Multimodel Parameters Identification for Main Steam Temperature of Ultra-Supercritical Units Using an Improved Genetic Algorithm

AbstractA parameter identification method based on genetic algorithm (GA) is presented to solve the multimodel parameters identification problem for the main steam temperature of ultra-supercritical (USC) units. Linear ranking selection, nonuniform linear crossover, and Gaussian mutation are employed in the algorithm design to enhance the convergence speed and the accuracy of the identification. Besides, the uniform design method is executed to initialize the population and the sigmoid function with adaptation is applied to adjust the probabilities of crossover and mutation. Simulations carried out with the field operation data from Haimen USC units, including two processes—parameters identification and model verification. The simulation results show that the improved genetic algorithm performs well in global parameters searching and the proposed identification methodology offers good results for the multimodel parameters identification.

[1]  Xavier Blasco,et al.  Nonlinear Parametric Model Identification with Genetic Algorithms. Application to a Thermal Process , 2001 .

[2]  Y. Maeda,et al.  Parameter estimation using simultaneous perturbation stochastic approximation , 2006 .

[3]  Jizhen Liu,et al.  Adaptive Immune Genetic Algorithm and its application in PID parameter optimization for main steam temperature control system , 2010, Third International Workshop on Advanced Computational Intelligence.

[4]  R. Hinterding,et al.  Gaussian mutation and self-adaption for numeric genetic algorithms , 1995, Proceedings of 1995 IEEE International Conference on Evolutionary Computation.

[5]  Mukesh Sharma,et al.  Identification and Quantification of Environmental Issues of Aging Coal-Based Power Plant—Case Study , 2001 .

[6]  Alireza Yazdizadeh,et al.  Neural Network-Based IMC-PID Controller Design for Main Steam Temperature of a Power Plant , 2009, ISNN.

[7]  Li Shen,et al.  Application of Internal Model and Self-Adaptive PSD Controller in the Main Steam Temperature System , 2005, 2005 International Conference on Machine Learning and Cybernetics.

[8]  Ji-Zhen Liu,et al.  Multi-model adaptive control for the superheated steam temperature , 2002, Proceedings. International Conference on Machine Learning and Cybernetics.

[9]  Ian F. C. Smith,et al.  Improving Knowledge of Structural System Behavior through Multiple Models , 2008 .

[10]  Yiping Jiang,et al.  Main steam temperature's adaptive control with Kautz model , 2010, 2010 8th World Congress on Intelligent Control and Automation.

[11]  E. Parloo,et al.  Modal parameter estimation from input–output Fourier data using frequency-domain maximum likelihood identification , 2004 .

[12]  Peter Winker,et al.  Lower bounds for centered and wrap-around L2-discrepancies and construction of uniform designs by threshold accepting , 2003, J. Complex..

[13]  Xiaoming Dai,et al.  Allele Gene Based Adaptive Genetic Algorithm to the Code Design , 2011, IEEE Transactions on Communications.

[14]  Tariq Muneer,et al.  Modeling and Experimental Verification of Solar Radiation on a Sloped Surface, Photovoltaic Cell Temperature, and Photovoltaic Efficiency , 2013 .

[15]  Enrique Alba,et al.  A survey of parallel distributed genetic algorithms , 1999 .

[16]  Lin Gao,et al.  Modeling of combined cycle power plant based on a genetic algorithm parameter identification method , 2010, 2010 Sixth International Conference on Natural Computation.

[17]  Robert E. Kearney,et al.  A least-squares parameter estimation algorithm for switched Hammerstein systems with applications to the VOR , 2005, IEEE Transactions on Biomedical Engineering.

[18]  Jia Ming-xing,et al.  Uniform Design of Initial Population and Operational Parameters of Genetic Algorithm , 2005 .

[19]  Liu Changliang,et al.  The application of genetic algorithm in model identification , 2002, 2002 IEEE Region 10 Conference on Computers, Communications, Control and Power Engineering. TENCOM '02. Proceedings..

[20]  Jing Zhang,et al.  Model Parameter Identification of Excitation System based on a Genetic Algorithm Techniques , 2006, 2006 International Conference on Power System Technology.

[21]  Johan Meijer,et al.  Calculation and optimization of sample identification by laser induced breakdown spectroscopy via correlation analysis , 2007 .

[22]  Karl J. Friston,et al.  Population dynamics: Variance and the sigmoid activation function , 2008, NeuroImage.

[23]  Xiang-zhong Guan.  Multi-objective PID Controller Based on NSGA-II Algorithm with Application to Main Steam Temperature Control , 2009, 2009 International Conference on Artificial Intelligence and Computational Intelligence.

[24]  Zbigniew Michalewicz,et al.  Parameter control in evolutionary algorithms , 1999, IEEE Trans. Evol. Comput..

[25]  Kenneth Alan De Jong,et al.  An analysis of the behavior of a class of genetic adaptive systems. , 1975 .

[26]  Zhi-Yuan Wang,et al.  A kind of adaptive genetic algorithm and its application in model identification , 2005, 2005 International Conference on Machine Learning and Cybernetics.

[27]  Kusum Deep,et al.  A new crossover operator for real coded genetic algorithms , 2007, Appl. Math. Comput..

[28]  Alfredo Vaccaro,et al.  Parameter identification of power transformers thermal model via genetic algorithms , 2001 .

[29]  Sung-Ho Kim,et al.  Controller Design for a Large-Scale Ultrasupercritical Once-Through Boiler Power Plant , 2010, IEEE Transactions on Energy Conversion.

[30]  Dong Wang,et al.  Application of Bayesian Model Averaging Approach to Multimodel Ensemble Hydrologic Forecasting , 2013 .

[31]  O. G. Kakde,et al.  Study of Operator’s adaptability and scale-up study for RAGA , 2007, 2007 IEEE Congress on Evolutionary Computation.

[32]  H. Christopher Frey,et al.  Modeling and Evaluation of Externally Fired Combined Cycle Using Aspen , 1997 .

[33]  Armando B. Corripio,et al.  Principles and Practice of Automatic Process Control , 1985 .

[34]  Ali Ghaffari,et al.  A simulated model for a once-through boiler by parameter adjustment based on genetic algorithms , 2007, Simul. Model. Pract. Theory.