Laurinda L. N. dos Reis

Robust Control Based on Generalized Predictive Control Applied to Switched Reluctance Motor Current Loop

This paper proposes a robust control based on generalized predictive control (GPC) applied to the current control loop for a switched reluctance motor (SRM) drive. The proposed controller has two degrees of freedom where the setpoint tracking is decoupled from the load disturbance rejection at the nominal case. In addition a filter design is proposed in order to achieve good relationship among robustness, load disturbance rejection, and noise attenuation. Simulation and real experimental results are shown to illustrate the controller performance. [DOI: 10.1115/1.4026128]

Neo-fuzzy neuron model for seasonal rainfall forecast: A case study of Ceara's eight homogenous regions

The knowledge about the seasonal rainfall in some Brazilian regions is essential for agriculture and the adequate management of water resources. For this purpose, linear and nonlinear models are commonly used for seasonal rainfall prediction, while some of them are based on Artificial Neural Networks, demonstrating great potential as shown in literature. According to this tendency, this work presents a rainfall seasonal forecast model based on a neuro-fuzzy technique called Neo-Fuzzy Neuron Model. Improved performance by using this approach has been obtained in terms of reduced root mean square error RMSE and increased correlation between predicted and real output when compared with dynamic downscaling model using the Regional Spectral Model. Experimental results show the effectiveness of the proposed method in predictions regarding the first four trimesters from year 2002 up to the current one.

Hybrid Control Robust Using Logic Fuzzy Applied to the Position Loop for Vector Control to Induction Motors

Abstract This article presents the study and implementation of position control for an induction motor drive using the vector control strategy to improve tracking of the motor shaft. Thus, a novel hybrid control technique is proposed in contrast to the proportional-integral-derivative controller with a fixed gain traditionally used in this type of application. The hybrid control relies on error techniques based on fuzzy rules systems weighting the control action of proportional-integral-derivative and generalized predictive controllers previously adjusted. The simulation and experimental results to check the performance and robustness of the control strategy demonstrate the merit of the proposed approach.

Speed control in switched reluctance motor based on generalized predictive control

This paper presents a robust control scheme based on a Generalized Predictive Controller (GPC) belonging to the Model Predictive Controller (MPC) Family applied to the speed loop of a SRM drive. The structure of the proposed controller is based on the design of a filter to allow a rapid response, disturbance rejection, noise attenuation and robustness with a low computational cost. The proposed controller, as well a traditional PID controller, in order to provide means of comparison of experimental results, are applied. The proposed controller was implemented and the experimental results compared with traditional PID controller and analyzed quantitatively by performance indexes.

Anti-windup predictive current controller applied to a DFIG-based wind turbine under low DC-link voltage

During unsymmetrical/symmetrical voltage sags, there is a drop in the DC-link voltage, thus it can lead to serious impacts, i.e. current and power oscillations, to a double fed induction generator (DFIG) with a simplified power converter (rotor side converter with three-phase passive diode rectifiers). Therefore, this paper deals with an anti-windup predictive current controller applied to a DFIG-based wind turbine under low DC-link voltage condition. In order to investigate the transient overshoots and ripples that appear in the rotors current and DC-link voltage, several experimental tests are carried out. The test results shows the effectiveness of the proposed anti-windup predictive controller when it is compared to the PI controller.

Robust analysis of a predictive controller of DFIG wind energy systems

This paper deals with the robustness analysis of an anti-windup predictive current controller applied to a DFIG-based wind turbine in the presence of parameter variation. Experimental results were carried out, under the changing of the rotor speed, power and voltage sags, for a 3.7kW DFIG to verify the robustness of the designed controller.

Robust Generalized Predictive Control applied to the mitigation of electromagnetic torque oscillations in a wind energy conversion system based on DFIG

In order to make wind power generation truly cost-effective and reliable, an advanced control technique must be used. This paper presents the development of a control strategy based on the rotor current loop using the Generalized Predictive Control (GPC) approach applied to a Doubly-Fed Induction Generator (DFIG)-based wind turbine. The controller is designed to minimize oscillations in the generator electromagnetic torque by filtering the control signal. Besides, a new methodology for tuning the controller is proposed based on a single parameter that allows good tradeoffs among noise attenuation of the control signal, disturbance rejection, and system robustness during operation. Simulation results are shown and discussed to demonstrate the merit of the proposal, as the performance and robustness of the algorithm are evaluated when applied to a DFIG-based wind energy conversion system (WECS).

Redes Neurais Aplicadas ao Diagnóstico de Falhas Incipientes em Transformadores de Potência Imersos em Óleos

Incipient fault in power transformer is closely related to isolation condition assessment. Dissolved gas analysis in transformer insulating oil is a well-known diagnostic technique. Produced gases serve as indicators of the type and severity of electrical stress. This paper presents a comparative study of multilayer perceptron and radial basis function neural networks for fault identification in oil-immersed power transformers. The neural networks are trained by using improved learning algorithms. The used data in experiments are taken out chromatographic analysis of COELCE (Electrical Company of Ceara) transformers. The simulation results indicate the potentialities of the tested neural networks for diagnostic in oil-immersed power transformers.

Robust generalized predictive control applied to the rotor side converter of a wind power generator system based on DFIG

This paper presents the model and the control of the rotor current loop a double-fed induction generator based on a robust generalized predictive controller. The controller is designed to minimize the control signal and ensure the reference tracking quickly and with a minimum of overshoot. Furthermore, it is proposed a new methodology for controller tuning based on a single parameter that allows a balance between noise attenuation of the control signal, disturbance rejection and robustness of the system during operation. Through computer simulation, the relationship between the tuning parameter and the system dynamics is analyzed. The simulations results validate the performance of the proposed controller when applied to a DFIG-based wind generation system.

Hybrid position controller for an indirect field-oriented induction motor drive

This paper presents a conception and implementation of a hybrid controller technique applied in an indirect field-oriented (FOC) induction motor drive. The main goal is to improve the position tracking performance with a hybrid controller based on the classical proportional (P) controller and the generalized predictive control (GPC) approach. It has shown through experimental results, the effectiveness of the proposed hybrid controller. Indeed, the controller has the fast tracking capability as a P controller and the robustness as a GPC controller.