Partial discharge pattern recognition of DC XLPE cables based on convolutional neural network

In view of the fact that the statistical feature quantity of traditional partial discharge (PD) pattern recognition relies on expert experience and lacks certain generalization, this paper develops PD pattern recognition based on the convolutional neural network (cnn) and long-term short-term memory network (lstm). Firstly, we constructed the cnn-lstm PD pattern recognition model, which combines the advantages of cnn in mining local spatial information of the PD spectrum and the advantages of lstm in mining the PD spectrum time series feature information. Then, the transformer PD UHF (Ultra High Frequency) experiment was carried out. The performance of the constructed cnn-lstm pattern recognition network was tested by using different types of typical PD spectrums. Experimental results show that: (1) for the floating potential defects, the recognition rates of cnn-lstm and cnn are both 100%; (2) cnn-lstm has better recognition ability than cnn for metal protrusion defects, oil paper void defects, and surface discharge defects; and (3) cnn-lstm has better overall recognition accuracy than cnn and lstm.

Different types of partial discharge (PD) cause different damage to gas-insulated switchgear (GIS), so it is very important to correctly identify the type of PD for evaluating the GIS insulation condition. The traditional PD pattern recognition algorithm has the limitations of low recognition accuracy and slow recognition speed in engineering applications. This work proposed a Convolutional Block Attention Module Residual Network (CBAM-ResNet) for the GIS PD pattern recognition. In particular, the Squeeze-and-Excitation mechanism was used to improve the recognition accuracy. The GIS phase resolved partial discharge (PRPD) image is used as the input of the pattern recognition model. The results show that the recognition accuracy of the proposed method is as high as 89%, and has a recognition accuracy of 92.72%, 90.19%, 89.36%, and 93.62% for the four PD types, respectively. Compared with normal CNN and SVM recognition algorithms, the PD pattern recognition accuracy of the proposed method has been greatly improved. And the CBAM-ResNet for PD pattern recognition has a good application prospect in engineering practice.

This paper discusses the results of an on-going project on the application of artificial intelligence techniques for recognition of partial discharge (PD) patterns from hydrogenerators. The main purpose of the project is to classify data according to the origin of a PD source and create an early warning system for stator winding insulation. More than nine hundreds PD patterns recorded during last two years on more than thirty generators were analysed. The patterns in the form of the maximum and mean pulse height and pulse count discharge distributions were described by statistical parameters such as the skewness, kurtosis, etc., and stored in a data base as a fingerprint. Classification of unknown discharge patterns was carried out by statistical pattern recognition techniques. The results indicate that satisfactory classifications took place.

<span>Partial discharge (PD) pattern recognition is useful to diagnose insulation condition. PD measurement data is commonly represented in phase-resolved partial discharge (PRPD) format. PRPD is useful as it provides a visible pattern for different PD source and various features can be extracted for PD pattern recognition. Shorter PRPD duration will enable more training data but the information in each data is less and vice versa. This works aims to investigate the effects of using very short duration PRPD data on the accuracy of PD pattern recognition. The results conclude that machine learning models such as Artificial Neural Network (ANN) and Support Vector Machine (SVM) are robust enough such that reduction of PRPD duration from 15-seconds to 1-second causes less than 5 % drop in the classification accuracy. However, this is only true for noise free condition. <span>When the same PD data is overlapped with random noise, the classification accuracy suffers a significant reduction up to 19%. Therefore, longer PRPD duration is recommended to withstand the effects of noise contamination.</span></span>

In order to improve the recognition accuracy of partial discharge (PD) by making full use of the time-frequency characteristics of PD signals and employing deep learning theory, a kind of PD pattern recognition method based on variational mode decomposition (VMD)-Choi-Williams distribution (CWD) spectrum and optimized convolutional neural network (CNN) with cross-layer feature fusion is proposed in this paper. Firstly, a PD signal is decomposed into several components by VMD algorithm, and the CWD analysis of the obtained components is carried out to obtain the VMD-CWD time-frequency spectrum. Secondly, the cross-layer feature fusion and optimization CNN (CFFO-CNN) is constructed by introducing cross-layer connection and optimization algorithm. Thirdly, the VMD-CWD is regarded as the input vector to train CFFO-CNN to learn and extract the intrinsic features of the spectrum. Finally, the trained network is used to recognize the PD types of the testing samples. The proposed method is compared with traditional recognition methods such as BP neural network (BPNN) and support vector machine (SVM), as well as some commonly used deep learning algorithms. The experimental results indicate that the recognition performance of the proposed method is significantly better than that of existing recognition methods with accuracy up to 99.5%. It is proved that CFFO-CNN has superior feature extraction ability, which can extract the internal features of the VMD-CWD spectrum independently with higher recognition accuracy and wider application prospect.

An experimental study of pattern recognition of partial discharge (PD) in a crosslinked polyethylene (XLPE) cable by using a neural network (NN) system is described. The NN system was a three-layer artificial neural network system with feedforward connections, and its learning method was a backpropagation algorithm incorporating an external teacher signal. Input information for the NN was a combination of the discharge magnitude, the number of pulse counts and the phase angle of applied voltage in which PD is produced. PD measurement was carried out using a PD pulse recorder for a 66 kV XLPE cable with an artificial defect under a 38 kV AC applied voltage. After learning 30 typical input patterns, the NN discriminated unknown patterns with 90% correct responses. The time duration including measuring time required for the NN to discriminate PD signal was approximately 30 s. In a long-term performance test of a 66 kV XLPE cable with an artificial defect, the NN-based alarm processor was able to recognize the presence of PD 1 h before breakdown of the cable, and successfully alerted the operator. >

This paper presents a feature extraction algorithm combining S transform (ST) and two-directional two-dimensional principal component analysis ((2D) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> PCA) for partial discharge (PD) pattern recognition. S transform (ST) is firstly employed to obtain a time-frequency representation of the recorded UHF signals. Then, (2D) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> PCA is applied to compress the ST amplitude(STA) matrices to extract various feature vectors with different (d <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , d <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ) combinations, i.e. (5, 5), (5, 10), (10, 5) and (10, 10). The extracted features are examined by both PSO-SVM classifier and BPNN. Experimental results show that the classification accuracies by PSO-SVM are all higher than that by BPNN under four circumstances of (d <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , d <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ) combinations. The success rates of the PSO-SVM with the four feature vectors are above 94% in all cases. It can be found that the proposed feature extraction and classification algorithm can be effectively applied to PD pattern recognition.

Distinguishing the types of partial discharge (PD) caused by different insulation defects in gas-insulated switchgear (GIS) is a great challenge in the power industry, and improving the recognition accuracy of the relevant models is one of the key problems. In this paper, a convolutional neural network and long short-term memory (CNN-LSTM) model is proposed, which can effectively extract and utilize the spatiotemporal characteristics of PD input signals. First, the spatial characteristics of higher-level PD signals can be obtained through the CNN network, but because CNN is a deep feedforward neural network, it does not have the ability to process time-series data. The PD voltage signal is related to the time dimension, so LSTM saves and analyzes the previous voltage signal information, realizes the modeling of the time dependence of the data, and improves the accuracy of the PD signal pattern recognition. Finally, the pattern recognition results based on CNN-LSTM are given and compared with those based on other traditional analysis methods. The results show that the pattern recognition rate of this method is the highest, with an average of 97.9%, and its overall accuracy is better than that of other traditional analysis methods. The CNN-LSTM model provides a reliable reference for GIS PD diagnosis.

The power system on the offshore platform is of great importance since it is the power source for oil and gas exploitation, procession and transportation. Transformers constitute key equipment in the power system, and partial discharge (PD) is its most common fault that should be monitored and identified ın a timely and accurate manner. However, the existing PD classifiers cannot meet the demand for real-time online monitoring due to their disadvantages of high memory consumption and poor timeliness. Therefore, a new MobileNets convolutional neural network (MCNN) model is proposed to identify the PD pattern of transformers based on the phase resolved partial discharge (PRPD) spectrum. The model has the advantages of low computational complexity, fast reasoning speed and excellent classification performance. Firstly, we make four typical defect models of PD and conduct a test in a laboratory to collect the PRPD spectra as the data sample. In order to further improve the feature expression ability and recognition accuracy of the model, the lightweight attention mechanism Squeeze-and-Excitation (SE) module and the nonlinear function hard-swish (h-swish) are added after constructing the MCNN model to eliminate the potential accuracy loss in PD pattern recognition. The MCNN model is trained and tested with the pre-processed PRPD spectrum, and a variety of methods are used to visualize the model to verify the effectiveness of the model. Finally, the performance of MCNN is compared with many existing PD pattern recognition models based on convolutional neural network (CNN), the results show that the proposed MCNN can further reduce the number of parameters of the model and improve the calculation speed to achieve the best performance on the premise of good recognition accuracy.

Pattern recognition of partial discharge (PD) has various beneficial applications in academia and industry. However, it is hard and expensive to obtain extensively annotated PD data to build a high-performance classification model. Given there is potential to use PD data more effectively, this paper presents a novel data generative model based on generative adversarial networks (GAN). GAN are able to learn deep feature representations of existing PD signals and synthesizes more extensive UHF PD signals. An original PD data set of UHF PD signals is established by the partial discharge experiment. With the result of the original PD signals dataset as baseline, we evaluated the performance of some classifiers on the augmented dataset. The results show that UHF PD classification model benefits from GAN-based data augmentation techniques. Clearly, the GAN-based model has good potential in industry diagnosis of PD, especially when the data are sparse.

Optimal selection of features of Partial Discharge (PD) signals recorded from defects in High Voltage (HV) cables will contribute not only to the improvement of PD pattern recognition accuracy and efficiency but also to PD parameter visualization in HV cable condition monitoring and diagnostics. This paper presents a novel Random Forest (RF)-based feature-selection algorithm for PD pattern recognition of HV cables. The algorithm is applied to feature selection of both PD signals and interference signals with the aim of obtaining the optimal features for data processing. First, the experimental data acquisition and feature extraction processes are introduced. PD signals were captured from faults created in a cable to obtain the raw PD data, and then, a set of 3500 transient PD pulses and a set of 3500 typical interference pulses were extracted, based on which 34 PD features were extracted for further processing. Furthermore, 119 two-dimensional features and 1082 three-dimensional features were generated. The paper then discusses the basic principle of the RF algorithm. Finally, RF-based feature selection was implemented to determine the optimal features for PD pattern recognition. The results were obtained and evaluated with the Back Propagation Neural Network and Support Vector Machine. Results show that the proposed RF-based method is effective for PD feature selection of HV cables with the potential for application to additional HV power apparatus.

Partial Discharge (PD) characteristics of typical defects in oil-paper insulation under AC, DC and AC-DC combined voltages have been studied extensively. However, there is still limited material available for the pattern recognition of partial discharge under actual stress withstood by valve side windings in converter transformers. In this paper, the PD of needle-plane defect is measured under actual stress in converter transformers. The stress is DC voltage superimposed harmonics, which has been analyzed in PSCAD at first. Then DC voltage superimposed both single harmonic and multiple harmonics, including simulated actual stress and approximate square waveform, have been applied on electrode. PD parameters and patterns are acquired by detector using pulse current method. The test results indicate that the inception voltage and pulse repetition rate of PD have no direct relationship with harmonic order due to the accumulation of space charges injected by DC voltage. PD amplitude shows a growing trend and phase distributions of PD expand with the increase of harmonic order. PD patterns are also closely related to the waveform of applied voltage. PD amplitude rises with the increase of voltage change rate, and the majority of PD pulses occur in the phase range which has a higher voltage change rate. Experimental results show that harmonic order exerts a great effect on PD characteristics of needle-plane defect under DC superimposed harmonic waveforms. The findings of the current study are useful in further research on the pattern recognition of PD and diagnose in converter transformers.

Different types of partial discharge (PD) cause different damages to gas-insulated substation (GIS), so it is very important to correctly identify the type of PD for evaluating the GIS insulation condition. The traditional PD pattern recognition algorithm has the limitations of low recognition accuracy and slow recognition speed in engineering applications. To effectively diagnose the GIS PD type and safeguard the safe and reliable operation of the distribution network, a GIS PD method based on improved CBAM-ResNet was proposed in this paper. And the improved CBAM-ResNet takes advantage of the residual neural network and attention mechanism. In particular, the channel attention module and the spatial attention module are connected in parallel in the improved CBAM. The experimental results showed that the GIS PD pattern recognition method proposed herein has a recognition rate of 93.58%, 95.00%, 93.55%, and 93.88% against the four PD types. Compared with the traditional PD pattern recognition algorithm, the algorithm has the advantages of a lightweight model and more accurate recognition results, which carry better engineering application values.

Convolutional 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.

Accurate identification of partial discharge (PD) and its types is essential for assessing the operating conditions of electrical equipment. To enhance PD pattern recognition under imbalanced and limited sample conditions, a method based on a Deep Autoencoder-embedded Generative Adversarial Network (DAE-GAN) is proposed. First, the Deep Autoencoder (DAE) is embedded within the Generative Adversarial Network (GAN) to improve the realism of generated samples. Then, complementary PD data samples are introduced during GAN training to address the issue of limited sample size. Lastly, the model’s discriminator is fine-tuned with augmented and balanced training data to enable PD pattern recognition. The DAE-GAN method is used to augment data and recognize patterns in experimental PD signals. The results demonstrate that, under imbalanced and small sample conditions, DAE-GAN generates more authentic PD samples with improved probability distribution fitting compared to other algorithms, leading to varying levels of enhancement in pattern recognition accuracy.