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A survey of applications of the Kruskal Wallis test in wind power generation

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Warianty tytułu
PL
rzegląd zastosowań testu Kruskala Wallisa w energetyce wiatrowej
Języki publikacji
EN
Abstrakty
EN
The authors present the results of a survey on the use of the Kruskal Wallis test in wind power generation research. An overall assessment of the qualifying publications suggested that they could be categorized into 4 logical application areas. The time series of the annual number of publications indicated a steady trend in the numbers produced annually and most publications were in the category of environmental issues. The survey contributes to the body of knowledge on wind power generation and also creates a depository of references in one source.
PL
e. Autorzy przedstawiają wyniki ankiety dotyczącej wykorzystania testu Kruskala Wallisa w badaniach energetyki wiatrowej. Ogólna ocena kwalifikujących się publikacji sugeruje, że można je podzielić na 4 logiczne obszary zastosowań. Szeregi czasowe rocznej liczby publikacji wskazywały na stały trend w liczbach wydawanych rocznie, a większość publikacji dotyczyła kwestii środowiskowych. Ankieta wzbogaca wiedzę o energetyce wiatrowej, a także tworzy depozyt referencji w jednym źródle.
Rocznik
Strony
140--144
Opis fizyczny
Bibliogr. 30 poz. rys., tab.
Twórcy
  • University of South Africa, 28 Pioneer Ave, Florida Park, Roodepoort, 1709, South Africa
  • Eskom Rotek Industries, Lower Germiston Road, Rosherville, 2022, South Africa
Bibliografia
  • 1 M. Cheng, J. Zhu, N.A. Rahim and K.H. Solangi. (2014) “The state of the art of wind energy conversion systems and technologies: A review.” Energy Conversion and Management88 (2014): 332-347.
  • 2 N. Mbuli, B. Mendu, “A survey of applications of simple and multiple linear regression in wind power generation.” PRZEGLĄD ELEKTROTECHNICZNY, ISSN 0033-2097, R. 98 NR 3/2022, (2022) 1-7.
  • 3 X. Sun, D. Huang and G. Wu. (2012) “The current state of offshore wind energy technology development.” Energy 41 (2012): 298-312.
  • 4 R. Saidur, M.R. Islam, N.A. Rahim and K.H. Solangi. (2010) “Areview on global wind energy policy, Renewable and Sustainable Energy Reviews.” 14 (2010): 1744-1762.
  • 5 World Energy Council. (2016) “Variable Renewables Integration in Electricity Systems: How to get it Right. World Energy Perspectives: Renewables Integration 2016.
  • 6 M. Ram, D. Bogdanov, A. Aghahosseini, A. S. Oyewo, A. Gulagi, M. Child, H.-J. Fell and C. Breyer. (2017) “Global Energy Systems based on 100% Renewables-Power Sector.” Study by Lappeenranta University of Technology and Energy Watch Group, Lappeenranta, Berlin, November 2017.
  • 7 M. Haffejee and A. C. Brent. (2008) “Evaluation of an Integrated Asset Life-Cycle Management (ACLM) Model and Assessment of Practices in the Water Utility Sector.” Water SA34.2 (2008) 285-290.
  • 8 P. Sprent and N. C. Smeeton. (2001) “Applied Nonparametric Statistical Methods.” 3rd Edition, Chapman and Hall/CRC, Florida, USA 2001.
  • 9 G. Keller. (2014) “Statistics for Management and Economics,”10th Edition, CENGAGE: Connecticut, USA 2014
  • 10 H. V. Haghi, M. T. Bina, and M. A. Golkar. (2013) “Nonlinear Modeling of Temporal Wind Power Variations.” IEEE Transactions on Sustainable Energy 4.4 (2013): 838-847.
  • 11 S. Perkin, D. Garrett, P. Jensson. (2015) “Optimal wind turbineselection methodology: A case-study for Búrfell, Iceland.” Renewable Energy 75 (2015) 165-172.
  • 12 G. Ertek, M. M. Tunç, E. Kurtaraner and D. Kebude. (2012) “Insights into the Efficiencies of On-Shore Wind Turbines: A Data-Centric Analysis.” 2012 International Symposium on Innovations in Intelligent Systems and Applications, Trabzon, Turkey 2-4 July 2012.
  • 13 Ü. Saglam.(2017) “A two-stage data envelopment analysis model for efficiency assessments of 39 state’s wind power in the United States.” Energy Conversion and Management 146 (2017): 52–67.
  • 14 M. Bilbao and E. Alba. (2010) “CHC and SA Applied to the Distribution of Wind Turbines on Irregular Fields.”16th Argentine Congress Of Computer Sciences, Cacic (2010): 72-81
  • 15 D. S. Callaway. (2020) “Sequential Reliability Forecasting for Wind Energy: Temperature Dependence and Probability Distributions.” IEEE transactions on energy conversion 25.2 (2010): 577-585
  • 16 R. Mbuvha, M. Jonsson, N. Ehn and P. Herman. (2017) “Bayesian Neural Networks for One-Hour Ahead Wind Power Forecasting.” 6th International Conference on Renewable Energy Research and Application, San Deigo,CA.USA 5-8 Nov (2017): 591-596
  • 17 E. M. Nascimento, and J. D. de Souza. (2017) “Hybrid Power Plants: Viability for Cities in Minas Gerais.” Engineering Journal21.5(2017): 37-52.
  • 18 G. F. Cruz-Delgado1, D. A. Wiedenfeld, and J. A. González. (2010) “Assessing the potential impact of wind turbines on the endangered Galapagos Petrel Pterodroma phaeopygia at San Cristóbal Island.” Biodivers Conserv 19 (2010): 679–694 https://doi.org/10.1007/s10531-009-9727-y.
  • 19 M. Pescadora, J. I. G. Ramírezb and S. J. Perisc. (2019) “Effectiveness of a mitigation measure for the lesser kestrel (Falco naumanni) in wind farms in Spain.” Journal of Environmental Management 231 (2019) 919–925.
  • 20 M. R. Perrow, J. J. Gilroy, E. R. Skeate and M. L. Tomlinson. (2011) “Effects of the construction of Scroby Sands offshore wind farm on the prey base of Little tern Sternula albifrons at its most important UK colony.” Marine Pollution Bulletin 62 (2011) 1661–1670
  • 21 S. M. Xirouchakus, E. Armeni, S. Nikdopoulou and J. Halley. (2019) “Estimating the potential motality of Griffon Valtures (Gyps fulvus) Due to wind energy development on the Island of Crete (Greece).” Springer Nature Switzerland AG 2019 R. Bispo et al. (eds.), Wind Energy and Wildlife Impacts (2019): 205-222 https://doi.org/10.1007/978-3-030-05520-2_13
  • 22 K. Arikan and S. L. Turan. (2017) “Estimation Of Bird Fatalities Caused By Wind Turbines In Turkey.” Fresenius Environmental Bulletin 26.11(2017 ): 6543-6550.
  • 23 M. Abbasi, M. R. Monnazzam, S. A. Zakerian and A. Yousefzadeh.(2015) “Effect of Wind Turbine Noise on Workers’ Sleep Disorder: A Case Study of ManjilWind Farm in Northern Iran.” World Scientific Publishing Company, Fluctuation and Noise Letters 14. 2 (2015) 1550020 (15 pages).
  • 24 T. Bauwensa, P. Devine-Wright. (2018) “Positive energies? An empirical study of community energy participation and attitudesto renewable energy.” Energy Policy 118 (2018) 612-625
  • 25 H. Thomson, W. Kempton. (2018) “Perceptions and attitudes of residents living near a wind turbine compared with those livingnear a coal power plant.” Renewable Energy 123 (2018) 301-311.
  • 26 L. A. de Sena, P. Ferreira and A. C. Braga. (2016) “Social acceptance of wind and solar power in the Brazilian electricitysystem.” Environ Dev Sustain, Springer Science and Business Media, Dordrecht 2016.
  • 27 Y. Cronin, E. Wolsztynski and V. Cummins. (2020) “Public Perception of Offshore Wind Farms Report Part 2.” EirWind Project Deliverable D4.7 Report, MaREI Centre, ERI, University College Cork, Ireland.(2020) DOI: http://doi.org/10.5281/zenodo.3948450
  • 28 H. V. Haghi, and S. Lotfifard. (2015) “Spatiotemporal Modellingof Wind Generation for Optimal Energy Storage Sizing.” IEEE Transactions on Sustainable Energy 6.1 (2015): 113-121.
  • 29 Y.Z. Li, Q.H. Wu, M.S. Li, J.P. Zhan. (2014) “Mean-variance Model for Power System Economic Dispatch with Wind Power Integrated.” Energy 72 (2014) 510-520.
  • 30 C. Jung and D. Schindler. (2018) “Sensitivity analysis of the system of wind speed distributions.” Energy Conversion and Management 177 (2018) 376–384.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-fdf9c4ee-23ea-40c4-b5c2-fb2a53fb42e3
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