nach oben

Cluster Computing

Erschienen in:

23.02.2018

Performance analysis of artificial intelligent controllers in PEM fuel cell voltage tracking

verfasst von: R. Vinu, Varghese Paul

Erschienen in: Cluster Computing | Sonderheft 2/2019

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

The main criteria to be kept in mind while designing any application using fuel cell is the Voltage Control under sudden load variations. As a standard practice the output voltage of a fuel cell is controlled and maintained to the reference by introducing Intelligent Controllers. This paper shows the performance analysis of various intelligent controllers that can track the output voltage of fuel cell. In this paper, the state space model of Proton Exchange Membrane Fuel cell is considered for analyzing various controllers. Additionally the transient response of the fuel cell is analyzed and compared for the different controllers. The performance of the controllers is evaluated by estimating the time response characteristics of the system and also by calculating the system errors.

Vorheriger Artikel An intelligent reversible watermarking system for authenticating medical images using Wavelet and PSO

Nächster Artikel Optimized gradient histogram preservation with block wise SURE shrinkage for noise free image restoration

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

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!

Jetzt informieren

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!

Jetzt informieren

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!

Jetzt informieren

T-Raissi, A., Banerjee, A.: Current technology of fuel cell systems. In: Proceedings of the 32nd Intersociety on Energy Conversion Engineering Conference, IECEC-97, vol. 3, pp. 1953–1957 (1997)

Samrat Nandikesan, P., Mohanty, S., Shaneeth, M., Kamlakaran, K.P.: Control strategy for PEM fuel cell power plant. In: IEEE I International Conference on Power and Energy in NERIST (ICPEN), pp. 1–3. IEEE 978-1-4673-1669-9/12 (2012)

Mohamed Ali, E.A., Abhudhahir, A.: A survey of the relevance of control systems for PEM fuel cells. In: IEEE International Conference on Computer, Communication and Electrical Technology-ICCCET, pp. 322–326. ICCCET.978-1-4244-9394-4/11 (2011)

Karimi, M., Imanzadeh, M., Farhadi, P., Ghadimi, N.: Voltage control of PEMFC using a new controller based on reinforcement learning. Int. J. Inf. Electron. Eng. 2(5), 752–756 (2012). https://doi.org/10.7763/ijiee.2012.v2.200

Rezazadeh, A., Askarzadeh, A., Sedighizadeh, M.: Adaptive inverse control of proton exchange membrane fuel cell using RBF neural network. Int. J. Electrochem. Sci. 6, 3105–3117 (2011)

Mansour, A.M., Saad, N.H., Sattar, A.A.: Maximum power point tracking of ten parameter fuel cell model. J. Am. Sci. 8(8), 941–946 (2012)

Rezai, M., Mohseni, M.: A predictive control based on neural network for dynamic model of proton exchange membrane fuel cell. J. Fuel Cell Sci Technol 10(3), 035001 (2013)

Borujeni, M.S., Zarabadipour, H.: Fuel cell voltage control using neural network based on model predictive control. In: 2014 Iranian Conference on Intelligent Systems (ICIS) (2014). https://doi.org/10.1109/iraniancis.2014.6802609

Fan, L., Liu, Y.: Fuzzy logic based constant power control of a proton exchange membrane fuel cell. Przeglad Elektrotechniczny (Electrical Review), 72–75 (2012)

10.

Cao, J.-Y., Cao, B.-G.: Design of fractional order controller based on particle swarm optimization. Int. J. Control Autom. Syst. 4(6), 775–781 (2006)

11.

Chen, Y.Q., Petras, I., Xue, D.: Fractional order control-a tutorial. In: 2009 American Control Conference AACC, pp. 1397–1411 (2009). WeC02.1. 978-1-4244-4524-0/09

12.

Barbosa, R.S., Jesus, I.S.: Comparitive study of fuzzy integer and fractional PID controller. In: 39th Annual Conference of the IEEE Industrial Electronics Society, IECON 2013, pp. 3392–3397 (2013). https://doi.org/10.1109/iecon.2013.6699673

13.

Singhal, R., Padhee, S., Kaur, G.: Design of fractional order PID controller for speed control of DC motor. Int. J. Sci. Res. Publ. 2(6), 1–8 (2012)

14.

Zhang, Y., Li, J.: Fractional order PID controller tuning based on genetic algorithm. In: 2011 IEEE International Conference on Business Management and Electronic Information, pp. 764–767 (2011). https://doi.org/10.1109/icbmei.2011.5920371

15.

Bettayeb, M., Rachid, M.: IMC-PID fractional order filter controllers design for integer order systems. In: ISA Transactions Elsevier, pp. 1620–1628 (2014). http://dx.doi.org/10.1016/j.isatra.2014.05.007

16.

Farhadi, P., Sojoudi, T.: PEMFC voltage control using PSO-tuned PID controller. In: Proceedings of IEEE NW Russia Young Researches in Electrical and Electronics Engineering Conference, pp. 32–35 (2014). https://doi.org/10.1109/elconrusnw.2014.6839194

17.

Shamel, A., Ghadimi, N.: Hybrid PSTOTVAC/BFA technique for tuning of robust PID controller of fuel cell voltage. Indian J. Chem. Technol. 23, 171–178 (2016)

18.

Khoeiniha, M., Zarabadipuour, H.: Optimal control design for proton exchange membrane fuel cell via genetic algorithm. Int. J. Electrochem. Sci. 7, 6302–6312 (2012)

19.

Houshyar, A., Mohammadi, M., Rasouli Nezhad, R., Ghadimi, N.: Designing PID controller for fuel cell voltage using evolutionary programming algorithms. J. Basic Appl. Sci. Res. 2(2), 1981–1987 (2012)

20.

Sachin Puranik, V., Keyhani, Ali, Khorrami, Farshad: State space modelling of proton exchange membrane fuel cell. IEEE Trans. Energy Convers. 25(3), 804–813 (2010). https://doi.org/10.1109/TEC.2010.2047725

21.

Bilbao, M.N., Del Ser, J., Geem, Z.W., Gil-Lopez, S., Landa-Torres, I., Manjarres, D., Salcedo-Sanz, S.: A Survey on the Applications of the harmony search algorithm. Eng. Appl. Artif. Intell. 26(8), 1818–1831 (2013). https://doi.org/10.1016/j.engappai.2013.05.008

22.

Kassim, N., Sulaiman, S.I., Musirin, I.: Harmony search based optimization of artificial neural network for predicting AC power from a photovoltaic system. In: IEEE 8th International Power Engineering and Optimization Conference (PEOCO2014). https://doi.org/10.1109/peoco.2014.6814481

23.

Askarzadeh, A., Rezazadeh, A.: An innovative global harmony search algorithm for parameter identification of a PEM fuel cell model. IEEE Trans. Ind. Electron. 59(9), 3473–3480 (2011). https://doi.org/10.1109/tie.2011.2172173

24.

Vinu, R., Paul, V.: Robust optimized artificial neural network based PEM fuelcell voltage tracking. Adv. Intell. Syst. Comput. 424, 79–91 (2015). https://doi.org/10.1007/978-3-319-28031-8

25.

Vinu, R., Paul, V.: Harmony search optimized fractional order PID controller for voltage control of fuel cell. Asian J. Res. Soc. Sci. Hum. 6(10), 815–829 (2016). https://doi.org/10.5958/2249-7315.2016.01055.8

Titel: Performance analysis of artificial intelligent controllers in PEM fuel cell voltage tracking
verfasst von: R. Vinu
Varghese Paul
Publikationsdatum: 23.02.2018
Verlag: Springer US
Erschienen in: Cluster Computing / Ausgabe Sonderheft 2/2019
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI: https://doi.org/10.1007/s10586-018-1992-7

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Sonderheft 2/2019

Grey three index prediction method based on data assimilation preprocessing

Design and development of sports training system based on image processing technology

Intelligent Bézier curve-based path planning model using Chaotic Particle Swarm Optimization algorithm

A smart power saving protocol for IoT with wireless energy harvesting technique

Power allocation algorithm based on mixed integer nonlinear programming in WSN

Distributed collaborative filtering recommendation algorithm based on DHT