nach oben

Erschienen in:

2015 | OriginalPaper | Buchkapitel

Review and Improvement of Several Optimal Intelligent Pitch Controllers and Estimator of WECS via Artificial Intelligent Approaches

verfasst von : Hadi Kasiri, Hamid Reza Momeni, Mohammad Saniee Abadeh

Erschienen in: Complex System Modelling and Control Through Intelligent Soft Computations

Verlag: Springer International Publishing

Einloggen

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

search-config

KI-gestützte Suche

Aus

Abstract

Wind turbines in megawatt classification ordinarily rotate at variable speed in wind farm. Therefore turbine operation must be managed in order to maximize the conversion efficiency below rated power and reduce loading on the drive-train. In addition, to control the energy captured throughout operation above and below rated wind speed, researchers particularly employ pitch control of the blades. Thus, we could manage the energy captured throughout operation above and below rated wind speed using pitch control of the blades. This chapter suggests six new plans to conquer wind fluctuation problems based on a new Nero Fuzzy and Nero Fuzzy Genetic Controller where the fuzzy knowledge based are tuned automatically by Genetic Algorithm (GA) as known Tuned Fuzzy Genetic System (TFGS). Additionally In this Chapter, a new hybrid control has been trained that Wind Energy Conversion System (WECS) has optimal performance. This method contains a Multi-Layer Perceptron (MLP) Neural Network (NN) (MLPNN) and a Fuzzy Rule extraction from a Trained Artificial Neural Network using Genetic Algorithm (FRENGA). Proposed Hybrid method recognizes disturbance wind with sensors and it generates desired pitch angle control by comparison between FRENGA and MLPNN results. One of them has better signal control is selected to send to pitch blade controller. Consequently Proposed strategies reject wind disturbance in Wind Energy Conversion Systems (WECSs) input with pitch angel control generation. Consequently, proposed approaches have regulated output aerodynamic power and torque in the nominal range. Results indicate that the new proposed Artificial Intelligent (AI) methods extraction system outperform the best and earliest methods in controlling the output during wind fluctuation.

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

Vorheriges Kapitel Modeling, Identification and Control of Irrigation Station with Sprinkling: Takagi-Sugeno Approach

Nächstes Kapitel Secondary and Tertiary Structure Prediction of Proteins: A Bioinformatic Approach

Abdelkarim, M., Achraf, A., & Lotfi, K. (2011). Electric power generation based on variable speed wind turbine under load disturbance. Energy, 36, 5016–5026.CrossRef

Agrawal, R., Imielinski, T., & Swami, A. (1993). Mining association rules between sets of items in large databases (pp. 207–216). Washington D.C: SIGMOD.

Andrew, K., & Haiyang, Z. (2010). Optimization of wind turbine energy and power factor with an evolutionary computation algorithm. Energy, 35, 1324–1332.CrossRef

Azar, A. T. (2010b). Adaptive neuro-fuzzy system. In A. T Azar (Ed.) Fuzzy systems. Vienna: IN-TECH. ISBN 978-953-7619-92-3.

Azar, A. T. (2012). Overview of Type-2 fuzzy logic Systems. International Journal of Fuzzy System Applications (IJFSA), 2(4), 1–28.CrossRefMathSciNet

Baesens, B., Setiono, R., Mues, C., & Vanthienen, J. (2003). Using neural network rule extraction and decision tables for credit-risk evaluation. Management Science, 49(3), 313–329.CrossRef

Bianchi, F. D., Battista, H. D., & Mantz, R. J. (2006). Wind turbine control systems: Principles, modeling and gain scheduling. New York: Springer.

Bianchi, F. D., Battista, H. D., & Mantz, R. J. (2008). Optimal gain-scheduled control of fixed-speed active stall wind turbines. IET Renewable Power Generation, 14–29. doi:10.1049/ iet-rpg: 20070106.

Bishop, C. M. (1995). Neural networks for pattern recognition. U.K: Oxford University Press.

Bossanyi, E. A. (2000). The design of closed loop controllers for wind turbines. Wind Energy, 3, 149–163.CrossRef

Boukhezzar, B., Lupu, L., Siguerdidjane, H., & Hand, M. (2007). Multivariable control strategy for variable speed, variable pitch wind turbines. Renewable Energy, 32, 1273–1287.CrossRef

Brice, B., Tarek, A. A., & Mohamed, E. H. B. (2009). High-order sliding-mode control of variable-speed wind turbines. IEEE Transactions on Industrial Electronics, 56(9), 361–376.

Camblong, H. (2004). Minimisation de l’impact des perturbations d’origine éoliennes dans la génération d’électricité par des aérogénérateurs à vitesse variable.

Carlin, P. W., Laxson, A. S., & Muljadi, E. B. (2001). The history and state of the art variable-speed wind turbine technology. NREL. Technical Report.

Chen, S. M., & Yeh, M. S. (1997). Generating fuzzy rules from relational database systems for estimating null values. Cybernetics and Systems: An International Journal, 28(8), 695–723.CrossRefMATH

Cotrell, J. (2004). Motion technologies CRADA CRD-03-130: Assessing the potential of amechnical continuously variable transmission. NREL, Technical Report.

Craven, M. W., & Shavlik, J. W. (1996). Extracting tree-structured representations of trained networks. In Advances in Neural Information Processing Systems 8, 24–30.

Darbari, A. (2000). Rule extraction from trained ANN: A survey. Technical report Institute of Artificial intelligence, Department of Computer Science, TU Dresden.

Endusa, B. M., & Aki, U. (2009). LQG design for megawatt-class WECS with DFIG based on functional models’ fidelity prerequisites. IEEE Transactions on Energy Conversion, 24(4), 321–340.

Gallant, S. I. (1998). Connectionist expert systems. Communications of the ACM, 31(2), 152–169.CrossRef

Gen, M., & Cheng, R. (1997). Genetic algorithms and engineering design (pp. 32–55). New York: Wiley.

Giles, C. L., Miller, C. B., Chen, D., Chen, H., Sun, G. Z., & Lee, Y. C. (1992). Learning and extracting finite state automata with second-order recurrent neural networks. Neural Computation, 4(3), 393–405.CrossRef

Giles, C. L., & Omlin, C. W. (1993). Extraction, insertion, and refinement of symbolic rules in dynamically driven recurrent networks. Connection Science, 5(3–4), 307–328.CrossRef

Gjengedal, T. (2004). Large scale wind power farms as power plants. In Proceedings Nordic Wind Power Conference (pp. 48–55).

Glorennec, P. Y. (1997). Coordination between autonomous robots. International Journal of Approximate Reasoning, 17(4), 433–446.CrossRefMATH

Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison Wesley.MATH

Golea, M. (1996). On the complexity of rule extraction from neural networks and network querying. In Proceedings of the AISB’96 Workshop on Rule Extraction from Trained Neural Networks (pp. 51–59), Brighton, UK.

Hand, M. M. (1999). Variable-speed wind turbine controller systematic design methodology: A comparaison of nonlinear and linear model-based designs. NREL report TP-500-25540, National Renewable Energy Laboratory, Golden, CO, July.

Heider, H., & Drabe, T. (1997). A cascaded genetic algorithm for improving fuzzy-system design. International Journal of Approximate Reasoning, 17(4), 351–368.CrossRefMATH

Hisao, I., Tomoharu, N., & Tadahiko, M. (1999). Techniques and applications of genetic algorithm-based methods for designing compact fuzzy classification systems. (Chap. 40) Fuzzy Theory Systems: Techniques and Applications, 3, 35–45.

Hong, Y. Y., Chang, H. L., & Chiu, C. S. (2010). Hour-ahead wind power and speed forecasting using simultaneous perturbation stochastic approximation (SPSA) algorithm and neural network with fuzzy inputs. Energy, 3, 3870–3876.CrossRef

Hong, T. P., Kuo, C. S., & Chi, S. C. (2001). Trade-off between time complexity and number of rules for fuzzy mining from quantitative data. International Journal Uncertain Fuzziness Knowledge-Based Systems, 9(5), 587–604.CrossRefMATH

Horiuchi, N., & Kawahito, T. (2001). Torque and power limitations of variable speed wind turbines using pitch control and generator power control. IEEE Power Engineering Society Summer Meeting, 2(1), 638–643.CrossRef

Jianjun, L., Shozo, T., & Yoshikazu, I. (2006). Explanatory rule extraction based on the trained neural network and the genetic programming. Journal of the Operations Research Society of Japan, 49(1), 66–82.MATHMathSciNet

Kaneko, T., Uehara, A., Senjyu, T., Yona, A., & Urasaki, N. (2010). An integrated control method for a wind farm to reduce frequency deviations in a small power system. Applied Energy, 88, 1049–1058.CrossRef

Kanellos, F. D., Papathanassiou, S. A., & Hatziargyriou, N. D. (2000). Dynamic analysis of a variable speed wind turbine equipped with a voltage source ac/dc/ac converter interface and a reactive current control loop. In 10th Mediterranean Electrotechnical Conference, IEEE, 3, pp. 986–989.

Kasabov, N. K. (1998). Foundation of neural networks, fuzzy systems and knowledge engineering (2nd ed.), Cambridge: A Bradford Book/the MIT Press.

Kasiri, H., Momeni, H. R., Azimi, M., & Motavalian, A. R. (2011b). A New hybrid optimal control for WECS using MLP neural network and genetic neuro fuzzy. In 2nd IEEE International Conference on Control, Instrumentation and Automation (ICCIA) (pp. 361–366), 978-1-4673-1690-3 IEEE.

Kasiri, H., Momeni, H. R., & Kasiri, A. (2012a). Optimal intelligent control for wind turbulence rejection in WECS using ANNs and genetic fuzzy approach. International Journal of Soft Computing and Soft Engineering, Jscse, 2(9), 16–34. doi:10.7321/jscse.v2.n9.2.CrossRef

Kasiri, H., Sanee Abadeh, M., & Momeni, H. R. (2012b). Optimal estimation and control of WECS via a genetic neuro fuzzy approach. Energy, 40, 438–444.CrossRef

Kasiri, H., Sanee Abadeh, M., Momeni, H. R., & Motavalian, A. R. (2011a). Fuzzy rule extraction from a trained artificial neural network using genetic algorithm for WECS control and parameter estimation. In Proceedings of the 8th IEEE international Conference on Fuzzy Systems and Knowledge Discovery (FSKD11) (pp. 635–639), Shanghai, China. doi:978-1-61284-181-6/11.

Kathryn, E. J., Lucy, Y. P., Mark, J. B., & Lee, J. F. (2006). Standard and adaptive techniques for maximizing energy capture. IEEE Control Systems Magazine (June), 3(2), 232–240.

Kaya, M., & Alhajj, R. (2003). A clustering algorithm with genetically optimized membership functions for fuzzy association rules mining. In IEEE International Conference on Fuzzy Systems (pp. 881–886), St. Louis, Missouri.

Kuo, R. J. (1995). Intelligent diagnosis for turbine blade faults using artificial neural networks and fuzzy logic. Engineering Applications of Artificial Intelligence, 8(1), 25–34. doi:10.1016/0952-1976(94)00082-X.CrossRef

Laks, J. H., Pao, L. Y., & Wright, A. D. (2009). Control of wind turbines: Past, present, and future. In American Control Conference Hyatt Regency Riverfront (pp. 10–12).

Lin, W. M., & Hong, C. M. (2010). Intelligent approach to maximum power point tracking control strategy for variable-speed wind turbine generation system. Energy, 35, 2440–2447.CrossRef

Lin, W. M., Hong, C. M., & Fu, S. C. (2010a). Fuzzy neural network output maximization control for sensorless wind energy conversion system. Energy, 35, 592–601.CrossRef

Lin, W. M., Hong, C. M., & Fu, S. C. (2010b). On-line designed hybrid controller with adaptive observer for variable-speed wind generation system. Energy, 35, 3022–3030.CrossRef

Litipu, Z., & Nagasaka, K. (2004). Improve the reliability and environment of power system based on optimal allocation of WPG. IEEE Power System Conference Exposition Proceedings (Vol. 1, pp. 524–532).

Luo, F. L., & Unbehaben, R. (1998). Applied neural networks for signal processing. Cambridge: Cambridge University Press.

Ma, X. (1997). Adaptive extremum control and wind turbine control PhD thesis, Danemark.

Magdalena, L. (2001). Genetic fuzzy systems—Evolutionary tuning and learning of fuzzy knowledge bases. Singapore: World Scientific.MATH

Mangialardi, L., & Mantriota, G. (1996). Dynamic behaviour of wind power systems equipped with automatically regulated continously variable transmission. Renewable Energy, 7(2), 185–203.CrossRef

Martin, F., Purellku, I., & Gehlhaar, T. (2014). Modelling of and simulation with grid code validated wind turbine models. Germanischer Lloyd Industrial Services GmbH, Competence Centre Renewables Certification, Steinhöft 9, 20459 Hamburg, Germany.

Mitra, S. (1994). Fuzzy MLP based expert system for medical diagnosis. Fuzzy Sets and Systems, 65(2–3), 285–296.CrossRef

Mitra, S., & Hayashi, Y. (2000). Neuro-fuzzy rule generation: Survey in soft computing framework. IEEE Transaction on Neural Networks, 11(3), 748–768.CrossRef

Mitra, S., Pal, S. K., & Mitra, P. (2002). Data mining in soft computing framework: A survey. IEEE Transactions on Neural Networks, 13(1), 3–14.CrossRef

Moyano, C. F., & Lopes, J. A. (2009). An optimization approach for wind turbine commitment and dispatch in a wind park. Electric Power Systems Research, 79, 71–79.CrossRef

Muhando, E. B. (2008). Modeling-based design of intelligent control paradigms for modern wind generating systems. (Doctoral dissertation, University of the Ryukyus, Nishihara, Japan).

Nauck, N. (2000). Data analysis with neuro-fuzzy methods. (Habilitation Thesis University of Magdeburg).

Oh, S. H. (2010). Error back-propagation algorithm for classification of imbalanced data. Neurocomputing, 5(3), 23–35. doi:10.1016/j.neucom.2010.11.024.

Omlin, C. W., Giles, C. L., & Miller, C. B. (1992). Heuristics for the extraction of rules from discrete time recurrent neural networks. In Proceedings of the International Joint Conference on Neural Networks (Vol. 1, pp. 33–38), Baltimore, MD.

Pao, Y. H. (1989). Adaptive pattern recognition and neural networks. Reading, MA: Addison-Wesley Publishing Co., Inc.

Prakash, A., Chan Felix, T. S., & Deshmukh, S. G. (2011). FMS scheduling with knowledge based genetic algorithm approach. Expert Systems with Applications, 38, 3161–3171.CrossRef

Roy, A. (2000). On connectionism, rule extraction, and brain-like learning. IEEE Transactions on Fuzzy Systems, 8(2), 222–227.CrossRef

Saito, K., & Nakano, R. (2002). Extracting regression rules from neural networks. Neural Networks, 15(10), 1279–1288.CrossRef

Sakamoto, R., Senjyu, T., Kinjo T., Urasaki, N., Funabashi, T., & Fujita, H. (2005). Output power leveling of wind turbine generator for all operation regions by pitch angle control. In IEEE Power Engineering Society General Meeting (pp. 2274–2281).

Salman, K. S., & Teo, A. L. J. (2003). Windmill modeling consideration and factors influencing the stability of a grid-connected wind power-based embedded generator. IEEE Transactions on Power Systems, 18, 793–802.CrossRef

Santos, R., Nievola, J., & Freitas, A. (2000). Extracting comprehensible rules from neural networks via genetic algorithm. In Proceedings of the IEEE Symposium on Combination of Evolutionary Algorithm and Neural Network (pp. 130–139), S. Antonio, RX, USA.

Senjyu, T., Sakamoto, R., Urasaki, N., Funabashi, T., & Sekine, H. (2006). Output power leveling of wind farm using pitch angle control with fuzzy neural network. 1-4244-0493-2 © IEEE.

Setiono, R., Leow, W. K., & Zurada, J. M. (2002). Extraction of rules from artificial neural networks for nonlinear regression. IEEE Transactions on Neural Networks, 13(3), 564–577.CrossRef

van der Hooft, E. L, & van Engelen, T. G. (2003). Feed forward control of estimated wind speed. Technical report ECN-C-03-137, ECN Windenergie.

van der Hooft, E. L, & van Engelen, T. G. (2004). Estimated wind speed feed forward control for wind turbine operation optimisation. In European Wind Energy Conference Proceedings (pp. 35–42), London.

Verdonschot, M. J. (2009). Modeling and control of wind turbines using a continuously variable transmission. Master’s thesis, Eindhoven University of Technology Department Mechanical Engineering Dynamics and Control Technology Group, April.

W2000 2 MW Wind Turbine, Wikov Wind in partnership with WINDTEC ORBITAL2, September 2007, Technical brochure.

Wang, W., & Bridges, S.M. (2000). Genetic algorithm optimization of membership functions for mining fuzzy association rules. In International Joint Conference on Information Systems, Fuzzy Theory and Technology Conference (pp. 1–4), Atlantic City.

Wang, X. Z., Zhang, T., & He, L. (2010). Application of fuzzy adaptive back-propagation neural network in thermal conductivity gas analyzer. Neurocomputing, 73, 679–683.CrossRef

Wermter, S., & Sun, R. (2000). Hybrid neural systems. Berlin: Springer.CrossRef

Witten, I. H., & Frank, E. (1999). Data mining. practical machine learning tools and techniques with java implementations. San Diego: Academic Press.

Xing, Z., Li, Q., Su, X., & Guo, H. (2009). Application of BP neural network for wind turbines. In Second International Conference on Intelligent Computation Technology and Automation (pp. 10–18). doi:10.2119.

Yingduo, H., Zonghong, W., Qi, C., & Shaohua, T. (1997). Artificial-neural-network-based fast valving control in a power-generation system. Engineering Applications of Artificial Intelligence, 10(2), 139–155. doi:10.1016/S0952-1976(96)00071-1.CrossRef

Yue, S., Tsang, E., Yeung, D., & Shi D. (2000). Mining fuzzy association rules with weighted items. In IEEE International Conference on Systems, Man and Cybernetics (pp. 1906–1911), Nashville, Tennessee.

Zhou, Z. H., Chen, S. F., & Chen, Z. Q. (2000). A statistics based approach for extracting priority rules from trained neural networks. In Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (Vol. 3, pp. 401–406), Como, Italy.

Titel: Review and Improvement of Several Optimal Intelligent Pitch Controllers and Estimator of WECS via Artificial Intelligent Approaches
verfasst von: Hadi Kasiri
Hamid Reza Momeni
Mohammad Saniee Abadeh
Verlag: Springer International Publishing
Buch: Complex System Modelling and Control Through Intelligent Soft Computations
Print ISBN: 978-3-319-12882-5

Electronic ISBN: 978-3-319-12883-2

Copyright-Jahr: 2015
DOI: https://doi.org/10.1007/978-3-319-12883-2_18

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"