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.

Abstract
Acoustic emission monitoring is often used in the diagnosis of electrical and mechanical incipient faults in high voltage apparatus. Partial discharges are a major source of insulation failure in power transformers and the differentiation from other sources of acoustic emissions is of the utmost importance. Also, it is important to give an indication of the PD source location in order to obtain a useful diagnosis. This paper reports the developments in partial discharges source location through the associated acoustic emissions using three different algorithms.

Abstract
Partial discharges are a major source of insulation failure in electric power transformers. Their detection can be achieved through the associated acoustic emissions, and this work reports on the investigation of a fibre laser sensor based on a phase-shifted chirped fibre grating for acoustic emission detection in the power transformer environment. The performance of the sensing head is characterized and compared for different surrounding media: air, water and oil.

Abstract
Incipient fault diagnosis is closely related to isolation condition assessment. There are a great number of methods available for condition monitoring and diagnosis of power transformer isolation systems, but only few of them can take direct measurements inside the transformer. Fibre optic sensors can be applied to incipient fault diagnosis. In special, acoustic sensors are being developed for the detection and location of partial discharges in oil-filled power transformers. Towards such objective, this work reports investigation on the applicability of fibre Fabry-Perot interferometers for acoustic detection both in air and in liquids.