Partial Discharge Pattern Recognition Research Articles

GIS Partial Discharge Pattern Recognition Based on IWOA-SVM Algorithm

Abstract To effectively recognize partial discharge (PD) types in gas-insulated switchgear (GIS) equipment, a novel PD pattern recognition method is proposed. This method leverages an improved whale optimization algorithm (IWOA) to optimize the support vector machine (SVM). Firstly, typical PD defects were simulated in the GIS chamber. The PRPS results demonstrated significant differences among four PD defects. Secondly, multiple feature values of PD results were calculated for feature extraction. Finally, the IWOA-SVM algorithm was employed for the recognition of PD patterns in GIS. Experimental results indicate that the IWOA-SVM algorithm can effectively recognize different PD types and the predicted accuracy rate can reach 99.1667%, which is much higher than SVM and WOA-SVM algorithms, providing information to construction units for maintenance.

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.

Pattern Recognition of Partial Discharge Faults in Switchgear Using a Back Propagation Neural Network Optimized by an Improved Mantis Search Algorithm.

The dependable functioning of switchgear is essential to maintain the stability of power supply systems. Partial discharge (PD) is a critical phenomenon affecting the insulation of switchgear, potentially leading to equipment failure and accidents. PDs are generally grouped into metal particle discharge, suspended discharge, and creeping discharge. Different types of PDs are closely related to the severity of a PD. Partial discharge pattern recognition (PDPR) plays a vital role in the early detection of insulation defects. In this regard, a Back Propagation Neural Network (BPNN) for PDPR in switchgear is proposed in this paper. To eliminate the sensitivity to initial values of BPNN parameters and to enhance the generalized ability of the proposed BPRN, an improved Mantis Search Algorithm (MSA) is proposed to optimize the BPNN. The improved MSA employs some boundary handling strategies and adaptive parameters to enhance the algorithm's efficiency in optimizing the network parameters of BPNN. Principal Component Analysis (PCA) is introduced to reduce the dimensionality of the feature space to achieve significant time saving in comparable recognition accuracy. The initially extracted 14 feature values are reduced to 7, reducing the BPNN parameter count from 183 with 14 features to 113 with 7 features. Finally, numerical results are presented and compared with Decision Tree (DT), k-Nearest Neighbor classifiers (KNN), and Support Vector Machine (SVM). The proposed method in this paper exhibits the highest recognition accuracy in metal particle discharge and suspended discharge.

Research on fault diagnosis method of vehicle cable terminal based on time series segmentation for graph neural network model

In the field of partial discharge (PD) pattern recognition for vehicle cable terminals, the existing recognition methods often lead to reduced accuracy due to inadequate time–frequency features. This study introduces a novel method for time sequence segmentation to construct graph signals. Additionally, we flexibly integrate the graph self-attention convolution layer (GAT) and the self-attention graph pooling layer (SAG) to build a diagnostic model, allowing for robust feature extraction through multiple attention heads of GAT and effective integration of global features via the SAG pooling layer. High-frequency pulse current was utilized for PD testing on four defect models, with subsequent evaluation of outcomes. Furthermore, examinations of cable terminations in real trains provide further support for our approach by improving recognition accuracy and enhancing train operational stability.

Improving power cable partial discharge pattern recognition through gustafson-kessel fuzzy clustering techniques

Existing methods for recognizing partial discharge patterns in power cables do not utilize fuzzy clustering of the discharge signals, resulting in poor quality and low recall and precision of the pattern recognition. To address this, we propose a new approach for partial discharge pattern recognition in cables using Gustafson-Kessel(GK) Fuzzy Clustering. The method involves acquiring signals from a power cable partial discharge monitoring system and then processing the signals with GK fuzzy clustering. The clustered discharge signals are filtered with wavelet packet transforms before input into an improved adaptive resonance theory(ART) neural network for final pattern recognition. Experiments demonstrate the new technique achieves up to 98.7% recall and 85.6% precision for discharge pattern recognition, with discharge signal Signal Noise Ratio(SNR) between 55 dB and 62 dB and maximum recognition accuracy reaching 98%. The proposed fuzzy clustering-based pattern recognition approach significantly enhances partial discharge diagnostics for power cable monitoring.

Fine-grained partial discharge pattern recognition method based on substation equipment knowledge graph

Discharge-type faults are one of the main types of faults in substation equipment, and partial discharge (PD) faults are an important cause of equipment discharge faults. When different types of PD faults occur in equipment, their fault handling plans also vary accordingly. Therefore, the identification of PD types in equipment is very important. Aiming at the problem that the recognition accuracy of the existing methods is not high enough due to the low quality of PD data and unbalanced samples in the process of equipment PD pattern recognition, we propose a fine-grained method to recognize PD patterns based on substation equipment knowledge graph. This model combines a visual encoder with a knowledge graph and uses the fine-grained attribute features and the association feature information of the PD entity provided by the knowledge graph for the research of equipment PD pattern recognition. After experimental verification, the accuracy of PD pattern recognition in our proposed method is significantly higher than that of previous methods.

Partial discharge pattern recognition algorithm of overhead covered conductors based on feature optimization and bidirectional LSTM‐GRU

AbstractRecognition of partial discharge (PD) patterns is essential for insulation diagnosis of covered conductors in overhead lines. Current research has not sufficiently addressed the complex background noise in real environments, and most detection methods depend primarily on feature engineering or deep learning, suggesting potential for improvement in accuracy and efficiency. This has led the authors to propose a PD pattern recognition algorithm that integrates feature selection and deep learning. This algorithm incorporates the design of a discrete wavelet denoising function specifically tailored to the characteristics of PD for data preprocessing. It employs Bayesian optimization algorithms and light gradient boosting machines for characterizing corona discharge features. Furthermore, it develops multi‐scale clustering features and phase‐resolved features for feature fusion, and constructs insightful features based on the light gradient boosting machine. Finally, a novel deep learning model is formulated, demonstrating exceptional detection performance for early faults in covered conductors. Experimental results show that this algorithm attains an Matthews correlation coefficient score of 0.814, a 13.2% improvement over the baseline algorithm's 0.719, and a speed increase of 39.18%. The final accuracy amounts to 97.85%. This algorithm demonstrates exceptional performance in detecting early insulation faults in conductors.

Multi-source partial discharge pattern recognition algorithm based on DCGAN-Yolov5

This study aims to overcome the complication that deep learning-based pattern recognition in partial discharge (PD) diagnosis of gas-insulated switchgear (GIS) can only identify single-source partial discharge but not multi-source partial discharge. Specifically, a GIS multi-source partial discharge detection algorithm based on Deep Convolution Generative Adversarial Networks and You Only Look Once (DCGAN-YOLOv5) is proposed. First, the Phase Resolved Partial Discharge (PRPD) features of multi-source PD are analyzed, a GIS experiment platform is established, and four typical PD defects are simulated. Besides, sample data are collected, and the DCGAN network is used for sample expansion. Then, a YOLOv5 network model is designed, and a spatial and channel attention mechanism is added to the feature extraction network with a positive sample equilibrium strategy. Finally, the effectiveness of the proposed algorithm is verified using laboratory data and field data collected from a 220 kV substation. The experimental results demonstrate that the proposed algorithm can effectively detect the multi-source PD features in PRPD patterns under complex noise and thus successfully identify the types of multi-source PD. The mean Average Precision (mAP) can reach 98.4%. The precision of single-source PD and multi-source PD can reach 95.2% and 89.3%, when testing with field data.

Generator Stator Partial Discharge Pattern Recognition Based on PRPD-Grabcut and DSC-GoogLeNet Deep Learning

Partial discharge (PD) pattern recognition of typical defects from generator stator bars is the basis of generator condition monitoring and fault diagnosis. A PD pattern recognition method for generator stator bars based on a phase-resolved partial discharge graph cut (PRPD-Grabcut) and Depthwise Separable Convolution GoogLeNet (DSC-GoogLeNet) deep learning neural network is proposed in this paper. First, five typical defects are designed on stator bars in the laboratory. A long-term high voltage test is carried out, and 37500 PRPD original graphs are obtained. Second, a PRPD-Grabcut method based on image segmentation is proposed, which is designed to extract key components of the PRPD map. Finally, a DSC-GoogLeNet based PD pattern recognition method is proposed, which combines the GoogLeNet inception module and depth-wise separable convolution. Experimental results show that the PRPD-Grabcut contributes to certain improvements in the recognition accuracy and training efficiency of the neural network. The DSC-GoogLeNet shows superiority in recognition accuracy, cross-entropy loss, and training time compared to a variety of existing lightweight neural networks. In addition, compared with other networks, the DSC-GoogLeNet greatly improves the recognition accuracy of PD types with similar but different risk levels for stator insulation.

Feature extraction based on time-series topological analysis for the partial discharge pattern recognition of high-voltage power cables

In the partial discharge (PD) pattern recognition of power cables, the existing time–frequency features often exert an impact on recognition accuracy because of insufficient discrimination. A novel PD feature extraction and identification method on the basis of time-series topological data analysis (TDA) was proposed in this paper. Firstly, original PD sequence was reconstructed as point cloud in phase space based on optimized symbolic entropy. Then, a PD topological space is constituted with point cloud to extract its persistent homology features. On this basis, persistence diagrams and barcodes were calculated and visually expressed as Betty curves. Finally, Betty curves were input into an optimized 1D convolution neural network (1D-CNN) model to recognize four typical PD patterns and carry out comparison experiments. The visualization produced by t-distributed stochastic neighbor embedding (t-SNE) shows that TDA features possess significant discrimination, experiencing an increase of 11.25% in the overall recognition accuracy and reaching 98.00% compared with original PD sequence and time–frequency features. Meanwhile, the computation cost of the proposed algorithm is optimized within the permissible range for real-time applications.

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.

Time-frequency Fusion Method via Convolutional Neural Network for Partial Discharge Classification

To improve the accuracy of partial discharge (PD) pattern recognition by jointing time-domain (TD) and frequency-domain (FD) information, a time-frequency (TF) fusion method via convolution neural network (CNN) is proposed in this paper. Firstly, PD signals are represented by PD waveform images and transformed into the envelope of variational mode decomposition-based Hilbert marginal spectrum (VHMS). Secondly, a fusion network, FuNet involving a 2-dimensional CNN (2D-CNN), a 1D-CNN, and a multilayer perceptron (MLP), is established to join TF information. In FuNet, the 2D-CNN inputted by PD waveform images and 1D-CNN adopted the envelope of VHMS of PD signal as its input are all improved by drawing on the complementary strengths of different convolution layers’ features. Then the MLP will fuse the extracted TD and FD features and classify the PD defects.

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

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

A novel method for small and unbalanced sample pattern recognition of gas insulated switchgear partial discharge using an auxiliary classifier generative adversarial network

AbstractConvolutional neural networks (CNNs) have been widely used for gas‐insulated switchgear (GIS) partial discharge (PD) pattern recognition due to their powerful feature extraction ability. However, there is commonly a scarcity of fault samples due to low insulation failure rate of GIS equipment, which degrades the diagnostic performance of these CNN networks when directly applied to small and unbalanced datasets. Therefore, we propose a novel auxiliary classifier generative adversarial network for GIS PD pattern recognition for small and unbalanced samples. First, we propose using synchrosqueezed wavelet transform to extract time‐frequency characteristics of PD pulses and obtain a time‐frequency image with high energy aggregation and time‐frequency distribution rate. Then, we propose an improved generative adversarial network with an auxiliary classier and self‐attention mechanism, which can generate high‐quality PD samples for situations with few classes. Experiments show that our proposed method can reach 95.75% recognition accuracy for small datasets, which is the highest among several comparable methods. Furthermore, the proposed method has excellent and stable recognition performance for various unbalanced datasets.

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.

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 Pattern Recognition Based on a Multifrequency F–P Sensing Array, AOK Time–Frequency Representation, and Deep Learning

Recognizing the patterns of partial discharge (PD) is the key to assess the hazard level of PD. This article proposes an oil-immersed transformer PD pattern recognition method based on a multifrequency fiber-optic Fabry–Perot (F–P) ultrasound sensing array and deep learning (DL). Three F–P ultrasonic probes with different resonance bands were used to form a sensing array for collecting ultrasonic signals excited by PD. Five types of PD models were prepared, including metal tip (MT) discharge in oil, PD in the air cavity (AC), and surface discharge (SD) on the pressboard, in addition to SD after pressboard blocking or after pressboard plus transformer winding blocking, to study the influence of ultrasonic attenuation on recognition accuracy. The collected PD ultrasound signal time-frequency matrix was obtained using adaptive optimal-kernel time–frequency representation to reinforce the differences in PD patterns. Then, based on the features of the PD ultrasonic signals, the signal time and frequency band for analysis were determined. The intercepted time–frequency matrix of the signals from the three F–P probes was formed into a (3, 300, 375) tensor. A modified ResNet-18 net was used for PD pattern recognition, which achieved a 98% recognition accuracy. The probabilities given by the softmax function were used to study the confidence of the model’s predictions of signals belonging to known and unknown types.

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

Examination of insulation diagnosis in substation by neural network with phase‐resolved partial discharge pattern reconstruction

AbstractSeveral studies for partial discharge (PD) pattern recognition using artificial neural network (ANN) were reported in the early 1990s. Usually, in an actual field such as a substation, data on partial discharge is scarcely available, or even rare. In many cases, the power supply phase required for the PRPD pattern cannot be easily obtained. We propose an ANN method that shifts the phase in which the maximum signal intensity detected with PD sensors is generated and used it as training and input data, even for the few phases resolved PD data available in the field. This ANN method was applied to the PRPD pattern obtained in a practical field. As a result, it was shown that the discrimination rate between PD and noise was improved, and therefore the proposed ANN method was found to be effective.

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.