Abstract
The authors present a novel automatic transient stability classification approach, based on a two-stage hierarchical classifier obtained through the combination of two different pattern recognition structures. The first-level classifier uses low-cost computational variables evaluated at the fault occurrence moment. The classification structure used in this first level is a linear one and is obtained through the combination of a Fisher discriminant transformation and a Bayesian probabilistic approach for the independent term determination. The second-level classifier uses a new set of transient variables, directly related to kinetic and potential energies at the fault clearance moment. The classification structure in this second level is based on a weighted K nearest-neighbor rule, using a Euclidian metric defined in an optimum discriminant plane, where all data have been previously projected. Results obtained in the CIGRE test system are presented, showing that this method provides an efficient tool for online transient stability assessment of electric power systems.

Abstract
A candidate methodology for online transient stability assessment in power systems is presented. The method uses dynamic system variables as primary features. Linear security functions are derived by applying Fisher discriminant transformations and probabilistic considerations. Test results obtained on the CIGRE system are presented and discussed.

Abstract
In the usual operation, wind park production is strongly dependent on the instantaneously available wind power. When energy storage is accessible, the wind park operation can be improved, aiming to a better exploitation of the available wind power resource. The present work proposes an optimization approach to determinate the most probable range of the output production, to be used in the definition of production profiles that will help the participation of wind power in the market. Two production profiles strategies were analyzed and compared. The forecasted wind power is represented as a stochastic variable and real wind power Portuguese conditions have been used in the evaluation of the model.

Abstract
This paper describes the main steps needed to apply neural networks in the domain of dynamic security assessment (DSA). The paper provides also an overview of the application of neural networks in DSA. Special emphasis is given to the application of this approach in a medium size real system where a large penetration of wind power is foreseen.

Abstract
Under normal operating conditions, a MicroGrid (MG) is interconnected with the Medium Voltage (MV) network. However, planned or unplanned events like maintenance or faults in the MV network, respectively, may lead to MG islanding. In order to deal with islanded operation and even black start following a general blackout, an emergency operation mode must be envisaged. Two possible control strategies were investigated and are described in this paper in order to operate a MG under emergency mode. A sequence of actions for a well succeeded black start procedure, involving microgeneration units, has also been identified contributing for an increase in distribution network reliability.

Abstract
This paper deals with the problem of large wind power integration and its potential impact on systems small signal stability. Factors like grid configuration, load and wind power integration are considered. An automatic algorithm is used to search the space formed by such parameters and generate a representative set of possible operation conditions. The oscillation modes are then calculated for each operation point. Some of these points of operation are subjected to a deeper analysis and the movement of oscillation modes on the complex plane is evaluated. Finally, non-linear time domain simulations are presented.