Performance evaluation of an autoencoder state estimator with realistic low voltage grids reconstructed from open data - CentraleSupélec Accéder directement au contenu
Communication Dans Un Congrès Année : 2023

Performance evaluation of an autoencoder state estimator with realistic low voltage grids reconstructed from open data

Résumé

Distribution systems operators' (DSO) upcoming challenge is to monitor and control low voltage (LV) grids. Real time measurements' acquisition from LV grids has become possible thanks to the deployment of smart meters (SM) but it is still limited by technical constraints. Thus, a real time state estimator (SE) is needed to ensure the LV grid's observability. A machine learning model can be trained using the SM historical data and then run in real time using few available measurements to estimate the system's state. For the purpose of evaluating the performance of a machine learning based SE on a realistic LV grid, an auto-encoder based SE is presented in this paper. An accuracy enhancement using pseudomeasurements is proposed and a method for reconstructing realistic LV grid models is also described.
Fichier principal
Vignette du fichier
PERFORMANCE EVALUATION OF AN AUTOENCODER STATE ESTIMATOR WITH REALISTIC LOW VOLTAGE GRIDS RECONSTRUCTED FROM OPEN DATA.pdf (710.83 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04198569 , version 1 (07-09-2023)

Identifiants

Citer

Elio El Semaan, Dat Tien Nguyen, Alessio Iovine, Trung Dung Le, Philippe Dessante, et al.. Performance evaluation of an autoencoder state estimator with realistic low voltage grids reconstructed from open data. 27th International Conference on Electricity Distribution (CIRED 2023), Jun 2023, Rome, Italy. ⟨10.1049/icp.2023.0618⟩. ⟨hal-04198569⟩
48 Consultations
23 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More