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.

Abstract
The State Estimation is nowadays considered the fundamental element of modern electrical power networks control centers. In this paper we develop a theoretically robust and computationally efficient state estimator algorithm, to solve the WLS problem by using parallel processing, The computational aspects of the parallel processing, was analyzed and tested using the IEEE 14, 57 and 118 bus systems. Computational experiments are compared with standard WLS methods, in the integral and distributed version. An evaluation of the degree of natural decoupling in the state estimation problem is rise performed. The results indicate that a distributed processing for state estimation, is the better way to adopt the parallel computing in power systems energy.

Abstract
Today, the State Estimation is considered as the heart of nearly all modern network control centers. Among other factors, the technical developments in fast data communication network technology opens up the possibility of parallel and distributed implementations of the state estimation function. This paper provides an overview of the state of the art in Power Systems State Estimation.

Abstract
The software package TRANsySTEM developed for teaching and research purposes of the transient security assessment of a multimachine power system is presented. This package run in Windows environment.