An Algorithm for the Selection of Structure for Artificial Networks. Case Study: Solar Thermal Energy Systems

Despite perceived simplicity of solar thermal collectors, failures occur during operation. Therefore fault detection and isolation tools for these systems should be investigated. One of the critical parts for the development of fault detection and isolation is model selection. Within the paper, a specific algorithm for the selection of fault detection and an isolation model is elaborated and presented. The developed algorithm was applied for a solar and pellet combisystem. Through application of the proposed algorithm, a model based approach with recurrent structure of artificial neural networks is chosen for the development of a fault detection and isolation model.