Forecasting-Aided Monitoring for the Distribution System State Estimation

Carcangiu S.;Fanni A.;Pegoraro P. A.;Sias G.;Sulis S.
2020

Abstract

In this paper, an innovative approach based on an artificial neural network (ANN) load forecasting model to improve the distribution system state estimation accuracy is proposed. High-quality pseudomeasurements are produced by a neural model fed with both exogenous and historical load information and applied in a realistic measurement scenario. Aggregated active and reactive powers of small or medium enterprises and residential loads are simultaneously predicted by a one-step ahead forecast. The correlation between the forecasted real and reactive power errors is duly kept into account in the definition of the estimator together with the uncertainty of the overall measurement chain. The beneficial effects of the ANN-based pseudomeasurements on the quality of the state estimation are demonstrated by simulations carried out on a small medium-voltage distribution grid.
eng
2020
1
15
15
https://www.hindawi.com/journals/complexity/2020/4281219/
Esperti anonimi
internazionale
Scientifica
Neural networks; Reactive power; State estimation; Uncertainty analysis
Article ID 4281219
no
Carcangiu, S.; Fanni, A.; Pegoraro, P. A.; Sias, G.; Sulis, S.
1.1 Articolo in rivista
info:eu-repo/semantics/article
1 Contributo su Rivista::1.1 Articolo in rivista
262
5
open
File in questo prodotto:
File Dimensione Formato  
2020_Complexity_Forecasting-Aided Monitoring.pdf

accesso aperto

Descrizione: articolo
Tipologia: versione editoriale
Dimensione 1.81 MB
Formato Adobe PDF
1.81 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Questionario e social

Condividi su:
Impostazioni cookie