Abstract
Incipient fault diagnosis is closely related to insulation condition assessment. A great number of methods are available for condition monitoring and diagnosis of power transformer insulation systems, but only few of them can take direct measurements inside the transformer. Fiber optic sensors can be applied to incipient fault diagnosis. In particular, acoustic sensors have been developed for detection and location of partial discharges in oil-filled power transformers. We report the study of extrinsic and intrinsic fiber Fabry-Perot sensors that can be used to detect the acoustic waves that are generated by a partial discharge inside a power transformer. A comparative analysis is done to determine the best sensing head configuration and some methods to improve the parameter readout sensitivity are proposed. The sensing head behaviour when immersed in different fluids (air, water, and oil) is also investigated. (c) 2009 Society of Photo-Optical Instrumentation Engineers. [DOI: 10.1117/1.3080752]

Abstract
This paper presents a sequential Monte Carlo simulation algorithm that can simultaneously assess composite system adequacy and detect wind power curtailment events. A simple procedure at the end of the state evaluation stage is proposed to categorize wind power curtailment events according to their cause. Furthermore, the dual variables of the DC optimal power flow procedure are used to identify which transmission circuits are restricting the use of the total wind power available. In the first set of experiments, the composite system adequacy is assessed, incorporating different generation technologies. This is conducted to clarify the usual comparisons made between wind and thermal technologies which, in fact, depend on the performance measure selected. A second set of experiments considering several wind penetration scenarios is also performed to determine the operational rules or system components responsible for the largest amount of wind energy curtailed. The experiments are carried out on configurations of the IEEE-RTS 79 power system.

Abstract
This paper presents an application of evolutionary particle swarm optimization (EPSO)-based methods to evaluate power system reliability. Population-based (PB) methods appear as competitors to the traditional Monte Carlo simulation (MCS), because they are computationally efficient in estimating a variety of reliability indices. The work reported in this paper demonstrates that EPSO variants can focus the search in the region of the state space where contributions to the formation of a reliability index may be found, instead of conducting a blind sampling of the space. The results obtained with EPSO are compared to MCS and with other PB methods.

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.