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.

Abstract
This paper presents a new and efficient methodology for distribution network reconfiguration integrated with optimal power flow (OPF) based on a Benders decomposition approach. The objective minimizes power losses, load balancing among feeders, and is subject to constraints: capacity limit of branches, minimum and maximum power limits of substations or distributed generators, minimum deviation of bus voltages, and radial optimal operation of networks. A specific approach of the Generalized Benders decomposition algorithm is applied to solve the problem. The formulation can be embedded under two stages: the first one is the Master problem and is formulated as a mixed integer nonlinear programming problem. This stage determines the radial topology of the distribution network. The second stage is the Slave problem and is formulated as a nonlinear programming problem. This stage is used to determine the feasibility of the Master problem solution by means of an OPF and provides information to formulate the linear Benders cuts that connect both problems. The model is programmed in the General Algebraic Modeling System. The effectiveness of the proposal is demonstrated through three examples extracted from the literature.

Abstract
The integration of large shares of wind generation in power systems requires the development of new algorithms and forecasting tools for making decisions in the operational domain taking into account wind generation forecast uncertainties. One of these decisions regards operating reserve requirements to meet load and wind variations. The aim of this paper is therefore to address this issue from a risk evaluation perspective, showing that it is possible to describe the consequences of each possible reserve level through a set of risk indices useful for decision-making. The new reserve management tool described in this paper is intended to support the Transmission System Operator (TSO) in defining on-line the operating reserve needs for the daily and intraday markets. Decision strategies like setting an acceptable risk level or finding a compromise between economic issues and the risk of loss of load are explored. A case-study based on the Portuguese power system demonstrates the usefulness and efficiency of the tool.