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1
Sun, Zhang, Shi, and Gou. "Extraction of Partial Discharge Pulses from the Complex Noisy Signals of Power Cables Based on CEEMDAN and Wavelet Packet." Energies 12, no. 17 (2019): 3242. http://dx.doi.org/10.3390/en12173242.
Texte intégralRésumé :
While both periodic narrowband noise and white noise are significant sources of interference in the detection and localization of partial discharge (PD) signals in power cables, existing research has focused nearly exclusively on white noise suppression. This paper addresses this issue by proposing a new signal extraction method for effectively detecting random PD signals in power cables subject to complex noise environments involving both white noise and periodic narrowband noise. Firstly, the power cable signal was decomposed using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and the periodic narrowband noise and frequency aliasing in the obtained signal components were suppressed using singular value decomposition. Then, signal components contributing significantly to the PD signal were determined according to the cross-correlation coefficient between each component and the original PD signal, and the PD signal was reconstructed solely from the obtained significant components. Finally, the wavelet packet threshold method was used to filter out residual white noise in the reconstructed PD signal. The performance of the proposed algorithm was demonstrated by its application to synthesized PD signals with complex noise environments composed of both Gaussian white noise and periodic narrowband noise. In addition, the time-varying kurtosis method was demonstrated to accurately determine the PD signal arrival time when applied to PD signals extracted by the proposed method from synthesized signals in complex noise environments with signal-to-noise ratio (SNR) values as low as −6 dB. When the SNR was reduced to −23 dB, the arrival time error of the PD signal was only one sampling point.
2
Zhou, Kai, Mingzhi Li, Yuan Li, Min Xie, and Yonglu Huang. "An Improved Denoising Method for Partial Discharge Signals Contaminated by White Noise Based on Adaptive Short-Time Singular Value Decomposition." Energies 12, no. 18 (2019): 3465. http://dx.doi.org/10.3390/en12183465.
Texte intégralRésumé :
To extract partial discharge (PD) signals from white noise efficiently, this paper proposes a denoising method for PD signals, named adaptive short-time singular value decomposition (ASTSVD). First, a sliding window was moved along the time axis of a PD signal to cut a whole signal into segments with overlaps. The singular value decomposition (SVD) method was then applied to each segment to obtain its singular value sequence. The minimum description length (MDL) criterion was used to determine the number of effective singular values automatically. Then, the selected singular values of each signal segment were used to reconstruct the noise-free signal segment, from which the denoised PD signal was obtained. To evaluate ASTSVD, we applied ASTSVD and two other methods on simulated, laboratory-measured, and field-detected noisy PD signals, respectively. Compared to the other two methods, the denoised PD signals of ASTSVD contain less residual noise and exhibit smaller waveform distortion.
3
Zhang, Anan, Cong He, Maoyi Sun, Qian Li, Hong Wei Li, and Lin Yang. "Partial discharge signal self-adaptive sparse decomposition noise abatement based on spectral kurtosis and S-transform." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 37, no. 1 (2018): 293–306. http://dx.doi.org/10.1108/compel-03-2017-0126.
Texte intégralRésumé :
Purpose Noise abatement is one of the key techniques for Partial Discharge (PD) on-line measurement and monitoring. However, how to enhance the efficiency of PD signal noise suppression is a challenging work. Hence, this study aims to improve the efficiency of PD signal noise abatement. Design/methodology/approach In this approach, the time–frequency characteristics of PD signal had been obtained based on fast kurtogram and S-transform time–frequency spectrum, and these characteristics were used to optimize the parameters for the signal matching over-complete dictionary. Subsequently, a self-adaptive selection of matching atoms was realized when using Matching Pursuit (MP) to analyze PD signals, which leading to seldom noise signal element was represented in sparse decomposition. Findings The de-noising of PD signals was achieved efficiently. Simulation and experimental results show that the proposed method has good adaptability and significant noise abatement effect compared with Empirical Mode Decomposition, Wavelet Threshold and global signal sparse decomposition of MP. Originality/value A self-adaptive noise abatement method was proposed to improve the efficiency of PD signal noise suppression based on the signal sparse representation and its MP algorithm, which is significant to on-line PD measurement.
4
Kui Fern, Chin, Chai Chang Yii, Asfarina Abu Bakar, et al. "Adaptive Wavelet De-noising Algorithm using Absolute Difference Optimization Technique for Partial Discharge Signal." Journal of Engineering and Science Research 7, no. 3 (2023): 26–31. http://dx.doi.org/10.26666/rmp.jesr.2023.3.4.
Texte intégralRésumé :
Discrete Wavelet Transform (DWT) de-noising method is widely used for one-dimension partial discharge (PD) signals measured from medium voltage underground cable. However, DWT de-noising has several drawbacks that prevent the DWT de-noising from improving its de-noising effectiveness In DWT de-noising, the two most important parameters are decomposition level and mother wavelet. The aforementioned parameters must be varied according to the noise level in the measured PD signal in order to effectively suppress the noise of the measured PD signal. In this paper, an adaptive DWT de-noising algorithm based on the Absolute Difference Optimizing (ADO) technique is presented to effectively suppress the varying noise levels in measured PD signal. First, the measured PD signal will be de-noised using a Daubechies 3 (db3) mother wavelet and a DWT decomposition level ranging from 1 to 10. Second, the de-noised PD signal will be subjected to the ADO technique. The sum of the absolute difference of local maxima in the de-noised PD signal will be used as an indicator to select the best decomposition level for the de-noised PD signal. Finally, the best-selected de-noised PD signal by using the ADO technique will be used to estimate the PD location on the underground cable. The results of PD location error using the ADO technique and normal DWT de-noising will be compared. The findings show that the ADO-based adaptive DWT de-noising algorithm significantly improved the de-noising process of the measured PD signal.
5
Chen, Baichao, Weiqiang Qi, Jiaxin Yuan, and Yihong You. "Recognition of High-Voltage Cable Partial Discharge Signal Based on Adaptive Fuzzy C-Means Clustering." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 06 (2017): 1759009. http://dx.doi.org/10.1142/s0218001417590091.
Texte intégralRésumé :
Partial discharge (PD) detection is an effective means to find high-voltage cable defects. However, various interference signals affect the PD signal in practical applications, resulting in wrong judgment. In order to improve the accuracy of PD detection of high voltage cables, an adaptive fuzzy C-means (FCM) clustering was proposed to identify PD signals. The adaptive threshold pulse extraction algorithm based on fixed interval width was employed. The threshold was changed adaptively to extract the effective PD pulse waveform according to the change of the background noise and the degree of PD. Then PD pulse features were analyzed in time and frequency domain by employing the equivalent time frequency analysis method. The adaptive fuzzy clustering algorithm was used to classify the signals. The phase distribution concentration of all kinds of pulse signal was calculated. The phase standard deviation of various types of pulse was taken as the index that measures the concentration of density to distinguish between PD and interference signals. Results show that the adaptive FCM clustering algorithm, compared with the traditional method, can not only identify the PD signal accurately, but also be conscious of the PD category. The PD recognition method proposed in this paper has strong applicability and high accuracy, which is particularly suitable for application in the field of engineering.
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Guo, Bowen, Songyuan Li, Qinghua Tang, Lin Li, and Pengfei Li. "A Fuzzy Interpolation Cross-Correlation Method for Time-Delay Estimation of Partial Discharge UHF Pulse Signals." Journal of Electrical and Computer Engineering 2021 (October 23, 2021): 1–10. http://dx.doi.org/10.1155/2021/8532714.
Texte intégralRésumé :
Time-delay estimation of partial discharge (PD) ultrahigh-frequency (UHF) pulse signals is one of the effective means to diagnose the local defect of electrical equipment. In order to improve the time-delay estimation accuracy of a multielement PD UHF pulse signal sensor array and reduce the cost of the PD detection system, a fuzzy interpolation cross-correlation method for time-delay estimation of PD UHF pulse signals was proposed in this paper. Improving the signal sampling rate is an effective way to improve the time-delay estimation accuracy. In this work, the fuzzy interpolation inference method was applied to interpolate intermediate values into the feature area of the PD UHF signal to improve the sampling rate, and then the cross-correlation method was used to estimate the time delay. This method not only improves the system sampling rate by fuzzy interpolation inference, which can make up for the deficiency of system equipment sampling performance, but also reduces the estimation error caused by noninteger multiple sampling intervals of signal time delay. The comparative experiment results demonstrate the power of the proposed method in improving the accuracy of the time-delay estimation of PD UHF pulse signals.
7
Muhammad Izwan Abdul Halim, Nur Zahirah MohdRazaly, Mohamad Nur Khairul Hafizi Rohani, et al. "Multiple Partial Discharge Signal Classification Using Artificial Neural Network Technique in XLPE Power Cable." Journal of Advanced Research in Applied Sciences and Engineering Technology 29, no. 3 (2023): 214–27. http://dx.doi.org/10.37934/araset.29.3.214227.
Texte intégralRésumé :
According to partial discharge (PD) damage in the electrodes that are not entirely bridging, the presence of PD in the high voltage (HV) power cable might lead to insulation failure. PD defects can damage cross-linked polyethylene (XLPE) cables directly, which is one of the most critical electrical issues in the industry. Poor workmanship during cable jointing, aging, or exposure to the surrounding environment is the most common cause of PD in HV cable systems. As a result, the location of the PD signals that occur cannot be classified without identifying the multiple PD signals present in the cable system. In this study, the artificial neural network (ANN) based feedforward back propagation classification technique is used as a diagnostic tool thru MATLAB software in which the PD signal was approached to determine the accuracy of the location PD signal. In addition, statistical feature extraction was added to compare the accuracy of classification with the standard method. The three-point technique is also an approach used to locate PD signals in a single line 11 kV XLPE underground power cable. The results show that the statistical feature extraction had been successful classify the PD signal location with the accuracy of 80% compared to without statistical feature extraction. The distance between PD signals and the PD source affected the result of the three-point technique which proved that a lower error means a near distance between them.
8
Wang, Yanwen, Peng Chen, Yongmei Zhao, and Yanying Sun. "A Denoising Method for Mining Cable PD Signal Based on Genetic Algorithm Optimization of VMD and Wavelet Threshold." Sensors 22, no. 23 (2022): 9386. http://dx.doi.org/10.3390/s22239386.
Texte intégralRésumé :
When the pulse current method is used for partial discharge (PD) monitoring of mining cables, the detected PD signals are seriously disturbed by the field noise, which are easily submerged in the noise and cannot be extracted. In order to realize the effective separation of the PD signal and the interference signal of the mining cable and improve the signal-to-noise ratio of the PD signal, a denoising method for the PD signal of the mining cable based on genetic algorithm optimization of variational mode decomposition (VMD) and wavelet threshold is proposed in this paper. Firstly, the genetic algorithm is used to optimize the VMD, and the optimal value of the number of modal components K and the quadratic penalty factor α is determined; secondly, the PD signal is decomposed by the VMD algorithm to obtain K intrinsic mode functions (IMF). Then, wavelet threshold denoising is applied to each IMF, and the denoised IMFs are reconstructed. Finally, the feasibility of the denoising method proposed in this paper is verified by simulation and experiment.
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Alhaidar, Abdul Rahman, Mohamed Yacin Sikkandar, and Abdulaziz A. Alkathiry. "Reconstruction of dual tasking gait pattern in Parkinson’s disease subjects using radial basis function based artificial intelligence." Journal of Intelligent & Fuzzy Systems 39, no. 4 (2020): 5437–48. http://dx.doi.org/10.3233/jifs-189027.
Texte intégralRésumé :
Vertical Ground Reaction Force (VGRF) is a force obtained during gait cycle beneath the feet and is used to screen the severity of Parkinson’s disease (PD) patient’s in clinical environment. This article investigates the VGRF signals (left and right) semblance nature among PD patients and control subjects as a function of time and possibility of reconstructing dual tasking VGRF signal from normal walking VGRF signals using radial basis function (RBF) based artificial intelligence (AI). There are many traditional methods for gait analysis and these methods are purely subjective and none made semblance analysis of same subjects gait pattern in different tasking. In order to overcome the difficulties faced by PD patients, RBF based AI is proposed in this research to reconstruct the dual tasking VGRF signal from normal walking VGRF signal. 93 PD patients with mean age: 66.3 years (63% men), and 73 healthy controls with mean age: 66.3 years (55% men) datasets are used in this work. Proposed RBF network is trained on VGRF signals obtained in normal walking and dual tasking conditions from control. The network was trained with 60% of VGRF data and tested on remaining 40% data. Semblance analysis results are encouraging, and it shows that semblance is high in PD patients than control subjects during dual tasking (P < 0.05). In order to test the findings of semblance analysis, we explicitly reconstruct VGRF signal of clinically significant dual tasking from VGRF signal of normal walking by the proposed RBF method. Findings proved that the proposed RBF network can reconstruct dual tasking VGRF signal of PD patients from their normal walking VGRF signal with high cross correlation (P < 0.0001). These findings pave way for a new adjunct tool to diagnose the gait dynamics of PD patients using the proposed reconstruction method.
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Bohari, Z. H., M. Isa, A. Z. Abdullah, P. J. Soh, and M. F. Sulaima. "A smart partial discharge classification SOM with optimized statistical transformation feature." Bulletin of Electrical Engineering and Informatics 10, no. 2 (2021): 1054–62. http://dx.doi.org/10.11591/eei.v10i2.2751.
Texte intégralRésumé :
Condition-based monitoring (CBM) has been a vital engineering method to assess high voltage (HV) equipment and power cables conditions or health levels. One of the effective CBM methods is partial discharge (PD) measurement or detection. PD event is the phenomenon that always associated with insulation healthiness. PD has been measured and evaluated in this paper to discriminate PD signals from a good signal. A mixed-signal being fed at an AI technique with statistical modified input data to do fast classification (less than five seconds) with nearly zero error. In this paper, an unsupervised neural network is applied for PD classification. The methods combine the self-organizing maps (SOMs) and feature statistical transformation. By the combination of these methods, the ‘range’ normalization method produced the best classification outcomes. This development decided that PD information was effectively correlated and grouped by means of MATLAB’s SOM Toolbox and transformation device to discriminate the normal signal from the PD signal.
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Ma, Xiao, and Hoi Wai Choi. "Observation of ground loop signals in GaN monolithically integrated devices." Journal of Vacuum Science & Technology B 41, no. 1 (2023): 012207. http://dx.doi.org/10.1116/6.0002245.
Texte intégralRésumé :
The observation of ground loop signals in nonelectrically isolated GaN monolithic systems has prompted an investigation on its origins. The study is carried out with devices comprising monolithic light-emitting diodes (LED) and photodetectors (PD) that are either electrically isolated by completely etching through the GaN epitaxial layers, or nonelectrically isolated devices where the etch terminates at the n-GaN layer, through TCAD simulations and experiments. While the devices behave identically to DC input signals, a signal can be observed across the PD of the nonelectrically isolated devices when an AC signal is fed to the LED, even at voltages below the LED’s turn-on voltage. The [Formula: see text] phase difference of the output PD potential with respect to the input LED potential indicates that the signal, regarded as a ground loop signal, couples through the junction capacitance of the LED and PD. The ground loop signal increases with increasing frequency due to the frequency-dependence of the junction impedance. The insertion of a grounded metal line between the LED and PD reduces the ground loop signal, but not to a sufficient extent not to affect the photovoltage. The findings illustrate the necessity of electrical isolation among devices for GaN monolithic systems, especially those operating at higher frequencies, such as photonic integrated systems.
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Faizol, Zulbirri, Farid Zubir, Norhafezaidi Mat Saman, et al. "Detection Method of Partial Discharge on Transformer and Gas-Insulated Switchgear: A Review." Applied Sciences 13, no. 17 (2023): 9605. http://dx.doi.org/10.3390/app13179605.
Texte intégralRésumé :
The detection of partial discharge (PD) activities in high-voltage equipment can be conducted according to several mechanisms of signal detection, including electromagnetic wave signal detection, acoustic signal detection, chemical reactions, electrical signal detection, and optical emission detection. Recently, multiple methods of detection and localization of partial discharge activities, which occurred in power transformers and gas-insulated switchgear (GIS), have been proposed to monitor the health condition of high-voltage equipment, especially when the awareness regarding preventive maintenance has been emphasized at the industrial level and among electrical providers. In aligning the needs of the industrial sector and the improvement of PD-detection methods, this manuscript focuses on reviewing the current practice methods for the detection and localization of PD signals in high-voltage equipment, comparing their efficacy, and summarizing the future direction of research work-related methods of PD detection. The comparative reviews are discussed in terms of the mechanism of PD signal detection, indication parameters, calibration techniques, and the advantages and limitations of each method of PD measurement in detail.
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Adriana Azhar, Nayli, Norazizah Mohd Aripin, Goh Chin Hock, Nayla Ferdous, and Saidatul Hamidah. "A t-shaped partial ground microstrip patch antenna based UHF sensor for partial discharge detection." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 2 (2020): 704. http://dx.doi.org/10.11591/ijeecs.v20.i2.pp704-711.
Texte intégralRésumé :
Continuous partial discharge (PD) monitoring and early PD detection is important in making sure the necessary preventative measures can be taken accordingly. This paper proposed a T-shaped partial ground microstrip patch antenna that is able to detect PD signal within the UHF range. The antenna was designed and simulated using CST Microwave Studio. The antenna was then fabricated using FR4 substrate material and tested for reception test. The simulation results and the analysis from the fabricated antenna confirmed that the proposed antenna able to detect PD signal at UHF range (specifically at about 500 MHz) and fulfilled the design requirements in terms of the return loss, VSWR, bandwidth and gain. Reception test had confirmed that the proposed antenna was able to detect PD signals that are located at maximum distance, ranges from 37 cm to 70 cm (depending on the PD signal strength). The proposed antenna also had succesfully detected PD occurances at 300 MHz to 700 MHz. In conclusion, the proposed T-shaped partial ground microstrip patch antenna had been successfully designed and able to detect PD signal emitted in the UHF range.
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Shi, Zhongyan, Lei Ding, Xingyu Han, et al. "Multimodal biofeedback for Parkinson’s disease motor and nonmotor symptoms." Brain Science Advances 9, no. 2 (2023): 136–54. http://dx.doi.org/10.26599/bsa.2023.9050015.
Texte intégralRésumé :
Parkinson’s disease (PD) is a neurodegenerative disorder characterized by motor retardation, myotonia, quiescent tremor, and postural gait abnormality, as well as nonmotor symptoms such as anxiety and depression. Biofeedback improves motor and nonmotor functions of patients by regulating abnormal electroencephalogram (EEG), electrocardiogram (ECG), photoplethysmography (PPG), electromyography (EMG), respiration (RSP), or other physiological signals. Given that multimodal signals are closely related to PD states, the clinical effect of multimodal biofeedback on patients with PD is worth exploring. Twenty-one patients with PD in Beijing Rehabilitation Hospital were enrolled and divided into three groups: multimodal (EEG, ECG, PPG, and RSP feedback signal), EEG (EEG feedback signal), and sham (random feedback signal), and they received biofeedback training five times in two weeks. The combined clinical scale and multimodal signal analysis results revealed that the EEG group significantly improved motor symptoms and increased Berg balance scale scores by regulating β band activity; the multimodal group significantly improved nonmotor symptoms and reduced Hamilton rating scale for depression scores by improving θ band activity. Our preliminary results revealed that multimodal biofeedback can improve the clinical symptoms of PD, but the regulation effect on motor symptoms is weaker than that of EEG biofeedback.
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Yaacob, Mohd Muhridza, and Malik Abdulrazzaq Alsaedi. "Partial Dicharge Signal Analysis in Insulation Oil Using Optical Chromatic Technique." Applied Mechanics and Materials 530-531 (February 2014): 332–35. http://dx.doi.org/10.4028/www.scientific.net/amm.530-531.332.
Texte intégralRésumé :
In this work optical chromatic approch has been use to analyze PD signals in insulation oil. The study of chromatic approach of acoustic emission for PD phenomenon addressing PD signals with three detectors red, green and blue (R,G,B) having non-orthogonal (overlapping) responses. The outputs of the three detectors are transformed into a number of chromatic parameters designated x, y, z as well as hue, lightness and saturation (H,L,S). The x, y, z represents the proportion of the signal from each of the three processors while H, L, S represents the dominant phase, effective strength and notional spread of the PD signals respectively. This technique provides a high degree of traceability in relating the chromatic parameters to the raw PD signals. Changes in the values of the chromatic parameters are shown to reflect various PD trends and to provide early indications of incipient insulation failure and PD source discrimination.
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Sornsen, Isara, Chatchai Suppitaksakul, and Pollakrit Toonkum. "Mother Wavelet Performance Evaluation for Noise Removal in Partial Discharge Signals." ECTI Transactions on Electrical Engineering, Electronics, and Communications 20, no. 3 (2022): 450–60. http://dx.doi.org/10.37936/ecti-eec.2022203.247521.
Texte intégralRésumé :
This article aims to study the pattern of partial discharge (PD) signals occurring on the insulators of high-voltage systems. A mother wavelet comparison and wavelet decomposition are presented to detect and locate PD signals by dividing them into three processes: 1) Signal test generation, employing RC and RLC impedance circuits whereby the output voltage pulses in the RC impedance circuit are expressed as damped exponential pulses (DEPs) and those of the RLC as damped oscillatory pulses (DOPs). White Gaussian noise (AWGN) is then added, which mimics the effect of many random processes in measurement systems. The concept involves applying noise to the original signal and removing it with wavelet transform using various wavelet templates such as Daubechies, Coiflet, Symlet, and biorthogonal to separate the signal components. The experiment results are then compared, and a performance evaluation performed using mean square error (MSE) as in the first two signals. 2) DEPs and DOPs are added with a sine wave to simulate a virtual measurement from a measuring instrument according to the superposition principle using a band-pass filter with the frequency range specified by the two elements to determine the frequency of the resulting PD. 3). The results of the PD signal experiments developed in the laboratory are also evaluated for efficiency by subjective measurement by a PD signal specialist. Therefore, partial discharge denoizing is evaluated using the mother wavelet as a preprocessing step for feature extraction and classification.
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Yun, Yu Xin. "Clustering Research on Fractal Parameter of GIS PD UHF Signals." Applied Mechanics and Materials 380-384 (August 2013): 3818–21. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.3818.
Texte intégralRésumé :
The distribution of fractal parameters (box dimension and lacunarity) vary with noise level of GIS PD UHF signal is the main obstacle to the use of fractal theory in GIS PD pattern recognition. According to the characteristic of fractal parameters, box dimension and wavelet method have been used in control the noise level and gather the fractal parameters of each kind of GIS PD UHF signals. Simulations show that, the fuzzy control parameters filtering algorithm based on the box dimension will lead to the dispersive of lacunarity and the distribution of fractal parameters changes as the noise level of GIS PD UHF signal changes. However, the wavelet method has a good performance in gathering those two fractal parameters of different GIS PD UHF signals. And its a promising approach to expand the applicability of classifiers used in GIS PD pattern recognition.
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Yang, Jingjie, Ke Yan, Zhuo Wang, and Xiang Zheng. "A Novel Denoising Method for Partial Discharge Signal Based on Improved Variational Mode Decomposition." Energies 15, no. 21 (2022): 8167. http://dx.doi.org/10.3390/en15218167.
Texte intégralRésumé :
Partial discharge (PD) online monitoring is a common technique for high-voltage equipment diagnosis. However, due to field interference, the monitored PD signal contains a lot of noise. Therefore, this paper proposes a novel method by integrating the flower pollination algorithm, variational mode decomposition, and Savitzky–Golay filter (FPA-VMD-SG) to effectively suppress white noise and narrowband noise in the PD signal. Firstly, based on the mean envelope entropy (MEE), the decomposition number and quadratic penalty term of the VMD were optimized by the FPA. The PD signal containing noise was broken down into intrinsic mode functions (IMFs) by optimized parameters. Secondly, the IMFs were classified as the signal component, the noise dominant component, and the noise component according to the kurtosis value. Thirdly, the noise dominant component was denoised using the SG filter, and the denoised signal was mixed with the signal component to reconstruct a new signal. Finally, threshold denoising was used to eliminate residual white noise. To verify the performance of the FPA-VMD-SG method, compared with empirical mode decomposition with wavelet transform (EMD-WT) and adaptive singular value decomposition (ASVD), the denoising results of simulated and real PD signals indicated that the FPA-VMD-SG method had excellent performance.
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Thuc, Vu Cong, and Han Soo Lee. "Partial Discharge (PD) Signal Detection and Isolation on High Voltage Equipment Using Improved Complete EEMD Method." Energies 15, no. 16 (2022): 5819. http://dx.doi.org/10.3390/en15165819.
Texte intégralRésumé :
Electricity has a crucial function in contemporary civilization. The power grid must be stable to ensure the efficiency and dependability of electrical equipment. This implies that the high-voltage equipment at the substation must be reliably operated. As a result, the appropriate and dependable use of systems to monitor the operating status of high-voltage electrical equipment has recently gained attention. Partial discharge (PD) analysis is one of the most promising solutions for monitoring and diagnosing potential problems in insulation systems. Noise is a major challenge in diagnosing and detecting defects when using this measurement. This study aims to denoise PD signals using a data decomposition method, improved complete ensemble empirical mode decomposition with adaptive noise algorithm, combined with statistical significance test to increase noise reduction efficiency and to derive and visualize the Hilbert spectrum of the input signal in time-frequency domain after filtering the noise. In the PD signal analysis, both artificial and experimental signals were used as input signals in the decomposition method. For these signals, this study has yielded significant improvement in the denoising and the PD detecting process indicated by statistical measures. Thus, the signal decomposition by using the proposed method is proven to be a useful tool for diagnosing the PD on high voltage equipment.
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Xu, Wencong, Bingshu Chen, Yue Hu, and Jianxun Li. "A Novel Wide-Band Directional MUSIC Algorithm Using the Strength Proportion." Sensors 23, no. 9 (2023): 4562. http://dx.doi.org/10.3390/s23094562.
Texte intégralRésumé :
The directional multiple signal classification (Dir-MUSIC) algorithm based on the antenna gain array manifold has been proposed to find the direction of the partial discharge (PD) source in substations. However, PD signals are wideband signals and the antenna gain pattern functions are always different at different frequencies; therefore, the accuracy can be improved using a wideband Dir-MUSIC algorithm. In this paper, wideband Dir-MUSIC algorithms are discussed and a novel wideband Dir-MUSIC algorithm using the strength proportion (DirSP) is proposed. This algorithm estimates a focusing PD signal at a certain frequency using the strength proportion among different directions, and then the Dir-MUSIC algorithm can process the focusing PD signal at this frequency. In simulations, when the antenna gain functions among different frequency bins are quite different, the Dir-MUSIC algorithm loses accuracy; meanwhile, DirDP performs very well. In the experiments, we deal with six sets of samples, and the mean error and standard deviation are both smaller than 4° better than other methods.
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Li, Yanan, and Zhaohui Li. "Application of a Novel Wavelet Shrinkage Scheme to Partial Discharge Signal Denoising of Large Generators." Applied Sciences 10, no. 6 (2020): 2162. http://dx.doi.org/10.3390/app10062162.
Texte intégralRésumé :
Partial Discharge (PD) measurements of large generators are extremely affected and hampered by noise, making the denoising of PD signal an inevitable issue. Wavelet shrinkage is the most commonly employed method for PD signal denoising. The appropriate mother wavelet and decomposition level are critically important for the denoising performance. In consideration of the PD signal characteristics of large generators, a novel wavelet shrinkage scheme for PD signal denoising is presented. In the scheme, a scale dependent wavelet selection method is proposed; the core idea is that the optimum wavelet at each scale is selected as the one maximizing the energy ratio of coefficients beside and inside the range formed by the threshold, which correspond to the signal to be reserved and noise to be removed, respectively. In addition, taking into account the influence of mother wavelet at each scale on the decomposition level, an approach for decomposition level determination is put forward based on the energy composition after decomposition at each scale. The application results on the simulated signals with different SNR obtained by combining the various pulses and measured signal on-site show the effectiveness of the proposed scheme. Besides, the denoising results are compared with that of the existing wavelet selection methods and the proposed wavelet selection method shows an obvious advantage.
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Sun, Kang, Tong Wu, Xinwei Li, and Jing Zhang. "Robust Estimation of Arrival Time of Complex Noisy Partial Discharge Pulse in Power Cables Based on Adaptive Variational Mode Decomposition." Applied Sciences 10, no. 5 (2020): 1641. http://dx.doi.org/10.3390/app10051641.
Texte intégralRésumé :
Periodic narrowband signals and white noise are the main interferences in online detection and localization of cable partial discharge (PD), however, existing research has always focused on the white noise suppression only, which is not in line with the actual scene. A novel de-noising method for effectively extracting random PD pulse from complex and strong interferences is proposed in this paper and applied to PD localization. Firstly, an improved adaptive variational mode decomposition (AVMD) is used to decompose periodic narrowband interference, white noise, and PD signal into different intrinsic mode. According to the characteristic that the power of intrinsic mode component of periodic narrowband interference in the discrete Fourier transformation (DFT) power spectrum is much larger than that of PD and white noise, the periodic narrowband is removed out. In order to effectively filter out white noise, a scale adaptive wavelet packet decomposition method based on correlation coefficient is proposed, which decomposes the signal into high, middle, and low-frequency components. The components with low frequency, small amplitude are removed out as the white noise interference according to the threshold method, and the residual is the de-noising PD signal. Experimental results show that the proposed method can robustly suppress the interference of periodic narrowband signal and white noise, and effectively preserve the essential characteristics of the real PD signal. In the multi-sensor travelling wave based localization system of cable PD source using time-varying kurtosis, accurate estimation of first arrival time of PD pulse can be achieved by the de-noising results.
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Gourishetti, Saichand, David Johnson, Sara Werner, András Kátai, and Peter Holstein. "Partial discharge monitoring using deep neural networks with acoustic emission." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 263, no. 3 (2021): 3312–23. http://dx.doi.org/10.3397/in-2021-2373.
Texte intégralRésumé :
The occurrence of partial discharge (PD) indicates failures in electrical equipment. Depending on the equipment and operating conditions, each type of PD has its own acoustic characteristics and a wide frequency spectrum. To detect PD, electrical equipment is often monitored using various sensors, such as microphones, ultrasonic, and transient-earth voltage, whose signals are then analyzed manually by experts using signal processing techniques. This process requires significant expertise and time, both of which are costly. Advancements in machine learning, aim to address this issue by automatically learning a representation of the signal, minimizing the need for expert analysis. To this end, we propose a deep learning-based solution for the automatic detection of PD using airborne sound emission in the audible to the ultrasonic range. As input to our proposed model, we evaluate common time-frequency representations of the acoustic signal, such as short-time Fourier, continuous wavelet transform and Mel spectrograms. The extracted spectrum from the PD signal pulses is used to train and evaluate the proposed deep neural network models for the detection of different types of PD. Compared to the manual process, the automatic solution is seen as beneficial for maintenance processes and measurement technology.
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Zhou, Huan, Zhong Ma, Xiunan Chu, et al. "Small window cross-correlation accumulation time difference algorithm based on peak judgment method and generalized cross-correlation method." Journal of Physics: Conference Series 2369, no. 1 (2022): 012043. http://dx.doi.org/10.1088/1742-6596/2369/1/012043.
Texte intégralRésumé :
Based on peak judgment method and generalized correlation method, this paper proposes a new ultra high frequency partial discharge signal time difference algorithm. This algorithm avoids the defect that the traditional cross-correlation algorithm has a large correlation error for signals with lower amplitude. At the same time, by accumulating the correlation results of N groups of data, the influence of random interference of UHF PD signal on the correlation results is reduced. N times of correlation analysis were performed and correlation results were accumulated in a small area near the peak of the UHF PD signal, not only avoiding the deficiencies of the traditional peak judgment method that the peak value of the UHF PD signal is not easy to determine, but also minimizing the effect of PD white noise. In order to verify the effectiveness of the proposed algorithm, the time difference calculated by different algorithm are compared, and the results show that the proposed algorithm is superior to other existing algorithms.
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Liu, Tingliang, Jing Yan, Yanxin Wang, Yifan Xu, and Yiming Zhao. "GIS Partial Discharge Pattern Recognition Based on a Novel Convolutional Neural Networks and Long Short-Term Memory." Entropy 23, no. 6 (2021): 774. http://dx.doi.org/10.3390/e23060774.
Texte intégralRésumé :
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.
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Wang, Hui, Xiu Wei Li, Yu Xin Yun, and Hai Yan Yuan. "Reassigned Time Frequency Analysis for PD Signals in GIS." Applied Mechanics and Materials 448-453 (October 2013): 1959–62. http://dx.doi.org/10.4028/www.scientific.net/amm.448-453.1959.
Texte intégralRésumé :
Partial discharge signal in GIS is a kind of typical non-stationary signal, using the time or frequency domain simply is not enough to describe the time-varying information of PD. Based on the reason above, this paper introduces a joint time-frequency analysis method according to the reassignment theory for analyzing the PD of GIS. After the processing of the PD signals simulated and on field, we conclude that this method provides a higher concentration in the time-frequency plane and reduces the most influence of the cross-interference terms.
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Suo, Chunguang, Jingjing Zhao, Xuehua Wu, Zhipeng Xu, Wenbin Zhang, and Mingxing He. "Partial Discharge Detection Technology for Switchgear Based on Near-Field Detection." Electronics 12, no. 2 (2023): 336. http://dx.doi.org/10.3390/electronics12020336.
Texte intégralRésumé :
In view of the fact that the partial discharge (PD) signal energy is mainly concentrated below hundreds of megahertz, the ultra-high frequency part of the energy is weak, and the interior space of the switchgear is narrow, this paper proposes a new method for PD detection of the switchgear based on near-field detection. Firstly, based on the principle of PD, the field characteristics of the signal in the switchgear are analyzed. After that, the probe is designed with an electric small loop structure. Based on its equivalent circuit, its measurement principle and amplitude frequency characteristics are analyzed. The influence of probe size and material on amplitude frequency characteristics is obtained by using simulation software High Frequency Structure Simulator (HFSS), and the probe parameters suitable for PD detection in the switchgear are determined. Finally, the performance of the probe is measured by network analyzer, and the PD signal is tested on the simulated PD test platform. The results show that the probe works in the frequency band of 10–200 MHz and can receive PD signals containing more energy information. In the operating frequency band, the reflection coefficient of the probe port is very large, and its interference to the signal near field is particularly small. The probe also has good frequency response characteristics, and the fluctuation in the frequency band is less than 5 dB, which can obtain more accurate PD signal characteristics in subsequent processing. In addition, the probe is passive, with dimensions of 166 mm in length, 104 mm in width, and 2 mm in thickness, which is suitable for placing in the switchgear with small internal space. The results of PD receiving test show that the probe can reflect the occurrence of PD remarkably and accurately.
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Kozioł, Michał, Łukasz Nagi, Tomasz Boczar, and Zbigniew Nadolny. "Quantitative Analysis of Surface Partial Discharges through Radio Frequency and Ultraviolet Signal Measurements." Energies 16, no. 9 (2023): 3641. http://dx.doi.org/10.3390/en16093641.
Texte intégralRésumé :
In high voltage insulation systems, dielectric materials may be exposed to partial discharges (PD), which can lead to equipment failures and safety hazards. Therefore, it is crucial to detect and characterize PD activity on the surface of insulation systems. Techniques such as radio frequency signal analysis and ultraviolet radiation emission detection are commonly used for this purpose. In this research study, an analysis was conducted on the signals emitted by surface PD in the radio frequency and ultraviolet radiation emission ranges. The goal was to indicate possible directions for further basic research aimed at building a knowledge base and improving measurement methods. The analysis confirmed that radio frequency and ultraviolet signal analysis can provide important information about the activity and location of PD on the surface, including the intensity and nature of PD. The experimental investigation presented in this paper provides valuable insights into the potential for using radio frequency and ultraviolet signals to enhance diagnostic techniques for monitoring the condition of insulation systems in high-voltage equipment.
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Yang, Yan, Xiao Feng Chen, and Qing Liu. "Development of GIS PD Location System Based on LabVIEW." Advanced Materials Research 1044-1045 (October 2014): 1469–72. http://dx.doi.org/10.4028/www.scientific.net/amr.1044-1045.1469.
Texte intégralRésumé :
On-line detecting partial discharge inside GIS and locating the source of partial discharge spatially can discover the premonition of GIS insulation defects. This article introduces a system for partial discharge on-line detection and source locating, which is based on the hardware consisting essentially of ultrasonic sensors, prepositive signal amplifiers, a TDS3034C oscilloscope and a laptop. The system is a virtual instrument based on LabVIEW platform, and it can sample the ultrasonic signals generated by partial discharge in real time, and provide a friendly human-computer interaction interface. This article deeply discussed the partial discharge signal acquisition, signal de-noising, multi-way signal delay calculation and algorithm based on the time lag. The result implies that the system can validly detect and locate partial discharge, and spatially demonstrate the coordinate of the source of partial discharge through human-computer interaction interface.
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Z. Abdullah, A., M. Isa, S. N. M. Arshad, et al. "Wavelet based de-noising for on-site partial discharge measurement signal." Indonesian Journal of Electrical Engineering and Computer Science 16, no. 1 (2019): 259. http://dx.doi.org/10.11591/ijeecs.v16.i1.pp259-266.
Texte intégralRésumé :
<span>This paper presents, wavelet based de-noising technique for on-site partial discharge (PD) measurement signal. The signal is measured from medium voltage power cable at 11 kV distribution substation. The best mother wavelet, decomposition level and the type of threshold for the de-noising technique are selected based on the signal to noise ratio (SNR) aggregation. The SNR aggregation is determined based on the minimum, maximum, mean and standard deviation parameters. The same standard de-noising procedure is applied for two different PD signals and the selection parameters are done based on the accuracy of de-noising analysis. The analysis is performed in MATLAB software environment and Daubechies 2 (db2) is found as the best mother wavelet at tenth decomposition levels with soft threshold type. This study is specifically performed to develop the de-noising procedure for on-site PD measurement. Overall results indicate that the right selection of the de-noising procedure will help to improve the PD signal detection from on–site measurement.</span>
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Pereira, José Carlos, Arthur Oscar Schelp, Arlindo Neto Montagnoli, Ana Rita Gatto, André Augusto Spadotto, and Lídia Raquel de Carvalho. "Residual signal auto-correlation to evaluate speech in Parkinson’s disease patients." Arquivos de Neuro-Psiquiatria 64, no. 4 (2006): 912–15. http://dx.doi.org/10.1590/s0004-282x2006000600004.
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OBJECTIVE: To evaluate the maximum residual signal auto-correlation also known as pitch amplitude (PA) values in patients with Parkinson’s disease (PD) patients. METHOD: The signals of 21 Parkinson’s patients were compared with 15 healthy individuals, divided according age and gender. RESULTS: Statistical difference was seen between groups for PA, 0.39 for controls and 0.25 for PD. Normal value threshold was set as 0.3; (p<0.001). In the Parkinson’s group 80.77%, and in the control group only 12.28%, had a PA<0.3 demonstrating an association between these variables. The dispersion diagram for age and PA for PD individuals showed p=0.01 and r=0.54. There was no significant difference in relation to gender and PA between groups. CONCLUSION: The significant differences in pitch’s amplitude between PD patients and healthy individuals demonstrate the methods specificity. The results showed the need of prospective controlled studies to improve the use and indications of residual signal auto-correlation to evaluate speech in PD patients.
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Yang, Jing Gang, Yong Yong Jia, Zhi Cheng Zhou, and Jun Hao Li. "The Measurement System of Partial Discharge under Impulse Voltage." Applied Mechanics and Materials 543-547 (March 2014): 1046–49. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.1046.
Texte intégralRésumé :
Partial discharge (PD) is the important detection parameter for power equipment, with the development of high voltage test technology, PD detection under impulse voltage is more attention. In order to detect the partial discharge signal under the impulse voltage, this paper designs a high-frequency current sensor and broadband measurement impedance. A set of partial discharge measurement system under impulse voltage is built up. The frequency characteristics of current sensor and measurement impedance are detected. In order to test the measurement system, the PD signals of a needle defect under impulse voltage is detected, the test results show that the PD measurement system can detect the PD signals effectively.
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Samaitis, Vykintas, Liudas Mažeika, Audrius Jankauskas, and Regina Rekuvienė. "Detection and Localization of Partial Discharge in Connectors of Air Power Lines by Means of Ultrasonic Measurements and Artificial Intelligence Models." Sensors 21, no. 1 (2020): 20. http://dx.doi.org/10.3390/s21010020.
Texte intégralRésumé :
According to the statistics, 40% of unplanned disruptions in electricity distribution grids are caused by failure of equipment in high voltage (HV) transformer substations. These damages in most cases are caused by partial discharge (PD) phenomenon which progressively leads to false operation of equipment. The detection and localization of PD at early stage can significantly reduce repair and maintenance expenses of HV assets. In this paper, a non-invasive PD detection and localization solution has been proposed, which uses three ultrasonic sensors arranged in an L shape to detect, identify and localize PD source. The solution uses a fusion of ultrasonic signal processing, machine learning (ML) and deep learning (DL) methods to classify and process PD signals. The research revealed that the support vector machines classifier performed best among two other classifiers in terms of sensitivity and specificity while classifying discharge and surrounding noise signals. The proposed ultrasonic signal processing methods based on binaural principles allowed us to achieve an experimental lateral source positioning error of 0.1 m by using 0.2 m spacing between L shaped sensors. Finally, an approach based on DL was suggested, which allowed us to detect a single PD source in optical images and, in such a way, to provide visual representation of PD location.
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Xie, Qing, Dan Liu, Ying Zhang, et al. "Application Research of the Sparse Representation of Eigenvector on the PD Positioning in the Transformer Oil." International Journal of Antennas and Propagation 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/1343194.
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The partial discharge (PD) detection of electrical equipment is important for the safe operation of power system. The ultrasonic signal generated by the PD in the oil is a broadband signal. However, most methods of the array signal processing are used for the narrowband signal at present, and the effect of some methods for processing wideband signals is not satisfactory. Therefore, it is necessary to find new broadband signal processing methods to improve detection ability of the PD source. In this paper, the direction of arrival (DOA) estimation method based on sparse representation of eigenvector is proposed, and this method can further reduce the noise interference. Moreover, the simulation results show that this direction finding method is feasible for broadband signal and thus improve the following positioning accuracy of the three-array localization method. And experimental results verify that the direction finding method based on sparse representation of eigenvector is feasible for the ultrasonic array, which can achieve accurate estimation of direction of arrival and improve the following positioning accuracy. This can provide important guidance information for the equipment maintenance in the practical application.
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Fang, Weixing, Guojin Chen, Wenxin Li, et al. "A PRPD-Based UHF Filtering and Noise Reduction Algorithm for GIS Partial Discharge." Sensors 23, no. 15 (2023): 6763. http://dx.doi.org/10.3390/s23156763.
Texte intégralRésumé :
The online detection of partial discharge (PD) in gas-insulated switchgear (GIS) is a crucial and powerful tool for maintaining their reliability. However, extracting weak discharge signals from strong disturbances is a significant challenge. The presence of noise can hamper the identification and localization of PD types, making the extraction of pure PD signals the focus of current research. This paper proposes a PRPD-based PD filtering algorithm that analyzes interference using the output information from PRPD and sets threshold parameters for noise reduction processing. This method is mainly used for secondary noise reduction at a later stage, without analyzing the noise source, to achieve effective signal acquisition while retaining the characteristics of the PD signals, thereby improving the system’s sensitivity and the signal’s purity.
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Zhang, Fei, Jiao Fu, and Jie Li. "The Propagation Characteristics of Partial Discharge Signals in Enclose Busbar." Applied Mechanics and Materials 260-261 (December 2012): 420–25. http://dx.doi.org/10.4028/www.scientific.net/amm.260-261.420.
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Time-domain waveform of partial discharge (PD) signals of generator stator is distorted in enclosed busbar by the influence of distribution parameters of electrical equipment and different connection structure of enclose busbar. To research propagation characteristics of PD signals, a model of enclosed busbar between generator busbar exports and main transformer is established by using EMTP-ATP software. To analyze the propagation characteristics of time-domain waveform, voltage and current wave of PD signals are simulated. Voltage wave and current wave will be carried out fast fourier transform (FFT) algorithm by the use of MATLAB, frequency-domain characteristics are analyzed. It follows that the influence of time-domain waveform and frequency-domain characteristics of PD signals. The basic theories is provided for the location of PD signal monitoring sensor.
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Xie, Hong Ling, Bo Yi Shen, Fei Long Wang, and Yan Qing Li. "Research on Method for Obtaining Time Difference of Localization of Transformer PD Source." Applied Mechanics and Materials 291-294 (February 2013): 2222–27. http://dx.doi.org/10.4028/www.scientific.net/amm.291-294.2222.
Texte intégralRésumé :
Ultrasonic positioning accuracy of transformer partial discharge is not high. The main reason is that it is difficult to obtain accurate time difference of the ultrasonic signals reaching two different sensors, especially when the signal-to-noise ratio is not high enough. To solve this problem, Hilbert-Huang transform (HHT) is applied in signal processing to extract the precise moment, and in this way the precise time difference is easily obtained. The signal-to-noise ratio (SNR) of received signal is low, and even worse PD signal is drowned in the strong interference signal. For this, the method named fast independent component analysis (FastICA) is applied in ultrasonic signal denoising before obtaining the time difference. FastICA is not effective in separating the ultrasonic signal from the separated components and in order to solve the problem, envelope identification method is proposed in this paper. In the method, the upper envelope of the signal waveform is sampled and the average of the sampling points is calculated to extract the ultrasonic signal. The numerical result confirmed the practicality of all the mentioned methods.
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Maheswari, R. V., B. Vigneshwaran, and L. Kalaivani. "Genetic algorithm based automated threshold estimation in translation invariant wavelet transform for denoising PD signal." COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering 34, no. 4 (2015): 1252–69. http://dx.doi.org/10.1108/compel-12-2014-0332.
Texte intégralRésumé :
Purpose – The purpose of this paper is to investigate the condition of insulation in high-voltage equipments using partial discharge (PD) measurements. It proposes the methods to eliminate several noises like white noise, random noise and discrete spectral interferences which severely pollutes the PD signals. The study aims to remove these noises from the PD signal effectively by preserving the signal features. Design/methodology/approach – This paper employs fast Fourier transform, discrete wavelet transform and translational invariant wavelet transform (TIWT) for denoising the PD signals. The simulated damped exponential pulse and damped oscillatory pulse with low- and high-level noises and a measured PD signal are considered for this analysis. The conventional wavelet denoising approach is also improved by estimating the automated global optimum threshold value using genetic algorithm (GA). The statistical parameters are evaluated and compared. Among these methods, GA-based TIWT approach provides robustness and reduces computational complexity. Findings – This paper provides effective condition monitoring of power apparatus using GA-based TIWT approach. This method provides the low value of mean square error, pulse amplitude distortion and also high reduction in noise level due to its robustness and reduced computational complexity. It suggests that this approach works well for both signals immersed in noise as well as for noise immersed in signals. Research limitations/implications – Because of the chosen PD signals, the research results may lack for multiple discharges. Therefore, researchers are encouraged to test the proposed propositions further. Practical implications – The paper includes implication for the development of online testing for equipment analysis and diagnostics during normal operating condition. Corrective actions can be planned and implemented, resulting in reduced unscheduled downtime. Social implications – This PD-based analysis often present well in advance of insulation failure, asset managers can monitor it over time and make informed strategic decisions regarding the repair or replacement of the equipment. These predictive diagnostics help society to prioritize investments before an unexpected outage occurs. Originality/value – This paper provides an enhanced study of condition monitoring of HV power apparatus by which life time of insulation can be increased by taking preventive measures.
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Lu, Jia, Xiaoxing Zhang, and Hao Xiong. "A New Method for Suppressing Periodic Narrowband Interference Based on the Chaotic van der Pol Oscillator." International Journal of Bifurcation and Chaos 25, no. 09 (2015): 1550120. http://dx.doi.org/10.1142/s0218127415501205.
Texte intégralRésumé :
The chaotic van der Pol oscillator is a powerful tool for detecting defects in electric systems by using online partial discharge (PD) monitoring. This paper focuses on realizing weak PD signal detection in the strong periodic narrowband interference by using high sensitivity to the periodic narrowband interference signals and immunity to white noise and PD signals of chaotic systems. A new approach to removing the periodic narrowband interference by using a van der Pol chaotic oscillator is described by analyzing the motion characteristic of the chaotic oscillator on the basis of the van der Pol equation. Furthermore, the Floquet index for measuring the amplitude of periodic narrowband signals is redefined. The denoising signal processed by the chaotic van der Pol oscillators is further processed by wavelet analysis. Finally, the denoising results verify that the periodic narrowband and white noise interference can be removed efficiently by combining the theory of the chaotic van der Pol oscillator and wavelet analysis.
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Hou, Shan Shan, Qing Xie, Le Xian Shi, and Wei Tao Hu. "Research on Direct Wave Separation of PD Ultrasonic Signal Based on ICA." Advanced Materials Research 734-737 (August 2013): 2819–23. http://dx.doi.org/10.4028/www.scientific.net/amr.734-737.2819.
Texte intégralRésumé :
For partial discharge of transformer ultrasonic detection method exists positioning accuracy problems, the main reason mostly due to the time delay selected error is analyzed, while the main reason for time delay selected error is due to the transformer PD ultrasonic signal propagation which is extremely complex, the signals received by the ultrasonic sensor are the direct wave, non-direct wave and a mixture of all kinds of noise superimposed, so in order to improve positioning accuracy the correct separation of the direct waves time delay is important, this paper proposes a method of transformer PD direct wave ultrasonic signal separation based on independent component analysis (ICA), by a separate analysis of the direct wave signal, non-direct wave signal and noise characteristics of each principal component, the use of ICA to separate from the mixed signal to extract the direct wave signal and select the time delay, simulation and experimental results demonstrate the feasibility of the method.
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Jia, Rong, Yongtao Xie, Hua Wu, Jian Dang, and Kaisong Dong. "Power Transformer Partial Discharge Fault Diagnosis Based on Multidimensional Feature Region." Mathematical Problems in Engineering 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/4835694.
Texte intégralRésumé :
Effectively extracting power transformer partial discharge (PD) signals feature is of great significance for monitoring power transformer insulation condition. However, there has been lack of practical and effective extraction methods. For this reason, this paper suggests a novel method for the PD signal feature extraction based on multidimensional feature region. Firstly, in order to better describe differences in each frequency band of fault signals, empirical mode decomposition (EMD) and Hilbert-Huang transform (HHT) band-pass filter wave for raw signal is carried out. And the component of raw signals on each frequency band can be obtained. Secondly, the sample entropy value and the energy value of each frequency band component are calculated. Using the difference of each frequency band energy and complexity, signals feature region is established by the multidimensional energy parameters and the multidimensional sample entropy parameters to describe PD signals multidimensional feature information. Finally, partial discharge faults are classified by sphere-structured support vector machines algorithm. The result indicates that this method is able to identify and classify different partial discharge faults.
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Dhandapani, Ragavesh, Imene Mitiche, Scott McMeekin, Venkateswara Sarma Mallela, and Gordon Morison. "Enhanced Partial Discharge Signal Denoising Using Dispersion Entropy Optimized Variational Mode Decomposition." Entropy 23, no. 12 (2021): 1567. http://dx.doi.org/10.3390/e23121567.
Texte intégralRésumé :
This paper presents a new approach for denoising Partial Discharge (PD) signals using a hybrid algorithm combining the adaptive decomposition technique with Entropy measures and Group-Sparse Total Variation (GSTV). Initially, the Empirical Mode Decomposition (EMD) technique is applied to decompose a noisy sensor data into the Intrinsic Mode Functions (IMFs), Mutual Information (MI) analysis between IMFs is carried out to set the mode length K. Then, the Variational Mode Decomposition (VMD) technique decomposes a noisy sensor data into K number of Band Limited IMFs (BLIMFs). The BLIMFs are separated as noise, noise-dominant, and signal-dominant BLIMFs by calculating the MI between BLIMFs. Eventually, the noise BLIMFs are discarded from further processing, noise-dominant BLIMFs are denoised using GSTV, and the signal BLIMFs are added to reconstruct the output signal. The regularization parameter λ for GSTV is automatically selected based on the values of Dispersion Entropy of the noise-dominant BLIMFs. The effectiveness of the proposed denoising method is evaluated in terms of performance metrics such as Signal-to-Noise Ratio, Root Mean Square Error, and Correlation Coefficient, which are are compared to EMD variants, and the results demonstrated that the proposed approach is able to effectively denoise the synthetic Blocks, Bumps, Doppler, Heavy Sine, PD pulses and real PD signals.
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Bu, Xia, Melissa Bu, Ping Hua, Baogong Zhu, Arlene H. Sharpe, and Gordon J. Freeman. "Intracellular Signaling triggered by engagement of PD-1 by PD-L1." Journal of Immunology 204, no. 1_Supplement (2020): 78.24. http://dx.doi.org/10.4049/jimmunol.204.supp.78.24.
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Abstract Blockade of PD-1/PD-L1 is in effective anti-tumor immunotherapy. To further elucidate the mechanism by which PD-1/PD-L1 engagement causes T cell dysfunction, we investigated intracellular signaling triggered by PD-1/PD-L1 ligation in T cells. We describe a novel antibody that detects PD-1 signaling. This antibody detects phosphorylation of the immunotyrosine switch motif (ITSM) in the intracellular tail of both human and mouse PD-1 (phospho-PD-1). We first examined the expression of PD-1 and phosphorylated PD-1 in T cells from human PBMC. We found that without CD3 and CD28 mAb stimulation, PD-1 and phospho-PD-1 had minimal expression on CD3+CD4+ and CD3+CD8+ cell. Upon stimulation with CD3 and CD28 mAb, we observed increased expression of PD-1 and phospho PD-1 in both CD3+CD4+ and CD3+CD8+ cells. Furthermore, upon CD3 and CD28 mAb stimulation and PD-L1 recombinant protein treatment, the level of phospho PD-1 increased in CD3+CD4+ and CD3+CD8+ cells. Utilizing a co-culture system of Jurkat-hPD-1 and 300-hPD-L1, we mimicked the cell-cell interaction through molecular ligation of PD-1 and PD-L1. Upon stimulation with CD3 and CD28 mAb in this co-culture system, we detected increased phospho PD-1 signal and phospho SHP-2 signal by Western blot. When CD3 and CD28 mAb stimulated Jurkat-hPD-1 cells were treated with recombinant PD-L1 protein, we observed decreased IL-2 secretion which indicates PD-1/PD-L1 ligation suppresses T cell activation and corresponds with phospho PD-1 expression. This study examines the intrinsic intracellular signals upon PD-1/PD-L1 engagement, and the results may promote a better understanding of the mechanism of T cell dysfunction by PD-1/PD-L1 ligation.
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Zhang, Deyi, Xiujie Sun, Harshita B. Gupta, Ryan M. Reyes, Robert S. Svatek, and Tyler J. Curiel. "Cell-intrinsic PD-L1 and PD-1 signal effects in bladder cancer." Journal of Immunology 200, no. 1_Supplement (2018): 166.27. http://dx.doi.org/10.4049/jimmunol.200.supp.166.27.
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Abstract PD-1/PD-L1 provides a mechanism of immune escape, the blockage of which has reinvigorated interest in the treatment of urothelial cancer. We recently reported on cell-intrinsic PD-L1 effects in melanoma and ovarian cancer, and considered that effects in bladder cancer cells could differ based on distinct mutational landscapes. Using CRISPER/Cas9 methodology, we knockout PD-L1 in the murine bladder cancer cell line MB49 and the human bladder caner cell line RT. We show here that cell-intrinsic MB49 and RT4 cells express PD-L1 and PD-1 that each mediate cell-intrinsic signals. PD-L1 knockout has little effect on cells proliferation in vitro, but significant difference could be found in tumor size when we used MB49 Ctrl or MB49 PD-L1KO cells to challenge C57B16 mice. αPD-L1 and αPD-1 antibodies reduced bladder cancer cell proliferation in vitro demonstrating direct signaling effects. Bladder cancer cell-intrinsic PD-L1 promoted mTORC1 and tumor initiating cell generation similar to melanoma and ovarian cancer cells. By contrast to melanoma and ovarian cancer cells, bladder cancer cell PD-L1 promoted autophagy and had little effect on in vivo immune-independent growth. Base one the functional role, PD-L1 knockout increased proliferation inhibition induced by Rapamycin in both mouse and human bladder cancer cells. Furthermore, we found intrinsic PD-L1 suppress cytokine production, including CXCL10. Corresponding with increased CXCL10 secretion, more CXCR3+ NK cells could be attracted by PD-L1KO cancer cells. Overall, our findings further illustrated the intrinsic role of PD-L1 in bladder cancer development and found PD-1 expression on human bladder cancer cells for the first time.
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Dong, Yue, Yong Qian, Hai Feng Ye, and Xiu Chen Jiang. "FEM Simulation of PD Acoustic Signal Propagation in Transformers." Applied Mechanics and Materials 448-453 (October 2013): 2278–85. http://dx.doi.org/10.4028/www.scientific.net/amm.448-453.2278.
Texte intégralRésumé :
In order to study propagation process of partial discharge ultrasonic signal in power transformer, the finite element method is used for simulation modeling and calculation. Ultrasonic waves can be activated by partial discharges (PD) in power transformers. The ultrasonic method is used for evaluating the insulation condition of power transformers by analyzing the partial discharge signals information which is detected by AE sensors. Compared with other diagnostic methods the AE method causes relatively low disturbance, and measuring apparatus is simple and easy to use. This technique is noninvasive and immune to electromagnetic noise. Simulate partial discharge sources of different positions respectively. Achieved results indicate that the space and time distributions of the acoustic pressure depend on the induction position. Furthermore, a greater pressure gradient is observed in domains with higher speed of sound while the signal amplitude decays when it moves away from the PD source.
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Yun, Yuxin, Guangke Xu, Weiwei Zhang, Xing Li, and Lingying Chen. "Simulation Analysis on Partial Discharge Signals Produced by Multiple Insulation Defects in GIS." E3S Web of Conferences 165 (2020): 06040. http://dx.doi.org/10.1051/e3sconf/202016506040.
Texte intégralRésumé :
In this paper, the simulation analysis on partial discharge (PD) signals produced by multiple insulation defects in gas insulated substation (GIS) has been researched by the simulation software of electromagnetic field (XFDTD). The theoretical analysis and simulation results show that the single partial discharge signal is the linear convolution of the partial discharge current signal and its corresponding impact response in GIS. The mixed partial discharge signals are the linear mixtures of every partial discharge signal.
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Sun, Zhiyong, Chunjuan Shi, and Hailin Tian. "Simulation and analysis of partial pulse remained jamming to HPRF PD radar." MATEC Web of Conferences 355 (2022): 03049. http://dx.doi.org/10.1051/matecconf/202235503049.
Texte intégralRésumé :
To solve the problem that the duty cycle of HPRF PD radar is not easy to use the general tow-and-pull jamming, a method of partial pulse remained jamming is proposed. Taking the transmitting signal of PD radar acquired by DRFM as an example, the jamming effect of the signal is simulated and analyzed. The results show that the jamming signal is modulated by the radar signal acquired by DRFM, the jamming signal generated has a strong correlation with the target Echo Signal, and it can effectively jam the HPRF PD radar with less power, which proves the effectiveness of the method.
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Alemami, Yahia, and Laiali Almazaydeh. "Pathological Voice Signal Analysis Using Machine Learning Based Approaches." Computer and Information Science 11, no. 1 (2017): 8. http://dx.doi.org/10.5539/cis.v11n1p8.
Texte intégralRésumé :
Voice signal analysis is becoming one of the most significant examination in clinical practice due to the importance of extracting related parameters to reflect the patient's health. In this regard, various acoustic studies have been revealed that the analysis of laryngeal, respiratory and articulatory function may be efficient as an early indicator in the diagnosis of Parkinson disease (PD). PD is a common chronic neurodegenerative disorder, which affects a central nervous system and it is characterized by progressive loss of muscle control. Tremor, movement and speech disorders are the main symptoms of PD. The diagnosis decision of PD is obtained by continued clinical observation which relies on expert human observer. Therefore, an additional diagnosis method is desirable for most comfortable and timely detection of PD as well as faster treatment is needed. In this study, we develop and validate automated classification algorithms, which are based on Naïve Bayes and K- Nearest Neighbors (KNN) using voice signal measurements to predict PD. According to the results, the diagnostic performance provided by the automated classification algorithm using Naïve Bayes was superior to that of the KNN and it is useful as a predictive tool for PD screening with a high degree of accuracy, approximately 93.3%.
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Yang, Yuan, Jian Gang Bi, Hong Jie Wang, and Nan Meng. "Study of the Ultrasonic Characteristics of Typical Partial Discharge on GIS." Advanced Materials Research 753-755 (August 2013): 2200–2207. http://dx.doi.org/10.4028/www.scientific.net/amr.753-755.2200.
Texte intégralRésumé :
This Paper studied the typical PD in GIS by ultrasonic method. A GIS PD simulating and testing system is established, including a PD simulating system, an ultrasonic detecting system and a conventional pulse current method measurement system. The thesis focuses on four typical kinds of PD signals in GIS, including metal particles, bad contacts on high and grounding potential conductor, the protrusion on the high potential conductor. The statistics characteristics are obtained which include N-Φ spectrogram (relationship between discharge frequency and discharge phase Spectrogram) and Umax-Φ spectrogram (relationship between discharge amplitude and discharge phase Spectrogram). Experimental results show that low-frequency ultrasonic sensors can detect this four PD signals， signal spectra analysis shows that ultrasonic signals of different statistical model are with different characteristics. The results provide test data for the pattern recognition of Partial Discharge in GIS.
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Wang, Yulong, Xiaohong Zhang, Yancheng Li, Lili Li, Junguo Gao, and Ning Guo. "Multi-Scale Analysis and Pattern Recognition of Ultrasonic Signals of PD in a Liquid/Solid Composite of an Oil-Filled Terminal." Energies 13, no. 2 (2020): 366. http://dx.doi.org/10.3390/en13020366.
Texte intégralRésumé :
In order to analyze the partial discharge (PD) characteristics of a liquid/solid composite medium in an oil-filled submarine cable terminal; we have designed five discharge models including needle-plate, plate-to-plate air gap, surface, slide-flash and suspension potential. At the same time, the ultrasonic signals of PD have been extracted through the typical fault model research platform of oil-filled submarine cable equipment. First, we use SureShrink threshold wavelet denoising to suppress the ultrasonic signal noise. Secondly, through the multi-scale analysis of the signal, the energy distribution maps of five different types of PD are obtained; the analysis found that needle-plate discharge, suspension discharge, and slide-flash discharge have better resolution; and plate-to-plate air gap discharge and creeping discharge have similar characteristics and are not easy to distinguish. Finally, we designed six characteristic parameters of the ultrasound signal, and screened three feature quantities by a back propagation (BP) neural network to distinguish between plate-to-plate air gap discharge and surface discharge. In summary, the method of combining multi-scale analysis and neural networks is used to distinguish the five discharge types by extracting the characteristic values of the characteristic signals.