Effectiveness of artificial neural networks for first swing stability determination of practical systems

Hobson, Elizabeth; Allen, G. N.

doi:10.1109/59.317625

Cited by 41 publications

(12 citation statements)

References 25 publications

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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%

Estimation of the critical clearing time using MLP and RBF neural networks

Karami

2008

Int Trans Elec Energy Syst

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SUMMARYThis paper presents multi-layer perceptron (MLP) and radial basis function (RBF) neural networks (NNs) based methods for the estimation of the critical clearing time (t cr ) as an index for power systems transient stability analysis (TSA). The t cr evaluation involves elaborate computations that often include time-consuming solutions of nonlinear on-fault and post-fault systems equations. Knowing that for a particular fault scenario (contingency), the t cr is a function of the pre-fault system operating point, the objective of this paper is to show how one may develop the MLP and the RBF NNs based methods for estimating the t cr by using only the pre-fault operating conditions as the inputs of the NNs. The paper uses the proposed MLP and RBF NNs based methods to estimate the t cr under different topological as well as operating conditions of the 10-machane 39-bus New England test power system, and results are given. The simulation results show that both NNs are able to retain past learned information almost instantaneously. However, compared to the RBF NN, the MLP NN makes us have a more accurate estimation for the t cr .

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Section: Introductionmentioning

confidence: 99%

Estimation of the critical clearing time using MLP and RBF neural networks

Karami

2008

Int Trans Elec Energy Syst

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“…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%

Transient stability assessment of power systems described with detailed models using neural networks

Karami

Esmaili

2013

International Journal of Electrical Power & Energy Systems

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“…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%