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.

Abstract
In this paper is presented a Game Theory based methodology to allocate transmission costs, considering cooperation and competition between producers. As original contribution, it finds the degree of participation on the additional costs according to the demand behavior. A comparative study was carried out between the obtained results using Nucleolus balance and Shapley Value, with other techniques such as Averages Allocation method and the Generalized Generation Distribution Factors method (GGDF). As example, a six nodes network was used for the simulations. The results demonstrate the ability to find adequate solutions on open access environment to the networks.

Abstract
This paper proposes a swarm intelligence long-term hedging tool to support electricity producers in competitive electricity markets. This tool investigates the long-term hedging opportunities available to electric power producers through the use of contracts with physical (spot and forward) and financial (options) settlement. To find the optimal portfolio the producer risk preference is stated by a utility function (U) expressing the trade-off between the expectation and the variance of the return. Variance estimation and the expected return are based on a forecasted scenario interval determined by a long-term price range forecast model, developed by the authors, whose explanation is outside the scope of this paper. The proposed tool makes use of Particle Swarm Optimization (PSO) and its performance has been evaluated by comparing it with a Genetic Algorithm (GA) based approach. To validate the risk management tool a case study, using real price historical data for mainland Spanish market, is presented to demonstrate the effectiveness of the proposed methodology.