Partial Discharge Recognition Research Articles

Diagnostics of porcelain insulators by partial discharges characteristics

RELEVANCE of the research is in the development of a non-destructive method for diagnosing porcelain insulators of high-voltage electrical equipment based on analysis of the characteristics of partial discharges (PDs). The problem of the final stage of breakdown of both the discharge gap and porcelain insulators recognizing is currently has not solved. THE PURPOSE. Recognition of PDs in solid insulation, study of PDs characteristics in a pre- breakdown situation, recognition of defective insulators based on PDs characteristics analysing. METHODS. The study of PDs characteristics for a defective and functional porcelain insulator were carried out. To study the characteristics of various types of PDs, including in the pre-breakdown situation, a system of surface-needle electrode system was used. RESULTS. The article describes compares the PDs characteristics obtained using standard R-400 device and a digital storage oscilloscope (DSO) using a telescopic antenna. A technique for recognizing the development of the pre-breakdown situation of the surface-needle discharge gap and the porcelain insulator was developed. The technique for recognizing a defective porcelain insulator from the amplitude-phase diagrams (APD) of PDs was developed. The method for assessing the breakdown voltage of porcelain insulators based on the characteristics of partial discharges was developed. CONCLUSION. At the moment of transition to the pre-breakdown stage were observed a sharp increase in the counter-movement of charges of opposite polarity. In the stage immediately before the breakdown, near zero values of the applied voltage of negative polarity, ordered PDs of the same polarity as the applied voltage were recorded in large numbers. These discharges led to an increase in leakage current and were interpreted as initial corona discharges. These discharges were recorded both by a telescopic antenna in a surface-needle system and in porcelain insulators.

Noise Separation and Discriminative Feature Learning for Partial Discharge Recognition

Noise Separation and Discriminative Feature Learning for Partial Discharge Recognition

A Semi-Supervised Approach for Partial Discharge Recognition Combining Graph Convolutional Network and Virtual Adversarial Training

A Semi-Supervised Approach for Partial Discharge Recognition Combining Graph Convolutional Network and Virtual Adversarial Training

Partial Discharge Pattern Recognition Based on an Ensembled Simple Convolutional Neural Network and a Quadratic Support Vector Machine

Partial discharge (PD) is a crucial and intricate electrical occurrence observed in various types of electrical equipment. Identifying and characterizing PDs is essential for upholding the integrity and reliability of electrical assets. This paper proposes an ensemble methodology aiming to strike a balance between the model complexity and the predictive performance in PD pattern recognition. A simple convolutional neural network (SCNN) was constructed to efficiently decrease the model parameters (quantities). A quadratic support vector machine (QSVM) was established and ensembled with the SCNN model to effectively improve the PD recognition accuracy. The input for QSVM consisted of the circular local binary pattern (CLBP) extracted from the enhanced image. A testing prototype with three types of PD was constructed and 3D phase-resolved pulse sequence (PRPS) spectrograms were measured and recorded by ultra-high frequency (UHF) sensors. The proposed methodology was compared with three existing lightweight CNNs. The experiment results from the collected dataset emphasize the benefits of the proposed method, showcasing its advantages in high recognition accuracy and relatively few mode parameters, thereby rendering it more suitable for PD pattern recognition on resource-constrained devices.

Recognition of partial discharge in GIS based on image feature fusion

<p>Partial discharge (PD) is a significant electrical fault in gas-insulated switchgear (GIS), with various types posing different risks to insulation. Accurate identification of PD types is essential for enhancing GIS management and ensuring the reliability of electrical grids. This study proposes a novel approach for PD identification in GIS integrating completed local binary pattern (CLBP) feature extraction, feature engineering, and an optimized support vector machine (SVM). PD faults were simulated in GIS and phase-resolved pulse sequence (PRPS) data for four different forms of PD were gathered. CLBP was used to extract image features, and then the support vector machine recursive feature elimination (SVM-RFE) algorithm was used to evaluate feature importance. Then, linear discriminant analysis (LDA) was used to fuse the selected features and reduce redundancy. The fused features were classified using a bald eagle search algorithm combined with differential evolution (IBES)-optimized SVM, achieving a recognition accuracy of 99.38%. The results indicate that the proposed method effectively distinguishes between different PD PRPS patterns in GIS.</p>

Edge-Computing-Based Knowledge Distillation and Multitask Learning for Partial Discharge Recognition

Edge-Computing-Based Knowledge Distillation and Multitask Learning for Partial Discharge Recognition

Partial Discharge Pattern-Recognition Method Based on Embedded Artificial Intelligence

This paper proposes a method for detecting and recognizing partial discharges in high-voltage (HV) equipment. The aim is to address issues commonly found in traditional systems, including complex operations, high computational demands, significant power consumption, and elevated costs. Various types of discharges were investigated in an HV laboratory environment. Discharge data were collected using a high-frequency current sensor and a microcontroller. Subsequently, this data underwent processing and transformation into feature sets using the phase-resolved partial discharge analysis technique. These features were then converted into grayscale map samples in PNG format. To achieve partial discharge classification, a convolutional neural network (CNN) was trained on these samples. After successful training, the network model was adapted for deployment on a microcontroller, facilitated by the STM32Cube.AI ecosystem, enabling real-time partial discharge recognition. The study also examined storage requirements across different CNN layers and their impact on recognition efficacy. To assess the algorithm’s robustness, recognition accuracy was tested under varying discharge voltages, insulation media thicknesses, and noise levels. The test results demonstrated that the algorithm could be effectively implemented on a microcontroller, achieving a recognition accuracy exceeding 98%.

Audio General Recognition of Partial Discharge and Mechanical Defects in Switchgear Using a Smartphone

Mechanical defects and partial discharge (PD) defects can appear in the indoor switchgear of substations or distribution stations, making the switchgear a safety hazard. However, traditional acoustic methods detect and identify these two types of defects separately, ignoring the general recognition of audio signals. In addition, the process of using testing equipment is complex and costly, which is not conducive to timely testing and widespread application. To assist technicians in making a quick preliminary diagnosis of defect types for switchgear, improve the efficiency of the subsequent overhaul, and reduce the cost of detection, this paper proposes a general audio recognition method for identifying defects in switchgear using a smartphone. Using this method, we can analyze and identify audio and video files recorded with smartphones and synchronously distinguish background noise, mechanical vibration, and PD audio signals, which have good applicability within a certain range. When testing the feasibility of using smartphones to identify three types of audio signal, through characterizing 12 sets of live audio and video files provided by technicians, it was found that there were similarities and differences in these characteristics, such as the autocorrelation, density, and steepness of the waveforms in the time domain, and the band energy and harmonic components of the frequency spectrum, and new combinations of features were proposed as applicable. To compare the recognition performance for features in the time domain, frequency band energy, Mel-frequency cepstral coefficient (MFCC), and this method, feature vectors were input into a support vector machine (SVM) for a recognition test, and the recognition results showed that the the present method had the highest recognition accuracy. Finally, a set of mechanical defects and PD defects were set up for a switchgear, for practical verification, which proved that this method was general and effective.

Analysis of the main problems arising during the recognition of the defect type based on the results of the dissolved gases analysis

The article presents the results of the analysis of the main problems that arise when recognising the defect type in oil-filled equipment of electric power networks based on the results of the dissolved gases analysis. On the example of interpreting the actual results of the dissolved gases analysis, the main problems arising in the recognition of partial discharges, spark discharges, discharges with low and high energy density, local overheating and combined defects are analysed using the graphical method from the IEC 60599 standard, the ETRA square and the Duval triangle. The analysis revealed significant discrepancies in the norms and criteria regulated by different standards and methods for interpreting the results of the dissolved gases analysis to recognise the same defect. The research results showed that for almost all the analysed defects, the non-recognition is caused by the absence of normalised values of diagnostic criteria (values of gas ratios, percentage of gases and ratios of gas concentrations to the gas with the maximum content) for some defects or combinations of several defects. False diagnoses are made when identifying the type of defects in oil-filled equipment based on the results of the dissolved gases analysis because the values of individual gas ratios or percentage content of individual gases are not taken into account. The analysis revealed contradictions in the diagnoses made when different diagnostic criteria (ratios of characteristic gases and percentage of gases) are used for the same results of the analysis of gases dissolved in oil. Ensuring reliable recognition of the type of defect in oil-filled equipment based on the results of dissolved gas analysis is possible thanks to an integrated approach that includes not only the analysis of gas ratios, but also the analysis of the percentage of gases and defect nomograms. In addition, it is crucial to take into account the physicochemical laws of gas formation in oil, in particular, the dependence of gas content on the temperature or energy of defects.

A Contemporary Review of High Voltage Partial Discharge Detection and Recognition Techniques

This review article provides a summary of the most advanced approaches and advancements in the detection and recognition of high voltage partial discharge (PD). It discusses numerous detecting technologies, such as electrical, acoustic, and optical approaches, as well as their merits and disadvantages. It also discusses current developments in signal processing and pattern recognition algorithms used for PD detection and classification. Lastly, the study covers the challenges and limitations in high voltage PD detection and identification studies, as well as potential future solutions.

Validation of a Qualification Procedure Applied to the Verification of Partial Discharge Analysers Used for HVDC or HVAC Networks

The insulation condition of HVDC grids consisting of cable systems, GIS, and converters should be monitored by partial discharge (PD) analysers using artificial intelligence (AI) tools for efficient insulation diagnosis. Although there are many experiences of PD monitoring solutions developed for the supervision of the insulation condition of HVAC grids using PD analysers, there are no standardised requirements for their qualification available yet. The international technical specification TS IEC 62478 provides general rules for PD measurements using electromagnetic methods but does not define performance requirements for qualification tests. HVDC and HVAC PD analysers must be tested by unambiguous test procedures. This paper compiles experiences of using PD analysers with HFCT sensors in HVAC grids (cable systems, GIS, and AIS) to define a qualification procedure for HVAC systems. This procedure is applicable to HVDC grids (cable systems, GIS, AIS, and converters) because the particularities related to the insulation behaviour under HVDC voltage are also considered. Representative PD sources are discussed in HVAC and HVDC positive and negative polarity. The PD pulse trend of representative insulation defects in HVDC cable systems is quite different from that of HVAC grids. Special attention should be paid to the acquisition of PD signals in HVDC grids since few pulses appear in solid insulations, mainly during voltage changes (polarity reversals or surges), but rarely in continuous operation with constant direct voltage. A synthetic PD simulator has been developed to reproduce trains of PD pulses or noise signals, similar to those that can appear in the power network. A set of three functionality tests has been developed for qualification of the diagnostic capabilities of PD analysers working up to 30 MHz addressed to HVDC or HVAC grids: (1) PD recognition test, (2) PD clustering test, and (3) PD location test. This qualification procedure has been validated by means of a round-robin test performed by five research institutes (RISE, FFII, TUDelft, TAU, and UPM) using commercial and in-development AI PD recognition and clustering tools to demonstrate its robustness and applicability. Applying this qualification procedure, two PD methods for electrical detection and prevention of insulation defects have been approved, one for HVAC and the other for HVDC grids.

Partial Discharge Data Augmentation and Pattern Recognition for Unbalanced and Small Sample Scenarios

To improve the accuracy of partial discharge pattern recognition under unbalanced and small sample conditions, a method of partial discharge data augmentation and pattern recognition using the generative adversarial network embedded deep auto-encoder (DAE-GAN) is proposed. First, deep Auto-encoder (DAE) is embedded into a Generative Adversarial Network (GAN), and DAE is used to guide the generation process to improve the authenticity of generated samples. Second, complement samples of PD samples are added to the training process of the Generative Adversarial Network to solve the problem of small PD samples. Finally, extended equalization training samples are used to fine-tune the discriminator of the model to realize PD pattern recognition. DAE-GAN is used for data augmentation and pattern recognition of partial discharge experimental signals. The results show that, compared with other algorithms, the authenticity and probability distribution fitting accuracy of partial discharge samples generated by DAE-GAN are higher and the accuracy of partial discharge recognition is improved by 8.24% after data augmentation.

Radiometric Partial Discharge Detection: A Review

One of the most common failures or breakdowns that can occur in high-voltage (HV) equipment is due to partial discharges (PDs). This occurs as a result of inadequate insulation, aging, harsh environmental effects, or manufacturing flaws. PD detection and recognition methods have gained growing attention and have seen great progress in the past decades. Radiometric methods are one of the most investigated detection approaches due to their immunity to electromagnetic interference (EMI) and their capabilities to detect and locate PD activities in different applications such as transformers, cables, etc. Several review articles have been published to classify and categorize these works. Nonetheless, some concepts are missing, and some improvement techniques, such as PD detection at high-frequency (HF) and very high-frequency (VHF), have been overlooked. We present in this paper an exhaustive review study of state-of-the-art PD detection based on radiometric methods at different usable radiofrequency bands (i.e., HF, VHF, and UHF). Accordingly, we propose a new generic categorization approach based on the detected electromagnetic wave component (magnetic or electric fields) and pick-up location, either from free space or ground cable.

Partial Discharges Monitoring for Electric Machines Diagnosis: A Review

Online monitoring of Partial Discharges (PDs) in rotating electrical machines is an useful tool for machine prognosis, as it presents reduced costs compared to intrusive inspections and is associated with relevant problems. Although this monitoring method has been developed for almost 50 years, the recent advancements in processes automation and signal processing techniques allow improvements that are still being studied by academic and industrial researchers. To analyze the current context of PDs monitoring, this article presents a literature review based on concepts of PDs in rotating machines, data acquisition techniques, state-of-the art commercial equipment, and recent methodologies for detection and pattern recognition of PDs. The challenges identified in the literature that motivate the development of more reliable and robust PD monitoring systems are presented and discussed.

Pattern Recognition of Partial Discharge in Power Transformer Based on InfoGAN and CNN

Pattern Recognition of Partial Discharge in Power Transformer Based on InfoGAN and CNN

Power-Line Partial Discharge Recognition with Hilbert–Huang Transform Features

Partial discharge (PD) has caused considerable challenges to the safety and stability of high voltage equipment. Therefore, highly accurate and effective PD detection has become the focus of research. Hilbert–Huang Transform (HHT) features have been proven to have great potential in the PD analysis of transformer, gas insulated switchgear and power cable. However, due to the insufficient research available on the PD features of power lines, its application in the PD recognition of power lines has not yet been systematically studied. In the present study, an enhanced light gradient boosting machine methodology for PD recognition is proposed; the HHT features are extracted from the signal and added to the feature pool to improve the performance of the classifier. A public power-line PD recognition contest dataset is introduced to evaluate the effectiveness of the proposed feature. Numerical studies along with comparison results demonstrate that the proposed method can achieve promising performances. This method which includes the HHT features contributes to the detection of PD in power lines.

Cable-Partial-Discharge Recognition Based on a Data-Driven Approach with Optical-Fiber Vibration-Monitoring Signals

The effective pattern recognition of cable partial discharges (PDs) enables prompt corresponding measures to ensure cable safety. Traditional PD monitoring methods have limitations in online monitoring and accurate positioning, and the feature extraction of the monitored electrical signals requires significant prior knowledge. Therefore, this paper reports the performance of the distributed optical-fiber vibration-sensing monitoring of PDs on a cable with different insulation defects, and proposes a data-driven recognition approach based on the monitoring signals. The time series of the backscattered Rayleigh light intensity (BRLI) changes at the PD position were collected as the sample data. The coefficients of the time series’ autoregressive moving average (ARMA) models were extracted as features. Next, a classification model trained by the random forest (RF) algorithm was established. After the model’s validation with the experimental data and a comparative analysis with previously published methods, the PD recognition model was simply optimized based on the RF principle. The results showed that the proposed method achieved a high recognition accuracy, of about 98%, indicating that the data-driven approach—combining the ARMA model and the RF—is effective for cable-PD pattern recognition in distributed optical-fiber vibration-sensing systems.

Knowledge-Driven Recognition Methodology of Partial Discharge Patterns in GIS

Recognition of partial discharge (PD) patterns in gas insulated switchgear (GIS), as a basis of fault diagnosis, provides essential information for the condition assessment of GIS. However, traditional recognition methods are limited to handcrafted feature extraction or are extremely sensitive to the quantity and quality of datasets. Therefore, this article proposes a knowledge-driven algorithm, composed of the feature space and the knowledge space, to automatically extract PD features and improve the performance on noised, insufficient, and imbalanced datasets. First, the deep residual network (ResNet) in feature space extracts features from raw signals. Second, in knowledge space, the graph convolutional network (GCN) extracts additional information from the knowledge graph, which compensates for the missing information of original datasets. Finally, the algorithm recognizes patterns by ranking similarities between feature vectors and knowledge vectors. Verified by the comparison experiment, the proposed algorithm outperforms traditional methods with the accuracy of 99.58% on the experimental dataset and 95.67% on the online-detected dataset. Moreover, the accuracy of the proposed algorithm achieves 88.79% and 70.37% on noised and insufficient datasets, respectively, while the F-measure is higher than those of the comparison methods by 12.95% <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 18.69% on imbalanced datasets.

Dynamic characteristics of partial discharge from typical defects of high voltage cables

High voltage cables are one of the indispensable equipment in power transmission and distribution. The partial discharge characteristics of high voltage cables can provide guidance for cables status assessment and maintenance. A partial discharge testing is carried out for four typical defects that may occur in high voltage cables. After that, three parameters of the partial discharge are extracted and discussed, the discharge magnitude, the pulse amplitude and the rise time. The dynamic withstand voltage characteristics of partial discharge induced by typical defect types of the high voltage cables are summarized. The results indicate that the dynamic withstand voltage characteristics of partial discharge induced by different defective are quite different, and the variation trends and variation intervals of different characteristic parameters are obviously discriminate. Based on the analysis and comparison of the dynamic withstand voltage characteristics of partial discharge for the high voltage cables, the defect type of the cables can be effectively identified, which has reference significance for the pattern recognition of partial discharge and the evaluation of cables status.

The detection and classification of partial discharges in power transformer using the acoustic method

Partial discharges are the main causes of degradation of the insulation system in power transformers. Their permanent monitoring is therefore of fundamental importance, in the way that it ensures a better assessment of the level of electrical insulation. Several types of partial discharges are present in power transformers depending on the components of the electric field to which the insulation is subjected. Many on-line methods are used to the continuous measurement of partial discharges, including the acoustic method which doing both measurement of electrical parameters and localization of the sources of partial discharges. This article deals with both recognition and classification of partial discharges by analysis of acoustic signals. The goal is to reproduce various types of partial discharges present in the transformers (surface, internal, and corona discharges) by several structures of electrodes in a prototype transformer filled with insulating oil. The generation of partial discharges is done using an experimental setup consisting of a controlled 220V/50kV HV transformer connected to a prototype of transformer containing an electrode system. The experimental procedure are carried out according to the protocol of the IEC 61270 standard. The measurement of the acoustic signals on the tank is carried out using a conventional broadband sensor from the company ACOUSTIC EMISSION (GI150). Both signal analysis obtained in the frequency domain using the time-frequency response and high-resolution frequency analysis (MUSIC) makes it possible to clearly distinguish the type of discharge produced.