Neural-network-based maximum power point tracking methods for photovoltaic systems operating under fast changing environments

Photovoltaic (PV) generation systems (PGSs) have become an attractive option among renewable energy sources because they are clean, maintenance-free and environmental friendly. For PGSs, a simple and fast maximum power point tracking (MPPT) algorithm is essential. Although the static tracking efficiency of conventional MPPT method is usually high, it drops noticeably in case of rapidly changing irradiance conditions. In this paper, two fast and accurate digital MPPT methods for fast changing environments are proposed. By using piecewise line segments or cubic equation to approximate the maximum power point (MPP) locus, two high-speed, low-complexity MPPT techniques can be developed. To make the developed system more convenient for common PGS users, neural network (NN)-based program which can be used to calculate the parameters of the emulated MPP locus is also developed and embedded into the proposed digital MPPT system. Theoretical derivation and detailed design procedure will be provided in this paper. The advantages of the proposed system include low computation requirement, fast tracking speed and high static/dynamic tracking efficiencies. To validate the effectiveness and correctness of the proposed methods, simulation and experimental results of a 230 W PV system will also be provided.