Stability assessment of electric power systems using growing neural gas and self-organizing maps

Liberalized competitive electrical energy markets need tools for realtime stability assessment to link the technical with the market issues. Analytical tools are available but time-consuming. Alternatively, knowledge based systems speed up the stability assessment but most of them need extensive and assessed training data. Unsupervised learning methods like Growing Neural Gas or SelfOrganizing Maps use training situations and the information of stability separately. Doing this, the calculation of training data is less time consuming. The use of the two methods within a fully automated tool for stability assessment is discussed in this paper. Aspects of self-learning, quality of the assessment and application to real power systems are considered.