TY - GEN
T1 - An ANN-based maximum power point tracking method for fast changing environments
AU - Chiu, Yi Hsun
AU - Luo, Yi Feng
AU - Huang, Jia Wei
AU - Liu, Yi Hua
PY - 2012
Y1 - 2012
N2 - Photovoltaic generation system (PGS) is becoming increasingly important as renewable energy sources due to its advantages such as absence of fuel cost, low maintenance requirement and environmental friendliness. For PGS, a simple and fast maximum power point tracking (MPPT) algorithm is vital. Although the static tracking efficiency of conventional MPPT method is usually high, it drops noticeably under fast changing environments. In this paper, a simple and fast MPPT method is proposed. By using piecewise line segments (PLS) to approximate the maximum power point (MPP) locus, a highspeed, low-complexity MPPT technique can be developed. To simplify the design procedure, an artificial neural network (ANN) is also developed to calculate the parameters of the MPP locus. Theoretical derivation and design procedure will be provided in this paper. The proposed methods can achieve high static and dynamic tracking efficiencies. To validate the feasibility of the proposed methods, simulation and experimental results of a 230 W PV system will also be provided.
AB - Photovoltaic generation system (PGS) is becoming increasingly important as renewable energy sources due to its advantages such as absence of fuel cost, low maintenance requirement and environmental friendliness. For PGS, a simple and fast maximum power point tracking (MPPT) algorithm is vital. Although the static tracking efficiency of conventional MPPT method is usually high, it drops noticeably under fast changing environments. In this paper, a simple and fast MPPT method is proposed. By using piecewise line segments (PLS) to approximate the maximum power point (MPP) locus, a highspeed, low-complexity MPPT technique can be developed. To simplify the design procedure, an artificial neural network (ANN) is also developed to calculate the parameters of the MPP locus. Theoretical derivation and design procedure will be provided in this paper. The proposed methods can achieve high static and dynamic tracking efficiencies. To validate the feasibility of the proposed methods, simulation and experimental results of a 230 W PV system will also be provided.
KW - Artificial Neural Network
KW - Maximum power point tracking (MPPT)
KW - Photovoltaic (PV)
UR - https://www.scopus.com/pages/publications/84877822077
U2 - 10.1109/SCIS-ISIS.2012.6505228
DO - 10.1109/SCIS-ISIS.2012.6505228
M3 - 会议稿件
AN - SCOPUS:84877822077
SN - 9781467327428
T3 - 6th International Conference on Soft Computing and Intelligent Systems, and 13th International Symposium on Advanced Intelligence Systems, SCIS/ISIS 2012
SP - 715
EP - 720
BT - 6th International Conference on Soft Computing and Intelligent Systems, and 13th International Symposium on Advanced Intelligence Systems, SCIS/ISIS 2012
T2 - 2012 Joint 6th International Conference on Soft Computing and Intelligent Systems, SCIS 2012 and 13th International Symposium on Advanced Intelligence Systems, ISIS 2012
Y2 - 20 November 2012 through 24 November 2012
ER -
