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Zero-phase CARIMA Filtering and Application in Wind-storage System Sizing and Power Dispatch Optimization

CSEE Journal of Power and Energy Systems 2024年第6期10卷 2283-2295页

作者： Wei Wang Peng Chen Guorui Ren Jizhen Liu Fang Fang Zhe Chen State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power UniversityBeijing 102206China School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China Department of Energy Technology Aalborg University9220 AalborgDenmark

Hybrid energy storage system(HESS)is an effective way to mitigate wind power fluctuations on multi-time scale,and can improve influence of large-scale grid-connected wind power on stability and reliability of power system.A novel methodology named zero-phase controlled auto-regressive integrated moving-average(CARIMA)filter is proposed to integrate HESS to smooth wind power ***,a design method for zero-phase CARIMA filter is provided,and then used to determine grid-connected power for a wind storage system and size *** reasons,direct current(DC)component caused by energy storage efficiency and grid-connected power delay caused by phase shift,for causing superfluous energy storage configuration are *** addition,a nonlinear programming scheduling strategy considering battery degradation is *** imbalance caused by efficiency difference during dynamic adjustment of energy storage output power is ***,thermostatically controlled loads(TCLs)are integrated in sizing and scheduling HESS to reduce energy storage demand and improve operating conditions of energy ***,effectiveness of the proposed strategy is verified by a case study.

关键词： CARIMA demand response energy storage optimization wind power zero-phase

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Surrogate submodel generation for an MEA-based post-combustion CO2 capture absorber 18

Surrogate submodel generation for an MEA-based post-combusti...

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18th IEEE Conference on industrial Electronics and Applications, ICIEA 2023

作者： Wang, Yurun Huang, Yi Ye, Lingjian Huzhou University School of Engineering Huzhou China Huzhou University Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems School of Engineering Huzhou China

ISBN: (纸本)9798350312201

The absorber is the core operating unit of the MEA-based post-combustion CO2 capture process. Research on absorber modeling has been ongoing over the past few decades. However, the mechanistic model based on complex thermodynamics and reaction kinetics is computationally expensive and time-consuming. For example, the reaction balance typically needs iterative computations within the rigorous absorber simulator, for the computation of the true component concentrations distributed along the absorber. The objective of this paper is to develop a surrogate submodel for the reaction equilibrium, by establishing an explicit function to replace the equality relationships, the intensive iterations are avoided. The surrogate submodel is trained via the neural networks with data generated from the simulator using the Hammersley sequence. The results show that the surrogate submodel can substantially reduce the computation loads, while maintaining satisfactory accuracies compared with the rigorous simulator. © 2023 IEEE.

关键词： Carbon dioxide

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Multi-scale network for single image deblurring based on ensemble learning module

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Multimedia Tools and Applications 2025年第11期84卷 9045-9064页

作者： Wu, WenBo Pan, Yun Su, Na Wang, JingTao Wu, ShaoChuan Xu, ZeKun Yu, YouJian Liu, YaPeng State Key Laboratory of Media Convergence and Communication Communication University of China Beijing100024 China School of Computer and Cyberspace Security Communication University of China Beijing100024 China School of Electronics and Information Engineering Harbin Institute of Technology Harbin150080 China College of Computer and Control Engineering Northeast Forestry University Harbin150040 China School of Computer and Information Engineering Tianjin Chengjian University Tianjin300384 China School of Computer Science and Technology Anhui University Hefei230601 China

In this paper, we have identified two primary issues with current multi-scale image deblurring methods. On the one hand, the blurring scale is ignored. On the other hand, the context information of images is not fully utilized, and the spatial detail information of images is lost. To better solve the above problems, on top of that, this paper proposes a multi-scale network for single image deblurring. The designed network contains two parts: 1) Ensemble Learning Model: The Weak Learning Model in this module connects multiple weak learners in series to increase network depth. Moreover, through multi-scale feature ensemble module, the output features and intermediate features of different weak learner are integrated, and finally strong learner is used to enhance the features with semantic information at different scales;2) Spatial Feature Enhancement Module: This module can preserve spatial detail information of images and compensate for the disadvantages of losing image spatial details due to continuous downsampling in the Ensemble Learning Model structure. Our proposed deblurring model has been extensively evaluated on several benchmark datasets, and the model achieves superior performance compared to the most advanced deblurring methods. In particular, the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) values are 33.57 dB and 0.965 on the GoPro dataset, respectively, with PSNR and SSIM improving by 0.49 dB and 0.002 over NAFNet, and 0.65 dB and 0.004 over Restormer. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Learning systems

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Backstepping Method and control Allocation for a Fully-Actuated Tri-Rotor

Backstepping Method and Control Allocation for a Fully-Actua...

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International Conference on Guidance, Navigation and control, ICGNC 2020

作者： Wang, Yunhe Zhu, Zhangzhen Zhang, Yu State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou310027 China

ISBN: (纸本)9789811581540

This paper considers a tilting tri-rotor unmanned aerial vehicle (UAV) with each rotor having a tilting degree of freedom. The dynamic model of this UAV is obtained in this paper, which is a fully-actuated system instead of an underactuated system. Although it is not a redundant system, it also needs control allocator to distribute virtual control commands to actuators. Therefore, we propose a new control allocation method to solve this nonlinear problem. Meanwhile, the Backstepping method is used to build the entire control architecture. Finally, several simulation experiments are conducted to demonstrate our proposed method. © 2022, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Backstepping

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Attention-based Stacked Supervised Poisson Autoencoders for Defects Prediction in Casting-rolling process 11

Attention-based Stacked Supervised Poisson Autoencoders for ...

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11th IEEE Data Driven control and Learning Systems Conference, DDCLS 2022

作者： Zhang, Xinmin Song, Zhihuan Ge, Zhiqiang College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

In this paper, we propose an attention-based stacked supervised Poisson autoencoders (ASSPAE) modeling framework to predict the occurrence of defects in the casting-rolling process. ASSPAE is a novel deep learning model, which is designed based on the stacked supervised Poisson autoencoders and attention-based multi-feature fusion strategy. Stacked supervised Poisson autoencoders are a novel deep neural networks model, which is designed by integrating the Poisson regression network layer into the deep autoencoders framework. Compared with the conventional autoencoder-based models, the proposed stacked supervised Poisson autoencoders not only can capture the quality-relevant deep features of the data, but also is suitable for predicting the count-type quality variable. Furthermore, in order to efficiently leverage information present in different layers of the network, an attention-based multi-feature fusion strategy is integrated into the stacked supervised Poisson autoencoders modeling framework. The effectiveness of the proposed ASSPAE method is evaluated by its application to the cast-rolling process of a steelmaking plant. The application results show that the prediction accuracy of the proposed ASSPAE modeling method was improved by 32%, 31%, 24%, and 13% in Root Mean Square Error (RMSE) compared with the partial least square regression, support vector regression, Poisson regression, and stacked autoencoders-Poisson regression, respectively. © 2022 IEEE.

关键词： Network layers

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FTEKFNet: Hybridizing Physical and Data-driven Estimation Algorithms for Vehicle State

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-11页

作者： Wang, Yan Hu, Fengqing Tian, Cheng Li, Panshuo Huang, Hailong Yin, Guodong Huang, Chao Department of Industrial and System Engineering The Hong Kong Polytechnical University Hong Kong Department of Aeronautical and Aviation Engineering The Hong Kong Polytechnical University Hong Kong School of Automation and the Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control Guangdong University of Technology Guangzhou China School of Mechanical Engineering Southeast University Nanjing China

Sideslip angle and vehicle velocity are crucial for both traditional and autonomous vehicles. They play essential roles in chassis stability control, as well as in tasks such as path planning and tracking control. However, these states cannot be directly measured by onboard sensors, therefore various vehicle state estimation algorithms have been developed. Most of these algorithms assume that the noise characteristics are known, ignoring the impact of missing measurement data, and cannot simultaneously handle the effects of colored noise and white noise. To address these issues, we propose a fault-tolerant extended Kalman filter network (FTEKFNet), which integrates both physics-based and data-driven methods for vehicle state estimation. Based on the Fault Tolerant Extended Kalman Filter (FTEKF) iterative framework, a pre-trained artificial neural network is utilized to directly predict the Kalman gain, and it is combined with FTEKF to form FTEKFNet. Experimental results under different conditions demonstrate that FTEKFNet can simultaneously deal with unknown noise and data loss problems and has good adaptability to color noise. The estimation performance of the proposed algorithm is better than the traditional FTEKF and EKF methods. IEEE

关键词： Extended Kalman filters

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Blast Furnace Charging State Recognition based on YOLOv5

Blast Furnace Charging State Recognition based on YOLOv5

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2022 Chinese Automation Congress, CAC 2022

作者： Li, Songchen Tang, Xiao-Yu Wang, Xin Fu, Xiayang Yang, Chunjie Zhejiang University State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Hangzhou310027 China

ISBN: (纸本)9781665465335

For modern iron and steel industry, blast furnace (BF) burden charging is one of the key procedures for cast iron making. Recognition of the different states during the charging process is the premise of burden layer profile observation and the hot gas monitoring in the BF throat. Conventional methods only provide limited information from the measurement spots where the sensors are located. Infrared thermal imaging technique provides continuous and field information of the BF throat, and is able to monitor the charging process. In the paper, the computer vision technique is applied to implement the charging state recognition of the BF. After preprocessing of the infrared images, the YOLOv5 algorithm is adopted to accomplish the target detection of the markers in the BF throat. A state identification template is designed to tell the different states during the charging process. The proposed charging state recognition method in this paper is verified with the infrared videos from an actual BF. The recognition accuracy is better than 95.5%, which has shown the effectiveness of the proposed method. © 2022 IEEE.

关键词： Blast furnaces

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Spatio-Temporal View GAIN for Data Imputation and Dynamic Soft Sensor 11

Spatio-Temporal View GAIN for Data Imputation and Dynamic So...

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11th IEEE Data Driven control and Learning Systems Conference, DDCLS 2022

作者： Ren, Jiayi Chen, Xu Zhao, Chunhui College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

Soft sensor plays a key role in the safe operation of industrial processes and product quality control. Affected by closed-loop feedback, process data often demonstrate certain dynamic characteristics. In addition, traditional soft sensor methods are often based on the assumption of completeness. Once part of the data is missing, the above methods will collapse. In this paper, we propose a Spatio-Temporal view Generative Adversarial Imputation Network (GAIN) for data imputation and apply dynamic soft sensor for block missing accompanied with completely random missing data. Initially, a data pre-imputed strategy is designed to first impute the missing data through GAIN from the perspective of data distribution. Secondly, on the basis of pre-imputation, a data imputation strategy that integrates time and space information is proposed to impute the data precisely, which can comprehensively consider the time series of process data and the correlation between variables, and further improve the accuracy of data imputation. Finally, we propose a dynamic soft sensor method to make real-time predictions of the data. The effectiveness of the proposed method is verified by the Tennessee Eastman (TE) process. © 2022 IEEE.

关键词： Generative adversarial networks

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A traffic signal control method based on improved deep reinforcement learning

A traffic signal control method based on improved deep reinf...

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2024 China Automation Congress, CAC 2024

作者： Bao-Lin, Ye Dong, Chen Peng, Wu Weimin, Wu Li, Lingxi Bin, Chen School of Information Science and Engineering Jiaxing University Jiaxing China School of Information Science and Engineering Zhejiang Sci-Tech University Hangzhou China State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China Elmore Family School of Electrical and Computer Engineering Purdue University West Lafayette United States Jiaxing Key Laboratory of Smart Transportations Jiaxing University Jiaxing China

ISBN: (纸本)9798350368604

In order to solve the problem that the existing traffic signal control methods usually need to predefine the phase sequence or phase duration, a single intersection traffic signal control method based on improved deep reinforcement learning Double DQN (DDQN) was proposed. Firstly, a traffic signal control model based on deep reinforcement learning DDQN was constructed to optimize the estimated value of action-value function and the iterative process of target value. Secondly, the original one-dimensional action space was discretized into a three-dimensional action space composed of phase selection, phase duration and loss factor, and the next phase and phase green time to be executed were intelligently decided. Finally, a new loss function is designed based on the loss factor, which can dynamically adjust the loss weight of different actions according to the difference of importance. The simulation test is carried out based on the microscopic simulation platform SUMO. The experimental results show that, compared with the baseline method, the proposed method reduces the average waiting time by 28.1%, the average queue length by 29.7%, and the average number of parking vehicles by 18.8%. © 2024 IEEE.

关键词： Deep reinforcement learning

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Symplectic analysis for regulating wave propagation in a one-dimensional nonlinear graded metamaterial

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Applied Mathematics and Mechanics(English Edition) 2023年第5期44卷 745-758页

作者： Yunping ZHAO Xiuhui HOU Kai ZHANG Zichen DENG Department of Applied Mathematics Northwestern Polytechnical UniversityXi’an 710072China School of Mechanics Civil Engineering and ArchitectureNorthwestern Polytechnical UniversityXi’an 710072China Ministry of Industry and Information Technology Key Laboratory of Dynamics and Control of Complex SystemsNorthwestern Polytechnical UniversityXi’an 710072China State Key Laboratory of Structural Analysis for Industrial Equipment Dalian University of TechnologyDalian 116024China

An analytical method,called the symplectic mathematical method,is proposed to study the wave propagation in a spring-mass chain with gradient arranged local resonators and nonlinear ground *** with the linearized perturbation approach,the symplectic transform matrix for a unit cell of the weakly nonlinear graded metamaterial is derived,which only relies on the state *** results of the dispersion relation obtained with the symplectic mathematical method agree well with those achieved by the Bloch *** is shown that wider and lower frequency bandgaps are formed when the hardening nonlinearity and incident wave intensity ***,the displacement response and transmission performance of nonlinear graded metamaterials with finite length are *** dual tunable effects of nonlinearity and gradation on the wave propagation are explored under different excitation *** small excitation frequencies,the gradient parameter plays a dominant role compared with the *** reason is that the gradient tuning aims at the gradient arrangement of local resonators,which is limited by the critical value of the local resonator *** contrast,for larger excitation frequencies,the hardening nonlinearity is dominant and will contribute to the formation of a new bandgap.

关键词： symplectic mathematical method nonlinear graded metamaterial tunable bandgap

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