ŞEBEKEDEN BAĞIMSIZ FV SİSTEMDE MAKSİMUM GÜÇ NOKTASI TAKİP ALGORİTMALARININ DEĞİŞKEN HAVA ŞARTLARI ALTINDA KARŞILAŞTIRMALI ANALİZİ
Abstract
Güneş enerjisi en uygun alternatif enerji
kaynağıdır; buna ek olarak güneş enerjisi teknolojilerinin uygulanması,
elektrik enerjisi taleplerini güvenceye almanın yanı sıra çevre kirliliği ve
elektrik üretim maliyetlerini de azaltabilir. Bu çalışmada, maksimum güç
noktası takip (MGNT) sistemlerinde kullanılan üç algoritmanın değerlendirilmesi
ele alınmaktadır. Bunlar sırası ile Değiştir ve Gözetle (D&G), Artımlı
İletkenlik (Aİ) ve Bulanık Mantık (BM) tabanlı algoritmalardır. Bu algoritmalar
basitliği ve gerçekleştirme kolaylığı nedeniyle MGNT'de en çok kullanılanlar
olarak kabul edilirler. Algoritmalar Matlab/Simulink ortamında tasarlanmış ve
üç algoritmanın matematik modelleri çeşitli hava koşullarında test edilmiştir.
Benzetimler sonunda bu üç algoritma içerisinden bulanık mantık algoritmasının
diğerlerine oranla daha fazla enerji sağlayabildiği, daha az salınım yaptığı ve
değişken hava koşulları altında daha hızlı yanıt verdiği gözlenmiştir.
Supporting Institution
Selçuk Üniversitesi Bilimsel Araştırma Projeleri (BAP) Koordinatörlüğü
Project Number
17401144
Thanks
17401144 numaralı projeye sağlamış olduğu destekten dolayı, Selçuk Üniversitesi Bilimsel Araştırma Projeleri (BAP) Koordinatörlüğü’ne teşekkür ederiz.
References
- S. Mulel, R. Hardas, and N. Kulkarni, “P&O, IncCon and Fuzzy Logic Implemented MPPT Scheme for PV Systems using PIC18F452,” in IEEE WiSPNET Conference, 2016.
- P. Takun, S. Kaitwanidvilai, and C. Jettanasen, “Maximum Power Point Tracking using Fuzzy Logic Control for Photovoltaic Systems,” in International Multi conference of Engineers and Computer Scientists, Vol II, Hong Kong, March 2011.
- Y.Yi Hong, “Real-Time Simulation of Maximum Power Point Tracking Control U sing Fuzzy Logic for Stand Alone PV System” IEEE Transactions on Industrial Electronics, 2017.
- R. Mahalakshmi, A. Kumar, “Design of Fuzzy Logic Based Maximum Power Point Tracking Controller for Solar Array for Cloudy Weather Conditions.” IEEE Towards Sustainable Energy, 2014.
- Ch. Yan Chuang, P. Syun Chen, “Novel maximum power point tracker for PV systems using interval type-2 fuzzy logic controller,” IEEE Transactions on Industrial Electronics, 2017.
- N. Karamia, N. Moubayedb, and R. Outbibc, “General review and classification of different MPPT Techniques,” in Renewable and Sustainable Energy Reviews, pp. 1–18, 2017.
- A. Safari and S. Mekhilef, “Simulation and Hardware Implementation of Incremental Conductance MPPT with Direct Control Method Using Cuk Converter,” IEEE Transactions on Industrial Electronics, Vol. 58, No. 4, April 2011.
- M. Kumar, S. Kapoor, R. Nagar, and A. Verma, “Comparison between IC and Fuzzy Logic MPPT Algorithm Based Solar PV System using Boost Converter,” International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol. 4, Issue 6, June 2015.
- S. Soltani, and M. Kouhanjani. “Fuzzy Logic Type-2 Controller Design for MPPT in Photovoltaic System,” 22nd Electrical Power Distribution Conference, April 2017.
Comparative Analysis of Maximum Power Point Tracking Algorithms under Various Weather Conditions for Standalone PV System
Abstract
Solar energy is the most viable alternative
source; furthermore, the implementation of solar energy technologies can reduce
the problems of environmental pollution and electricity production costs
besides securing the demands of electrical power. This research addresses the
evaluation of three algorithms used in maximum power point tracking systems
(MPPT). These algorithms are Perturbation & Observation (P&O),
Incremental Conductance (IC) and Fuzzy Logic (FL). They are considered as the
most used in MPPT due to their simplicity and ease of realization. Based on
Matlab/Simulink environment, the mathematical models of the three algorithms
are designed and tested under various weather conditions. Collected simulation
results illustrated the effectiveness of Fuzzy logic algorithm to draw more
energy, decrease oscillation and provide a fast response under variable weather
condition. The final simulation results show the fuzzy logic algorithm exhibits
a better performance compared to both perturbation & observation and incremental
conductance algorithms.
Project Number
17401144
References
- S. Mulel, R. Hardas, and N. Kulkarni, “P&O, IncCon and Fuzzy Logic Implemented MPPT Scheme for PV Systems using PIC18F452,” in IEEE WiSPNET Conference, 2016.
- P. Takun, S. Kaitwanidvilai, and C. Jettanasen, “Maximum Power Point Tracking using Fuzzy Logic Control for Photovoltaic Systems,” in International Multi conference of Engineers and Computer Scientists, Vol II, Hong Kong, March 2011.
- Y.Yi Hong, “Real-Time Simulation of Maximum Power Point Tracking Control U sing Fuzzy Logic for Stand Alone PV System” IEEE Transactions on Industrial Electronics, 2017.
- R. Mahalakshmi, A. Kumar, “Design of Fuzzy Logic Based Maximum Power Point Tracking Controller for Solar Array for Cloudy Weather Conditions.” IEEE Towards Sustainable Energy, 2014.
- Ch. Yan Chuang, P. Syun Chen, “Novel maximum power point tracker for PV systems using interval type-2 fuzzy logic controller,” IEEE Transactions on Industrial Electronics, 2017.
- N. Karamia, N. Moubayedb, and R. Outbibc, “General review and classification of different MPPT Techniques,” in Renewable and Sustainable Energy Reviews, pp. 1–18, 2017.
- A. Safari and S. Mekhilef, “Simulation and Hardware Implementation of Incremental Conductance MPPT with Direct Control Method Using Cuk Converter,” IEEE Transactions on Industrial Electronics, Vol. 58, No. 4, April 2011.
- M. Kumar, S. Kapoor, R. Nagar, and A. Verma, “Comparison between IC and Fuzzy Logic MPPT Algorithm Based Solar PV System using Boost Converter,” International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol. 4, Issue 6, June 2015.
- S. Soltani, and M. Kouhanjani. “Fuzzy Logic Type-2 Controller Design for MPPT in Photovoltaic System,” 22nd Electrical Power Distribution Conference, April 2017.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Engineering |
| Journal Section | Research Article |
| Authors | |
| Project Number | 17401144 |
| Publication Date | September 1, 2019 |
| Submission Date | January 21, 2019 |
| Acceptance Date | February 27, 2019 |
| Published in Issue | Year 2019 |