Small-signal modelling of maximum power point tracking for photovoltaic systems

Abstract

In grid connected photovoltaic (PV) generation systems, inverters are used to convert the generated DC voltage to an AC voltage. An additional dc-dc converter is usually connected between the PV source and the inverter for Maximum Power Point Tracking (MPPT). An iterative MPPT algorithm searches for the optimum operating point of PV cells to maximise the output power under various atmospheric conditions. It is desirable to be able to represent the dynamics of the changing PV power yield within stability studies of the AC network. Unfortunately MPPT algorithms tend to be nonlinear and/or time-varying and cannot be easily combined with linear models of other system elements. In this work a new MPPT technique is developed in order to enable linear analysis of the PV system over reasonable time scales. The new MPPT method is based on interpolation and an emulated-load control technique. Numerical analysis and simulations are employed to develop and refine the MPPT. The small-signal modelling of the MPPT technique exploits the fact that the emulated-load control technique can be linearised and that short periods of interpolation can be neglected. A small-signal PV system model for variable irradiation conditions was developed. The PV system includes a PV module, a dc-dc boost converter, the proposed controller and a variety of possible loads. The new model was verified by component-level time-domain simulations. Be cause measured signals in PV systems contain noise, it is important to assess the impact of that noise on the MPPT and design an algorithm that operates effectively in pr esence of noise. For performance assessment of the new MPPT techniques, the efficiencies of various MPPT techniques in presence of noise were compared. This comparison showed superiority of the interpolation MPPT and led to conclusions about effective use of existing MPPT methods. The new MPPT method was also experimentally tested.