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| Content Provider | IET Digital Library |
|---|---|
| Author | Natsheh, Emad Maher Natsheh, Abdel Razzak Albarbar, Alhussein |
| 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. |
| Starting Page | 191 |
| Ending Page | 200 |
| Page Count | 10 |
| ISSN | 17518822 |
| Volume Number | 7 |
| e-ISSN | 17518830 |
| Issue Number | Issue 4, Jul (2013) |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/iet-smt/7/4 |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/iet-smt.2013.0011 |
| Journal | IET Science, Measurement & Technology |
| Access Restriction | Open |
| Rights Holder | © The Institution of Engineering and Technology |
| Subject Keyword | Adaptive Control Adaptive Load Flow Management Strategy Automated Buildings Backpropagation Backpropagation Neural Network Controller Battery Management System Battery Storage Control of Electric Power System Current Flow Energy Source Feedforward Fuel Cell Fuzzy Control Fuzzy Logic Controller Hierarchical Controller Hierarchical System Home Automation Hybrid Power System Intelligent Controller Linear PI Controller Technique Linear System Load Distribution Load Flow Control Load Profile Load Resistance Change Maximum Power Point Trackers Maximum Power Point Tracking Controller System Multivariable Control System Neural Computing Technique Neurocontrollers Nonlinear Control System Nonlinear Passivity-based Controller Online Energy Management Perturb And Observe Algorithm Perturbation Technique Photovoltaic Panel Photovoltaic Power System PI Control Power Converter Power Distribution Power Supplies to Apparatus Power System Control Power System Economics Power System Managemen Power System Operation Proton Exchange Membrane Fuel Cell PV Control System PV Panel Robust Control Robustness Secondary Cell Self Adjusting Control System Smart House Application Solar Irradiance Solar Power Stations Standalone Hybrid Power System |
| Content Type | Text |
| Resource Type | Article |
| Subject | Atomic and Molecular Physics, and Optics Electrical and Electronic Engineering |
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