Nowadays, the use of the hardware in the loop (HIL) simulation has gained popularity among researchers all over the world. One of its main applications is the simulation of power electronics converters. However, the equipment designed for this purpose is difficult to acquire for some universities or research centers, so ad-hoc solutions for the implementation of HIL simulation in low-cost hardware for power electronics converters is a novel research topic. However, the information regarding implementation is written at a high technical level and in a specific language that is not easy for non-expert users to understand. In this paper, a systematic methodology using LabVIEW software (LabVIEW 2018) for HIL simulation is shown. A fast and easy implementation of power converter topologies is obtained by means of the differential equations that define each state of the power converter. Five simple steps are considered: designing the converter, modeling the converter, solving the model using a numerical method, programming an off-line simulation of the model using fixed-point representation, and implementing the solution of the model in a Field-Programmable Gate Array (FPGA). This methodology is intended for people with no experience in the use of languages as Very High-Speed Integrated Circuit Hardware Description Language (VHDL) for Real-Time Simulation (RTS) and HIL simulation. In order to prove the methodology’s effectiveness and easiness, two converters were simulated—a buck converter and a three-phase Voltage Source Inverter (VSI)—and compared with the simulation of commercial software (PSIM^® v9.0) and a real power converter.

This paper presents a different Finite Control Set – Model Predictive Control (FCS-MPC) for grid-connected three-phase bidirectional power inverters. These are typically used in dc or ac renewable-based microgrids (MGs), where bidirectional operation and fast dynamic response is required. The bidirectional grid-connected inverters are an essential part of MG, which inject energy into the ac grid or demand energy from it. The dynamic behavior of the system is a major concern since the current can suddenly change depending on the hierarchical controller. This paper proposes a different cost function using sliding mode theory, which offers a good dynamic response, reduced computational burden, and a parameter-free control model. The operation principle of the proposed controller is given and evaluated using a Hardware-In-the-Loop (HIL) system, but also experimentally with a 1kW laboratory prototype. The final results demonstrate the advantages of using this approach in grid-connected three-phase bidirectional power inverters in terms of dynamic response and reduced computational burden, making this solution technically attractive and viable.

A zero-ripple input current is known to improve the lifetime of battery sets and fuel cells and to assure maximum power point tracking in PV panels. To perform current ripple elimination in a floating interleaved boost converter (FIBC), one of the typical linear inductors is substituted by a variable inductor, and phases of the converter have complementary duty cycles. This variable inductor is controlled using a switched current-source converter, which adjusts the input current ripple. An equivalent model for the variable inductor is presented, including uncertainties in the component description. To achieve current stabilization, a variable inductor controller was designed using the sliding modes approach via fixed frequency. An experimental prototype is implemented and tested with an output voltage controller to compare with the conventional FIBC. The results demonstrate that the input current ripple of the proposed converter is eliminated without significantly decreasing the efficiency.

Although PV systems are one of the most widely used alternatives as a renewable energy source due to their well-known advantages, there are significant challenges to address related to voltage fluctuations and reverse power flow caused by high PV penetration scenarios. As a potential solution to this problem, an active power management strategy is proposed in this work using a residential cluster as a benchmark. The proposed strategy is analyzed and experimentally verified, offering a simple way to reduce the voltage fluctuations by regulating the active power delivered by the PV system, achieving also relevant functionalities for the system, such as the regulation of the DC bus voltage, maximum power point tracking (MPPT), synchronization with the grid voltage, detection of high penetration conditions, and a simple strategy for the main controller with an effective performance. The proposal system shows satisfactory results being able to maintain grid voltage fluctuations within the voltage standard specifications.

Leakage current is one of the main issues for transformerless grid-connected photovoltaic (PV) inverters, and its reduction is a primary focus of various studies reported in the literature. This paper proposes and validates a finite control set model predictive controller (FCS-MPC) with a novel cost function (CF) to mitigate leakage current in a single-phase full-bridge (FB) grid-connected inverter with an LCL filter, making no modifications to the traditional FB topology and without adding any extra components. Furthermore, mathematical modeling is based on the fully actuated system (FAS) model, allowing a reduction in the number of variables sensed during implementation, resulting in only grid current and voltage being measured.

Power quality is one of the most critical fields in the energy sector. The connection of electric vehicles to the electrical grid can cause a degradation of energy quality when charging or discharging the battery. Identifying sudden voltage drops, surges, and network interruptions is essential to avoid network failure. A multiple fault classification/monitoring system has been developed based on artificial neural networks and implemented in an FPGA. The system’s performance has been evaluated using a set of data obtained from MATLAB/Simulink 2024. Different dataset combinations are implemented, trained, validated, and tested on three multilayer perceptron (MLP) models of various configurations. The analysis shows that the MLP designed with 12-bit fixed-point data precision is the most efficient MLP implementation in classification accuracy, compared to 8, 12, 20, and 24-bit fixed-point data. The system achieved 99.95% classification accuracy across various fault conditions, demonstrating its capability for real-time deployment.