Localización de fallas en microredes eléctricas basado en un modelo Markoviano.

The present paper analyzes the effect produced by the appearance of a state or failure event on the system and its environment, component or control structure from the result of algorithms of fault location or FDIs for Electric Microgrids, using a Markovian model. This method will help to understand the propagation of faults and their effect on the Microgrids, in addition to allowing the identification and location of the important faults and its control with fault tolerance techniques. This method not only implements a fault location algorithm, it also achieves a scheme to analyze the fault propagation that occur in Microgrids (MG). For this case, fault locations, the detection of critical points in which a failure can occur, as well as the determination of the most probable route for its propagation, are considered the aim to be resolved. The model was created using the Markov process. It is important to consider that in order for the Markov process to obtain results close to reality, it is necessary to consider not only a model that simulates the dynamic behavior of the system, but also to have more in-depth studies that provide statistical and probabilistic data on failure events, their propagation and decision making once located.El presente trabajo analiza el efecto producido por la aparición de un estado o evento de fallo sobre el sistema y su entorno, componente o estructura de control usando un modelo Markoviano, y a partir del resultado de algoritmos de localización de fallas o FDIs para Microredes Eléctrica. El método ayudará a comprender la propagación de fallas y su efecto en las Micro-redes, permitiendo además identificar y localizar las fallas importantes a tratar y controlar con técnicas de tolerancia a fallas. Este método no solo implementa un algoritmo de localización de falla, además logra un esquema para analizar la propagación de las fallas que se presentan en Micro-redes (MG). Para el caso propuesto, la localización de la falla, la detección de los puntos críticos en los que puede ocurrir una falla, así como la determinación de la ruta más probable para la propagación de las mismas, son considerados como un punto clave a resolver. Es importante considerar que, para que el proceso de Markov obtenga resultados cercanos a la realidad es necesario considerar no solo un modelo que simule el comportamiento dinámico del sistema, también, contar con estudios más profundos que brinden datos estadísticos y probabilísticos de los eventos de fallos, su propagación, y toma de decisiones una vez que estos son localizados

Active Fault-Tolerant Control for Wind Turbine with Simultaneous Actuator and Sensor Faults

The purpose of this paper is to show a novel fault-tolerant tracking control (FTC) strategy with robust fault estimation and compensating for simultaneous actuator sensor faults. Based on the framework of fault-tolerant control, developing an FTC design method for wind turbines is a challenge and, thus, they can tolerate simultaneous pitch actuator and pitch sensor faults having bounded first time derivatives. The paper’s key contribution is proposing a descriptor sliding mode method, in which for establishing a novel augmented descriptor system, with which we can estimate the state of system and reconstruct fault by designing descriptor sliding mode observer, the paper introduces an auxiliary descriptor state vector composed by a system state vector, actuator fault vector, and sensor fault vector. By the optimized method of LMI, the conditions for stability that estimated error dynamics are set up to promote the determination of the parameters designed. With this estimation, and designing a fault-tolerant controller, the system’s stability can be maintained. The effectiveness of the design strategy is verified by implementing the controller in the National Renewable Energy Laboratory’s 5-MW nonlinear, high-fidelity wind turbine model (FAST) and simulating it in MATLAB/Simulink