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- W2057213734 abstract "This paper proposes a new methodology which combines supervised and unsupervised learning for evaluating power system dynamic security. Based on the concept of stability margin, pre-fault power system conditions are assigned to the output neurons on the two-dimensional grid with the growing hierarchical self-organizing map technique (GHSOM) via supervised artificial neural networks (ANNs) which perform an estimation of post-fault power system state. The technique estimates the dynamic stability index that corresponds to the most critical value of synchronizing and damping torques of multimachine power systems. ANN-based pattern recognition is carried out with the growing hierarchical self-organizing feature mapping in order to provide adaptive neural network architecture during its unsupervised training process. Numerical tests, carried out on a IEEE 9 bus power system are presented and discussed. The analysis using such method provides accurate results and improves the effectiveness of system security evaluation." @default.
- W2057213734 created "2016-06-24" @default.
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- W2057213734 date "2005-09-01" @default.
- W2057213734 modified "2023-09-26" @default.
- W2057213734 title "Combined use of supervised and unsupervised learning for power system dynamic security mapping" @default.
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- W2057213734 doi "https://doi.org/10.1016/j.engappai.2005.01.005" @default.
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