Abstract
The paper describes the on-line dynamic security assessment functions developed within the European Union, DGXII programme, CARE. These functions are based exclusively on the application of machine learning techniques. A description of the problem and the data set generation procedure for the Crete island power system are included. Comparative results regarding performances of Decision Trees, Kernel Regression Trees and Neural Networks are presented and discussed.The paper describes the on-line dynamic security assessment functions developed within the European Union, DGXII programme, CARE. These functions are based exclusively on the application of machine learning techniques. A description of the problem and the data set generation procedure for the Crete island power system are included. Comparative results regarding performances of Decision Trees, Kernel Regression Trees and Neural Networks are presented and discussed.

Abstract
In this paper, preliminary results from MORE CARE, a European R&D project financed within the Energy Program are described. This project has as main objective the development of an advanced control software system, aiming to optimize the overall performance of isolated and weakly interconnected systems in liberalized market environments by increasing the share of wind energy and other renewable forms, including advanced on-line security functions. The main features of the control system comprise advanced software modules for load and wind power forecasting, unit commitment and economic dispatch of the conventional and renewable units and on-line security assessment capabilities integrated in a friendly Man-Machine environment. Pilot installations of advanced control functions are foreseen on the islands of Crete, Ireland and Madeira. © 2001 IEEE.