Jefferson Souza Costa

This article presents a novel time derivative-free bounded nonlinear controller (BNLC), including a refined integral action, for a class of boost DC/DC converters. The proposed strategy avoids the complexity of optimization-based methods through an explicit analytical form and mitigates potential noise issues associated with derivative-based controllers. The proposed controller guarantees boundedness via explicit duty cycle limits, with a complete closed-loop stability analysis provided. Validation on an experimental testbed compares the method against advanced proportional integral (PI) and sliding mode plus PI (SMPI) controllers. Results demonstrate superior and consistent transient response across the entire operational range, particularly in highly nonlinear low-current regions where it is 72% faster than robust benchmarks. In addition, it shows significantly higher noise immunity [12–20 dB higher signal-to-noise ratio (SNR)] and robustness under PV shading and large parametric variations.

The growing penetration of wind generation, particularly systems based on Doubly-Fed Induction Generators (DFIG), requires advanced control strategies to ensure stable and reliable operation under adverse conditions such as distorted voltages and parametric variations. Finite Control Set Model Predictive Control (FCS-MPC) has emerged as a promising solution, but the lack of formal stability guarantees hinders its practical adoption. This paper addresses this gap by proposing a rigorous FCSMPC framework for rotor current control of DFIGs, whose exponential stability is formally proven using Lyapunov theory. Experimental validation demonstrates high-performance operation, achieving steady-state tracking errors below 2%, settling times under 1 ms, negligible overshoot (< 1%), and Total Harmonic Distortion (THD) within 5%, even under distorted grid voltages. Comparative analysis with conventional MPC and Deadbeat control highlights the superiority and robustness of the proposed approach, establishing it as an effective solution for modern wind energy conversion systems.

Model predictive control (MPC) has emerged as a highly regarded control strategy in power electronics for renewable energy applications. While it minimizes tracking errors and control effort, a significant challenge is the lack of systematic tuning strategies to meet these systems’ energy quality performance requirements. This paper proposes a comprehensive MPC tuning methodology for grid-integrating converters with LCL filters, incorporating modulation and delay compensation. We conduct a stability analysis to define precise constraints for cost function weights. The fine-tuning strategy systematically maps a Figure of Merit (FoM) for system performance using an advanced computational model, revealing that optimal tunings reside in narrow parameter regions. Experimental validation on a 2 kW workbench confirmed that all proposed MPC tunings met IEEE Std. 519-2014 power quality criteria and consistently outperformed a two-sample deadbeat controller, exhibiting enhanced dynamic response and power quality.

This article presents an improvement to the Model Predictive Control-Finite Control Set (MPC-FCS) applied to grid-connected Doubly-Fed Induction Generators (DFIG) for operation under distorted voltage conditions. The MPC-FCS controller uses the discrete system model to predict the future behavior of the rotor currents, minimizing a cost function based on the error between predictions and references. The proposed approach incorporates the Decoupled Double Synchronous Reference Frame Phase-Locked Loop (DDSRF-PLL), allowing precise synchronization with the grid, even in distorted voltage scenarios, and optimizing the selection of voltage vectors applied to the rotor. Experimental tests on a 3 kW/220 V DFIG bench setup demonstrated that the proposed enhancements deliver superior performance to conventional MPC-FCS, SRF-PLL, Deadbeat, and classical PI controllers. The proposed method, integrated with DDSRF-PLL, exhibited superior performance compared to the other strategies, standing out in waveform quality and dynamic response. The proposed controller ensured the highest power quality among all evaluated strategies, even under distorted grid voltage conditions. Notably, the traditional MPC-FCS control strategy failed to meet the current THD requirement of less than 5%, as specified by the IEEE Std. 519-2014. Additionally, it showed fast transient response, low overshoot, and high steady-state accuracy, outperforming MPC-FCS variations without proper synchronization and the other controllers compared. It also contributes significantly to the literature, addressing the gap in studies on the performance of classic MPC-FCS in grid-connected wind generation systems with DFIG.

The Model Predictive Controller (MPC) is commonly used in power systems, especially in renewable energy generation, due to its fixed switching frequency and multivariable control. In this context, application to the doubly-fed induction generator (DFIG) minimizes torque and current oscillations, leading to greater stability in dynamics. A significant challenge in the design of the weighting factors within the MPC is that inappropriate selections can adversely affect the system’s performance, given their direct impact on the cost function. The methodology presented aims to simplify the design of the weighting factors in the MPC applied to the DFIG using the Root Locus Method (RLM), based on ensuring the stability of closed-loop eigenvalues while optimizing performance related to power regulation and energy quality. Furthermore, performance maps were created to evaluate the effects of weighting factors and predictive horizons on system dynamics. Therefore, this article proposes to design MPC’s weighting factors to ensure stability and performance of grid-injected energy by regulating DFIG rotor currents, with experimental validation on a test bench.

Electric vehicles (EVs) are the best solution to tackle the critical challenge of reducing carbon emissions in the transportation sector. However, the widespread adoption of EVs relies on advancing fast-charging infrastructure technology. This includes overcoming challenges related to operating under disturbed conditions, which can impact the stability of the internal control loop. This paper presents a method for robustly tuning a Generalized Predictive Control (GPC) for an EV charger grid converter. This approach aims to enhance its performance in the face of disturbances in the grid voltage and internal filter parameters. One significant scientific gap in applying GPC in grid-tied converters concerns systematic tuning. This article addresses this gap by explicitly analyzing the impact of tuning on the stability and robustness of the GPC controller. The concept of robust stability margin, derived from singular value decomposition, is used for this purpose. Experimental results obtained from an EV charger prototype validated the tuning proposal aimed at maximizing the robustness and performance of the grid converter. The tests with different internal filters guaranteed a performance level within the defined error band. Furthermore, experimental tests have shown that the proposed controller is more robust than conventional MPC.

The large adoption of electric vehicles (EVs) to reduce carbon emissions in the transportation sector presents various technological obstacles that need to be addressed, such as improving power quality when operating in vehicle-to-grid (V2G) mode as a distributed energy resource for the electricity grid. Model Predictive Control (MPC) is an advanced control technique gaining popularity in power electronics applications, particularly in EV chargers. MPC uses the plant's mathematical model to predict the future behavior of the state variables. Predictive Repetitive Control (PRC) combines MPC and repetitive control to increase robustness against disturbances such as parametric errors or significant perturbations in grid voltage. This paper proposes a robust PRC direct power control (PRC-DPC) of an EV charger grid-side converter operating under disturbed conditions. An explicit stability and robustness analysis is provided using the robust margins derived from the structured Singular Value Decomposition (SVD). The analyses highlight the impact of the PRC-DPC controller tuning on its robustness. Experimental tests were conducted on a 2 kW prototype EV charger in V2G operation mode to validate the robustness of the proposed PRC-DPC controller. The proposed controller presented superior robustness compared to the conventional MPC.