Neural Network Based Approach Applied to Review Transient Stability of an Alternator Connected to Infinite Bus

Priyaranjan Mandal

doi:https://doi.org/10.14445/22315381/IJETT-V38P259

Research Article | Open Access | Download PDF

Volume 38 | Number 2 | Year 2016 | Article Id. IJETT-V38P259 | DOI : https://doi.org/10.14445/22315381/IJETT-V38P259

Neural Network Based Approach Applied to Review Transient Stability of an Alternator Connected to Infinite Bus

Priyaranjan Mandal

Citation :

Priyaranjan Mandal, "Neural Network Based Approach Applied to Review Transient Stability of an Alternator Connected to Infinite Bus," International Journal of Engineering Trends and Technology (IJETT), vol. 38, no. 2, pp. 320-325, 2016. Crossref, https://doi.org/10.14445/22315381/IJETT-V38P259

Abstract

This paper approaches to analyse the transient stability of an alternator connected to infinite bus. A three phase fault is considered to happen to the total system. The characteristics behaviour of the alternator relative torque angle is studied in time domain Simulink model, based on MATLAB, of a single machine connected to an infinite bus system is assumed to suffer severe three phase short circuit faults. The transient state behaviour of the rotor relative torque angle shows a sustained oscillations indicating an uncertainty of the system to reach the steady state. The behaviour of the system is improved by providing a damping to the system. Inclusion of negative feedback technique is utilized to provide the damping. The damped system shows an underdamped behaviour in the post fault conditions ensuring a good tendency of the system to reach to steady state quickly. A neural network (NN) is assumed to be configured for prediction of the behaviour of the transient stability of the actual system. The artificial neural network is configured by learning and training of the actual system by the Levenberg-Marquardt method of training (trainlm) to predict the actual system. The NN based system predicts the actual system successfully showing its good strength and reliability.

Keywords

Transient Stability, neural network, relative torque angle, training (trainlm).

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