TY - JOUR
T1 - Reference-Voltage-Line-Aided Power Incremental Algorithm for Photovoltaic GMPPT and Partial Shading Detection
AU - Li, Xingshuo
AU - Zhu, Yinxiao
AU - Wen, Huiqing
AU - Du, Yang
AU - Xiao, Weidong
N1 - Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant 52177195, in part by the Research Development Fund of XJTLU under Grants RDF-16-01-10, RDF- 16-02-31, and RDF-17-01-28, in part by the Research Enhancement Fund of XJTLU under Grant REF-17-01-02, in part by Suzhou Prospective Application Programme underGrant SYG202016, and in part by the XJTLUKey Programme Special Fund under Grants KSF-A-08, KSF-E-13, KSF-E-65, and KSF-T-04.
Publisher Copyright:
© 2010-2012 IEEE.
PY - 2022/7
Y1 - 2022/7
N2 - Conventional global maximum power point tracking (GMPPT) algorithms show limited tracking speed and accuracy due to the unnecessary peak search considering the complexity of partial shading conditions (PSCs). This paper proposes a novel power incremental (PI) algorithm for photovoltaic GMPPT and partial shading detection with the aid of the reference voltage line (RVL), representing the voltage of possible peaks based on the derived analytical expressions. The proposed analytical solution can detect the occurrence of PSCs easily and never overlook GMPP. Furthermore, both the tracking speed and the efficiency can be improved due to the quick allocation of GMPP and the reduced search area. The proposed RVL-PI-GMPPT algorithm is straightforward without sophisticated searching iterations, which can reduce the computational burden. Combined with the low-cost feature without additional temperature or irradiance sensors, the proposed algorithm is very suitable for the practical engineering design. Both the simulations and experiments under various PSC patterns validated the effectiveness of the proposed algorithm. Conducted fair comparisons with other popular GMPPT methods, the average percentage change of tracking time by implementing the proposed RVL-PI-GMPPT algorithm under three-peaks PSC patterns is 108.9% compared with the PSO method and 209.9% compared with the 0.8Voc model method, respectively.
AB - Conventional global maximum power point tracking (GMPPT) algorithms show limited tracking speed and accuracy due to the unnecessary peak search considering the complexity of partial shading conditions (PSCs). This paper proposes a novel power incremental (PI) algorithm for photovoltaic GMPPT and partial shading detection with the aid of the reference voltage line (RVL), representing the voltage of possible peaks based on the derived analytical expressions. The proposed analytical solution can detect the occurrence of PSCs easily and never overlook GMPP. Furthermore, both the tracking speed and the efficiency can be improved due to the quick allocation of GMPP and the reduced search area. The proposed RVL-PI-GMPPT algorithm is straightforward without sophisticated searching iterations, which can reduce the computational burden. Combined with the low-cost feature without additional temperature or irradiance sensors, the proposed algorithm is very suitable for the practical engineering design. Both the simulations and experiments under various PSC patterns validated the effectiveness of the proposed algorithm. Conducted fair comparisons with other popular GMPPT methods, the average percentage change of tracking time by implementing the proposed RVL-PI-GMPPT algorithm under three-peaks PSC patterns is 108.9% compared with the PSO method and 209.9% compared with the 0.8Voc model method, respectively.
KW - Global maximum power point tracking (GMPPT)
KW - partial shading condition (PSC)
KW - photovoltaic (PV) energy
KW - power incremental algorithm
KW - reference voltage line
UR - http://www.scopus.com/inward/record.url?scp=85132515410&partnerID=8YFLogxK
U2 - 10.1109/TSTE.2022.3174614
DO - 10.1109/TSTE.2022.3174614
M3 - Article
AN - SCOPUS:85132515410
SN - 1949-3029
VL - 13
SP - 1756
EP - 1770
JO - IEEE Transactions on Sustainable Energy
JF - IEEE Transactions on Sustainable Energy
IS - 3
ER -