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In the current landscape of the power industry, computer-based algorithms play a pivotal role in monitoring and controlling distributed energy resources. The prevalence of artificial intelligence algorithms has underscored the need for stability and fault tolerance in power systems, particularly in the realm of smart control, to prevent disruptions such as blackouts. This paper introduces a fault-tolerant multi-agent framework with a bottom-up approach designed for the control and management of energy flow within a Microgrid (MG). Within this framework, a tender system is implemented to effectively distribute tasks among agents. To incentivize power sharing among agents, an iterative first-price sealed-bid algorithm is employed, fostering a competitive environment within the multi-agent system (MAS) aimed at minimizing electricity costs in the MG. Each agent, functioning as a bidder, exercises autonomous decision-making, presenting its offer price to the tenderer during each time period to partake in the tender and secure rewards. The proposed framework not only facilitates power allocation but also integrates fault tolerance and system redevelopment capabilities. Theoretical analysis and empirical results affirm that the proposed approach yields a robust smart control system, ensuring stability, flexibility, and fault tolerance within the overall system. Simulation outcomes demonstrate that in instances where an agent encounters a fault, the demand can seamlessly be met by the remaining agents, thereby preserving the stability of the system., This work was supported by the Secretaria d’Universitats i Recerca del Departament d’Empresa i Coneixement de la Generalitat de Catalunya under 2021 SGR 00392. In addition, this research work was funded by Institutional Fund Projects under grant no. IFPIP: 1217- 135-1443. The authors gratefully acknowledge technical and financial support provided by the Ministry of Education and King Abdulaziz University (KAU), DSR, Jeddah, Saudi Arabia., Peer Reviewed, Postprint (author's final draft)

A novel fault diagnosis procedure is proposed in this paper to estimate faults using a linear parameter varying (LPV) model whose scheduling parameters depend on the fault. A wrong determination of the operating conditions could lead the system to an undesired performance or even to an unstable situation, when classical fault diagnosis approaches are applied. This paper addresses this issue by formulating fault diagnosis as a dynamic optimization problem, solved by using a novel hybrid technique that combines a Luenberger-based observer with artificial intelligent (AI) optimization-based algorithms. The observer supervises the health of the system, while AI-based algorithms are able to reconstruct the faulty signal in real-time when the observer determines that the system is under a fault. The efficiency of the proposed fault diagnosis scheme, the three AI-based algorithms based on artificial bee colony and particle swarm optimization, and the gradient-based algorithm developed in this paper, are assessed using a numerical example., This work has been co-financed by the Spanish State Research Agency (AEI) and the European Regional Development Fund (ERFD) through the project SaCoAV (ref. MINECO PID2020-114244RB-100), by the European Regional Development Fund of the European Union in the framework of the ERDF Operational Program of Catalonia 2014-2020 (ref. 001-P-001643 Looming Factory) and by the DGR of Generalitat de Catalunya (SAC group ref. 2017/SGR/482)., Peer Reviewed, Postprint (author's final draft)

An electric arc is a common distribution grid error, even more so with the use of electric vehicles and renewable energy sources that increase the likelihood of grid errors. Several control systems and strategies are under development to minimize electric-arc faults and to guarantee reliable grid performance. All the control strategies must be validated through emulations of the different fault modes at laboratory scale where there is no risk to either the researchers or the facilities. In this study, a novel system is proposed for the emulation of electric arc current waveforms, based on both real measurements and equation-based modeling. The proposed modular system provides the capability to emulate different levels of arcing currents. Validation of the system was demonstrated, in view of the design, the underlying theoretical analysis, and a set of control system simulations and experiments., This work was supported in part by the Government of the Basque Country within the fund for research groups of the Basque University system IT1440-22, by the Agencia Estatal de Investigación within the project PID2021-122821NB-I00, by the Generalitat de Catalunya within the project 2021 SGR 00376, and by the research project PT10709 funded by i-DE Redes Inteligentes S.A.U. through its Global Smart Grids Innovation Hub. Open Access funding provided by the University of Basque Country UPV/EHU., Peer Reviewed, Postprint (published version)