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This book explores the application of artificial intelligence (AI) techniques for fault detection, diagnosis, and reconfiguration in a three-phase voltage source inverter (VSI) feeding an induction motor drive. The study focuses on improving the reliability and efficiency of induction motor drives by addressing common inverter faults, such as open-circuit and short-circuit faults, using AI-based methods like Artificial Neural Networks (ANN), Fuzzy Logic Control (FLC), and Convolutional Neural Networks (CNN). The research proposes a fault-tolerant system that integrates intelligent control…mehr

Produktbeschreibung
This book explores the application of artificial intelligence (AI) techniques for fault detection, diagnosis, and reconfiguration in a three-phase voltage source inverter (VSI) feeding an induction motor drive. The study focuses on improving the reliability and efficiency of induction motor drives by addressing common inverter faults, such as open-circuit and short-circuit faults, using AI-based methods like Artificial Neural Networks (ANN), Fuzzy Logic Control (FLC), and Convolutional Neural Networks (CNN). The research proposes a fault-tolerant system that integrates intelligent control strategies, including Direct Torque Control (DTC) and Direct Torque Control with Space Vector Modulation (DTC-SVM), to enhance the robustness of the motor drive system. The proposed methods are validated through simulations, demonstrating high accuracy in fault detection and diagnosis, as well as effective reconfiguration of the inverter to maintain system stability under fault conditions.
Autorenporträt
Mi chiamo Younes Tamissa, nato il 13 agosto 1986 ad Algeri. Ho conseguito il dottorato di ricerca in Automazione e Informatica Industriale presso l'Università di Ouargla. La mia ricerca si concentra sui sistemi a tolleranza di errore, in particolare negli inverter a sorgente di tensione per azionamenti di motori a induzione, utilizzando tecniche intelligenti come le reti neurali artificiali e la logica fuzzy.