Rüzgar Enerjisi Olasılık Yoğunluk Dağılımı Fonksiyonları Parametrelerinin Meta-Sezgisel Algoritmalar Kullanılarak Tahmini

Abstract

Rüzgar enerjisi oldukça popüler bir yenilenebilir enerji kaynağıdır ve çevre dostu olması, yenilenebilir olması ve büyük rezervlere sahip olması gibi faydaları nedeniyle dünya çapında bir enerji kaynağı olarak kullanılmaktadır. Rüzgar hızının olasılık yoğunluk dağılımı, rüzgar gücü yoğunluğunu tahmin etmek için kullanılabilmektedir. Bu çalışmada, toplam rüzgar gücünün tek bir rastgele değişkenle tanımlandığı varsayımını analitik olarak ortadan kaldırmak ve rüzgar gücü olasılık yoğunluk dağılımını hesaplamak için Weibull ve Rayleigh yoğunluk dağılımları kullanılmıştır. Karmaşık yüksek boyutlu stokastik rüzgar enerjisinin modellenmesinde, çeşitli matematiksel yaklaşımlarla çözülebilmesine rağmen genellikle çok sayıda jeneratör, bara, planlama periyodu ve doğrusal olmayan stokastik değişkenler içeren büyük ölçekli güç sistemleri bulunduğundan oldukça yavaştır. Optimum noktayı ararken çoğu zaman çözümler gerçeklikten uzak olmaktadır. Sonuç olarak, rüzgar parametrelerinin elde edilmesinde günümüzde klasik matematiksel yöntemlerin yerini sezgisel yöntemler almıştır. Dolayısıyla sezgisel yöntemlerin klasik yöntemlere göre avantajı, daha kısa sürede ve daha yüksek hassasiyetle etkin çözümler üretebilmesidir. Bu nedenle, bu çalışmada rüzgar gücü parametre hesaplamalarının doğruluğunu araştırmak için bilinen başarılı metasezgisel algoritmalardan Symbiosis Organisms Search (SOS) ve Yapay Arı Kolonisi (ABC) algoritmaları ile klasik istatistiksel yöntemlerden Enerji Eğilim Faktörü ve Maksimum Olabilirlik yöntemleri kullanılmıştır. Elde edilen sonuçlara göre hata analizleri hesaplanmış ve yöntemlerin doğrulukları karşılaştırılmıştır.

Keywords

• Güç Sistemleri
• Rüzgar enerjisi
• Optimizasyon
• Meta Sezgisel Algoritmalar

Estimation of Wind Power Probability Density Distribution Functions Parameters By Using Meta-Heuristic Algorithms

Abstract

Wind energy is a very popular renewable energy resource and is used as an energy source global because of its benefits of being environmentally friendly, renewable and having great reserves. The probability density distribution of wind speed can be used to estimate wind power density. In this study, Weibull and Rayleigh density distributions were employed to analytically eliminate the presumption that the total wind power is described by a single random variant and to calculate the wind power probability density distribution. In the modeling of complex high-dimensional stochastic wind power, although it can be solved with various mathematical approaches, since there are generally large-scale power systems containing many generators, buses, planning periods and non-linear stochastic variables, it is quite leisurely in searching for the optimum point and most of the time the solutions are far from reality. Consequently, heuristic methods have now substituted classical mathematical methods in obtaining wind parameters. Therefore, the advantage of heuristic methods compared to classical methods is that they can produce efficient solutions in a shorter time and with greater precision. Therefore, in this study, the main metaheuristic algorithms Symbiosis Organisms Search (SOS) and Artificial Bee Colony (ABC) algorithms and the classical statistical methods Energy Pattern Factor and Maximum Likelihood Method were employed to investigate the accuracy of wind power parameter calculations. According to the results obtained, error analyzes were calculated and the accuracies of the methods were compared.

Keywords

• Power Systems
• Wind Energy
• Optimization
• Metaheuristic Search Algorithms

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