Intelligent controller for managing power flow within standalone hybrid power systems. Issue 4 (1st July 2013)

• Record Type: Journal Article
• Authors: Natsheh, Emad Maher; Natsheh, Abdel Razzak; Albarbar, Alhussein
• Is Part Of: IET science, measurement & technology. Volume 7:Issue 4(2013)
• Page Start: 191
• Page End: 200
• Publication Date: 2013-07-01
• Subjects: adaptive control -- backpropagation -- battery management systems -- feedforward -- fuzzy control -- hierarchical systems -- home automation -- hybrid power systems -- linear systems -- load distribution -- load flow control -- neurocontrollers -- nonlinear control systems -- perturbation techniques -- photovoltaic power systems -- PI control -- proton exchange membrane fuel cells -- robust control -- maximum power point trackers; intelligent controller -- standalone hybrid power system -- adaptive load flow management strategy -- online energy management -- hierarchical controller -- energy source -- photovoltaic panel -- battery storage -- proton exchange membrane fuel cell -- feedforward -- backpropagation neural network controller -- PV panel -- fuzzy logic controller -- power distribution -- current flow -- perturb and observe algorithm -- linear PI controller technique -- nonlinear passivity‐based controller -- maximum power point tracking controller system -- PV control system -- solar irradiance -- robustness -- load resistance change -- load profile -- smart house application; Measurement -- Periodicals; Electrical engineering -- Periodicals; Electronics -- Periodicals; Nanotechnology -- Periodicals; Electromagnetism -- Periodicals; Medical instruments and apparatus -- Periodicals; 621.3
• DOI: 10.1049/iet-smt.2013.0011
• Languages: English
• ISSNs: 1751-8822

Abstract

Abstract : 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.