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

Yıl 2023, Cilt: 11 Sayı: 2, 551 - 563, 28.06.2023

Erşan Ömer Yüzer , Altuğ Bozkurt , İbrahim Çağrı Barutçu

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

Cited By: 2

Öz

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.

Anahtar Kelimeler

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

Öz

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.

Anahtar Kelimeler

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

Kaynakça

• Ali A.I. M., Mohamed H. R. A.., 2022. Improved P&O MPPT algorithm with efficient open-circuit voltage estimation for two-stage grid-integrated PV system under realistic solar radiation. International Journal of Electrical Power & Energy Systems, 137, 107805.
• Ağbulut, Ü., Gürel, A. E., Biçen, Y., 2021. Prediction of daily global solar radiation using different machine learning algorithms: Evaluation and comparison. Renewable and Sustainable Energy Reviews, 135, 110114.
• Loukriz A., Haddadi M., Messalti S.., 2016. Simulation and experimental design of a new advanced variable step size Incremental Conductance MPPT algorithm for PV systems. ISA Transactions, 62, 30–38.
• Sozen A., Arcaklioglu E., 2005. Effect of relative humidity on solar potential. Applied Energy, 82 (4), 345–367.
• Sozen A., Arcaklioglu E., Ozalp M.., 2004. Estimation of solar potential in Turkey by artificial neural networks using meteorological and geographical data. Energy Conversion and Management, 45 (18–19), 3033–3052.
• Hao D., Qi L., Tairab A. M., Ahmed A., Azam A., Luo D., Pan Y., Zhang Z., Yan J., 2022. Solar energy harvesting technologies for PV self-powered applications: a comprehensive review. Renewable Energy, 188, 678–697.
• Gul E., Baldinelli G., Bartocci P., Bianchi F., Piergiovanni D., Cotana F., Wang J., 2022. A techno-economic analysis of a solar PV and DC battery storage system for a community energy sharing. Energy, 244, 123191.
• Chepp E. D., Krenzinger A., 2021. A methodology for prediction and assessment of shading on PV systems. Solar Energy, 216, 537–550.
• Praynlin E., Jensona J. I.., 2017. Solar radiation forecasting using artificial neural network. In Innovations in Power and Advanced Computing Technologies (i-PACT), 1–7.
• Rodríguez F., Fleetwood A., Galarza A., Fontán L., 2018. Predicting solar energy generation through artificial neural networks using weather forecasts for microgrid control. Renewable Energy, 126, 855–864.
• Wang F., Zhen Z., Mi Z., Sun H., Su S., Yang G., 2015. Solar irradiance feature extraction and support vector machines based weather status pattern recognition model for short-term photovoltaic power forecasting. Energy and Buildings, 86, 427–438.
• Wang H., Liu Y., Zhou B., Li C., Cao G., Voropai N., Barakhtenko E., 2020. Taxonomy research of artificial intelligence for deterministic solar power forecasting. Energy Conversion and Management, 214, 112909.
• Majumder I., Behera M. K., Nayak N., 2017. Solar power forecasting using a hybrid EMD-ELM method. In international conference on circuit, power and computing technologies (ICCPCT), 1–6.
• Lv K., Wang F., Che J., Wang W., Zhen Z., 2019. A novel solar irradiance forecast model using complex network analysis and classification modeling. In IEEE Innovative Smart Grid Technologies-Asia (ISGT Asia), 882–887.
• Lyu L., Kantardzic M., Arabmakki E., 2014. Solar irradiance forecasting by using wavelet based denoising, In IEEE symposium on computational intelligence for engineering solutions (CIES), 110–116.
• Martín L., Zarzalejo L. F., Polo J., Navarro A., Marchante R., Cony M., 2010. Prediction of global solar irradiance based on time series analysis: Application to solar thermal power plants energy production planning. Solar Energy, 84 (10), 1772–1781.
• Koondhar M. A., Laghari I. A., Asfaw B. M., Kumar R. R., Lenin A. H.., 2022. Experimental and simulation-based comparative analysis of different parameters of PV module. Scientific African, 16, e01197.
• Swarupa M. L., Kumar E. V., Sreelatha K.., 2021. Modeling and simulation of solar PV modules based inverter in MATLAB-SIMULINK for domestic cooking. Materials Today: Proceedings, 38, 3414–3423.
• Wang M., Xu X., Yan Z., Wang H., 2021. An online optimization method for extracting parameters of multi-parameter PV module model based on adaptive Levenberg-Marquardt algorithm, Energy Conversion and Management, 245, 114611.
• Catelani M., Ciani L., Kazimierczuk M. K., Reatti A., 2016. Matlab PV solar concentrator performance based on triple junction solar cell model. Measurement, 88, 310–317.
• Shankar N., SaravanaKumar N., 2020. Reduced partial shading effect in multiple PV array configuration model using MPPT based enhanced particle swarm optimization technique. Microprocessors and Microsystems, 103287.
• Obiwulu, A. U., Erusiafe, N., Olopade, M. A., Nwokolo, S. C., 2020. Modeling and optimization of back temperature models of mono-crystalline silicon modules with special focus on the effect of meteorological and geographical parameters on PV performance. Renewable Energy, 154, 404-431.
• Mahela O. P., Shaik A. G., 2017. Power quality recognition in distribution system with solar energy penetration using S-transform and Fuzzy C-means clustering. Renewable Energy, 106, 37–51.
• Bevilacqua P., Perella S., Bruno R., Arcuri N., 2021. An accurate thermal model for the PV electric generation prediction: long-term validation in different climatic conditions. Renewable Energy, 163, 1092–1112.
• Pachauri R. K., Thanikanti S. B., Bai J., Yadav V. K., Aljafari B., Ghosh S., Alhelou H. H., 2022. Ancient Chinese magic square-based PV array reconfiguration methodology to reduce power loss under partial shading conditions. Energy Conversion and Management, 253, 115148.
• Ahmed R., Sreeram V., Mishra Y., Arif M. D., 2020. A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization. Renewable and Sustainable Energy Reviews, 124, 109792.
• Deo R. C., Sahin M., 2017. Forecasting long-term global solar radiation with an ANN algorithm coupled with satellite-derived (MODIS) land surface temperature (LST) for regional locations in Queensland. Renewable and Sustainable Energy Reviews, 72, 828–848.
• Ayeng’o S. P., Axelsan H., Haberschusz D., Sauer D. U., 2019. A model for direct-coupled PV systems with batteries depending on solar radiation, temperature and number of serial connected PV cells. Solar Energy, 183, 120–131.
• Das U. K., Tey K. S., Seyedmahmoudian M., Mekhilef S., Idris M. Y. I., Van Deventer W., Horan B., Stojcevski A., 2018. Forecasting of photovoltaic power generation and model optimization: a review. Renewable and Sustainable Energy Reviews, 81, 912–928.
• Das U. K., Tey K. S., Seyedmahmoudian M., Idris M. Y. I., Mekhilef S., Horan B., Stojcevski A., 2017. SVR-based model to forecast PV power generation under different weather conditions. Energies, 10 (7), 876.
• Vakili, M., Sabbagh-Yazdi, S. R., Khosrojerdi, S., Kalhor, K., 2017. Evaluating the effect of particulate matter pollution on estimation of daily global solar radiation using artificial neural network modeling based on meteorological data. Journal of cleaner production, 141, 1275-1285.
• Vakitbilir, N., Hilal, A., Direkoğlu, C., 2022. Hybrid deep learning models for multivariate forecasting of global horizontal irradiation. Neural Computing and Applications, 34 (10), 8005-8026.
• Sumathi V., Javapragash R., Bakshi A., Akella P. K., 2017. Solar tracking methods to maximize PV system output – a review of the methods adopted in recent decade. Renewable and Sustainable Energy Reviews, 74, 130–138.