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Noise-scaled euclidean distance: a metric for maximum likelihood estimation of the PV model parameters

Noise-scaled euclidean distance: a metric for maximum likelihood estimation of the PV model parameters
Noise-scaled euclidean distance: a metric for maximum likelihood estimation of the PV model parameters
This article revisits the objective function (or metric) used in the extraction of photovoltaic (PV) model parameters. A theoretical investigation shows that the widely used current distance (CD) metric does not yield the maximum likelihood estimates (MLE) of the model parameters when there is noise in both voltage and current samples. It demonstrates that the Euclidean distance (ED) should be used instead, when the voltage and current noise powers are equal. For the general case, a new noise-scaled Euclidean distance (NSED) metric is proposed as a weighted variation of ED, which is shown to fetch the MLE of the parameters at any noise conditions. This metric requires the noise ratio (i.e., ratio of the two noise variances) as an additional input, which can be estimated by a new noise estimation (NE) method introduced in this study. One application of the new metric is to employ NSED regression as a follow-up step to existing parameter extraction methods toward fine-tuning of their outputs. Results on synthetic and experimental data show that the so-called NSED regression “add-on” improves the accuracy of five such methods and validate the merits of the NSED metric.
Euclidean distance (ED), Fitting, Noise extraction (NE), Orthogonal distance, Parameter estimation, Parameter extraction, Parameter identification, Photovoltaic (PV) model, Regression
2156-3381
815 - 826
Batzelis, Stratis
2a85086e-e403-443c-81a6-e3b4ee16ae5e
Blanes, Jose
7c85e85d-d45f-47e1-be6a-2be23d4274b4
Toledo, F. Javier
72ef6fd0-9d57-4b45-8a7b-bc99688520ac
Galiano, Vicente
241d4b06-7a9a-4153-9398-98f0b969e01d
Batzelis, Stratis
2a85086e-e403-443c-81a6-e3b4ee16ae5e
Blanes, Jose
7c85e85d-d45f-47e1-be6a-2be23d4274b4
Toledo, F. Javier
72ef6fd0-9d57-4b45-8a7b-bc99688520ac
Galiano, Vicente
241d4b06-7a9a-4153-9398-98f0b969e01d

Batzelis, Stratis, Blanes, Jose, Toledo, F. Javier and Galiano, Vicente (2022) Noise-scaled euclidean distance: a metric for maximum likelihood estimation of the PV model parameters. IEEE Journal of Photovoltaics, 12 (3), 815 - 826. (doi:10.1109/JPHOTOV.2022.3159390).

Record type: Article

Abstract

This article revisits the objective function (or metric) used in the extraction of photovoltaic (PV) model parameters. A theoretical investigation shows that the widely used current distance (CD) metric does not yield the maximum likelihood estimates (MLE) of the model parameters when there is noise in both voltage and current samples. It demonstrates that the Euclidean distance (ED) should be used instead, when the voltage and current noise powers are equal. For the general case, a new noise-scaled Euclidean distance (NSED) metric is proposed as a weighted variation of ED, which is shown to fetch the MLE of the parameters at any noise conditions. This metric requires the noise ratio (i.e., ratio of the two noise variances) as an additional input, which can be estimated by a new noise estimation (NE) method introduced in this study. One application of the new metric is to employ NSED regression as a follow-up step to existing parameter extraction methods toward fine-tuning of their outputs. Results on synthetic and experimental data show that the so-called NSED regression “add-on” improves the accuracy of five such methods and validate the merits of the NSED metric.

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Noise-Scaled_Euclidean_Distance_A_Metric_for_Maximum_Likelihood_Estimation_of_the_PV_Model_Parameters (1) - Version of Record
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Published date: 1 May 2022
Keywords: Euclidean distance (ED), Fitting, Noise extraction (NE), Orthogonal distance, Parameter estimation, Parameter extraction, Parameter identification, Photovoltaic (PV) model, Regression

Identifiers

Local EPrints ID: 456599
URI: http://eprints.soton.ac.uk/id/eprint/456599
ISSN: 2156-3381
PURE UUID: 16dd43a2-91c0-450f-94d2-1357f09af739
ORCID for Stratis Batzelis: ORCID iD orcid.org/0000-0002-2967-3677

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Date deposited: 05 May 2022 16:47
Last modified: 28 Jan 2023 03:00

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Contributors

Author: Stratis Batzelis ORCID iD
Author: Jose Blanes
Author: F. Javier Toledo
Author: Vicente Galiano

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