Search Results (50)

As a key equipment in the power system, the insulation state of oil-immersed transformer is directly related to the safe and stable operation of the power grid. To explore the feasibility of optical detection methods for detecting transformer insulation defects and further analyze the trend of partial discharge optical signal characteristics under typical oil-paper insulation defects in transformers, this paper proposes a method for detecting insulation defects in transformers based on photon detection technology. This method can not only reflect the periodicity and phase characteristics of photon signals, but also exhibits higher sensitivity compared to the traditional PRPD method. Firstly, the study builds an experimental platform for optoelectronic combined transformer partial discharge based on photon detection technology and carries out partial discharge simulation experiments on four typical insulation defect models through the step-up method to collect their pulse current signals and photon signals. Then, a phase-resolved photon counting (PRPC) method is proposed to analyze the signals during the development of partial discharges. Finally, the optical signal characteristics of the four defect models are extracted for comparative analysis. The results show that the optical signals of partial discharges can effectively respond to the generation and development process of partial discharges inside the transformer, and different types of insulation defects and development stages can be clearly distinguished according to the phase distribution characteristics and characteristic parameters of the optical signals. This study verifies the effectiveness of photon detection technology and provides a new effective tool for the detection of transformer oil-paper insulation defects. Full article

Partial discharge (PD) is not only a critical indicator but also a major accelerating factor of insulation degradation in power transformers. Accurate identification of PD types is essential for diagnosing insulation defects and locating faults in transformers. Traditional methods based on phase-resolved partial discharge (PRPD) patterns typically rely on expert interpretation and manual feature extraction, which are increasingly being supplanted by Convolutional Neural Networks (CNNs) due to their ability to automatically extract features and deliver high classification accuracy. However, the inherent subtlety and diversity of characteristic differences among PRPD patterns, coupled with substantial noise resulting from complex electromagnetic interference, present significant hurdles to achieving accurate identification. This paper proposes a transformer partial discharge identification method based on Deep Residual Shrinkage Network (DRSN) to address these challenges. The method integrates dual-path feature extraction to capture both local and global features, incorporates a channel-domain adaptive soft-thresholding mechanism to effectively suppress noise interference, and utilizes the Focal Loss function to enhance the model’s attention to hard-to-classify samples. To validate the proposed method, given the scarcity of diverse real-world transformer PD data, an experimental platform was utilized to generate and collect PD data by artificially simulating various discharge defect models, including tip discharge, surface discharge, air-gap discharge and floating discharge. Data diversity was then enhanced through sample augmentation and noise simulation, to minimize the gap between experimental data and real-world on-site data. Experimental results demonstrate that the proposed method achieves superior partial discharge recognition accuracy and strong noise robustness on the experimental dataset. For future work, it is essential to collect more real transformer PD data to further validate and strengthen the model’s generalization capability, thereby ensuring its robust performance and applicability in practical scenarios. Full article

The growing complexity of electrical infrastructure has elevated partial discharge (PD) detection to a crucial methodology for ensuring power system safety. Current PD pattern recognition approaches encounter persistent challenges in low-data scenarios, particularly regarding classification accuracy and model generalizability. This study develops a Firefly Algorithm with a Black Hole Mechanism-ResNet18 (FBH-ResNet18) framework that synergistically integrates the Firefly Algorithm with the Black Hole Mechanism (FBH algorithm) optimization with residual neural networks for PD signal classification using phase-resolved partial discharge (PRPD) mappings. A dedicated experimental platform first acquires PD signals through UHF sensors, which are subsequently converted into two-dimensional PRPD representations. The FBH algorithm systematically optimizes four key hyperparameters within the ResNet18 architecture during network training. The Black Hole Mechanism and improved population dynamics enhance optimization efficiency, resulting in more accurate hyperparameter tuning and improved model performance. Comparative evaluations demonstrate the enhanced performance of this parameter-optimized model against alternative configurations. Experimental results indicate that the improved ResNet18 achieves fast convergence and strong generalization on small-sample datasets, significantly enhancing recognition accuracy. During the first 80 generations of training, the classification accuracy reaches 89.11%, and in the final iteration, the model’s recognition accuracy increases to 92.55%, outperforming other models with accuracies generally below 90%. Additionally, the model shows excellent performance on the test set, with a loss function value of 0.250785, significantly lower than that of other models, indicating superior performance on small sample datasets. This research provides an effective solution for power cable fault diagnosis, offering high practical value. Full article

As critical infrastructure in modern distribution systems, power cables face progressive insulation degradation from partial discharge (PD), while conventional recognition methods struggle with feature extraction and model generalizability. This study develops an integrated experimental platform for PD pattern recognition in power cable systems, comprising a control console, high-voltage transformer, high-frequency current transformer, and ultra-high-frequency (UHF) signal acquisition equipment. Four distinct types of discharge-defective models are constructed and tested through this dedicated high-voltage platform, generating a dataset of phase-resolved partial discharge (PRPD) spectra. Based on this experimental foundation, an improved Swin Transformer-based framework with adaptive learning rate optimization is developed to address the limitations of conventional methods. The proposed architecture demonstrates superior performance, achieving 94.68% classification accuracy with 20 training epochs while reaching 97.52% at the final 200th epoch. Comparisons with the original tiny version of the Swin Transformer model show that the proposed Swin Transformer with an adaptive learning rate attains a maximum improvement of 6.89% over the baseline model in recognition accuracy for different types of PD defect detection. Comparisons with other deeper Convolutional Neural Networks illustrate that the proposed lightweight Swin Transformer can achieve comparable accuracy with significantly lower computational demands, making it more promising for application in real-time PD defect diagnostics. Full article

This paper investigates partial discharge (PD) characteristics and breakdown behavior in five electrode configurations fabricated from fiberglass circuit boards, including parallel electrode (PE), triangular electrode (TE), right-angled electrode (RE), and two floating electrode (FE) designs with 0.3 and 0.5 mm insulation gaps. The electrodes are tested according to IEC 60270:2000 using a commercial device with eight samples per configuration. Key parameters such as the PD inception voltage (PDIV) and breakdown voltage are measured. The TE configuration exhibited the highest breakdown voltage of 14.9 kV, with a PDIV of 6.13 kV and RPDIV of 8.04 kV, indicating strong dielectric properties. The RE configuration showed a PDIV of 7 kV, RPDIV of 8.5 kV, and a breakdown voltage of 13.3 kV. The FE_0.5 mm sample exhibited surface discharges, whereas the FE_0.3 mm sample experienced breakdown at 18 kV with an average breakdown voltage of 15.3 kV. The results indicate that the electrode geometry and insulation spacing strongly influence PD behavior and breakdown resilience. The phase-resolved partial discharge (PRPD) patterns at PDIV and breakdown provide further understanding of the dielectric stability. These findings offer critical insights into designing insulation systems under electrical stress. Full article

In order to address the issue of the accuracy of partial discharge pattern recognition being constrained by unbalanced samples and the deep structure of the deep learning network, a method for partial discharge data enhancement and pattern recognition based on a convolutional autoencoder auxiliary classifier generative adversarial network (CAE-ACGAN) and a residual network (ResNet) is proposed. The initial step involves the preprocessing of the acquired partial discharge signals, with the phase resolved partial discharge (PRPD) spectra subsequently employed as the training samples. Secondly, a CAE-ACGAN is constructed. The model combines the advantages of a deep convolutional self-coding structure and a generative adversarial paradigm to generate high-quality phase resolved partial discharge spectrograms. Subsequently, a ResNet is employed as the classifier for partial discharge pattern recognition, utilising the CAE-ACGAN-enhanced partial discharge dataset for network training to achieve accurate recognition of partial discharge signals. The experimental findings demonstrate that the SSIM and PSNR indexes of the CAE-ACGAN model utilised in this study are 0.92 and 45.88 dB, respectively. The partial discharge pattern method employing the CAE-ACGAN and ResNet exhibits superiority in identifying partial discharges, attaining an identification accuracy of 98%, which is 7.25% higher than the pre-enhancement level. Full article

Deep learning approaches have been successfully applied to perform automatic classification of phase-resolved partial discharge (PRPD) diagrams. Under the supervised learning paradigm, however, the performance of classifiers strongly depends on the availability of large and previously labeled data sets. Labeling is an intensive and time-consuming labor, typically involving the manual annotation of a large number of data examples by an expert. In this work, we propose a label propagation algorithm applied to PRPD data sets, aiming to reduce the time necessary to manually label PRPDs. Our basic pipeline is composed of three phases: pre-processing, dimensionality reduction procedures, and clustering. Different configurations of the basic pipeline are tested by using PRPDs obtained from online measurements in hydrogenerators. The performance of each configuration is assessed by using the Silhouette, Caliński–Harabasz, and Davies–Bouldin scores. The clustering of the best three configurations is compared with annotated PRPDs by using the Fowlkes-Mallows index. Results suggest our strategy can substantially decrease the time for manual labeling. Full article

In this work, a new methodology is proposed for the online and non-invasive extraction of partial discharge (PD) pulses from raw measurement data obtained using a simplified setup. This method enables the creation of sub-windows with optimized size, each containing a single candidate PD pulse. The proposed approach integrates mathematical morphological filtering (MMF) with kurtosis, a first-order Savitzky-Golay smoothing filter, the Otsu method for thresholding, and a specific technique to associate each sub-window with the phase angle of the applied voltage waveform, enabling the construction of phase-resolved PD (PRPD) patterns. The methodology was validated against a commercial PD detection device adhering to the IEC (International Electrotechnical Commission) standard. Experimental results demonstrated that the proposed method, utilizing an off-the-shelf 8-bit resolution data acquisition system and a low-cost high-frequency current transformer (HFCT) sensor, effectively diagnoses and characterizes PD activity in high-voltage equipment, such as surge arresters and instrument transformers, even in noisy environments. It was able to characterize PD activity using only a few cycles of the applied voltage waveform and identify low amplitude PD pulses with low signal-to-noise ratio signals. Other contribution of this work is the diagnosis and fault signature obtained from a real surge arrester (SA) with a nominal voltage of 192 kV, corroborated by destructive disassembly and internal inspection of the tested equipment. This work provides a cost-effective and accurate tool for real-time PD monitoring, which can be embedded in hardware for continuous evaluation of electrical equipment integrity. Full article

With the digital transformation of the grid, partial discharge (PD) recognition using deep learning (DL) and big data has become essential for intelligent transformer upgrades. However, labeling on-site PD data poses challenges, even necessitating the removal of covers for internal examination, which makes it difficult to train DL models. To reduce the reliance of DL models on labeled PD data, this study proposes a semi-supervised approach for PD fault recognition by combining the graph convolutional network (GCN) and virtual adversarial training (VAT). The approach introduces a novel PD graph signal to effectively utilize phase-resolved partial discharge (PRPD) information by integrating numerical data and region correlations of PRPD. Then, GCN autonomously extracts features from PD graph signals and identifies fault types, while VAT learns from unlabeled PD samples and improves the robustness during training. The approach is validated using test and on-site data. The results show that the approach significantly reduces the demand for labeled samples and that its PD recognition rates have increased by 6.14% to 14.72% compared with traditional approaches, which helps to reduce the time and labor costs of manually labeling on-site PD faults. Full article

In general, a high voltage (HV) substation can be made up of multiple insulation subsystems: an air insulation subsystem (AIS), gas insulation subsystem (GIS), liquid insulation subsystem (power transformers), and solid insulation subsystem (power cables), all of them with their grounding structures interconnected and linked to the substation earth. Partial discharge (PD) pulses, which are generated in a HV apparatus belonging to a subsystem, travel through the grounding structures of the others. PD analyzers using high-frequency current transformer (HFCT) sensors, which are installed at the connections between the grounding structures, are sensitive to these traveling pulses. In a substation made up of an AIS, several non-critical PD sources can be detected, such as possible corona, air surface, or floating discharges. To perform the correct diagnosis, non-critical PD sources must be separated from critical PD sources related to insulation defects, such as a cavity in a solid dielectric material, mobile particles in SF6, or surface discharges in oil. Powerful diagnostic tools using PD clustering and phase-resolved PD (PRPD) pattern recognition have been developed to check the insulation condition of HV substations. However, a common issue is how to determine the subsystem in which a critical PD source is located when there are several PD sources, and a critical one is near the boundary between two HV subsystems, e.g., a cavity defect located between a cable end and a GIS. The traveling direction of the detected PD is valuable information to determine the subsystem in which the insulation defect is located. However, incorrect diagnostics are usually due to the constraints of PD measuring systems and inadequate PD diagnostic procedures. This paper presents a diagnostic procedure using an appropriate PD analyzer with multiple HFCT sensors to carry out efficient insulation condition diagnoses. This PD procedure has been developed on the basis of laboratory tests, transient signal modeling, and validation tests. The validation tests were carried out in a special test bench developed for the characterization of PD analyzers. To demonstrate the effectiveness of the procedure, a real case is also presented, where satisfactory results are shown. Full article

This work presents a methodology for the generation and classification of phase-resolved partial discharge (PRPD) patterns based on the use of a printed UHF monopole antenna and signal conditioning circuit to reduce hardware requirements. For this purpose, the envelope detection technique was applied. In addition, test objects such as a hydrogenerator bar, dielectric discs with internal cavities in an oil cell, a potential transformer and tip–tip electrodes immersed in oil were used to generate partial discharge (PD) signals. To detect and classify partial discharges, the standard IEC 60270 (2000) method was used as a reference. After the acquisition of conditioned UHF signals, a digital signal filtering threshold technique was used, and peaks of partial discharge envelope pulses were extracted. Feature selection techniques were used to classify the discharges and choose the best features to train machine learning algorithms, such as multilayer perceptron, support vector machine and decision tree algorithms. Accuracies greater than 84% were met, revealing the classification potential of the methodology proposed in this work. Full article

This paper proposes a new method for recognizing, extracting, and processing Phase-Resolved Partial Discharge (PRPD) patterns from two-dimensional plots to identify specific defect types affecting electrical equipment without human intervention while retaining the principals that make PRPD analysis an effective diagnostic technique. The proposed method does not rely on training complex deep learning algorithms which demand substantial computational resources and extensive datasets that can pose significant hurdles for the application of on-line partial discharge monitoring. Instead, the developed Cosine Cluster Net (CCNet) model, which is an image processing pipeline, can extract and process patterns from any two-dimensional PRPD plot before employing the cosine similarity function to measure the likeness of the patterns to predefined templates of known defect types. The PRPD pattern recognition capabilities of the model were tested using several manually classified PRPD images available in the existing literature. The model consistently produced similarity scores that identified the same defect type as the one from the manual classification. The successful defect type reporting from the initial trials of the CCNet model together with the speed of the identification, which typically does not exceed four seconds, indicates potential for real-time applications. Full article

Partial discharge (PD) is a localized discharge phenomenon in the insulator of electrical equipment resulting from the electric field strength exceeding the local dielectric breakdown electric field. Partial-discharge signal identification is an important means of assessing the insulation status of electrical equipment and critical to the safe operation of electrical equipment. The identification effect of traditional methods is not ideal because the PD signal collected is subject to strong noise interference. To overcome noise interference, quickly and accurately identify PD signals, and eliminate potential safety hazards, this study proposes a PD signal identification method based on multiscale feature fusion. The method improves identification efficiency through the multiscale feature fusion and feature aggregation of phase-resolved partial-discharge (PRPD) diagrams by using PMSNet. The whole network consists of three parts: a CNN backbone composed of a multiscale feature fusion pyramid, a down-sampling feature enhancement (DSFB) module for each layer of the pyramid to acquire features from different layers, a Transformer encoder module dominated by a spatial interaction–attention mechanism to enhance subspace feature interactions, a final categorized feature recognition method for the PRPD maps and a final classification feature generation module (F-Collect). PMSNet improves recognition accuracy by 10% compared with traditional high-frequency current detection methods and current pulse detection methods. On the PRPD dataset, the validation accuracy of PMSNet is above 80%, the validation loss is about 0.3%, and the training accuracy exceeds 85%. Experimental results show that the use of PMSNet can greatly improve the recognition accuracy and robustness of PD signals and has good practicality and application prospects. Full article

Partial discharge (PD) is the dominant insulating defect in Gas-Insulated Switchgear (GIS). The existing detection methods are mainly divided into built-in wire-connected disk antennas with destructive drilling and external ultra-high frequency antennas with poor anti-interference ability. This research introduces a passive wireless PD sensor implanted inside GIS on the observation window. The sensor is implemented by a sheeting branch-inductor with multiple resonances which is able to enhance detection sensitivity. A coaxially aligned readout circuit, positioned outside the GIS, interrogates the PD sensor to wirelessly obtain the PD signal. The proposed sensing scheme improves signal-to-noise ratio and ensures minimal disruption to the electric field distribution inside GIS. An experimental setup was established in a controlled laboratory environment to benchmark the multi-resonant sensor against the commercial UHF sensor. A 2.5-times enhancement of signal strength was observed. Since our sensor was implanted inside the GIS, a high signal-to-noise ratio (68.82 dB) was obtained. Moreover, we constructed a wireless calibration test to investigate the accuracy of the proposed sensor. The precision of the signal test was as high as 0.72 pC. The pulse phase distribution information was collected to demonstrate a phase-resolved partial discharge (PRPD) pattern. The experiment results validate the effectiveness of the proposed method and demonstrate excellent performance in PD detection. Full article

In this paper, we present an unprecedented method based on Kohonen networks that is able to automatically recognize partial discharge (PD) classes from phase-resolved partial discharge (PRPD) diagrams with features of various simultaneous PD patterns. The PRPD diagrams were obtained from the stator windings of a real-world hydro-generator rotating machine. The proposed approach integrates classification probabilities into the Kohonen method, producing self-organizing probability maps (SOPMs). For building SOPMs, a group of PRPD diagrams, each containing a single PD pattern for training the Kohonen networks and single- and multiple-class-featured samples for obtaining final SOPMs, is used to calculate the probabilities of each Kohonen neuron to be associated with the various PD classes considered. At the end of this process, a self-organizing probability map is produced. Probabilities are calculated using distances, obtained in the space of features, between neurons and samples. The so-produced SOPM enables the effective classification of PRPD samples and provides the probability that a given PD sample is associated with a PD class. In this work, amplitude histograms are the features extracted from PRPDs maps. Our results demonstrate an average classification accuracy rate of approximately 90% for test samples. Full article