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Transforming Conventional Waveform Measurements Into Synchro-Waveforms: A data-driven method for Event Signature Alignment and Synchronization Operator Estimation

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IEEE TRANSACTIONS ON SMART GRID 2025年第2期16卷 1495-1509页

作者： Ye, Zong-Jhen Mohsenian-Rad, Hamed Univ Calif Riverside Dept Elect & Comp Engn Riverside CA 92521 USA

A series of methodologies are proposed to transform conventional waveform measurements from legacy power quality meters into synchro-waveforms. This study is motivated by the presence of thousands of legacy power quality meters in operation worldwide that provide event-triggered waveform measurements but lack time-synchronization among their data. Consequently, the waveform measurements from these legacy meters cannot be directly used as synchro-waveforms, limiting their applicability in the promising synchro-waveform applications that have been introduced in the literature in recent years. We address this issue without requiring legacy power quality meters to be equipped with GPS receivers or other time-synchronization hardware. Our data-driven methods operate in two steps: first, they perform optimization-based event signature alignment, and then they use the results to estimate a synchronization operator between any two legacy meters. The proposed methods are accurate, robust, and computationally efficient. All case studies presented in this paper are based on real-world waveform measurements.

关键词： Sensors Power quality Synchronization Power measurement Voltage measurement Time measurement Current measurement Meters Clocks Sensor systems Synchro-waveforms synchronization operator conventional waveform sensors data-driven method real-world measurements event signature alignment optimization

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Fatigue life prediction of cold expansion hole using physics-enhanced data-driven method

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INTERNATIONAL JOURNAL OF FATIGUE 2025年 190卷

作者： Mao, Jian-Xing Xian, Zhi-Fan Wang, Xin Hu, Dian-Yin Pan, Jin-Chao Wang, Rong-Qiao Zou, Shi-Kun Gao, Yang Beihang Univ Res Inst Aeroengine Beijing 100191 Peoples R China United Res Ctr Midsmall Aeroengine Beijing 100191 Peoples R China Beijing Key Lab Aeroengine Struct & Strength Beijing 100191 Peoples R China AECC Beijing Inst Aeronaut Mat Beijing 100095 Peoples R China Beihang Univ Sch Energy & Power Engn Beijing 100191 Peoples R China AVIC Mfg Technol Inst Beijing 100024 Peoples R China AECC Guiyang Engine Res Inst Guiyang 550081 Peoples R China

Cold expansion (CE) serves as a practical surface enhancement to improve the fatigue life of hole structures by improving surface integrity in both macro-scale and micro-scale. Due to the inaccessibility and high cost of experimental measurements, the physical relation between surface integrity and fatigue life are always implicit, serving as the major challenge for accurate life prediction. To address this issue, a novel method is proposed by introducing physical information to traditional data-driven method, where surface integrity enriched by multiscale simulation is mapped to fatigue life via machine learning (ML) mechanism. As integrated to four typical ML algorithms, the proposed physics-enhanced data-driven method exhibit outstanding capability for accuracy improvement, decreasing the scatter band by amplitude between 27.3 % and 71.4 %. The proposed method offers a promising option for fatigue life prediction on surface treated structures with limited physical information.

关键词： Cold expansion Surface integrity data-driven method Machine learning

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A data-driven method for Determining DRASTIC Weights to Assess Groundwater Vulnerability to Nitrate: Application in the Lake Baiyangdian Watershed, North China Plain

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APPLIED SCIENCES-BASEL 2025年第5期15卷 2866-2866页

作者： Hou, Xianglong Peng, Liqin Zhang, Yuan Zhang, Yan Wang, Yunxia Feng, Wenzhao Yang, Hui Hebei Acad Sci Inst Geog Sci Shijiazhuang 050011 Peoples R China Hebei Technol Innovat Ctr Geog Informat Applicat Shijiazhuang 050011 Peoples R China Hebei GEO Univ Hebei Prov Collaborat Innovat Ctr Sustainable Util Shijiazhuang 050031 Peoples R China Chinese Acad Sci Inst Genet & Dev Biol Ctr Agr Resources Res Key Lab Agr Water ResourcesHebei Key Lab Agr Wate Shijiazhuang 050001 Peoples R China

Nitrate pollution due to agricultural activities challenges the management of groundwater resources. The most popular technique used for groundwater vulnerability assessments is the DRASTIC. The subjectivity introduced by the DRASTIC has always been questioned. Therefore, the determination of rating scores and weights of parameters has become the main difficulty in DRASTIC applications. In this paper, a new data-driven weighting method based on Monte Carlo or genetic algorithm was developed. The new method considers both single factors and the relationship among factors, overcomes the subjectivity of weight determination, and is theoretically applicable to various hydrogeological environments and as a general weight determination method. In addition, a new method for the verification of the evaluation results on a temporal scale was established, which is based on changes in the nitrate concentration over the past 20 years. To verify and test these methods, they were used for the evaluation of groundwater vulnerability to nitrate in the plain area of the Baiyangdian watershed in the North China Plain and compared with other commonly used methods. The Pearson correlation coefficient increased by 15%. From a time perspective, the changes in nitrate concentration confirmed that the correctness of the assessment is 88%. In this study, the effect of the revision of the rating ranges on the improvement of the evaluation results is very obvious. Therefore, the focus of future work should be on determining the rating ranges and their rating scores, and whether the corresponding weights based on the data-driven method will yield more reliable results.

关键词： groundwater vulnerability nitrate DRASTIC data-driven method North China plain

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Interpretable data-driven method for sub/super-synchronous oscillation stability evaluation

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ELECTRIC POWER SYSTEMS RESEARCH 2025年 241卷

作者： Zhan, Ying Luo, Yonghong Wang, Linguang Xie, Xiaorong China Univ Min & Technol Beijing Sch Mech & Elect Engn Beijing 100083 Peoples R China Huawei Digital Power Technol Co Ltd Digital Power Shenzhen 518031 Peoples R China Tsinghua Univ Dept Elect Engn State Key Lab Power Syst Operat & Control Beijing 100084 Peoples R China

The stability of sub/super-synchronous oscillation (SSO) in wind power integrated power systems is closely related to system operating conditions. To avoid SSO issues, it is necessary to conduct the online stability evaluation. The data-driven methods have the potential to achieve fast and accurate stability evaluations for large-scale power systems. However, the lack of interpretability poses a significant obstacle to its practical application. To address the issue, an interpretable data-driven method is proposed in this paper. Firstly, the SSO stability evaluation model is built based on neural network (NN) and the SSO stability boundary is derived. Then, the regularization and pruning techniques are adopted to streamline the model and activation functions are piecewise linearized to obtain SSO stability classification hyperplanes, so that the SSO stability rules can be extracted. Furthermore, the input sensitivity and SSO stability margin index are proposed to obtain more information on the current operating points. Finally, the proposed method is applied on a single-wind-farm system and a practical multiple-wind-farms system. The NN evaluation model is built and the SSO stability rules are extracted to clarify the evaluation logic. Compared with existing interpretable methods, the accuracy is greatly improved by the proposed method.

关键词： Interpretability data-driven method Stability evaluation Sub/super-synchronous oscillation

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data-driven method for Real-Time Reconstruction of the Structural Displacement Field

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JOURNAL OF AEROSPACE ENGINEERING 2024年第3期37卷

作者： Yan, Jun Du, Hongze Bu, Yufeng Jiang, Lizhe Xu, Qi Zhao, Chunyu Dalian Univ Technol Dept Engn Mech State Key Lab Struct Anal Ind Equipment Dalian 116024 Peoples R China Dalian Univ Technol Ningbo Res Inst Marine Equipment Strength Anal & Innovat Design Te Ningbo 315016 Peoples R China

Accurate real-time displacement field reconstruction based on limited measurement points is crucial for spacecraft on-orbit monitoring. This study proposes a data-driven displacement field reconstruction method called stacked convolutional autoencoder with denoising autoencoder and filter. Precise reconstruction of the structural displacement from a small number of local strains was made possible by the two primary components of the method: low-resolution displacement field reconstruction and result optimization. Given the significant imbalance between the limited strain information input and the structural displacement field output, a deep learning model with multiple deconvolution layers was built in the low-resolution displacement field reconstruction part using the layer-wise training property of a stacked autoencoder and the sparse mapping property of a convolutional neural network. The result optimization part utilized a denoising autoencoder and a linear density filter to effectively alleviate the checkerboard phenomenon and displacement field discontinuity caused by the deconvolution operation. The results of the case study indicate that the proposed method can accurately reconstruct the structural displacement field of both simple regular geometric structures and irregular geometric structures with complex boundaries without prior information. Additionally, the method exhibits excellent robustness to unavoidable measurement noise, providing a new implementation approach for real-time monitoring of spacecraft.

关键词： Real-time displacement field reconstruction data-driven method Deep learning

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data-driven method using DNN for PD location in substations

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IET SCIENCE MEASUREMENT & TECHNOLOGY 2020年第3期14卷 314-321页

作者： Liu, Jiangting Hu, Yue Peng, Hui Zaki, Atta ul Munim Shanghai Jiao Tong Univ Dept Elect Engn 800 Rd Dongchuan Shanghai Peoples R China State Grid Chongqing Elect Power Co Chongqing Peoples R China

Partial discharge (PD) detection and location based on ultra-high frequency (UHF) sensor array have been employed for the power equipment in the whole substation for assessing the insulating condition of electrical equipment and determining a precondition for further implementing insulation diagnosis. At present, one of the widely used localisation methods is time difference of arrival (TDOA) based, localisation result of which is extremely sensitive to time delay estimation and usually time-consuming to solve. Motivated by this, a data-driven method using the deep neural network (DNN) is proposed in this study to significantly speed up the solving process of non-linear TDOA equations and simultaneously guarantee the accuracy of results. It works with sequences of time delay measured from the UHF sensor array as the input of the network and with the corresponding coordinates of PD source as output to train the network. Simulation results demonstrate that the proposed method shows relatively higher accuracy and efficiency in PD location. In addition, many factors such as array shape, error type added to time delay, and detailed structure and parameters of network are taken into consideration for error analysis, laying foundation to more reliable localisation of PD.

关键词： error analysis substations partial discharges neural nets power apparatus time-of-arrival estimation sensor arrays UHF detectors insulation power engineering computing data-driven method PD location substation ultra-high frequency sensor array power equipment electrical equipment time delay estimation deep neural network nonlinear TDOA equation partial discharge detection error analysis

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data-driven method for a single-input multi-output dual-rate system

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2018年第12期13卷 1837-1838页

作者： Ito, Sho Sato, Takao Araki, Nozomu Konishi, Yasuo Univ Hyogo Grad Sch Engn 2167 Shosha Himeji Hyogo 6712280 Japan

We propose a new data-driven method for a single-input multi-output dual-rate system, in which the sampling interval of plant outputs is an integer multiple of the holding interval of a control input. In the proposed method, the dual-rate system is converted to a multi-input multi-output single-rate system, and controller parameters are optimized using one-shot closed-loop data. Furthermore, the ripple-free condition is clarified. Finally, the effectiveness of the proposed method is shown through a numerical example. (c) 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

关键词： data-driven method dual-rate closed-loop data lifting method single-input multi-output FRIT

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Rapid identification of switched systems: A data-driven method in variational framework

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Science China(Technological Sciences) 2021年第1期64卷 148-156页

作者： LI ChunJiang HUANG ZhiLong WANG Yong JIANG HanQing Department of Engineering Mechanics Zhejiang UniversityHangzhou 310027China School for Engineering of Matter Transport and EnergyArizona State UniversityTempe AZ 85287USA

Switched systems, i.e., systems changing the parameter values(even structural forms) abruptly and randomly at arbitrary instants, have been extensively utilized in many fields of modern industries. Rapid identification of switched systems, i.e.,capturing all the changing instants and reconstructing the mathematical models rapidly, is of great significance for behavior prediction, performance evaluation and possible control, but is restricted by small data amount available. Here, the rapid identification problem is successfully solved by a data-driven method in variational framework. The data-driven method only requires a small amount of data due to the compact form of the variational description, and is robust to data noise due to the holistic viewpoint. Two numerical examples, i.e., Duffing oscillator and van der Pol system(as two representative systems in nonlinear dynamics), are adopted to illustrate its application, efficiency and robustness to noise.

关键词： switched system rapid identification data-driven method small data amount robustness

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data-driven method for Fault Isolation in Technical Systems 20

Data-Driven Method for Fault Isolation in Technical Systems

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20th International Conference on Process Control (PC)

作者： Zhirabok, Alexey Pavlov, Sergey Far Eastern Fed Univ Dept Automat & Control Vladivostok Russia

ISBN: (纸本)9781467366274

The paper is devoted to the problem of fault isolation in technical systems described by nonlinear dynamic models containing non-smooth nonlinearities. So-called "model-free" or "data-driven" method is used to solve the problem. The feature of this method is that parameters of the system under consideration may be unknown. The algebra of functions is used to solve the problem under consideration.

关键词： nonlinear dynamic systems fault isolation data-driven method algebra of functions

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data-driven method for Fault Isolation in Technical Systems

Data-Driven Method for Fault Isolation in Technical Systems

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International Siberian Conference on Control and Communications (SIBCON)

作者： Zhirabok, Alexey Pavlov, Sergey Far Eastern Fed Univ Dept Automat & Control Vladivostok Russia

ISBN: (纸本)9781479971039

关键词： nonlinear dynamic systems fault isolation data-driven method algebra of functions

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