FOTOVOLTAİK SİSTEM ÇIKIŞ GÜCÜNÜN YAPAY SİNİR AĞLARI VE MATLAB/SİMULİNK MODELLERİNİN ENTEGRASYONU İLE BELİRLENMESİ

Year 2023, Volume: 11 Issue: 2, 551 - 563, 28.06.2023

Erşan Ömer Yüzer , Altuğ Bozkurt , İbrahim Çağrı Barutçu

https://doi.org/10.21923/jesd.1163411

Cited By: 2

Abstract

PV sistemlerin çıkış gücü, temel olarak güneş ışınımına ve diğer atmosferik koşullara bağlıdır. Bu çalışmada, Türkiye’nin Güneydoğusunda yer alan Hakkâri ilinde ölçülmüş olan meteorolojik veriler, tahminleme çalışmalarında yaygın olarak kullanılan Yapay Sinir Ağları (YSA) modelinde giriş değişkenleri olarak değerlendirmeye alınmış olup, bu modelin çıkışında güneş ışınımının tahmin değerleri belirlenmiştir. Farklı atmosferik koşullarda maksimum gücün belirlenebilmesi için DC – DC yükseltici (boost) güç elektroniği dönüştürücüsüne uygulanan artımlı iletkenlik maksimum güç noktası izleme (MPPT) algoritması bulunan PV sistemin Matlab / Simulink modeli göz önünde bulundurulmuştur. Gerçek güneş ışınımı, ortam sıcaklığı ile YSA modelinde tahmin edilen güneş ışınımı değerleri ayrı ayrı göz önüne alınarak Matlab / Simulink ortamındaki PV sistemin çıkış güçleri hesaplanmıştır. İlk olarak gerçek güneş ışınımı ve ortam sıcaklığı değerleri daha sonra ise tahmin edilen güneş ışınımı ve ortam sıcaklığı değerleri, ilgili PV sistem modelinde ele alınarak belirlenen PV sistem çıkış güçleri karşılaştırılmıştır. Karşılaştırma sonuçları literatürde yaygın olarak kullanılan değerlendirme metrikleri ile hesaplanmış ve güneş ışınımı için 0,9705 ve PV sistem çıkış gücü için 0,9668 belirleme katsayısı (R2) değeri ile başarılı sonuçlar elde edilmiştir.

Keywords

PV Sistem, Meteorolojik Veri, Yapay Sinir Ağları, Tahminleme, PV Çıkış Gücü

DETERMINATION OF PHOTOVOLTAIC SYSTEM OUTPUT POWER BY INTEGRATION OF ARTIFICIAL NEURAL NETWORKS AND MATLAB/SIMULINK MODELS

Abstract

The output power obtained from PV systems depends mainly on solar radiation and other atmospheric conditions. In this study, meteorological data measured in Hakkari province in the Southeast of Turkey has been evaluated as input parameters in the Artificial Neural Networks (ANN) model, which is widely used in the literature in forecasting studies, and the prediction values of solar radiation have been determined at the output of this model. Matlab/Simulink model of PV system with incremental conductivity maximum power point tracking (MPPT) algorithm applied to DC–DC boost power electronics converter has been considered to determine the maximum power under different atmospheric conditions. Output powers of the PV system in Matlab/Simulink environment have been calculated by considering the real solar radiation, ambient temperature and the solar radiation values estimated in the ANN model separately. First, the actual solar radiation and ambient temperature values and then the predicted solar radiation and ambient temperature values have been handled to compare the output powers in the relevant PV system model. Comparison results have been calculated with evaluation metrics commonly used in the literature, and successful results have been obtained with a determination coefficient (R2) value of 0.9705 for solar radiation and 0.9668 for PV system output power.

Keywords

PV System, Meteorological Data, Artificial Neural Networks, Forecasting, PV Output Power

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