Application of autoassociative neural networks to solve large scale problems in power systems

This paper synthetizes the work conducted concerning the application of autoassociative neural networks (autoencoders) to solve large scale problems in power systems. Autoencoders have been shown effective in providing a useful tool to compress and expand information in various domains. This work explores the autoencoders' ability to reduce dimensionality by applying an evolutionary optimization metaheuristic into a space of reduced dimension. This idea was firstly pursued with the goal of reducing computational effort, which is known to be significantly high for large scale problems. The bew methodology developed, “Hybrid”, allows an evolutionary metaheuristic to evolve in a reduced dimension space S’, controlling its evolution in the original space S. The results obtained are detailed.