Eduardo Giraldo

Universidad Tecnológica de Pereira, Cartago Viejo, Risaralda, Colombia

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Publications (24)3.75 Total impact

  • Eduardo Giraldo, Alejandro Garces
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    ABSTRACT: This paper proposes a new adaptive control strategy for a wind energy conversion system based on a permanent magnet synchronous generator and a pulse-width modulated current source converter. Most of the studies on wind farms are based on double fed induction technology. Nevertheless, the proposed conversion system is a good alternative due to its high efficiency and reliability. Electrolytic capacitors are not required in this type of converter and the voltage in the DC-link as well as the generated reactive power can be dynamically modified according to the wind velocity, being even negative if required. However, it is challenging from the control and stability standpoint. Capacitive filters placed on the AC side, which are required for safe commutation, can create resonances with the power grid. Reactive power is generated according to the capacity of the converter, the wind velocity and the load profile. The adaptive control strategy uses an adaptive PI which is self-tuned based on a linear approximation of the power system calculated at each sample time. A model reference is also proposed in order to reduce the post-fault voltages. Simulation results demonstrate the advantages of the proposed control.
    IEEE Transactions on Power Systems 05/2014; 29(3):1446-1453. DOI:10.1109/TPWRS.2013.2283804 · 3.53 Impact Factor
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    Carlos David Zuluaga Ríos, Eduardo Giraldo
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    ABSTRACT: This paper presents a methodology for designing a minimum variance control- (MVC) and Kalman filter- (KF) based adaptive system. MVC is a technique of great interest, and it is widely used because it can reduce either energy or material consumption, or else, it can increase production performance. The Kalman filter is a recursive method that provides stochastic support for adaptive systems, showing feasibility and good results for dynamic system identification. The methodology implementation was conducted in a multiplatform integrated development environment called Qt Creator Qt 4.7-based, yielding good results when applied to the reference tracking problem. Moreover, it can be observed that the adaptive control scheme exhibits good settling times and notoriously appropriate overshoots.
    06/2013; 17(36):41-49.
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    Eduardo Giraldo, César G. Castellanos
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    ABSTRACT: En este artículo de investigación se presenta la estimación de la actividad neuronal y la dinámica del cerebro a partir de señales electroencefalográficas (EEG) usando un filtro de Kalman dual. El comportamiento dinámico del cerebro se representa a partir de modelos lineales con base en restricciones fisiológicas. Como resultado, se obtiene un mejor desempeño en la solución en el caso de modelos variantes con el tiempo, al compararla con modelos invariantes en el tiempo, y con la solución estática. Se presenta una evaluación de la carga computacional donde es claro que la estimación de la actividad neuronal con el filtro de Kalman basada en modelos dinámicos lineales, es hasta 10 veces más rápida, que la solución para el caso estático.
    06/2013; 12(22):169-180.
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    ABSTRACT: A Weighted Dynamic Inverse Problem Solution is proposed for electroencephalographic current density reconstruction. The method considers physiologically based models that takes both spatial and temporal dynamics into account and a weighting stage to obtain the covariance of the measurement equation from observations. The calculated weighting matrix is included in the cost function used to solve the dynamic inverse problem. Additionally, an iterative method for inverse problem solution is proposed which considers the time varying weighting matrix in the solution. In this way, a weighted dynamic inverse problem solution is proposed including a time varying weighting matrix. The method performance is analyzed in terms of standard error, projected error, and residual error for several SNRs by using simulated signals through complex dynamical models.
    Neural Engineering (NER), 2013 6th International IEEE/EMBS Conference on; 01/2013
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    Dyna (Medellin, Colombia) 12/2012; 79(176):64-70. · 0.22 Impact Factor
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    ABSTRACT: The estimation of current distributions from electroencephalographic recordings poses an inverse problem, which can approximately be solved by including dynamical models as spatio-temporal constraints onto the solution. In this paper, we consider the electrocardiography source localization task, where a specific structure for the dynamical model of current distribution is directly obtained from the data by fitting multivariate autoregressive models to electroencephalographic time series. Whereas previous approaches consider an approximation of the internal connectivity of the sources, the proposed methodology takes into account a realistic structure of the model estimated from the data, such that it becomes possible to obtain improved inverse solutions. The performance of the new method is demonstrated by application to simulated electroencephalographic data over several signal to noise ratios, where the source localization task is evaluated by using the localization error and the data fit error. Finally, it is shown that estimating MVAR models makes possible to obtain inverse solutions of considerably improved quality, as compared to the usual instantaneous inverse solutions, even if the regularized inverse of Tikhonov is used.
    Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 08/2011; 2011:7151-4. DOI:10.1109/IEMBS.2011.6091807
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    ABSTRACT: Epilepsy is a brain pathology that affects approximately 40 million people in the world. The most utilized clinical test for epilepsy diagnose is the electroencephalogram (EEG). For this reason, nowadays are being developed multiple tools devised for automatic seizure detection on EEG signals. In this work, several approaches of TFR estimation for detection of epileptic events in EEG recordings are compared. Parametric (stochastic evolving and local estimation) TFR estimators as well as non-parametric (STFT, SPWV and CWT) are under study. Compari- son is made according with the achieved performance using a recently proposed methodology for TFR based classification. Results show similar outcomings with all approaches for TFR estimation, achieving accuracy rates from 96 to 99%. Best performance was found for STFT and STTVAR approaches for TFR estimation. Index Terms—Time frequency representations, epileptic activity
    International IEEE/EMBS Conference on Neural Engineering 01/2011; DOI:10.1109/NER.2011.5910510
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    ABSTRACT: A new electroencephalographic current density reconstruction method is introduced using a physiologically based nonlinear modeling that describes better the dynamic behavior of the neural activity. In addition, time-variant parameters are considered into the model to capture the dynamics for normal and pathological states measured from signals. The method is implemented by Unscented Kalman filtering approach. The performance of the new method is evaluated (in terms of mean square error) by application to simulated EEG data over several noise conditions, and a considerable improvement over linear estimation approaches is found. I. INTRODUCTION Widely used functional neuroimaging techniques are functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) source reconstruction. Latter one is a technique that reconstructs the sources of electrical currents (i.e. the current distribution) within the brain that give rise to recordable potential fields at the scalp. Both techniques map neuronal activity and are complementary in the sense that fMRI is known to provide a high spatial resolution but a relatively low temporal resolution, whereas EEG source reconstruction, allows a high temporal resolution, but a relatively low spatial resolution. This paper will focus on EEG source reconstruction, which is known to be an ill-posed inverse problem that cannot be solved without some kind of regularization. Until recently, most attempts to solve the inverse problem were based on scalp recordings at one single time point (i.e., the static case) (1).
    International IEEE/EMBS Conference on Neural Engineering 01/2011; DOI:10.1109/NER.2011.5910528
  • S.S. Acevedo, E. Giraldo, E.D. Trejos
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    ABSTRACT: Renewable energy sources have focused a special attention in wind energy conversion systems, where the goal is maximal power extraction. This paper presents an evaluation of the linear controllers eigen structure assignment, linear quadratic regulator, and the robust loop shaping design procedure actuating on a small wind energy conversion system with a permanent magnet synchronous generator, the rectification is made by a diodes bridge, and the direct current is controlled by a DC-DC converter. The analysis is made calculating the mean square error of the optimal tip speed ratio and the tip speed ratio developed by each regulator, the numerical results also show the performance of the power coefficient, power extracted from a wind profile, and the operation around the optimal regime characteristic. The proposed system works under variable wind speed, and validate the methodology applied.
    Electronics, Robotics and Automotive Mechanics Conference (CERMA), 2010; 11/2010
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    ABSTRACT: This paper presents a new method to estimate dynamic neural activity from EEG signals. The method is based on a Kalman filter approach, using physiological models that take both spatial and temporal dynamics into account. The filter's performance (in terms of estimation error) is analyzed for the cases of linear and nonlinear models having either time invariant or time varying parameters. The best performance is achieved with a nonlinear model with time-varying parameters.
    Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 01/2010; 2010:2914-7. DOI:10.1109/IEMBS.2010.5626281
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    ABSTRACT: Time-frequency representations (TFR) are one of the most popular characterization methods for non-stationary biosignals. Despite of their potential advantages, these representations suffer of large quantity of redundant and irrelevant data which makes them difficult to use for classification purposes. In this work, a methodology for reduction of irrelevant and redundant data is explored. This approach consists on removing irrelevant data, applying a relevance measure on the t-f plane that measures the dependence of each t-f point with the class labels. Then, principal component analysis (PCA) and partial least squares (PLS) are used as non-supervised and supervised linear decomposition approaches to reduce redundancy of remaining t-f points. Results show that the proposed methodology improves the performance of classifier up to 3% when no relevance and redundancy on TFRs is reduced.
    Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 01/2010; 2010:4010-3. DOI:10.1109/IEMBS.2010.5627999
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    ABSTRACT: In order to get the maximum power from the wind, the variable-speed turbine should run at different speed when wind speed changes.The dynamic characteristics of the control system must be properly defined and designed to achieve satisfactory generated power quality and system security, availability and reliability. Therefore great attention has to be kept in the theoretical design task, as well as in the experimental validation phase, in order to assure the correct operation of the control system both in normal and in perturbed conditions. This paper presents the design of an PI controller, the control system is trained to find the gain of the regulator by minimizing the overshoot and the tracking error. The tuning problem is solved with optimizing by Particles Swarm; the speed of synchronous generator is controlled in the wind turbine, the system is connected to a chopper converter represented by a variable resistive load, the turbine uses the model of rigid axis. in order to development and test the control system the model in the system coordinate of the rotor is used.
  • S.S. Acevedo, Eduardo Giraldo, Didier Giraldo
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    ABSTRACT: This paper presents a robust control system over the induction motor based on the H<sub>infin</sub> and loop shaping procedure. It has a set of simples steps to design the linear control law, the model used is the rotor-flux oriented coordinated system and is linearized by the Taylor's series. Finally the output feedback controller test shows the performance of the drive in presence of disturbances.
    Electronics, Robotics and Automotive Mechanics Conference, 2008. CERMA '08; 11/2008
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    EDUARDO GIRALDO, SANTIAGO SÁNCHEZ A.
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    ABSTRACT: The modelling of the induction machine like a drive have a great importance in the controller design, a model that make simple this work is the rotor-flux field oriented coordinated system described in this paper, also a simple technique to control the motor in open loop with voltage-frequency is analyzed to present a start-up of the device.
    01/2008; XIV(39):89-93.
  • XXIII Congreso Anual de la Sociedad Española de Ingeniería Biomédica CASEIB05, Madrid; 11/2005
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    Eduardo Giraldo, Alvaro orozco, Gustavo Betancourt
    II Congreso Colombiano de Bioingeniería e Ingeniería Biomédica; 10/2005
  • X Simposio de Tratamiento de Señales, Imágenes y Visión Artificial, Cali; 09/2005
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    Eduardo Giraldo, Alvaro orozco, Gustavo Betancourt
    IEEE Colombian Workshop on Robotics and Automation; 08/2005
  • EDUARDO GIRALDO, GRUPO DE INVESTIGACIÓN
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    ABSTRACT: We present a sliding mode control using parametric estimation of a multivariable system. The controller design implies that the system is decoupled and the internal coupling can be considered as perturbations. Controller test are performed over a second order multivariable system simulated over an analog computer.