Abstract
This paper presents a modified negative selection algorithm for the diagnosis of disturbance in distribution electrical systems. This study analyzes voltage disturbances and high-impedance faults, based on three phase current and voltage electric measures, which are obtained at the substations. The principal application is to support operation decision aid during faults, as well as to supervise the protection system. To evaluate the performance of the proposed method, simulations were executed using the EMTP software for a distribution test system containing 134 bus. The results obtained were compared with the specialized literature.

Abstract
This paper reports the research that was developed to predict biding curves submitted by generation players to the Market Operator of the Iberian Electricity Market. In this scope, we used a data set based on publicly available information from the website of the Market Operator to develop a two-step ANN prediction model. The first step involves the prediction of the amount of energy bidden at zero price and the second ANN predicts the parameters of the equation of the line that better approximates the remaining bid curve. The tests were done using information of a large generation player but this approach can be replicated to other players so that the individual predicted curves can be composed in order to obtain the aggregated selling curve for each hour of the next day.

Abstract
Load profiles are a crucial tool for power system planning and operation, and also in several operations of electricity markets. This article proposes a new methodology for the determination of load profiles based on a two-step approach. The first phase employs a neural network autoencoder to reduce the dimensionality of the input vectors. The second phase is a clustering process based on the Kohonen Self- Organizing Maps, to identify cohesive consumers' classes. The implemented approach produces classes based on load diagrams and, simultaneously, a class identification based on consumers' billing data.

Abstract
This paper describes the application of the clonal selection algorithm to the reconfiguration problem of distribution networks considering non-uniform demand levels. The Clonal Algorithm, CLONALG, is a combinatorial optimization technique inspired in the immunologic bio system and it aims at reproducing the main properties and functions of this system. The reconfiguration problem of distribution networks with non-uniform demand levels is a complex problem that aims at identifying the most adequate radial topology of the network that complies with all technical constraints in every demand level while minimizing the cost of active losses along an extended operation period. This work includes results of the application of the Clonal algorithm to distribution systems with 33, 84 and 136 buses. These results demonstrate the robustness and efficiency of the proposed approach.

Abstract
This paper describes the application of the specialized genetic algorithm of Chu-Beasley to solve the Distribution System Reconfiguration, DSR, problem considering different demand scenarios. This algorithm is an approach inspired in the natural selection and evolution of species. The reconfiguration problem of distribution networks taking into account different demand scenarios aims at identifying the most adequate radial topology of a distribution system assuming that this topology is used for all demand scenarios under study. This search is driven by the minimization of the cost of energy losses in the network along a full operation year. The performance of the algorithm is evaluated considering test systems having 33, 70, 84 and 136 buses and a real system with 417 buses. The obtained results confirm the robustness and efficiency of the developed approach and its potential to be used in distribution control centers.

Abstract
This paper describes a hybrid tool to perform Static Transmission Expansion Planning, STEP, studies and its application to the Garver6-Bus academic system and to the Southern Brazilian Transmission equivalent real system. The developed STEP tool integrates two phases as follows. The first one uses Constructive Heuristic Algorithms (CHA) to reduce the search space, and the second uses Particle Swarm Optimization (PSO) to identify the final solution. This hybridization between CHAs and PSO proved to be very effective and shows good performance to reduce the size of the STEP search space and to identify good quality solutions. These are relevant issues given the combinatorial nature of investment problems leading to the explosion of the number of alternative plans, one of the greatest difficulties faced in this planning problem.