New data set for validating PV module performance models

Marion, Bill; Anderberg, A.; Deline, Chris; Cueto, J. del; Muller, Matthew; Perrin, Greg; Rodriguez, Jose; Rummel, S.; Silverman, Timothy J.; Vignola, Frank; Kessler, Rich; Peterson, Josh; Barkaszi, Stephen; Jacobs, M.E.; Riedel, Nick; Pratt, Lawrence E; King, Bruce H.

doi:10.1109/pvsc.2014.6925171

Cited by 55 publications

(47 citation statements)

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“…The irradiance and temperature distribution of this dataset is shown in Figure 3a,b, the former varying within [20,1440] W/m 2 and the latter within [−19, +73] • C. A large number of I-V curves were recorded under low and very low-irradiance conditions, which are known to be challenging for the single-diode model and therefore for the respective parameter extraction methods. More information on this dataset may be found in [42]. The comparative assessment that follows is first performed for all 17 methods, repeated afterwards for those that rely only on datasheet information and for those that require the SC or/and OC slope measurements.…”

Section: Comparative Assessmentmentioning

confidence: 99%

“…To shed some light on these topics, this paper performs a comprehensive review and comparison of the non-iterative parameter extraction methods that are based on the single-diode PV model. Seventeen such methods are identified dating since 1984 [12,[14][15][16][17][18][19][20][21][22][23][24][25][26][27], all of which are implemented and assessed on a common set of I-V curves provided by National Renewable Energy Laboratories (NREL) [42]. This dataset contains 1,025,665 I-V curves measured over a one-year period in the USA for 22 PV modules under a wide range of irradiance and temperature conditions.…”

Section: Introductionmentioning

confidence: 99%

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Non-Iterative Methods for the Extraction of the Single-Diode Model Parameters of Photovoltaic Modules: A Review and Comparative Assessment

Batzelis

2019

Energies

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The extraction of the photovoltaic (PV) model parameters remains to this day a long-standing and popular research topic. Numerous methods are available in the literature, widely differing in accuracy, complexity, applicability, and their very nature. This paper focuses on the class of non-iterative parameter extraction methods and is limited to the single-diode PV model. These approaches consist of a few straightforward calculation steps that do not involve iterations; they are generally simple and easy to implement but exhibit moderate accuracy. Seventeen such methods are reviewed, implemented, and evaluated on a dataset of more than one million measured I-V curves of six different PV technologies provided by the National Renewable Energy Laboratories (NREL). A comprehensive comparative assessment takes place to evaluate these alternatives in terms of accuracy, robustness, calculation cost, and applicability to different PV technologies. For the first time, the irregularities found in the extracted parameters (negative or complex values) and the execution failures of these methods are recorded and are used as an assessment criterion. This comprehensive and up-to-date literature review will serve as a useful tool for researchers and engineers in selecting the appropriate parameter extraction method for their application.

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Section: Comparative Assessmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Non-Iterative Methods for the Extraction of the Single-Diode Model Parameters of Photovoltaic Modules: A Review and Comparative Assessment

Batzelis

2019

Energies

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“…The GTI-DIRINT model was evaluated using solar irradiance data for three climatically diverse locations: Cocoa, Florida (subtropical climate); Eugene, Oregon (marine west coast climate); and Golden, Colorado (semiarid climate). The data for the Cocoa and Eugene locations were measured previously as part of work to provide a publicly available data set for use in validating models that estimate the performance of flat-plate PV modules (Marion et al, 2014). The data used for this work are a subset and included the DNI, DHI, and the GTI for a tilt angle from the horizontal equaling the latitude (28.5° for Cocoa and 44° for Eugene) and south-facing.…”

Section: Gti-dirint Model For θ ≥ 90°mentioning

confidence: 99%

A model for deriving the direct normal and diffuse horizontal irradiance from the global tilted irradiance

Marion

2015

Solar Energy

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A separation model was developed for deriving the direct normal irradiance (DNI) and the diffuse horizontal irradiance (DHI) from the global tilted irradiance (GTI). The model is based on the DIRINT model, but substitutes the use of the GTI in place of the global horizontal irradiance (GHI) for determining the global clearness index (K t). Additionally, an iterative method is used to adjust K t to improve the derived values of the DNI and the DHI. The model is referred to as the GTI-DIRINT model. When using for model input the GTI measured with a pyranometer tilted at a small angle from the horizontal (10°), the deviations between the measured DNI and DHI and the GTI-DIRINT-modeled DNI and DHI were essentially the same as those for the DIRINT model when using the GHI for model input. Increasing the tilt angle from horizontal (as great as 44° for Eugene, Oregon) increased the deviations between the modeled and measured DNI and DHI, but the results were still reasonable. The GTI-DIRINT model provides reasonable values of DNI and DHI from the GTI, and if they are subsequently used with the Perez tilted surface model to model the GTI for orientations within 30° of the azimuth and tilt of the GTI used for model input, the result is as good or better than may be obtained when using a measured DNI and DHI, and without their significant instrumentation cost.

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“…For example, pyranometers, which have been used to measure solar radiation flux density, have been affected by moisture. After precipitation occurs, the data from pyranometers should not be used for prediction until the moisture is removed or vaporized from the equipment [17].…”

Section: Introductionmentioning

confidence: 99%

Very Short-Term Photovoltaic Power Forecasting Using Stochastic Factors

Khumma

Tamee

2020

ECTI-CIT

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This paper proposes a photovoltaic (PV) power forecasting model, using the application of a Gaussian blur algorithm ﬁltering technique to estimate power output and the creation of a stochastic forecasting model. As a result, aﬀected power can be forecasted from stochastic factors with machine learning and an artiﬁcial neural network. This model focuses on very short-term forecasting over a ﬁve minute period. As it uses only endogenous data, no exogenous data is needed. To evaluate the model, results were compared to the persistence model, which has good short-term forecasting accuracy. This proposed PV forecasting model gained higher accuracy than the persistence model using stochastic factors.

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New data set for validating PV module performance models

Cited by 55 publications

References 1 publication

Non-Iterative Methods for the Extraction of the Single-Diode Model Parameters of Photovoltaic Modules: A Review and Comparative Assessment

Non-Iterative Methods for the Extraction of the Single-Diode Model Parameters of Photovoltaic Modules: A Review and Comparative Assessment

A model for deriving the direct normal and diffuse horizontal irradiance from the global tilted irradiance

Very Short-Term Photovoltaic Power Forecasting Using Stochastic Factors

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