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Probabilistic Feasible Region Characterization of Active Distribution Networks Driven by data-model fusion

IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2025年第2期61卷 2791-2802页

作者： Hu, Anni Li, Gengyin Zhang, Tiance Zhou, Ming Wang, Jianxiao North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewable Beijing 102206 Peoples R China Peking Univ Natl Engn Lab Big Data Anal & Applicat Beijing 100871 Peoples R China

With the large-scale intergration of distributed energy resources (DERs) into distribution networks, the traditional paradigm of regarding the distribution networks as a static parameter load has become increasingly obsolete. However, this shift has highlighted the necessity of characterizing the equivalent model of active distribution network (ADN) amidst profound uncertainty. Therefore, the concept of probabilistic feasible region (PFR) considering the stochastic characteristics and temporal-coupling characteristics of DERs is proposed in this paper, enabling ADN to provide equivalent models with different confidence levels for power system operators (PSOs). Based on chance constraints programming and feasible region projection theory, we theoretically derive the characterization method of PFR, which can be characterized as a constraint set formed by the extreme points of the dual space of the ADN optimization model under different confidence interval. To overcome the inefficiency of analytical methods, an intelligent method driven by data-model fusion is proposed to accurately and efficiently characterize PFR. Furthermore, a fused neural network algorithm is employed to map the relationship between operational data and security constraints, and the loss function is improved according to the results of the theoretical algorithms to correct the model, which avoids the problem of violating security constraints due to overgeneralization. Case studies based on a modified IEEE 33-bus distribution system validate the effectiveness and computational efficiency of the proposed method.

关键词： Uncertainty Security Power systems Probabilistic logic Distribution networks Computational modeling Load modeling data models Active distribution networks Accuracy Active distribution network (ADN) chance constraint data-model fusion distributed energy resource Probabilistic feasible region

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Cross-Condition Fault Diagnosis of Planetary Gearboxes Driven by data-model fusion Based on Improved Domain-Adversarial Transfer Learning

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SHOCK AND VIBRATION 2025年第1期2025卷

作者： Chen, Long Feng, Guojin Zhen, Dong Jing, Hongxiang Liang, Xiaoxia Gu, Fengshou Hebei Univ Technol Sch Mech Engn Tianjin 300401 Peoples R China Natl Univ Def Technol Natl Key Lab Equipment State Sensing & Smart Suppo Changsha 410073 Peoples R China Adv Equipment Res Inst Co Ltd HEBUT Tianjin 300401 Peoples R China Univ Huddersfeld Ctr Efciency & Performance Engn Huddersfeld HD1 3DH England

data-driven methods have been extensively applied in the intelligent fault diagnosis of planetary gearboxes. Building reliable deep learning models typically requires a substantial amount of labeled data. However, in real-world industrial environments, obtaining a large volume of sample data under various working conditions and for different types of faults is often challenging. Mechanistic models can generate simulated data under various working conditions and for different types of faults through numerical simulations, thereby addressing the issue of insufficient labeled data. However, the significant distributional discrepancies between simulated and measured data may compromise the diagnostic performance of the models. To address the aforementioned issue, this paper proposes an improved domain-adversarial neural network (DANN) model that integrates mechanism and data fusion for cross-condition fault diagnosis of planetary gear systems. First, a translation-rotation dynamics model considering the impact of crack faults is developed to generate simulation data, compensating for the deficiency of experimental data. Furthermore, envelope preprocessing is applied to reduce noise interference in the real data, while a DANN model with an integrated subdomain discriminator is designed. By aligning the conditional distributions of simulation and experimental data, classification accuracy and robustness are enhanced. Finally, the method is applied to the operational condition transfer of the 2K-H planetary gearbox, validating its diagnostic performance under different conditions. Comparative studies with classical models show that, even with limited data, the proposed method is capable of fault diagnosis under different operating conditions and demonstrates superior diagnostic accuracy compared to other methods.

关键词： data-model fusion fault diagnosis planetary gear systems transfer learning

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Performance and parameter prediction of SCR-ORC system based on data-model fusion and twin data-driven

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ENERGY 2024年 290卷

作者： Lu, Shengdong Yang, Xinle Bu, Shujuan Li, Weikang Yu, Ning Wang, Xin Dai, Wenzhi Liu, Xunan Liaoning Tech Univ Sch Mech Engn Fuxin 123000 Peoples R China

This paper introduced an integrated approach of data-model fusion coupled with twin data-driven to address the bottlenecks of low accuracy and poor stability in machine learning for performance prediction and parameter reverse prediction in the Separation Compression Recirculation Organic Rankine Cycle (SCR-ORC). Initially, a cyclic database of SCR-ORC for five distinct working fluids was established through rigorous thermodynamic calculations. Subsequently, a Back Propagation Neural Network (BPNN) data-driven scheme of Grey Wolf Optimization (GWO) was developed for precise predictions of SCR-ORC thermal efficiency, exergy efficiency, and key parameters. Finally, utilizing the prediction results, Response Surface Methodology (RSM) was employed to analyze interactions among various parameters affecting the performance of the SCR-ORC, and further research on parameter optimization was conducted. The results indicate that GWO-BPNN achieves prediction errors 1-2 orders of magnitude lower than BPNN in performance prediction;regarding parameter prediction, the relative errors in GWO-BPNN predictions exhibit greater stability, with relative errors for each parameter prediction ranging from 0.01 % to 1.12 %. The proposed method notably enhances prediction accuracy, and the optimization outcomes of the agent model closely align with those of the traditional thermodynamic model.

关键词： data-model fusion Twin data-driven SCR-ORC system System performance prediction Parameter reverse prediction Interactive effect

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A novel data-model fusion state-of-health estimation approach for lithium-ion batteries

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APPLIED ENERGY 2019年 237卷 836-847页

作者： Ma, Zeyu Yang, Ruixin Wang, Zhenpo Beijing Inst Technol Natl Engn Lab Elect Vehicles Sch Mech Engn Beijing 100081 Peoples R China Beijing Inst Technol Collaborat Innovat Ctr Elect Vehicles Beijing Sch Mech Engn Beijing 100081 Peoples R China

In order to ensure the efficient, reliable, and safe operation of the lithium-ion battery system, an accurate battery state-of-health estimation is essential and remaining challenges. Here we propose a novel data-model fusion battery state-of-health estimation approach based on open-circuit-voltage parametric modeling considering the correlation between capacity degradation and the open-circuit-voltage changes. An open-circuit-voltage model is built to capture the aging behavior associated with the reactions progress in the cell. Then the battery state-of health estimation approach is developed based on the correlation between capacity fade and the changes of the open-circuit-voltage model parameters. In addition, a data-driven based method is applied to identify the parameters of the proposed battery model to obtain the open-circuit-voltage online. The proposed state-of-health estimation approach has been verified by the cells experienced different aging paths. The results show that the average relative errors of the state-of-health estimation for all cells are less than 3% against different aging paths and levels.

关键词： Lithium-ion battery State of health estimation data-model fusion Degradation mechanisms Thermal and cycle aging

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data–model fusion Methods and Applications toward Smart Manufacturing and Digital Engineering

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Engineering 2025年

作者： Fei Tao Yilin Li Yupeng Wei Chenyuan Zhang Ying Zuo Digital Twin Research Center International Frontier Interdisciplinary Science Research Institute Beihang University Beijing 100191 China School of Automation Science and Electrical Engineering Beihang University Beijing 100191 China

As pivotal supporting technologies for smart manufacturing and digital engineering, model-based and data-driven methods have been widely applied in many industrial fields, such as product design, process monitoring, and smart maintenance. While promising, both methods have issues that need to be addressed. For example, model-based methods are limited by low computational accuracy and a high computational burden, and data-driven methods always suffer from poor interpretability and redundant features. To address these issues, the concept of data–model fusion (DMF) emerges as a promising solution. DMF involves integrating model-based methods with data-driven methods by incorporating big data into model-based methods or embedding relevant domain knowledge into data-driven methods. Despite growing efforts in the field of DMF, a unanimous definition of DMF remains elusive, and a general framework of DMF has been rarely discussed. This paper aims to address this gap by providing a thorough overview and categorization of both data-driven methods and model-based methods. Subsequently, this paper also presents the definition and categorization of DMF and discusses the general framework of DMF. Moreover, the primary seven applications of DMF are reviewed within the context of smart manufacturing and digital engineering. Finally, this paper directs the future directions of DMF.

关键词： data-model fusion model-based methods data-driven methods Smart manufacturing Digital engineering

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Quantitative reconstruction of Holocene hydroclimate changes in northeastern China and implications for East Asian monsoon dynamics

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PALAEOGEOGRAPHY PALAEOCLIMATOLOGY PALAEOECOLOGY 2025年 666卷

作者： Yu, Shi-Yong Xu, Runzhe Jiangsu Normal Univ Key Lab Reg Sustainable Dev Syst Anal & Simulat Sch Geog Geomat & Planning Higher Educ Inst Xuzhou Peoples R China

The pattern of Holocene climate change remains unclear due to unresolved issues such as the "temperature conundrum" and the uncertain relationship between temperature and precipitation, particularly in monsoonal regions. Variability in regional climate drivers, including changes in ice volume, atmospheric circulation, and monsoon dynamics, further complicates Holocene climate dynamics, leading to spatially and temporally heterogeneous climate responses. As a result, reconstructing the precise timing, magnitude, and drivers of Holocene climate change remains challenging, requiring more detailed data and advanced statistical methods. In this study, we present a quantitative reconstruction of Holocene hydroclimate changes in northeastern China through a data-model fusion approach. By integrating water chemistry data from ostracod shells in a closed-basin lake with a hydrological balance model, we estimate growing-season temperature anomalies and relative lake-level changes during the Holocene. Our results highlight a mid-Holocene hydrological peak corresponding to the Holocene Thermal Maximum in the region. This peak lagged behind the early-Holocene summer insolation maximum, likely due to the influence of remnant ice sheets in the Northern Hemisphere. A comparison of our relative lake-level record with data of regional pollen-based precipitation reconstructions and TraCE-21 k modeling reveals a consistent pattern of increased monsoonal rainfall during the middle Holocene, followed by a steady decline after 5 kyr BP, reflecting a weakening of the East Asian summer monsoon. Additionally, our EOF analyses identify an early-Holocene tripolar precipitation pattern, which reversed in the middle Holocene, driven by shifts in atmospheric circulation. These findings advance our understanding of Holocene climate variability and offer insights for future climate predictions in monsoonal regions.

关键词： Holocene climate variability Hydroclimate reconstruction East Asian monsoon data-model fusion Atmospheric circulation shifts

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modeled production, oxidation, and transport processes of wetland methane emissions in temperate, boreal, and Arctic regions

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GLOBAL CHANGE BIOLOGY 2023年第8期29卷 2313-2334页

作者： Ueyama, Masahito Knox, Sara H. Delwiche, Kyle B. Bansal, Sheel Riley, William J. Baldocchi, Dennis Hirano, Takashi McNicol, Gavin Schafer, Karina Windham-Myers, Lisamarie Poulter, Benjamin Jackson, Robert B. Chang, Kuang-Yu Chen, Jiquen Chu, Housen Desai, Ankur R. Gogo, Sebastien Iwata, Hiroki Kang, Minseok Mammarella, Ivan Peichl, Matthias Sonnentag, Oliver Tuittila, Eeva-Stiina Ryu, Youngryel Euskirchen, Eugenie S. Goeckede, Mathias Jacotot, Adrien Nilsson, Mats B. Sachs, Torsten Osaka Metropolitan Univ Grad Sch Agr Sakai Japan Univ British Columbia Dept Geog Vancouver BC Canada Univ Calif Berkeley Dept Environm Sci Policy & Management Berkeley CA USA US Geol Survey Northern Prairie Wildlife Res Ctr Jamestown ND USA Lawrence Berkeley Natl Lab Climate & Ecosyst Sci Div Berkeley CA USA Hokkaido Univ Res Fac Agr Sapporo Japan Univ Illinois Dept Earth & Environm Sci Chicago IL USA Rutgers Univ Newark Dept Earth & Env Sci Newark NJ USA US Geol Survey Water Mission Area Menlo Pk CA USA NASA Biospher Sci Lab Goddard Space Flight Ctr Greenbelt MD USA Stanford Univ Dept Earth Syst Sci Stanford CA USA Michigan State Univ Dept Geog Environm & Spatial Sci E Lansing MI USA Univ Wisconsin Dept Atmospher & Ocean Sci Madison WI USA Univ Rennes 1 ECOBIO Ecosyst Biodivers Evolut CNRS UMR 6553 Rennes France Shinshu Univ Fac Sci Dept Environm Sci Matsumoto Japan Natl Ctr Agro Meteorol Seoul South Korea Univ Helsinki Inst Atmospher & Earth Syst Res Phys Fac Sci Helsinki Finland Swedish Univ Agr Sci Dept Forest Ecol & Management Umea Sweden Univ Montreal Dept Geog Montreal PQ Canada Univ Eastern Finland Sch Forest Sci Joesnuu Finland Seoul Natl Univ Dept Landscape Architecture & Rural Syst Engn Seoul South Korea Univ Alaska Inst Arctic Biol Fairbanks AK USA Max Planck Inst Biogeochem Dept Biogeochem Signals Jena Germany INRAE UMR 1069 SAS Rennes France GFZ German Res Ctr Geosci Potsdam Germany

Wetlands are the largest natural source of methane (CH4) to the atmosphere. The eddy covariance method provides robust measurements of net ecosystem exchange of CH4, but interpreting its spatiotemporal variations is challenging due to the co-occurrence of CH4 production, oxidation, and transport dynamics. Here, we estimate these three processes using a data-model fusion approach across 25 wetlands in temperate, boreal, and Arctic regions. Our data-constrained model-iPEACE-reasonably reproduced CH4 emissions at 19 of the 25 sites with normalized root mean square error of 0.59, correlation coefficient of 0.82, and normalized standard deviation of 0.87. Among the three processes, CH4 production appeared to be the most important process, followed by oxidation in explaining inter-site variations in CH4 emissions. Based on a sensitivity analysis, CH4 emissions were generally more sensitive to decreased water table than to increased gross primary productivity or soil temperature. For periods with leaf area index (LAI) of >= 20% of its annual peak, plant-mediated transport appeared to be the major pathway for CH4 transport. Contributions from ebullition and diffusion were relatively high during low LAI (< 20%) periods. The lag time between CH4 production and CH4 emissions tended to be short in fen sites (3 +/- 2 days) and long in bog sites (13 +/- 10 days). Based on a principal component analysis, we found that parameters for CH4 production, plant-mediated transport, and diffusion through water explained 77% of the variance in the parameters across the 19 sites, highlighting the importance of these parameters for predicting wetland CH4 emissions across biomes. These processes and associated parameters for CH4 emissions among and within the wetlands provide useful insights for interpreting observed net CH4 fluxes, estimating sensitivities to biophysical variables, and modeling global CH4 fluxes.

关键词： Bayesian optimization data-model fusion Eddy covariance methane emissions methane model multi-site synthesis

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Design of Digital Twin Sensing Strategies Via Predictive modeling and Interpretable Machine Learning

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JOURNAL OF MECHANICAL DESIGN 2022年第9期144卷 091710-091710页

作者： Kapteyn, Michael G. Willcox, Karen E. Univ Texas Austin Oden Inst Computat Engn & Sci Austin TX 78712 USA Univ Texas Austin Oden Inst Computat Engn & Sci Dept Aerosp Engn & Engn Mech Austin TX 78712 USA

This work develops a methodology for sensor placement and dynamic sensor scheduling decisions for digital twins. The digital twin data assimilation is posed as a classification problem, and predictive models are used to train optimal classification trees that represent the map from observed data to estimated digital twin states. In addition to providing a rapid digital twin updating capability, the resulting classification trees yield an interpretable mathematical representation that can be queried to inform sensor placement and sensor scheduling decisions. The proposed approach is demonstrated for a structural digital twin of a 12 ft wingspan unmanned aerial vehicle. Offline, training data are generated by simulating scenarios using predictive reduced-order models of the vehicle in a range of structural states. These training data can be further augmented using experimental or other historical data. In operation, the trained classifier is applied to observational data from the physical vehicle, enabling rapid adaptation of the digital twin in response to changes in structural health. Within this context, we study the performance of the optimal tree classifiers and demonstrate how they enable explainable structural assessments from sparse sensor measurements and also inform optimal sensor placement.

关键词： digital twin data-model fusion model updating reduced-order model unmanned aerial vehicle

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data-driven physics-based digital twins via a library of component-based reduced-order models

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INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING 2022年第13期123卷 2986-3003页

作者： Kapteyn, M. G. Knezevic, D. J. Huynh, D. B. P. Tran, M. Willcox, K. E. MIT 77 Massachusetts Ave Cambridge MA 02139 USA Akselos Inc Brookline MA USA Akselos SA Lausanne Switzerland Univ Texas Austin Austin TX 78712 USA

This work proposes an approach that combines a library of component-based reduced-order models with Bayesian state estimation in order to create data-driven physics-based digital twins. Reduced-order modeling produces physics-based computational models that are reliable enough for predictive digital twins, while still being fast to evaluate. In contrast with traditional monolithic techniques for model reduction, the component-based approach scales efficiently to large complex systems, and provides a flexible and expressive framework for rapid model adaptation-both critical features in the digital twin context. data-driven model adaptation and uncertainty quantification are formulated as a Bayesian state estimation problem, in which sensor data are used to infer which models in the model library are the best candidates for the digital twin. This approach is demonstrated through the development of a digital twin for a 12-ft wingspan unmanned aerial vehicle. Offline, we construct a library of pristine and damaged aircraft components. Online, we use structural sensor data to rapidly adapt a physics-based digital twin of the aircraft structure. The data-driven digital twin enables the aircraft to dynamically replan a safe mission in response to structural damage or degradation.

关键词： data-model fusion digital twin model updating reduced-order model unmanned aerial vehicle

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A neural-corrector method for prediction of the vertical motions of a high-speed craft?

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OCEAN ENGINEERING 2022年 262卷

作者： Marlantes, Kyle E. Maki, Kevin J. Univ Michigan Dept Naval Architecture & Marine Engn Ann Arbor MI 48109 USA

A machine learning method is developed which is capable of making predictions of nonlinear ship motions in a range of wave conditions when trained on response data from only a single seaway. The method is formulated around the equations of motion in the time domain, but the equations are augmented with data-driven terms that act to correct the force-balance in the equations. The data-driven nonlinear forcing terms are modeled using Long Short-Term Memory (LSTM) recurrent neural networks. The resulting hybrid governing equations are solved numerically. Predictions from the method are compared to nonlinear test data of 2-DOF motion of a Generic Prismatic Planing Hull (GPPH) at forward speed in head seas, with time histories given for both regular and irregular waves. The training data requirements to classify a specific seaway are investigated and quantified. Predictions over a range of significant wave heights and peak periods are performed using training data from only a single seaway to show the effectiveness of the method in generalizing across different environmental conditions.

关键词： Hybrid method data-model fusion LSTM Neural network High-speed Seakeeping

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