Abstract
An interest to integrate solar collectors in heat supply systems is receiving a lot of attention. Despite perceived simplicity of solar collectors, failures can occur during the operation. Meanwhile, for the most installations, absent heat gains from solar collectors are covered by an auxiliary energy source. Thus the objective of the paper is to present the development of an automatic fault detection system, which can deal with abrupt in solar combisystems. The application of the artificial neural networks (ANN) for the fault detection has considerable advantages over other models, because the ANN can deal with complex problems, where traditional deterministic algorithms are exhausted. For the input and output layers the historical data about the fault-free operation parameters from the experimental solar combisystem and from the TRNSYS simulation tool is feed to the ANN. Learning algorithms are applied for the hidden layer in order to “train” the ANN. The results show that application of the proposed methodology increases the performance reliability of solar combisystem. The fault detection systems could be integrated in the solar combisystems, thus reducing the consumption of auxiliary energy and decreasing emissions during operation.
