This paper presents a novel simplified method to implement the finite set -model predictive control technique for photovoltaic generation systems connected to the ac network. This method maintains the advantages of the conventional finite set -model predictive control, such as fast response, simple implementation, and easy understanding; but it also eliminates the use of a cost function and hence the weighting factors, instead, it finds the optimal operating state directly from the model and the discrete number of valid states of the converter. Although the proposed algorithm does not compute a cost function, it is able to select the inverter state that minimizes the tracking error by using a hexagonal convergence region. The main advantage of this technique is to reduce the computational cost in 43% of the algorithm that selects the best state, presenting a simple and complete algorithm without compromising the predictive control performance. The proposed algorithm properly operates under various conditions such as changes in the network frequency and changes in the system parameters.INDEX TERMS Predictive control, Solar power generation, ac-dc power converters, Fast MPC.
Solar power generation has become a solution to mitigate the severe effects on the everyday higher prices of fossil fuels. Additionally, renewable energies operation -as solar-results in a non-polluting way to supply energy, being of special interest into highly contaminated cities and/or countries. The solar energy efficiency injection system is known to be high and mainly due to the power converters effectiveness, which is over of 95% for low and medium voltage. However, this efficiency is reduced when the solar array is partially shaded because traditional maximum power point tracking (MPPT) algorithms are not able to find the maximum power point (MPP) under irregular radiation. This work presents a new algorithm to find the global MPP (GMPP) based upon two MPPTs algorithms used regularly in uniform solar condition (USC), these are the Measuring Cell (MC) and the Perturb and Observe (P&O) methods. The MC ensures to find the surroundings of every local MPP (LMPP) faster and then choose among them the surroundings of the GMPP. Once the surroundings of GMPP are found, the P&O is used to get closer to the GMPP but reducing the DC voltage oscillation to zero hence overcoming the main issue of the P&O. Thus, the proposed algorithm finds the GMPP in two main steps and eliminates the oscillations around the GMPP in steady state, despite the utilization of the P&O. The algorithm is detailed mathematically, illustrated by means of a block diagram, and validated in simulated and experimental results.
Several control strategies have been proposed with the aim to get a desired behavior in the power converter variables. The most employed control techniques are linear control, nonlinear control based on linear and nonlinear feedback, and predictive control. The controllers associated with linear and nonlinear algorithms usually have a fixed switching frequency, featuring a defined spectrum given by the pulse width modulation (PWM) or space vector modulation (SVM) time period. On the other hand, finite set model predictive control (FS-MPC) is known to present a variable switching frequency that results too high for high power applications, increasing losses, reducing the switches lifetime and, therefore, limiting its application. This paper proposes a predictive control approach using a very low sampling frequency, allowing the use of predictive control in high power applications. The proposed method is straightforward to understand, is simple to implement, and can be computed with off-the-shelf digital systems. The main advantage of the proposed control algorithm comes from the combination of the model predictive control and the SVM technique, drawing the principal benefits of both methods. The provided experimental results are satisfactory, displaying the nature of space vector-based schemes but at the same time the fast response as expected in predictive control.
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