Abstract
When inference is performed with the compositional rule of inference (CRI), fuzzy rules can be mathematically modelled by a pair of operators, the implication function and the modus ponens generating function, each pair being a possible model for the rule. Analyzing the firing condition of fuzzy rules, that is the condition the hypothesis and the observation must verify to infer a non trivial conclusion, it is possible to get a deeper understanding of the behaviour of the different models that can be used, and valuable semantical criteria can be obtained to select the best suitable model. The firing condition of any model can be expressed by means of a generalized possibility or necessity measure generated by its implication and modus ponens generating functions, measures that quantify the amount of uncertainty or possibilistic uncertainty of the conclusion. Two different types of possibility distributions can be identified as those concluded from different types of models, that is necessary and possible possibility distributions, corresponding to necessary and possible conclusions.

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

Abstract
In this paper one formulates the coordination problem of a set of directional overcurrent relays installed in meshed networks. After analysing the topology of the network to identify the primary/back up pairs, the coordination problem is organized in terms of a linear programming problem to be solved using the Simplex method. As results, this application gives the instantaneous relay settings, the relay pick up taps and the time dial settings. The methodology is illustrated using a small network mainly conceived for didactic purposes.

Abstract
This paper presents an approach to reliability evaluation of generation/transmission power systems using the Monte Carlo simulation method. In this approach uncertainties are represented using two different frameworks. For one side, probability concepts are used to represent the failure-repair cycle of system components. Loads are represented through classified load diagrams in which each time step corresponds to a fuzzy number. This simulation method allows the user to obtain estimates of the expected exposure and robustness indices and of the Power Not Supplied membership function.