On-line dynamic security assessment based on kernel regression trees

This paper presents a new approach to perform online dynamic security assessment and monitoring of electric power systems exploiting a statistical hybrid learning technique-kernel regression trees. This technique, besides producing fast security classification, can still quantify, in real-time, the security degree of the system, by emulating continuous security indices that translate the power system dynamic behavior. Moreover it can provide interpretable security structures. The feasibility of this approach was demonstrated in the dynamic security assessment of isolated systems with large amounts of wind power production, like in the Crete island electric network (Greece). Comparative results regarding performances of decision trees and neural networks are also presented and discussed. From the obtained results, the proposed approach showed to provide good predicting structures whose performance stands up to the performance of the two other existing methods