TY - GEN
T1 - Predicting the Global Maximum Power Point Locus using Shading Information
AU - Ma, Jieming
AU - Bi, Ziqiang
AU - Man, Ka Lok
AU - Liang, Hai Ning
AU - Smith, Jeremy S.
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/6
Y1 - 2019/6
N2 - The power-voltage characteristic curves of a series-connected photovoltaic (PV) system exhibit multiple peaks in partial shading scenario (PSS). However, there lacks a feasible model that obtains the capability of predicting the global maximum power point (GMPP) locus. This paper analyzes the GMPP characterization and demonstrates that the voltage at the GMPP is located over a large range in various PSS. With the aim of improving the prediction performance, multiple Gaussian process regression (MGPR) models are proposed to predict the GMPP locus by using shading information, such as the shading rate and shading strength. The performance of the method is evaluated using a dataset obtained in a variety of environmental conditions. The results show that it outperforms the existing prediction models in terms of accuracy. With its high prediction capability, the proposed method can be used to increase the maximum power point tracking performance in PV systems.
AB - The power-voltage characteristic curves of a series-connected photovoltaic (PV) system exhibit multiple peaks in partial shading scenario (PSS). However, there lacks a feasible model that obtains the capability of predicting the global maximum power point (GMPP) locus. This paper analyzes the GMPP characterization and demonstrates that the voltage at the GMPP is located over a large range in various PSS. With the aim of improving the prediction performance, multiple Gaussian process regression (MGPR) models are proposed to predict the GMPP locus by using shading information, such as the shading rate and shading strength. The performance of the method is evaluated using a dataset obtained in a variety of environmental conditions. The results show that it outperforms the existing prediction models in terms of accuracy. With its high prediction capability, the proposed method can be used to increase the maximum power point tracking performance in PV systems.
KW - Gaussian process regression
KW - Global maximum power Point
KW - partial shading scenario
UR - http://www.scopus.com/inward/record.url?scp=85070836682&partnerID=8YFLogxK
U2 - 10.1109/EEEIC.2019.8783326
DO - 10.1109/EEEIC.2019.8783326
M3 - Conference Proceeding
AN - SCOPUS:85070836682
T3 - Proceedings - 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2019
BT - Proceedings - 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2019
Y2 - 11 June 2019 through 14 June 2019
ER -