TY - JOUR
T1 - Improved differential evolution-based MPPT algorithm using SEPIC for PV systems under partial shading conditions and load variation
AU - Tey, Kok Soon
AU - Mekhilef, Saad
AU - Seyedmahmoudian, Mehdi
AU - Horan, Ben
AU - Than Oo, Amanullah
AU - Stojcevski, Alex
PY - 2018/10
Y1 - 2018/10
N2 - Photovoltaic arrays subject to partial shading conditions have more than one maximum power point (MPP), and conventional algorithms are unable to track the global maximum power point (GMPP) accurately. Thus, an improved global search space differential evolution algorithm for tracking the GMPP is introduced in this paper. The main contribution of the proposed algorithm are the following: capability in tracking GMPP and faster respond against load variation; optimization algorithm can search for the GMPP within a larger operating region as it is implemented by using a single-ended primary-inductor converter; and easy tuning as less parameter has to be set in the algorithm. The proposed system is first simulated in PSIM to ensure its capability. The feasibility of the approach is validated through physical implementation and experimentation. Results demonstrate that the proposed algorithm has the capability to track the GMPP within 2 s with an accuracy of 99% and respond to load variation within 0.1 s.
AB - Photovoltaic arrays subject to partial shading conditions have more than one maximum power point (MPP), and conventional algorithms are unable to track the global maximum power point (GMPP) accurately. Thus, an improved global search space differential evolution algorithm for tracking the GMPP is introduced in this paper. The main contribution of the proposed algorithm are the following: capability in tracking GMPP and faster respond against load variation; optimization algorithm can search for the GMPP within a larger operating region as it is implemented by using a single-ended primary-inductor converter; and easy tuning as less parameter has to be set in the algorithm. The proposed system is first simulated in PSIM to ensure its capability. The feasibility of the approach is validated through physical implementation and experimentation. Results demonstrate that the proposed algorithm has the capability to track the GMPP within 2 s with an accuracy of 99% and respond to load variation within 0.1 s.
UR - http://www.scopus.com/inward/record.url?scp=85041216480&partnerID=8YFLogxK
U2 - 10.1109/TII.2018.2793210
DO - 10.1109/TII.2018.2793210
M3 - Article
AN - SCOPUS:85041216480
SN - 1551-3203
VL - 14
SP - 4322
EP - 4333
JO - IEEE Transactions on Industrial Informatics
JF - IEEE Transactions on Industrial Informatics
IS - 10
ER -