Leonardo Ortega

Multi-port converters are essential components in modern power distribution systems, interfacing diverse loads and sources. Among these, the Modular Multi-Active Bridge (MMAB) converter stands out for its scalability and simplified magnetic design, making it a compelling candidate for electric vehicles and more-electric aircraft. However, power flow control remains challenging due to the inherent Multiple-Input Multiple-Output (MIMO) nature of the MMAB, which introduces significant cross-coupling between ports. This paper presents a comprehensive sensitivity analysis to evaluate these coupling levels and identifies operational regions where decentralized control loops can be effectively implemented. The proposed strategy is validated using a five-port MMAB laboratory prototype. Experimental results demonstrate that the system achieves precise power regulation with steady-state errors below 0.2%, confirming the feasibility of decentralized control in complex multi-port architectures.

This paper elucidates the development of an advanced maturity detection system for cherry tomatoes, employing state-of-the-art computer vision technologies alongside automated irrigation systems. The proposed solution integrates sophisticated software with hardware components, incorporating moisture sensors and solenoid valves to ensure precise irrigation management. Central to this system is the YOLOv8 model, which has been specifically trained to differentiate between ripe and unripe tomatoes. A custom dataset comprising 1,442 images was meticulously curated and processed to facilitate the creation of the detection model utilizing the Roboflow platform. The training of the model was executed on Google Colab, capitalizing on its computational power to enhance model performance. Following comprehensive optimization, the YOLOv8 model attained an accuracy rate of 78.8%, establishing it as a reliable asset for agricultural applications. A user-friendly graphical interface, developed in Python with Tkinter, enables farmers to effectively monitor crop maturity and manage irrigation processes in real time. The validation results underscore the system’s remarkable efficacy in practical agricultural settings, emphasizing its potential to augment productivity and resource efficiency. This system signifies a considerable advancement in innovative agricultural technologies.

In the ongoing transition to renewable energy sources, power converters have become indispensable. Their prevalence is increasing, enabling efficient energy conversion, enhancing reliability and stability, and optimizing power extraction from renewable sources. Multi-port dc/dc power converters are widely used because they offer advantages in managing multiple sources and loads. However, designing an automatic control system for these converters presents a challenge due to their complexity. Many configurations for multi-port dc/dc power converters have been proposed, featuring diverse combinations of controllers, modulation techniques, and topologies tailored to specific applications. The body of knowledge on these configurations has grown. Yet, papers have been published according to the authors’ areas of specialization, thus generating a scattered and unorganized body of knowledge and making it difficult to discern research trends and open challenges. Previous studies have attempted to organize knowledge about these configurations, but they have not established a systematic mapping process that follows a rigorous and objective methodology. This paper conducts a systematic mapping study on Automatic Control Systems of multi-port dc/dc power converters. Our study analyzed 122 papers from the 777 papers found around the topic to find and organize the body of knowledge on topology, controller, efficiency, number of elements, modulation technique, and practical applications. This systematic mapping provides a foundational framework for researchers, aiming to inspire further exploration and the development of innovative controller systems in multi-port dc/dc power converters. We found the application of machine learning techniques in dc/dc power converters constitutes an open challenge in these devices.

Electrical distribution systems comprising different power sources and loads, such as electric vehicles, aircraft electrical distribution systems and residential microgrids, need for new power conversion architectures. Multi-port converters are an alternative to conventional multi-converter systems. This work studies a modular multi-active bridge (MMAB) converter, which is a multi-port converter based on the connection of active bridges and independent transformers. The converter is bidirectional, fully modular and scalable. It allows the connection of multiple power sources and loads that may change its power flow direction, allowing hot connection and disconnection of modules. The control of the power flow in each port depends on the relative phase shift among the control pulses of the others active bridges. An iterative algorithm is developed and implemented in a digital device to control the converter in real time. Besides a theoretical analysis, the main characteristics of the converter have been validated in a lab prototype with five modules. The experimental results validate the theoretical predictions. Power distribution among the modules has been kept under 9% of error with respect to the desired value, considering an open-loop control.

In this work, a battery-ultracapacitor based Hybrid Energy Storage System (HESS) is proposed to attain a constant output power on a 10kVA Virtual Power Plant (VPP) with three days autonomy. Firstly, the importance of a HESS is remarked and different topologies of battery-ultracapacitor based HESS are analyzed. Thus, the VPP scheme based on a photovoltaic farm, a wind energy conversion system and the proposed HESS is described. Furthermore, in order to achieve a suitable energy conversion, Sliding-Mode Control (SMC) on a cascade scheme is designed by using the space state representation of the DC-DC power converters. Finally, the system performance is presented remarking proper dynamic and steady-state responses under different solar irradiation, wind speed and load profiles.

This project implements a charger for lithium polymer batteries, using two different sources of energy: the grid and photovoltaic energy. The charge is switched so that, when the grid feeds the battery, the photovoltaic panel (PV) do not and similarly, when the PV feeds the battery, the grid does not. In this project, three algorithms for tracking the maximum power point (MPP) for a Photovoltaic Array are analyzed and simulated, and one of them is implemented. The power converters are modeled and simulated in order to analyze the general behavior of the system and to design the controllers that allow to regulate properly the involved variables.

In this work, Sliding-Mode Control (SMC) is employed in a cascade scheme in order to perform the maximum power point tracking (MPPT) of a permanent magnet synchronous generator (PMSG) based wind energy conversion system (WECS). In addition, SMC is used on the DC-AC power converter to achieve a suitable energy conversion and also the integration to a distribution network. Firstly, the WECS scheme is defined with a wind turbine generator (WTG) connected to an uncontrolled AC-DC rectifier, a controlled DC-DC chopper and a controlled DC-AC three-phase voltage source inverter. Then, the employed cascade scheme for each controlled power converter is detailed. SMC is achieved by using the space state representation of the converters. Finally, WECS performance is analyzed on different wind speeds and load profiles. The results are exposed remarking proper dynamic and steady-state responses.

The Einthoven's Triangle is a fundamental concept in electrophysiology and biomedical instrumentation. However, it is complicated to understand for students, because the concept is very abstract. Therefore, we present here the development of a didactic platform for teaching this concept interactively. The developed platform is constituted by the hardware for data acquisition, the software for signal processing and a user graphical interface. Its use let the students to identify the different points where the electrodes have to be connected to get the signal of the different leads and visualize those signals. Additionally, the graphical interface allows the selection of any two leads for the automatic deduction of the signal of the dipole that represents the heart as a unique source of current.

In this work, Sliding-Mode Control (SMC) is employed to control different DC-DC converters in a Photovoltaic-Wind-Battery based Virtual Power Plant (VPP). Firstly, the VPP scheme is detailed. Secondly, a DC-DC converter is employed on every Distributed Generator and Energy Storage System. Moreover, in order to integrate the output power of the DC-DC converters and for feeding a three-phase voltage source inverter synchronized to the distribution network, a DC bus is applied. Thus, the design of the proposed SMC controller is achieved by using space state small-signal average equations from the converters. Likewise, the system performance is analyzed on different scenarios and profiles. Finally, the results are exposed remarking proper dynamic and steady-state responses.

The present work develops the entire control system to achieve the active power regulation in a detailed model of a small-scale Virtual Power Plant (VPP). Firstly, the proposed VPP topology is exposed. A DC bus has been used for the integration of the different distributed generators and the storage system. Different DC-DC power converters schemes have been employed to fulfill this goal. Later, by means of an Energy Management System strategy who commands a three-leg three-phase inverter having a suitable modulation technique, the control of the demanded active power is performed. The results exhibit successful responses of the system for both, dynamic and steady state.