Wind missing data arrangement using wavelet based techniques for getting maximum likelihood
Long time series of wind data can have data gaps that may lead to errors in the subsequent analyses of the time series. This study proposes using the wavelet transform as a system to verify that a data completion technique is correct and that the data series behaves correctly, enabling the user to i...
- Autores:
-
Zapata-Sierra, Antonio Jesús
Cama-Pinto, Alejandro
Montoya, Francisco Gil
Alcayde, Alfredo
Manzano-Agugliaro, Francisco
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2019
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/4586
- Acceso en línea:
- https://hdl.handle.net/11323/4586
https://repositorio.cuc.edu.co/
- Palabra clave:
- Wind data
Wavelet transform
FFT
Missing data
Renewable energy
Data filling
Datos del viento
Transformada de wavelet
FFT
Datos perdidos
Energía renovable
Relleno de datos
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-sa/4.0/
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|
| dc.title.spa.fl_str_mv |
Wind missing data arrangement using wavelet based techniques for getting maximum likelihood |
| dc.title.translated.spa.fl_str_mv |
Disposición de datos faltantes del viento usando técnicas basadas en wavelets para obtener la máxima probabilidad |
| title |
Wind missing data arrangement using wavelet based techniques for getting maximum likelihood |
| spellingShingle |
Wind missing data arrangement using wavelet based techniques for getting maximum likelihood Wind data Wavelet transform FFT Missing data Renewable energy Data filling Datos del viento Transformada de wavelet FFT Datos perdidos Energía renovable Relleno de datos |
| title_short |
Wind missing data arrangement using wavelet based techniques for getting maximum likelihood |
| title_full |
Wind missing data arrangement using wavelet based techniques for getting maximum likelihood |
| title_fullStr |
Wind missing data arrangement using wavelet based techniques for getting maximum likelihood |
| title_full_unstemmed |
Wind missing data arrangement using wavelet based techniques for getting maximum likelihood |
| title_sort |
Wind missing data arrangement using wavelet based techniques for getting maximum likelihood |
| dc.creator.fl_str_mv |
Zapata-Sierra, Antonio Jesús Cama-Pinto, Alejandro Montoya, Francisco Gil Alcayde, Alfredo Manzano-Agugliaro, Francisco |
| dc.contributor.author.spa.fl_str_mv |
Zapata-Sierra, Antonio Jesús Cama-Pinto, Alejandro Montoya, Francisco Gil Alcayde, Alfredo Manzano-Agugliaro, Francisco |
| dc.subject.spa.fl_str_mv |
Wind data Wavelet transform FFT Missing data Renewable energy Data filling Datos del viento Transformada de wavelet FFT Datos perdidos Energía renovable Relleno de datos |
| topic |
Wind data Wavelet transform FFT Missing data Renewable energy Data filling Datos del viento Transformada de wavelet FFT Datos perdidos Energía renovable Relleno de datos |
| description |
Long time series of wind data can have data gaps that may lead to errors in the subsequent analyses of the time series. This study proposes using the wavelet transform as a system to verify that a data completion technique is correct and that the data series behaves correctly, enabling the user to infer the expected results. Wind speed data from three weather stations located in southern Europe were used to test the proposed method. The series consist of data measured every 10 min for 11 years. Various techniques are used to complete the data of one of the series; the wavelet transform is used as the control method, and its scalogram is used to visualize it. If the representation in the scalogram has zero magnitude, it shows the absence of data, so that if the data are properly filled in, then they have similar magnitudes to the rest of the series. The proposed method has shown that in case of data series inconsistencies, the wavelet transform can identify the lack of accuracy of the natural periodicity of these data. This result can be visually checked using the WT’s scalogram. Additionally, the scalograms provide valuable information on the variables studied, e.g. periods of higher wind speed. In summary, the wavelet transform has proven to be an excellent analysis tool that reveals the seasonal pattern of wind speed in periodograms at various scales. |
| publishDate |
2019 |
| dc.date.accessioned.none.fl_str_mv |
2019-05-21T12:42:10Z |
| dc.date.available.none.fl_str_mv |
2019-05-21T12:42:10Z |
| dc.date.issued.none.fl_str_mv |
2019-02-25 |
| dc.type.spa.fl_str_mv |
Artículo de revista |
| dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_2df8fbb1 |
| dc.type.coar.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_6501 |
| dc.type.content.spa.fl_str_mv |
Text |
| dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/article |
| dc.type.redcol.spa.fl_str_mv |
http://purl.org/redcol/resource_type/ART |
| dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
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http://purl.org/coar/resource_type/c_6501 |
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acceptedVersion |
| dc.identifier.issn.spa.fl_str_mv |
01968904 |
| dc.identifier.uri.spa.fl_str_mv |
https://hdl.handle.net/11323/4586 |
| dc.identifier.instname.spa.fl_str_mv |
Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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https://repositorio.cuc.edu.co/ |
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01968904 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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https://hdl.handle.net/11323/4586 https://repositorio.cuc.edu.co/ |
| dc.language.iso.none.fl_str_mv |
eng |
| language |
eng |
| dc.relation.references.spa.fl_str_mv |
Hernández-Escobedo Q, Perea-Moreno A-J, Manzano-Agugliaro F. Wind energy research in Mexico. Renewable Energy 2018;123:719–29. Manzano-Agugliaro F, Alcayde A, Montoya F, Zapata-Sierra A, Gil C. Scientific production of renewable energies worldwide: an overview. Renew Sustain Energy Rev 2013;18:134–43. Kiplangat D, Asokan K, Kumar K. Improved week-ahead predictions of wind speed using simple linear models with wavelet decomposition. Renewable Energy 2016;93:38–44. https://doi.org/10.1016/j.renene.2016.02.054. Clancy J, Gaffney F, Deane J, Curtis J, Gallachir B. Fossil fuel and co2 emissions savings on a high renewable electricity system – a single year case study for Ireland. Energy Policy 2015;83:151–64. https://doi.org/10.1016/j.enpol.2015.04.011. Nicholson M, Biegler T, Brook B. How carbon pricing changes the relative competitiveness of low-carbon baseload generating technologies. Energy 2011;36(1):305–13. https://doi.org/10.1016/j.energy.2010.10.039. Gentils T, Wang L, Kolios A. Integrated structural optimisation of offshore wind turbine support structures based on finite element analysis and genetic algorithm. Appl Energy 2017;199:187–204. https://doi.org/10.1016/j.apenergy.2017.05.009. Chitsaz H, Amjady N, Zareipour H. Wind power forecast using wavelet neural network trained by improved clonal selection algorithm. Energy Convers Manage 2015;89:588–98. https://doi.org/10.1016/j.enconman.2014.10.001. Dai J, Tan Y, Yang W, Wen L, Shen X. Investigation of wind resource characteristics in mountain wind farm using multiple-unit scada data in chenzhou: a case study. Energy Convers Manage 2017;148:378–93. Yan J, Ouyang T. Advanced wind power prediction based on data-driven error correction. Energy Convers Manage 2019;180:302–11. Okumus I, Dinler A. Current status of wind energy forecasting and a hybrid method for hourly predictions. Energy Convers Manage 2016;123:362–71. Claridge DE, Chen H. Missing data estimation for 1–6 h gaps in energy use and weather data using different statistical methods. Int J Energy Res 2006;30(13):1075–91. hler AT, Kirshner S, Ghil M, Robertson AW, Smyth P. Graphical models for statistical inference and data assimilation. Physica D 2007;230(1–2):72–87. i W, Chan C, Loh J, Choo F, Chen L. Solar radiation prediction using statistical approaches. In: Information, Communications and Signal Processing, 2009. ICICS 2009. 7th International Conference on, IEEE; 2009. p. 1–5. Segura JGL. Influence of mountains on extreme rainfall in semi-arid areas of europe: The sierra de los filabres (se spain) a case of study. J Food Agric Environ 2013;11(3–4):2458–60. Hernández-Escobedo Q, Manzano-Agugliaro F, Zapata-Sierra A. The wind power of mexico. Renew Sustain Energy Rev 2010;14(9):2830–40. Hernandez-Escobedo Q, Saldaña-Flores R, Rodríguez-García E, Manzano-Agugliaro F. Wind energy resource in northern Mexico. Renew Sustain Energy Rev 2014;32:890–914. 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Decomposition of hardy functions into square integrable wavelets of constant shape. SIAM J Math Anal 1984;15(4):723–36. Graps A. An introduction to wavelets. IEEE Comput Sci Eng 1995;2(2):50–61. Wang W, McFadden P. Application of wavelets to gearbox vibration signals for fault detection. J Sound Vibr 1996;192(5):927–39. Butler-Purry KL, Bagriyanik M. Characterization of transients in transformers using discrete wavelet transforms. IEEE Trans Power Syst 2003;18(2):648–56. Strang G. Wavelet transforms versus fourier transforms. Bull Am Math Soc 1993;28(2):288–305. https://doi.org/10.1090/S0273-0979-1993-00390-2. Bayram D, Seker S. Redundancy-based predictive fault detection on electric motors by stationary wavelet transform. IEEE Trans Ind Appl 2017;53(3):2997–3004. https://doi.org/10.1109/TIA.2016.2622231. Santoso S, Powers EJ, Grady W. Power quality disturbance data compression using wavelet transform methods. IEEE Trans Power Delivery 1997;12(3):1250–7. Chang SG, Yu B, Vetterli M. 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Discrete wavelet transform method for high flux nγdiscrimination with liquid scintillators. IEEE Trans Nucl Sci 2017;64(7):1927–33. https://doi.org/10.1109/TNS.2017.2708602. Chang T-P, Liu F-J, Ko H-H, Huang M-C. Oscillation characteristic study of wind speed, global solar radiation and air temperature using wavelet analysis. Appl Energy 2017;190:650–7. https://doi.org/10.1016/j.apenergy.2016.12.149. Liu H, Tian H-Q, Li Y-F. Comparison of new hybrid feemd-mlp, feemd-anfis, wavelet packet-mlp and wavelet packet-anfis for wind speed predictions. Energy Convers Manage 2015;89:1–11. Fei S-W, He Y. Wind speed prediction using the hybrid model of wavelet decomposition and artificial bee colony algorithm-based relevance vector machine. Int J Electr Power Energy Syst 2015;73:625–31. https://doi.org/10.1016/j.ijepes.2015. 04.019. Shao H, Wei H, Deng X, Xing S. Short-term wind speed forecasting using wavelet transformation and adaboosting neural networks in yunnan wind farm. IET Renew Power Gener 2017;11(4):374–81. https://doi.org/10.1049/iet-rpg.2016.0118. Mladenović I, Marković D, Milovančević M, Nikolić M. Extreme learning approach with wavelet transform function for forecasting wind turbine wake effect to improve wind farm efficiency. Adv Eng Softw 2016;96:91–5. https://doi.org/10. 1016/j.advengsoft.2016.02.011. Alam M, Rehman S, Al-Hadhrami L, Meyer J. Extraction of the inherent nature of wind speed using wavelets and FFT. Energy Sustainable Dev 2014;22(1):34–47. https://doi.org/10.1016/j.esd.2014.02.004. Alam M, Moriya M, Sakamoto H. Aerodynamic characteristics of two side-by-side circular cylinders and application of wavelet analysis on the switching phenomenon. J Fluids Struct 2003;18(3–4):325–46. https://doi.org/10.1016/j.jfluidstructs. 2003.07.005. Costa A, Crespo A, Navarro J, Lizcano G, Madsen H, Feitosa E. A review on the young history of the wind power short-term prediction. 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Expert Syst Appl 2017;85:292–304. https://doi.org/10. 1016/j.eswa.2017.04.028. Yu C, Li Y, Zhang M. An improved wavelet transform using singular spectrum analysis for wind speed forecasting based on elman neural network. Energy Convers Manage 2017;148:895–904. https://doi.org/10.1016/j.enconman.2017.05.063. Fornero S, Kehtarnavaz N, Swaminadham M, Phillips DA. Fourier and wavelet transform features for whirl tower diagnostics 4 (1999) 2267–2270. vol 4.https:// doi.org/10.1109/ICASSP.1999.758389. Jowder FA. Weibull and rayleigh distribution functions of wind speeds in kingdom of bahrain. Wind Eng 2006;30(5):439–45. Ahmed S, Mahammed H. A statistical analysis of wind power density based on the weibull and rayleigh models of penjwen region sulaimani/iraq. Jordan J Mech Ind Eng 2012;6(2):135–40. German-Sallo Z, German-Sallo M. Multiscale analysing methods in electrocardiogram signal processing and interpretation. Procedia Eng 2017;181:583–7. https://doi.org/10.1016/j.proeng.2017.02.437. Jung C, Schindler D. Sensitivity analysis of the system of wind speed distributions. Energy Convers Manage 2018;177:376–84. Zheng H, Li Z, Chen X. Gear fault diagnosis based on continuous wavelet transform. Mech Syst Signal Process 2002;16(2–3):447–57. Singh RR, Mishra R. Benefits of dual tree complex wavelet transform over discrete wavelet transform for image fusion. Int J Innovative Res Sci Technol 2015;1(11):259–63. Harkat A, Benzid R, Saidi L. Features extraction and classification of ecg beats using CWT combined to RBF neural network optimized by cuckoo search via levy flight. In: Electrical Engineering (ICEE), 2015 4th International Conference on, IEEE; 2015. p. 1–4. Nair S, Paul Joseph K. Wavelet based electroretinographic signal analysis for diagnosis. Biomed Signal Process Control 2014;9(1):37–44. https://doi.org/10.1016/ j.bspc.2013.09.008. Watson SJ, Xiang BJ, Yang W, Tavner PJ, Crabtree CJ. Condition monitoring of the power output of wind turbine generators using wavelets. IEEE Trans Energy Convers 2010;25(3):715–21. Liu H, Huang W, Wang S, Zhu Z. Adaptive spectral kurtosis filtering based on morlet wavelet and its application for signal transients detection. Signal Process 2014;96:118–24. Cohen MX. A better way to define and describe morlet wavelets for time-frequency analysis. bioRxiv 2018:397182 . Yaman S, Ozturk N, Çomelekoglu U, Degirmenci E. Determination of dichlorvos effect on uterine contractility using wavelet transform. IRBM 2016;37(5–6):264–70. https://doi.org/10.1016/j.irbm.2016.09.002. Grinsted A, Moore J, Jevrejeva S. Application of the cross wavelet transform and wavelet coherence to geophysical times series. Nonlinear Processes Geophys 2004;11(5–6):561–6. Montoya FG, Manzano-Agugliaro F, López-Márquez S, Hernández-Escobedo Q, Gil C. Wind turbine selection for wind farm layout using multi-objective evolutionary algorithms. Expert Syst Appl 2014;41(15):6585–95. |
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Zapata-Sierra, Antonio JesúsCama-Pinto, AlejandroMontoya, Francisco GilAlcayde, AlfredoManzano-Agugliaro, Francisco2019-05-21T12:42:10Z2019-05-21T12:42:10Z2019-02-2501968904https://hdl.handle.net/11323/4586Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Long time series of wind data can have data gaps that may lead to errors in the subsequent analyses of the time series. This study proposes using the wavelet transform as a system to verify that a data completion technique is correct and that the data series behaves correctly, enabling the user to infer the expected results. Wind speed data from three weather stations located in southern Europe were used to test the proposed method. The series consist of data measured every 10 min for 11 years. Various techniques are used to complete the data of one of the series; the wavelet transform is used as the control method, and its scalogram is used to visualize it. If the representation in the scalogram has zero magnitude, it shows the absence of data, so that if the data are properly filled in, then they have similar magnitudes to the rest of the series. The proposed method has shown that in case of data series inconsistencies, the wavelet transform can identify the lack of accuracy of the natural periodicity of these data. This result can be visually checked using the WT’s scalogram. Additionally, the scalograms provide valuable information on the variables studied, e.g. periods of higher wind speed. In summary, the wavelet transform has proven to be an excellent analysis tool that reveals the seasonal pattern of wind speed in periodograms at various scales.Zapata-Sierra, Antonio JesúsCama-Pinto, AlejandroMontoya, Francisco GilAlcayde, AlfredoManzano-Agugliaro, FranciscoengUniversidad de la Costahttp://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Wind dataWavelet transformFFTMissing dataRenewable energyData fillingDatos del vientoTransformada de waveletFFTDatos perdidosEnergía renovableRelleno de datosWind missing data arrangement using wavelet based techniques for getting maximum likelihoodDisposición de datos faltantes del viento usando técnicas basadas en wavelets para obtener la máxima probabilidadArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersionHernández-Escobedo Q, Perea-Moreno A-J, Manzano-Agugliaro F. Wind energy research in Mexico. Renewable Energy 2018;123:719–29. Manzano-Agugliaro F, Alcayde A, Montoya F, Zapata-Sierra A, Gil C. Scientific production of renewable energies worldwide: an overview. Renew Sustain Energy Rev 2013;18:134–43. Kiplangat D, Asokan K, Kumar K. Improved week-ahead predictions of wind speed using simple linear models with wavelet decomposition. Renewable Energy 2016;93:38–44. https://doi.org/10.1016/j.renene.2016.02.054. Clancy J, Gaffney F, Deane J, Curtis J, Gallachir B. Fossil fuel and co2 emissions savings on a high renewable electricity system – a single year case study for Ireland. Energy Policy 2015;83:151–64. https://doi.org/10.1016/j.enpol.2015.04.011. Nicholson M, Biegler T, Brook B. How carbon pricing changes the relative competitiveness of low-carbon baseload generating technologies. Energy 2011;36(1):305–13. https://doi.org/10.1016/j.energy.2010.10.039. Gentils T, Wang L, Kolios A. Integrated structural optimisation of offshore wind turbine support structures based on finite element analysis and genetic algorithm. Appl Energy 2017;199:187–204. https://doi.org/10.1016/j.apenergy.2017.05.009. Chitsaz H, Amjady N, Zareipour H. Wind power forecast using wavelet neural network trained by improved clonal selection algorithm. Energy Convers Manage 2015;89:588–98. https://doi.org/10.1016/j.enconman.2014.10.001. Dai J, Tan Y, Yang W, Wen L, Shen X. Investigation of wind resource characteristics in mountain wind farm using multiple-unit scada data in chenzhou: a case study. Energy Convers Manage 2017;148:378–93. Yan J, Ouyang T. Advanced wind power prediction based on data-driven error correction. Energy Convers Manage 2019;180:302–11. Okumus I, Dinler A. Current status of wind energy forecasting and a hybrid method for hourly predictions. Energy Convers Manage 2016;123:362–71. Claridge DE, Chen H. 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