An ANN-based Maximum Power Point Tracking Method for Fast Changing Environments

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.