Vitor Hugo Ferreira

The Induction Motor Parameter Estimation Using Genetic Algorithm

This paper presents a method to estimate electrical parameters in a three-phase induction motor (MIT) equivalent circuit, using genetic algorithm (GA). This method is applied in a MATLAB® Simulink environment. The methodology consist in minimize objective function to represent the estimated current error with GA. The stator current versus slip curve is a simulation result and it will be used to estimate electrical parameters of equivalent circuit. In this work two different theory MIT model are considered Chapman[1] and Wildi [2]. The goal is define the better current versus slip estimate curve for each model and to determine which one has the best motor simulate representation.

Classification of Events in Distribution Networks using Autonomous Neural Models

This paper presents a method for automatic clas- sification of faults and events related to quality of service in electricity distribution networks. The method consists in preprocessing event oscillographies using the wavelet transform and then classifying them using autonomous neural models. In the preprocessing stage, the energy present in each sub-band of the wavelet domain is computed in order to compose input feature vectors for the classification stage. The classifiers investigated are based in Multi-Layer Perceptron (MLP) feed-forward artificial neural networks and Support Vector Machines (SVM), which automatically promote input selection and structure complexity control simultaneously. Experiments using simulated data show promising results for the proposed application.

An Accurate Approach for Automatic Segmentation of Power Distribution Voltage Waveforms

This paper addresses one of the fundamental steps in automatic waveform analysis: transient segmentation. We present a new approach which incorporates the advantages of a multilevel wavelet decomposition and the representation of the support vector data description. Real data from a monitoring system developed for lightning overvoltage detection in overhead distribution power lines was used for comparison and validation of segmentation performance. The experiments involve the proposed segmentation approach and usual segmentation methods, such as Kalman filtering, autoregressive models, and standard discrete wavelet transform. The results show that the proposed segmentation method based on DWT+SVDD yields better overall accuracy for transient segmentation when compared to currently used methods, demonstrating the potential for applications in oscillographic recorders for smart distribution networks, where identification, characterization, and mitigation of events are critical for network operation and maintenance.

Natural optimization applied to medium-term hydrothermal coordination

This paper deals with the application of genetic algorithms and simulated annealing in order to solve the hydrothermal coordination problem through the optimal operation planning of large and nonlinear complex systems. Aiming to explore different solutions for this kind of problem, this paper suggests a comparison between the results of genetic algorithms and simulated annealing. The proposed techniques are applied in two hydrothermal test systems that are part of the Brazilian electric system, one composed by seven and other composed by fourteen hydro plants. The results show the effectiveness of the proposed techniques.

Automatic Kernel Based Models for Short Term Load Forecasting

The application of support vector machines to forecasting problems is becoming popular, lately. Several comparisons between neural networks trained with error backpropagation and support vector machines have shown advantage for the latter in different domains of application. However, some difficulties still deteriorate the performance of the support vector machines. The main one is related to the setting of the hyperparameters involved in their training. Techniques based on meta-heuristics have been employed to determine appropriate values for those hyperparameters. However, because of the high nonconvexity of this estimation problem, which makes the search for a good solution very hard, an approach based on Bayesian inference, called relevance vector machine, has been proposed more recently. The present paper aims at investigating the suitability of this new approach to the short-term load forecasting problem.

A new approach for event classification and novelty detection in power distribution networks

This paper presents a new approach for automatic oscillography classification in distribution networks, including the detection of patterns not initially presented to the classifier during training, which are defined as novelties. We performed experiments with coupled novelty detection and multi-class classification, and also in separate stages, using the following classifiers: Gaussian Mixture Models (GMM), K-means clustering (KM), K-nearest neighbors (KNN), Parzen Windows (PW), Support Vector Data Description (SVDD), and multi-class classification based on Support Vector Machines (SVM). Preliminary results for simulated data in the Alternative Transient Program (ATP) demonstrate the ability of the method to identify new classes of events in a dynamic learning environment. This work was partially supported by COPEL within the Research and Development Program of the Brazilian Electrical Energy Agency (ANEEL).

Chaos theory applied to input space representation of autonomous neural network-based short-term load forecasting models

After 1991, the literature on load forecasting has been dominated by neural network based proposals. However, one major risk in using neural models is the possibility of excessive training, i.e., data overfitting. The extent of nonlinearity provided by neural network based load forecasters, which depends on the input space representation, has been adjusted using heuristic procedures. The empirical nature of these procedures makes their application cumbersome and time consuming. Autonomous modeling including automatic input selection and model complexity control has been proposed recently for short-term load forecasting. However, these techniques require the specification of an initial input set that will be processed by the model in order to select the most relevant variables. This paper explores chaos theory as a tool from non-linear time series analysis to automatic select the lags of the load series data that will be used by the neural models. In this paper, Bayesian inference applied to multi-layered perceptrons and relevance vector machines are used in the development of autonomous neural models.

Harmonic analysis of distributed generation in Smart City Búzios project

The commercial and industrial sectors have come up against paradigm changes that concern the need for improving their activities, in order to develop processes with less environmental impact, sustainable and energy-efficient, aiming at reducing energy consumption. Technologies based on renewable sources for power generation, such as solar and wind, have emerged as an alternative for these sectors. With the development of distributed generation technologies, such as photovoltaic cells, wind turbines, and so on, it is necessary to identify and classify the disturbances that embedded electronic systems in these technologies may cause on distributed grids. This paper presents some data collected and analyzed on the Smart City Búzios project, where were installed three small wind turbines and solar photovoltaic systems in different grid points. This work presents and discusses some data collected on these systems regarding harmonic analysis. In the remaining of this paper is discussed data related to: vertical wind turbines, photovoltaic system for charging electric vehicles, photovoltaic system feeding computerized command center and photovoltaic system powering LED lamps in public area.

Fault Diagnosis in Transmission Lines: Trends and Main Research Areas

Fault diagnosis in transmission lines constitutes a problem in power systems with relevant technical and economic impact for independent system operators and also for the asset owner itself. Several techniques have been proposed in the literature to deal with this problem in a significant number of applications. This survey presents the main research areas and the trends in transmission line fault diagnosis in power systems. Classification of strategies and their relationship with applications are shown and discussed.

Fully Automatic Bayesian Neural Forecaster - NN GC1

This paper combines several techniques to generate a fully data-driven forecasting model. Input selection is performed, without user intervention, by applying chaos theory and Bayesian inference. Afterwards, neural network models are estimated, without cross-validation, relying on data partitioning and Bayesian regularization for complexity control. Automatic data clustering has been used for data partitioning. The proposed forecasting model has been tested with datasets provided by the competition organizers.