Please use this identifier to cite or link to this item: https://hdl.handle.net/11000/30460

Noise-Scaled Euclidean Distance: A Metric for Maximum Likelihood Estimation of the PV Model Parameters

Title:
Noise-Scaled Euclidean Distance: A Metric for Maximum Likelihood Estimation of the PV Model Parameters
Authors:
Batzelis, Efstratios  
Blanes, Jose M.  
Toledo Melero, Fco. Javier  
Galiano, Vicente  
Editor:
IEEE Xplore
Department:
Departamentos de la UMH::Ingeniería de Computadores
Issue Date:
2022-05
URI:
https://hdl.handle.net/11000/30460
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
Keywords/Subjects:
Euclidean distance (ED)
fitting
noise extraction (NE)
orthogonal distance
parameter estimation
parameter extraction
Knowledge area:
CDU: Ciencias aplicadas: Ingeniería. Tecnología
Type of document:
info:eu-repo/semantics/article
Access rights:
info:eu-repo/semantics/openAccess
DOI:
https://doi.org/ 10.1109/JPHOTOV.2022.3159390
Appears in Collections:
Artículos Ingeniería de computadores



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