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A REVIEW ON SYSTEM IDENTIFICATION IN POWER GENERATION SYSTEMS

Yıl 2018, Cilt: 60 Sayı: 2, 147 - 162, 01.08.2018

Öz

Power
generation systems with multiple input-multiple output have a wide operating
range and may not be fully defined by a fixed model due to high-order nonlinear
dynamics. As the parameters of the conventional excitation and speed governor
controllers are determined by the system model which is linearized around one
operating point, the performances of the controllers at different operating
points can be reduced. Large disturbances encountered in the system can cause
the controllers to operate outside the linear region. In addition, when the
plant's operating structure changes with time or with changing environmental
conditions, it is necessary to readjust the controller parameters. This
readjustment is needed because the controller parameters that are set to
provide the best performance at one operating point may not provide the same
performance when the operating points change. In order to avoid the degradation
in controller performance, system identification can be performed so that the
controller parameters will have an adaptive structure. At the same time, it
will be possible to make predictive maintenance, determine optimum operating
points, diagnose faults and estimate performance by means of the power plant
model built on the basis of system identification. In order to meet these
requirements, system identification methods used in power generation systems
have been examined throughout this review study and the performances obtained
as a result of the changes made in the controllers have been compared.



 

Kaynakça

  • G.A.H. Munoz, S. P. Mansoor and D.I. Jones, 2013. Modelling and Controlling Hydropower Plants. Springer, 286, New York.
  • L. Ljung, 1987. System Identification: Theory for the User. Prentice Hall, New Jersey.
  • W. Sun et al., Parameter Identification Method for Turbine Speed Governor System Based on Particle Swarm Optimization, Applied Mechanics and Materials, 448/453 (2014) 2511-2515.
  • B. Zaker, G.B. Gharehpetian, M. Karrari and N. Moaddabi, Simultaneous Parameter Identification of Synchronous Generator and Excitation System Using Online Measurements, IEEE Transactions on Smart Grid, 7/3 (2016) 1230-1238.
  • S. Simani, S. Alvisi and M. Venturini, Study of the Time Response of a Simulated Hydroelectric System, Journal of Physics: Conference Series, 570/5 (2014).
  • C. Li, J. Zhou, Parameters identification of hydraulic turbine governing system using improved gravitational search algorithm, Energy Conversion and Management, 52/1 (2011) 374-381.
  • Z. Chen, X. Yuan, Y. Yuan, H.H.C. Iu and T. Fernando, Parameter Identification of Integrated Model of Hydraulic Turbine Regulating System With Uncertainties Using Three Different Approaches, IEEE Transactions on Power Systems, 32/5 (2017) 3482-3491.
  • Z. Chen, X. Yuan, H. Tian, B. Ji, Improved gravitational search algorithm for parameter identification of water turbine regulation system, Energy Conversion and Management, 78 (2014) 306-315.
  • G.K. Venayagamoorthy and R.G. Harley, A continually online trained neurocontroller for excitation and turbine control of a turbogenerator, IEEE Transactions on Energy Conversion, 16/3 (2001) 261-269.
  • M. Negnevitsky and V. Pavlovsky, Neural networks approach to online identification of multiple failures of protection systems, IEEE Transactions on Power Delivery, 20/2 (2005) 588-594.
  • J.W. Park, G.K. Venayagamoorthy and R.G. Harley, MLP/RBF neural-networks-based online global model identification of synchronous generator, IEEE Transactions on Industrial Electronics, 52/6 (2005) 1685-1695.
  • N. Kishor, S.P. Singh, Simulated response of NN based identification and predictive control of hydro plant, Expert Systems with Applications, 32/1 (2007) 233-244.
  • N. Kishor, S.P. Singh and A.S. Raghuvanshi, Adaptive intelligent hydro turbine speed identification with water and random load disturbances, Engineering Applications of Artificial Intelligence, 20/6 (2007) 795-808.
  • W.A. Albukhanajer, H.A. Lefta and A.A. Ali, Effective identification of a turbogenerator in a SMIB power system using Fuzzy Neural Networks, 2014 International Joint Conference on Neural Networks (IJCNN), Beijing, (2014) 2804-2811.
  • T. Abdelazim, O.P. Malik, Identification of nonlinear systems by Takagi–Sugeno fuzzy logic grey box modeling for real-time control, Control Engineering Practice, 13/12 (2005) 1489-1498.
  • M. Rasouli and M. Karrari, Nonlinear identification of a brushless excitation system via field tests, IEEE Transactions on Energy Conversion, 19/4 (2004) 733-740.
  • M. Mori, Y. Kagami, S. Kanemoto, T. Tamaoki, M. Enomoto and S. Kawamura, New proposal of reactivity coefficient estimation method using a gray-box model in nuclear power plants, Progress in Nuclear Energy, 46/3-4 (2005) 241-252.
  • H. B. Karayaka, A. Keyhani, G. T. Heydt, B. L. Agrawal and D. A. Selin, Synchronous generator model identification and parameter estimation from operating data, IEEE Transactions on Energy Conversion, 18/1 (2003) 121-126.
  • L. Saarinen, P. Norrlund and U. Lundin, Field Measurements and System Identification of Three Frequency Controlling Hydropower Plants, IEEE Transactions on Energy Conversion, 30/3 (2015) 1061-1068.
  • M.R.B. Tavakoli, M. Power, L. Ruttledge and D. Flynn, Load Inertia Estimation Using White and Grey-Box Estimators for Power Systems with High Wind Penetration, IFAC Proceedings Volumes, 45/21 (2012) 399-404.
  • J. Zhang and H. Xu, Online Identification of Power System Equivalent Inertia Constant, IEEE Transactions on Industrial Electronics, 64/10 (2017) 8098-8107.
  • D.T. Duong, K. Uhlen and E. A. Jansson, Estimation of hydro turbine-governor system's transfer function from PMU measurements, 2016 IEEE Power and Energy Society General Meeting (PESGM), Boston, MA, (2016) 1-5.
  • N. C. Pahalawatha, G. S. Hope, O. P. Malik and K. Wong, Real time implementation of a MIMO adaptive power system stabiliser, IEE Proceedings C - Generation, Transmission and Distribution, 137/3 (1990) 186-194.
  • S. Ichikawa, M. Tomita, S. Doki and S. Okuma, Sensorless control of permanent-magnet synchronous motors using online parameter identification based on system identification theory, IEEE Transactions on Industrial Electronics, 53/2 (2006) 363-372.
Yıl 2018, Cilt: 60 Sayı: 2, 147 - 162, 01.08.2018

Öz

Kaynakça

  • G.A.H. Munoz, S. P. Mansoor and D.I. Jones, 2013. Modelling and Controlling Hydropower Plants. Springer, 286, New York.
  • L. Ljung, 1987. System Identification: Theory for the User. Prentice Hall, New Jersey.
  • W. Sun et al., Parameter Identification Method for Turbine Speed Governor System Based on Particle Swarm Optimization, Applied Mechanics and Materials, 448/453 (2014) 2511-2515.
  • B. Zaker, G.B. Gharehpetian, M. Karrari and N. Moaddabi, Simultaneous Parameter Identification of Synchronous Generator and Excitation System Using Online Measurements, IEEE Transactions on Smart Grid, 7/3 (2016) 1230-1238.
  • S. Simani, S. Alvisi and M. Venturini, Study of the Time Response of a Simulated Hydroelectric System, Journal of Physics: Conference Series, 570/5 (2014).
  • C. Li, J. Zhou, Parameters identification of hydraulic turbine governing system using improved gravitational search algorithm, Energy Conversion and Management, 52/1 (2011) 374-381.
  • Z. Chen, X. Yuan, Y. Yuan, H.H.C. Iu and T. Fernando, Parameter Identification of Integrated Model of Hydraulic Turbine Regulating System With Uncertainties Using Three Different Approaches, IEEE Transactions on Power Systems, 32/5 (2017) 3482-3491.
  • Z. Chen, X. Yuan, H. Tian, B. Ji, Improved gravitational search algorithm for parameter identification of water turbine regulation system, Energy Conversion and Management, 78 (2014) 306-315.
  • G.K. Venayagamoorthy and R.G. Harley, A continually online trained neurocontroller for excitation and turbine control of a turbogenerator, IEEE Transactions on Energy Conversion, 16/3 (2001) 261-269.
  • M. Negnevitsky and V. Pavlovsky, Neural networks approach to online identification of multiple failures of protection systems, IEEE Transactions on Power Delivery, 20/2 (2005) 588-594.
  • J.W. Park, G.K. Venayagamoorthy and R.G. Harley, MLP/RBF neural-networks-based online global model identification of synchronous generator, IEEE Transactions on Industrial Electronics, 52/6 (2005) 1685-1695.
  • N. Kishor, S.P. Singh, Simulated response of NN based identification and predictive control of hydro plant, Expert Systems with Applications, 32/1 (2007) 233-244.
  • N. Kishor, S.P. Singh and A.S. Raghuvanshi, Adaptive intelligent hydro turbine speed identification with water and random load disturbances, Engineering Applications of Artificial Intelligence, 20/6 (2007) 795-808.
  • W.A. Albukhanajer, H.A. Lefta and A.A. Ali, Effective identification of a turbogenerator in a SMIB power system using Fuzzy Neural Networks, 2014 International Joint Conference on Neural Networks (IJCNN), Beijing, (2014) 2804-2811.
  • T. Abdelazim, O.P. Malik, Identification of nonlinear systems by Takagi–Sugeno fuzzy logic grey box modeling for real-time control, Control Engineering Practice, 13/12 (2005) 1489-1498.
  • M. Rasouli and M. Karrari, Nonlinear identification of a brushless excitation system via field tests, IEEE Transactions on Energy Conversion, 19/4 (2004) 733-740.
  • M. Mori, Y. Kagami, S. Kanemoto, T. Tamaoki, M. Enomoto and S. Kawamura, New proposal of reactivity coefficient estimation method using a gray-box model in nuclear power plants, Progress in Nuclear Energy, 46/3-4 (2005) 241-252.
  • H. B. Karayaka, A. Keyhani, G. T. Heydt, B. L. Agrawal and D. A. Selin, Synchronous generator model identification and parameter estimation from operating data, IEEE Transactions on Energy Conversion, 18/1 (2003) 121-126.
  • L. Saarinen, P. Norrlund and U. Lundin, Field Measurements and System Identification of Three Frequency Controlling Hydropower Plants, IEEE Transactions on Energy Conversion, 30/3 (2015) 1061-1068.
  • M.R.B. Tavakoli, M. Power, L. Ruttledge and D. Flynn, Load Inertia Estimation Using White and Grey-Box Estimators for Power Systems with High Wind Penetration, IFAC Proceedings Volumes, 45/21 (2012) 399-404.
  • J. Zhang and H. Xu, Online Identification of Power System Equivalent Inertia Constant, IEEE Transactions on Industrial Electronics, 64/10 (2017) 8098-8107.
  • D.T. Duong, K. Uhlen and E. A. Jansson, Estimation of hydro turbine-governor system's transfer function from PMU measurements, 2016 IEEE Power and Energy Society General Meeting (PESGM), Boston, MA, (2016) 1-5.
  • N. C. Pahalawatha, G. S. Hope, O. P. Malik and K. Wong, Real time implementation of a MIMO adaptive power system stabiliser, IEE Proceedings C - Generation, Transmission and Distribution, 137/3 (1990) 186-194.
  • S. Ichikawa, M. Tomita, S. Doki and S. Okuma, Sensorless control of permanent-magnet synchronous motors using online parameter identification based on system identification theory, IEEE Transactions on Industrial Electronics, 53/2 (2006) 363-372.
Toplam 24 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Review Articles
Yazarlar

Derya Ozkaya Bu kişi benim 0000-0003-3331-4826

İlhan Kosalay Bu kişi benim 0000-0001-9231-416X

Yayımlanma Tarihi 1 Ağustos 2018
Gönderilme Tarihi 23 Mayıs 2017
Kabul Tarihi 3 Aralık 2018
Yayımlandığı Sayı Yıl 2018 Cilt: 60 Sayı: 2

Kaynak Göster

APA Ozkaya, D., & Kosalay, İ. (2018). A REVIEW ON SYSTEM IDENTIFICATION IN POWER GENERATION SYSTEMS. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering, 60(2), 147-162.
AMA Ozkaya D, Kosalay İ. A REVIEW ON SYSTEM IDENTIFICATION IN POWER GENERATION SYSTEMS. Commun.Fac.Sci.Univ.Ank.Series A2-A3: Phys.Sci. and Eng. Ağustos 2018;60(2):147-162.
Chicago Ozkaya, Derya, ve İlhan Kosalay. “A REVIEW ON SYSTEM IDENTIFICATION IN POWER GENERATION SYSTEMS”. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering 60, sy. 2 (Ağustos 2018): 147-62.
EndNote Ozkaya D, Kosalay İ (01 Ağustos 2018) A REVIEW ON SYSTEM IDENTIFICATION IN POWER GENERATION SYSTEMS. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering 60 2 147–162.
IEEE D. Ozkaya ve İ. Kosalay, “A REVIEW ON SYSTEM IDENTIFICATION IN POWER GENERATION SYSTEMS”, Commun.Fac.Sci.Univ.Ank.Series A2-A3: Phys.Sci. and Eng., c. 60, sy. 2, ss. 147–162, 2018.
ISNAD Ozkaya, Derya - Kosalay, İlhan. “A REVIEW ON SYSTEM IDENTIFICATION IN POWER GENERATION SYSTEMS”. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering 60/2 (Ağustos 2018), 147-162.
JAMA Ozkaya D, Kosalay İ. A REVIEW ON SYSTEM IDENTIFICATION IN POWER GENERATION SYSTEMS. Commun.Fac.Sci.Univ.Ank.Series A2-A3: Phys.Sci. and Eng. 2018;60:147–162.
MLA Ozkaya, Derya ve İlhan Kosalay. “A REVIEW ON SYSTEM IDENTIFICATION IN POWER GENERATION SYSTEMS”. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering, c. 60, sy. 2, 2018, ss. 147-62.
Vancouver Ozkaya D, Kosalay İ. A REVIEW ON SYSTEM IDENTIFICATION IN POWER GENERATION SYSTEMS. Commun.Fac.Sci.Univ.Ank.Series A2-A3: Phys.Sci. and Eng. 2018;60(2):147-62.

Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering

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