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A Comparison of One- and Two-Diode Model Parameters at Indoor Illumination Levels
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.
Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.ORCID iD: 0000-0001-9572-3639
2020 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 8, p. 172057-172064Article in journal (Refereed) Published
Abstract [en]

Indoor photovoltaic (PV) application gains in attraction for low-power electronic systems, which requires accurate methods for performance predictions in indoor environments. Despite this, the knowledge on the performance of commonly used photovoltaic device models and their parameter estimation techniques in these scenarios is very limited. Accurate models are an essential tool for conducting feasibility analyses and component dimensioning for indoor photovoltaic systems. In this paper, we therefore conduct a comparison of the one- and two-diode models with parameters estimated based on two well-known methods. We evaluate the models' performance on datasets of photovoltaic panels intended for indoor use, and illumination conditions to be expected in indoor environments lit by artificial light sources. The results demonstrate that the one-diode model outperforms the two-diode model with respect to the estimation of the overall I-V characteristics. The two-diode model results instead in lower maximum power point errors. Both models show a sensitivity to initial conditions, such as the selection of the diode ideality factor, as well as the curve form of the photovoltaic panel to be modeled, which has not been acknowledged in previous research.

Place, publisher, year, edition, pages
2020. Vol. 8, p. 172057-172064
Keywords [en]
Parameter estimation, Integrated circuit modeling, Mathematical model, Lighting, Photovoltaic systems, Estimation, Indoor photovoltaics, energy harvesting, photovoltaic cell models, one-diode model, two-diode model
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:miun:diva-40274DOI: 10.1109/ACCESS.2020.3025146ISI: 000575887300001Scopus ID: 2-s2.0-85102839809OAI: oai:DiVA.org:miun-40274DiVA, id: diva2:1478382
Available from: 2020-10-22 Created: 2020-10-22 Last updated: 2021-03-30
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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Bader, SebastianMa, XinyuOelmann, Bengt

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