Bal Porsuğu Algoritması ve Vahşi At Optimize Edici ile Fotovoltaik Modellerin Parametre Çıkarımı

Yıl 2023, Cilt: 26 Sayı: 4, 1453 - 1465, 01.12.2023

Kezban Koç Mehmet Demirtaş İpek Çetinbaş

https://doi.org/10.2339/politeknik.1155696

Öz

Fotovoltaik (FV) sistemlerin kurulum konfigürasyonunun belirlenmesinden, maksimum güç noktasında çalıştırılmasına, teknik ve ekonomik fizibilite çalışmasından üretim yapması planlanan bölgeye sağlayacağı pozitif katkısına kadar olan süreçlerin analizinin yapılması FV sistemlerin doğru ve verimli simülasyon modellerine bağlıdır. FV hücrelerin ve modüllerin detaylı modellenmesi ve bu sistemlerin davranışının taklit edilebilmesi için FV parametre çıkarımı son derece önemli olup son zamanlarda sıklıkla çalışılan bir konudur. Bu sebeple bu çalışmada, FV parametre çıkarımı konusunda çalışılmış ve bu optimizasyon problemi bal porsuğu algoritması (BPA) ve vahşi at optimize edici (VAO) ile çözülmüştür. FV hücre ve modüller tek diyotlu model (TDM) ve çift diyotlu model (ÇDM) ile modellenmiştir. Bu modellerin test edilmesinde ise gerçek ölçüm verileri kullanılmıştır. Amaç fonksiyonu olarak hata kareler ortalamasının karekökü (RMSE) seçilmiş ve sonuçlar, hesaplama doğruluğu ve zamanı açılarından değerlendirme metrikleri ile karşılaştırılmıştır. Dört FV modelin sonuçlarına göre; BPA 9,9318E-04 ile 1,7011E-03 aralığında ve VAO ise 9,8602E-04 ile 1,7298E-03 aralığında RMSE değerleri hesaplanmıştır. Sonuç olarak her iki algoritma da PV parametre çıkarımında başarılı, kararlı ve hızlı sonuçlar vermesine rağmen VAO daha iyi sonuçlar vermiştir. 

Anahtar Kelimeler

Çift diyotlu FV model, bal porsuğu algoritması, FV parametre çıkarımı, tek diyotlu FV model, vahşi at optimize edici

Parameter Extraction of Photovoltaic Models by Honey Badger algorithm and Wild Horse Optimizer

Öz

Analyzing the processes ranging from the determination of the installation configuration of the photovoltaic (PV) systems to the operation at the maximum power, from the technical and economic feasibility study to the positive contribution to the region where the production is planned are just possible with the accurate and efficient simulation models of the PV systems. PV parameter extraction, which is a topic frequently discussed recently, is crucial for the detailed modeling of PV cells and modules and simulating the behavior of these systems. For this reason, the current study examined PV parameter extraction and solved this optimization problem with the honey badger algorithm (HBA) and wild horse optimizer (WHO). PV cells and modules were modeled with the single diode model (SDM) and double diode model (DDM) and tested with actual measurement data. The root-mean-square error (RMSE) was chosen as the objective function, and the results were compared with the evaluation metrics for computational accuracy and time. Based on four PV model results, RMSE values were calculated between 9.9318E-04 to 1.7011E-03 for HBA and between 9.8602E-04 and 1.7298E-03 for WHO. As a result, even though both algorithms produce successful, stable, and fast results in PV parameter extraction, the WHO yielded better results. 

Anahtar Kelimeler

Double diode PV model, honey badger algorithm, PV parameter extraction, single diode model, wild horse optimizer

Kaynakça

• [1] Li S., Gong W., Wang L., Yan X. and Hu C., “A hybrid adaptive teaching–learning-based optimization and differential evolution for parameter identification of photovoltaic models”, Energy Conversion and Management, 225: 113474, (2020).
• [2] Gümüş Z. ve Demi̇rtaş M., “Fotovoltaik sistemlerde maksimum güç noktası takibinde kullanılan algoritmaların kısmi gölgeleme koşulları altında karşılaştırılması”, Politeknik Dergisi, 24 (3): 853–865, (2020).
• [3] Coşgun A. E. and Demi̇r H., “The experimental study of dust effect on solar panel efficiency,” Journal of Polytechnic, (Erken Görünüm), (2021).
• [4] Madeti S. R. and Singh S. N., “Online fault detection and the economic analysis of grid-connected photovoltaic systems”, Energy, 134: 121–135, (2017).
• [5] Li Y., Ding K., Zhang J., Chen F., Chen X. and Wu J., “A fault diagnosis method for photovoltaic arrays based on fault parameters identification”, Renewable Energy, 143: 52–63, (2019).
• [6] Houssein E. H., Zaki G. N., Diab A. A. Z. and Younis E. M. G., “An efficient Manta Ray Foraging Optimization algorithm for parameter extraction of three-diode photovoltaic model”, Computers & Electrical Engineering, 94: 107304, (2021).
• [7] Yesilbudak M. and Colak M., “Efficient parameter estimation of double diode-based PV cell model using marine predators algorithm”, 2021 10th International Conference on Renewable Energy Research and Application (ICRERA), İstanbul, 26-29 September 2021, 376–380, (2021).
• [8] Pan J., Gao Y., Qian Q., Feng Y., Fu Y., Sun M. and Sardari F., “Parameters identification of photovoltaic cells using improved version of the chaotic grey wolf optimizer”, Optik, 242:167150, (2021).
• [9] Xiong G., Li L., Mohamed A. W., Yuan X. and Zhang J., “A new method for parameter extraction of solar photovoltaic models using gaining–sharing knowledge based algorithm”, Energy Reports, 7:3286–3301, (2021).
• [10] Arabshahi M. R., Torkaman H. and Keyhani A., “A method for hybrid extraction of single-diode model parameters of photovoltaics”, Renewable Energy, 158: pp. 236–252, (2020).
• [11] Gari̇p Z., Çi̇men M. E. and Boz A. F., “Fotovoltaik modellerin parametre çıkarımı için geliştirilmiş bir kaotik tabanlı balina optimizasyon algoritması,” Politeknik Dergisi, 25(3): 1041-1054, (2022).
• [12] Rizk-Allah R. M. and El-Fergany A. A., “Emended heap-based optimizer for characterizing performance of industrial solar generating units using triple-diode model”, Energy, 237:121561, (2021).
• [13] Wang M., Zhao X., Heidari A. A. and Chen H., “Evaluation of constraint in photovoltaic models by exploiting an enhanced ant lion optimizer”, Solar Energy, 211: 503–521, (2020).
• [14] Yeşi̇lbudak M., “Extraction of photovoltaic cell and photovoltaic module parameters using african vultures optimization algorithm”, GU J Sci, Part C, 9(4): 708–725, (2021).
• [15] Ndi F. E., Perabi S. N., Ndjakomo S. E., Ondoua Abessolo G. and Mengata G.M, “Estimation of single-diode and two diode solar cell parameters by equilibrium optimizer method”, Energy Reports, 7: 4761–4768, (2021).
• [16] Pourmousa N., Ebrahimi S. M., Malekzadeh M. and Alizadeh M., “Parameter estimation of photovoltaic cells using improved lozi map based chaotic optimization Algorithm”, Solar Energy, 180: 180–191, (2019).
• [17] Long W., Wu T., Xu M., Tang M. and Cai S., “Parameters identification of photovoltaic models by using an enhanced adaptive butterfly optimization algorithm”, Energy, 229: 120750, (2021).
• [18] Yu K., Chen X., Wang X. and Wang Z., “Parameters identification of photovoltaic models using self-adaptive teaching-learning-based optimization”, Energy Conversion and Management, 145: 233–246, (2017).
• [19] Pardhu B. S. S. G. and Kota V. R., “Radial movement optimization based parameter extraction of double diode model of solar photovoltaic cell”, Solar Energy, 213: 312–327, (2021).
• [20] Chin V. J. and Salam Z., “Coyote optimization algorithm for the parameter extraction of photovoltaic cells”, Solar Energy, 194: 656– 670, (2019).
• [21] Diab A. A. Z., Sultan H. M., Aljendy R., Al-Sumaiti A. S., Shoyama M. and Ali Z. M., “Tree growth based optimization algorithm for parameter extraction of different models of photovoltaic cells and modules”, IEEE Access, 8:119668–119687, (2020).
• [22] Shaheen A. M., Ginidi A. R., El-Sehiemy R. A. and Ghoneim S. S. M., “A forensic-based investigation algorithm for parameter extraction of solar cell models”, IEEE Access, 9: 1–20, (2021).
• [23] Diab A. A. Z., Sultan H. M., Do T. D., Kamel O. M. and Mossa M. A., “Coyote optimization algorithm for parameters estimation of various models of solar cells and PV modules”, IEEE Access, 8: 111102–111140, (2020).
• [24] Premkumar M., Jangir P., Sowmya R., Elavarasan R. M. and Kumar B. S., “Enhanced chaotic JAYA algorithm for parameter estimation of photovoltaic cell/modules”, ISA Transactions, 116: 139–166, (2021).
• [25] Zhou W., Wang P., Heidari A. A., Zhao X., Turabieh H., Mafarja M. and Chen H., “Metaphor-free dynamic spherical evolution for parameter estimation of photovoltaic modules”, Energy Reports, 7: 5175–5202, (2021).
• [26] Hashim F. A., Houssein E. H., Hussain K., Mabrouk M. S. and Al-Atabany W., “Honey badger algorithm: new metaheuristic algorithm for solving optimization problems”, Mathematics and Computers in Simulation, 192: 84–110, (2022).
• [27] Naruei I. and Keynia F., “Wild horse optimizer: a new meta-heuristic algorithm for solving engineering optimization problems”, Engineering with Computers, (2021).
• [28] Easwarakhanthan T., Bottin J., Bouhouch I. and Boutrit C., “Nonlinear minimization algorithm for determining the solar cell parameters with microcomputers”, International Journal of Solar Energy, 4: 1-12, (1986).
• [29] Tong N. T. and Pora W., “A parameter extraction technique exploiting intrinsic properties of solar cells”, Applied Energy, 176: 104–115, (2016).
• [30] Song S., Wang P., Heidari A. A., Zhao X. and Chen H., “Adaptive harris hawks optimization with persistent trigonometric differences for photovoltaic model parameter extraction”, Engineering Applications of Artificial Intelligence, 109: 104608, (2022).
• [31] Guo L., Meng Z., Sun Y. and Wang L., “Parameter identification and sensitivity analysis of solar cell models with cat swarm optimization algorithm”, Energy Conversion and Management, 108: 520–528, (2016).
• [32] Gao X., Cui Y., Hu J., Xu G., Wang Z., Qu J. and Wang H., “Parameter extraction of solar cell models using improved shuffled complex evolution algorithm”, Energy Conversion and Management, 157: 460–479, (2018).
• [33] Ebrahimi S. M., Salahshour E., Malekzadeh M. and Gordillo F., “Parameters identification of PV solar cells and modules using flexible particle swarm optimization algorithm”, Energy, 179: 358–372, (2019).
• [34] Oliva D., Cuevas E. and Pajares G., “Parameter identification of solar cells using artificial bee colony optimization”, Energy, 72: 93– 102, (2014).
• [35] Demirtas M. and Koc K., “Parameter extraction of photovoltaic cells and modules by INFO algorithm”, IEEE Access, 10: 87022–87052, (2022).
• [36] Yesilbudak M., “Parameter extraction of photovoltaic cells and modules using grey wolf optimizer with dimension learning-based hunting search strategy”, Energies, 14(18): 5735, (2021).