Kouro Renaer, Samir Felipe

Energy structures from non-conventional energy source has become highly demanded nowadays. In this way, the maximum power extraction from photovoltaic (PV) systems has attracted the attention, therefore an optimization technique is necessary to improve the performance of solar systems. This article proposes the use of ABC (artificial bee colony) algorithm for the maximum power point tracking (MPPT) of a PV system using a DC-DC converter. The procedure of the ABC MPPT algorithm is using data values from PV module, the P-V characteristic is identified and the optimal voltage is selected. Then, the MPPT strategy is applied to obtain the voltage reference for the outer PI control loop, which in turn provides the current reference to the predictive digital current programmed control. A real-time and high-speed simulator (PLECS RT Box 1) and a digital signal controller (DSC) are used to implement the hardware-in-the-loop system to obtain the results. The general system does not have a high computational cost and can be implemented in a commercial low-cost DSC (TI 28069M). The proposed MPPT strategy is compared to the conventional perturb and observe method, results show the proposed method archives a much superior performance.

This paper proposes a new method for maximum power point tracking (MPPT) of the photovoltaic (PV) system while using a DC-DC boost converter. The conventional perturb and observe (P&O) method has a fast tracking response, but it presents oscillation around the maximum power point (MPP) in steady state. Therefore, to satisfy transient and steady-state responses, this paper presents a MPPT method using support vector machines (SVMs). The use of SVM will help to improve the tracking speed of maximum power point of the PV system without oscillations near MPP. A boost converter is used to implement the MPPT method, where the input voltage of the DC-DC converter is regulated using a double loop where the inner loop is a current control that is based on passivity. The MPPT structure is validated by hardware in the loop, a real time and high-speed simulator (PLECS RT Box 1), and a digital signal controller (DSC) are used to model the PV system and implement the control strategies, respectively. The proposed strategy presents low complexity and it is implemented in a commercial low-cost DSC (TI 28069M). The performance of the MPPT proposed is presented under challenging experimental profiles with solar irradiance and temperature variations across the panel. In addition, the performance of the proposed method is compared with the P&O method, which is traditionally most often used in MPPT under demanding tests, in order to demonstrate the superiority of the strategy presented.

The articles in this special issue focus on the latest achievements of modular multilevel converters regarding the development of new circuit configurations, converter models, modulation strategies, and control schemes.

Photovoltaic (PV) microinverters have grown rapidly in the small-scale PV market, where typical two-stage converters are used to connect one PV module to the single-phase AC grid. This configuration achieves better performance in terms of energy yield compared with other PV configurations. However, the conversion efficiency of a two-stage system is the main drawback, especially when a high-voltage gain effort is required. In this context, single-stage microinverter topologies have been recently proposed since only one power conversion stage is required to extract the maximum power of the PV module and inject the AC power to the grid. This single-stage configuration allows considerable improvement of the overall efficiency of microinverters by reducing the number of elements in the system. However, the main challenge of these topologies is their control, since all variables of the converter are composed by the AC waveform with DC-bias. In this paper, four control strategies are analyzed for the mainstream single-stage topology, which is the dual-boost inverter (DBI). Classical linear control and three non-linear strategies, namely finite control set–model predictive control, flatness-based control, and sliding mode control, are detailed. The main contribution of this work is a complete comparison of the control strategies, to give insights into the most suitable control strategy for the DBI in PV microinverter application.