Rogerio Andrade Flauzino

Intelligent Systems for the Detection of Internal Faults in Power Transmission Transformers

This chapter presents an approach based on expert systems, which is intended to identify and to locate internal faults in power transformers, as well as to provide an accurate diag‐ nosis (predictive, preventive and corrective), so that proper maintenance can be per‐ formed. In fact, the main difficulty in using conventional methods, based on analysis of acoustic emissions or dissolved gases, lies in how to relate the measured variables when there is an internal fault in a transformer. This kind of situation makes it difficult to de‐ sign optimized systems, because it prevents the efficient location and identification of pos‐ sible defects with sufficient rapidity. In addition, there are many cases where the equipment must be turned off for such tests to be carried out. Thus, this chapter proposes an architec‐ ture for an intelligent expert system for efficient fault detection in power transformers us‐ ing different diagnosis tools, based on techniques of artificial neural networks and fuzzy inference systems. Based on acoustic emission signals and the concentration of gases present in insulating mineral oil and electrical measurements, intelligent expert systems are able to provide, as a final result, the identification, characterization and location of any electrical fault occurring in transformers.

Intelligent Expert System for Protection Optimization Purposes in Electric Power Distribution Systems

The decision process taken into account by the expert system is based on information provided by the software “SimSurto”, which was especially developed to simulate the voltage transients caused by atmospheric discharges in distribution lines, and its objective is the computation of several parameters related to the respective transients, considering the equipments already installed, the geographical location of the distribution line and the respective incidence of atmospheric discharges in the distribution system.

Recurrent Neural Approach for Solving Several Types of Optimization Problems

An artificial neural network, more commonly known as neural network, is a mathematical model for information processing based on the biological nervous system, which has a natural propensity for storing experiential knowledge and making it available for use (Haykin, 1999). The main advantage of a neural network is in its ability to approximate functional relationships, particularly nonlinear relationships. Neural networks have been applied to several classes of optimization problems and have shown promise for solving such problems efficiently. Most of the neural architectures proposed in the literature solve specific types of optimization problems (Dillon & O’Malley, 2002; Kakeya & Okabe, 2000; Xia et al., 2002). In contrast to these neural models, the network proposed here is able to treat several kinds of optimization problems using a unique network architecture. The approach described in this chapter uses a modified Hopfield network, which has equilibrium points representing the solution of the optimization problems. The Hopfield network is modified by presenting an optimization process carried out in two distinct stages, which are represented by two energy functions. The internal parameters of the network have been computed using the valid-subspace technique (Aiyer et al., 1990; Silva et al., 1997). This technique allows us to define a subspace, which contains only those solutions that represent feasible solutions to the problem analyzed. It has also been demonstrated that with appropriately set parameters, the network confines its output to this subspace, thus ensuring convergence to a valid solution. Also in contrast to other neural approaches that use an energy function for each constraint to be satisfied, the mapping of optimization problems using the modified Hopfield network always consists of determining just two energy functions, which are denoted by Econf and Eop. The function Econf is a confinement term that groups all structural constraints associated with the problems, and Eop is an optimization term that leads the network output to the equilibrium points corresponding to optimal solutions. In this chapter, the proposed approach has been applied to solve combinatorial optimization problems, dynamic programming problems and nonlinear optimization problems. In addition to providing a new approach for solving several classes of optimization problems through a unique neural network architecture, the main advantages of using the modified O pe n A cc es s D at ab as e w w w .ite ch on lin e. co m

Efficient parametric adjustment of fuzzy inference system using unconstrained optimization

This paper presents a new methodology for the adjustment of fuzzy inference systems, which uses technique based on error back-propagation method. The free parameters of the fuzzy inference system, such as its intrinsic parameters of the membership function and the weights of the inference rules, are automatically adjusted. This methodology is interesting, not only for the results presented and obtained through computer simulations, but also for its generality concerning to the kind of fuzzy inference system used. Therefore, this methodology is expandable either to the Mandani architecture or also to that suggested by Takagi-Sugeno. The validation of the presented methodology is accomplished through estimation of time series and by a mathematical modeling problem. More specifically, the Mackey-Glass chaotic time series is used for the validation of the proposed methodology.

An Approach Based on Neural Networks for Identification of Fault Sections in Radial Distribution Systems

The main objective involved with this paper consists of presenting the results obtained from the application of artificial neural networks and statistical tools in the automatic identification and classification process of faults in electric power distribution systems. The developed techniques to treat the proposed problem have used, in an integrated way, several approaches that can contribute to the successful detection process of faults, aiming that it is carried out in a reliable and safe way. The compilations of the results obtained from practical experiments accomplished in a pilot radial distribution feeder have demonstrated that the developed techniques provide accurate results, identifying and classifying efficiently the several occurrences of faults observed in the feeder.

Study on the emergence and expansion of smart grids in divergent cities

Along with innovative expert technologies that are emerging, the smart grid stands out as an ideal vision for future power systems. In recent years, various studies have highlighted the smart grids in different initiatives, either in the area of network automation or in services aimed at consumers. Pilot projects have been also developed in various countries with different economic and political status, whose main objective is to create a set of sustainable and reliable networks, as well as find energy solutions that meet future needs. Therefore, this paper aims to explore the prospects that smart grids are being developed internationally. Finally, future directions involving smart grid are outlined and conclusions about its current status are compiled.

Alocação eficiente de indicadores de faltas em um sistema de distribuição real usando computação evolutiva

This work proposes an evolutionary computing strategy to solve the problem of fault indicator placement in primary distribution feeders. The problem is solved using Genetics Algorithm (GA) technique in order to obtain an efficient configuration of fault indicator placement in the main feeder. The results with actual data confirm the efficiency of the GA methodology to solve the fault indicator placement problem. As a result, the distribution reliability indices of quality are improved attending the financial costs.