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PERFORMANCE OF PSO BASED CLASSİCAL AND INTELLİGENT CONTROLLERS FOR WATER LEVEL CONTROL OF A STEAM GENERATOR

Year 2017, Volume: 19 Issue: 57, 835 - 844, 01.09.2017

Abstract

In this paper, different controller techniques based on particle swarm optimization (PSO) algorithm are proposed to control the water level of a steam generator with multiple inputmultiple output (MIMO) characteristics. The techniques employed are classical proportional+integral+derivative (PID) control, fuzzy logic control (FLC) and fuzzy tuned proportional-integral control (FTPIC). Gains of PID controller and parameters of FLC (the core and the boundaries of triangular membership functions in input and output spaces) are optimized by the PSO. Validations of the proposed PSO based PID control (PSO-PID), PSO based fuzzy logic control (PSO-FLC) and PSO based fuzzy tuned PI control (PSOFTPIC) techniques are done with numerical simulation in using MATLAB. The simulation results show that the PSO-PID provides better performance for controlling the water level of a steam generator compared to the others

References

  • [1] Liu, X.J., Lara-Rosano, F., Chan, C.W. 2003. Neurofuzzy Network Modelling and Control of Steam Pressure in 300 MW Steam-Boiler System, Engineering Applications of Artificial Intelligence, Vol. 16, No. 5- 6, pp. 431-440. DOI: 10.1016/ j.enga ppai.2003.08.006
  • [2] Klauco, M., Kvasnica, M. 2017. Control of A Boiler-Turbine Unit Using MPC-based Reference Governors, Applied Thermal Engineering, Vol. 110, pp. 1437- 1447. DOI: 10.1016/j.applthermale ng.2016.09.041
  • [3] Liu, X.J., Chan, C.W. 2006. NeuroFuzzy Generalized Predictive Control of Boiler Steam Temperature, IEEE Transactions on Energy Conversion, Vol. 21, No. 4, pp. 900-908. DOI: 10.1109/TEC.2005. 853758
  • [4] Ku, C.C. 2015. Robust Controller Design for Nonlinear Uncertain Stochastic Drum-Boiler System. 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), 15-17 August, Zhangjiajie, China, 2378-2383.
  • [5] Niva, L., Yli-Korpela, A. 2012. Control of A Benchmark Boiler Process Model with DMC and QDMC, IFAC Proceedings, Vol. 45, No. 21, pp. 190-195. DOI: 10.3182/20120902-4-FR2032.00035
  • [6] Lawrynczuk, M. 2017. Nonlinear Predictive Control of A BoilerTurbine Unit: A State-Space Approach with Successive On-Line Model Linearisation and Quadratic Optimisation, ISA Transactions, Vol. 67, pp. 476-495. DOI: 10.1016/ j.isatra.2017.01.016
  • [7] Wei, L., Fang, F. 2017. H∞-LQRBased Coordinated Control for Large Coal-Fired Boiler-Turbine Generation Units. IEEE Transactions on Industrial Electronics, Vol. 64, No. 6, pp. 5212- 5221. DOI: 10.1109/TIE. 2016.2622233
  • [8] Moradi, H., Abbasi, M.H., Moradian, H. 2016. Improving The Performance of a Nonlinear BoilerTurbine Unit Via Bifurcation Control of External Disturbances: A Comparison Between Sliding Mode and Feedback Linearization Control Approaches, Nonlinear Dynamics, Vol. 85, No. 1, pp. 229-243. DOI: 10.1007/s11071-016-2680-x
  • [9] Zhang F., Wu, X., Shen, J. 2017. Extended State Observer Based Fuzzy Model Predictive Control for Ultra-Supercritical Boiler-Turbine Unit, Applied Thermal Engineering, Vol. 118, pp. 90-100. DOI: 10.1016/ j.applthermaleng.2017.01.111
  • [10] Alamoodi, N., Daoutidis, P. 2017. Nonlinear Control of Coal-Fired Steam Power Plants, Control Engineering Practice, Vol. 60, pp. 63-75. DOI: 0.1016/j.conengprac.2016. 12.005
  • [11] González, J. A., Barreiro, A., Dormido, S., Baños, A. 2017. Nonlinear Adaptive Sliding Mode Control With Fast Non-Overshooting Responses And Chattering Avoidance, Journal of the Franklin Institute, Vol. 354, No. 7, pp. 2788-2815. DOI: 10.1016/ j.jfranklin.2017.01.025
  • [12] Ait Sahed, O., Kara, K., Benyoucef, A., Hadjili, M.L. 2016. An Efficient Artificial Bee Colony Algorithm with Application to Nonlinear Predictive Control, International Journal of General Systems, Vol. 45, No. 4, pp. 393-417. DOI: 10.1080/03081079. 2015.1086344
  • [13] Jamali, B., Jazayeri-Rad, H. 2010. Application of Adaptive Local Linear Model Tree for Nonlinear Identification of Heat Recovery Steam Generator System Based on Experimental Data. 2010 Fourth UKSim European Symposium on Computer Modeling and Simulation, 17-19 November, Pisa, Italy, 16-20.
  • [14] Ozkop E., Sharaf A., Altas I.H. 2016. An Adaptive Fuzzy PI Controlled Bus Quantity Enhancer for Wave Energy Systems, Turkish Journal of Electrical Engineering and Computer Sciences, Vol. 24, pp. 2454-2468. DOI: 10.3906/elk-1312- 198
  • [15] Castro, P.A.D., Camargo, H.A. 2004. Learning and Optimization of Fuzzy Rule Base by Means of Self-Adaptive Genetic Algorithm, IEEE International Conference on Fuzzy Systems; 25-29 July, Budapest, Hungary. New York, NY, USA
  • [16] Li, H.X., Gatland, H.B. 1995. New Methodology for Designing A Fuzzy Logic Controller, IEEE T Syst Man Cy, Vol. 25, pp. 505-512
  • [17] Altas, I.H., Sharaf, A.M. 2007. A Generalized Direct Approach for Designing Fuzzy Logic Controllers in MATLAB/ Simulink GUI Environment, International Journal of Information Technology and Intelligent Computing, Vol. 1, No. 4, pp. 1-27
  • [18] Liu ,J., Ma, D., Ma, T.B, Zhang, W. 2017. Ecosystem Particle Swarm Optimization, Soft Computing, Vol. 21, No. 7, pp. 1667-1691. DOI: 10.1007/s00500-016-2111-4
  • [19] Yang, Q.H., Tian, J.P., Si, W. 2017. An Improved Particle Swarm Optimization Based on Difference Equation Analysis, Journal of Difference Equations and Applications, Vol. 23, No. 1-2, pp. 135-152. DOI: 10.1080/10236198. 2016.1199691
  • [20] Cui, H.Q., Shu, M.L., Song, M., Wang, Y.L. 2017. Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks Localization, Sensors, Vol. 17, No. 3, pp. 1-18. DOI: 10.3390/ s17030487
  • [21] Bouallègue, S., Haggège, J., Ayadi, M., Benrejeb, M. 2012. PID-Type Fuzzy Logic Controller Tuning Based on Particle Swarm Optimization, Engineering Applications of Artificial Intelligence, Vol. 25, No. 3, pp. 484-493. DOI: 10.1016/j.engappai.2011. 09.018
  • [22] Khare, A., Rangnekar, S. 2013. A Review of Particle Swarm Optimization and Its Applications in Solar Photovoltaic System, Applied Soft Computing, Vol. 13, No. 5, pp. 2997-3006. DOI: 10.1016/j.asoc. 2012.11.033
  • [23] Coban, R. 2011. A Fuzzy Controller Design for Nuclear Research Reactors Using The Particle Swarm Optimization Algorithm, Nuclear Engineering and Design, Vol. 241, No. 5, pp. 1899-1908. DOI: 10.1016/ j.nucengdes.2011.01.045
  • [24] Del Valle, Y., Venayagamoorthy, G.K., Mohagheghi, S., Hernandez, J.C., Harley, R.G. 2008. Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems, IEEE Transactions on Evolutionary Computation, Vol. 12, No. 2, pp. 171-195. DOI: 10.1109/ TEVC.2007.896686

BUHAR GENERATÖRÜNÜN SU SEVIYESI DENETIMI IÇIN PSO TEMELLI KLASIK VE AKILLI DENETLEYICILERIN PERFORMANSI

Year 2017, Volume: 19 Issue: 57, 835 - 844, 01.09.2017

Abstract

Bu çalışmada çok giriş-çok çıkış (ÇGÇÇ) özelliğine sahip buhar generatörünün su seviyesi denetimi için parçacık sürü optimizasyonu (PSO) algoritmasına dayanan farklı kontrol teknikleri önerilmektedir. Bu teknikler, klasik oransal-integraltürevsel (PID) denetim, bulanık mantık denetim (BMD) ve bulanık ayarlı oransal-integral denetimdir. PID denetleyicilerin kazançları ve BMD’nin parametreleri (giriş ve çıkıştaki üçgen üyelik fonksiyonların merkezleri ve sınırları) PSO tarafından en uygun hale getirilmektedir. Önerilen PSO temelli PID denetim (PSO-PID), PSO temelli bulanık mantık denetim (PSO-BMD) ve PSO temelli bulanık ayarlı PI denetim (PSO-BAPI) tekniklerinin gerçeklemesi, MATLAB kullanılarak sayısal benzetim ile doğrulanmaktadır. Benzetim sonuçları, buhar generatörünün su seviyesi denetim için PSO-PID tekniğini diğerlerine göre daha iyi performans sergilediğini göstermektedir

References

  • [1] Liu, X.J., Lara-Rosano, F., Chan, C.W. 2003. Neurofuzzy Network Modelling and Control of Steam Pressure in 300 MW Steam-Boiler System, Engineering Applications of Artificial Intelligence, Vol. 16, No. 5- 6, pp. 431-440. DOI: 10.1016/ j.enga ppai.2003.08.006
  • [2] Klauco, M., Kvasnica, M. 2017. Control of A Boiler-Turbine Unit Using MPC-based Reference Governors, Applied Thermal Engineering, Vol. 110, pp. 1437- 1447. DOI: 10.1016/j.applthermale ng.2016.09.041
  • [3] Liu, X.J., Chan, C.W. 2006. NeuroFuzzy Generalized Predictive Control of Boiler Steam Temperature, IEEE Transactions on Energy Conversion, Vol. 21, No. 4, pp. 900-908. DOI: 10.1109/TEC.2005. 853758
  • [4] Ku, C.C. 2015. Robust Controller Design for Nonlinear Uncertain Stochastic Drum-Boiler System. 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), 15-17 August, Zhangjiajie, China, 2378-2383.
  • [5] Niva, L., Yli-Korpela, A. 2012. Control of A Benchmark Boiler Process Model with DMC and QDMC, IFAC Proceedings, Vol. 45, No. 21, pp. 190-195. DOI: 10.3182/20120902-4-FR2032.00035
  • [6] Lawrynczuk, M. 2017. Nonlinear Predictive Control of A BoilerTurbine Unit: A State-Space Approach with Successive On-Line Model Linearisation and Quadratic Optimisation, ISA Transactions, Vol. 67, pp. 476-495. DOI: 10.1016/ j.isatra.2017.01.016
  • [7] Wei, L., Fang, F. 2017. H∞-LQRBased Coordinated Control for Large Coal-Fired Boiler-Turbine Generation Units. IEEE Transactions on Industrial Electronics, Vol. 64, No. 6, pp. 5212- 5221. DOI: 10.1109/TIE. 2016.2622233
  • [8] Moradi, H., Abbasi, M.H., Moradian, H. 2016. Improving The Performance of a Nonlinear BoilerTurbine Unit Via Bifurcation Control of External Disturbances: A Comparison Between Sliding Mode and Feedback Linearization Control Approaches, Nonlinear Dynamics, Vol. 85, No. 1, pp. 229-243. DOI: 10.1007/s11071-016-2680-x
  • [9] Zhang F., Wu, X., Shen, J. 2017. Extended State Observer Based Fuzzy Model Predictive Control for Ultra-Supercritical Boiler-Turbine Unit, Applied Thermal Engineering, Vol. 118, pp. 90-100. DOI: 10.1016/ j.applthermaleng.2017.01.111
  • [10] Alamoodi, N., Daoutidis, P. 2017. Nonlinear Control of Coal-Fired Steam Power Plants, Control Engineering Practice, Vol. 60, pp. 63-75. DOI: 0.1016/j.conengprac.2016. 12.005
  • [11] González, J. A., Barreiro, A., Dormido, S., Baños, A. 2017. Nonlinear Adaptive Sliding Mode Control With Fast Non-Overshooting Responses And Chattering Avoidance, Journal of the Franklin Institute, Vol. 354, No. 7, pp. 2788-2815. DOI: 10.1016/ j.jfranklin.2017.01.025
  • [12] Ait Sahed, O., Kara, K., Benyoucef, A., Hadjili, M.L. 2016. An Efficient Artificial Bee Colony Algorithm with Application to Nonlinear Predictive Control, International Journal of General Systems, Vol. 45, No. 4, pp. 393-417. DOI: 10.1080/03081079. 2015.1086344
  • [13] Jamali, B., Jazayeri-Rad, H. 2010. Application of Adaptive Local Linear Model Tree for Nonlinear Identification of Heat Recovery Steam Generator System Based on Experimental Data. 2010 Fourth UKSim European Symposium on Computer Modeling and Simulation, 17-19 November, Pisa, Italy, 16-20.
  • [14] Ozkop E., Sharaf A., Altas I.H. 2016. An Adaptive Fuzzy PI Controlled Bus Quantity Enhancer for Wave Energy Systems, Turkish Journal of Electrical Engineering and Computer Sciences, Vol. 24, pp. 2454-2468. DOI: 10.3906/elk-1312- 198
  • [15] Castro, P.A.D., Camargo, H.A. 2004. Learning and Optimization of Fuzzy Rule Base by Means of Self-Adaptive Genetic Algorithm, IEEE International Conference on Fuzzy Systems; 25-29 July, Budapest, Hungary. New York, NY, USA
  • [16] Li, H.X., Gatland, H.B. 1995. New Methodology for Designing A Fuzzy Logic Controller, IEEE T Syst Man Cy, Vol. 25, pp. 505-512
  • [17] Altas, I.H., Sharaf, A.M. 2007. A Generalized Direct Approach for Designing Fuzzy Logic Controllers in MATLAB/ Simulink GUI Environment, International Journal of Information Technology and Intelligent Computing, Vol. 1, No. 4, pp. 1-27
  • [18] Liu ,J., Ma, D., Ma, T.B, Zhang, W. 2017. Ecosystem Particle Swarm Optimization, Soft Computing, Vol. 21, No. 7, pp. 1667-1691. DOI: 10.1007/s00500-016-2111-4
  • [19] Yang, Q.H., Tian, J.P., Si, W. 2017. An Improved Particle Swarm Optimization Based on Difference Equation Analysis, Journal of Difference Equations and Applications, Vol. 23, No. 1-2, pp. 135-152. DOI: 10.1080/10236198. 2016.1199691
  • [20] Cui, H.Q., Shu, M.L., Song, M., Wang, Y.L. 2017. Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks Localization, Sensors, Vol. 17, No. 3, pp. 1-18. DOI: 10.3390/ s17030487
  • [21] Bouallègue, S., Haggège, J., Ayadi, M., Benrejeb, M. 2012. PID-Type Fuzzy Logic Controller Tuning Based on Particle Swarm Optimization, Engineering Applications of Artificial Intelligence, Vol. 25, No. 3, pp. 484-493. DOI: 10.1016/j.engappai.2011. 09.018
  • [22] Khare, A., Rangnekar, S. 2013. A Review of Particle Swarm Optimization and Its Applications in Solar Photovoltaic System, Applied Soft Computing, Vol. 13, No. 5, pp. 2997-3006. DOI: 10.1016/j.asoc. 2012.11.033
  • [23] Coban, R. 2011. A Fuzzy Controller Design for Nuclear Research Reactors Using The Particle Swarm Optimization Algorithm, Nuclear Engineering and Design, Vol. 241, No. 5, pp. 1899-1908. DOI: 10.1016/ j.nucengdes.2011.01.045
  • [24] Del Valle, Y., Venayagamoorthy, G.K., Mohagheghi, S., Hernandez, J.C., Harley, R.G. 2008. Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems, IEEE Transactions on Evolutionary Computation, Vol. 12, No. 2, pp. 171-195. DOI: 10.1109/ TEVC.2007.896686
There are 24 citations in total.

Details

Other ID JA74BH53SU
Journal Section Research Article
Authors

Emre Özkop This is me

İsmail Hakkı Altaş This is me

Publication Date September 1, 2017
Published in Issue Year 2017 Volume: 19 Issue: 57

Cite

APA Özkop, E., & Altaş, İ. H. (2017). BUHAR GENERATÖRÜNÜN SU SEVIYESI DENETIMI IÇIN PSO TEMELLI KLASIK VE AKILLI DENETLEYICILERIN PERFORMANSI. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen Ve Mühendislik Dergisi, 19(57), 835-844.
AMA Özkop E, Altaş İH. BUHAR GENERATÖRÜNÜN SU SEVIYESI DENETIMI IÇIN PSO TEMELLI KLASIK VE AKILLI DENETLEYICILERIN PERFORMANSI. DEUFMD. September 2017;19(57):835-844.
Chicago Özkop, Emre, and İsmail Hakkı Altaş. “BUHAR GENERATÖRÜNÜN SU SEVIYESI DENETIMI IÇIN PSO TEMELLI KLASIK VE AKILLI DENETLEYICILERIN PERFORMANSI”. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen Ve Mühendislik Dergisi 19, no. 57 (September 2017): 835-44.
EndNote Özkop E, Altaş İH (September 1, 2017) BUHAR GENERATÖRÜNÜN SU SEVIYESI DENETIMI IÇIN PSO TEMELLI KLASIK VE AKILLI DENETLEYICILERIN PERFORMANSI. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi 19 57 835–844.
IEEE E. Özkop and İ. H. Altaş, “BUHAR GENERATÖRÜNÜN SU SEVIYESI DENETIMI IÇIN PSO TEMELLI KLASIK VE AKILLI DENETLEYICILERIN PERFORMANSI”, DEUFMD, vol. 19, no. 57, pp. 835–844, 2017.
ISNAD Özkop, Emre - Altaş, İsmail Hakkı. “BUHAR GENERATÖRÜNÜN SU SEVIYESI DENETIMI IÇIN PSO TEMELLI KLASIK VE AKILLI DENETLEYICILERIN PERFORMANSI”. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi 19/57 (September 2017), 835-844.
JAMA Özkop E, Altaş İH. BUHAR GENERATÖRÜNÜN SU SEVIYESI DENETIMI IÇIN PSO TEMELLI KLASIK VE AKILLI DENETLEYICILERIN PERFORMANSI. DEUFMD. 2017;19:835–844.
MLA Özkop, Emre and İsmail Hakkı Altaş. “BUHAR GENERATÖRÜNÜN SU SEVIYESI DENETIMI IÇIN PSO TEMELLI KLASIK VE AKILLI DENETLEYICILERIN PERFORMANSI”. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen Ve Mühendislik Dergisi, vol. 19, no. 57, 2017, pp. 835-44.
Vancouver Özkop E, Altaş İH. BUHAR GENERATÖRÜNÜN SU SEVIYESI DENETIMI IÇIN PSO TEMELLI KLASIK VE AKILLI DENETLEYICILERIN PERFORMANSI. DEUFMD. 2017;19(57):835-44.

Dokuz Eylül Üniversitesi, Mühendislik Fakültesi Dekanlığı Tınaztepe Yerleşkesi, Adatepe Mah. Doğuş Cad. No: 207-I / 35390 Buca-İZMİR.