Significant Improvement in PV Module Performance Prediction Accuracy Using a New Model Based on IEC-61853 Data: Preprint

Janine Keith; Aron Dobos

Significant Improvement in PV Module Performance Prediction Accuracy Using a New Model Based on IEC-61853 Data: Preprint

Strategic Energy Analysis Center

Research output: Contribution to conference › Paper

Abstract

This paper presents a significant improvement in accuracy for photovoltaic (PV) module performance prediction using a new approach based on IEC-61853 test data. The method is based on the concept of a single diode model and calibrates its performance across a broad set of operating conditions. The new method reduces irradiance-weighted error in maximum power point (MPP) power relative to the standard 6-parameter single diode model, on average across 20 different module types, by 88% across the entire range of measured irradiance and temperature conditions, while retaining a continuous mathematical representation of the full current-voltage characteristic that allows for accurate off-MPP module modeling.

Original language	American English
Number of pages	7
State	Published - 2019
Event	2017 IEEE 44th Photovoltaic Specialist Conference (PVSC) - Washington, D.C. Duration: 25 Jun 2017 → 30 Jun 2017

Conference

Conference	2017 IEEE 44th Photovoltaic Specialist Conference (PVSC)
City	Washington, D.C.
Period	25/06/17 → 30/06/17

NREL Publication Number

NREL/CP-6A20-68637

Keywords

maximum power point
modeling
photovoltaic
PV

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

https://www.nrel.gov/docs/fy19osti/68637.pdf

Cite this

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title = "Significant Improvement in PV Module Performance Prediction Accuracy Using a New Model Based on IEC-61853 Data: Preprint",

abstract = "This paper presents a significant improvement in accuracy for photovoltaic (PV) module performance prediction using a new approach based on IEC-61853 test data. The method is based on the concept of a single diode model and calibrates its performance across a broad set of operating conditions. The new method reduces irradiance-weighted error in maximum power point (MPP) power relative to the standard 6-parameter single diode model, on average across 20 different module types, by 88% across the entire range of measured irradiance and temperature conditions, while retaining a continuous mathematical representation of the full current-voltage characteristic that allows for accurate off-MPP module modeling.",

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author = "Janine Keith and Aron Dobos",

year = "2019",

language = "American English",

note = "2017 IEEE 44th Photovoltaic Specialist Conference (PVSC) ; Conference date: 25-06-2017 Through 30-06-2017",

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T1 - Significant Improvement in PV Module Performance Prediction Accuracy Using a New Model Based on IEC-61853 Data: Preprint

AU - Keith, Janine

AU - Dobos, Aron

PY - 2019

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N2 - This paper presents a significant improvement in accuracy for photovoltaic (PV) module performance prediction using a new approach based on IEC-61853 test data. The method is based on the concept of a single diode model and calibrates its performance across a broad set of operating conditions. The new method reduces irradiance-weighted error in maximum power point (MPP) power relative to the standard 6-parameter single diode model, on average across 20 different module types, by 88% across the entire range of measured irradiance and temperature conditions, while retaining a continuous mathematical representation of the full current-voltage characteristic that allows for accurate off-MPP module modeling.

AB - This paper presents a significant improvement in accuracy for photovoltaic (PV) module performance prediction using a new approach based on IEC-61853 test data. The method is based on the concept of a single diode model and calibrates its performance across a broad set of operating conditions. The new method reduces irradiance-weighted error in maximum power point (MPP) power relative to the standard 6-parameter single diode model, on average across 20 different module types, by 88% across the entire range of measured irradiance and temperature conditions, while retaining a continuous mathematical representation of the full current-voltage characteristic that allows for accurate off-MPP module modeling.

KW - maximum power point

KW - modeling

KW - photovoltaic

KW - PV

M3 - Paper

T2 - 2017 IEEE 44th Photovoltaic Specialist Conference (PVSC)

Y2 - 25 June 2017 through 30 June 2017

ER -

Significant Improvement in PV Module Performance Prediction Accuracy Using a New Model Based on IEC-61853 Data: Preprint

Abstract

Conference

NREL Publication Number

Keywords

UN SDGs

Access to Document

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Cite this