Different Types Of Partial Discharge Research Articles

Multi-source partial discharge diagnosis in gas-insulated switchgear via zero-shot learning

Partial discharge (PD) diagnosis in gas insulation switchgear (GIS) has been continuously studied as a hot issue. The diagnostic model developed at this stage requires the possible combination of all single PDs, and each combination has sufficient data. However, the demand for the number of multi-source PD samples increases in the form of an index due to the accidental and concurrency of different types of PD, where it is unrealistic to collect sufficient PD samples. To this end, we proposed zero-shot learning (ZL) to diagnose multi-source PD. The ZL diagnosis multi-source PD only required single PD for training. Specifically, an attention residual network was introduced for feature extraction. Then, a novel semantic descriptor was proposed to obtain the semantic attributes, and the semantic embedded features were mapped to the visual space with a full connection network. Finally, the relationship network adopted the similarity between the visual features and semantic embedded features, and the multi-source PD diagnosis was realized. In addition, the multi-source PD experiment in GIS was constructed to verify the proposed ZL. The experimental results demonstrated that the ZL could achieve 90.25 % accuracy when only single PD participated in the training. It provided novel solutions for the diagnosis of multi-source PD in GIS.

GIS Partial Discharge Pattern Recognition Based on Time-Frequency Features and Improved Convolutional Neural Network

Different types of partial discharge (PD) in gas-insulated switchgear (GIS) cause different damage to GIS insulation, correctly identifying the PD type is very important for evaluating the insulation status of GIS. This paper proposes a PD pattern recognition method based on an improved feature fusion convolutional neural network (IFCNN) to fully use the time-frequency features of PD pulses to realize PD pattern recognition. Firstly, the one-dimensional time-domain feature sequence of the PD pulse and the corresponding wavelet time-frequency diagram are applied as inputs. Secondly, the convolutional neural network (CNN) with two parallel channels is used for feature extraction, the extracted fault information is fused, and the shallow features of the wavelet time-frequency diagram are fused to prevent feature loss caused by pooling operation. Finally, the extracted features are sent to the classifier to recognize different types of PD. The discharge data of different types of PD are obtained for testing by experiments and simulation. Compared with 1-D CNN and 2-D CNN under the same specification, the proposed method can mine more potential local features of discharge pulses by fusing the time-frequency features of PD pulses in different dimensions, and improves the recognition accuracy to 95.8%.

Partial discharge ultrasonic signals pattern recognition in transformer using BSO-SVM based on microfiber coupler sensor

Detection and pattern recognition of different types of partial discharge (PD) signals in transformer are of great significance for the safe and stable operation of transformers. This paper demonstrates the application of microfiber coupler sensor (MFCS) to the pattern recognition of PD ultrasonic signals with an optimized support vector machine (BSO-SVM) algorithm. First, a PD ultrasonic detection system based on MFCS is built, compared with the traditional piezoelectric sensor, the MFCS can be directly implanted in the transformer oil to detect the PD signals, avoiding the attenuation of ultrasonic signals as they propagate to the transformer outer wall. Then, four kinds of PD ultrasonic signals are collected by the PD detection system, and feature extraction is carried out for the time domain and frequency domain characteristics of PD ultrasonic signals. Finally, a BSO-SVM classification algorithm is proposed, which uses the beetle antennae search (BAS) and the particle swarm optimization (PSO) algorithm to jointly optimize the support vector machine (SVM) algorithm. The BSO-SVM classifier is used to classify four kinds of PD signals, the classification accuracy is 93%, and the number of convergence is 5. In addition, the BSO-SVM algorithm is compared with the GA-SVM and PSO-SVM algorithms. The results show that the BSO-SVM algorithm has higher recognition accuracy and faster convergence speed. The research results show that it is feasible to use MFCS combined with BSO-SVM to identify PD patterns of transformers, and it has certain significance for transformer fault diagnosis.

Partial discharge monitoring using deep neural networks with acoustic emission

The occurrence of partial discharge (PD) indicates failures in electrical equipment. Depending on the equipment and operating conditions, each type of PD has its own acoustic characteristics and a wide frequency spectrum. To detect PD, electrical equipment is often monitored using various sensors, such as microphones, ultrasonic, and transient-earth voltage, whose signals are then analyzed manually by experts using signal processing techniques. This process requires significant expertise and time, both of which are costly. Advancements in machine learning, aim to address this issue by automatically learning a representation of the signal, minimizing the need for expert analysis. To this end, we propose a deep learning-based solution for the automatic detection of PD using airborne sound emission in the audible to the ultrasonic range. As input to our proposed model, we evaluate common time-frequency representations of the acoustic signal, such as short-time Fourier, continuous wavelet transform and Mel spectrograms. The extracted spectrum from the PD signal pulses is used to train and evaluate the proposed deep neural network models for the detection of different types of PD. Compared to the manual process, the automatic solution is seen as beneficial for maintenance processes and measurement technology.

Particularities of measuring partial discharges in stator windings insulation systems of high-voltage electrical machines

The purpose of the article is to provide potential tools that can make a significant contribution to the identification of partial discharges (PD). Different types of partial discharges occur in stator winding insulation and a few partial discharges may occur simultaneously. Internal partial discharges are electrical discharges that occur in voids in the insulation of the stator winding. In typical stator insulation systems that use epoxy bonded mica tapes, insulation degradation due to internal partial discharges is usually slow (many years or decades). External partial discharges (slot PD and surface PD in the end-winding) are more dangerous and lead to the destruction of the insulation in a short time (several months or years). Therefore, the identification of insulation defects is essential. The analysis of existing methods for identification of defects in the insulation of high-voltage electrical machines using the results of measuring the partial discharges characteristics is carried out. The advantages and disadvantages of each of the groups of identification methods are characterized. It is shown that among the models of knowledge representation in solving problems of diagnostics of insulation systems for high-voltage electrical machines, identification methods, including field tests using training samples, are among the most suitable ones. It is noted that detection of insulation defects and their identification cannot be carried out only by direct measurements of PD characteristics and other dielectric parameters (electrical resistance, dielectric loss, polarization index). For this, special computing programs based on pattern recognition methods should be used. Results are presented of identification of technological defects in the insulation of the stator winding at the stage of factory testing, obtained using the PD identification method developed by the authors

A Review of Knowledge-Based Defect Identification via PRPD Patterns in High Voltage Apparatus

Partial discharge (PD) as one of well-known signatures of defects in the insulation system of high voltage equipment has been studied over eight decades. It is of a great interest to power grid operators and owners to identify and distinguish local weak spots in insulation system of the key components of power system by using the obtained PD information to make timely decision thus to avoid any unexpected failures in the system. This article reviews the research progress of PD in high voltage apparatus, including motors and generators, cables, transformers and gas insulated equipment, with attempts to assess the severity of different types of PD initiating defects as well as key PD features that can be selected for classifying defects. Experimental and on-site test results for each type of discharge and defect associated are summarized and discussed. Also, development of these defects during operation of equipment caused by changes in physical and chemical parameters of defect and/or insulation, and its impact on phase-resolved PD pattern variation are thoroughly explained. It is expected that this paper can provide a comprehensive guideline for PD data analysis of available PD information extracted by conventional PD acquisition instruments for both engineers and asset managers of high voltage apparatus.

Detection, Classification and Location of Sources of Partial Discharges Using the Radiometric Method: Trends, Challenges and Open Issues

In this article, it is provided a comprehensive survey on the most relevant research in the area of detection, classification and location of partial discharge (PD) sources using the radiometric or ultra high frequency (UHF) method, focusing mainly on the works related to the use of antennas. To the best of the authors knowledge, this is the first review paper that focuses on the application of antennas based on UHF method in the complete diagnosis of PD. Specifically, it is first presented the main contributions to the development and optimization of UHF sensors for application in the detection of PD sources. Second, articles that used the UHF method to identify different types of PD in high voltage equipment are described. Finally, the main contributions regarding the location of sources of defects using the radiometric method are presented. Summary of works in the area of UHF detection sensors, PD classification and PD location, with their respective contributions, are shown. Finally, research trends, challenging and open issues are discussed. The purpose of this article is to provide important information about the complete diagnosis of PD, which could be used to guide the development of more efficient techniques in this research area.

A Study on the Extraction Method of Partial Discharge Features in Gas Insulated Switchgear Based on Ultra-High Frequency Signal Envelope

Because the UHF detection method has the advantages of wide detection range and high sensitivity, it is of great significance for partial discharge detection and defect type identification in gas insulated electrical equipment. Therefore, this paper proposes a new type of partial discharge UHF signal pattern recognition algorithm, which can effectively identify the type of partial discharge defects. The low frequency signal modulated by high frequency signal is demodulated by Hilbert transform. This paper presents a method to extract the time domain characteristics of UHF signals emitted by gas insulated switchgears (GIS) partial discharge (PD). By using the Hilbert transform, the envelope of partial discharge signal can be obtained, and then the key points related to features in time domain can be obtained. The artificial insulation fault test platform is developed. Experimental data of three typical partial discharge signals are obtained, and the algorithm is analyzed and verified. The result shows that the method can recognize different types of PD well. So it is practical and effective.

Classification and comparison of AC and DC partial discharges by pulse waveform analysis

This study aims to classify and compare AC and DC partial discharge (PD) based on PD pulse waveform analysis. To achieve this goal, we designed a testbed that enables precise measurements of individual PD pulses. The testbed is used for collecting data from four different types of PDs including cavity, surface, corona, and floating potential discharges generated by individual PD source samples. All samples were examined under AC, positive DC, and negative DC electrical stresses, through which we captured thousands of PD pulses. We classify the waveforms of each PD type into representative groups associated to their discharge mechanisms. The statistical data of the measured pulses are utilized to identify the differences between AC and DC PDs while the clustered patterns of PD amplitude versus their temporal characteristics serve as a means to classify the types of PDs under AC and DC electrical stresses.

Phased-resolved partial discharge patterns of 10 kV stator coils under different experimental conditions

The most five common types of partial discharge (PD) activities involving 10 kV motors, namely internal discharge, slot discharge, corona discharge, surface tracking and bar-to-bar discharge, were researched in this paper. PD was induced on some 10 kV coils with modern epoxy-mica insulation system through off-line experiment under different conditions. The phased-resolved partial discharge (PRPD) patterns of different types of PDs were recorded to characterize different PDs. This research can provide a reference in off-line fault diagnosis and be useful for the PD online monitoring of high voltage motors.

Multi-Scale Analysis and Pattern Recognition of Ultrasonic Signals of PD in a Liquid/Solid Composite of an Oil-Filled Terminal

In order to analyze the partial discharge (PD) characteristics of a liquid/solid composite medium in an oil-filled submarine cable terminal; we have designed five discharge models including needle-plate, plate-to-plate air gap, surface, slide-flash and suspension potential. At the same time, the ultrasonic signals of PD have been extracted through the typical fault model research platform of oil-filled submarine cable equipment. First, we use SureShrink threshold wavelet denoising to suppress the ultrasonic signal noise. Secondly, through the multi-scale analysis of the signal, the energy distribution maps of five different types of PD are obtained; the analysis found that needle-plate discharge, suspension discharge, and slide-flash discharge have better resolution; and plate-to-plate air gap discharge and creeping discharge have similar characteristics and are not easy to distinguish. Finally, we designed six characteristic parameters of the ultrasound signal, and screened three feature quantities by a back propagation (BP) neural network to distinguish between plate-to-plate air gap discharge and surface discharge. In summary, the method of combining multi-scale analysis and neural networks is used to distinguish the five discharge types by extracting the characteristic values of the characteristic signals.

Multiple Partial Discharge Source Localization in Power Cables Through Power Spectral Separation and Time-Domain Reflectometry

Insulated power cables are becoming increasingly popular in today’s developing distribution and transportion networks. However, due to aging, deterioration, and various operational and environmental stresses, insulation defects may appear and so the cable needs to be monitored in a timely manner to avoid unexpected failures. Many of these defects are responsible for partial discharge (PD) activity. The localization of the sources of these discharges is a highly decisive facet in the condition-based monitoring of power cables. The techniques for the localization of single-PD defects in insulated power cables are well presented in the current bibliography. However, when several simultaneous PD sources are active, the localization of the sources becomes quite complex. This paper develops an efficient technique for the separation and localization of multiple PD sources in a medium voltage cable. The experimental results are obtained with single-end-based measurements using a high-frequency current transformer in a laboratory environment. The data processing based on the spectral characteristics of the signals is carried out by using the power ratios technique in order to determine the presence of different types of PD. Once the signals are separated, the PD sources can be localized with an individualized analysis of each source through time-domain reflectometry. The proposed methodology can be very valuable to improve the location diagnostic capability of the condition-based monitoring solutions, especially for underground cables.

A novel approach to identify slot discharges in the presence of end winding corona discharges

Different types of partial discharges occur in stator winding insulation and a few partial discharges may occur simultaneously. Among them, one of the important discharges, i.e. slot discharge, may get masked in the presence of end winding corona discharge. In such cases, recognizing the occurrence of slot discharge during partial discharge measurements is a challenging task. Therefore, in this paper, a novel approach has been used to identify the slot discharge in the presence of end-winding corona discharge. This approach is based on partial discharge measurements at 50 Hz and at a low frequency, in particular 0.1 Hz. Similar phase resolved patterns were seen at 50 and 0.1 Hz, for both discharges. In this method, per cycle positive integrated charge is measured and is compared at different PD threshold values. This unique method of determining the integrated charge as a function of PD threshold value clearly indicates the existence of slot discharges in the presence of end winding corona discharge. Otherwise, the occurrence of slot discharge would have gone unnoticed if the measurements were carried out only at power frequency, as it is usually done.

Separating Multi-Source Partial Discharge Signals Using Linear Prediction Analysis and Isolation Forest Algorithm

Partial discharge (PD) detection is an effective way to find defects and diagnose the insulation condition of power equipment. During manufacturing and operating, there may exist multi-source PD signals in the equipment, which could seriously affect the accuracy of subsequent defect analyses. In this paper, a universal separation methodology using linear prediction analysis (LPA) and the isolation forest algorithm (IFA) is introduced. By approximating the present waveform point by a linear combination of several points in the past, a 12-D linear prediction cepstrum coefficient (LPCC) feature space can be established which can accurately characterize PD waveforms. In this paper, we applied principal component analysis (PCA) to reduce the feature space to 2-D space. The IFA was adopted to separate multi-source PD signals which quantified the degree of clustering and added one more parameter based on the original features. Thus, the proposed method can separate multiple PD sources effectively even if the difference in the features of different types of PDs is small. In addition, the proposed algorithm easily removed the noise points during the separation process. The algorithm was applied to different types of PD signals on 35-kV transformers and a three-source PD data set on a 252-kV gas-insulated switchgear (GIS) platform. The separation results confirm that the proposed algorithm can effectively separate and distinguish various PD signals.

Partial discharge detection using PLC receivers in MV cables: A theoretical framework

Partial discharge (PD) activity is a key indicator of the cable insulation health in medium-voltage networks. Many of these cables are used as transmission media, because power line communication (PLC) modems are installed at their ends.This paper proposes two techniques that allow broadband PLC receivers to detect PDs at the same time that the PLC signal is being demodulated. The proposed techniques are based on how PDs alter both, null, and zero-bit loaded carriers, commonly used in orthogonal frequency division multiplexing modems. Using the proposed techniques, a simple firmware modification is sufficient to add this new functionality to the already installed modems.This paper is mainly concerned with theoretical aspects of the proposed techniques, and no experimental validation has been done. Nevertheless, simulation results show that the proposed techniques can be used to detect different types of PDs. Four cases studies are analyzed: short- and long-distance PDs, and low- and high-energy PDs. Finally, the impact of the inter-carrier interference on the detection capability is studied, and some recommendations are made to mitigate its effects.

Behavior of an Inductive Loop Sensor in the Measurement of Partial Discharge Pulses with Variations in Its Separation from the Primary Conductor.

Ideally, an insulation system must be capable of electrically insulating the active components of a machine or device subjected to high voltages. However, due to the presence of polluting agents or imperfections inside or on the surface of the insulation, small current pulses called partial discharges (PDs) are common, which partially short-circuit the insulation and cause it to lose its insulating properties, and thus its insulation capacity, over time. In some cases, measurements of this phenomenon are limited by the type of sensor used; if it is not adequate, it can distort the obtained results, which can lead to a misdiagnosis of the state of the device. The inductive loop sensor has experimentally been demonstrated to be capable of properly measuring different types of PDs. However, because of its current design, there are several practical limitations on its use in real devices or environments. An example is the presence of a primary conductor located at a fixed distance from the sensor, through which PD pulses must flow for the sensor to capture them. In this article, the sensor’s behavior is studied at different separation distances from the line through which the PD pulses flow. In addition, the measuring capacity of the sensor is tested by removing the presence of the primary conductor and placing the sensor directly over the line through which the PD pulses of a real device flow.

Design and development of a portable partial discharge generator

Intensive study of Partial Discharge (PD) phenomenon is of great importance for detecting and distinguishing different types of partial discharge. A portable device for partial discharge generation is designed and developed in this paper, which possesses advantage of miniaturization, dependability and flexibility. Such device is composed of power supply module, PD generating module and PD output interface. An implementing example of the partial discharge generator is given here in detail. Simulated high voltage signal is generated by a waveform generator and applied on physical PD generating model after being amplified by power amplifier and being boosted by a high voltage transformer with high transformation ratio. The generated PD pulse sequence are acquired and further analysed by MATLAB. Point to plane discharge is used as an example and verifies the performances of this device.

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.

Robust Condition Assessment of Electrical Equipment with One Class Support Vector Machines Based on the Measurement of Partial Discharges

This paper presents a system for the detection of partial discharges (PD) in industrial applications based on One Class Support Vector Machines (OCSVM). The study stresses the detection of Partial Discharges (PD) as they represent a major source of information related to degradation in the equipment. PD measurement is a widely extended technique for condition monitoring of electrical machines and power cables to avoid catastrophic failures and the consequent blackouts. One of the most important keystones in the interpretation of partial discharges is their separation from other signals considered as not-PD especially in low SNR measurements. In this sense, the OCSVM is an interesting alternative to binary SVMs since it does not need a training set with examples of all the output classes correctly labelled. On the contrary, the OCSVM learns a model of the signals acquired when the equipment is in PD-free mode, defined as a state where no degradation mechanism is active, so one only needs to make sure that the training signals were recorded under this setting. These default mode signals are easier to characterize and acquire in industrial environments than PD and lead to more robust detectors that practically do not need domain adaptation to perform in scenarios prone to different types of PD. In fact, the experimental results show that the performance of the OCSVM is comparable to that achieved by a binary SVM trained using both noise and PD pulses. Finally, the method is successfully applied to a more realistic scenario involving the detection of PD in a damaged distribution power cable.

Identification of acoustic signals of corona discharges under different contamination levels using wavelet transform

Partial discharges (PDs) emit energy in several ways, producing electromagnetic emissions in the form of radio waves, light and heat, and acoustic emissions in the audible and ultra-sonic ranges. These emissions enable us to detect, locate, measure, and analyse PD activity in order to identify faults before the development of failures, because once present, the damage caused by PDs always increases, leading to asset losses, outages, protection-system failure, disaster, and huge energy losses. Therefore, it is of great importance to identify different types of PDs and to assess their severity. This paper investigates the acoustic emissions associated with corona discharge (CD) from different types of sources in the time domain, and based on these it is used to detect, identify, and characterize the acoustic signals due to CD activity. Which usually takes place on polluted glass insulators used in high-voltage transmission lines and hence to differentiate abnormal operating conditions from normal ones. A laboratory experiment was conducted by preparing prototypes of the discharge. This study suggests a feature extraction and classification algorithm for CD classification. A wavelet signal processing toolbox is used to recover the CD acoustic signals by eliminating the noisy portion and to reduce the dimensions of the feature input vector. The proposed model is proven to characterize the PD activity with a high degree of integrity, which is attributed to the effect of the wavelet technique. The test results show that the proposed approach is efficient and reliable.