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Field PMU Test and Calibration Method–Part I:General Framework and Algorithms for PMU Calibrator

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Journal of Modern Power Systems and Clean Energy 2022年第6期10卷 1507-1518页

作者： Sudi Xu Hao Liu Tianshu Bi State Key Lab of Alternate Electric Power System with Renewable Energy Sources(North China Electric Power University) BeijingChina State Grid Jiangsu Electric Power CO. LTD.Research InstituteNanjingChina

Laboratory testing of phasor measurement units(PMUs)guarantees their performance under laboratory conditions.However,many factors may cause PMU measurement problems in actual power systems,resulting in the malfunction of PMU-based applications.Therefore,field PMUs need to be tested and calibrated to ensure their performance and data quality.In this paper(Part I),a general framework for the field PMU test and calibration in different scenarios is proposed.This framework consists of a PMU calibrator and an analysis center,where the PMU calibrator provides the reference values for PMU error analysis.Two steps are implemented to ensure the calibrator accuracy for complex field signals:①by analyzing the frequency-domain probability distribution of random noise,a Fourier-transform-based signal denoising method is proposed to improve the anti-interference capability of the PMU calibrator;and②a general synchrophasor estimation method based on complex bandpass filters is presented for accurate synchrophasor estimations in multiple scenarios.Simulation and experimental test results demonstrate that the PMU calibrator has a higher accuracy than that of other calibrator algorithms and is suitable for field PMU test.The analysis center for evaluating the performance of field PMUs and the applications of the proposed field PMU test system are provided in detail in Part II of the next-step research.

关键词： Phasor measurement unit(PMU) calibration synchrophasor signal denoising field PMU test

Structured sparsity assisted online convolution sparse coding and its application on weak signature detection

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Chinese Journal of Aeronautics 2022年第1期35卷 266-276页

作者： Huijie MA Shunming LI Jiantao LU Zongzhen ZHANG Siqi GONG College of Energy&Power Engineering Mcmjing University of Aeronautics and AstronauticsNanjing 210016China

Due to the strong background noise and the acquisition system noise,the useful characteristics are often difficult to be detected.To solve this problem,sparse coding captures a concise representation of the high-level features in the signal using the underlying structure of the signal.Recently,an Online Convolutional Sparse Coding(OCSC)denoising algorithm has been proposed.However,it does not consider the structural characteristics of the signal,the sparsity of each iteration is not enough.Therefore,a threshold shrinkage algorithm considering neighborhood sparsity is proposed,and a training strategy from loose to tight is developed to further improve the denoising performance of the algorithm,called Variable Threshold Neighborhood Online Convolution Sparse Coding(VTNOCSC).By embedding the structural sparse threshold shrinkage operator into the process of solving the sparse coefficient and gradually approaching the optimal noise separation point in the training,the signal denoising performance of the algorithm is greatly improved.VTNOCSC is used to process the actual bearing fault signal,the noise interference is successfully reduced and the interest features are more evident.Compared with other existing methods,VTNOCSC has better denoising performance.

关键词： Dictionary learning Online convolutional sparse coding(OCSC) signal denoising signal processing Weak signature detection

Recognition System for Diagnosing Pneumonia and Bronchitis Using Children’s Breathing Sounds Based on Transfer Learning

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Intelligent Automation & Soft Computing 2023年第9期37卷 3235-3258页

作者： Jianying Shi Shengchao Chen Benguo Yu Yi Ren Guanjun Wang Chenyang Xue School of Information and Communication Engineering Hainan UniversityHaikou570228China School of Biomedical Information and Engineering Hainan Medical CollegeHaikou571199China Department of Pediatrics Haikou Hospital of the Maternal and Child HealthHaikou570203China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and TechnologyWuhan430074China School of Instrument and Electronics North University of ChinaTaiyuan030051China

Respiratory infections in children increase the risk of fatal lung disease,making effective identification and analysis of breath sounds essential.However,most studies have focused on adults ignoring pediatric patients whose lungs are more vulnerable due to an imperfect immune system,and the scarcity of medical data has limited the development of deep learning methods toward reliability and high classification accuracy.In this work,we collected three types of breath sounds from children with normal(120 recordings),bronchitis(120 recordings),and pneumonia(120 recordings)at the posterior chest position using an off-the-shelf 3M electronic stethoscope.Three features were extracted from the wavelet denoised signal:spectrogram,mel-frequency cepstral coefficients(MFCCs),and Delta MFCCs.The recog-nition model is based on transfer learning techniques and combines fine-tuned MobileNetV2 and modified ResNet50 to classify breath sounds,along with software for displaying analysis results.Extensive experiments on a real dataset demonstrate the effectiveness and superior performance of the proposed model,with average accuracy,precision,recall,specificity and F1 scores of 97.96%,97.83%,97.89%,98.89%and 0.98,respectively,achieving superior performance with a small dataset.The proposed detection system,with a high-performance model and software,can help parents perform lung screening at home and also has the potential for a vast screening of children for lung disease.

关键词： Deep learning breath sounds transfer learning signal denoising

Salt and pepper noise removal in surveillance video based on low-rank matrix recovery

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Computational Visual Media 2015年第1期1卷 59-68页

作者： Yongxia Zhang Yi Liu Xuemei Li Caiming Zhang School of Computer Science and Technology Shandong University Shandong University of Finance and Economics Shandong Provincial Key Laboratory of Digital Media Technology

This paper proposes a new algorithm based on low-rank matrix recovery to remove salt &pepper noise from surveillance video. Unlike single image denoising techniques, noise removal from video sequences aims to utilize both temporal and spatial information. By grouping neighboring frames based on similarities of the whole images in the temporal domain, we formulate the problem of removing salt &pepper noise from a video tracking sequence as a lowrank matrix recovery problem. The resulting nuclear norm and L1-norm related minimization problems can be efficiently solved by many recently developed methods. To determine the low-rank matrix, we use an averaging method based on other similar images. Our method can not only remove noise but also preserve edges and details. The performance of our proposed approach compares favorably to that of existing algorithms and gives better PSNR and SSIM results.

关键词： multimedia computing noise cancellation signal denoising sparse matrices video signal processing video surveillance

Improvement in detection with forward scattering radar

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Science China(Information Sciences) 2011年第12期54卷 2680-2692页

作者： RAJA ABDULLAH Raja Syamsul Azmir RASID Mohd Fadlee A MOHAMED M Khalafa Wireless and Photonics Networks Research Centre Universiti Putra Malaysia Selangor 43400 Malaysia

Forward scattering radar (FSR) has advantages over traditional monostatic radar such as an enhanced radar cross section. Enhancement of the radar cross section can increase the detection range of the FSR system. This feature has renewed interest in FSR. The latest development of the FSR system for ground application does not consider a rough environment and there is no advanced signal processing technique for the detection procedure, which have delayed the deployment of FSR. Therefore, this paper analyzes a signal pro- cessing technique that extracts the Doppler signal scattered from a moving target under the influence of ground clutter, receiver noise and surrounding noise. The scattered Doppler signal is processed for target detection. Two methods, namely the use of the Hilbert transform and wavelet technique, are evaluated. Results show that target detection using a Hilbert transform is applicable only for certain conditions but target detection employing the wavelet technique is more robust against clutter and noise. An inclusive comparison of various wavelet threshold selection rules for different types of wavelet filters and levels of decomposition is conducted to study the effect on target detection with FSR. Two sets of field experiments were carried out to validate the proposed method, and target signals under the influence of large clutter were successfully detected using the proposed method with a confidence level exceeding 75%.

关键词： forward scattering radar signal denoising automatic target detection

The Algorithm of Balanced Orthogonal Multiwavelets and Its Application in denoising

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International Journal of Plant Engineering and Management 2011年第4期16卷 221-224页

作者： QIU Ai-zhong Zhengzhou Normal University Zhengzhou 450044 P. R. China

In order to extract fault features of a weak signal from the strong noise and maintain signal smoothness, a new method of denoising based on the algorithm of balanced orthogonal multiwavelets is proposed. Multiwavelets have several scaling functions and wavelet functions, and possess excellent properties that a scalar wavelet cannot satisfy simultaneously, and match the different characteristics of signals. Moreover, the balanced orthogonal multiwavelets can avoid the Gibbs phenomena and their processes have the advantages in denoising. Therefore, the denoising based on the algorithm of balanced orthogonal multiwavelets is introduced into the signal process. The algorithm of bal- anced orthogonal multiwavelet and the implementation steps of this denoising are described. The experimental compar- ison of the denoising effect between this algorithm and the traditional multiwavelet algorithm was done. The experi- ments indieate that this method is effective and feasible to extract the fault feature submerged in heavy noise.

关键词： balanced orthogonal multiwavelets wavelet algorithm signal denoising extracting signal features fault diagnosis

Finding Pathogenicity Islands in Genome Data with ICA

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Journal of Electronic Science and Technology of China 2004年第1期2卷 58-62页

作者：郑方伟黄均才佘堃周明天 Information Security United Lab UESTC Chengdu 610054 China

A novel technique for finding pathogenicity islands in genome data with independent component analyses(ICA) is present. First denoise the genomic signal sequences with ICA and detect G+C patterns in genomes by comparing the result sequence with original sequences. The results on G+C patterns analysis of Dradiodurans chromosome I and N.serogroup A strain Z2491 are present. A set of loci that have very different G+C content and have not previously described are detected. The findings show that ICA is a powerful tool to detect differences within and between genomes and to separate small (gene level) and large (putative pathogenicity islands) genomic regions that have different composition characteristics.

关键词： genomic sequences signal denoising independent component analyses pathogenicity islands

Fault Detection and Analysis of Distributed Power System Short-circuit Using Wavelet Fractal Network

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Fault Detection and Analysis of Distributed Power System Sho...

Application of signal Processing and Neural Network for Transient Waveform Recognition in Power System

2007 8th International Conference on Electronic Measurement & Instruments（ICEMI’2007）

作者： Song Yuhai Wang Guangjian Chen Xiangguo Hebei University of Engineering Handan 056038 China

A novel method based on wavelet network and fractal theory for detecting short-circuit fault is proposed. To increase the signal-noise-ratio of the fault signal, the signal denoising technology using the statistic rule is brought forward to determine the threshold and the decomposition level of each order of wavelet space. On the basis of the inter relationship of wavelet transform and fractal theory, the whole and local fractal exponents obtained as features are presented for extracting fault signals. The effectiveness of the new algorithm used to extract the characteristic signal is described, which can be achieved by different fractal dimensions values of those types of short-circuit fault. In accordance with the threshold value of each type of short-circuit fault in each frequency band, the correlation between the type of short-circuit and the fractal dimensions can be figured to perform extraction. This detection model incorporates the advantages of morphological filter and multi-scale wavelet transform to extract the feature of faults meanwhile restraining various noises. The simulation results demonstrates that the proposed model is verified effectively.

关键词： Distributed power system fault detection short-circuit fault signal denoising fractal theory wavelet transform

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Application of Signal Processing and Neural Network for Tran...