Artificial Neural Network Based Power System Stabilizer on a Single Machine Infinite Bus Modelled in Digsilent Powerfactory and MATLAB

TL;DR

This paper explores the application of artificial neural networks as a predictive control strategy to replace traditional power system stabilizers, aiming to improve damping of low-frequency oscillations in complex power systems.

Contribution

It introduces a neural network-based control scheme modeled in MATLAB and Digsilent Powerfactory, demonstrating its potential as a practical alternative to conventional stabilizers.

Findings

Neural network controllers effectively dampen oscillations.

The proposed method outperforms traditional stabilizers in simulation.

Model exchange between MATLAB and Powerfactory enhances control design.

Abstract

In this paper the use of artificial neural network in power system stability is studied. A predictive controller based on two neural networks is designed and tested on a single machine infinite bus system which is used to replace conventional power system stabilizers. They have been used for decades in power system to dampen small amplitude low frequency oscillation in power systems. The increases in size and complexity of power systems have cast a shadow on efficiency of conventional method. New control strategies have been proposed in many researches. Artificial Neural Networks have been studied in many publications but lack of assurance of their functionality has hindered the practical usage of them in utilities. The proposed control structure is modelled using a novel data exchange established between MATLAB and