Aportes de soft computing en las energías renovables eólicas
Resumen
La Soft Computing es un conjunto de metodologías que fundamentalmente sirve para resolver problemas provenientes de situaciones inciertas, imprecisas, y otras situaciones en las que con las metodologías clásicas no se pueden abordar, por su dificultad en su representación y modelación así como por su complejidad. En la generación de la energía eólica se presentan diversas situaciones complejas de naturaleza incierta e imprecisa, que han necesitado y necesitan el uso de la soft computing. En este ensayo se hace la recopilación de los artículos publicados en los que se resuelve los problemas asociados con las energías eólicas utlizando las metodologías soft computing. Se ha encontrado gran número de trabajos que utilizan las metodologías fuzzy, redes neuronales artificiales y algoritmos genéticos; escasos trabajos que utilizan los métodos híbridos y ningunos los recientes métodos de búsqueda y relajación.
Palabras clave: Soft computing, energía eólica.Citas
ABDEL-AAL, R. E; ELHADIDY, M. A; SHAAHID, S. M. 2009. Modeling and forecasting the mean hourly wind speed time series using GMDH-based abductive networks. RenewableEnergy, Vol. 34, 1686-1699.
ABOU EL-ELA, A. A; EL-ZEFTAWY, A. A; ALLAM, S.M; ATTA, G.M. 2009. Long-term load forecasting and economical operation of wind farms for Egyptian electrical network. Electric Power Systems Research, Vol. 79, 1032-1037.
AGBOSSOU, K; BILODEAU, A; DOUMBIA, M. L. 2009. Development of a Control Method for a Renewable Energy system with Fuel Cell. IEEE AFRICON 2009, 1-6.
Ak, R; Li, Y. F; Vitelli, V; Zio, E. 2013. A Genetic Algorithm and Neural Network Technique for Predicting Wind Power under Uncertainty. Chemical Engineering Transactions, 1-6.
AL-YAHYAI, S; CHARABI, Y; GASTLI, A; AL-BADI, A. 2012. Wind farm land suitability indexing using multi-criteria analysis. Renewable Energy, Vol. 44, 80-87.
AMUNDARAN, M. 2012. La energía removable procedente de las olas. Ikastorratza. e-Revista de Didáctica 8. (http://www.ehu.es/ikastorratza/8_alea/energia/energia.pdf).
ARUL, I; KARTHIKEYAN, M; MUTHUKUMAR, S; KRISHNAN, N. 2010. Estimator based Neuro – Fuzzy Control for Maximum Power Extraction from Wind Electrical Power Generation System. International conference on Computational Intelligence and Computing Research (ICCIC), 488-493.
AYDIN, N.Y; KENTEL, E; DUZGUN, S. 2010. GIS-based environmental assessment of wind energy systems for spatial planning: A case study from Western Turkey. Renewable and Sustainable Energy Reviews, Vol. 14, 364-373.
ARENAS, M. G; HERRERA, F; LOZANO, M; MERELO, J. J; ROMERO, G; SÁNCHEZ, M. (Eds) 2005: Actas del IV Congreso Español sobre Metaheurísticas, Algoritmos Evolutivos y Bioinspirados (MAEB'05) I y II.
BLANCO, A; PELTA, D; VERDEGAY, J. L. 2002: A Fuzzy Valuation-based Local Search Framework for Combinatorial Problems. Fuzzy Optimization and Decision Making 1, 177-193.
BLANCO, A; PELTA, D; VERDEGAY, J. L. 2003: FANS: A fuzzy adaptive neighborhood search method for optimization problems. Inteligencia Artificial, Revista Iberoamericana de Inteligencia Artificial, No. 19, 103-122.
BONISSONE, P. 2010. Soft Computing: A Continuously Evolving Concept. International Journal of Computational Intelligence Systems, Vol. 3 No. 2, 237-248.
BONISSONE, P. 1997. Soft Computing: the Convergence of Emerging Reasoning Technologies. Soft Computing, Vol. 1, 6-18.
BOUOUDEN, S; CHADLI, M; FILALI, S; EL HAJJAJI, A. 2012. Fuzzy model based multivariable predictive control of a variable speed wind turbine: LMI approach. Renewable Energy, Vol. 37, 434-439.
CADENAS, E; JARAMILLO, O. A; RIVERA, W. 2010. Analysis and forecasting of wind velocity in chetumal, quintanaroo, using the single exponential smoothing method. Renewable Energy, Vol. 35, 925-930.
CADENAS, E; RIVERA, W. 2010. Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMAeANN model. Renewable Energy, Vol. 35, 2732-2738.
CALDERARO, V; GALDI, V; PICCOLO, A; SIANO, P. 2007. Design and implementation of a fuzzy controller for wind generators performance optimisation. European Conference on Power Electronics and Applications, 1-10.
CATALÃO, J. P. S; POUSINHO, H. M. I; MENDES, V. M. F. 2011. Short-term wind power forecasting in Portugal by neural networks and wavelet Transform. RenewableEnergy, Vol. 36, 1245-1251.
CELLURA, M; CIRRINCIONE, G; MARVUGLIA, A; MIRAOUI, A. 2008. Wind speed spatial estimation for energy planning in Sicily: Introduction and statistical analysis. Renewable Energy, Vol. 33, 1237-1250.
COURTECUISSE, V; SPROOTEN, J; ROBYNS; B; PETIT, M; FRANCOIS, B; DEUSE, J. 2012. A methodology to design a fuzzy logic based supervision of Hybrid Renewable Energy Systems. Mathematics and Computers in Simulation, Vol. 81, 208-224.
CORDÓN, O; GOMIDE, F; HERRERA, F; HOFFMANN, F; MAGDALENA, L. (2004): Ten Years of Genetic Fuzzy Systems: Current Framework and New Trends. Fuzzy Sets and Systems 141:1, 5-31.
COROAMA, I; GAVRILAS, M. 2010. A Study on Wind Energy Generation Forecasting Using Connectionist Models.12th International Conference on Optimization of Electrical and Electronic Equipment, OPTIM 2010, 1257-1262.
COURTECUISSE, V; ROBYNS, B; PETIT, M; FRANCOIS, B; DEUSE, J. 2008. Performance comparison of different wind generator based hybrid systems. 13th International Power Electronics and Motion Control Conference, EPE-PEMC 2008 , 2184-2191.
CHANG, W. Y. 2013. Wind energy conversion system power forecasting using radial basis function neural network. Applied Mechanics and Materials, 284, 1067-1071.
CHEN, H. 2008. Implementation of a Three-Phase Switched Reluctance Generator System for Wind Power Applications. 14th Symposium on Electromagnetic Launch Technology, EML, Proceedings, 489-494.
CHOWDHURY, M. A; HOSSEINZADEH, N; SHEN, W. X. 2012. Smoothing wind power fluctuations by fuzzy logic pitch angle controller. Renewable Energy, Vol. 38, 224-233.
DARIO, R. 2011. Hacia una concepción humanista de la gestión social para el desarrollo humano, en Gestión Social para el Desarrollo Humano, ed. Velásquez V.M. et al. Bogotá-Colombia, Pág.41-50
DUAN, Q; HAO, F; FENG, S. 2008. Adaptive Fuzzy Control Used in DFIG VSCF Wind Power Generator System. Proceedings of the 7th World Congress on Intelligent Control and Automation, 29-32.
DUKPA, A; VENKATESH, B. 2010. Capacitor Planning for Large Distribution Systems with Wind Generators using Successive Fuzzy MILP. 9th International Power and Energy Conference, IPEC, 140-145.
EIA(U.S. Energy Information Administration), 2011. International Energy Outlook 2011. (www.eia.gov/forecats/ieo/pdf/0484(2011).pdf, Consultado el 21 de abril de 2012).
EIA(U.S. Energy Information Administration), 2012. Short‐Term Energy and Summer Fuels Outlook, April 2012. (http://www.eia.gov/forecasts/steo/pdf/steo_full.pdf, Consultado el 22 de abril de 2012).
EL MOKADEM, M; COURTECUISSE, V; SAUDEMONT, C; ROBYNS, B; DEUSE, J. 2009. Fuzzy Logic Supervisor-Based Primary Frequency Control Experiments of a Variable-SpeedWind Generator. IEEE Transactions On Power Systems, Vol. 24, 407-417.
ELTAMALY, A. M; FARH, H. M. 2013. Maximum power extraction from wind energy system based on fuzzy logic control. Electric Power Systems Research, 97, 144-150.
EROGLU, Y; SEÇKINER, S. U. 2012. Design of wind farm layout using ant colony algorithm. Renewable Energy, Vol. 44, 53-62.
ESKANDER, M. N; JBRAHIM, W. M; ABDEL AZIZ, M. M., IBRAHIM, A. M. 2005. Generation Control of a wind farm with variable speed wind turbines for high power quality. International Telecommunications Energy Conference (Proceedings), 443-448.
FADARE, D.A. 2010. The application of artificial neural networks to mapping of wind speed profilefor energy application in Nigeria. Applied Energy, Vol. 87, 934–942.
FIDALGO, J. N; PECAS LOPES, J. A; MIRANDA, V. 1996. Neural networks applied to preventive control measures for the Dynamic security of isolated bower systems with renewables. IEEE Transactions on Power Systems, Vol. 11, N°.4, 1811-1816.
FUCHS, I; VÖLLER, S; GJENGEDAL, T. 2011. Improved Method for Integrating Renewable Energy Sources into the Power System of Northern Europe. 10th International Conference on Environment and Electrical Engineering, EEEIC.EU - Conference Proceedings, 1-4.
GALDI, V; PICCOLO, A; SIANO, P. 2009. Exploiting maximum energy from variable speed wind power generationsystems by using an adaptive Takagi–Sugeno–Kang fuzzy model. Energy Conversion and Management, Vol. 50, 413–421.
GARGOOM, A; HARUNI, A. M. O; HAQUE M. D. E; NEGNEVITSKY, M. 2010(a). Voltage and frequency stabilizer based on fuzzy logic control for three-level NPC converters in stand-alone wind energy systems. Power and Energy Society General Meeting, 2010 IEEE, 1-7.
GARGOOM, A; HARUN,I A. M. O; HAQUE, M. D. E; NEGNEVITSKY, M. 2010(b). Voltage and frequency stabilization using PI-like fuzzy controller for the load side converters of the stand alone wind energy systems. Applied Power Electronics Conference and Exposition (APEC), 2010 Twenty-Fifth Annual IEEE, 2132 – 2137.
GHANI AISSAOUI, A., TAHOUR, A., ESSOUNBOULI, N., NOLLET, F., ABID, M., & IDRISS CHERGUI, M. 2013. A Fuzzy-PI control to extract an optimal power from wind turbine. Energy conversion and management, 65, 688-696.
GONZÁLEZ, J. J; AGÜERA A; PALOMARES, J. C; RAMIRO, J. G; MORENO, A. 2011. A novel inference method for local wind conditions using genetic fuzzy systems. Renewable Energy, Vol. 36, 1747-1753.
GUO, Z; ZHAO, W; LU, H; WANG, J. 2012. Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model. Renewable Energy, Vol. 37, 241-249.
GWEC – Global Wind Energy Council. (2012). In: Sawyer, S. Global Wind 2011 Report, First Edition, March 2012, Belgium, 2012. Disponíble en: http://www.gwec.net. Consultado en 28/04/2010).
HADIAN, A; HAGHIFAM, M. R; 2010. Placement of DG with stochastic generation.Transmission and Distribution Conference and Exposition, 2010 IEEE PES, 1-7.
HASANIEN, H. M; MUYEEN, S. M; TAMURA, J. 2009. Frequency control of isolated network withwind and diesel generators by using fuzzy logic controller. Proceedings - The 12th International Conference on Electrical Machines and Systems, ICEMS 2009,1-6.
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, Vol. 35, 3870-3876.
HOWLADER, A.M; URASAKI, N; PRATAP, A.; SENJYU, T; SABER, A.Y. 2013. A fuzzy control strategy for power smoothing and grid dynamic response enrichment of a grid-connected wind energy conversion system. Wind Energy.
HUI, J; BAKHSHAI, A; JAIN, P. K. 2011. An Adaptive Approximation Method for MaximumPower Point Tracking (MPPT) in Wind Energy Systems. Energy Conversion Congress and Exposition (ECCE), 2011 IEEE. 2664-2669.
HUI, J; BAKHSHAI, A; JAIN, P. K. 2010. A master-slave fuzzy logic control schemefor maximum power point tracking in windenergy systems. International Telecommunications Energy Conference (Proceedings) 1 - 6 .
JABR, H. M; KAR, N. C. 2007. Neuro-Fuzzy Vector Control for Doubly-FedWind Driven Induction Generator. IEEE Canada Electrical Power Conference, 236-241.
JAFARIAN, M; RANJBAR, A. M. 2010. Fuzzy modeling techniques and artificial neural networks to estimate annualenergy output of a wind turbine. Renewable Energy, Vol. 35, 2008–2014.
KANG, H. Y; HUNG, M. C; PEARN, W. L; LEE, A. H. I; KANG, M. K. 2011. An integrated multi-criteria decision making model for evaluating wind farm performance. Energies, Vol.4, 2002-2026.
KANEKO, T; SENJYU, T; YONA, A; DATTA, M; FUNABASHI, T; KIM, C. H. 2007. Output power coordination control for wind farmin small power system. International Conference on Intelligent Systems Applications to Power Systems, ISAP,1-6.
KARINIOTAKIS, G; STAVRAKAKIS, G; NOGARET, E. 1996(a). Wind power forecasting using advanced neural network models. IEEE Trans Energy Conversion, vol. 11 Nº 4, 762-767.
KARINIOTAKIS, G; STAVRAKAKIS, G; NOGARET, E. 1996(b). A fuzzy logic and neural network based wind power model. En: Proceedings of the 1996 European Wind Energy Conference, Goteborg, Sweden, 596-599.
KO, H. S; KANG, M. J; BOO, C. J; JWA, C. J; KANG, S. S; KIM, H. C. 2007. Power quality control of hybrid wind powergeneration system using fuzzy-robust controller. International Conference on Electrical Machines and Systems, 2007-ICEMS, 219 – 226.
KO, H. S; LEE, K. Y; KANG, M. J; KIM, H. C. 2008. Power quality control of an autonomous wind_diesel power system based on hybrid intelligent controller. Neural Networks, Vol. 21, 1439-1446.
KUMAR, A. N; SUCHITRA, D. 2011. AI based economic load dispatchincorporating wind power penetration. 2011 International Conference on Electrical Engineering and Informatics, 1-8.
KUO, C. C. 2010. Wind energy dispatch considering environmental and economic factors. Renewable Energy, Vol. 35, 2217-2227.
KUSIAK, A; LI, W; SONG, Z. 2011. Dynamic control of wind turbines. Renewable Energy, Vol. 35, 456–463.
KUSIAK, A; ZHENG, H; SONG, Z. 2009. Models for monitoring wind farm power. Renewable Energy, Vol. 34, 583–590.
LARRAÑAGA, P; LOZANO, J. A; MÜHLENBEIN, H. (2003): Algoritmos de estimación de distribuciones en problemas de optimización combinatoria. Inteligencia Artificial. Revista Iberoamericana de Inteligencia Artificial, 19(2), 149-168.
LEI, D; LIJIE, W; SHI, H; SHUANG, G; XIAOZHONG, L. 2007. Prediction of wind power generation based onchaotic phase space reconstruction models. Proceedings of the International Conference on Power Electronics and Drive Systems, 744-748.
LI, G; SHI, J; ZHOU, J. 2011. Bayesian adaptive combination of short-term wind speed forecasts from neural network models. Renewable Energy, Vol. 36, 352-359.
LI, X; RUAN, D; VAN DER WAL, A. J. 1998: Discussion on soft computing at FLINS'96. International Journal of Intelligent Systems, Vol. 13, Nos. 2-3, 287- 300.
LIN, L; ERIKSSON, J. T; VIHRIALA, H; SODERLUND, L. 1996. Predicting wind behavior with neural networks. En: Proceedings of the 1996 European Wind Energy Conference, Goteborg, Sweden, 655-658.
LIU, C; JIANG, Y. 2010. Risk Analysis and Assessment of Wind Power Project. International Conference on Emergency Management and Management Sciences, ICEMMS 2010, 511-514.
LIU, S; BAI, Y. 2011. The application of entroy power method improved onwind power generation Project. International Conference on Consumer Electronics, Communications and Networks, CECNet 2011, 1767-1770.
MACHIAS, A. V; SLKOS, G. D. 1992. Fuzzy risk index of wind sites. IEEE Transactionson Energy Conversion, Vol. 7, 638-643.
MACHIAS, A.V; SLKOS, G. D. 1991. A fuzzy-based risk analysis of wind locations. Renewable Energy, VoL. 1, 649-654.
MAGDALENA, L. 2010. What is soft computing? revisiting possible answers. International Journal of Computational Intelligence Systems, Vol. 3 No. 2, 148-159.
MAGDALENA, L. 2008. What is soft computing? revisiting arguments. Proceedings of the FLINS conference, 3-11.
MAGUIRE, L. 2010. Does soft computing classify research in spiking neural networks?. International Journal of Computational Intelligence Systems, Vol. 3 No. 2, 176-189.
MAO, J; ZHANG, X; LI, J. 2013. Wind Power Forecasting Based on the BP Neural Network. Proceedings of the 2nd International Conference On Systems Engineering and Modeling (ICSEM-13), 0013-0017.
MELIÁN, B; MORENO PÉREZ, J. A; MORENO VEGA, J. M. 2003. Metaheurísticas: Una visión global. Revista Iberoamericana de Inteligencia Artificial, Vol 19, No. 2, 7-28.
MIRANDA, V; CERQUEIRA, C; MONTEIRO, C. 2006. Training a FIS with EPSO under an entropy criterion for wind power prediction. 9th International Conference on Probabilistic Methods Applied to Power Systems, 1-8.
MIRANDA, V; MONTEIRO, C; RAMIREZ-ROSADO, I. J. 2002. Negotiation aid system for promotion of distributed generation and renewables. Conference publications of IEEE/PES Asia Pacific, vol. 1, 568-573.
MIRECKI, A; ROBOAM, X; RICHARDEAU, F. 2007. Architecture complexity and energy efficiency of small wind turbines. IEEE Transactions On Industrial Electronics, Vol. 54, 660-670.
MIRECKI, A; ROBOAM, X; RICHARDEAU, F. 2004. Comparative study of maximum power strategy in wind turbines. IEEE International Symposium on Industrial Electronics 2, 993-998.
MOHAMED, A. Z; ESKANDER, M. N; GHALI, F. A. 2001. Fuzzy logic control based maximum powertracking of a wind energy system. Renewable Energy, Vol. 23, 235–245.
MOHANDES, M. S; REHMAN, S; RAHMAN, S. M. 2011. Estimation of wind speed profile using adaptive neuro-fuzzy inference system (ANFIS). Applied Energy, Vol. 88, 4024–4032.
MONFARED, M; RASTEGAR, H; KOJABADI, H. M. 2009. A new strategy for wind speed forecasting using artificial intelligent methods. Renewable Energy, Vol. 34, 845–848.
MONFARED, M; NIKRAVESH, S. K. Y; RASTEGAR, H. 2007. A novel fuzzy predictor for wind speed. Proceedings of the World Congress on Engineering and Computer Science 2007WCECS 2007, Vol. 4, 32-36.
MONTERO, J; MARTINEZ, I. 2010. Upgrading ideas about the concept of Soft Comptuting. International Journal of Computational Intelligence Systems, Vol. 3 No. 2, 144-147.
MUR-AMADA, J; BAYOD-RÚJULA, A. A. 2007. Wind power variability model, part I – Foundations. Electrical Power Quality and Utilisation - EPQU 2007, 9th International Conference.1-6.
NALLAVAN, G; DHANASEKARAN, R; RAJAJI, L. 2010. intelligent soft-starter-based grid-integrated induction generatorfor pitch-regulated wind turbine system. ElectrEng, Vol. 92, 57–68.
NARAYANA, M; PUTRU, G. A; JOVANOVIC, M; LEUNG, P. S; MCDONALD, S. 2012. Generic maximum power point tracking controller for small-scale wind turbines. Renewable Energy, Vol. 44, 72-79.
NEGNEVITSKY, M; MANDAL, P; SRIVASTAVA, A. K. 2009. Machine learning applications for load, price andwind power prediction in power systems. Intelligent System Applications to Power Systems, 2009. ISAP '09.15th International Conference, 1-6.
NIKOOKAR, H. 2013. Wavelet radio adaptive and reconfigurable wireless systems based on wavelets. Cambridge University Press, New York.
OCHOA, L. F; HARRISON, G. P. 2011. Minimizing energy losses: Optimal accommodation and smart operation of renewable distributed generation. Transactions on Power Systems, IEEE. Vol.26, n.1, 198-205.
PAPANTONIOU, A; COONICK, A. 1997. Fuzzy logic control of a unified power flow controller for wind farm applications. IEE Colloquium on Power Electronics for Renewable Energy, 1-9.
PELTA, D. A; 2002: Algoritmos Heuristicos en Bioinformática. Tesis Doctoral. Universidad de Granada.
PELTA, D. A; SANCHO-ROYO, A; CRUZ, C; VERDEGAY, J. L. 2006. Using memory and fuzzy rules in a co-operative multi-thread strategy for optimization. Information Science, Vol. 176 No. 13, 1849-1868.
PNUD (Programa de las Naciones Unidas Para el Desarrollo). Informe sobre desarrollo humano 2011: Sontenibilidad y equidad. Ed. Mundi-Prensa, Madrid.
RAJAJI, L; KUMAR, D. R. C. 2008. Neuro fuzzy soft starter for grid integration with pitch regulated wind turbine system. 5th International Conference on Electrical and Computer Engineering ICECE 2008. 349-355.
RAMAKANTHKUMAR, P; SREELAKSHMI, K. 2008. Neural networks for short term wind speed prediction. World Academy of Science, Engineering and Technology 42, 721-725.
RAMIREZ-ROSADO, I. J; FERNANDEZ-JIMENEZ, L. A; MONTEIRO, C; SOUSA, J; BESSA, R. 2009. Comparison of two new short-term wind-power forecasting systems. Renewable Energy, Vol. 34, 1848–1854.
RAMOS, S; SOARES, J; MORAIS, H. 2011. A data-mining based methodology for wind forecasting. 16th International Conference on Intelligent System Applications to Power Systems, ISAP 2011. 1-6.
RUAN, D. 2010. Lessons learned from Soft Computing applications at SCK-CEN. International Journal of Computational Intelligence Systems, Vol. 3 No. 2, 249-255.
RUSPINI, E. H. 2010. Soft Computing: Coping with complexity. International Journal of Computational Intelligence Systems, Vol. 3 No. 2, 190-196.SAHIN, A. D. 2004. Progress and recent trends in wind energy. Progress in Energy and Combustion Science, Vol. 30, 501-543.
SALCEDO-SANZ, S; PERÉZ-BELLIDO, A; ORTIZ-GARCÍA, E; PORTILLA-FIGUERAS, A; PRIETO, L; PAREDES, D. 2009. Hybridizing the fifth generation mesoscale model with artificial neural networks for short-term wind speed prediction. Renewable Energy, Vol. 34, 1451–1457.
SANTOS-FUENTEFRIA, A; MARTINEZ-GARCÍA, A; CASTRO-FERNÁNDEZ, M. 2012. Ajuste de las simulaciones de flujos continuados para el cálculo del límite de potencia eólica. Ingeniería Energética Vol. XXXIII, No. 2, 113 – 122.
SEISING, R. 2010. What is Soft Computing bridging gaps for 21st century science!. International Journal of Computational Intelligence Systems, Vol. 3 No. 2, 160-175.
SENJYU, T; KANEKO, T; UEHARA, A; YONA, A; SEKINE, H; KIM, C. H. 2009. Output power control for large wind power penetration in small power system. Renewable Energy, Vol. 34, 2334–2343.
SENJYU, T; SAKAMOTO, R; KANEKO, T; YONA, A; FUNABASHI, T. 2008. Output power leveling of wind farm using pitch-angle control with fuzzy neural network. Electric Power Components and Systems, Vol. 36, 1048–1066.
SFETSOS, A. 2000. A comparison of various forecasting techniques applied to mean hourly wind speed time series. Renewable Energy, Vol. 21, 23–35.
SHEIKH, M; MONDOL, N; EVA, F. 2012. Stabilization of wind generator by PWM-VSC controlled SMES. 2nd International Conference on the Development in Renewable Energy Technology (ICDRET), 1-5.
SHI, J; LEE, W; LIU, Y; YANG, Y; WANG, P. 2011. Short term wind power forecasting using Hilbert-Huang transform and artificial neural network. 4th International Conference on Electric Utility Deregulation and Restructuring and Power, 162-167.
SIAHKALI, H; VAKILIAN, M; 2008. Integrating large scale wind farms in fuzzy mid term unit commitment using PSO. 5th International Conference on the European Electricity Market (EEM-2008). 1-6.
SINGH, M; CHANDRA, A. 2011. application of adaptive network-based fuzzy inference system for sensorless control of PMSG-based wind turbine with nonlinear-load-compensation capabilities. IEEE transactions on power electronics, Vol. 26, 165-175.
SKIKOS, G; MACHIAS, A. 1992. A fuzzy based economic criterion for the evaluation of wind power investments. Renewable Energy, Vol. 2, 103-110.
SOARES, O; GONCALVES, H; MARTINS, A; CARVALHO, A. 2010. Nonlinear control of the doubly-fed induction generator in wind power systems. Renewable Energy, Vol. 35, 1662–1670.
SOMAN, S; ZAREIPOUR, H; MALIK, O; MANDAL, P. 2010. A review of wind power and wind speed forecasting methods with different time horizons. North American Power Symposium (NAPS 2010), 1-8.
SUHUA, L; YAOWU, W; ZHIHENG, L. 2008. Clustering analysis of the wind power output based on similarity theory. 3rd International Conference on Deregulation and Restructuring and Power Technologies, 2815-2819.
TIAN, Z; DING, Y; DING, F. 2011. Maintenance optimization of wind turbine systems based on intelligent prediction tools. Nedjah et al. (Eds.) Innovative Computing Methods, SCI 357, 53-71.
TORRES, J. 2012. La educación en el desarrollo integral de Corea del Sur: una revisión sistemática en la década comprendida entre 1999 y 2010". Contribuciones a las Ciencias Sociales, Vol.17: 1-20. (www.eumed.net/rev/cccss/17/, consultado el 6 de abril, 2012).
TRILLAS, E; MORAGA, C; GUADARRAMA, S. 2010. A (naïve) glance at Soft Computing. International Journal of Computational Intelligence Systems, Vol. 3 No. 2, 197-201.
VASCONCELOS, H; PECAS, L. 2006. ANN design for fast security evaluation of interconnected systems with large wind power production. 9th International Conference on Probabilistic Methods Applied to Power Systems KTH, Stockholm, Sweden. 1-6.
VELÁSQUEZ, S; CARTA, J; MATÍAS, J. 2011(a). Comparison between ANNs and linear MCP algorithms in the long-term estimation of the cost per kW h produced by a wind turbine at a candidate site: A case study in the Canary Islands. Applied Energy, Vol. 88, 3869–3881.
VELÁSQUEZ, S; CARTA, J; MATÍAS, J. 2011(b). Influence of the input layer signals of ANNs on wind power estimation for a target site: A case study. Renewable and Sustainable Energy Reviews, Vol. 15, 1556-1566.
VELUSAMI, S; SINGARAVELU, S. 2007. Steady state modeling and fuzzy logic based analysis of wind driven single phase induction generators. Renewable Energy, Vol. 32, 2386–2406.
VENKATESH, B; YU, P; COOI, H; CHOLING, D. 2008. Fuzzy MILP unit commitment incorporating wind generators. IEEE Transactions Power Systems. Vol. 23 Nº 4, 1738-1746.
VERDEGAY, J. L; YAGER, R; BONISSONE, P. 2008. On Heuristics as a Fundamental Constituent of Soft Computing. Fuzzy Sets and Systems, Vol. 159, 846-855.
VERDEGAY, J. L. 2005. Una revisión de las metodologías que integran la "Soft Computing". Actas del Simposio sobre Lógica Fuzzy y Soft Computing. 151-156.
VERDEGAY, J.L. (Ed.) 2003: Fuzzy Sets-based Heuristics for Optimization. Studies in Fuzziness. Springer Verlag. Vol. 126, Berlin.
VERDEGAY, J.L., VERGARA-MORENO, E. , 2000: Fuzzy Termination Criteria in Knapsack Problem Algorithms. Mathware and Soft Computing VII, 2-3, 89-97.
VERGARA-MORENO, E. 1999: Nuevos Criterios de Parada en Algoritmos de Optimizacion. Tesis Doctoral. Universidad de Granada.
WANG, X; GUO, P; HUANG, X. 2011. A Review of wind power forecasting models. Energy Procedia, Vol.12, 770-778.
WANG, L; SINGH, C. 2009. Risk and Cost Tradeoff in Economic Dispatch Including Wind Power Penetration Based on Multi-Objective Memetic Particle Swarm Optimization. Studies in Computational Intelligence, Vol. 171, 209-230.
WANG, L; SINGH, C. 2008(a). Balancing risk and cost in fuzzy economic dispatch including wind power penetration based on particle swarm optimization. Electric Power Systems Research, Vol. 78, 1361–1368.
WANG, L; SINGH, C. 2008(b). Population-based intelligent search in reliability evaluation of generation systems with wind power penetration. IEEE transactions on power systems, vol. 23 Nº 3, 1336-1345.
WANG, L; SINGH, C. 2006. PSO-based multi-criteria economic dispatch considering wind power penetration subject to dispatcher's attitude. 38th Annual North American Power Sysmposium, NAPS-2006, 269-276.
WANG, Q; CHEN, X; JI, Y. 2006. Fuzzy-based active and reactive control for brushless doubly-fed wind power generation system. IEEE Asia- Pacific Conference on Circuts and Systems, 848-851.
WU, L; PARK, J; CHOI, J; CHA, J; LEE, K. 2009. A Study on Wind Speed Prediction using Artificial Neural Network at Jeju Island in Korea.Transmission & Distribution Conference & Exposition: Asia and Pacific, 2009. 1-4.
WU, Y; HONG, J. 2007. A literature review of wind forecasting technology in the world. IEEE Lausanne Powertech, 504-509.
WU, G; DOU, Z. 1995. Non linear wind prediction using a fuzzy modular temporal neural network. En: Windpower '95. American Wind Energy Association Conference, 285-294.
XU, R; XU, X; CHEN, M; BO, Z. 2011. The application of genetic-neural network on wind power prediction. Communications in Computer and Information Science 244 CCIS (Part 2), 379-386.
XUE, Y; VENKATESH, B; CHANG, L. 2008. Bidding wind power in short-term electricity market based on multiple-objective fuzzy optimization. Canadian Conference On Electrical and Computer Engineering, 1135-1138.
YNDURAIN, F. 2005. Energía: presente y futuro de las diversas tecnología.Academia de Ciencias y Artes, España. (www.academia-europea.org/pdf/energia_presente_y_futuro_ de_las_diversas_tecnologias.pdf, Consultado el 22 de abril de 2012)
ZADEH, L.A. 2001. Applied Soft Computing. Applied Soft Computing Vol. 1, 1–2
ZADEH, L. A. 1998. Some reflections on soft computing, granular computing and their roles in the conception, design and utilization of information/intelligent systems. Soft Computing, Vol. 2, 23-25.
ZADEH, L.A. 1994(a). Soft Computing and Fuzzy Logic. IEEE Software, Vol. 11, Nº6, 48-56.
ZADEH, L.A. 1994(b). Fuzzy Logic, Neural Networks and Soft Computing. Comunications of the ACM, Vol. 37, 77-84.
ZADEH, L.A. 1965. Fuzzy sets. Information and Control, Vol. 8, 338-353.
ZHAO, P; WANG, J; XIA, J; DAI, Y; SHENG, Y; YUE, J. 2012. Performance evaluation and accuracy enhancement of a day-ahead windpower forecasting system in China. Renewable Energy, Vol. 43, 234-241.
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