Intelligent controller for managing power flow within standalone hybrid power systems

This study presents a novel adaptive management strategy for power flow in standalone hybrid power systems. The method introduces an on-line energy management by using a hierarchical controller between three energy sources: photovoltaic (PV) panels, battery storage and proton exchange membrane fuel cell. The proposed method includes a feed-forward, back-propagation neural network controller in the first layer, which is added in order to achieve the maximum power point for the different types of PV panels. In the second layer, a fuzzy logic controller has been developed to optimise performance by distributing the power inside the hybrid system and by managing the charge and discharge of the current flow. Finally, and in the third layer, local controllers are presented to regulate the fuel cell/battery set points in order to reach to best performance. Moreover, perturb and observe algorithm with two different controller techniques - the linear proportional-integral (PI) and the non-linear passivity-based controller - are provided for a comparison with the proposed maximum power point tracking controller system. The comparison revealed the robustness of the proposed PV control system for solar irradiance and load resistance changes. Real-time measured parameters and practical load profiles are used as inputs for the developed management system. The proposed model and its control strategy offer a proper tool for optimising the hybrid power system performance, such as the one used in smart-house applications.