An intelligent security alert system for power system pre-emergency control

Recent large-scale blackouts have demonstrated that secure operation of large interconnected power systems cannot be achieved without full understanding of the system behavior during abnormal and emergency conditions. This paper is focused on applying learning clustering algorithms for identifying critical states in power systems. The authors propose an intelligent security alert system for early detection of alarm states using the clustering ensemble concept. The security assessment clustering ensemble is realized in STATISTICA 6.0 and GA Fuzzy Clustering. Matlab and Power System Analysis Toolbox are used as the modeling tools. We demonstrated the approach on the modified IEEE One Area RTS-96 power system. Preliminary results demonstrate that our security alert system can identify potentially dangerous system states.