Rüzgâr enerjisi potansiyelini değerlendirirken önemli hususlar

Yıl 2023, , 947 - 962, 07.10.2022

Mohammed Wadi , Wisam Elmasry Furkan Ahmet Tamyiğit

https://doi.org/10.17341/gazimmfd.1066351

Cited By: 1

Öz

Rüzgâr rejimi dağılım modelinin belirlenmesi birkaç nedenden dolayı gereklidir, rüzgâr gücü çıktısını tahmin etmek en önemli konulardan biridir. Bu açıdan rüzgâr hızı dağılımını modellemek için Weibull, Gamma ve Rayleigh dağılımları en yaygın olarak kullanılan dağılımlardır. Ancak, tüm rüzgâr modellerini modellemede üstün olmayabilirler. Sonuç olarak, yerine geçecek dağılım fonksiyonlarının çalışılması gerekmektedir. Bu makale, rüzgâr hızı dağılımını tanımlamak için Weibull, Uç Değer, Ters Gauss, Lojistik, Log-Lojistik, Yarı-Normal, Burr Tipi XII, Genelleştirilmiş Uç Değer, Genelleştirilmiş Pareto ve T Konum-Ölçeği adlı on farklı dağılım fonksiyonlarını kapsamlı bir şekilde sunar. Ayrıca, her dağılımın parametre değerlerini optimize etmek için iki metasezgisel optimizasyon yöntemi olan Genetik Algoritması ve Parçacık Sürü Optimizasyonu kullanılmaktadır. Sunulan dağılımların iyi durumlarını (good-of-fitness) karşılaştırmak için yedi istatistiksel tanımlayıcı ile birlikte altı hata kriteri kullanılmıştır.

Anahtar Kelimeler

CDF, Rüzgâr Enerjisi Modellemesi, İstatistiksel Dağılımlar, PDF, TCDF, GA, PSO

Destekleyen Kurum

Yok

Proje Numarası

Yok

Important considerations while evaluating wind energy potential

Öz

Rayleigh, Gamma, and Weibull distributions are the most widely-used distributions for modeling wind speed distribution. However, they may not be outstanding for modeling all wind patterns. Consequently, substitute distribution functions are required to be studied. This study presents a comprehensive analysis of ten different distributions to represent wind speed patterns: Weibull, Extreme Value, Inverse Gaussian, Logistic, Log-Logistic, Half-Normal, Burr Type XII, Generalized Extreme Value, Generalized Pareto, and T Location-Scale. Additionally, two optimization methods, Genetic Algorithms and Particle Swarm Optimization, are utilized to select the optimal parameter values for each distribution. The good-of-fitness, six error measures, and seven statistical descriptors are employed.

Anahtar Kelimeler

CDF, Wind Energy Modeling, Probability Distribution Function, ICDF, GA, PSO

Proje Numarası

Yok

Kaynakça

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