Incremental learning on a budget and its application to quick maximum power point tracking of photovoltaic systems

Abstract

Maximum power point tracking(MPPT) is an essential technique to improving the efficiency of renewable energy systems. Although various techniques exist that can realize MPPT, there are fewer techniques that can realize quick control using conventional circuit design. In this paper, we propose a quick MPPT converter using a limited general regression neural network (LGRNN). The proposed LGRNN is an incremental learning method for regression on a budget [1]. Therefore, the LGRNN is able to work on small embedded systems, which allows the MPPT converter to be constructed at low cost using the normal combination of a chopper circuit and a controlling microcomputer. This means that the MPPT converter can be constructed in a low cost. The LGRNN learns the maximum power point (MPP) found by the perturb and observe (P&O) method, and sets the reference voltage of the converter immediately after a sudden irradiation change. By using this strategy, the MPPT quickly without predetermination of parameters. The experimental results suggests that after learning, the proposed converter controls the chopper circuit within about 20 [ms] after sudden irradiation changes. Moreover, the converter was designed to be attached to each solar panel to obtain the MPP of each panel. The proposed converter was tested with two series-connected solar panels. The results shows that the proposed system maintains a high efficiency even if one of the two panels is shadowed.