Forecasting transient stability of power system by an ensemble classifier
Corressponding author's email:
ngocau@hcmute.edu.vnKeywords:
Transient stability Forecast, Feature Selection, Power System, Neural Networks, Ensemble classifierAbstract
A large oscillation caused by faults leads power system to instability state. This makes fast forecast a necessity to drive power system into stability state, avoid the risk of blackouts. In recent years, an ensemble classifier has been emerged as a promising approach to enable online transient stable forecast (TSF). The paper proposed an ensemble classifier (EC) that is combined by parallel single classifiers. The single classifiers can compensate for the others by combining in parallel. Then, the EC can improve classification accuracy. The paper proposed the use of Multi-layer Perceptron Networks (MLPN) to build EC. The study is tested on IEEE 39-bus power system network
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