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Pré-Publication, Document De Travail Année : 2021

AUTOMATIC DETECTION OF DISTRIBUTED SOLAR GENERATION BASED ON EXOGENOUS INFORMATION

Résumé

In recent years, the importance of PV generation data for distribution system operations has increased. However, there are still a lot of behind-the-meter solar installations that are not registered by the system operator and are not monitored. This "hidden" generation, therefore, increases the difficulty to operate securely and efficiently the distribution grid. This paper introduces a tool for the automatic detection of such "hidden" behind-the-meter solar generation. It is designed to discriminate the nodes with and without PV generation and is aimed at a high accuracy. The tool consists of a neural network coupled with an analytical classification algorithm, which considers an exogenous information (i.e. node consumption and temperature data). Open-access data about consumption and solar radiation were used to simulate the electrical grid and validate the proposed approach. The implemented solution was tested across all the nodes of the grid and its sensitivity has been analysed with regard to the level of PV penetration and period of observation. The tool is able to recognize the nodes with a new PV installation with an accuracy of up to 100%, depending on the exogenous conditions.
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Dates et versions

hal-03368870 , version 1 (07-10-2021)

Identifiants

  • HAL Id : hal-03368870 , version 1

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Aleksandr Petrusev, Rémy Rigo-Mariani, Vincent Debusschere, Patrick Reignier, Nouredine Hadjsaid. AUTOMATIC DETECTION OF DISTRIBUTED SOLAR GENERATION BASED ON EXOGENOUS INFORMATION. 2021. ⟨hal-03368870⟩
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