caa a22 4500 606205098 CHVBK 20210128101005.0 cr unu---uuuuu 210128e20150901xx s 000 0 eng 10.1007/s00034-015-9972-0 doi (NATIONALLICENCE)springer-10.1007/s00034-015-9972-0 An Adaptive Signal Processing Framework for PV Power Maximization [Elektronische Daten] [André Vidal, Vítor Tavares, José Príncipe] This paper discusses the possibility of using adaptive signal processing techniques for maximum power point tracking controllers, in order to extract peak power from individual photovoltaic modules. A new technique grounded on unsupervised Hebbian learning theory (maximum eigenvector of the output power) is presented, which works on-online and is capable of operating without a desired response. Important modifications were made to the generic Hebbian adaptation to accommodate the intrinsic feedback loop between the controller and the plant. Analytic derivation of the new update rule is presented, as well as stability analysis by means of Lyapunov theory. Simulation results showing its effectiveness are presented, as well as experimental results. Springer Science+Business Media New York, 2015 Adaptive maximum power point tracking nationallicence MPPT nationallicence Hebbian learning nationallicence PV optimization nationallicence Vidal André INESC Technology and Science (INESC TEC), Faculty of Engineering of the University of Porto (FEUP), Rua Dr. Roberto Frias, 378, 4200-465, Porto, Portugal aut Tavares Vítor INESC Technology and Science (INESC TEC), Faculty of Engineering of the University of Porto (FEUP), Rua Dr. Roberto Frias, 378, 4200-465, Porto, Portugal aut Príncipe José Computational NeuroEngineering Laboratory, University of Florida, Gainesville, FL, USA aut Circuits, Systems, and Signal Processing Springer US; http://www.springer-ny.com 34/9(2015-09-01), 2973-2992 0278-081X 34:9<2973 2015 34 34 https://doi.org/10.1007/s00034-015-9972-0 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00034-015-9972-0 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Vidal André INESC Technology and Science (INESC TEC), Faculty of Engineering of the University of Porto (FEUP), Rua Dr. Roberto Frias, 378, 4200-465, Porto, Portugal aut NATIONALLICENCE 700 1- Tavares Vítor INESC Technology and Science (INESC TEC), Faculty of Engineering of the University of Porto (FEUP), Rua Dr. Roberto Frias, 378, 4200-465, Porto, Portugal aut NATIONALLICENCE 700 1- Príncipe José Computational NeuroEngineering Laboratory, University of Florida, Gainesville, FL, USA aut NATIONALLICENCE 773 0- Circuits, Systems, and Signal Processing Springer US; http://www.springer-ny.com 34/9(2015-09-01), 2973-2992 0278-081X 34:9<2973 2015 34 34