Comparação do controle do inversor trifásico conectado à rede com filtro LCL considerando o amortecimento passivo e ativo

Distributed generation systems must fulfill standards specifications of current harmonics injected to the grid. In order to satisfy these grid requirements, passive filters are connected between inverter and grid. This work compares the characteristic response of the traditional inductive (L) filter with the inductive-capacitive-inductive (LCL) filter. It is shown that increasing the inductance L leads to a good ripple current suppression around the inverter switching frequency. The LCL filter provides better harmonic attenuation and reduces the filter size. The main drawback is the LCL filter impedance, which is characterized by a typical resonance peak, which must be damped to avoid instability. Passive or active techniques can be used to damp the LCL resonance. To address this issue, this dissertation presents a comparison of current control for PV grid-tied inverters with L filter and LCL filter and also discuss the use of active and passive damping for different regions of resonance frequency. From the mathematical models, a design methodology of the controllers was developed and the dynamic behavior of the system operating in closed loop was investigated. To validate the studies developed during this work, experimental results are presented using a three-phase 5kW experimental platform. The main components and their functions are discussed in this work. Experimental results are given to support the theoretical analysis and to illustrate the performance of grid-connected PV inverter system. It is shown that the resonant frequency of the system, and sampling frequency can be associated in order to calculate a critical frequency, below which is essential to perform the damping of the LCL filter. Also, the experimental results show that the active buffer per virtual resistor, although with a simple development, is effective to damp the resonance of the LCL filter and allow the system to operate stable within predetermined parameters.

Análise e proposição de estratégias de estimação e controle de velocidade para motores de indução trifásicos

This study presents a proposal of speed servomechanisms without the use of mechanical sensors (sensorless) using induction motors. A comparison is performed and propose techniques for pet rotor speed, analyzing performance in different conditions of speed and load. For the determination of control technique, initially, is performed an analysis of the technical literature of the main control and speed estimation used, with their characteristics and limitations. The proposed technique for servo sensorless speed induction motor uses indirect field-oriented control (IFOC), composed of four controllers of the proportional-integral type (PI): rotor flux controller, speed controller and current controllers in the direct and quadrature shaft. As the main focus of the work is in the speed control loop was implemented in Matlab the recursive least squares algorithm (RLS) for identification of mechanical parameters, such as moment of inertia and friction coefficient. Thus, the speed of outer loop controller gains can be self adjusted to compensate for any changes in the mechanical parameters. For speed estimation techniques are analyzed: MRAS by rotóricos fluxes MRAS by counter EMF, MRAS by instantaneous reactive power, slip, locked loop phase (PLL) and sliding mode. A proposition of estimation in sliding mode based on speed, which is performed a change in rotor flux observer structure is displayed. To evaluate the techniques are performed theoretical analyzes in Matlab simulation environment and experimental platform in electrical machinery drives. The DSP TMS320F28069 was used for experimental implementation of speed estimation techniques and check the performance of the same in a wide speed range, including load insertion. From this analysis is carried out to implement closed-loop control of sensorless speed IFOC structure. The results demonstrated the real possibility of replacing mechanical sensors for estimation techniques proposed and analyzed. Among these, the estimator based on PLL demonstrated the best performance in various conditions, while the technique based on sliding mode has good capacity estimation in steady state and robustness to parametric variations.

Implante neural controlado em malha fechada

One of the challenges to biomedical engineers proposed by researchers in neuroscience is brain machine interaction. The nervous system communicates by interpreting electrochemical signals, and implantable circuits make decisions in order to interact with the biological environment. It is well known that Parkinson’s disease is related to a deficit of dopamine (DA). Different methods has been employed to control dopamine concentration like magnetic or electrical stimulators or drugs. In this work was automatically controlled the neurotransmitter concentration since this is not currently employed. To do that, four systems were designed and developed: deep brain stimulation (DBS), transmagnetic stimulation (TMS), Infusion Pump Control (IPC) for drug delivery, and fast scan cyclic voltammetry (FSCV) (sensing circuits which detect varying concentrations of neurotransmitters like dopamine caused by these stimulations). Some softwares also were developed for data display and analysis in synchronously with current events in the experiments. This allowed the use of infusion pumps and their flexibility is such that DBS or TMS can be used in single mode and other stimulation techniques and combinations like lights, sounds, etc. The developed system allows to control automatically the concentration of DA. The resolution of the system is around 0.4 µmol/L with time correction of concentration adjustable between 1 and 90 seconds. The system allows controlling DA concentrations between 1 and 10 µmol/L, with an error about +/- 0.8 µmol/L. Although designed to control DA concentration, the system can be used to control, the concentration of other substances. It is proposed to continue the closed loop development with FSCV and DBS (or TMS, or infusion) using parkinsonian animals models.