Artificial neural network based algorithm for early prediction of transient stability using wide area measurements

Mahdi M., Genç V. M. İ.

5th International Istanbul Smart Grids and Cities Congress and Fair, ICSG 2017, İstanbul, Turkey, 19 - 21 April 2017, pp.17-21 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • Doi Number: 10.1109/sgcf.2017.7947611
  • City: İstanbul
  • Country: Turkey
  • Page Numbers: pp.17-21
  • Keywords: Classification, neural networks, prediction, transient stability, wide area measurements, CLASSIFICATION
  • Istanbul Technical University Affiliated: Yes


© 2017 IEEE.Early prediction of the transient stability of power systems after fault occurrences has a great impact on the performance of wide area protection and control systems designed against transient instabilities. In this paper, an artificial neural networks based methodology is proposed for predicting the power system stability directly after clearing the fault. A dataset is generated to train a multilayer perceptron off-line, which is then used for early online prediction of any transient instability. The neural network is fed by the inputs, which are the pre-fault, during-fault, and post-fault voltage magnitude measurements collected from the phasor measurement units. The success and the effectiveness of the proposed method are demonstrated, as it is applied to the 37-generator 127-bus power test system and an accuracy above 99% is obtained in the early prediction of transient instabilities.