Abstract
This paper provides a description of a new approach for steady state security evaluation, using fuzzy nearest prototype classifiers, The basic method has an off-line training phase, used to design the fast classifiers for on-line purposes, allowing more than the two traditional security classes. A battery of these fuzzy classifiers, valid for a specific configuration of the network, is adopted to produce a global evaluation for all relevant single Contingencies. An important feature of this approach is that it selects automatically the most appropriate number of security clusters for each selected contingency. Natural language-labeling is also used to produce standardized sentences about the security level of the system, improving in this way the communication process between the system and the operator. The paper is completed by an example on a realistic model of the Hellenic interconnected power system, where seven contingencies were simulated.

Abstract
This paper proposes a method to quickly and accurately predict the dynamic response of a power system during an under-frequency load shedding scenario. Emergency actions in a power system due to loss of generation typically calls for under-frequency load shedding measures to avoid potential collapse due to the lack of time in which to correct the imbalance via other means. Due to the slow and repetitious use of dynamic simulators the need for a fast and accurate procedure is evident when calculating optimal bad-shedding strategies A neural network (NN) seems to he an ideal solution for a quick and accurate way to replace standard dynamic simulations. The steps taken to produce a viable NN and corresponding results will he discussed.

Abstract
In this paper we describe a multiobjective formulation for the long term planning of distribution networks considering a number of important features. The model admits fuzzy representations for loads and evaluates nodal long term marginal prices. It integrates a number of criteria related to investment, operational and reliability costs, risk index measuring the ability to accommodate load uncertainties and the remuneration collected using long term marginal prices. After using a Simulated Annealing approach to identify efficient expansion plans, it is finally conducted a decision analysis in order to select the most adequate plan. At a final section, we illustrate the formulation with a case study based on a Portuguese distribution network.

Abstract
In this paper we describe a new approach to identify the combination of tap transformer positions, capacitor bank steps together with the minimum amount of toad to be shed that assures one to obtain a specified security degree of a power system. The basic approach is designed to identify the most adequate actions to be taken for a given contingency. This identification procedure uses Genetic Algorithms given their adequacy to model discrete actions. However, Genetic Algorithms are known for their usually large computation time. In order to address this issue and having in mind the objective of developing a real time tool, we incorporated a classification procedure based on Neural Networks. The paper includes results obtained using the developed approach both to evaluate the quality of the solutions for a number of contingencies and the quality of the overall performance when using the Neural Network tool. Results obtained for a reduced version of the Brazilian Mate Grosso transmission system are presented and discussed.

Abstract
In this paper we describe an application corresponding to a DMS system - Distribution Management System - that is organized in terms of a distributed multitask client-server architecture. The system is implemented according to the Object Oriented paradigm leading to a number of objects related with electric devices and specific algorithms. At this level of development, these algorithms correspond to topology processor, state estimation, power flow and short circuit analysis. They correspond to main coordinator objects that can be achieved by clients of the system in terms of Java applets running within a WEB browser. fn the paper we detail the structure of the system and describe some objects and applications.

Abstract
This paper presents a new approach to perform on-line dynamic security assessment and monitoring of electric power systems exploiting a statistical hybrid learning technique - the Kernel Regression Trees This technique, besides producing fast;security classification, can still quantify, hi real-time, the security degree of the system, by emulating continuos security indices that translate the power system dynamic behavior. Moreover it can provide interpretable security structures. The feasibility of this approach was demonstrated in the dynamic security assessment of isolated systems with large amounts of wind power production, like In the Crete island electric network (Greece) Comparative results regarding performances of Decision Trees and Neural Networks are also presented and discussed. From the obtained results, the proposed approach showed to provide good predicting structures whose performance stands up to the performance of the two other existent methods.