Abstract
In order to deal with the power fluctuations that come from wind uncertainties, this paper presents a generating reliability assessment of the real generation system of Bosnia and Herzegovina (BH) including wind power as an planning exercise for a given horizon. For this purposes, the sequential Monte Carlo simulation is used not only to assess conventional reliability indices as loss of load probability, loss of load expectation, loss of load frequency, and loss of load duration, but also to discuss an alternative measure of risk-based level called Well-being Analysis. © 2011 IEEE.

Abstract
This paper describes a Load Allocation model to be used in a DMS environment. A process of rough allocation is initiated, based on information on actual measurements and on data about installed capacity and power and energy consumption at LV substations. This process generates a fuzzy load allocation, which is then corrected by a fuzzy state estimator procedure in order to generate a crisp power flow compatible set of load allocations, coherent with available real time measurements recorded in the SCADA.

Abstract
This paper presents an artificial neural network (ANN) based approach for the definition of preventive control strategies of autonomous power systems with a large renewable power penetration. For a given operating point, a fast dynamic security evaluation for a specified wind perturbation is performed using an ANN. If insecurity is detected, new alternative stable operating points are suggested, using a hybrid ANN-optimization approach that checks several feasible possibilities, resulting from changes in power produced by diesel and wind generators, and other combinations of diesel units in operation, Results obtained from computer simulations of the real power system of Lemnos (Greece) support the validity of the developed approach.

Abstract
This paper presents a Monte-Carlo algorithm considering loads defined by fuzzy numbers. In this methodology states are sampled according to the probabilistic models governing the life cycle of system components while fuzzy concepts are used to model uncertainty related to future load behavior. This model can be used to evaluate generation/transmission power system reliability for long term planning studies as one uses the more adequate uncertainty models for each type of data. For each sampled state a Fuzzy Optimal Power Flow is run so that one builds its power not supplied membership function. The paper proposes new indices reflecting the integration of probabilistic models and fuzzy concepts and discusses the application of variance reduction techniques if loads are defined by fuzzy numbers. A case-study based on the IEEE 30 bus system illustrates this methodology.

Abstract
A new methodology to evaluate well-being indices for a composite generation and transmission system, based on non-sequential Monte Carlo simulation and pattern recognition techniques, is presented. To classify the success operating states into healthy and marginal, an artificial neural network based on group method data handling techniques is used to capture the patterns of these state classes, during the beginning of the simulation process. The idea is to provide the simulation process with an intelligent memory, based on polynomial parameters, to speed up the evaluation of the operating states. The proposed methodology is applied to the IEEE reliability test system (IEEE-RTS), to the IEEE-RTS-96 and to a configuration of the Brazilian South-Southeastern system.

Abstract
The goal of this paper is to coordinate directional overcurrent relays using the Evolutionary Particle Swarm Optimization (EPSO) Algorithm. EPSO Algorithm has gained a lot of interest for its simplicity, robustness and easy implementation. Coordinate directional overcurrent relays on a meshed network deals with a large volume of data, with many calculations and constraints. So that, this work shows the viability of how EPSO algorithm can solve a non-linear coordination problem.