Open AccessANN‐based grid voltage and frequency forecaster
Author(s) -
Massi Pavan Alessandro,
Chettibi Nadjwa,
Mellit Adel,
Feehally Thomas,
Forsyth Andrew J.,
Todd Rebecca
Publication year - 2019
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2018.8162
Subject(s) - dspace , controller (irrigation) , voltage , artificial neural network , computer science , grid , control theory (sociology) , simulation , control (management) , electrical engineering , engineering , artificial intelligence , algorithm , mathematics , geometry , agronomy , biology
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 windows: 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 >0.85, and mean absolute percentage errors of <0.2%.