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Field PMU Test and Calibration Method——PartⅡ:Test signal identification Methods and Field Test Applications

Journal of Modern Power Systems and Clean Energy 2023年第1期11卷 243-253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) Beijing 102206China State Grid Jangsu Electric Power CO. LTD Research InstituteNanjing 211103China

Synchrophasor measurement units(PMUs)provide synchronized measurement data for wide-area *** improve the effectiveness of synchrophasor-based applications,field PMUs must be tested to ensure their performance and data *** the companion paper(Part I),we proposed a field PMU test and calibration framework consisting of a PMU calibrator and analysis *** I presents the development and test of the PMU *** paper focuses on the analysis center and field test ***,the critical component of the analysis center is the signal identification module,for which the step and oscillation signal identification methods are ***,the performance evaluation criteria of PMU in these two cases are different from *** methods include a step signal detection method based on singular value decomposition(SVD),which has the capability of weak step detection to account for energy leakage of the signal during the step process,and an oscillation signal identification method based on SVD and fast Fourier transform,which can accurately extract oscillation components that benefit from the adaptive threshold setting ***,the analysis center software is implemented based on identification *** integrating the PMU calibrator in Part I with the analysis center in Part II,we can examine in depth the field PMU test applications in three test scenarios,including standard,playback,and field signal *** demonstrate the effectiveness and applicability of the proposed field PMU test methods from both Parts I and II.

关键词： Phasor measurement unit(PMU) field test signal identification oscillation

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Research on Image signal identification Based on Adaptive Array Stochastic Resonance

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Journal of Systems Science & Complexity 2022年第1期35卷 179-193页

作者： ZHAO Jingjing MA Yumei PAN Zhenkuan ZHANG Huage College of Computer Science&Technology Qingdao UniversityQingdao 266071China.

Aiming at the problems of low accuracy of image signal identification and poor anti-noise signal interference ability under strong noise environment,a signal identification method of correlated noisy image based on adaptive array stochastic resonance(SR)is proposed in this ***,the two-dimensional grayscale image is transformed to a one-dimensional binary pulse amplitude modulation(BPAM)signal with periodicity by the row or column scanning method,encoding and ***,the one-dimensional low signal-to-noise ratio BPAM signal can be applied to the saturating nonlinearity array SR module for image signal identification processing and part of the noise energy is converted into signal ***,the one-dimensional image signal processed by the nonlinearities is demodulated,decoded and reverse scanned to get the restored grayscale *** simulation results show that the image signal identification method proposed in this paper is highly efficient and accurate for the identification of noisy image signals of different sizes,and the bit error rate(BER)is also significantly reduced.

关键词： Array stochastic resonance bit error rate image restoration saturating nonlinearity signal identification

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维普期刊数据库

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Seismic signal recognition using improved BP neural network and combined feature extraction method

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Journal of Central South University 2014年第5期21卷 1898-1906页

作者：彭朝琴曹纯黄姣英刘秋生 School of Automation Science and Electrical Engineering Beihang University Science&Technology on Reliability&Environmental Engineering Laboratory Beihang University Ordnance Engineering College

Seismic signal is generally employed in moving target monitoring due to its robust characteristic.A recognition method for vehicle and personnel with seismic signal sensing system was proposed based on improved neural *** analyzing the seismic signal of the moving objects,the seismic signal of person and vehicle was acquisitioned from the seismic sensor,and then feature vectors were extracted with combined methods after filter ***,these features were put into the improved BP neural network designed for effective signal *** with previous ways,it is demonstrated that the proposed system presents higher recognition accuracy and validity based on the experimental *** also shows the effectiveness of the improved BP neural network.

关键词： seismic signal feature extraction BP neural network signal identification

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OFDM and FH signal Recognition Based on Cumulants and SPWVD Spectrum Cancellation for Internet of Things

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China Communications 2012年第5期9卷 141-148页

作者： Zhang Hongxin Wu Yanling Wang Haiqing Chen Canfeng Beijing Univ Posts & Telecommun Sch Elect Engn Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Beijing Key Lab Work Safety Intelligent Monitorin Beijing 100876 Peoples R China Nokia Res Ctr China Beijing 100078 Peoples R China

Considering the Gaussian asymptotic features of OFDM signals, the identification meth-od of it is proposed in this paper by using the cu-mulants of the wavelet transform coefficients in different layer in a low SNR circumstance. Further-more, taking the coexistence of the OFDM and Frequency Hopping （FH） signals into account, a new way to separate FH and OFDM signals is pro- posed based on SPWVD spectrum cancellation, and it can be used to estimate the FH parameters. The simulation resuks show that the OFDM and single-carrier signals can be identified with a high correct rate of 95% even at-6 dB SNR; mean-while, the separation of mixed OFDM and FH sig-nals can be achieved with a low SNR of-6 dB, and FH parameters can be estirmted *** shows that the recognition performance is improved by about 5 dB compared with the traditional method.

关键词： OFDM FH signal identification IoT SPWVD

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dentification of blasting vibration and coal-roc fracturing microseismic signals

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Applied Geophysics 2018年第2期15卷 280-289,364页

作者： Zhang Xing-Li Jia Rui-Sheng Lu Xin-Ming Peng Yan-Jun Zhao Wei-Dong College of Computer Science and Engineering Shandong University of Science and Technology Qingdao 266590 China Shandong Province Key Laboratory of Wisdom Mine Information Technology Shandong University of Science andTechnology Qingdao 266590 China

A new method based on variational mode decomposition （VMD） is proposed to distinguish between coal-rock fracturing and blasting vibration microseismic signals. First, the signals are decomposed to obtain the variational mode components, which are ranked by frequency in descending order. Second, each mode component is extracted to form the eigenvector of the energy of the original signal and calculate the center of gravity coefficient of the energy distribution plane. Finally, the coal-rock fracturing and blasting vibration signals are classified using a decision tree stump. Experimental results suggest that VMD can effectively separate the signal components into coal-rock fracturing and blasting vibration signals based on frequency. The contrast in the energy distribution center coefficient after the dimension reduction of the energy distribution eigenvector accurately identifies the two types of microseismic signals. The method is verified by comparing it to EMD and wavelet packet decomposition.

关键词： Coal-rock fracturing microseismic blasting vibration variational modedecomposition signal identification

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