ANN-based grid voltage and frequency forecaster

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 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%.

Keywords

• load forecasting
• power engineering computing
• power grids
• neural nets
• grid quantities
• different time windows
• developed ANNs
• dSPACE-based real-time controller
• forecasters
• ANN-based grid voltage
• frequency forecaster
• forecasting
• battery energy storage system
• low-voltage distribution grid
• optimal management
• artificial neural network-based technique