TY - JOUR
T1 - Detection and Assessment of Partial Shading Scenarios on Photovoltaic Strings
AU - Ma, Jieming
AU - Pan, Xinyu
AU - Man, Ka Lok
AU - Li, Xingshuo
AU - Wen, Huiqing
AU - On Ting, Tiew
N1 - Publisher Copyright:
© 1972-2012 IEEE.
PY - 2018/11/1
Y1 - 2018/11/1
N2 - There has been growing interest in using photovoltaic (PV) energy harvesting technology to reduce reliance on mineral-based energy. Partial shading scenarios (PSS) significantly affect the electrical characteristics of a PV generator. However, a few studies have devoted to the detection and assessment of the PSS. In this paper, shading rate and shading strength are used to characterize the PSS. A shading pattern detection algorithm is proposed to estimate the number of shaded modules in a PV string and distinguish the PSS from uniform irradiation scenarios. A multiple-output support vector regression is applied to estimate the shading strength. In addition, the maximum power point voltage of the applied PV generation system can be predicted from measured data. Simulation and experimental validation show the feasibility of the proposed method in the face of various environmental conditions. It could be used as a preliminary step toward automatic supervision and monitoring PV generation system.
AB - There has been growing interest in using photovoltaic (PV) energy harvesting technology to reduce reliance on mineral-based energy. Partial shading scenarios (PSS) significantly affect the electrical characteristics of a PV generator. However, a few studies have devoted to the detection and assessment of the PSS. In this paper, shading rate and shading strength are used to characterize the PSS. A shading pattern detection algorithm is proposed to estimate the number of shaded modules in a PV string and distinguish the PSS from uniform irradiation scenarios. A multiple-output support vector regression is applied to estimate the shading strength. In addition, the maximum power point voltage of the applied PV generation system can be predicted from measured data. Simulation and experimental validation show the feasibility of the proposed method in the face of various environmental conditions. It could be used as a preliminary step toward automatic supervision and monitoring PV generation system.
KW - Measurement
KW - partial shading conditions
KW - partial shading detection
KW - photovoltaic (PV) cells
KW - support vector regression (SVR)
UR - http://www.scopus.com/inward/record.url?scp=85048598028&partnerID=8YFLogxK
U2 - 10.1109/TIA.2018.2848643
DO - 10.1109/TIA.2018.2848643
M3 - Article
AN - SCOPUS:85048598028
SN - 0093-9994
VL - 54
SP - 6279
EP - 6289
JO - IEEE Transactions on Industry Applications
JF - IEEE Transactions on Industry Applications
IS - 6
M1 - 8387864
ER -