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Global data-driven modeling of wind turbines in the presence of turbulence

CONTROL ENGINEERING PRACTICE 2013年第4期21卷 441-454页

作者： van der Veen, G. J. van Wingerden, J. W. Fleming, P. A. Scholbrock, A. K. Verhaegen, M. Delft Univ Technol Delft Ctr Syst & Control NL-2628 CD Delft Netherlands Natl Renewable Energy Lab Golden CO 80401 USA

This paper presents a practical approach to identify a global model of a wind turbine from operational data, while it operates in a turbulent wind field with a varying mean wind speed and under closed-loop control. The approach is based on the realization that the nonlinearities are dominated by the aerodynamics of the rotor, which change with the operating condition. The dynamics of a wind turbine can be decomposed into a nonlinear static part, governed by the torque and thrust characteristics of the rotor, and a linear time-invariant dynamic part The multi-input-multi-output linear dynamics are estimated using a recent closed-loop subspace identification method. The practical applicability of the algorithm is demonstrated by applying it to data obtained from the NREL CART 3 research turbine. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Closed-loop system identification Wind turbines data-driven modeling Hammerstein systems Turbulence

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Full Feedback Dynamic Neural Network with Exogenous Inputs for Dynamic data-driven modeling in Nonlinear Dynamic Power Systems

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2023年第6期18卷 876-890页

作者： Zhang, Zhenhui Zhang, Zhengjiang Zhao, Sheng Hong, Zhihui Huang, Shipei Li, Quanfang Wenzhou Univ Natl & Local Joint Engn Lab Elect Digital Design T Wenzhou 325000 Zhejiang Peoples R China Wenzhou Univ Key Lab Low Voltage Apparat Intellectual Technol Z Wenzhou 325035 Peoples R China Zhejiang Juchuang Smart Technol Co Res & Dev Dept Wenzhou 325035 Peoples R China

Dynamic neural networks (DNNs) are widely used in data-driven modeling of nonlinear control systems. Due to the complexity of the actual operating nonlinear power systems, rigorous dynamic models are always unknown. DNNs can focus on methods that only use input and output information to establish accurate dynamic models and reduce noise in measured data, which is called data-driven modeling. The core of the DNN is the feedback with memory function. This paper analyzes the traditional Elman neural network (ENN) and nonlinear auto-regression with exogenous input (NARX) neural network with different structure feedback structures, and proposes a full feedback dynamic neural network (FF-DNN) with exogenous input. Eight different kinds of neural networks (including ENN, NARX, etc.) are compared and analyzed. The eight kinds of neural networks are applied on the experimental data of the DC-AC inverter and the power system of Zhejiang Juchuang Smart Technology Company Park in Wenzhou. The experimental results are used to compare the performance of the data-driven models established by eight different kinds of neural networks under different noise conditions, verify the robustness and generalization performance of dynamic data-driven modeling based on FF-DNN, and demonstrate the feasibility and effectiveness of FF-DNN in actual power systems. (C) 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

关键词： dynamic neural network data-driven modeling feedback structure power system

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A Universal Automated data-driven modeling Framework for Truck Traffic Volume Prediction

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IEEE ACCESS 2021年 9卷 105341-105356页

作者： Mahdavian, Amirsaman Shojaei, Alireza Salem, Milad Laman, Haluk Eluru, Naveen Oloufa, Amr A. Univ Cent Florida Dept Civil Environm & Construct Engn Orlando FL 32816 USA Virginia Polytech Inst & State Univ Myers Lawson Sch Construct Blacksburg VA 24061 USA Univ Cent Florida Dept Elect & Comp Engn Orlando FL 32816 USA

Knowledge of the truck traffic volumes on state and interstate highways is critical for highway authorities and federal organizations. Increased urbanization, population growth, and economic development have led to an increased demand for freight travel. Several planning applications demand reliable and accurate truck traffic prediction. A review of the available literature indicated that limited research had been performed on the development and utilization of a universal automatic framework for truck traffic volume prediction. As a result, there is a gap to incorporate inclusive predictors, a broad dataset, a comprehensive feature selection approach, and a robust cross-validation method that utilizes both linear and non-linear algorithms. The present study uses a hyperparameter optimization framework to select the appropriate feature selection method and modeling approach among a comprehensive list of available state of the art approaches. Distinct from models based on individual case studies, the proposed framework allows for greater customization and minimized MAPE error. The developed framework automates much of the traffic count forecasting process, and the resulting method is less labor-intensive and may be utilized without the need for experienced data analysts. Florida's interstate highways historical traffic data were used to test the feasibility of the proposed framework. The results of the Florida Case Study revealed the superiority of non-linear models in the generalization and prediction of traffic volumes over linear models. The random forest algorithm results on the test dataset in this study demonstrate this model's ability to predict truck traffic with 86% accuracy. Spatial variables were the most significant variable group, followed by road characteristics.

关键词： Predictive models Forecasting Mathematical model Prediction algorithms Transportation Biological system modeling Solid modeling data-driven modeling truck traffic traffic volume prediction model regression analysis forecasting machine learning

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Combined Physics- and data-driven modeling for the Design and Operation Optimization of an Energy Concept Including a Storage System

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ENERGIES 2024年第2期17卷 350页

作者： Kansara, Rushit Lockan, Michael Serrano, Maria Isabel Roldan Inst Low Carbon Ind Proc German Aerosp Ctr D-03046 Cottbus Germany

The industrial sector accounts for a huge amount of energy- and process-related CO2 emissions. One decarbonization measure is to build an energy concept that provides electricity and heat for industrial processes using a combination of different renewable energy sources, such as photovoltaic, wind turbine, and solar thermal collector systems, integrating also energy conversion power-to-heat components such as heat pumps, electric boilers, and thermal energy storage. The challenge for the industries is the economic aspect of the decarbonization, as industries require a cost-efficient solution. Minimizing cost and emissions together is a complex problem, which requires two major tasks: (I) modeling of components and (II) multi-objective coupled design and operation optimization of the energy concept. The optimal design and capacity of the components and optimal system operation depend majorly on component modeling, which is either physics-driven or data-driven. This paper shows different types of physics- and data-driven modeling of energy components for multi-objective coupled optimization in order to minimize costs and emissions of a specific industrial process as a case study. Several modeling techniques and their influence on the optimization are compared in terms of computational effort, solution accuracy, and optimal capacity of components. The results show that the combination of physics- and data-driven models has a computational time reduction of up to 37% for an energy concept without thermal energy storage and 29% for that with thermal energy storage, both with high-accuracy solutions compared to complete physics-driven models for the considered case study.

关键词： energy concept renewable energy sources coupled optimization data-driven modeling

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Evolutionary computational approaches for data-driven modeling of multi-dimensional memory-dependent systems

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SMART STRUCTURES AND SYSTEMS 2015年第3期15卷 897-911页

作者： Bolourchi, Ali Masri, Sami F. Univ So Calif Viterbi Sch Engn Los Angeles CA 90089 USA

This study presents a novel approach based on advancements in Evolutionary Computation for data-driven modeling of complex multi-dimensional memory-dependent systems. The investigated example is a benchmark coupled three-dimensional system that incorporates 6 Bouc-Wen elements, and is subjected to external excitations at three points. The proposed technique of this research adapts Genetic Programming for discovering the optimum structure of the differential equation of an auxiliary variable associated with every specific degree-of-freedom of this system that integrates the imposed effect of vibrations at all other degrees-of-freedom. After the termination of the first phase of the optimization process, a system of differential equations is formed that represent the multi-dimensional hysteretic system. Then, the parameters of this system of differential equations are optimized in the second phase using Genetic Algorithms to yield accurate response estimates globally, because the separately obtained differential equations are coupled essentially, and their true performance can be assessed only when the entire system of coupled differential equations is solved. The resultant model after the second phase of optimization is a low-order low-complexity surrogate computational model that represents the investigated three-dimensional memory-dependent system. Hence, this research presents a promising data-driven modeling technique for obtaining optimized representative models for multi-dimensional hysteretic systems that yield reasonably accurate results, and can be generalized to many problems, in various fields, ranging from engineering to economics as well as biology.

关键词： computational intelligence genetic algorithms differential equations hysteretic behavior data-driven modeling identification multi-dimensional systems genetic programming

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Manufacturing-induced stochastic constitutive behaviors of additive manufactured specimens: testing, data-driven modeling, and optimization

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RAPID PROTOTYPING JOURNAL 2024年第4期30卷 662-676页

作者： Chen, Baixi Mao, Weining Lin, Yangsheng Ma, Wenqian Hu, Nan Univ Wisconsin Madison Dept Mech Engn Madison WI USA Nanyang Technol Univ Sch Mech & Aerosp Engn Singapore Singapore South China Univ Technol Sch Civil Engn & Transportat Guangzhou Peoples R China

PurposeFused deposition modeling (FDM) is an extensively used additive manufacturing method with the capacity to build complex functional components. Due to the machinery and environmental factors during manufacturing, the FDM parts inevitably demonstrated uncertainty in properties and performance. This study aims to identify the stochastic constitutive behaviors of FDM-fabricated polylactic acid (PLA) tensile specimens induced by the manufacturing ***/methodology/approachBy conducting the tensile test, the effects of the printing machine selection and three major manufacturing parameters (i.e., printing speed S, nozzle temperature T and layer thickness t) on the stochastic constitutive behaviors were investigated. The influence of the loading rate was also explained. In addition, the data-driven models were established to quantify and optimize the uncertain mechanical behaviors of FDM-based tensile specimens under various printing *** indicated by the results, the uncertain behaviors of the stiffness and strength of the PLA tensile specimens were dominated by the printing speed and nozzle temperature, respectively. The manufacturing-induced stochastic constitutive behaviors could be accurately captured by the developed data-driven model with the R2 over 0.98 on the testing dataset. The optimal parameters obtained from the data-driven framework were T = 231.3595 degrees C, S = 40.3179 mm/min and t = 0.2343 mm, which were in good agreement with the *** implicationsThe developed data-driven models can also be integrated into the design and characterization of parts fabricated by extrusion and other additive manufacturing ***/valueStochastic behaviors of additively manufactured products were revealed by considering extensive manufacturing factors. The data-driven models were proposed to facilitate the description and optimization of the FDM products and control their quality.

关键词： Fused deposition modelling (FDM) Polylactic acid (PLA) Printing parameters Stochastic constitutive behaviors data-driven modeling

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A data-driven modeling Method of Virtual Synchronous Generator Based on LSTM Neural Network

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第4期20卷 5428-5439页

作者： Tian, Jiangbin Zeng, Guohui Zhao, Jinbin Zhu, Xiangchen Zhang, Zhenhua Shanghai Univ Engn Sci Sch Elect & Elect Engn Shanghai 201620 Peoples R China Shanghai Univ Elect Power Offshore Wind Power Res Inst Shanghai 201399 Peoples R China Aalborg Univ Dept Energy Technol DK-9220 Aalborg Denmark

The virtual synchronous generator (VSG) exhibits high-dimensional complexity, necessitating a tradeoff between accuracy and complexity when constructing small-signal models. To address this issue, this article proposes a data-driven modeling approach based on long short-term memory (LSTM) neural networks. The focus is on mapping relationships between electrical quantities, considering the influence of irrational factors on model accuracy. A detailed data-driven modeling approach for VSG is proposed and verified in this article. Due to the time-series correlation in the electrical data generated during VSG operation, the LSTM algorithm, known for its excellent time-series prediction capabilities, is chosen to construct the VSG data-driven model. Several complex VSG application scenarios are used to validate the effectiveness and accuracy of the proposed modeling approach. In conclusion, the LSTM-based VSG data-driven modeling outperforms small-signal models and recurrent neural network data-driven models in terms of accuracy and stability.

关键词： data-driven modeling long short-term memory (LSTM) virtual synchronous generator (VSG)

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A new data-driven modeling method for fermentation processes

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2016年 152卷 88-96页

作者： Yang, Qiangda Gao, Hongbo Zhang, Weijun Chi, Zhongyuan Yi, Zhi Northeastern Univ Sch Met Shenyang 110819 Peoples R China Northeastern Univ Sch Mech Engn & Automat Shenyang 110819 Peoples R China

An accurate model is the premise for successfully implementing fermentation process optimization. Most data-driven models that are widely applied to fermentation processes are unfit for optimization or provide low precision. This paper presents a new data-driven modeling method for directly developing an ANN-based differential model that is fit for optimization. Moreover, this model can provide high precision because it can be discretized using the sampling period of the control variables as the step length. The lack of data pairs is addressed by transforming the model-training problem into a dynamic system parameter identification problem. Further, a particle swarm optimization algorithm with a time-varying escape mechanism (PSOE) is constructed to determine the model parameters. Finally, the uniform design method is used to select the model structure. The results of experiments conducted using practical data for a lab-scale nosiheptide batch fermentation process confirm the effectiveness of the proposed modeling method and PSOE algorithm. (C) 2016 Elsevier B.V. All rights reserved.

关键词： data-driven modeling Artificial neural networks Particle swarm optimization Fermentation process Nosiheptide

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A Neural-Network-Based Sensitivity Analysis Approach for data-driven modeling of Ship Motion

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IEEE JOURNAL OF OCEANIC ENGINEERING 2020年第2期45卷 451-461页

作者： Cheng, Xu Li, Guoyuan Skulstad, Robert Chen, Shengyong Hildre, Hans Petter Zhang, Houxiang Tianjin Univ Technol Sch Comp Sci & Technol Tianjin 300384 Peoples R China Norwegian Univ Sci & Technol Dept Ocean Operat & Civil Engn N-6009 Alesund Norway

Researchers have been investigating data-driven modeling as a key way to achieve ship intelligence for years. This paper presents a novel data analysis approach to data-driven modeling of ship motion. We propose a global sensitivity analysis (GSA) approach combining artificial neural network (ANN) and sparse polynomial chaos expansion (SPCE) techniques to accommodate high-dimensional sensor data collected from ship motion. An ANN is constructed as a surrogate model to associate ship sensor data with a certain type of ship motion. To account for the computational efficiency of GSA, an SPCE is integrated into the GSA to decrease the need for Monte Carlo (MC) samples generated by the ANN. A probe variable is designed to couple with the MC samples, which plays a role in determining the degree of convergence of variable importance. A test on benchmark function demonstrates the efficiency and accuracy of the proposed approach. A case study of ship heading with and without environment effects is conducted. The experimental results show that the proposed approach can identify and rank the most sensitive factors of ship motion. The proposed approach highlights the application of GSA in data-driven modeling for ship intelligence.

关键词： Marine vehicles data models Analytical models Computational modeling Probes Sensitivity analysis data-driven modeling environment effect global sensitivity analysis (GSA) ship intelligence ship motion

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Anomaly detection in streaming environmental sensor data: A data-driven modeling approach

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ENVIRONMENTAL MODELLING & SOFTWARE 2010年第9期25卷 1014-1022页

作者： Hill, David J. Minsker, Barbara S. Rutgers State Univ Dept Civil & Environm Engn Piscataway NJ 08854 USA Univ Illinois Dept Civil & Environm Engn Urbana IL 61801 USA

The deployment of environmental sensors has generated an interest in real-time applications of the data they collect. This research develops a real-time anomaly detection method for environmental data streams that can be used to identify data that deviate from historical patterns. The method is based on an autoregressive data-driven model of the data stream and its corresponding prediction interval. It performs fast, incremental evaluation of data as it becomes available, scales to large quantities of data, and requires no pre-classification of anomalies. Furthermore, this method can be easily deployed on a large heterogeneous sensor network. Sixteen instantiations of this method are compared based on their ability to identify measurement errors in a windspeed data stream from Corpus Christi, Texas. The results indicate that a multilayer perceptron model of the data stream, coupled with replacement of anomalous data points, performs well at identifying erroneous data in this data stream. (C) 2009 Published by Elsevier Ltd.

关键词： Coastal environment data-driven modeling Anomaly detection Machine learning Real-time data Sensor networks data quality control Artificial intelligence

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