A Lyapunov function based model predictive control for three phase grid connected photovoltaic converters

In this paper, a predictive direct power control (PDPC) method based on Lyapunov function approach is developed for control of grid connected photovoltaic (PV) converter. The predictive control algorithm utilizes the discrete model of the PV converter and predicts the future active and reactive power of the system by calculating a cost function for all voltage vectors. Subsequently, the optimal voltage vector that minimizes the cost function is selected to calculate the model variables in the next sampling instant. The proposed controller predicts error before the switching signal is applied to the high-gain multilevel PV converter and thus is able to choose the next switch event to minimize error between the commanded and actual converter operation. This technique improves the speed at which the controller can track rapid changes in solar insolation and results in an increase in the overall system output power. In the proposed technique, there is no need to adjust the control parameters under changing loads or model parameters uncertainties. Also, the use of many classical controllers is eliminated and only one PI controller is used for DC link voltage. Moreover, contrary to the conventional PDPC, the optimal switching vector is determined only based on active and reactive power and hence, the computation burden is significantly reduced and the convergence is speed up. The detail modeling and design of the proposed controller are verified through computer simulations under different operating conditions. Also, the robustness of the control approach is illustrated in the presence of parameters uncertainties and change in solar irradiations. Simulation results show that the proposed control strategy maintains the active and reactive powers as close as possible to the desired values under different operation mode.