Abstract
This paper presents a new methodology for reliability evaluation of composite generation and transmission systems, based on nonsequential Monte Carlo simulation (MCS) and artificial neural network (ANN) concepts. ANN techniques are used to classify the operating states during the Monte Carlo sampling. A polynomial network, named Group Method Data Handling (GMDH), is used, and the states analyzed during the beginning of the simulation process are adequately selected as input data for training and test sets. Based on this procedure, a great number of success states are classified by a simple polynomial function, given by the ANN model, providine siginificant reductions in the computational cost. Moreover, all types of composite reliability indices (i.e., loss of load probability, frequency, duration, and energy/power not supplied) can be assessed not only for the overall system but also for areas and buses. The proposed methodology is applied to the IEEE Reliability Test System (IEEE-RTS), to the IEEE-RTS 96, and to a configuration of the Brazilian South-Southeastern System.

Abstract
The letter describes a new economic dispatch algorithm for systems with uncertain wind generation prediction, similar to the classical thermal dispatch model with load on a single bus. The optimization is achieved in a compromise between fuzzy constraints in the magnitude of wind penetration and the variation of running costs. The model includes also the attitudes of the dispatcher toward risk (security) and cost.

Abstract
This paper presents a new multicriteria decision aid system (DAS) to obtain acceptable power line paths integrating the diverse socioeconomic interests of the different groups involved in the planning process, such as utilities, environmental agents, or local and regional authorities. The DAS is based on the intensive use of geographic information systems, as well as multicriteria weighting techniques reflecting all group interests. This new DAS can be used to overcome the problems raised by initially opposing positions among different groups stemming from diverse technological, economic, environmental, and/or social interests. The technique is illustrated by an intensive simulation example from a case study reproducing some of the phases of a negotiation process.

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 is trained, so that each becomes tuned with a particular fault mode or no fault condition. The scarce data available forms clusters that are densified using an Information Theoretic Mean Shift algorithm, allowing all real data to be used in the validation process. 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 of 100% is achieved with this architecture, in a validation data set using all real information available.

Abstract
Acoustic emission monitoring is often used in the diagnosis of electrical and mechanical incipient faults in high-voltage apparatus. Partial discharges are a source of failure in power transformers, and the differentiation from other sources of acoustic emissions is of the utmost importance. This paper reports the development of a new sensor concept-mandrel-based fiber-optic sensor-for the detection of incipient faults in oil-filled power transformers, taking direct measurements inside a transformer. These sensors can be placed in the inner surface of the transformer tank wall, not affecting the insulation integrity of the structure, and improving fault detection and location. The applicability of these acoustic sensors in air, water, and oil is investigated and the paper presents the promising results obtained, which will enable the industrial development of practical solutions.

Abstract
The paper describes how mapping a neural network into a rule-based fuzzy inference system leads to knowledge extraction. This mapping makes explicit the knowledge implicitly captured by the neural network during the learning stage, by transforming it into a set of rules. By applying the method to transformer fault diagnosis using dissolved gas-in-oil analysis, one could not only develop intelligent diagnosis systems, providing better results than the application of the IIEC 60599 Table, but also generate a new rule table whose application also leads to better diagnosis results.