Skip to main content
Top
Published in: Neural Computing and Applications 9/2019

07-01-2019 | Original Article

Design of neural network predictive controller based on imperialist competitive algorithm for automatic voltage regulator

Author: M. Elsisi

Published in: Neural Computing and Applications | Issue 9/2019

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

This paper proposes the neural network (NN) predictive controller that combines the advantages of NN and predictive control for the automatic voltage regulator (AVR). The NN predictive controller is suggested as a new intelligence controller rather than the conventional controllers for the AVR. This is the first application of the NN predictive controller for AVR. There are five parameters of the NN predictive controller which need a proper tuning to get a good performance by using the NN predictive controller. In recent papers, the parameters of NN predictive controller are typically set by trial and error or by the designer’s expertise. The imperialist competitive algorithm (ICA) is introduced in this paper as a new artificial intelligence technique instead of the trial-and-error or the designer’s expertise methods to get the optimal parameters of NN predictive controller in order to overcome the deviations of the voltage. The performance of the designed NN predictive controller based on the ICA is compared with the designed NN predictive controller based on the genetic algorithm and the conventional proportional–integral–derivative controller based on Ziegler–Nichols technique. The comparison emphasizes the superiority of the suggested NN predictive controller based on the ICA.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Appendix
Available only for authorised users
Literature
1.
go back to reference Kundur P (1994) Power system stability and control. McGraw-Hill, New York Kundur P (1994) Power system stability and control. McGraw-Hill, New York
2.
go back to reference Saadat H (2002) Power system analysis. Tata Mcgraw-Hill, New Delhi Saadat H (2002) Power system analysis. Tata Mcgraw-Hill, New Delhi
3.
go back to reference Chatterjee S, Mukherjee V (2016) PID controller for automatic voltage regulator using teaching–learning based optimization technique. Int J Electr Power Energy Syst 77:418–429CrossRef Chatterjee S, Mukherjee V (2016) PID controller for automatic voltage regulator using teaching–learning based optimization technique. Int J Electr Power Energy Syst 77:418–429CrossRef
4.
go back to reference Hasanien HM (2013) Design optimization of PID controller in automatic voltage regulator system using Taguchi combined genetic algorithm method. IEEE Syst J 7(4):825–831CrossRef Hasanien HM (2013) Design optimization of PID controller in automatic voltage regulator system using Taguchi combined genetic algorithm method. IEEE Syst J 7(4):825–831CrossRef
5.
go back to reference Devaraj D, Selvabala B (2009) Real-coded genetic algorithm and fuzzy logic approach for real-time tuning of proportional–integral–derivative controller in automatic voltage regulator system. IET Gener Transm Distrib 3(7):641–649CrossRef Devaraj D, Selvabala B (2009) Real-coded genetic algorithm and fuzzy logic approach for real-time tuning of proportional–integral–derivative controller in automatic voltage regulator system. IET Gener Transm Distrib 3(7):641–649CrossRef
6.
go back to reference Kansit S, Assawinchaichote W (2016) Optimization of PID controller based on PSOGSA for an automatic voltage regulator system. Procedia Comput Sci 86:87–90CrossRef Kansit S, Assawinchaichote W (2016) Optimization of PID controller based on PSOGSA for an automatic voltage regulator system. Procedia Comput Sci 86:87–90CrossRef
7.
go back to reference Gaing ZL (2004) A particle swarm optimization approach for optimum design of PID controller in AVR system. IEEE Trans Energy Convers 19(2):384–391CrossRef Gaing ZL (2004) A particle swarm optimization approach for optimum design of PID controller in AVR system. IEEE Trans Energy Convers 19(2):384–391CrossRef
8.
go back to reference Panda S, Sahu BK, Mohanty PK (2012) Design and performance analysis of PID controller for an automatic voltage regulator system using simplified particle swarm optimization. J Frankl Inst 349(8):2609–2625MathSciNetCrossRef Panda S, Sahu BK, Mohanty PK (2012) Design and performance analysis of PID controller for an automatic voltage regulator system using simplified particle swarm optimization. J Frankl Inst 349(8):2609–2625MathSciNetCrossRef
9.
go back to reference Chatterjee A, Mukherjee V, Ghoshal SP (2009) Velocity relaxed and craziness-based swarm optimized intelligent PID and PSS controlled AVR system. Int J Electr Power Energy Syst 31(7):323–333CrossRef Chatterjee A, Mukherjee V, Ghoshal SP (2009) Velocity relaxed and craziness-based swarm optimized intelligent PID and PSS controlled AVR system. Int J Electr Power Energy Syst 31(7):323–333CrossRef
10.
go back to reference Kim DH, Cho JH (2006) A biologically inspired intelligent PID controller tuning for AVR systems. Int J Control Autom Syst 4(5):624–636 Kim DH, Cho JH (2006) A biologically inspired intelligent PID controller tuning for AVR systems. Int J Control Autom Syst 4(5):624–636
11.
go back to reference Dos Santos Coelho L (2009) Tuning of PID controller for an automatic regulator voltage system using chaotic optimization approach. Chaos, Solitons Fractals 39(4):1504–1514CrossRef Dos Santos Coelho L (2009) Tuning of PID controller for an automatic regulator voltage system using chaotic optimization approach. Chaos, Solitons Fractals 39(4):1504–1514CrossRef
12.
go back to reference Aguila-Camacho N, Duarte-Mermoud MA (2013) Fractional adaptive control for an automatic voltage regulator. ISA Trans 52(6):807–815CrossRef Aguila-Camacho N, Duarte-Mermoud MA (2013) Fractional adaptive control for an automatic voltage regulator. ISA Trans 52(6):807–815CrossRef
13.
go back to reference Prasad LB, Gupta HO, Tyagi B (2014) Application of policy iteration technique based adaptive optimal control design for automatic voltage regulator of power system. Int J Electr Power Energy Syst 63:940–949CrossRef Prasad LB, Gupta HO, Tyagi B (2014) Application of policy iteration technique based adaptive optimal control design for automatic voltage regulator of power system. Int J Electr Power Energy Syst 63:940–949CrossRef
14.
go back to reference Zhang H, Shi F, Liu Y (2014) Enhancing optimal excitation control by adaptive fuzzy logic rules. Int J Electr Power Energy Syst 63:226–235CrossRef Zhang H, Shi F, Liu Y (2014) Enhancing optimal excitation control by adaptive fuzzy logic rules. Int J Electr Power Energy Syst 63:226–235CrossRef
15.
go back to reference Hasan AR, Martis TS, Ula AS (1994) Design and implementation of a fuzzy controller based automatic voltage regulator for a synchronous generator. IEEE Trans Energy Convers 9(3):550–557CrossRef Hasan AR, Martis TS, Ula AS (1994) Design and implementation of a fuzzy controller based automatic voltage regulator for a synchronous generator. IEEE Trans Energy Convers 9(3):550–557CrossRef
16.
go back to reference Li H, Li F, Xu Y, Rizy DT, Kueck JD (2010) Adaptive voltage control with distributed energy resources: algorithm, theoretical analysis, simulation, and field test verification. IEEE Trans Power Syst 25(3):1638–1647CrossRef Li H, Li F, Xu Y, Rizy DT, Kueck JD (2010) Adaptive voltage control with distributed energy resources: algorithm, theoretical analysis, simulation, and field test verification. IEEE Trans Power Syst 25(3):1638–1647CrossRef
17.
go back to reference Mao C, Malik OP, Hope GS, Fan J (1990) An adaptive generator excitation controller based on linear optimal control. IEEE Trans Energy Convers 5(4):673–678CrossRef Mao C, Malik OP, Hope GS, Fan J (1990) An adaptive generator excitation controller based on linear optimal control. IEEE Trans Energy Convers 5(4):673–678CrossRef
18.
go back to reference Camacho E, Bordons C (2004) Model predictive control. Springer, BerlinMATH Camacho E, Bordons C (2004) Model predictive control. Springer, BerlinMATH
19.
go back to reference Farina M, Guagliardi A, Mariani F, Sandroni C, Scattolini R (2015) Model predictive control of voltage profiles in MV networks with distributed generation. Control Eng Pract 34:18–29CrossRef Farina M, Guagliardi A, Mariani F, Sandroni C, Scattolini R (2015) Model predictive control of voltage profiles in MV networks with distributed generation. Control Eng Pract 34:18–29CrossRef
20.
go back to reference Amraee T, Ranjbar AM, Feuillet R (2011) Adaptive under-voltage load shedding scheme using model predictive control. Electr Power Syst Res 81(7):1507–1513CrossRef Amraee T, Ranjbar AM, Feuillet R (2011) Adaptive under-voltage load shedding scheme using model predictive control. Electr Power Syst Res 81(7):1507–1513CrossRef
21.
go back to reference Kassem AM, Yousef AM (2013) Voltage and frequency control of an autonomous hybrid generation system based on linear model predictive control. Sustain Energy Technol Assess 4:52–61 Kassem AM, Yousef AM (2013) Voltage and frequency control of an autonomous hybrid generation system based on linear model predictive control. Sustain Energy Technol Assess 4:52–61
22.
go back to reference Hassan LH, Moghavvemi M, Almurib HA, Steinmayer O (2013) Current state of neural networks applications in power system monitoring and control. Int J Electr Power Energy Syst 51:134–144CrossRef Hassan LH, Moghavvemi M, Almurib HA, Steinmayer O (2013) Current state of neural networks applications in power system monitoring and control. Int J Electr Power Energy Syst 51:134–144CrossRef
23.
go back to reference Kassem AM (2010) Neural predictive controller of a two-area load frequency control for interconnected power system. Ain Shams Eng J 1(1):49–58CrossRef Kassem AM (2010) Neural predictive controller of a two-area load frequency control for interconnected power system. Ain Shams Eng J 1(1):49–58CrossRef
24.
go back to reference Lachman T, Mohamad TR (2009) Neural network excitation control system for transient stability analysis of power system. In: TENCON 2009-2009 IEEE Region 10 Conference. IEEE, pp 1–6 Lachman T, Mohamad TR (2009) Neural network excitation control system for transient stability analysis of power system. In: TENCON 2009-2009 IEEE Region 10 Conference. IEEE, pp 1–6
25.
go back to reference Bahmanyar AR, Karami A (2014) Power system voltage stability monitoring using artificial neural networks with a reduced set of inputs. Int J Electr Power Energy Syst 58:246–256CrossRef Bahmanyar AR, Karami A (2014) Power system voltage stability monitoring using artificial neural networks with a reduced set of inputs. Int J Electr Power Energy Syst 58:246–256CrossRef
26.
go back to reference Suykens JAK (1996) Artificial neural networks for modelling and control of non-linear systems, 1st edn. Kluwer Academic Publishers, BostonCrossRef Suykens JAK (1996) Artificial neural networks for modelling and control of non-linear systems, 1st edn. Kluwer Academic Publishers, BostonCrossRef
27.
go back to reference Fausett L (1994) Fundamentals of neural networks, architectures, algorithms and applications, 2nd edn. Prentice Hall, Englewood CliffsMATH Fausett L (1994) Fundamentals of neural networks, architectures, algorithms and applications, 2nd edn. Prentice Hall, Englewood CliffsMATH
28.
go back to reference Yan G, Li C (2011) An effective refinement artificial bee colony optimization algorithm based on chaotic search and application for PID control tuning. J Comput Inf Syst 7(9):3309–3316 Yan G, Li C (2011) An effective refinement artificial bee colony optimization algorithm based on chaotic search and application for PID control tuning. J Comput Inf Syst 7(9):3309–3316
29.
go back to reference Abachizadeh M, Yazdi MRH, Yousefi-Koma A (2010) Optimal tuning of PID controllers using artificial bee colony algorithm. In: 2010 IEEE/ASME international conference on advanced intelligent mechatronics (AIM), pp 379–384 Abachizadeh M, Yazdi MRH, Yousefi-Koma A (2010) Optimal tuning of PID controllers using artificial bee colony algorithm. In: 2010 IEEE/ASME international conference on advanced intelligent mechatronics (AIM), pp 379–384
30.
go back to reference Kumar SR, Ganapathy S (2014) Artificial cooperative search algorithm based load frequency control of deregulated power system with SMES unit. J Theor Appl Inf Technol 63(1):20–29 Kumar SR, Ganapathy S (2014) Artificial cooperative search algorithm based load frequency control of deregulated power system with SMES unit. J Theor Appl Inf Technol 63(1):20–29
31.
go back to reference Walters DC, Sheble GB (1993) Genetic algorithm solution of economic dispatch with valve point loading. IEEE Trans Power Syst 8(3):1325–1332CrossRef Walters DC, Sheble GB (1993) Genetic algorithm solution of economic dispatch with valve point loading. IEEE Trans Power Syst 8(3):1325–1332CrossRef
32.
go back to reference Elsisi M, Soliman M, Aboelela MAS, Mansour W (2016) Bat inspired algorithm based optimal design of model predictive load frequency control. Int J Electr Power Energy Syst 83:426–433CrossRef Elsisi M, Soliman M, Aboelela MAS, Mansour W (2016) Bat inspired algorithm based optimal design of model predictive load frequency control. Int J Electr Power Energy Syst 83:426–433CrossRef
33.
go back to reference Ghoshal SP (2004) Optimizations of PID gains by particle swarm optimizations in fuzzy based automatic generation control. Electr Power Syst Res 72(3):203–212CrossRef Ghoshal SP (2004) Optimizations of PID gains by particle swarm optimizations in fuzzy based automatic generation control. Electr Power Syst Res 72(3):203–212CrossRef
34.
go back to reference Elsisi M, Soliman M, Aboelela MAS, Mansour W (2017) Model predictive control of plug-in hybrid electric vehicles for frequency regulation in a smart grid. IET Gener Transm Distrib 11(16):3974–3983CrossRef Elsisi M, Soliman M, Aboelela MAS, Mansour W (2017) Model predictive control of plug-in hybrid electric vehicles for frequency regulation in a smart grid. IET Gener Transm Distrib 11(16):3974–3983CrossRef
35.
go back to reference Ardalan Z, Karimi S, Poursabzi O, Naderi B (2015) A novel imperialist competitive algorithm for generalized traveling salesman problems. Appl Soft Comput 26:546–555CrossRef Ardalan Z, Karimi S, Poursabzi O, Naderi B (2015) A novel imperialist competitive algorithm for generalized traveling salesman problems. Appl Soft Comput 26:546–555CrossRef
36.
go back to reference Ogata K (2001) Modern control engineering. Prenctice Hall, Upper Saddle RiverMATH Ogata K (2001) Modern control engineering. Prenctice Hall, Upper Saddle RiverMATH
37.
go back to reference Kumar A, Kumar A, Chanana S (2010) Genetic fuzzy PID controller based on adaptive gain scheduling for load frequency control. In: 2010 Joint international conference on power electronics, drives and energy systems (PEDES) & 2010 power India. IEEE, pp 1–8 Kumar A, Kumar A, Chanana S (2010) Genetic fuzzy PID controller based on adaptive gain scheduling for load frequency control. In: 2010 Joint international conference on power electronics, drives and energy systems (PEDES) & 2010 power India. IEEE, pp 1–8
38.
go back to reference Selvakumaran S, Rajasekaran V, Karthigaivel R (2014) Genetic algorithm tuned IP controller for Load Frequency Control of interconnected power systems with HVDC links. Arch Electr Eng 63(2):161–175CrossRef Selvakumaran S, Rajasekaran V, Karthigaivel R (2014) Genetic algorithm tuned IP controller for Load Frequency Control of interconnected power systems with HVDC links. Arch Electr Eng 63(2):161–175CrossRef
39.
go back to reference Dwivedi A, Ray G, Sharma AK (2016) Genetic algorithm based decentralized PI type controller: load frequency control. J Inst Eng (India) Ser B 97(4):509–515CrossRef Dwivedi A, Ray G, Sharma AK (2016) Genetic algorithm based decentralized PI type controller: load frequency control. J Inst Eng (India) Ser B 97(4):509–515CrossRef
40.
go back to reference Soheilirad M, Karami K, Othman ML, Farzan P (2013) PID controller adjustment for MA-LFC by using a hybrid Genetic-Tabu Search Algorithm. In: System engineering and technology (ICSET), 2013 IEEE 3rd international conference on IEEE 2013, pp 197–202 Soheilirad M, Karami K, Othman ML, Farzan P (2013) PID controller adjustment for MA-LFC by using a hybrid Genetic-Tabu Search Algorithm. In: System engineering and technology (ICSET), 2013 IEEE 3rd international conference on IEEE 2013, pp 197–202
41.
go back to reference Mahto T, Mukherjee V (2015) Frequency stabilisation of a hybrid two-area power system by a novel quasi-oppositional harmony search algorithm. Proc IET Gener Transm Distrib 9(15):2167–2179CrossRef Mahto T, Mukherjee V (2015) Frequency stabilisation of a hybrid two-area power system by a novel quasi-oppositional harmony search algorithm. Proc IET Gener Transm Distrib 9(15):2167–2179CrossRef
42.
go back to reference Ghoshal SP, Roy R (2008) Evolutionary computation based comparative study of TCPS and CES control applied to automatic generation control. In: Power system technology and IEEE power India conference, 2008. POWERCON 2008 Ghoshal SP, Roy R (2008) Evolutionary computation based comparative study of TCPS and CES control applied to automatic generation control. In: Power system technology and IEEE power India conference, 2008. POWERCON 2008
43.
go back to reference Atashpaz-Gargari E, Lucas C (2007) Designing an optimal PID controller using Imperialist Competitive Algorithm. In: First joint congress on fuzzy and intelligent systems. Ferdowsi University of Mashhad, pp 29–31 Atashpaz-Gargari E, Lucas C (2007) Designing an optimal PID controller using Imperialist Competitive Algorithm. In: First joint congress on fuzzy and intelligent systems. Ferdowsi University of Mashhad, pp 29–31
44.
go back to reference Atashpaz-Gargari, E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: IEEE congress on evolutionary computation, 2007. CEC 2007, pp 4661–4667 Atashpaz-Gargari, E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: IEEE congress on evolutionary computation, 2007. CEC 2007, pp 4661–4667
Metadata
Title
Design of neural network predictive controller based on imperialist competitive algorithm for automatic voltage regulator
Author
M. Elsisi
Publication date
07-01-2019
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 9/2019
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-018-03995-9

Other articles of this Issue 9/2019

Neural Computing and Applications 9/2019 Go to the issue

S.I. : Emergence in Human-like Intelligence towards Cyber-Physical Systems

A new method of online extreme learning machine based on hybrid kernel function

Premium Partner