Browsing by Author "Rodriguez, Jose"

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.

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.

Finite Control Set Model Predictive Control (FCS-MPC), thanks to its simple and powerful concept, has been applied widely to control the multilevel converters. FCS-MPC allows the control of several objectives and can incorporate restrictions, even nonlinear, into the mathematical model of the system under consideration. However, FCS-MPC needs to evaluate all the switching states of the multilevel converter to find the best switching state to be applied in the next sampling time. For higher-level multilevel converters, this iterative action requires computational capacity that is far beyond the digital controller’s capacity in the current market. This paper proposes a new predictive geometric pre-filtering strategy to reduce the iterations and computational burden without affecting the dynamic performance of FCS-MPC. This method consists of a novel pre-filtering stage that uses the predictive model of the system and geometrical properties to find the sector where the reference vector is located and evaluates few vectors that constitute the optimal sector. The proposed method is experimentally validated using a four-level three-cell flying capacitor converter with 512 voltage vectors, obtaining a 64% reduction in the computational burden, while achieving excellent electrical performance indices and maintains the high dynamic performance of the standard FCS-MPC.

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.

Multilevel inverters (MLIs) have lately become important due to their extended application to electrical transmission and distribution systems. At the same time, the control and modulation of MLIs are especially challenging due to the high number of switching states, many of them redundant in terms of output voltage generation, and their nonlinear characteristics. In order to ease their implementation in real environment, model predictive control (MPC) is often considered, where the main control targets are: 1) to generate a the desired output current and 2) to keep the internal converter capacitor voltages at their reference value. However, a major issue with the implementation of MPC in MLIs is that the number of calculations to be done online increases dramatically with the number of levels, making it almost impossible to apply MPC in some practical cases. For these reasons, one of the main research trend in MPC for MLIs is to provide an algorithm which can reduce the computational burden necessary to operate the control. The article proposes a review of such control techniques. Starting from the basic MPC implementation and using a flying capacitor converter as an example the article review the basic strategies to avoid calculating the weighting factor in the cost function, simplifying the implementation. Also, methods to reduce the number of calculations necessary to implement MPC are shown and applied to cascaded H-bridge converters. These techniques allow to keep an high load current quality while reducing more than 95% in the number of calculations necessary to implement the control. Finally, other operation improvements of MPC are also included, such as fixed switching frequency operation and multistep MPC, reaching an important performance improvement compared to the basic MPC strategy.

Multilevel inverters (MLIs) have lately become important due to their extended application to electrical transmission and distribution systems. At the same time, the control and modulation of MLIs are especially challenging due to the high number of switching states, many of them redundant in terms of output voltage generation, and their nonlinear characteristics. In order to ease their implementation in real environment, model predictive control (MPC) is often considered, where the main control targets are: 1) to generate a the desired output current and 2) to keep the internal converter capacitor voltages at their reference value. However, a major issue with the implementation of MPC in MLIs is that the number of calculations to be done online increases dramatically with the number of levels, making it almost impossible to apply MPC in some practical cases. For these reasons, one of the main research trend in MPC for MLIs is to provide an algorithm which can reduce the computational burden necessary to operate the control. The article proposes a review of such control techniques. Starting from the basic MPC implementation and using a flying capacitor converter as an example the article review the basic strategies to avoid calculating the weighting factor in the cost function, simplifying the implementation. Also, methods to reduce the number of calculations necessary to implement MPC are shown and applied to cascaded H-bridge converters. These techniques allow to keep an high load current quality while reducing more than 95% in the number of calculations necessary to implement the control. Finally, other operation improvements of MPC are also included, such as fixed switching frequency operation and multistep MPC, reaching an important performance improvement compared to the basic MPC strategy.

This article presents the use of model predictive control (MPC) in multilevel inverters for some applications, such as, first, wind generation and, second, photovoltaics, showing that the particular restrictions of each of them can be very easily included in the control algorithm, which is an important advantage of this technique. Another application is in modular multilevel cascaded converters, where it is demonstrated that MPC can operate with very few calculations and fixed switching frequency. The second part of this article is dedicated to comparing MPC with linear control and pulsewidth modulation for multilevel inverters. The main comparison criteria are the switching losses, the distortion in the load current, and the number of commutations. The main conclusion is that MPC is a competitive alternative to linear control for application in multilevel inverters.

This article presents the use of model predictive control (MPC) in multilevel inverters for some applications, such as, first, wind generation and, second, photovoltaics, showing that the particular restrictions of each of them can be very easily included in the control algorithm, which is an important advantage of this technique. Another application is in modular multilevel cascaded converters, where it is demonstrated that MPC can operate with very few calculations and fixed switching frequency. The second part of this article is dedicated to comparing MPC with linear control and pulsewidth modulation for multilevel inverters. The main comparison criteria are the switching losses, the distortion in the load current, and the number of commutations. The main conclusion is that MPC is a competitive alternative to linear control for application in multilevel inverters.

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