A Novel Implementation for Generator Rotor Angle Stability Prediction Using an Adaptive Artificial Neural Network Application for Dynamic Security Assessment

Author

Al-Masri, Ahmed Naufal ; Ab Kadir, M.Z.A. ; Hizam, H. ; Mariun, N.

Author_Institution

Fac. of Inf. Sci. & Eng., Manage. & Sci. Univ., Shah Alam, Malaysia

Volume

28

Issue

3

fYear

2013

fDate

Aug. 2013

Firstpage

2516

Lastpage

2525

Abstract

This paper addresses a new approach for predicting the generator rotor angle using an adaptive artificial neural network (AANN) for power system stability. The aim of this work is to predict the stability status for each generator when the system is under a contingency. This is based on the initial condition of an operating point, which is represented by the generator rotor angle at a certain load level. An automatic data generation algorithm is developed for the training and testing process. The proposed method has been successfully tested on the IEEE 9-bus test system and the 87-bus system for Peninsular Malaysia.

Keywords

electric generators; neural nets; power engineering computing; power system security; power system stability; rotors; AANN; IEEE 87-bus system; IEEE 9-bus test system; Peninsular Malaysia; adaptive artificial neural network application; automatic data generation algorithm; dynamic security assessment; generator rotor angle stability prediction; power system stability; Generators; Power system stability; Rotors; Security; Stability criteria; Training; Artificial neural network (ANN); contingency analysis; dynamic security assessment (DSA); rotor angle stability;

fLanguage

English

Journal_Title

Power Systems, IEEE Transactions on

Publisher

ieee

ISSN

0885-8950

Type

jour

DOI

10.1109/TPWRS.2013.2247069

Filename

6472123