## Abstract

The authors present a method for designing a neural network (NN) for potential application in real-time system security analysis. Specifically, the authors formulate the contingency classification problem as a pattern recognition problem and then design a NN to classify the system states (i.e., normal, alert and emergency). A two-layered NN with a fully-connected asynchronous binary model for each layer is developed. An optimization technique, which calculates the weights and thresholds of the NN, is used to maximize the probability of classifying the correct state. This procedure is illustrated through a 17-bus example system for which the post-contingency voltage drop limits are considered.

Original language | English (US) |
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Pages (from-to) | 1121-1124 |

Number of pages | 4 |

Journal | Proceedings - IEEE International Symposium on Circuits and Systems |

Volume | 2 |

State | Published - 1991 |

Externally published | Yes |

Event | 1991 IEEE International Symposium on Circuits and Systems Part 4 (of 5) - Singapore, Singapore Duration: Jun 11 1991 → Jun 14 1991 |

## All Science Journal Classification (ASJC) codes

- Electrical and Electronic Engineering