An ANN-based grid voltage and frequency forecaster

Research output: Contribution to conferencePaper

  • External authors:
  • Alessandro Massi Pavan
  • N. Chettibi
  • A Mellit


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 %.

Bibliographical metadata

Original languageEnglish
Publication statusPublished - 2018
EventIET International Conference on Power Electronics, Machines and Drives (PEMD) - Liverpool, United Kingdom
Event duration: 17 Apr 201819 Jul 2018


ConferenceIET International Conference on Power Electronics, Machines and Drives (PEMD)
CountryUnited Kingdom
Internet address