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Software-Defined Power Electronics

Tuesday 28 March 2023 at 9:00 AM EST, 3:00 PM CET By Matthias Preindl (Columbia University, USA) Register now using the link below: Abstract: Software-defined power electronics leverage a real-time control framework to form modular, reconfigurable, and partially redundant power converters. This talk explores a hierarchical control architecture that is composed of three layers: (1) the physical layer consists of optimized Lego-like power electronic converters called autoconverter modules (ACM) that consist of power conversion hardware, real-time control, and protection schemes implemented in microcontrollers; the power electronic complexities, e.g. switching and PWM are solved locally and the power conversion capabilities are exposed to higher control layers; (2) the interconnection layer defines the real-time communication that exposes the sensors, hardware peripherals, and actuators, e.g. pulse width modulators (PWM) to a central processor; (3) the application layer defines the application layer controls, e.g. renewable loads/sources including single/three-phase grid, solar, battery, and electric motor. The software-defined power electronics is shown on two example implementations: an EV traction inverter and EV charging equipment. Presenter’s bio: Matthias Preindl received the B.Sc. degree from the University of Padua (summa cum laude, 2008), the M.Sc. degree from ETH Zurich (2010), and the Ph.D. degree from the University ...
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The Bees Algorithm and Its Applications

Thursday 30 March 2023 at 2:00 PM BST (3:00 PM CET, 9:00 AM EST) By Duc Truong Pham (University of Birmingham, United Kingdom) Register now using the link below: Abstract: The Bees Algorithm is a swarm-based intelligent optimisation metaheuristic inspired by the foraging behaviour of honey bees. The algorithm is conceptually elegant and simple to apply. All it needs to solve an optimisation problem is a means to evaluate the quality of potential solutions. This is the reason why, since the algorithm was first published in 2005, it has attracted users from virtually all fields of engineering and natural, physical, medical and social sciences.  This presentation will introduce the algorithm and its main variants developed over the years including the latest incarnation which is the simplest version to date.  Unlike its predecessors which have six or seven user-selected parameters, the new version involves just two parameters and thus is even easier to implement than the original algorithm. The application of the different renditions of the Bees Algorithm to signal processing and machine learning problems such as design of microwave filters and training of deep learning systems will be reviewed to demonstrate its versatility and effectiveness.  The aim of ...
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