Dyanamic stability assessment of power system using multilayer feedforward neural networks with reduced feature selection
Corressponding author's email:
aunn@hcmute.edu.vnKeywords:
dynamic stability assessment, neural networks, feature/variable selectionAbstract
This paper presents an application of Multilayer Feed-forward Neural Networks (MLFN) for Dynamic Stability Assessment (DSA) with feature reduction techniques. Dynamic stability of the power system is first determined based on the generator relative rotor angles obtained from time domain simulations. Simulations were carried out on the IEEE 9-bus test system considering three phase faults on at different loading conditions. The data collected from the time domain simulations are then used as inputs to the MLFN. Reduced feature inputs based on Fisher Discrimination (FD) and correlation analysis (CA). MLFN results show that the stability condition of the power system can be predicted with high accuracy and less misclassification rate.
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