Study of Neural Network Models for Security Assessment in Power Systems
نویسنده
چکیده
This paper presents the application of different Neural Network (NN) models for classifying the power system states as secure/insecure. Traditional method of security evaluation involves performing load flow and transient stability analysis for each contingency, making it infeasible for real time application. Pattern Recognition (PR) approach is recognized as an alternative tool. The NN models adopted for classification includes Multilayer Perceptron (MLP), Learning Vector Quantization (LVQ), Probabilistic Neural Network (PNN) and Adaptive Resonance Theory Mapping (ARTMAP). The NN models designed are tested on 14 Bus, 30 bus and 57 Bus IEEE standard test systems. The performance of various NN models are studied in training and testing phases and the results are compared.
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