A.N. de Souza

Detection and Identification of Abnormalities in Customer Consumptions in Power Distribution Systems

This paper proposes a computational technique for the classification of electricity consumption profiles. The methodology is comprised of two steps. In the first one, a C-means-based fuzzy clustering is performed in order to find consumers with similar consumption profiles. Afterwards, a fuzzy classification is performed using a fuzzy membership matrix and the Euclidean distance to the cluster centers. Then, the distance measures are normalized and ordered, yielding a unitary index score, where the potential fraudsters or users with irregular patterns of consumption have the highest scores. The approach was tested and validated on a real database, showing good performance in tasks of fraud and measurement defect detection.

New Insights on Nontechnical Losses Characterization Through Evolutionary-Based Feature Selection

Although nontechnical losses automatic identification has been massively studied, the problem of selecting the most representative features in order to boost the identification accuracy and to characterize possible illegal consumers has not attracted much attention in this context. In this paper, we focus on this problem by reviewing three evolutionary-based techniques for feature selection, and we also introduce one of them in this context. The results demonstrated that selecting the most representative features can improve a lot of the classification accuracy of possible frauds in datasets composed by industrial and commercial profiles.

The application of neural networks to the analysis of dissolved gases in insulating oil used in transformers

The state of insulating oils used in transformers is determined through the accomplishment of physical chemical tests, which determine the state of the oil, as well as the chromatography test, which determines possible faults in the equipment. The article concentrates on determining, from a new methodology, a relationship among the variation of the indices obtained from the physical-chemical tests with those indices supplied by the chromatography tests. The determination of the relationship among the tests is accomplished through the application of neural networks. From the data obtained by physical-chemical tests, the network is capable of determining the relationship among the concentration of the main gases present in a certain sample, which were detected by the chromatography tests. More specifically, the proposed approach uses neural networks of perceptron type comprising multiple layers. After the process of network training, it is possible to determine the existing relationship between the physical chemical tests and the amount of gases present in the insulating oil.

A modified Hopfield model for solving the N-Queens problem

A neural network model for solving the N-Queens problem is presented in this paper. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the convergence of the network to the equilibrium points. The network is shown to be completely stable and globally convergent to the solutions of the N-Queens problem. Simulation results are presented to validate the proposed approach.

Nitrogen content identification in crop plants using spectral reflectance and artificial neural networks

The accurate identification of the nitrogen content in crop plants is extremely important since it invokes economic aspects and environmental impacts. Several experimental tests have been carried out to obtain characteristics and parameters associated with the health of plants and their growing. The nitrogen content identification involves a lot of nonlinear parameters and complex mathematical models. The paper describes an approach for identification of nitrogen content through spectral reflectance of plant leaves using artificial neural networks. The network acts as identifier of relationships among pH of soil, fertilizer treatment, spectral reflectance and nitrogen content in the plants. So, nitrogen content can be estimated and generalized from an input parameter set. This approach can form the basis for development of an accurate real time nitrogen applicator.

Evaluation and identification of lightning models by artificial neural networks

This paper describes a novel approach for mapping lightning models using artificial neural networks. The networks acts as identifier of structural features of the lightning models so that output parameters can be estimated and generalised from an input parameter set. Simulation examples are presented to validate the proposed approach. More specifically, the neural networks are used to compute electrical field intensity and critical disruptive voltage taking into account several atmospheric and structural factors, such as pressure, temperature, humidity, distance between phases, height of bus bars, and wave forms. A comparative analysis with other approaches is also provided to illustrate this new methodology.

Design of digital PID controller with gain planning based on CMAC

Presents the development of a proportional integral derivative (PID) digital controller with gain planning based on the (CMAC) cerebellar model articulation controller. This digital controller operates in closed loop and has a dynamic gain adjustment. The control strategy uses an algorithm that calculates the proportional, integral and derivative parcels. This algorithm provides the soft start requirements. Both, the gain planning and the soft start requirements, uses auxiliary variables to determine an appropriate and dynamic setup of the digital controller. These auxiliary variables impose several restrictions on the digital controller and this one must have adaptive characteristics. The artificial neural network is used to estimate these auxiliary variables. The learning process depends on the data acquisition or the mathematical model simulations. The digital controller can operate in real-time conditions and the sample frequency can reach 10 kHz.

Proposals for improvement of methodology and process of collecting and analyzing compatibility of power quality indicators in distribution systems

This article aims to present proposals for improvement of key standards and resolutions concerned about the methodology for calculating the indicator of total harmonic voltage distortion, and should contribute to the process of examining the compatibility of potentially disturbing loads in electric power quality in distribution systems. These proposals were drawn from the analysis of results from measurement campaigns conducted in a case study including analysis of the connection of a new induction furnace in a foundry served by a distributor of São Paulo state. A general historical situating the quality of electric energy in the electricity sector is presented, and methodological guidelines and procedures used in experimental trials are shown. The analysis and discussion of results are prepared to answer the main questions that arise during the implementation of standards, resolutions and procedures.

A barrier method for constrained nonlinear optimization using a modified Hopfield network

The ability of neural networks to realize some complex nonlinear function makes them attractive for system identification. The paper describes a barrier method using artificial neural networks to solve robust parameter estimation problems for a nonlinear model with unknown-but-bounded errors and uncertainties. This problem can be represented by a typical constrained optimization problem. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the network convergence to the equilibrium points. A solution for the robust estimation problem with unknown-but-bounded error corresponds to an equilibrium point of the network. Simulation results are presented as an illustration of the proposed approach.

Design and analysis of neural networks for systems optimization

Artificial neural networks are dynamic systems consisting of highly interconnected and parallel nonlinear processing elements that are shown to be extremely effective in computation. This paper presents an architecture of artificial neural networks that can be used to solve several classes of optimization problems. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid-subspace technique. Among the problems that can be treated by the proposed approach include combinational optimization problems and dynamic programming problems.