Articles producció científica> Enginyeria Electrònica, Elèctrica i Automàtica

Map optimization fuzzy logic framework in wind turbine site selection with application to the usa wind farms

  • Dades identificatives

    Identificador: imarina:9229345
    Autors:
    Abdelmassih, GorgAl-Numay, MohammedEl Aroudi, Abdelali
    Resum:
    In this study, we analyze observational and predicted wind energy datasets of the lower 48 states of the United States, and we intend to predict an optimal map for new turbines placement. Several approaches have been implemented to investigate the correlation between current wind power stations, power capacity, wind seasonality, and site selection. The correlation between stations is carried out according to Pearson correlation coefficient approach joined with the spherical law of cosines to calculate the distances. The high correlation values between the stations spaced within a distance of 100 km show that installing more turbines close to the current farms would assist the electrical grid. The total power capacity indicates that the current wind turbines are utilizing approximately 70% of the wind resources available in the turbine’s sites. The Power spectrum of Fourier’s spectral density indicates main, secondary, and harmonic frequencies correspond to yearly, semiyearly, and daily wind-speed periodic patterns. We propose and validate a numerical approach based on a novel fuzzy logic framework for wind turbines placement. Map optimizations are fitted considering different parameters presented in wind speed, land use, price, and elevation. Map optimization results show that suitable sites for turbines placement are in general agreement with the direction of the correlation approach.
  • Altres:

    Autor segons l'article: Abdelmassih, Gorg; Al-Numay, Mohammed; El Aroudi, Abdelali
    Departament: Enginyeria Electrònica, Elèctrica i Automàtica
    Autor/s de la URV: Cid Pastor, Angel / El Aroudi Chaoui, Abdelali
    Paraules clau: Wind turbines Wind seasonality Renewable energy Power capacity Offshore wind Map optimization Fuzzy logic Correlation wind turbines wind seasonality variability speed resource power capacity power placement noise map optimization impact fuzzy logic emissions correlation
    Resum: In this study, we analyze observational and predicted wind energy datasets of the lower 48 states of the United States, and we intend to predict an optimal map for new turbines placement. Several approaches have been implemented to investigate the correlation between current wind power stations, power capacity, wind seasonality, and site selection. The correlation between stations is carried out according to Pearson correlation coefficient approach joined with the spherical law of cosines to calculate the distances. The high correlation values between the stations spaced within a distance of 100 km show that installing more turbines close to the current farms would assist the electrical grid. The total power capacity indicates that the current wind turbines are utilizing approximately 70% of the wind resources available in the turbine’s sites. The Power spectrum of Fourier’s spectral density indicates main, secondary, and harmonic frequencies correspond to yearly, semiyearly, and daily wind-speed periodic patterns. We propose and validate a numerical approach based on a novel fuzzy logic framework for wind turbines placement. Map optimizations are fitted considering different parameters presented in wind speed, land use, price, and elevation. Map optimization results show that suitable sites for turbines placement are in general agreement with the direction of the correlation approach.
    Àrees temàtiques: Zootecnia / recursos pesqueiros Renewable energy, sustainability and the environment Renewable energy, sustainability and the environm Interdisciplinar General computer science Fuel technology Engineering (miscellaneous) Engenharias iv Engenharias iii Engenharias ii Energy engineering and power technology Energy (miscellaneous) Energy & fuels Electrical and electronic engineering Economia Control and optimization Ciências ambientais Ciências agrárias i Ciência da computação Building and construction Biotecnología Biodiversidade Astronomia / física
    Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
    Adreça de correu electrònic de l'autor: angel.cid@urv.cat abdelali.elaroudi@urv.cat
    Identificador de l'autor: 0000-0001-8124-6210 0000-0001-9103-7762
    Data d'alta del registre: 2024-10-12
    Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
    Enllaç font original: https://www.mdpi.com/1996-1073/14/19/6127
    URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referència a l'article segons font original: Energies. 14 (19): 6127-
    Referència de l'ítem segons les normes APA: Abdelmassih, Gorg; Al-Numay, Mohammed; El Aroudi, Abdelali (2021). Map optimization fuzzy logic framework in wind turbine site selection with application to the usa wind farms. Energies, 14(19), 6127-. DOI: 10.3390/en14196127
    DOI de l'article: 10.3390/en14196127
    Entitat: Universitat Rovira i Virgili
    Any de publicació de la revista: 2021
    Tipus de publicació: Journal Publications
  • Paraules clau:

    Control and Optimization,Electrical and Electronic Engineering,Energy & Fuels,Energy (Miscellaneous),Energy Engineering and Power Technology,Engineering (Miscellaneous),Fuel Technology,Renewable Energy, Sustainability and the Environm,Renewable Energy, Sustainability and the Environment
    Wind turbines
    Wind seasonality
    Renewable energy
    Power capacity
    Offshore wind
    Map optimization
    Fuzzy logic
    Correlation
    wind turbines
    wind seasonality
    variability
    speed
    resource
    power capacity
    power
    placement
    noise
    map optimization
    impact
    fuzzy logic
    emissions
    correlation
    Zootecnia / recursos pesqueiros
    Renewable energy, sustainability and the environment
    Renewable energy, sustainability and the environm
    Interdisciplinar
    General computer science
    Fuel technology
    Engineering (miscellaneous)
    Engenharias iv
    Engenharias iii
    Engenharias ii
    Energy engineering and power technology
    Energy (miscellaneous)
    Energy & fuels
    Electrical and electronic engineering
    Economia
    Control and optimization
    Ciências ambientais
    Ciências agrárias i
    Ciência da computação
    Building and construction
    Biotecnología
    Biodiversidade
    Astronomia / física
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