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A Scalable, Data-driven Approach for Power Estimation of Photovoltaic Devices under Indoor Conditions
Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.
Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.ORCID iD: 0000-0002-8382-0359
Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.ORCID iD: 0000-0001-9572-3639
2019 (English)In: ENSsys'19 Proceedings of the 7th International Workshop on Energy Harvesting & Energy-Neutral Sensing Systems, New York, USA: ACM Digital Library, 2019, p. 29-34Conference paper, Published paper (Refereed)
Abstract [en]

For the output power estimation of photovoltaic devices in indoor applications, models are needed that perform accurately at the low illumination levels encountered. As a robust and scalable solution, we propose a data-driven modeling method, spanning an interpolated surface between two reference I-V curves. The proposed approach is evaluated based on experimental data of two exemplar PV panels at indoor illumination levels. The results are compared to two common parameter extraction methods for the one-diode circuit model. This investigation demonstrates that the proposed surface model has a high performance under all test conditions, whereas the reference models show a performance dependency on the PV panel type. It can be concluded that the surface model is a competitive alternative for output power estimations at indoor illumination levels, removing many of the uncertainties of traditionally used physical parameter extraction and scaling methods.

Place, publisher, year, edition, pages
New York, USA: ACM Digital Library, 2019. p. 29-34
Keywords [en]
photovoltaic panel, i-v characteristics, pv model, parameter scaling, indoor energy harvesting
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:miun:diva-37938DOI: 10.1145/3362053.3363494ISI: 000525878200005Scopus ID: 2-s2.0-85076604290ISBN: 978-1-4503-7010-3 (electronic)OAI: oai:DiVA.org:miun-37938DiVA, id: diva2:1376695
Conference
7th International Workshop on Energy Harvesting & Energy-Neutral Sensing Systems, New York, United States, November 2019
Funder
Knowledge Foundation, ASIS 20140323Available from: 2019-12-10 Created: 2019-12-10 Last updated: 2021-01-19Bibliographically approved
In thesis
1. Power Estimation for Indoor Light Energy Harvesting
Open this publication in new window or tab >>Power Estimation for Indoor Light Energy Harvesting
2021 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The growing popularity of indoor light energy harvesting for wireless sensor systems and low-power electronics has created a demand for systematic power estimation methods for different lighting conditions. Although existing research has recognized the critical role played by the spectral information on the output power of a photovoltaic cell, power estimation methods have rarely considered it. The vast majority of studies on the power estimation method in the past few years have focused on the conventional diode model, and even though scaling the parameters to other light conditions seems plausible, it is sometimes problematic to interpret the physical meanings of some parameters from theory. Therefore, a systematic investigation of the light condition characterization and PV cell modeling is fundamental to appropriately estimate the available light energy of an indoor environment. The power estimation method proposed in this thesis takes both spectral and intensity information into account and provides a data-driven approach to solve the scaling problem. We use low-cost sensors to measure spectral information and select an appropriate device model based on the classification of the light source. The evaluation results for both lab and real-world light conditions show that the proposed method achieves sufficient accuracy. This study provides new insights into the indoor light energy harvesting system design and makes a contribution to research on available energy estimation of the ambient environment.

Abstract [sv]

Intresset för att skörda energi från inomhusbelysning har ökat för att strömförsörja trådlösa sensorsystem och lågeffektelektronik och har skapat enefterfrågan på systematiska metoder för att estimera hur mycket effekt somkan skördas i olika ljusförhållanden. Även om befintlig forskning har visatden kritiska roll som spektralinformation spelar för solcellers uteffekt, så tasden inte i beaktad för effektestimeringen. De allra flesta studier om effektestimeringsmetoder under de senaste åren har fokuserat på den konventionella diodmodellen, och även om skalning av modellens parametrar till andra ljusförhållanden verkar rimliga är det ibland problematiskt att tolka den fysiskabetydelsen av vissa parametrar. Därför är en systematisk undersökning avkaraktäriseringen av ljusförhållanden och modellering av solceller grundläg-gande för att korrekt uppskatta den tillgängliga ljusenergin i en inomhus-miljö. Den effektestimeringsmetod som föreslås i den här avhandlingen tarhänsyn till både spektral- och intensitetsinformation och ger en datadrivenmetod för att lösa skalningsproblemet. Vi använder enkla ljussensorer för attmäta spektralinformation och utifrån spektralinformationen väljs en lämpligmodell för solcellen baserat på klassificering av ljuskällan. Resultaten förbåde labb och verkliga ljusförhållanden visar att den föreslagna metodenuppnår tillräcklig god noggrannhet. Denna studie ger nya insikter i dimen-sioneringen av energiskördesystemet för ljusenergi inomhus och bidrar tillforskning om tillgänglig energiuppskattning i den omgivande miljön.

Place, publisher, year, edition, pages
Sundsvall: Mid Sweden University, 2021. p. 52
Series
Mid Sweden University doctoral thesis, ISSN 1652-893X ; 338
National Category
Engineering and Technology
Identifiers
urn:nbn:se:miun:diva-40885 (URN)978-91-88947-86-4 (ISBN)
Public defence
2021-01-08, C312 och via Zoom, Holmgatan 10, Sundsvall, 13:00 (English)
Opponent
Supervisors
Available from: 2021-01-19 Created: 2021-01-18 Last updated: 2021-01-19Bibliographically approved

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Ma, XinyuBader, SebastianOelmann, Bengt

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