Artificial Neural Network Protection of Synchronous Generators Under Loss of Excitation

Mohammed, Rusul; Moravej, Zahra; Yaghobi, Hamid; Wali, Mousa Kadhim

doi:10.22075/mseee.2026.40707.1258

Artificial Neural Network Protection of Synchronous Generators Under Loss of Excitation

Articles in Press

Document Type : Research Article

Authors

Rusul Mohammed ¹

Zahra Moravej ¹

Hamid Yaghobi ¹

Mousa Kadhim Wali ²

¹ Faculty of Electrical & Computer Engineering, Semnan University, Semnan, Iran.

² Department of Computer Engineering Techniques, Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq.

10.22075/mseee.2026.40707.1258

Abstract

Loss of Excitation (LOE) poses a significant challenge for synchronous generators. It can lead to instability, overcurrent conditions, and potentially a loss of synchronization with the electrical grid. Conventional protection techniques based on impedance or power often lack adequate selectivity and are especially vulnerable to voltage fluctuations during major failures. This vulnerability can result in incorrect and undesired disconnections. This article presents an intelligent protection solution that utilizes an artificial neural network (ANN) method to classify and securely identify both entire and partial excitation losses in synchronous generators. The suggested technique uses electrical and electromechanical indications that show how the generator really behaves over time, such as terminal voltage, reactive power, load angle, and load angle derivative. A feedforward NN was trained to classify the difference between four operational states: normal, LOE, partial loss of excitation, and severe symmetric fault. The suggested method can isolate the generator within 0.46 seconds after the LOE or partial LOE (PLOE) happens in the generator terminal.

Keywords

Loss of Excitation

Artificial Neural Network

PLOE

MATLAB

Synchronous Generators

Subjects

Power System

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