Rui Esteves Araujo

Abstract
Matrix converters (MCs) allow the implementation of single-stage AC/AC power conversion systems (PCS) with inherent bidirectional power flow capability. By avoiding the typical DC-link capacitor, MCs have the potential to achieve higher power density with a more reliable operation and less maintenance when compared with conventional two-stage AC/DC/AC PCS. For these reasons, matrix converters have been receiving significant attention from the academic sector but have not yet been implemented on a large industrial scale. This article reviews the Direct Matrix Converter (DMC) and the Indirect Matrix Converter (IMC) along with the respective actual and most important modulation methods. Simulation results are provided to validate the theoretical analysis and to get a deep insight about the implementation of space vector modulation (SVM) and respective switching pattern generator.

Abstract

Abstract
This paper addresses the problem of the left inversion of switched linear systems from a diagnostics perspective. The problem of left inversion is to reconstruct the input of a system with the knowledge of its output, whose differentiation is usually required. In the case of this work, the objective is to reconstruct the system’s unknown inputs, based on the knowledge of its outputs, switching sequence and known inputs. With the inverse model of the switched linear system, a real-time Fault Detection and Isolation (FDI) algorithm with an integrated Fuzzy Logic System (FLS) that is capable of detecting and isolating abrupt faults occurring in the system is developed. In order to attenuate the effects of unknown disturbances and noise at the output of the inverse model, a smoothing strategy is also used. The results are illustrated with an example. The performance of the method is validated experimentally in a DC-DC boost converter, using a low-cost microcontroller, without any additional components. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.

Abstract
This paper presents theory, a new approach and validation results for fault detection and isolation (FDI) in DC-DC power converters, based on inversion method. The developed method consists on the inversion-based estimation of faults and change detection mechanisms adapted to the power converters context. With the inverse model of a switched linear system, we have designed a real-time FDI algorithm with an integrated fuzzy logic scheme which detects and isolates abrupt changes (faults) at unknown time instants. A smoothing strategy is used to attenuate the effect of unknown disturbances and noise that are present at the outputs of this inverse model. Once the fault event is detected, a dedicated fuzzy-logic-based scheme is proposed to isolate the four types of faults: switch, voltage and current sensor, and capacitor. The performance of the proposed method is verified experimentally to detect and isolate the mentioned faults in the DC-DC boost power converter.

Abstract
The main objective of modeling a switched reluctance machine is to derive a mathematical function to relate the outputs to the inputs. Due to the nonlinear relationship between the variables of torque, flux linkage, current and angular position of the rotor, Switched Reluctance Machine (SRM) modeling is a very challenging task and an open problem. Modeling is usually done in two situations, modeling a single machine, or modeling a set of machines. Each one must fulfill different requirements. This work presents a survey of different SRM modeling approaches, evaluating its advantages and limitations when modeling a single machine or a set of machines.