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Electrical power loss model for large-area monolithic organic photovoltaic module

Title
Electrical power loss model for large-area monolithic organic photovoltaic module
Author(s)
Lyu, Hong-KunJeong, SeonjuSim, Jun HyoungWoo, SunghoKim, Kang-PilShin, Jang-KyooHan, Yoon Soo
Issued Date
2011-01
Citation
Current Applied Physics, v.11, no.1, pp.S166 - S170
Type
Article
Author Keywords
Organic photovoltaic cellsElectrical power lossSeries resistanceMonolithic OPV
Keywords
Active AreaElectric Power Supplies to ApparatusElectrical LossElectrical ParameterElectrical PowerElectrical Power LossElectricityLoss PreventionMaximum Power OutputMonolithic OpvOrganic Photovoltaic CellsOrganic PhotovoltaicsPattern LengthPhotoelectrochemical CellsPHOTOVOLTAIC CELLSPhotovoltaic EffectsSERIES RESISTANCESeries ResistancesTransparent Conductive Oxides
ISSN
1567-1739
Abstract
We designed an electrical power loss model to minimize the electrical power losses in large-area monolithic organic photovoltaic (m-OPV) modules. Using the electrical power loss model, we calculated the parasitic electrical power losses on the transparent conductive oxide layer by considering the series resistance and shading losses. We fabricated a unit organic photovoltaic (OPV) cell as a reference and extracted its electrical parameters such as voltage and current density under the maximum power output condition. We calculated the electrical losses using the proposed electrical power loss model by applying these extracted parameters of the unit OPV cell. From the results of the electrical power loss model, the pattern length of the active area of a single OPV cell was determined to be 9 mm, indicating that we can place seven OPV cells in an active area of 84 mm × 90 mm. © 2010 Elsevier B.V. All rights reserved.
URI
http://hdl.handle.net/20.500.11750/5479
DOI
10.1016/j.cap.2010.11.084
Publisher
Elsevier B.V.
Related Researcher
  • 류홍근 Lyu, Hong-Kun
  • Research Interests Image-based AI application;Real-time AI system;AI-AMR System;AI farming machinery
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Appears in Collections:
Division of Energy & Environmental Technology 1. Journal Articles
Division of AI, Big data and Block chain 1. Journal Articles

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