Abstract
The evaluation of high-potential areas for integrating distributed resources is mainly dependent on the geographical characterization of resources, consumptions, energy technologies and infrastructures. The effect of distributed resources on electric distribution could be studied by a spatial evaluation of incremental costs. This paper presents a Spatial Support System (SSS), based on GIS methodologies, to evaluate the spatial impact caused by integration of distributed power resources on the power distribution network costs. © 2001 IEEE.

Abstract
A natural movement towards artificial intelligence (AI) techniques took place in the last years in power system analysis. Many research works have used AI topics like search techniques, knowledge representation, reasoning and learning systems, as well as heuristic tools to address power system problems. This paper focuses the discussion on power system reliability evaluation and this natural transition from AI topics to a more sophisticated software design, known as intelligent agent (IA) technology. Instead of applying AI techniques to improve a single stage of the Monte Carlo Simulation (MCS), the IA architecture explores new ways to support AI topics. However, this natural movement needs to be managed through the proposal of a modern framework of power system tools, where several different techniques have to be combined in order to maximize each one's benefits and advantages. © 2010 IEEE.

Abstract
This paper summarizes efforts in understanding the possible application of Information Theoretic Learning Principles to Power Systems. It presents the application of Renyi's Entropy combined with Parzen windows as a measure of information content of the error distribution in model parameter estimation in supervised learning. It illustrates the concept with an application to the prediction of power generated in a wind park, made by Takagi-Sugeno Fuzzy Inference Systems, whose parameters are discovered with an EPSO-Evolutionary Particle Swarm Optimization algorithm.

Abstract
This paper presents a new approach to incipient fault diagnosis in power transformers, based on the results of dissolved gas analysis. A set of autoassociative neural networks or autoencoders are trained, so that each becomes tuned with a particular fault mode. Then, a parallel model is built where the autoencoders compete with one another when a new input vector is entered and the closest recognition is taken as the diagnosis sought. A remarkable accuracy is achieved with this architecture, in a large data set used for result validation. © 2011 IEEE.

Abstract
This paper analyzes the application of clustering techniques for wind power scenario reduction. The results have shown the unimodal structure of the scenario generated under a Monte Carlo process. The unimodal structure has been confirmed by the modes found by the information theoretic learning mean shift algorithm. The paper also presents a new technique able to represent the wind power forecasting uncertainty by a set of representative scenarios capable of characterizing the probability density function of the wind power forecast. From an initial large set of sampled scenarios, a reduced discrete set of representative scenarios associated with a probability of occurrence can be created finding the areas of high probability density. This will allow the reduction of the computational burden in stochastic models that require scenario representation. © 2011 IEEE.

Abstract
Maintenance decisions in electricity markets are one of the most important strategic conflicts among power players. A generation company, for instance, is responsible for its assets but it is the Transmission System Operator's task to schedule the most suitable period for maintenance, preserving system reliability. This relationship is sometimes conflicting and can be seen as an obstacle to the maximization of profits of generation companies, as well as to the transmission system operator, since it is mandatory to keep the adequate risk-based level on overall system. In this paper, the attention is focused on this relationship. Moreover, it highlights the proposal of building a dedicated environment in order to improve and clarify the discussions about maintenance outage schedule for large equipments, using the agent-based technology. In order to illustrate the proposal, results with the IEEE-RTS are presented. © 2009 IEEE.