Real time Vulnerability Assessment in Cascading Failure Analysis Using an Intelligence Monitoring Model

Abstract

In this paper, A measurement-based model is proposed to assess the vulnerability of the transmission line under overloaded cascading blackout analysis in the power system online operation environment. The proposed Measurement-based model is constructed by the Artificial Neural Network due to its ability in nonlinear mapping between input and output vectors that its ability causes a suitable prediction. The Artificial Neural Network Training data set is provided by using analytical vulnerability assessment model in different operational condition to rank and obtain vulnerability status of each transmission line. Then, Arterial Neural Network links between operating conditions as the input vector and the vulnerability value of each transmission line as the output vector. The efficiency of the proposed measurement-based model in terms of speed and accuracy is investigated in the IEEE 39-, and IEEE 118-bus test case systems by comparing it to an analytical vulnerability assessment model. Finally, the security and reliability of the transmission network are enhanced by increasing the online situational awareness of the operator about the effects of each transmission line in propagating the cascading failure by using the proposed model.

Keywords

• cascading failure analysis
• multi-criteria vulnerability index
• freed forward multi-layer artificial neural network
• online vulnerability assessment