Abstract
This paper addresses the problem of large wind power integration and its impact on damping of the electromechnnical modes of oscillation. The viability of the doubly fed induction generator (DFIG) supplying additional damping is discussed and power system stabilizers (PSS) installed in DFIG wind power generators are simultaneously tuned to increase damping levels of a multimachine system.

Abstract
MicroGrids comprise low voltage distribution systems with distributed energy sources, storage devices and controllable loads, operated interconnected with the main power grid or autonomously, in a controlled coordinated way. Storage devices are absolutely essential for MicroGrid autonomous operation. However, depending on the operating conditions, their limited storage capacity is a major drawback and might compromise successful MicroGrid islanded operation. In this paper is proposed an index to evaluate MicroGrids security regarding an unplanned transition to islanded operation due to disturbances in the upstream Medium Voltage network. An Artificial Neural Network tool is proposed to forecast the security index for a credible range of operating scenarios. The usage of Artificial Neural Networks is emphasized due to its computational speed for on-line performance and its flexibility for providing corrective actions for insecure operating conditions in order to achieve a seamless transition from interconnected to islanded operation. © 2007 IEEE.

Abstract
Wind energy experiences in Portugal an increasing interest. Slightly more than 1700 MW were operating by the end of 2006, in a system with a global capacity of about 12 GW (8,5 GW peak demand). Several new wind farms are under construction and a considerable amount of connection points are or will be granted in the coming years. More than 5000 MW are expected to be connected to the grid around 2012, the global generating capacity being then about 16 GW. Clearly, a wind power forecasting system must be implemented that will help to deal with the significant penetration of the technology in the electrical system. A group of wind farm promoters, owning the majority of the capacity installed so far, ordered to a consortium of universities and research institutes the development of a forecasting tool, giving rise of the EPREV project, wholly financed by them. The system will have the following main characteristics: Wind speed and active power forecasting up to 72 hours; Evaluation of the forecasting uncertainty; Possibility of using the predictions of physical models and the information from the wind farm Supervisory Control And Data Acquisition (SCADA); Capacity of predicting only with SCADA information for very short term. The main components of the system are: A human-machine-interface, allowing the control of the system, the selection and aggregation of forecasting models and the visualization of results; A power forecasting model for individual wind turbines and for wind farms. A cascade of models is used, starting in the mesoscale simulation, with up to 2 km resolution. The outputs of the mesoscale models are corrected and statistically adapted to the fine scale conditions. Two models and different boundary conditions are run, in three nested domains (54x54, 18x18 and 6x6 km). The advantage of using a 2x2 km resolution is also tested. The statistical models are fed with recent information from the wind farms, after a learning process that made use of the historical information of its operation. Three different types of statistical models are employed: Power Curve Model (PCM), Auto Regressive (AR) and Neural Network Assembling Model (NNAM). The wind simulation at the wind farm scale is done both by linearized physical models and Computational Fluid Dynamics (CFD) models, namely using VENTOS®, a code developed at the University of Porto. The duration of the project is planned to be 1 year, including off-line tests of the complete system for 3 wind farms, for performance evaluation purposes.

Abstract
This paper evaluates parameter identification of induction motor (IM) using two different methods. The proposed methods provide an accurate estimation on the parameters for the IM steady state model. Both algorithms were tested with real data and then used to estimate the parameters of the motor. The disturbances in the acquired signals were reduced using some signal processing techniques. The processed signals were then applied in both identification procedures. The first method is based on optimization by nonlinear least squares algorithm that permits establishing convergence constrains, avoiding impossible physical results. The second is an indirect method that allows keeping the physical meaning of the continuous time parameters when converting to discrete time domain. The effectiveness of the proposed methods is verified by experimental tests and results of the methods are discussed.

Abstract
In this paper we present a new fully integrated control system in a System-on-Chip (SoC) suitable to implement the controller for a Neighbourhood Electric Vehicle (NEV) based on FPGA (Field Programmable Gate Array) technology. A prototype has been designed specifically to meet the requirement of low cost and it contains all of the active functions required to implement the electronic differential. The controller uses Field Oriented Control (FOC) techniques with offline parametric identification of the motors to enhance its performance. A PC based user interface allows easy controller configuration, data acquisition and performance analysis. The functionality of the electronic differential is verified through experiments with a bench test equipment equivalent to the actual system. The experimental results show that the proposed controller is able to follow the reference torque and curvature angle with a good dynamic and relatively low error.

Abstract
Long-term contractual decisions are the basis of an efficient risk management. However those types of decisions have to be supported with a robust price forecast methodology. This paper reports a different approach for long-term price forecast which tries to give answers to that need. Making use of regression models, the proposed methodology has as main objective to find the maximum and a minimum Market Clearing Price (MCP) for a specific programming period, and with a desired confidence level a. Due to the problem complexity, the meta-heuristic Particle Swarm Optimization (PSO) was used to find the best regression parameters and the results compared with the obtained by using a Genetic Algorithm (GA). To validate these models, results from realistic data are presented and discussed in detail.