1994
DOI: 10.1109/59.317625
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Effectiveness of artificial neural networks for first swing stability determination of practical systems

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Cited by 41 publications
(12 citation statements)
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“…Hobson and Allen [11] reported that the NNs have difficulty in returning consistent accurate answers under varying network conditions. Aboytes and Ramirez [12] used NNs to predict stability of a 53 generators system.…”
Section: Introductionmentioning
confidence: 99%
“…Hobson and Allen [11] reported that the NNs have difficulty in returning consistent accurate answers under varying network conditions. Aboytes and Ramirez [12] used NNs to predict stability of a 53 generators system.…”
Section: Introductionmentioning
confidence: 99%
“…Therefore, timedomain simulation is still the most accurate method for transient stability analysis and it can be applied to any level of power system models, but as mentioned before, the main problem of this method is that it is very time consuming. As a result, in recent years there have been several attempts in using computational intelligence based techniques like neural networks (NNs) for transient stability assessment (TSA) of power systems, e.g., see [14][15][16][17][18][19][20][21][22][23][24][25]. Neural networks are widely used for function approximation and/or classification problems, because no rigorous mathematical system modeling is required in order to train an NN to form the underlying mapping.…”
Section: Introductionmentioning
confidence: 99%
“…Hobson et al [5] have reported that ANN have difficulty in returning consistently accurate answers under varying network conditions. ANN was trained to predict CCT for three phase faults, for two systems(4 generator and 20 generator) at 5 load levels and 3 different topologies.…”
Section: Reviewmentioning
confidence: 99%