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SCNN Based Electrical Characteristics of Solar Photovoltaic Cell Model

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 7 No. 6 (2017)

DOI
10.11591/ijece.v7i6.pp3198-3206
ISSN
2088-8708, 2088-8708
Published
2017-12-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-nc/4.0

Abstract

Solar photovoltaic (PV) cell is one of the renewable energy sources and a main component of PV power systems. The design of PV power systems requires accurately its electrical output characteristics. The electrical characteristics of solar PV cell consist of I-V and P-V characteristics. They depend on the parameters of PV cell such as short circuit current, open circuit voltage and maximum power. Solar PV cell model can be described through an equivalent circuit including a current source, a diode, a series resistor and a shunt resistor. In this paper, the development solar PV cell model is built by using self constructing neural network (SCNN) methods. This SCNN technique is used to improve the accuracy of the electrical characteristic of solar PV cell model. SCNN solar PV cell model have three inputs and two outputs. They are respectively solar radiation, temperature, series resistance, current and power. The effectiveness of SCNN technique is verified using simulation results based on different physical and environmental conditions. Simulations are conducted by the change of the solar irradiation, temperature and series resistance. Simulation results show SCNN model can yield the I-V and P-V characteristics according to the characteristics of solar PV cell.

Collaboration network of the authors

3 authors · 3 co-authorship links

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  • Universitas Bhayangkara Surabaya
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>SCNN Based Electrical Characteristics of Solar Photovoltaic Cell Model</dc:title><dc:creator>Purwahyudi, Bambang</dc:creator><dc:creator>Kuspijani, Kuspijani</dc:creator><dc:creator>Ahmadi, Ahmadi</dc:creator><dc:description>&lt;jats:p&gt;Solar photovoltaic (PV) cell is one of the renewable energy sources and a main component of PV power systems. The design of PV power systems requires accurately its electrical output characteristics. The electrical characteristics of solar PV cell consist of I-V and P-V characteristics. They depend on the parameters of PV cell such as short circuit current, open circuit voltage and maximum power. Solar PV cell model can be described through an equivalent circuit including a current source, a diode, a series resistor and a shunt resistor. In this paper, the development solar PV cell model is built by using self constructing neural network (SCNN) methods. This SCNN technique is used to improve the accuracy of the electrical characteristic of solar PV cell model. SCNN solar PV cell model have three inputs and two outputs. They are respectively solar radiation, temperature, series resistance, current and power. The effectiveness of SCNN technique is verified using simulation results based on different physical and environmental conditions. Simulations are conducted by the change of the solar irradiation, temperature and series resistance. Simulation results show SCNN model can yield the I-V and P-V characteristics according to the characteristics of solar PV cell.&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2017-12-01</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v7i6.pp3198-3206</dc:identifier><dc:identifier>10.11591/ijece.v7i6.pp3198-3206</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 7 No 6 (2017); 3198</dc:source><dc:source>2088-8708</dc:source><dc:source>2088-8708</dc:source><dc:relation>http://ijece.iaescore.com/index.php/IJECE/article/viewFile/8690/7352</dc:relation><dc:rights>http://creativecommons.org/licenses/by-nc/4.0</dc:rights></oai_dc:dc>