Estimation of Temporary Overvoltages during Power System Restoration using Artificial Neural Network

The energization of power transformers following a complete or partial collapse of the system is an important issue. This paper presents an Artificial Neural Network (ANN)-based approach to estimate the temporary overvoltages (TOVs) due to transformer energization. In proposed methodology, Levenberg-Marquardt second order method is used to train the multilayer perceptron. The developed ANN is trained with the extensive simulated results, and tested for typical cases. Then the new algorithms are presented and demonstrated for a partial of 39-bus New England test system. The simulated results show that the proposed technique can estimate the peak values and duration of switching overvoltages with good accuracy.

I. Introduction

THE problem of restoring power systems after a complete or partial blackout is as old as the power industry itself. In recent years, due to economic competition and deregulation, power systems are being operated closer and closer to their limits. At the same time, power systems have increased in size and complexity. Both factors increase the risk of major power outages. After a blackout, power needs to be restored as quickly and reliably as possible and, consequently, detailed restoration plans are necessary [1], [2].