Abstract
This study is dedicated to the learning process to help students to deal with the complexity of a Lighting Design Project. In the Department of Electrical Engineering, preference was given to applying methods integrating technology in the learning and then evaluating the outcomes. Problem-based learning (PBL) was used as an instructional strategy of active learning, and adapted for use in Lighting Design course. In cooperative learning, students work together in small groups in an organized way, on a well-ordered activity They are individually responsible for their work,. on the other hand, the work of the group as a whole is also assessed. Teams need to be small enough so that everyone can play apart And the students' work must be clearly defined. By applying PBL it is expected that students work in a cooperative and collaborative learning way, developing positive interdependence, individual accountability, face-to-face promotive interaction, as well as group processing. Students see the potential of the method. PBL addresses many of the concerns of teachers and professional organizations. Above all, it encourages students to be in charge of their education. It emphasizes critical thinking skills, understanding, learning how to learn, and working cooperatively with others. The benefits of PBL in the Lighting Design course are seen in the way students improved the study and analysis of a Lighting Design project. This method can also he seen as a benefit for Electrical Engineering programme where educators strive for improved teaching and learning.

Abstract
The growing concern about global climate change has led the European Union and the Portuguese Government to set targets for the percentage of electricity to be produced from renewable sources. In order to achieve the defined targets, Distributed Generation (DG) is expected to be increasingly integrated into networks. However, the intermittency of some of those DGs (such as wind energy) may enhance network operating costs or decrease network security. Thus, Network Operators started to concern about these effects and in order to avoid them, new wind parks were required to provide ancillary services to the network. These ancillary services include the ride-through-fault capability. Although some wind parks can already supply ride-through-fault capability to the distribution network (i.e. wind parks with Double Fed Induction Generators (DFIG)), most of them are still largely unable to do so due to the current DG protection scheme. This work concentrates on the development of new settings for the DG protection scheme which aims at allowing DG to provide ride-through fault capability to the distribution network. A DFIG with ride-through-fault capability was modeled on PSCAD/EMTDC and tested under the Portuguese Distributed Generation Protection Scheme Regulation Code. New relay settings for the DG protection scheme are advanced and simulated on PSCAD/EMTDC software in order to permit DGs providing ride-through fault capability to the distribution network. Conclusions of the new relay settings performance are withdrawn and commented on.

Abstract
In this paper, two factors typical of large photovoltaic (PV) arrays are investigated: one is the current-voltage (I-V) mismatch consequent to the production tolerance; the other is the impact of reverse currents in different operating conditions. Concerning the manufacturing I-V mismatch, the parameters of the equivalent circuit of the solar cell are computed for several PV modules from flash reports provided by the manufacturers. The corresponding I-V characteristic of every module is used to evaluate the behavior of different strings and the interaction among the strings connected for composing PV arrays. Two real crystalline silicon PV systems of 8 x 250 kW and 20 kW are studied, respectively. The simulation results reveal that the impact of the I-V mismatch is negligible with the usual tolerance, and the insertion of the blocking diodes against reverse currents can be avoided with crystalline silicon technology. On the other hand, the experimental results on I-V characteristics of the aforementioned arrays put into evidence the existence of a remarkable power deviation (3%-4%) with respect to the rated power, linkable to the lack of measurement uncertainty in the manufacturer flash reports.

Abstract
Two three-phase squirrel-cage induction motors are used as a propulsion system of an electric vehicle (EV). A simple XC3S1000 FPGA is used to simultaneously control both electric motors, with field oriented control and space vector modulation techniques. To electronically distribute the torque between the two electric motors, a simple, yet effective, strategy based on a uniform torque distribution has been implemented. Experimental results obtained with a multi-motor EV prototype demonstrate the proper operation of the proposed system.

Abstract
A new FPGA based platform is presented for controlling a Multi-Motor Electric Vehicle (EV). By exploring the FPGA parallel processing capabilities, two induction motor controllers, based on Field Orientation Control and Space Vector Modulation techniques, were merged in a single and compact chip. Implementation issues related with the limited number of dedicated multipliers were overcome using an efficient computational block, based on resource sharing strategy. The developed IP Cores were carefully optimized to fit in a low cost XC3S1000. Experimental results, obtained with a multi-motor EV prototype, demonstrate the proper operation of the proposed propulsion system.

Abstract
This paper presents a methodology supported on the data base knowledge discovery process (KDD), in order to find out the failure probability of electrical equipments', which belong to a real electrical high voltage network. Data Mining (DM) techniques are used to discover a set of outcome failure probability and, therefore, to extract knowledge concerning to the unavailability of the electrical equipments such us power transformers and high-voltages power lines. The framework includes several steps, following the analysis of the real data base, the pre-processing data, the application of DM algorithms, and finally, the interpretation of the discovered knowledge. To validate the proposed methodology, a case study which includes real databases is used. This data have a heavy uncertainty due to climate conditions for this reason it was used fuzzy logic to determine the set of the electrical components failure probabitities in order to reestablish the service. The results reflect an interesting potential of this approach and encourage further research on the topic.