Presenting A New Method for Identifying the Initial Events of Cascading Failures with the Aim of Analyzing the Vulnerability of the Power System

Document Type : Power Article

Authors

1 Ph.D. Student, Department of Electrical Engineering, Islamic Azad University, Saveh Branch, Saveh, Iran

2 Department of Technical Engineering, Islamic Azad University, Saveh, Iran

3 Professor, Department of Electrical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran.

Abstract

Cascading failures are one of the most destructive states in the power systems, which may initially start with a small error and then spread with a chain effect like a domino, leading to large-scale blackouts. Therefore, it is necessary to prevent cascading failures, because our daily lives depend heavily on a stable and reliable power supply network. So far different methods have been proposed to identify the initial events of cascade failures, including fault-prone lines and transformers, with the aim of taking remedial action scheme to reduce the adverse effects of cascade failure. In this paper, several well-known methods with the potential to detect initial events that lead to catastrophic cascading failures are first implemented of the IEEE 39-bus test system and the Competencies and shortcomings of methods are compared. In the following, a new method is presented with appropriate speed and accuracy compared to other methods. The enumeration method based on William Fine risk assessment technique is used to validate the results of different methods. The enumeration method is a difficult and time-consuming method. Therefore, in order to reduce the dimensions of the mentioned method in order to validate the methods more appropriately and faster, the William Fine risk assessment technique is used. This technique identifies and ranks the potential and effective initial events in creating the chain of cascading failures in the power system by calculating the risk assessment score of combinations of possible N-K(1<K≤3) contingencies.

Keywords

References

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Journal of Modeling in Engineering
Volume 19, Issue 67
December 2021
Pages 191-205

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History

  • Receive Date: 17 February 2021
  • Revise Date: 13 June 2021
  • Accept Date: 10 July 2021

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