Abstract
This paper presents the Simulator of Intelligent Transportation Systems (SITS). The SITS is based on a microscopic simulation approach to reproduce real traffic conditions in an urban or non-urban network and considers different types of vehicles, drivers and roads. A dynamical analysis of several traffic phenomena is then addressed. The results of using classical system theory tools point out that it is possible to study traffic systems, taking advantage on the knowledge gathered with automatic control algorithms. In this line of thought, it was also presented a new modelling formalism based on the embedding of statistics and Fourier transform.

Abstract
This paper presents the Simulator of Intelligent Transportation Systems (SITS). The SITS is based on 2 microscopic simulation approach to reproduce real traffic conditions in an urban or non-urban network and considers, different types of vehicles, drivers and roads. A dynamical analysis of several traffic phenomena is then addressed.-The results of using classical system theory tools point out that it is possible to study traffic systems, taking advantage on the knowledge gathered with automatic control algorithms. In, this line of thought, it was also presented a new modelling formalism based on the embedding of statistics and Fourier transform.

Abstract
The paper presents the simulator of intelligent transportation systems (SITS). The SITS is based on a microscopic simulation approach to reproduce real traffic conditions in an urban or nonurban network and considers different types of vehicles, drivers, and roads. A dynamical analysis of several traffic phenomena is then addressed. The results of using classical system theory tools notes that it is possible to study traffic systems, taking advantage of the knowledge gathered with automatic control algorithms. In this line of thought, a new modeling formalism based on the embedding of statistics and Fourier transform is also presented.

Abstract
This paper discusses the problem of modelling and simulation of traffic systems, and presents the traffic simulator SITS (Simulator of Intelligent Transportation Systems). The SITS is based on a microscopic simulation approach considering different types of vehicles, drivers and roads. A dynamical analysis of several traffic phenomena is then addressed.

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
Voltage stability has been of the major concern in power system operation. To prevent these problems, technical staff evaluates frequently the distance of the operation state to the voltage collapse point. This distance normally is calculated with power flow equations. This classic technique is very slow for electric power systems with large dimension. In abnormal exploration situations it may introduce serious limitation in the voltage stability analysis process. So, the application of a fast and reliable evaluation technique is very important to diminish the evaluation time. This paper presents a study of the application of artificial neural network (ANN) to the evaluation of this distance to the voltage collapse point. To detection the point of collapse the new method FSQV was used.