Fotovoltaik Sistemlerde Maksimum Güç Noktası Takibinde Kullanılan Algoritmaların Kısmi Gölgeleme Koşulları Altında Karşılaştırılması

Year 2021, Volume: 24 Issue: 3, 853 - 865, 01.09.2021

Zeynep Gümüş , Mehmet Demirtaş

https://doi.org/10.2339/politeknik.725255

Cited By: 11

Abstract

Fotovoltaik (FV) sistemlerde verimliliği arttırmak için güç elektroniği dönüştürücüleri yardımıyla maksimum güç noktası takibi (MGNT) işlemi yapılmaktadır. Eşit dağılımlı ışıma koşullarında MGNT yapılırken geleneksel algoritmalardan biri olan değiştir ve gözle (D&G) algoritması oldukça verimlidir. Ancak kısmi gölgeleme koşulları meydana geldiğinde bu algoritma global maksimum güç noktasını bulamamakta ve yerel maksimum güç noktalarına takılmaktadır. Buna karşın parçacık sürü optimizasyonu (PSO) ve guguk kuşu optimizasyon (GKO) algoritması gibi doğadan esinlenen meta sezgisel algoritmalar global maksimum noktanın bulunmasında daha başarılı olmaktadır.
Bu çalışmada MATLAB/SIMULINK’de FV dizi, DA-DA yükselten dönüştürücü ve yükten oluşan bir sistem geliştirilmiştir. Bu sistem kullanılarak kısmi gölgeleme koşulları altında D&G, PSO ve GKO algoritmalarıyla MGNT işlemi gerçekleştirilmiş ve bu algoritmaların karşılaştırmalı analizi yapılmıştır. Bu algoritmalar üç farklı kısmi gölgeleme konfigürasyonu ile takip hızı ve doğruluğu açısından birbiriyle kıyaslanmıştır. Simülasyonlar sonucunda, D&G algoritması yerel bir maksimum güç noktasına yakalanırken PSO ve GKO algoritmaları global maksimum güç noktasının bulunmasında başarılı olmuştur. PSO ve GKO algoritması birbiriyle kıyaslandığında ise GKO algoritmasının PSO algoritmasından daha hızlı bir şekilde global maksimum güç noktasına ulaştığı görülmüştür.

Keywords

MGNT, kısmi gölgeleme, meta sezgisel algoritmalar

Comparison of the Algorithms Used in Maximum Power Point Tracking in Photovoltaic Systems under Partial Shading Conditions

Abstract

In order to increase efficiency in photovoltaic systems (PV), maximum power point tracking (MPPT) is performed with the help of power electronic converters. While MPPT is performed in uniformly distributed radiation conditions, one of the traditional algorithms, perturb and observe (P&O) algorithm is very efficient. However, when partial shading conditions occur, this algorithm cannot find global maximum power points and caught to local maximum power points. In contrast, nature inspired metaheuristic algorithms such as particle swarm optimization (PSO) and cuckoo search optimization (CSO) algorithm are more successful in finding the global maximum.
In this study, a system consisting of photovoltaic array, DC-DC boost converter and load has been developed in MATLAB/SIMULINK. Using this system, MPPT was performed with P&O, PSO and CSO algorithms under partial shading conditions and comparative analysis of these algorithms was performed. These algorithms were compared with three different partial shading configurations for tracking time and accuracy. As a result of the simulations, the P&O algorithm caught to a local point, while the PSO and CSO algorithms were successful in finding the global point. When PSO and GKO algorithm are compared, it is seen that CSO algorithm reaches global point faster than PSO algorithm.

Keywords

MPPT, partial shading, meta heuristic algorithms

References

• [1] Bholane R.R., Babu P.S.,”Grid connected PV System using FB-PSO”, International Conference on Smart Electric Drives S& Power System (ICSEDPS), (2018).
• [2] Cetinbas, I., Tamyürek, B., and Demirtas, M., “Energy management of a PV energy system and a plugged-in electric vehicle based micro-grid designed for residential applications,” 2019 8th International Conference on Renewable Energy Research and Applications (ICRERA), Brasov, Romania, 991-996, (2019).
• [3] Çetinbaş, I., Tamyürek, B., and Demirtaş, M., “Design, analysis, and optimization of a hybrid microgrid system using HOMER software: Eskişehir Osmangazi University Example”, Int. Journal of Renewable Energy Development, 8(1): 65-79, (2019).
• [4] Colak, I., Demirtas, M., and Kabalci, E., “Design, optimisation and application of a resonant DC link inverter for solar energy systems”, COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, 33(5): 1761-1776, (2014).
• [5] Koad R.B.A, Zobaa A.F. and El-Shahat A., “A novel MPPT algorithm based on particle swarm optimization for photovoltaic systems”, IEEE Transactions on Sustainable Energy, 8: 468-476, (2017).
• [6] Sawant P.T. and Bhattar C.L.,“ Optimization of PV system using particle swarm algorithm under dynamic weather conditions”, 2016 IEEE 6th International Conference on Advanced Computing (IACC), Bhimavaram, 208-213, (2016).
• [7] Chen L.R., Tsai C.H., Lin Y.L. and Lai Y.S.”A biological swarm chasing algorithm for tracking the PV maximum power point”, IEEE Transactions on Energy Conversion, 25: 484-493, (2010).
• [8] Kollimala S.K. and Mishra M.K., “Adaptive perturb & observe MPPT algorithm for photovoltaic system”, 2013 IEEE Power and Energy Conference at Illinois (PECI), Champaign, IL, 42-47, (2013).
• [9] Anoop K. and Nandakumar M,” A novel maximum power point tracking method based on particle swarm optimization combined with one cycle control”, 2018 International Conference on Power, Instrumentation, Control and Computing (PICC), Thrissur, 1-6, (2018).
• [10] Manickam C., Raman G.R., Raman G.P., Ganesan S.I. and Nagamani C.,“A hybrid algorithm for tracking of gmpp based on P&O and PSO with reduced power oscillation in string inverters”, IEEE Transactions on Industrial Eelctronics, 63: 6097-6106, (2016).
• [11] Fang G.J. and Lian K.L.,”A maximum power point tracking method based on multiple perturb-and-observe method for overcoming solar partial shaded problems”, 2017 6th International Conference on Clean Electrical Power (ICCEP), Santa Margherita Ligure, 68-73, (2017).
• [12] Nugraha D.A., Lian K.L. and Suwarno ”A novel MPPT method based on cuckoo search algorithm and golden section search algorithm for partially shaded pv system”, Canadian Journal of Electrical and Computer Engineering, 42:173-182, (2019).
• [13] Rakotomananandro, F.F., “Study of photovoltaic system”, Yüksek Lisans Tezi, The Ohio State University, Electrical and Computer Science Program, (2011).
• [14] Suryavanshi R., Joshi D.R. and Jangamshetti S.H.,“PSO and P&O based MPPT technique for spv panel under varying atmospheric conditions”, 2012 International Conference on Power, Signals, Controls and Computation, Thrissur, Kerala, 1-6, (2012).
• [15] Azharuddin M, ”Effects of shading on the power of photovoltaic arrays”, Yüksek Lisans Tezi, Purdue University, (2012).
• [16] Sagonda A.F. and Folly K.A.,”Maximum power point tracking in solar PV under partial shading conditions using stochastic optimization techniques”, 2019 IEEE Congress on Evolutionary Computation (CEC), Wellington, New Zealand, 1967-1974, (2019).
• [17] Chong B.V.P, Modelling and control of integrated photovoltaic-module and converter systems for partial shading operation using artificial ıntelligence, Doktora Tezi, The University of Leeds, (2010).
• [18] Elewa A., Elkholy M.M. and El-arini M., “Adaptive MPPT for PV systems under partial shadow condition and different loads using advanced optimization techniques”, 2017 Nineteenth International Middle East Power Systems Conference (MEPCON), Cairo, 152-162, (2017).
• [19] Behera T.K., Behera M.K. and Nayak N.,“Spider monkey based ımprove P&O MPPT controller for photovoltaic generation system”, 2018 Technologies for Smart-City Energy Security and Power (ICSESP), Bhubaneswar, 1-6, (2018).
• [20] Vijayakumar G. and Hemakumar K., ”Development of low cost high efficient DC-DC converter for photovoltaic system with fast converging MPPT algorithm”, 2013 International Conference on Renewable Energy and Sustainable Energy (ICRESE), Coimbatore, 98-104, (2013).
• [21] Dhivya P. and Kumar K.R., “MPPT based control of sepic converter using firefly algorithm for solar PV system under partial shaded conditions”, 2017 International Conference on Innovations in Green Energy and Healthcare Technologies (IGEHT), Coimbatore, 1-8, (2017).
• [22] Nigam A. and Gupta A.K., “Performance and simulation between conventional and improved perturb&observe MPPT algorithm for solar PV cell using MATLAB/Simulink”, 2016 International Conference on Control, Computing, Communication and Materials (ICCCCM), Allahbad, 1-4, (2016).
• [23] Riby J., Sheik M.S. and Richu Z.,“Variable step size perturb and observe Mppt algorithm for standalone solar photovoltaic system”, 2017 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS), Srivilliputhur, 1-6, (2017).
• [24] Sun Y., Lou Z., Xi Z., Bao Z., Li X. and Yan W., “Composite MPPT control algorithm with partial shading on PV arrays”, 2018 IEEE 4th Information Technology and Mechatronics Engineering Conference (ITOEC), Chongqing, China, 898-902, (2018).
• [25] Chaieb H. and Sakly A., “Comparison between P&O and P.S.O methods based mppt algorithm for photovoltaic systems”, 2015 16th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA), Monastir, 694-699, (2015).
• [26] Dwivedi M., Mehta G., Iqbal A. and Shekhar H., “Performance enhancement of solar PV system under partial shaded condition using PSO”, 2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Delhi, 1-7, (2017).
• [27] Azab M., “Optimal power point tracking for stand-alone PV system using particle swarm optimization”, 2010 IEEE International Symposium on Industrial Electronics, Bari, 969-973, (2010).
• [28] Liu G., Zhu J., Tao H., Wang W. and Blaabjerg F., “A MPPT algorithm based on PSO for PV array under partially shaded condition”, 2019 22nd International Conference on Electrical Machines and Systems (ICEMS), Harbin, China, 1-5, (2019).
• [29] Teo K.T.K., Lim P.Y., Chua B.L., Goh H.H. and Tan M.K., “Maximum power point tracking of partially shaded photovoltaic arrays using particle swarm optimization”, 2014 4th International Conference on Artificial Intelligence with Applications in Engineering and Technology, Kota Kinabalu, 247-252, (2014).
• [30] Yang X.S., “Nature-inspired metaheuristic algorithms”, University of Cambridge, LuniverPress, Second Edition, United Kingdom, (2010).
• [31] Ahmed J. and Salam Z.,“A soft computing MPPT for PV system based on cuckoo search algorithm”, 4th International Conference on Power Engineering, Energy and Electrical Drives, Istanbul, 558-562, (2013).
• [32] Pant S. and Saini R.P., “Comparative study of MPPT techniques for solar photovoltaic system”, 2019 International Conference on Electrical, Electronics and Computer Engineering (UPCON), ALIGARH, India, 1-6, (2019).
• [33] Ayas S., Doğan H., Gedikli E. and Ekinci M., “Sürü zekâsı optimizasyon algoritmaları tabanlı mikroskobik görüntü segmentasyonu”, Akıllı Sistemler ve Uygulamaları Dergisi, 1: 291-297, (2018).
• [34] Peng B.R., Ho K.C. and Liu Y.H., “A novel and fast MPPT method suitable for both fast changing and partially shaded conditions”, IEEE Transactions on Industrial Electronics, 65: 3240-3251, (2018).
• [35] Ho K.C., Lin C.C., Bagci F.S., Wang S.C. and Liu Y.H., ChengY.S., “Comparison of swarm ıntelligence based global maximum power point tracking methods for photovoltaic generation system”, 2019 10th International Conference on Power Electronics and ECCE Asia (ICPE 2019 - ECCE Asia), Busan, Korea (South), 2019, 1-6, (2019).
• [36] Kalaam R.N.A., “Controller parameter optimization for photovoltaic system using metaheuristic algorithm”, Yüksek Lisans Tezi, United Arab Emirates University, Electrical Engineering, (2015).
• [37] Köse U., “Zeki optimizasyon tabanlı destek vektör makineleri ile diyabet teşhisi”, Politeknik Dergisi, 22: 557-566, (2019).