In building failure diagnosis systems for discrete-event systems, two performance indexes must be specified: (i) the maximum time the diagnosis system takes to detect the failure occurrence, usually referred to as T-diagnosability; and (ii) if no time information is available, how many events must occur after the failure in order for the diagnosis system to detect and isolate its occurrence, usually referred to as K-diagnosability. In this technical note, we consider the problem of verifying if these specifications can be met for discrete-event systems modeled by finite weighted automaton; we regard the ordinary finite automaton as a particular case of weighted automaton with all weights equal to one. The resulting algorithms have polynomial time complexity, being the lowest worst case computational complexity among those already proposed in the literature for T- and K-codiagnosability verification of finite state automata.

We address, in this article, the problem of codiagnosability of networked discrete event systems with timing structure (NDESWTS) subject to delays and loss of observations of events between the measurement sites (MS) and local diagnosers (LD). To this end, we first introduce a new timed model that represents the dynamic behavior of the plant based on the, a priori, knowledge of the minimal activation time for each transition of the plant and on the maximal delays in the communication channels that connect MS and LD. We then convert this timed model into an equivalent untimed one, and add possible intermittent packet loss in the communication network. Based on this untimed model, we present necessary and sufficient conditions for NDESWTS codiagnosability and propose two tests for its verification: one that deploys diagnosers and another one that uses verifiers.

A principal desvantagem na implementação do diagnosticador tradicional de Sistemas a Eventos Discretos proposto na literatura é que seu conjunto de estados pode ser muito grande, requerendo o uso de uma quantidade de memória demasiadamente grande para armazenar o diagnosticador para sistemas grandes e complexos. Neste artigo, é proposto um algoritmo para computar um diagnosticador reduzido, que preserva tanto a diagnosticabilidade da linguagem do sistema quanto o mesmo atraso para o diagnóstico que o diagnosticador original. Além disso, é mostrado que a estratégia de redução proposta pode levar a um diagnosticador reduzido com menos estados do que outras estratégias propostas na literatura.

This paper presents a practical framework for the design and real-time implementation of a Power Management System (PMS) for microgrids based on Supervisory Control Theory (SCT) for discrete-event systems. A detailed step-by-step methodology is provided, which covers the entire process from defining discrete events, modeling microgrid components, synthesizing supervisory controllers, and realizing them in MATLAB (R2024b) Stateflow. This methodology is applied to a case study, where a decentralized supervisor controller is designed for a microgrid containing a Battery Energy Storage System (BESS), a generator set (Genset), a wind and a solar generation system, critical loads, and noncritical loads. Unlike previous works based on SCT, the proposed PMS addresses the following functionalities: $\left(i\right)$ grid-connected and islanded operation; $\left(ii\right)$ peak shaving; $\left(iii\right)$ voltage support; $\left(iv\right)$ load shedding. Finally, a CHIL testing is employed to validate the synthesized SCT-based PMS.