TECHNIQUES FOR THE DESIGN AUTOMATION OF OFF-GRID DC ENERGY SYSTEMS

Abstract

This work presents a set of novel techniques for the design automation of off-grid DC energy systems, in the form of a software tool. This tool combines the complementary characteristics of a Genetic Algorithm (GA) with those of a Perturbation and Observation (P&O) algorithm, offering a novel approach to multi-objective optimisation. At its core is a physical simulation that represents the system’s objective function, combining both established and newly developed models. A key innovation is a load profile estimation model for off-grid charging stations serving UAV flocks used in portable wireless networks. This model is based on an enhanced flight time estimation method, which accounts for the impact of flight altitude, velocity, and trajectory on UAV flight time and energy requirements. Additionally, this work proposes two new black-box techniques to infer the efficiency curve of DC-DC power converters from label characteristics. One employs classic regression methods, while the other utilises a machine learning approach. Both techniques aim to correlate efficiency with label characteristics and were developed through experimental measurements of five power devices from Lyra Electronics. This work advances the field by proposing a new design tool for off-grid DC energy systems, innovative techniques to model specific aspects of these systems, and various case studies to demonstrate how these can be applied to practical scenarios.

Date of Award	16 Aug 2024
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Andrew Forsyth (Supervisor) & Alessandra Parisio (Supervisor)