Decision trees and transient stability of electric power systems
Automatica (Journal of IFAC)
Automatic Learning Techniques in Power Systems
Automatic Learning Techniques in Power Systems
SETN '02 Proceedings of the Second Hellenic Conference on AI: Methods and Applications of Artificial Intelligence
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Modern Power Systems often operate close to their stability limits in order to meet the continuously growing demand, due to the difficulties in expanding the generation and transmission system. An effective way to face power system contingencies that can lead to instability is load shedding. In this paper we propose a method to assess the dynamic performance of the Greek mainland Power System and to propose a load shedding scheme in order to maintain voltage stability under various loading conditions and operating states in the presence of critical contingencies including outages of one or more generating units in the south part of the system. A Self Organizing Map is utilized in order to classify the Load profiles of the Power System. With a decision tree the dynamic performance of each class is assessed. The classification of Load Profiles by the SOM, provide the load shedding scheme.