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Granular Computing for Power Electronics Circuits Optimisation

Granular computing is an emerging computing paradigm of information processing. It concerns the processing of complex information entities called information granules, which arise in the process of data abstraction and derivation of knowledge from information. Generally speaking, information granules are collections of entities that usually originate at the numeric level and are arranged together due to their similarity, functional or physical adjacency, indistinguishability, and coherency. At present, granular computing is more a theoretical perspective than a coherent set of methods or principles. As a theoretical perspective, it encourages an approach to data that recognizes and exploits the knowledge present in data at various levels of resolution or scales. In this sense, it encompasses all methods which provide flexibility and adaptability in the resolution at which knowledge or information is extracted and represented. The development of power electronics results in a growing need for automatic design and optimization for power electronic circuits (PEC). This research work investigates the use of granular computing (GC) and particle swarm optimization (PSO) approach for the PECs design. The optimization problem is divided into two processes using a decoupled technique and PSO is employed to optimize the values of the circuit components in the power conversion stage (PCS) and the feedback network (FN), respectively. The GC approach will be used to develop a model of the circuits with the aim of modelling the circuit performance with respect to changes in the parameters and components.. The algorithm will be applied to the optimization of a buck regulator for meeting requirements under large-signal changes and at steady state.

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