A case-based reasoning approach for dynamic security assessment of power systems with large penetration of wind power

This paper presents Case-Based Reasoning (CBR), a novel methodology for dynamic security assessment of power systems with large penetration of wind power. CBR is a machine learning technique which belongs to the artificial intelligence family. The idea behind the CBR principle is to use solutions of old or previous cases to obtain accurate estimated solutions of new cases. The structure and functionalities of CBR are described. A test system model is used to demonstrate the efficiency of the described technique. Results suggest that dynamic security assessment using the CBR technique is robust and provide lesser computational time which can be applied for an on-line dynamic security assessment.