Open AccessNovel Neural network single sensor MPPT for Proton Exchange Membrane Fuel Cell
Author(s) -
Abdelghani Harrag
Publication year - 2021
Publication title -
journal of new materials for electrochemical systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.496
H-Index - 42
eISSN - 1480-2430
pISSN - 1480-2422
DOI - 10.14447/jnmes.v24i1.a08
Subject(s) - proton exchange membrane fuel cell , maximum power point tracking , maximum power principle , computer science , controller (irrigation) , overshoot (microwave communication) , artificial neural network , power (physics) , control theory (sociology) , wireless sensor network , voltage , engineering , fuel cells , electrical engineering , artificial intelligence , control (management) , telecommunications , physics , agronomy , computer network , quantum mechanics , chemical engineering , inverter , biology
This paper presents a new neural network single sensor maximum power point tracking algorithm controlling the DC-DC boost converter to guarantee the transfer of the proton exchange membrane fuel cell maximum generated power to the load. The implemented neural network single sensor controller has been developed and trained firstly in offline mode using single sensor maximum power point tracking data obtained previously; and secondly used in online mode to track the maximum output power of the fuel cell power system. Comparative simulation results prove the superiority of the proposed neural network single sensor maximum power point compared to the single sensor one especially in transit response reducing by the way the overshoot and the tracking time which leads to an overall energy losses reduction. In addition, the implemented neural network single sensor MPPT employs only one sensor which will reduce the complexity and the cost of PEM fuel cell power system. To our knowledge, this study is a pioneering work using a neural network single sensor controller as PEM fuel cell MPPT.