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7th IEEE International Conference on power and Renewable Energy, ICPRE 2022

作者： Cui, Chenyu Dong, Ze North China Electric Power University Hebei Technology Innovation Center of Simulation and Optimized Control for Power Generation School of Control and Computer Engineering Baoding China

ISBN: (数字)9781665450638

ISBN: (纸本)9781665450638

It is necessary to optimize the operation of micro power grid in order to improve its economy. Firstly, the mathematical model of optimal dispatching of microgrid is established. Taking the internal battery charge and discharge power, fan and photovoltaic output power of microgrid as the optimization vector and the minimum operation cost of microgrid as the optimization objective, based on the day ahead prediction data, the day is divided into 24h as a dispatching period for optimization. The Twin Delayed Deep Deterministic policy gradient algorithm (TD3) is used to solve the problem, and the power supply composition and cost data are obtained. The simulation results verify the effectiveness of the algorithm and optimal scheduling strategy. © 2022 IEEE.

关键词： Reinforcement learning

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Dynamic data modeling based on Kalman state estimation 8

Dynamic data modeling based on Kalman state estimation

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8th International Conference on power and Renewable Energy, ICPRE 2023

作者： Wang, Chengli He, Shenglin Zhang, Dapeng Yang, Huaiwang Dong, Ze North China Electric Power University Hebei Technology Innovation Center of Simulation & Optimized Control for Power Generation Baoding China Datang International Power Generation Co. Ltd Engineering Construction Department Baoding China Datang International Power Generation Co. Ltd Production and Environmental Protection Department Baoding China

ISBN: (纸本)9798350328813

In the process of conventional historical data-driven modeling, when the initial state of the system at the starting point of the selected modeling data is unsteady, the model of the system cannot be accurately obtained. In addition, for many control systems, due to the mismatch between the control system performance and the controlled object or the frequent change of the control system setpoint, the system is in an unstable state for a long time, and it is difficult to manually screen the steady-state data. To solve this problem, a dynamic historical data modeling method based on Kalman filter is proposed. The dynamic data is divided into three sections. The first section is used for Kalman state estimation, the second section is used for model identification, and the third section is used for model verification. The results show that the model identification accuracy of this modeling method is high by using dynamic data, and the effectiveness of this method is proved. © 2023 IEEE.

关键词： Dynamic historical data Kalman filtering Modeling State estimation

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Dynamic Data Modeling of Industrial System Based on Improved Bird SWARM Algorithm 7

Dynamic Data Modeling of Industrial System Based on Improved...

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7th IEEE International Conference on power and Renewable Energy, ICPRE 2022

作者： Liu, Baojie Dong, Ze North China Electric Power University School of Control and Computer Engineering Hebei Baoding China North China Electric Power University Hebei Technology Innovation Center of Simulation and Optimized Control for Power Generation Hebei Baoding China

ISBN: (数字)9781665450638

ISBN: (纸本)9781665450638

Aiming at the problem that the initial state of industrial system cannot be obtained in the process of data-driven modeling, a modeling method based on improved bird swarm algorithm and initial state optimization was proposed. The method described in this article on industrial system operation process in the historical data of dynamic data as the modeling data, by modeling the system at the end of the data input values of input of the system steady state component, with the system output steady state component, system initial state, the starting point of modeling data model parameters for the optimization of particle dimension, the application of the improved algorithm for optimization, system model is established. The modeling and simulation results of an industrial system show the effectiveness of the proposed method. © 2022 IEEE.

关键词： Birds

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Feedforward Tracking control of Main Steam Temperature with Disturbance Rejection 8

Feedforward Tracking Control of Main Steam Temperature with ...

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8th International Conference on power and Renewable Energy, ICPRE 2023

作者： Hu, Hongming Li, Jun Zhang, Dapeng Yang, Huaiwang Dong, Ze North China Electric Power University Hebei Technology Innovation Center of Simulation & Optimized Control for Power Generation Baoding China Datang International Power Generation Co. Ltd. Engineering Construction Department Baoding China Datang International Power Generation Co. Ltd. Production and Environmental Protection Department Baoding China

ISBN: (纸本)9798350328813

As a commonly used control system, main steam temperature control has the characteristics of large inertia and large lag, and the noise interference it receives can also have a significant impact on the system. Faced with controlled objects with large inertia and delay, traditional control methods based on feedback regulation are difficult to quickly adjust the system, and it is also difficult to identify and control disturbances in advance. At the same time, although using feedforward control based on inverse models can quickly regulate the system, the strong differential effect caused by its sensitivity to high-frequency noise can lead to a decrease in regulation quality, so a feedback channel needs to be designed to address the impact of noise. This article introduces the basic principles of feedforward control, feedforward tracking control, and active disturbance rejection control, and points out areas for improvement. On this basis, a feedforward tracking control algorithm for main steam temperature with disturbance suppression is proposed. Through simulation experiments, its characteristics were analyzed and its control effect, anti-interference ability, and robustness were verified. © 2023 IEEE.

关键词： Disturb rejection Feedforward control Main steam temperature

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Application of Virtual Reality Technology in the Maintenance of Steam Turbine

Application of Virtual Reality Technology in the Maintenance...

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2021 International Conference on Artificial Intelligence and Industrial Technology Applications, AIITA 2021

作者： Pan, Zijie Sun, Hairong Li, Li School of Control and Computer Engineering North China Electric Power University Baoding071000 China Hebei Engineering Research Center of Simulation Optimized Control for Power Generation North China Electric Power University Baoding071003 China

Traditional steam turbine maintenance work has many shortcomings, such as poor effectiveness, high cost, and long cycle. In order to solve this problem and improve the professional skills and maintenance efficiency of steam turbine maintenance personnel, we first collect steam turbine equipment drawings and 3D dimension data of parts, and use 3D modeling software to establish augmented reality scene model. Then combined with the virtual reality technology and image fusion technology to realize the maintenance procedure training and maintenance auxiliary guidance of the steam turbine unit. The practical application results show that this technology can realize the virtual and real operation of the model by users in augmented reality scenes, bring interactive and immersive experience to operators, and improve the work efficiency of maintenance personnel. © Published under licence by IOP Publishing Ltd.

关键词： Maintenance

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Deciphering environmental factors influencing phytoplankton community structure in a polluted urban river

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Journal of Environmental Sciences 2025年第2期148卷 375-386页

作者： Xiaxia Li Kai Chen Chao Wang Tianyu Zhuo Hongtao Li Yong Wu Xiaohui Lei Ming Li Bin Chen Beibei Chai School of Water Conservancy and Hydroelectric Power Hebei University of EngineeringHandan 056038China Collaborative Innovation Center for Intelligent Regulation and Comprehensive Management of Water Resources School of Water Conservancy and HydroelectricHebei University of EngineeringHandan 056038China Hebei Key Laboratory of Intelligent Water Conservancy Hebei University of EngineeringHandan 056038China Tianjin Water Group Binhai Water Co. Ltd.Tianjin 300308China State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin China Institute of Water Resources and Hydropower ResearchBeijing 100038China School of Architecture and Civil Engineering Xi’an University of Science and TechnologyXi’an 710054China Chengdu University of Technology Chengdu 610059China College of Natural Resources and Environment Northwest A&F UniversityYangling 712100China State Key Joint Laboratory of Environment Simulation and Pollution Control School of EnvironmentBeijing Normal UniversityBeijing 100875China

Tuojiang River Basin is a first-class tributary of the upper reaches of the Yangtze River—which is the longest river in *** phytoplankton are sensitive indicators of trophic changes inwater bodies,characterizing phytoplankton communities and their growth influencing factors in polluted urban rivers can provide new ideas for pollution ***,we used direct microscopic count and environmental DNA(eDNA)metabarcoding methods to investigate phytoplankton community structure in Tuojiang River Basin(Chengdu,Sichuan Province,China).The association between phytoplankton community structure and water environmental factors was evaluated by Mantel *** environmental monitoring data were used to pinpoint major factors that influenced phytoplankton growth based on structural equation *** the phylum level,the dominant phytoplankton taxa identified by the conventional microscopic method mainly belonged to Bacillariophyta,Chlorophyta,and Cyanophyta,in contrast with Chlorophyta,Dinophyceae,and Bacillariophyta identified by eDNA ***α-diversity analysis,eDNA metabarcoding detected greater species diversity and achieved higher precision than the microscopic *** growth was largely limited by phosphorus based on the nitrogen-to-phosphorus ratios>16:1 in all water *** analysis and structural equation modeling also confirmed that the nitrogen-to-phosphorus ratio was the principal factor influencing phytoplankton *** results could be useful for implementing comprehensive management of the river basin *** is recommended to control the discharge of point-and surface-source pollutants and the concentration of dissolved oxygen in areas with excessive nutrients(e.g.,Jianyang-Ziyang).Algae monitoring techniques and removal strategies should be improved in 201 Hospital,Hongrihe Bridge and Colmar Town areas.

关键词： Environmental DNA Microscopic count Phytoplankton growth Structural equation modeling Tuojiang River Basin

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research on Digital Twin Modeling Method for Combustion Process Based on Model Reduction

SSRN

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SSRN 2024年

作者： Zhang, Yue Li, Jiale Department of Automation North China Electric Power University Hebei Baoding071000 China Hebei Technology Innovation Center of Simulation & Optimized Control for Power Generation North China Electric Power University China Baoding Key Laboratory of State Detection and Optimization Regulation for Integrated Energy System China

In response to the difficulty in obtaining combustion information within coal-fired boiler furnaces, a method is proposed in this study to improve the reduced-order model using clustering segmentation. This approach aims to rapidly predict the combustion temperature field inside the furnace by establishing a twin model of the combustion temperature field. Initially, a finite element method is employed to analyze the combustion system of a 600MW subcritical boiler under various operating conditions. Subsequently, cross-sectional data from burner nozzle positions at each operating condition are extracted. These data are subjected to Proper Orthogonal Decomposition (POD), Spectral Proper Orthogonal Decomposition (SPOD), and Wavelet Transform-POD (WT-POD) for dimensionality reduction to obtain modal data. Comparative analyses are conducted on the modal data obtained from different methods. Furthermore, based on modal data analysis, a Support Vector Machine (SVM) regression model is selected to reconstruct the temperature field. The average absolute error of the reconstructed temperature fields from three methods under different operating conditions is then compared. Finally, the model is refined using clustering segmentation, resulting in an improvement of approximately 0.6% in reconstruction accuracy. This enhancement demonstrates that the clustered POD-SVR-GA model achieves higher accuracy in reconstructing combustion temperature fields after clustering-based improvements. © 2024, The Authors. All rights reserved.

关键词： Support vector machines

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Dynamic data modeling based on Kalman state estimation

Dynamic data modeling based on Kalman state estimation

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IEEE International Conference on power and power and Renewable Energy (ICPRE)

作者： Chengli Wang Shenglin He Dapeng Zhang Huaiwang Yang Ze Dong Hebei Technology Innovation Center of Simulation & Optimized Control for Power Generation North China Electric Power University Baoding China Engineering Construction Department Datang International Power Generation Co. Ltd Baoding China Production and Environmental Protection Department Datang International Power Generation Co. Ltd Baoding China

关键词：

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Feedforward Tracking control of Main Steam Temperature with Disturbance Rejection

Feedforward Tracking Control of Main Steam Temperature with ...

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IEEE International Conference on power and power and Renewable Energy (ICPRE)

作者： Hongming Hu Jun Li Dapeng Zhang Huaiwang Yang Ze Dong Hebei Technology Innovation Center of Simulation & Optimized Control for Power Generation North China Electric Power University Baoding China Engineering Construction Department Datang International Power Generation Co. Ltd. Baoding China Production and Environmental Protection Department Datang International Power Generation Co. Ltd. Baoding China

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Dynamic model for predicting nitrogen oxide concentration at outlet of selective catalytic reduction denitrification system based on kernel extreme learning machine

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Journal of Southeast University(English Edition) 2022年第4期38卷 383-391页

作者： Ma Ning Liu Lei Yang Zhenyong Yan Laiqing Dong Ze North China Electric Power Research Institute Co. Ltd.Beijing 100045China School of Electric Power Civil Engineering and ArchitectureShanxi UniversityTaiyuan 030006China Hebei Technology Innovation Center of Simulation and Optimized Control for Power Generation North China Electric Power UniversityBaoding 071003China

To solve the increasing model complexity due to several input variables and large correlations under variable load conditions,a dynamic modeling method combining a kernel extreme learning machine(KELM)and principal component analysis(PCA)was proposed and applied to the prediction of nitrogen oxide(NO_(x))concentration at the outlet of a selective catalytic reduction(SCR)denitrification ***,PCA is applied to the feature information extraction of input data,and the current and previous sequence values of the extracted information are used as the inputs of the KELM model to reflect the dynamic characteristics of the NO_(x)concentration at the SCR ***,the model takes the historical data of the NO_(x)concentration at the SCR outlet as the model input to improve its ***,an optimization algorithm is used to determine the optimal parameters of the *** with the Gaussian process regression,long short-term memory,and convolutional neural network models,the prediction errors are reduced by approximately 78.4%,67.6%,and 59.3%,*** results indicate that the proposed dynamic model structure is reliable and can accurately predict NO_(x)concentrations at the outlet of the SCR system.

关键词： selective catalytic reduction nitrogen oxides principal component analysis kernel extreme learning machine dynamic model

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