A Comparison of MPPT Control of Photovoltaic System using Conventional and Artificial Intelligence Techniques
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2016 by IJETT Journal|
|Year of Publication : 2016|
|Authors : Aneetta Raj, Manju Sreekumar
|DOI : 10.14445/22315381/IJETT-V38P204|
Aneetta Raj, Manju Sreekumar"A Comparison of MPPT Control of Photovoltaic System using Conventional and Artificial Intelligence Techniques", International Journal of Engineering Trends and Technology (IJETT), V38(1),16-19 August 2016. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
In a photovoltaic system, the maximum power point varies with insolation and cell temperature. Maximum power point tracking (MPPT) is implemented to identify the maximum power operating point and subsequently system is operated at that particular operating voltage for maximum power gaining. This algorithm is implemented in charge controllers for extracting maximum available power from PV module. Maximum power point tracking in photovoltaic systems using artificial intelligence methods are very popular. Adaptive- Neuro Fuzzy Inference Systems (ANFIS) are found to be very effective than simple conventional MPPT tracking. In the simulation part, a buck-boost converter feeding a permanent magnet dc motor load is achieved. The accuracy of the overall system depends on the fuzzy rule base and membership functions defined. The performance curves for comparison between MPPT tracking, fuzzy and ANFIS controllers were obtained using MATLAB/Simulink platform. Simulation results show that ANFIS based tracking has better performance in terms of response time, settling time and initial perturbance.
 Ahmed M. Othman, Mahdi M.M. El-arini , Ahmed Ghitas , Ahmed Fathy, “Realworld maximum power point tracking simulation of PV system based on Fuzzy Logic control”, NRIAG Journal of Astronomy and Geophysics, 2012.
 Basil M. Hamed, “Fuzzy Controller Design Using FPGA for Photovoltaic Maximum Power Point Tracking,”(IJARAI) International Journal of Advanced Research in Artificial Intelligence, Vol. 1, No. 3, 2012
 Ahmed M. Othman, Mahdi M.M. El-arini , Ahmed Ghitas , Ahmed Fathy, “Realworld maximum power point tracking simulation of PVsystem based on Fuzzy Logic control”, NRIAG Journal of Astronomy and Geophysics, 2012
 Navneet Walia, Harsukhpreet Singh, Anurag Sharma, “ANFIS: Adaptive Neuro-Fuzzy Inference System- A Survey”, International Journal of Computer Applications, Vol. 123, No.13, August 2015.
 Rasoul Rahmani, Mohammadmehdi Seyedmahmoudian, Saad Mekhilef and Rubiyah Yusof, “Implementation Of Fuzzy Logic Maximum Power Point Tracking Controller For Photovoltaic System”,American Journal of Applied Sciences, pp-209-218, 2013.
ANFIS- Adaptive-neuro fuzzy inference system, FLC-Fuzzy Logic Controller, MPPT-Maximum power point tracking, PV-Photo Voltaic.