Neuro-Fuzzy Decision Trees for Dynamic Security Control of Power Systems

This paper addresses the problem of dynamic security classification as well as security control of power systems., using class pattern recognition. More specifically, neuro-fuzzy decision trees (N-FDTs) are proposed i.e. fuzzy decision tree structure with neural like parameter adaptation strategy, in order to classify the security status of a power system. The method is applied on a realistic model of the Hellenic Power System, investigating two cases. The first case focuses on stressed operation of the power system and proposes corrective load shedding to avoid voltage instability. The second state investigates the scenario of large scale wind power integration to the system, and proposes wind power shedding as a preventive means to avoid.