Abstract
Pitch control has so far been the dominating method for power control in modern variable speed wind turbines. This paper proposes an improved control technique for pitching the blades of a variable speed wind turbine, using Artificial Neural Networks (ANN). The control objective is decided according the two states of operation: below rated operation and above rated operation. In the below rated power state, the aim of control is to extract maximum energy from the wind. In the above rated power, the control design problem is to limit and smooth the output electrical power. A model has been constructed and evaluated with experimental data obtained from Vestas V-47 660 kW wind turbine.

Abstract
Classifying consumers, namely LV consumers, in order to assign them typical load diagrams, was always a concern of the electric utilities, which used this kind of information to better manage their distribution networks. Now, with the transition to a completely open market, the need for settlement between distribution operators and traders requires hourly consumption records that are not generally available, so deriving load diagrams for LV consumers is a mandatory task. This paper presents a new methodology for this purpose that uses typical diagrams obtained in measurement campaigns to create classes defined in the commercial information space that maximize the compactness of the diagrams in each class. The methodology was developed in a project with EDP (the Portuguese distribution operator) and the result will probably be adopted by the regulatory authority. © 2005 ISAP.

Abstract
An artificial neural network concept has been developed for transformer fault diagnosis using dissolved gas-in-oil analysis (DGA). A new methodology for mapping the neural network into a rule-based inference system is described. This mapping makes explicit the knowledge implicitly captured by the neural network during the learning stage, by transforming it into a Fuzzy Inference System. Some studies are reported, illustrating the good results obtained.

Abstract
This paper provides evidence that Evolutionary Particle Swarm Algorithms outperform Genetic Algorithms in deriving optimal strategic decisions for an Energy Retailer, in the framework of a complex simulation of a multiple energy market, based on an Intelligent Agent FIPA-compliant open source platform.

Abstract
This paper describes a system for estimating load curves at low-voltage (LV) substations. The system is built by the aggregation of individual fuzzy inference systems of the Takagi-Sugeno type. The model was developed from actual measurements forming a base of raw data of consumer behavior. This database allowed one to build large test and,training sets of simulated LV substations, which led to the development of the fuzzy system. The results are compared in terms of accuracy with the ones obtained with a previous artificial neural network approach, with better performance.

Abstract
The development of techniques under geographical information system (GIS) platforms, such as geocomputational modeling, which increase the capabilities of GIS, allowing the systems to adapt to optimal distributed generation (DG) planning studies, is discussed. Using adequate software under the GIS platform, users can obtain useful information on the economic or technical viability of any distributed power generation facilities. GIS offers a variety of structured data models suitable for the storage, manipulation, and analysis of the information needed in DG planning. GIS can also be used in DG planning to study negotiation processes among different energy actors to look for geographical planning solutions.