Azauri Albano de Oliveira

Harmonic identification using parallel neural networks in single-phase systems

In this paper, artificial neural networks are employed in a novel approach to identify harmonic components of single-phase nonlinear load currents, whose amplitude and phase angle are subject to unpredictable changes, even in steady-state. The first six harmonic current components are identified through the variation analysis of waveform characteristics. The effectiveness of this method is tested by applying it to the model of a single-phase active power filter, dedicated to the selective compensation of harmonic current drained by an AC controller. Simulation and experimental results are presented to validate the proposed approach.

Harmonic distortion monitoring for nonlinear loads using neural-network-method

Nowadays, harmonic distortion in electric power systems is a power quality problem that has been attracting significant attention of engineering and scientific community. In order to evaluate the total harmonic distortion caused by particular nonlinear loads in power systems, the harmonic current components estimation becomes a critical issue. This paper presents an efficient approach to distortion metering, based on artificial neural networks applied to harmonic content estimation of load currents in single-phase systems. The harmonic content is computed using the estimation of amplitudes and phases of the first five odd harmonic components, which are carried out considering the waveform variations of current drained by nonlinear loads, within previously known limits. The proposed online monitoring method requires low computational effort and does not demand a specific number of samples per period at a fixed sampling rate, resulting in a low cost harmonic tracking system. The results from neural networks harmonic identification method are compared to the truncated fast Fourier transform algorithm. Besides, simulation and experimental results are presented to validate the proposed approach.

Speed Neuro-fuzzy Estimator Applied To Sensorless Induction Motor Control

This work proposes the development of an Adaptive Neuro-fuzzy Inference System (ANFIS) estimator applied to speed control in a three-phase induction motor sensorless drive. Usually, ANFIS is used to replace the traditional PI controller in induction motor drives. The evaluation of the estimation capability of the ANFIS in a sensorless drive is one of the contributions of this work. The ANFIS speed estimator is validated in a magnetizing flux oriented control scheme, consisting in one more contribution. As an open-loop estimator, it is applied to moderate performance drives and it is not the proposal of this work to solve the low and zero speed estimation problems. Simulations to evaluate the performance of the estimator considering the vector drive system were done from the Matlab/Simulink software. To determine the benefits of the proposed model, a practical system was implemented using a voltage source inverter (VSI) to drive the motor and the vector control including the ANFIS estimator, which is carried out by the Real Time Toolbox from Matlab/Simulink software and a data acquisition card from National Instruments.

Neural Network-Based Approach for Identification of the Harmonic Content of a Nonlinear Load in a Single-Phase System

In this paper an alternative method based on artificial neural networks is presented to determine harmonic components in the load current of a single-phase electric power system with nonlinear loads, whose parameters can vary so much in reason of the loads characteristic behaviors as because of the human intervention. The first six components in the load current are determined using the information contained in the time-varying waveforms. The effectiveness of this method is verified by using it in a single-phase active power filter with selective compensation of the current drained by an AC controller. The proposed method is compared with the fast Fourier transform.

Extended DQ Transformation for Vectorial Control Applications of Non-sinusoidal Permanent Magnet AC Machines

This paper presents an extended dq transformation model of non-sinusoidal permanent magnet synchronous machines (SM-PMSM). This transformation permits to obtain a simpler electromagnetic torque equation for this type of machine. The resultant electromagnetic torque equation obtained from this model is directly proportional to the machine quadrature stator current. Initially, the three-phase equations of the motor are presented. These equations are widely used for sinusoidal permanent magnetic machines, as well as for non-sinusoidal ones. The well known vectorial model for sinusoidal PM machines is shown. Then, a vectorial model for a non-sinusoidal PM machine is presented and the proposed vectorial model with variable dq axis magnitude is developed. Simulation results of the electromagnetic torque, produced by a brushless DC drive system using conventional six step open loop control are presented. These results are compared with those produced by the same system using the proposed model into an open loop system and into a closed loop system

Single-Stage Three-Phase Grid-Tied PV System With Universal Filtering Capability Applied to DG Systems and AC Microgrids

This paper proposes a single-stage three-phase four-wire grid-connected photovoltaic (PV) system operating with a dual compensating strategy and feedforward control loop (FFCL). Besides injection of active power into the grid, the PV system operates as a unified power quality conditioner (UPQC), suppressing load harmonic currents and compensating reactive power. Furthermore, regulated, balanced, and harmonic-free output voltages are provided to the load. Since the PV-UPQC is based on a dual compensation strategy, the series converter operates as a sinusoidal current source, whereas the parallel converter operates as a sinusoidal voltage source. Thus, seamless transition can be achieved from the interconnected to the islanding operation modes, and vice versa, without load voltage transients. Moreover, to overcome problems associated with sudden solar irradiation changes, fast power balance involving the PV array and the grid is obtained, since the FFCL acts on the generation of the series inverter current references. As a result, the dynamic responses of both inverter currents and dc-bus voltage are improved. Detailed analysis involving the active power flow through the inverters is performed allowing proper understanding of the PV-UPQC operation. Experimental results are presented to evaluate both dynamic and static performances of the PV-UPQC tied to the electrical distribution system.

Open-loop neuro-fuzzy speed estimator applied to vector and scalar induction motor drives

Abstract Scalar and vector drives have been the cornerstones of control of industrial motors for decades. In both the elimination of mechanical speed sensor consists in a trend of modern drives. This work proposes the development of an adaptive neuro-fuzzy inference system (ANFIS) angular rotor speed estimator applied to vector and scalar drives. A multi-frequency training of ANFIS is proposed, initially for a V / f scheme and after that a vector drive with magnetizing flux oriented control is proposed. In the literature ANFIS has been commonly proposed as a speed controller in substitution of the classical PI controller of the speed control loop. This paper investigates the ANFIS as an open-loop speed estimator instead. The subtractive clustering technique was used as strategy for generating the membership functions for all the incoming signal inputs of ANFIS. This provided a better analysis of the training data set improving the comprehension of the estimator. Additionally the subtractive cluster technique allowed the training with experimental data corrupted by noise improving the estimator robustness. Simulations to evaluate the performance of the estimator considering the V / f and vector drive system were realized using the Matlab/Simulink ® software. Finally experimental results are presented to validate the ANFIS open loop estimator.

Magnetic Field Analysis of a Brushless DC Motor Comparing Ferrite and NdFeB Magnets on Rotor

This paper presents comparisons between three-phase permanent-magnet motor with ferrite magnets on rotor and other with NdFeB (bonded). Related to this replacement, the advantages and disadvantages are quoted. The analysis of magnetic field is made through the finite element method. The used machine has nonsinusoidal flux. Its application main is in hermetic compressor household refrigeration systems. Also, the geometric and magnetic characteristics are presented.

Dynamic Performance Improvement of a Grid-Tied PV System Using a Feed-Forward Control Loop Acting on the NPC Inverter Currents

This paper presents a three-phase grid-connected photovoltaic (PV) system, which is implemented using the neutral-point-clamped (NPC) inverter. A current feed-forward control loop (FFCL) is proposed to improve the PV system dynamic behavior, due to the PV array being constantly subjected to sudden solar irradiance change, which causes voltage oscillations in the dc bus and, hence, interferes in proper PV system operation. As the current FFCL acts to speed up the calculation of the inverter current references, the dynamic response of the currents injected into the grid is improved. As a consequence, the dynamic behavior of the dc-bus voltage is also enhanced, reducing both settling time and overshoot. Besides the injection of active power into the grid, the PV system is also controlled to perform active power-line conditioning, so that load harmonic currents are suppressed, as well as load reactive power is compensated. However, the NPC inverter must be properly designed to guarantee that its power rating will not be exceeded, since both fundamental and nonfundamental current components flow through the grid-tied inverter. Extensive experimental results based on a digital signal processor are presented in order to evaluate the effectiveness, as well as the static and dynamic performance of the PV system.

A Versatile Unified Power Quality Conditioner Applied to Three-Phase Four-Wire Distribution Systems Using a Dual Control Strategy

This paper presents the study, analysis, and practical implementation of a versatile unified power quality conditioner (UPQC), which can be connected in both three-phase three-wire (3P3W) or three-phase four-wire distribution systems for performing the series-parallel power-line conditioning. Thus, even when only a 3P3W power system is available at a plant site, the UPQC is able to carry out power-line compensation for installed loads that require a neutral conductor to operate. Different from the control strategies used in the most of UPQC applications in which the controlled quantities are nonsinusoidal, this UPQC employs a dual compensation strategy such that the controlled quantities are always sinusoidal. Thereby, the series converter is controlled to act as a sinusoidal current source, whereas the parallel converter operates as a sinusoidal voltage source. Thus, because the controlled quantities are sinusoidal, it is possible to reduce the complexity of the algorithms used to calculate the compensation references. Therefore, since the voltage and current controllers are implemented into the synchronous reference frame, their control references are continuous, decreasing the steady-state errors when traditional proportional-integral controllers are employed. Static and dynamic performances, as well as the effectiveness, of the dual UPQC are evaluated by means of experimental results.