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 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
A tuning process of the PI (Proportional-Integral) controller gains of a doubly-fed induction generator's (DFIG) rotor side converter is described in this work. The purpose is to tune PI controllers to help the DFIG to survive to network faults, avoiding being tripped-off by under-voltage relays. The ride-through-fault capability of DFIGs improves system reliability and allows them to participate in the control and stabilization of a power system following system disturbances. The robust tuning of the DFIG rotor side converter's PI controllers may avoid undesired disconnections from the grid by, for instance, preventing over-currents in its variable frequency AC/DC/AC converter. This work presents an Evolutionary Particle Swarm Optimization-based (EPSO) approach to this tuning, with the aim of helping to limit the line-to-line voltage dip at the DFIG's terminals after a short-circuit, in order to avoid its tripping-off. The EPSO-based algorithm developed is validated at a typical Portuguese 15 kV Distribution Network with the integration of a DFIG, using the transient electromagnetic software package PSCAD/EMTDC (TM).

Abstract
Maintenance decisions in electricity markets are one of the most important strategic conflicts among power players. A generation company, for instance, is responsible for its assets but it is the Transmission System Operator's task to schedule the most suitable period for maintenance, preserving system reliability. This relationship is sometimes conflicting and can be seen as an obstacle to the maximization of profits of generation companies, as well as to the transmission system operator, since it is mandatory to keep the adequate risk-based level on overall system. In this paper, the attention is focused on this relationship. Moreover, it highlights the proposal of building a dedicated environment in order to improve and clarify the discussions about maintenance outage schedule for large equipments, using the agent-based technology. In order to illustrate the proposal, results with the IEEE-RTS are presented. © 2009 IEEE.

Abstract
Acoustic emission monitoring is often used in the diagnosis of electrical and mechanical incipient faults in high voltage apparatus. A great number of methods are available for condition monitoring and diagnosis of power transformer insulation, but only a few can take direct measurements inside a transformer. Partial discharges are a major source of insulation failure in power transformers and their detection can be achieved through the associated acoustic emissions. This paper 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. These sensors can be placed inside the transformer tank without affecting the insulation integrity, improving fault detection and location. 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.

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.