A Wind Energy Generator For Smart Grid Applications Using Wireless Coding Neuro-fuzzy Power Control

dc.creatorAzcue J.L.
dc.creatorSguarezi Filho A.J.
dc.creatorCapovilla C.E.
dc.creatorCasella I.R.S.
dc.creatorRuppert E.
dc.date2012
dc.date2015-06-25T20:26:56Z
dc.date2015-11-26T15:23:19Z
dc.date2015-06-25T20:26:56Z
dc.date2015-11-26T15:23:19Z
dc.date.accessioned2018-03-28T22:32:20Z
dc.date.available2018-03-28T22:32:20Z
dc.identifier9781467300223
dc.identifierProceedings - 2012 9th International Conference On Fuzzy Systems And Knowledge Discovery, Fskd 2012. , v. , n. , p. 525 - 530, 2012.
dc.identifier
dc.identifier10.1109/FSKD.2012.6233858
dc.identifierhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84872971992&partnerID=40&md5=afed4a0d2294ef1cd518a8ce1e9a1ee5
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/90615
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/90615
dc.identifier2-s2.0-84872971992
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1260472
dc.descriptionThe wind energy generation is the huge driver behind the push for supergrids and cross-border infrastructure for renewable energy systems into smart grids. To provide balance supply, demand, and storage of energy over a region in a much more efficient manner than it is done today, smart grids will need to use an advanced communication infrastructure into a robust control system. Towards this objective, it is proposed in this paper a wireless coding power control employing low density parity check coding to improve system robustness and reliability. For variable speed operation, the wind energy system uses a doubly-fed induction generator and a neuro-fuzzy controller. The performance improvements of the proposed system are investigated in different propagation conditions. © 2012 IEEE.
dc.description
dc.description
dc.description525
dc.description530
dc.descriptionStrzelecki, R., Benysek, G., (2008) Power Electronics in Smart Electrical Energy Networks, , Springer-Verlag
dc.descriptionIlic, M.D., From hierarchical to open access electric power systems (2007) Proceedings of the IEEE, 95 (5), pp. 1060-1084. , May
dc.descriptionLi, F., Qiao, W., Sun, H., Wan, H., Wang, J., Xia, Y., Xu, Z., Zhang, P., Smart transmission grid: Vision and frameworks (2010) IEEE Transactions on Smart Grid, 1 (2), pp. 186-192. , September
dc.descriptionKeyhani, A., Marwali, M.N., Dai, M., (2010) Integration of Green and Renewable Energy in Electric Power Systems
dc.descriptionManwell, J.F., McGowan, J.G., Rogers, A.L., (2010) Wind Energy Explained: Theory, Design and Application, , 2nd ed
dc.descriptionSimoes, M.G., Farret, F.A., (2004) Renewable Energy Systems with Induction Generators, , CRC Press
dc.descriptionDe Almeida, R.G., Lopes, J.A.P., Participation of doubly fed induction wind generators in system frequency regulation (2007) IEEE Transactions on Power Systems, 22 (3), pp. 944-950. , August
dc.descriptionDatta, R., Rangathan, V.T., Variable-speed wind power generation using doubly fed wound rotor induction machine-a comparison with alternative schemes (2002) IEEE Trans. on Energy Conversion, 17 (3), pp. 414-421. , September
dc.descriptionGallager, R.G., (1963) Low-Density Parity-Check Codes, , Cambridge
dc.descriptionLeonhard, W., (1985) Control of Electrical Drives, , Springer-Verlag Berlin Heidelberg New York Tokyo
dc.descriptionJang, J.-S., Sun, C.-T., Neuro-fuzzy modeling and control (1995) Proceed-ings of the IEEE, 83 (3), pp. 378-406. , Mar
dc.descriptionHaykin, S., (1998) Neural Networks: A Comprehensive Foundation, , 2nd ed. Prentice Hall
dc.descriptionJang, J.-S., Anfis: Adaptive-network-based fuzzy inference system (1993) IEEE Transactions on Systems, Man, and Cybernetics, 23 (3), pp. 665-685
dc.descriptionFilho, A.J.S., Ruppert, E., A deadbeat active and reactive power control for doubly-fed induction generators (2010) Electric Power Compo-nents and Systems, 38 (5), pp. 592-602
dc.descriptionSingh, B., Kyriakides, E., Singh, S.N., Intelligent control of grid connected unified doubly-fed induction generator (2010) IEEE Power and Energy Society General Meeting, pp. 1-7
dc.descriptionLin, S., Costello, D.J., (2004) Error Control Coding, , Prentice Hall
dc.descriptionTanner, R.M., A recursive approach to low complexity codes (1981) IEEE Transactions on Information Theory, 27 (5), pp. 533-547. , September
dc.descriptionZhang, Y., Ryan, W.E., Li, Y., Structured eira codes with low floors (2005) Proceedings of the International Symposium on Information Theory, pp. 174-178. , September
dc.descriptionBarbieri, A., Piemontese, A., Colavolpe, G., On the arma approximation for frequency-flat rayleigh fading channels (2007) IEEE International Symposium on Information Theory, pp. 1211-1215. , June
dc.descriptionDvb-s.2 (2005) Standard Specification, pp. 302-307. , ETSI, March
dc.descriptionFilho, A.J.S., De Oliveira Filho, M.E., Ruppert, E., A predictive power control for wind energy (2011) IEEE Transactions on Sustainable Energy, 2 (1), pp. 97-105. , January
dc.languageen
dc.publisher
dc.relationProceedings - 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2012
dc.rightsfechado
dc.sourceScopus
dc.titleA Wind Energy Generator For Smart Grid Applications Using Wireless Coding Neuro-fuzzy Power Control
dc.typeActas de congresos


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