Review of Control Strategies Employing Neural Network for Main Steam Temperature Control in Thermal Power Plant
DOI:
https://doi.org/10.11113/jt.v66.2488Keywords:
Main steam temperature, thermal power plant, neural networkAbstract
Main steam temperature control in thermal power plant has been a popular research subject for the past 10 years. The complexity of main steam temperature behavior which depends on multiple variables makes it one of the most challenging variables to control in thermal power plant. Furthermore, the successful control of main steam temperature ensures stable plant operation. Several studies found that excessive main steam temperature resulted overheating of boiler tubes and low main steam temperature reduce the plant heat rate and causes disturbance in other parameters. Most of the studies agrees that main steam temperature should be controlled within ±5 Deg C. Major factors that influenced the main steam temperature are load demand, main steam flow and combustion air flow. Most of the proposed solution embedded to the existing cascade PID control in order not to disturb the plant control too much. Neural network controls remains to be one of the most popular algorithm used to control main steam temperature to replace ever reliable but not so intelligent conventional PID control. Self-learning nature of neural network mean the load on the control engineer re-tuning work will be reduced. However the challenges remain for the researchers to prove that the algorithm can be practically implemented in industrial boiler control.
References
Ilhan Kocaarslan, Ertugrul Cam, Hasan Tiryaki. 2005. A Fuzzy Logic Controller Application for Thermal Power Plants. Energy Conversion and Management. 47: 442–458.
S. Matsumura, K. Ogata, S. Fujii, H. Shoya and H. Nakamura 1994. Adaptive Control for the Steam Temperature of Thermal Power Plants. Control Engineering Practice. 2(4): 567–575.
Hui Peng, Toru Ozaki, Yukihiro Toyoda, Keiji Oda 2001. Exponential ARX Model-Based Long-Range Predictive Control Strategy For Power Plants. Control Engineering Practice. 9: 1353–1360.
Tommy Moelbak 1998. Advanced Control of Superheater Steam Temperature – An Evaluation Based on Practical Applications. Control Engineering Practice. 7: 1–10.
Satish Kumar. 2004. Neural Network: A Classroom Approach. Tata McGraw-Hill Education Private Limited, New Delhi.
McCulloch, W. S., and Pitts, W. 1943. A Logical Calculus of the Ideas Immanent in Nervous Activity. Bull. Of Mathematical Biophysics. 5: 115–133.
von Neumann, J., 1951 The General and Logical Theory of Automata. Cerebral Mechanisms of Behavior: The Hixon Symposium. Wiley, New York, NY. 1–32.
Hebb, D. O. 1949. The Organization of Behavior. John Wiley, New York, NY.
Turing, A. 1950 Computing Machinery and Intelligence. Mind. 59: 433–460.
Rosenblatt, F. 1958. The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain. Psychological Review. 65: 386–408.
Rosenblatt, F. 1960. On the Convergence of Reinforcement Procedures in Simple Perceptrons. Tech. Rep. VG-1196-G-4, Cornell Aeronautical Laboratory, Buffalo, NY.
Rosenblatt, F. 1960. Perceptron Simulation Experiments. Proceedings of the Institute of Radio Engineers. 48: 301–309.
Rosenblatt, F. 1962. Principles of Neurodynamics. Spartan Books, Washington.
Minsky, M., and S. Papert 1969. Perceptrons: An Introduction to Computational Geometry. MIT Press, Cambridge, MA
Han Li and Zhang Zhen-Yu. 2012. The Application of Immune Genetic Algorithm in Main Steam Temperature of PID Control of BP Network. Physics Procedia. 24: 80–86.
Tian Haijun and Wang Jingru. 2012. Modeling of Power Plant Superheated Steam Temperature Based On Least Square Support Vector Machines. Energy Procedia. 17: 61–67.
Wen Zu and Jianping Sun. 2012. Based On The BP Neural Network-PID Series Control Boiler Main Steam Temperature System Research. Advance Materials Research.354-355 pg. 968-973.
Lijun Chen, Bo Sun, Jianchao Diao and Lili Zhao 2012. Improved CMAC Neural Network Control for Superheated Steam Temperature. Advanced Materials Research. 383–390: 111–117.
Liangyu Ma, Zhengxing Shi and Kwang Y. Lee 2012. Superheater Steam Temperature Control Based on the Expanded- Structure Neural Network Inverse Models. Advanced Materials Research. 443–444: 401–407.
Ma Liangyu, Ge Yinping and Cao Xing 2012. Superheated Steam Temperature Control Based on Improved Recurrent Neural Network and Simplified PSO Algorithm. Applied Mechanics and Materials. 128–129 1065–1069.
Liu Wei and Zhou Junmin 2012. Particle Swarm Optimization PID Neural Network Control Method In The Main Steam Temperature Control System. International Conference On Computer Science And Electronics Engineering. 137–140.
Daogang Peng, Hao Zhang, Conghua Huang, Fei Xia, Hui Li 2011. Immune PID Cascade Control Based on Neural Network For Main Steam Temperature System. Proceedings of the 8th World Congress on Intelligent Control and Automation. 480–484.
Liangyu Ma and Kwang Y. Lee 2011. Neural Network Based Superheater Steam Temperature Control for a Large-scale Supercritical Boiler Unit. Power and Energy Society General Meeting. 2011 IEEE Publication. 1–8.
Yang Xue and ZhenJie Yan. 2010. Research on RBF Tuning PID and Fuzzy Immune Control System on Superheat Temperature. Sixth Inter. Conference on Natural Computation. 3528–3531.
Yang Xue, ZhenJie Yan 2010. Research On Main Steam Temperature Control System In Power Plant Based On CMAC Neural Network And the Single-neuron PID Controller with Quadratic Index. Proceeding of The 9th International Conference on Machine Learning and Cybernetics, Qingdao. 56–60.
Yun Juan Li, Yan Jung Fang and Qi Li 2010. Application Of A Neural Network Predictive Control For The Supercritical Main Steam. 3rd International Conference on Advanced Computer Theory and Engineering ICACTE. 182–185.
J. Smrekar, M. Assadi, M. Fast, I. Kustrin, S. De. 2009. Development Of Artificial Neural Network Model for a Coal-fired Boiler Using Real Plant Data. Energy. 34: 144–152.
Lina He, Songming Jiao, Zijie Wang, Pu Han and Lihui Zhou 2008. Main Steam Temperature System Modeling Based on PCA and Neural Networks. 2008 International Symposium on Computational Intelligence And Design. 303–308.
Zhun Yu, Ying Bai Xie, You Ying Jing and Xu Ao Lu 2006. Applying Neural Networks to PID Controllers for Time Delay Systems. Proceedings of the Fifth International Conference on Machine Learning And Cybernetics. 3173–3176.
Jianhua Zhang, Guolian Hou and Jinfang Zhang 2006. Adaptive Neuro Control System For Superheated Steam Temperature Of Power Plant Over Wide Range Operation. Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications .
Zhi Ming Qin, Luan Ying Zhang, Jun Jie Gu and Lei Wang 2004. Single Neuron PSD Adaptive Control Based on Smith Predictor for the Main Steam Temperature. Proceedings of the Third International Conference On machine Learning and Cybernetics, Shanghai. 1088–1092.
Dao Gang Peng, Ping Yang, Zhi Ping Wang and Yan Hua Yang 2004. Adaptive Inverse Control Based on Parallel Self Learning Neural Networks and Its Application. Proceedings of the Third International Conference on Machine Learning and Cybernetics, Shanghai. 392–396.
G. Prasad, E. Swidenbank, B.W. Hogg 1998. A Neural Net Model-based Multivariable Long-range Predictive Control Strategy Applied in Thermal Power Plant Control. IEEE Transactions on Energy Conversion. 13(2): June 1998.
Xianzhong Cui and Kang G. Shin 1992. Application of Neural Networks to Temperature Control in Thermal Power Plants. Engineering Application & Artificial Intelligent. 5(6): 527–538.
Mazalan, N. A., Malek A. A., Mazlan A. Wahid, M. A. Mailah, M. Saat, Sies M. M. 2013. Main Steam Temperature Mod, Applied Mechanics and Materials. 388: 307–311.
Downloads
Published
Issue
Section
License
Copyright of articles that appear in Jurnal Teknologi belongs exclusively to Penerbit Universiti Teknologi Malaysia (Penerbit UTM Press). This copyright covers the rights to reproduce the article, including reprints, electronic reproductions, or any other reproductions of similar nature.
