measurement system built into the PD calibrator that uses two HFCT sensors to ensure that the current signal is the same at the input and output of the calibration cage where the HFCT of the PD analyzer is being calibrated. Signal reconstruction of the HFCT output signal to achieve the input signal is achieved by applying state variable theory using the transfer impedance of the HFCT sensor in the frequency domain.