Issue |
EPJ Photovolt.
Volume 15, 2024
Special Issue on ‘EU PVSEC 2024: State of the Art and Developments in Photovoltaics’, edited by Robert Kenny and Gabriele Eder
|
|
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Article Number | 33 | |
Number of page(s) | 12 | |
DOI | https://doi.org/10.1051/epjpv/2024028 | |
Published online | 21 October 2024 |
https://doi.org/10.1051/epjpv/2024028
Original Article
Uncertainty-aware estimation of inverter field efficiency using Bayesian neural networks in solar photovoltaic plants
1
GreenPowerMonitor a DNV company, Gran Via de les Corts Catalanes 130, Barcelona, Spain
2
DNV Denmark, Tuborg Parkvej 8, Hellerup, Denmark
* e-mail: gerardo.guerra@dnv.com
Received:
7
June
2024
Accepted:
2
September
2024
Published online: 21 October 2024
Solar inverters are one of the most important components in a Photovoltaic plant. Their main function is to convert the DC power produced by the solar modules into AC power that can be injected into the grid. Although inverter efficiency has reached exceptionally high values, thanks to recent technological advancements, it is typically measured at dedicated laboratories under strict testing conditions, which makes its validation after deployment extremely challenging, both from a logistic and financial point of view. This paper presents a methodology for the calculation of inverter field efficiency based on Bayesian neural networks. The goal of the neural network is to model inverter efficiency and its variance as a function of the inverter's operational state. Results show that an optimised Bayesian neural network can effectively model inverter efficiency with small reconstruction errors and negligible bias. Furthermore, the model has been proven useful to replicate the calculation of the European efficiency along with a full uncertainty characterisation.
Key words: Photovoltaic generation / inverter efficiency / Bayesian neural networks / machine learning / uncertainty
© G. Guerra et al., Published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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