Complex control structures are required for the operation of photovoltaic electrical energy systems. In this paper, a general review of the controllers used for photovoltaic systems is presented. This review is based on the most recent papers presented in the literature. The control architectures considered are complex hybrid systems that combine classical and modern techniques, such as artificial intelligence and statistical models. The main contribution of this paper is the synthesis of a generalized control structure and the identification of the latest trends. The main findings are summarized in the development of increasingly robust controllers for operation with improved efficiency, power quality, stability, safety, and economics.

High-order switched DC-DC converters, such as SEPIC, Ćuk and Zeta, are classic energy processing elements, which can be used in a wide variety of applications due to their capacity to step-up and/or step-down voltage characteristic. In this paper, a novel methodology for analyzing the previous converters operating in discontinuous conduction mode (DCM) is applied to obtain full-order dynamic models. The analysis is based on the fact that inductor currents have three differentiated operating sub-intervals characterized by a third one in which both currents become equal, which implies that the current flowing through the diode is zero (DCM). Under a small voltage ripple hypothesis, the currents of all three converters have similar current piecewise linear shapes that allow us to use a graphical method based on the triangular shape of the diode current to obtain the respective non-linear average models. The models’ linearization around their steady-state operating points yields full-order small-signal models that reproduce accurately the dynamic behavior of the corresponding switched model. The proposed methodology is applicable to the proposed converters and has also been extended to more complex topologies with magnetic coupling between inductors and/or an $RC$ damping network in parallel with the intermediate capacitor. Several tests were carried out using simulation, hardware-in-the-loop, and using an experimental prototype. All the results validate the theoretical models.

Coupled-inductor buck-boost dc-dc switching converter has emerged as an alternative to manage power in several hybrid system architectures. This is due to features such as a noninverting voltage step up and step down characteristic, high efficiency, wide-bandwidth, and the possibility to regulate its input or output currents as has been reported in previous works. All of them are based on a small-signal linearized model around an operating point. In this paper, a model predictive control strategy is proposed in order to increase the operation point domain. The proposal consists in the use of the mathematical model of the system in discrete time to obtain the optimal switching state to be applied in the converter, based on a cost function optimization which simultaneously improves the current tracking and reduces the converter power losses. Experimental results validate the proposal demonstrating that this is a good alternative for the control of this kind of power converters.

The digital implementation of all the control loops of a versatile buck-boost (VBB) dc–dc converter used in a stand-alone photovoltaic application is proposed in this paper to improve existing digital-analog sliding-mode-based implementations. All three control loops: maximum power point tracking (MPPT), fast input voltage regulation, and inner high-bandwidth current control, have been programmed in the same digital signal controller (DSC). A Model Predictive Control (MPC) based algorithm has satisfactorily solved the challenge of implementing the nominal 100 kHz switching frequency current loop. The MPC cost function is distributed throughout the algorithm to achieve three specific goals: the tracking of the reference current (G1), a quasi-constant steady-state switching frequency (G2), and the assurance that the duration of an interval is larger than the time required to calculate it (G3). The third goal requires the current control to toggle between peak- and valley-modes depending on the operating point. The correct fulfillment of these control objectives on the proposed MPC-based algorithm has been validated through simulations and experimental tests performed on a purpose built-prototype.

DC microgrid (DCMG) is a promising technology for integrating distributed resources, such as solar generation and energy storage devices, that are intrinsically DC. Recently, model predictive control (MPC) is one of the control techniques that has been widely used in microgrid applications due to its advantages, such as transient response and flexibility to nonlinearity inclusion. MPC applications can be centralized, distributed, or decentralized based on the communication architecture. A major disadvantage of the centralized model predictive control (CMPC) is the high computational effort. This paper proposes a CMPC for DCMG stabilization that uses the admittance matrix of a reduced DCMG in the prediction equation and the one-step prediction horizon to decrease the computational effort. The proposed CMPC also replaces the hierarchical architecture primary and secondary controls, achieving voltage or power regulation. A hardware-in-the-loop (HIL) tool, known as RT-Box 2, has been used to emulate an 8-node DC microgrid with versatile buck–boost converters at the supply and power consumption nodes. The proposed predictive control exhibited better performance in comparison with the averaged voltage control in the HIL experiments.

Model Predictive Control (MPC) has emerged as a promising alternative for controlling power converters, offering benefits such as flexibility, simplicity, and rapid control response, particularly when short-horizon algorithms are employed. This paper introduces a system using a short-horizon Finite Control Set MPC (FCS-MPC) strategy to specifically address the challenge of non-minimum phase behavior in boost converters. The non-minimum phase issue, which complicates the control process by introducing an initial inverse response, is effectively mitigated by the proposed method. A Proportional–Integral (PI) controller is integrated to dynamically adjust the reference current based on the output voltage error, thereby enhancing overall system stability and performance. Unlike conventional PI-MPC methods, where the PI controller has an influence on the system dynamics, the PI controller in this approach is solely used for tuning the reference current needed for the FCS-MPC controller. The PI controller addresses small deviations in output voltage, primarily due to model prediction inaccuracies, ensuring steady-state accuracy, while the FCS-MPC handles fast dynamic responses to adapt the controller’s behavior based on load conditions. This dual control strategy effectively balances the need for precise voltage regulation and rapid adaptation to varying load conditions. The proposed method’s effectiveness is validated through a multi-stage simulation test, demonstrating significant improvements in response time and stability compared to traditional control methods. Hardware-in-the-loop testing further confirms the system’s robustness and potential for real-time applications in power electronics.