Design of Adaptive Neuro-Fuzzy Inference Control Based One-Axis Solar Tracker on Battery Charging SystemShow others and affiliations
2020 (English)In: E3S Web of Conferences, EDP Sciences , 2020, p. 1-15, article id 00015Conference paper, Published paper (Refereed)
Abstract [en]
The photovoltaic (PV) panel can produce electrical energy that is very environmentally friendly and easy to use. The use of PV panels is suitable for supplying peak loads or at night using batteries as energy storage. However, the battery needs to manage for control, and the battery can last long. The solution to battery management problems is through research about the battery charging system. The DC-DC converter used is the Single Ended Primary Inductance Converter (SEPIC) type. Voltage Control of the battery charging using Adaptive Neuro-Fuzzy Inference System (ANFIS). In the simulation of bright conditions, ANFIS controls can track the charging point set point and obtain a voltage response with a rise time of 0.0028 s, a maximum overshoot of 0.027 %, a peak time of 0.008 s, and a settling time of 0.0193 s. When charging a solar tracker, PV battery gets a 0.25 % increase compared to a fixed PV panel. PV solar tracker can follow the direction of the sun's position. The irradiation value and maximum temperature affect the input voltage and input current that enters the converter.
Place, publisher, year, edition, pages
EDP Sciences , 2020. p. 1-15, article id 00015
Keywords [en]
Battery management, Electrical energy, Photovoltaic, Renewable energy, Solar tracker, Adaptive control systems, Battery management systems, DC-DC converters, Energy storage, Fuzzy control, Fuzzy inference, Fuzzy neural networks, Fuzzy systems, Photovoltaic cells, Secondary batteries, Adaptive neuro-fuzzy inference, Adaptive neuro-fuzzy inference system, Irradiation value, Maximum overshoot, Maximum temperature, Photovoltaic panels, Single ended primary inductance converters (SEPIC), Charging (batteries)
National Category
Energy Engineering
Research subject
Environmental Science, Environmental technology
Identifiers
URN: urn:nbn:se:mdh:diva-57901DOI: 10.1051/e3sconf/202019000015Scopus ID: 2-s2.0-85092461316OAI: oai:DiVA.org:mdh-57901DiVA, id: diva2:1651774
Conference
E3S Web of Conferences
2021-09-232022-04-13Bibliographically approved