 Artificial neural network based modified incremental conductance algorithm for maximum power point tracking in photovoltaic system under partial shading conditionsK. Punitha, D. Devaraj and S. Sakthivel Energy, 2013, vol. 62, issue C, 330-340 Abstract: In solar PV (photovoltaic) system, tracking the module's MPP (maximum power point) is challenging due to varying climatic conditions. Moreover, the tracking algorithm becomes more complicated under the condition of partial shading due to the presence of multiple peaks in the power voltage characteristics. This paper presents a NN (neural network) based modified IC (incremental conductance) algorithm for MPPT (maximum power point tracking) in PV system. The PV system along with the proposed MPPT algorithm was simulated using Matlab/Simulink simscape tool box. The simulated system was evaluated under uniform and non-uniform irradiation conditions and the results are presented. For comparison, P&O (perturb and observe) and Fuzzy based Modified Hill Climbing algorithms were used for MPP tracking, and the results show that the proposed approach is effective in tracking the MPP under partial shading conditions. To validate the simulated system hardware implementation of the proposed algorithm was carried out using FPGA (Field Programmable Gate Array). Keywords: Photovoltaic power generation; Maximum power point tracking; Neural network; Partial shading; FPGA (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:62:y:2013:i:c:p:330-340 DOI: 10.1016/j.energy.2013.08.022 Access Statistics for this article Energy is currently edited by Henrik Lund and Mark J. Kaiser More articles in Energy from Elsevier |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.