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
The goal of this paper is to coordinate directional overcurrent relays using the Evolutionary Particle Swarm Optimization (EPSO) Algorithm. EPSO Algorithm has gained a lot of interest for its simplicity, robustness and easy implementation. Coordinate directional overcurrent relays on a meshed network deals with a large volume of data, with many calculations and constraints. So that, this work shows the viability of how EPSO algorithm can solve a non-linear coordination problem.

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
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
The increasing levels of wind power penetration motivated a revisitation of methods for setting operating reserve requirements for the next and current day. System Operators (SO) are now moving from deterministic intro probabilistic approaches, and including wind power forecasts in their decision-making problems. In this manuscript, a probabilistic approach that evaluates the consequences of setting each possible reserve level through a set of risk indices is compared with frequently used deterministic rules and a probabilistic rule where wind power uncertainty is described by a Gaussian distribution. The comparison is performed over a period of five months for a realistic power system, using real load and wind power generation data. Results highlight the limitations of deterministic rules, challenge the Gaussian assumption and illustrate the usefulness of risk indices derived from the probabilistic forecast and using a full probabilistic methodology.