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IEEE Conference Anthology

作者： Song Ming Jiao Pu Han Hebei Engineering Research Center of Simulation & Optimized Control for Power Generation North China Electric Power University Baoding China Department of Automation North China Electric Power University Baoding China

Particle Swarm optimization (PSO) is a stochastic computational evolutionary technique which is based on intelligence of swarms but its convergence and search accuracy still need to be improved. Chaotic search, average initiate particle location, variable search zone, anti-prematurity strategy, inflection algorithm and changeable momentum weights were combined with PSO algorithm in order to solve these problems. This modified chaotic PSO was used to identify values of the transfer function on a circulation fluidized bed boiler combustion system. simulation result shows that combining analytical, intelligent and experimental approach is an effective and practicable modeling approach.

关键词： Chaos Combustion Temperature measurement Particle swarm optimization Coal Transfer functions Optimization

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Research on simulation and scheme of the double reheat steam temperature control system

Research on simulation and scheme of the double reheat steam...

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2013 International Conference on Future Energy, Environment, and Materials (FEEM 2013)

作者： Ze Dong Jialin Chai Ming Sun Dongfeng Wang Hui Wan Hebei Engineering Research Center of Simulation and Optimized Control for Power Generation North China Electric Power University Guodian Science and Technology Research Institute

ISBN: (纸本)9781845648572

The secondary reheat steam temperature system was designed and researched for the double reheat power unit. A simulator adopting modular modelling is chosen as the research platform to construct an ultra-supercritical double reheat unit. According to the analysis of the double reheat thermal system, the double reheat steam temperature control system is designed. The main control approach is surface type attemperator, which adopts cascade control loop and affiliates with spray type attemperator. After adding mathematical model of the second reheat steam temperature system into LN algorithm library, the system model is set up. According to the simulation results, the feasibility, advantages, and disadvantages of the surface type attemperator control is analyzed.

关键词： simulation ultra-supercritical unit with double reheat cycle steam temperature control surface type attemperator

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Combustion modeling for utility boiler based on multi-output least squares support vector machine

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International Journal of Advancements in Computing technology 2012年第14期4卷 60-68页

作者： Han, Pu Gao, Fang Zhai, Yong-Jie Zhuo, Yue Lu, Yuan Hebei Engineering Research Center of Simulation and Optimized Control for Power Generation North China Electric Power University Baoding China College of Electronics and Information Engineering Hebei University Baoding China Tianjin SDIC Jinneng Electric Power Company Limited Tianjin China

A novel regression algorithm of multi-output least squares support vector machine is proposed for the modeling of multi-output systems. The fitting error of each dimension output and the fitting error of overall output are defined. And combined with the equality constraints of least squares support vector machine, the linear equation of multi-output least squares support vector machine is deduced. A multioutput combustion system model for utility boiler is established based on above algorithm and the prediction of NOx emission, carbon content of fly ash, exhaust flue gas temperature and desuperheating water of reheater is achieved. In addition, the parameters of the multi-output model are optimized by particle swarm optimization. The performance of the multi-output least squares support vector machine model and multiple single-output least squares support vector machine are compared. The effectiveness of the multi-output model is verified and it provides a good foundation for combustion optimization.

关键词： Fly ash

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Research on a modified smith predictive control scheme of main steam temperature of circulating fluidized bed

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Research Journal of Applied Sciences, Engineering and technology 2012年第14期4卷 2216-2221页

作者： Ze, Dong Lei, Meng Pu, Han Hebei Engineering Research Center of Simulation Optimized Control for Power Generation North China Electric Power University Baoding 071003 China

In order to solve the current difficulties of modeling for designing Intelligent Service Mobile Robot (ISMR), a new modeling method based on metasynthesis is proposed from the macro and micro levels. And the system analysis and design agent-oriented based on POMDP are provided in the same time. Finally, the two case studies are given and the experimental results have shown efficiency and rationality of this modeling method. © Maxwell Scientific Organization, 2012.

关键词： Fuzzy control

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Computational intelligence in low NOx emission combustion for coal-fired power plants

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International Journal of Advancements in Computing technology 2012年第14期4卷 69-75页

作者： Gao, Fang Han, Pu Zhai, Yong-Jie Lu, Yuan Hebei Engineering Research Center of Simulation and Optimized Control for Power Generation North China Electric Power University Baoding China College of Electronics and Information Engineering Hebei University Baoding China

For low NOx emission from coal-fired power plants, the computational intelligence(CI) technologies for NOx emission modeling and optimization are summarized. The modeling technologies of CI for NOx emission include artificial neural networks and fuzzy systems. The optimization methods for low NOx emission combustion consist of evolutionary computation and swarm intelligence. Each method's advantage and disadvantage are comprised and the prospect of low NOx emission combustion is forecasted.

关键词： Fossil fuel power plants

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Coal ASH fusion temperature model based on SVM optimized by ACO

Coal ASH fusion temperature model based on SVM optimized by ...

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Symposium on ICT and Energy Efficiency and Workshop on Information Theory and Security (CIICT 2012)

作者： Pu Han Fang Gao Yong-jie Zhai Yuan Lu Hebei Engineering Research Center of Simulation & Optimized Control for Power Generation(North China Electric Power University) Baoding 071003 China College of Electric and Information Engineering Hebei University Baoding 071002 China

A coal ash fusion temperature model is constructed based on support vector machine(SVM). The compositions of coal ash are employed as the inputs and the ash fusion temperature is the output. A series of improvement is made on basic ant colony optimization(ACO) and it is used to optimize the parameters of the SVM model. The coal ash fusion temperature is predicted by the ACO-optimized SVM model. Some experiments are performed to compare the predicted and the measured temperature and the results show the ACO-optimized SVM model can achieve better predicting performance. The advantages of SVM model, such as small sampling, fast computing speed and real-time processing and predicting are also displayed.

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A new optimal PID control method for superheated steam temperature system

A new optimal PID control method for superheated steam tempe...

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International Conference on Intelligent control and Information Processing (ICICIP)

作者： Huang, Yu Li, Xiaohe Li, Yongling Han, Pu Hebei Engineering Research Center of Simulation and Optimized Control for Power Generation Baoding 071003 China North China Baoding Electric Power Voc. and Tech. College Baoding Hebei 071051 China

ISBN: (纸本)9781457708145

In order to design H2/H∞ PID controller, this paper proposed a novel niche quantum genetic algorithm that was based on chaotic mutation operator. The simulation results show that, it can obtain better effect when adopting this optimization algorithm to solve constrained optimization problems. The simulation of the super steam temperature control also demonstrates this method's validity, and meanwhile, this algorithm still performs well in model mismatch circumstances, and it's useful in engineering. © 2011 IEEE.

关键词： Genetic algorithms

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Hα filtering for linear parameter-varying systems with jumping parameters subject to data missing and quantization

Hα filtering for linear parameter-varying systems with jump...

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作者： Yao, Xiuming Zheng, Weixing Li, Tao Hebei Engineering Research Center of Simulation and Optimized Control for Power Generation North China Electric Power University Baoding 071003 China School of Computing and Mathematics University of Western Sydney Penrith NSW 2751 Australia Department of Information and Communication Nanjing University of Information Science and Technology Nanjing 210044 Jiangsu China

ISBN: (纸本)9789881725592

This paper is concerned with the problem of Hα filtering for a class of discrete-time linear parameter-varying (LPV) systems with Markovian switching under data missing and quantization. A stochastic variable is used to describe the model of the data missing phenomenon. Then a H α filter is designed to guarantee the filtering error dynamics is stochastically stable with Hα performance. The existence of the desired Hα filter is ensured by some sufficient conditions expressed in terms of parameterized linear matrix inequalities (PLMIs). The proposed theoretical findings are validated by numerical results. © 2011 Chinese Assoc of Automati.

关键词： Stochastic systems

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H∞ filtering for linear parameter-varying systems with jumping parameters subject to data missing and quantization

H∞ filtering for linear parameter-varying systems with jump...

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Chinese control Conference (CCC)

作者： Xiuming Yao Weixing Zheng Tao Li The Hebei Engineering Research Center of Simulation & Optimized Control for Power Generation North China Electric Power University Baoding 071003 China School of Computing and Mathematics University of Western Sydney Penrith NSW 2751 Australia Department of Information and Communication Nanjing University of Information Science and Technology 210044 Jiangsu China

This paper is concerned with the problem of H ∞ filtering for a class of discrete-time linear parameter-varying (LPV) systems with Markovian switching under data missing and quantization. A stochastic variable is used to describe the model of the data missing phenomenon. Then a H ∞ filter is designed to guarantee the filtering error dynamics is stochastically stable with H ∞ performance. The existence of the desired H ∞ filter is ensured by some sufficient conditions expressed in terms of parameterized linear matrix inequalities (PLMIs). The proposed theoretical findings are validated by numerical results.

关键词： Quantization Switches Electronic mail Filtering Stochastic processes Data models Linear matrix inequalities

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Quantizedfiltering for Markovian jump LPV systems with intermittent measurements

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International Journal of Robust and Nonlinear control 2011年第1期23卷

作者： Xiuming Yao Ligang Wu Wei Xing Zheng Hebei Engineering Research Center of Simulation and Optimized Control for Power Generation North China Electric Power University Baoding 071003 China School of Computing and Mathematics University of Western Sydney Penrith NSW 2751 Australia Space Control and Inertial Technology Research Center Harbin Institute of Technology Harbin 150001 China

This paper investigates the problem of quantized filtering for a class of discrete-time linear parameter-varying systems with Markovian switching under data missing. The measured output of the plant is quantized by a logarithmic mode-independent quantizer. The data missing phenomenon is modeled by a stochastic variable. The purpose of the problem addressed is to design a full-order filter such that the filtering error dynamics is stochastically stable and the prescribed noise attenuation level in the sense can be achieved. Sufficient conditions are derived for the existence of such filters in terms of parameterized linear matrix inequalities. Then the corresponding filter synthesis problem is transformed into a convex optimization problem that can be efficiently solved by using standard software packages. A simulation example is utilized to demonstrate the usefulness of the developed theoretical results. Copyright © 2011 John Wiley & Sons, Ltd.

关键词： linear parameter-varying (LPV) systems Markovian jump systems quantized filtering data missing parameterized linear matrix inequalities (PLMIs)

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