Feature extraction and severity assessment of partial discharge under protrusion defect based on fuzzy comprehensive evaluation

Zeng, Fuping; Dong, Yaomin; Tang, Ju

doi:10.1049/iet-gtd.2015.0403

Cited by 28 publications

(21 citation statements)

References 21 publications

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“…Support vector machine (SVM), decision trees (DT), BP neural network (BPNN), LeNet5, AlexNet, VGG16, and LCNN models were used for GIS PD pattern recognition. The recognition results are given in Table 1 (with reference to [49], the maximum value, root mean square deviation, standard deviation, skewness, kurtosis, and the peak-to-peak value were selected as feature parameters [49]). As can be seen in Table 1, the overall recognition rate of the LCNN reached 98.13% of 640 testing sets while the rates of SVM, BPNN, DT, LeNet5, AlexNet, and VGG16 were, respectively, 93.76%, 83.78%, 93.44%, 75.04%, 90.63%, and 86.41%.…”

Section: Accuracy Analysis Of Pattern Recognitionmentioning

confidence: 99%

Partial Discharge Pattern Recognition of Gas-Insulated Switchgear via a Light-Scale Convolutional Neural Network

et al. 2019

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Partial discharge (PD) is one of the major form expressions of gas-insulated switchgear (GIS) insulation defects. Because PD will accelerate equipment aging, online monitoring and fault diagnosis plays a significant role in ensuring safe and reliable operation of the power system. Owing to feature engineering or vanishing gradients, however, existing pattern recognition methods for GIS PD are complex and inefficient. To improve recognition accuracy, a novel GIS PD pattern recognition method based on a light-scale convolutional neural network (LCNN) without artificial feature engineering is proposed. Firstly, GIS PD data are obtained through experiments and finite-difference time-domain simulations. Secondly, data enhancement is reinforced by a conditional variation auto-encoder. Thirdly, the LCNN structure is applied for GIS PD pattern recognition while the deconvolution neural network is used for model visualization. The recognition accuracy of the LCNN was 98.13%. Compared with traditional machine learning and other deep convolutional neural networks, the proposed method can effectively improve recognition accuracy and shorten calculation time, thus making it much more suitable for the ubiquitous-power Internet of Things and big data.

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Section: Accuracy Analysis Of Pattern Recognitionmentioning

confidence: 99%

Partial Discharge Pattern Recognition of Gas-Insulated Switchgear via a Light-Scale Convolutional Neural Network

et al. 2019

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“…In [17], the rapid-charging-station model has been formulated based on queuing theory. 4-The fourth method is based on traffic studies and origin-destination (OD) concepts [18][19][20]. In addition, a comprehensive review and comparison of load-modeling techniques is available in [9].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Based PEVs Load Extraction and Analysis

et al. 2020

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Transformation of the energy sector due to the appearance of plug-in electric vehicles (PEVs) has faced the researchers with challenges in recent years. The foremost challenge is uncertain behavior of a PEV that hinders operators determining a deterministic load profile. Load forecasting of PEVs is so crucial in both operating and planning of the energy systems. PEV load demand mainly depends on traveling behavior of them. This paper tries to present an accurate model to forecast PEVs’ traveling behavior in order to extract the PEV load profile. The presented model is based on machine-learning techniques; namely, a generalized regression neural network (GRNN) that correlates between PEVs’ arrival/departure times and traveling behavior is considered in the model. The results show the ability of the GRNN to communicate between arrival/departure times of PEVs and the distance traveled by them with a correlation coefficient (R) of 99.49% for training and 98.99% for tests. Therefore, the trained and saved GRNN model is ready to forecast PEVs’ trip length based on training and testing with historical data. Finally, the results indicate the importance of implementing more accurate methods to predict PEVs to gain the significant advantages in the importance of electrical energy in vehicles in the years to come.

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“…The method extracts the signal with high efficiency, but when extracting the communication transmission signal, the accurate signal feature function cannot be obtained, the characteristics of the transmission signal extracted from the electronic communication network are too abstract, and the signal extraction precision is low. Zeng, F. et al [3] introduced a feature extraction method based on multi-dimensional signal feature fusion for transmission signals in electronic communication networks. The method extracts single multidimensional signal features in the time domain, frequency domain, and high-order spectral domain from the radio frequency signal of the communication target; fuses the extracted features; and then uses the support vector machine to classify the signal features to realize the transmission in electronic communication.…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction Method of Transmission Signal in Electronic Communication Network Based on Symmetric Algorithm

Song

2019

Symmetry

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Because the existing methods extract the signal characteristics of electronic communication networks, there is a problem of poor extraction. In this paper, a feature extraction method based on symmetric algorithm for transmission signals in electronic communication networks is proposed. The transmission signal in the time domain is decomposed by three-layer wavelet packet decomposition through threshold denoising and data dimension reduction. The adaptive floating threshold is used as a threshold to quantify the wavelet coefficients of the signal, which can effectively remove noise while retaining valuable transmission signal. Secondly, the feature extraction algorithm based on symmetric Holder coefficient is used to transform the transmitted signal from time domain to frequency domain, identify the signal sequence, and classify the signal sequence using neural network classifier. The simulation results show that the proposed method can extract the transmission signal of electronic communication network with the highest accuracy of 98.21%. This method can extract the amplitude and frequency characteristics of the transmission signal accurately under strong vibration environment. It is an efficient method for feature extraction of transmission signal.

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Feature extraction and severity assessment of partial discharge under protrusion defect based on fuzzy comprehensive evaluation

Cited by 28 publications

References 21 publications

Partial Discharge Pattern Recognition of Gas-Insulated Switchgear via a Light-Scale Convolutional Neural Network

Partial Discharge Pattern Recognition of Gas-Insulated Switchgear via a Light-Scale Convolutional Neural Network

Machine Learning Based PEVs Load Extraction and Analysis

Feature Extraction Method of Transmission Signal in Electronic Communication Network Based on Symmetric Algorithm

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