This paper presents a method for the forecasting of the voltage and the frequency at the point of connection between a battery energy storage system installed at The University of Manchester and the local low voltage distribution grid. The techniques are to be used in a real-time controller for optimal management of the storage system. The forecasters developed in this study use an Artificial Neural Network (ANN)-based technique and can predict the grid quantities with two different time widows: one second and one minute ahead. The developed ANNs have been implemented in a dSPACE based real-time controller and all forecasters show very good performance, with correlations coefficients greater than 0.85, and Mean Absolute Percentage Errors of less than 0.2 %.
An ANN-based grid voltage and frequency forecaster
Massi Pavan
2018-01-01
Abstract
This paper presents a method for the forecasting of the voltage and the frequency at the point of connection between a battery energy storage system installed at The University of Manchester and the local low voltage distribution grid. The techniques are to be used in a real-time controller for optimal management of the storage system. The forecasters developed in this study use an Artificial Neural Network (ANN)-based technique and can predict the grid quantities with two different time widows: one second and one minute ahead. The developed ANNs have been implemented in a dSPACE based real-time controller and all forecasters show very good performance, with correlations coefficients greater than 0.85, and Mean Absolute Percentage Errors of less than 0.2 %.| File | Dimensione | Formato | |
|---|---|---|---|
|
program+Pavan.pdf
Accesso chiuso
Tipologia:
Documento in Versione Editoriale
Licenza:
Digital Rights Management non definito
Dimensione
1.72 MB
Formato
Adobe PDF
|
1.72 MB | Adobe PDF | Visualizza/Apri Richiedi una copia |
Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
