Adson Silva Rocha

Method for the evaluation and classification of power transformer insulating oil based on physicochemical analyses

Power transformers are strategically important assets in the electric power system, because they enable electricity to be transmitted over long distances, with low power losses and voltage drops. Failures of these devices can cause unscheduled interruptions in the supply of electricity, resulting in major disruptions, impaired image of the companies responsible, including penalties imposed by the regulatory agency, and in worst case scenarios, irreparable breakdown of these assets. In this context, the degradation of the insulation system in a power transformer is one of the main causes of problems, which if not properly investigated and/or monitored, can lead to its forced removal from operation [1].

Metrics for partial discharge detection by the acoustic emission technique to support decision-making

The identification of metrics or ranges of parameters for the detection of partial discharges by the acoustic emission method for the diagnosis and/or classification of power transformers according to their state is essential to support decisions about equipment stoppages at the proper time, i.e., before a failure occurs. This paper discusses these parameters based on the analyses of several field trials, using an algorithm to generate decision trees, as well as a case study to validate the methodology, which was successfully applied in preventive maintenance.

An Improved Multiobjective Search Method Applied to Single Frequency Networks Planning

This paper proposes a simpler and efficient approach to optimize digital television coverage and costs planning using a modified version of the well-known local search algorithm Hill Climbing. The results, taken from a real scenario in central Brazil, show the efficiency and applicability of the method by comparing results with other memetic strategies based on hybridation.

Evolutionary Optimization of Microwave Filters

The optimization of circuits with the aim of improving performance, lowering costs, and more recently, to reduce the size and weight of electronic devices, among other objectives, has become a research field in the areas of mathematics and engineering around the world (Antoniou & Lu, 2007). The problem can be interpreted as: among all possible models for the circuit, considering topology and values of components, find a model with minimum size and appropriate parameters, able to meet a set of typically conflicting hard specifications. The deterministic methods are not effective in the design of new circuits, since it does not always have available accurate mathematical models of the processes to be optimized (Levy et al., 2001). However, a number of new stochastic algorithms have allowed the development of nearly optimal circuits, which are quite acceptable from the standpoint of their implementations. The techniques currently used require much prior knowledge about the circuit to be optimized and this knowledge is not always available. The challenge, then, is the development of robust techniques that rely less in specialist knowledge or even incorporating this knowledge, and show efficiency equal or superior to the traditional methods. In general, due to the complexity of the problems associated with different types of circuit designs, sub-optimal solutions are looked for in order to mitigate the mathematical complexity of the analytical models, generating approximate solutions, but with acceptable results (Levy et al., 2001). Evolutionary methods, so called because they use principles of evolution found in nature, are natural candidates to this task, since they are able to find optimal solutions or solutions near the optimum, using mechanisms of selection, crossover and mutation (Holland, 1970). Most methods in the literature consider an initial topology for the circuit and only optimize its parameters, such as, for example, in (Hsu & Huang, 2005). However, it is desirable that the topological structure also goes under optimization, allowing, among other things, the search for new topologies, which is a requirement of current applications. Other methods, using the traditional Genetic Programming as in the work of Koza (Koza et al., 1996), optimize the topology, without considering any prior knowledge, but demand a high computational cost and also have some methodological drawbacks such as premature convergence and stagnation, among others (Hu & Rosemberg, 2004).

A Performance Analysis of Mono and Multi-objective Evolutionary Algorithms Assisted by Meta-modeling

Evolutionary Algorithms can be inefficient in optimizing problems in which fitness evaluation of candidate solutions is computationally expensive. In this paper, single and multi-objective evolutionary methods assisted by meta-models are proposed and analyzed. Meta-models are used to identify promising regions of search space in order to save evaluations of objective-functions. The meta-models are produced using regularized Radial Basis Functions networks. The study in this work shows that the method assisted by meta-modeling accelerates the convergence of the evolutionary process in mono and multi-objectives optimizations.