IEEE Latin America Transactions 

About: IEEE Latin America Transactions is an academic journal published by Institute of Electrical and Electronics Engineers. The journal publishes majorly in the area(s): Computer science & Electric power system. It has an ISSN identifier of 1548-0992. Over the lifetime, 4061 publications have been published receiving 21721 citations. The journal is also known as: IEEE Latin America transactions & Revista do IEEE América Latina.

Comparative study of STATCOM and SVC performance on Dynamic Voltage Collapse of an Electric Power System with Wind Generation

Abstract: One of the major problems of voltage stability is the reactive power limit of the system. Improving the system's reactive power handling capacity via Flexible AC transmission System (FACTS) devices is a solution for prevention of voltage instability. In this paper it is studied the influence of Static Synchronous Compensators (STATCOM) and Static Var Compensators (SVC) in dynamic Voltage Stability during Low Voltage Ride Through (LVRT), in wind farms. The wind turbines are equipped with pitch control coupled with a Fixed Speed Induction Generator (FSIG). Due to the nature of asynchronous operation, system voltage instability of wind farms based on FSIG is largely caused by the excessive reactive power absorption by FSIG after a fault due to the large rotor slip gained during fault. Wind farm models based on FSIG and equipped with either STATCOM or SVC are developed in EUROSTAG. The Automatic Voltage Regulators (AVR) of the generating units and the turbine speed governors were modeled in detail. Different load models were used and the Under Load Tap Changers (ULTC) were also taken into account. Finally, some conclusions that provide a better understanding of the dynamic voltage stability of a system with FSIG model during LVRT, using various capacities of STATCOM and SVC are pointed out.

Electricity Prices Forecasting using Artificial Neural Networks

Abstract: This paper presents the results of the use of training algorithms for recurrent neural networks based on the extended Kalman filter and its use in electric energy price prediction, for both cases: one-step ahead and n-step ahead In addition, it is included the stability proof using the well-known Lyapunov methodology, for the proposed artificial neural network trained with an algorithm based on the extended Kalman filter Finally, the applicability of the proposed prediction scheme is shown by mean of the one-step ahead and n-step ahead prediction using data from the European power system

Trajectory tracking of a quadrotor using sliding mode control

Abstract: This paper presents the design of a sliding mode control (SMC) for trajectory tracking of an unmanned aerial vehicle (UAV), quadrotor. A simplified model of the quadrotor is used for the controller design. The robustness of the controller is verified through simulations, and also through data analysis from the experiments in the 3DR Arducopter platform. The SMC algorithms are implemented in a microcontroller that communicates with a human machine interface (HMI), which monitors the behavior and stability of the state variables. The results show effectiveness of the control technique for maintaining stability in the quadrotor under different operating scenarios.

WCI 04 On the Application of Ensembles of Classifiers to the Diagnosis of Pathologies of the Vertebral Column: A Comparative Analysis

Abstract: This paper reports results from a comprehensive performance comparison among standalone machine learning algorithms (SVM, MLP and GRNN) and their combinations in ensembles of classifiers when applied to a medical diagnosis problem in the field of orthopedics. All the aforementioned learning strategies, which currently comprises the classification module of the SINPATCO platform, are evaluated according to their ability in discriminating patients as belonging to one out of three categories: normal, disk hernia and spondylolisthesis. Confusion matrices of all learning algorithms are also reported, as well as a study of the effect of diversity in the design of the ensembles. The obtained results clearly indicate that the ensembles of classifiers have better generalization performance than standalone classifiers.

Annotated Plant Pathology Databases for Image-Based Detection and Recognition of Diseases

Abstract: Over the last few years, considerable effort has been spent by Embrapa in the construction of a plant disease database representative enough for the development of effective methods for automatic plant disease detection and recognition. In October of 2016, this database, called PDDB, had 2326 images of 171 diseases and other disorders affecting 21 plant species. PDDB size, although considerable, is not enough to allow the use of powerful techniques such as deep learning. In order to increase its size, each image was subdivided according to certain criteria, increasing the number of images to 46,513. Both the original (PDDB) and subdivided (XDB) databases are now being made freely available for academic research purposes, thus supporting new studies and contributing to speed up the advances in the area. Both collections are expected to grow continuously in order to expand their reach. PDDB and XDB can be accessed in the link https://www.digipathos-rep.cnptia.embrapa.br/.