Abstract
This paper presents a transformer failure diagnosis system based on Dissolved Gases Analysis that was developed by using a new methodology for extracting fuzzy rules from Kohonen Self-Organizing Map. Firstly, the Kohonen net was trained in order to capture the knowledge from a database of faulty transformers inspected in service. Once the knowledge was captured during the learning stage, it was transformed into the form of Zero-order Takagi-Sugeno fuzzy rules. In the form of fuzzy rules, the relationship between the variables of the system became explicit which have led to a more reliable diagnosis system. Additionally to the extraction of the fuzzy system, a fuzzyfication process was applied in the fuzzy system output. Experimental results demonstrated the efficiency of the diagnosis system proposed that had superior results as compared with other conventional and intelligent methods.

Abstract
This paper discusses the development of a Multi-Agent Systems (MAS) technology-based platform with potential applications in management and simulation processes in power systems. In order to explore some of the features of MAS, a new methodology is proposed to assess power systems reliability based on Monte Carlo simulation (MCS), exploiting the benefits of the distributed artificial intelligence area and, mainly, the use of the distributed capacity in two ways: building autonomous behaviors to the applications and mitigating computational effort. Through the use of this technology, it was possible to divide the MCS algorithm into distinct tasks and submit them to the agents' processing. Two different approaches to solve generating capacity reliability problems based on chronological MCS illustrate the potential of MAS in power systems reliability assessment.

Abstract
This work presents a model to evaluate the Distribution System Dynamic De-adaptation respecting its planning for a given period of Tariff Control. The starting point for modeling is brought about by the results from a multi-criteria method based on Fuzzy Dynamic Programming and on Analytic Hierarchy Processes applied in a mid/short-term horizon (stage 1). Then, the decision-making activities using the Hierarchy Analytical Processes will allow defining, for a Control of System De-adaptation (stage 2). a Vector to evaluate the System Dynamic Adaptation. It is directly associated to an eventual series of inbalances that take place during its evolution.

Abstract
This paper proposes a multi-objective approach to a distribution network planning process that deals with the challenges derived from the integration of Distributed Generation (DG). The proposal consists of a multi-objective version of the well-known Evolutionary Particle Swarm Optimization method (MEPSO). A broad performance comparison is made between the MEPSO and other multi-objective optimization meta-heuristics, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and a Multi-objective Tabu Search (MOTS), using two distribution networks in a given DG penetration scenario. Although the three methods proved to be applicable in distribution system planning, the MEPSO algorithm has presented promising attributes and a constant and high level performance when compared to other methods.

Abstract
This article reports the development of two sensing head configurations based on extrinsic and intrinsic fiber Fabry-Perot interferometers for the detection of incipient faults in oil-filled power transformers. The performances of the sensing heads are characterized and compared with the situations where it operates in air, water, and oil and promising results are obtained, which will allow the industrial development of practical solutions. (C) 2010 Wiley Periodicals, Inc. Microwave Opt Technol Lett 52: 1129-1134, 2010; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.25139

Abstract
This paper describes a new technique aimed at representing wind power forecasting uncertainty by a set of discrete scenarios able to characterize the probability density function of the wind power forecast. From an initial large set of sampled scenarios, a reduced discrete set of representative or focal scenarios associated with a probability of occurrence is created using clustering techniques. The advantage is that this allows reducing the computational burden in stochastic models that require scenario representation. The validity of the reduction methodology has been tested in a simplified Unit Commitment (UC) problem.