Design of Cloud Computing-Based Control Algorithm for Hybrid Power System in Smart Grid Applications

Hybrid renewable energy (HRE) models are those that have two or more renewable sources connected together with some conventional sources to serve the demand load. The objective of this article is to present a cloud-based HRE model in which a Legendre wavelet embedded neurofuzzy (NF) indirect adaptive (LNFIA) maximum power point tracking (MPPT) control of photovoltaic (PV) system is implemented for the extraction of maximum power and a Hermite wavelet-based NF indirect adaptive control (HNFIA) of solid oxide fuel cells (SOFCs) for obtaining a swift response in a grid-connected HRE system. The implementation of these two smart controls for PV systems and SOFC maintains the tradeoff among power generation and load demands. The proposed HRE model when connected with cloud can be implemented for large-scale applications. An extensive experimental analysis is carried out to ensure the effectiveness of the proposed model. The result analysis verified that the proposed model shows an effective performance.