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Resolver is an angular position sensor widely used in applications such as electric/hybrid vehicles, CNCs, antennas and robotics. However, the estimation of the angular position from resolver outputs is more difficult than the analysis of encoder signals, and it is still an open question. Most algorithms proposed in literature are based on type-I or type-II angle tracking ob- servers. Some type-III observers were proposed, but they require a high sampling frequency. This paper explores the use of synchronous demodulation of the resolver outputs to simplify the implementation of a type-III angle tracking observer. The resolver outputs are sampled at the peaks and valleys of the excitation resolver signal, being easy to get sine and cosine of the angular position. The proposed approach reduces the computational cost and the required sampling frequency to implement the type-III observer. Simulation and experimental results prove the accuracy of the proposed approach.

This paper presents the use of a radial basis function artificial neural network to estimate sensor readings exploring the analytical redundancy via auto-association. However, in order to guarantee optimal performance of the network, the training and optimization processes have been modified. In the conventional training algorithm, even if a stop criterion, such as summed squared error, is reached, one or more of the individual performance metrics, including: i) accuracy; ii) robustness; iii) spillover and iv) filtering of the neural network, may not be satisfactory while validating sensor measurements. Essentially, the proposed modification in the training algorithm is based on seeking to ensure that one or more of the metrics are met. This paper describes the proposed algorithm including all of its mathematical foundation. Afterward, a data set of a water injection pump for an oil and gas processing unit was used to train the RBF network using the conventional and the modified algorithm, and the performance of each was evaluated. Furthermore, the AAKR model is applied to the same dataset as a quality reference parameter. Finally, a comparison analysis of the developed models is presented for each of the performance metrics, as well as for overall effectiveness, demonstrating that the main advantage of the proposed approach is to obtain the estimation results equivalent or superior to the AAKR with shorter runtime and the disadvantage of having higher complexity during the model training.

Frequency domain multiplexing (FDM) allows simplifying the data acquisition in vector control when the resolver is used as a position sensor. However, the crosstalk reduction in the demultiplexing process is still a difficult task. This article proposes an improvement of the FDM-based acquisition system through synchronous sampling and Differential Evolution (DE). The resolver signals and the PWM carrier are synchronized so that the resolver outputs are zero when the PWM carrier reaches its valleys. Thus, the fundamental component of the stator currents can be easily estimated from the signals sent to the acquisition system. These estimated current signals and an angle tracking observer (ATO) are used to estimate the angular position. DE and a discretized model of the ATO are applied in order to improve the angle estimation transient response and to reduce crosstalk. Conditions to apply the proposed multiplexing approach will be defined. Simulation and experimental results show that the proposed approach allows an accurate estimation of the current fundamental components and the angular position when FDM is used, with better performance respect to other approaches in the literature. This is the first time that a DE is used to tune an ATO.

Transportation electrification is gaining notoriety in recent years as to fulfill the aim of reducing the fossil fuels consumption and to increase energy efficiency. Accordingly, integrated starter alternator is a compelling method to convert conventional vehicles into hybrid ones. A deal of benefits can be reached by using ISA systems. In general, it provides vehicles performance enhancement and also reduces fuel consumption. This paper aims to present simulation results and preliminary experimental achievements with a power electronics-based prototype driving a permanent magnet synchronous machine (PMSM). The ISA operation is demonstrated throughout providing cranking torque and assistant torque for constant power and constant torque conditions and afterward the generation mode. Details of the hardware design and control strategies, which include an ADALINE neural network for properly find the mechanical position and the application of a metaheuristic technique to tune the controllers of the motor control, have been also addressed.

Vibration and acoustic noise are considered as major factors preventing the widespread use of switched reluctance motor (SRM) drives in industrial and commercial applications. In this paper, a new active vibration cancellation strategy for radial vibration is proposed based on the optimal current switching instants. The mechanical impulse response method has been used to estimate the radial vibration of the stator and an optimization routine was developed to define the optimal instants for phase current switching in such a way that the resulting vibration would cancel the vibration caused by prior switching. Experimental verification was conducted on an 8/6 SRM and the results show significant reduction in the stator radial vibration which in turn contributes to reduction of acoustic noise.

Vibration and acoustic noise in switched reluctance motor (SRM) drives have been investigated and addressed frequently. However, the focus of most the paper is on vibration of the stator frame due to radial electromagnetic forces acting on the stator teeth. In this paper, a comprehensive vibration analysis for a SRM assembly using finite element (FE) method is conducted, which considers the stator and rotor vibrations due to radial electromagnetic force as well as the effect of mechanical coupling on the vibration between the stator and rotor. First, an experimental modal test was performed to extract modal parameters and to observe the coupling effect. The parameters were then used to adjust and validate the FE model that was used to obtain transfer functions for vibration characterization. Using the frequency domain representation of the radial magnetic force vector and the calculated transfer functions, radial and tangential vibration components have been predicted.

Resolver is a sensor used in applications that require a reliable angle measurement. However, getting the angular position from the resolver outputs is a difficult task. Observers called resolver-to-digital converters (RDCs) are used to get the angular position from resolver. In many researches, RDCs are estimation algorithms (software-based RDCs). The transient response and robustness against noise of the angle estimation depends on the parameters of the RDC. This paper proposes the use of a genetic algorithm (GA) to set the parameters of a type-III software-based RDC. GA is a heuristic algorithm that search a solution that minimize a fitness function. In this paper, the solution is composed by the gains of the angular tracking observer (ATO) that composes the RDC. On the other hand, the cost function is a linear combination of the error peak, the settling time and the effect of noise in the angle estimation. Simulations shows that the angle estimation using the proposed approach has accuracy, good transient response and robustness against noise. Besides, GA allows customizing the ATO in order for the RDC to give an estimation where the noise rejection or the settling time is priority.

This work presents the use of radial basis function artificial neural network to estimate the sensors measurements, exploring the analytical redundancy existent among different sensors in a process. However, in order to guarantee good performance of the network the training and optimization process was modified. In the conventional training algorithm, although the stop criteria, such as summed squared error, is reached, one or more of the individual performance metrics of the neural network may not be satisfactory. The performance metrics considered are Accuracy (training error), Sensitivity matrix (sensors propagated error to the estimations) and Filtering matrix (sensor propagated noise to the estimations). The paper describes the proposed method including all the mathematical foundation. A dataset of a petroleum refinery is used to train a RBF (Radial Basis Function) network using the conventional and the modified method and the performance of both will be evaluated. Furthermore, AAKR (Auto-Associative Kernel Regression) model is used to the same dataset. Finally, a comparison study of the developed models will be done for each of the performance metrics, as well as for the overall effectiveness in order to demonstrate the superiority of the proposed approach.

The necessity to create national to global-scale biodiversity monitoring systems as part of assessing progress toward biodiversity agendas presents a challenge for signatory countries. This is a brief review of ongoing Brazilian national initiatives that would allow the construction of a general biomonitoring network scheme in protected areas; with additional focus on linking independent monitoring schemes. We discuss some key aspects needed to include monitoring schemes under a single framework that will lead to better evaluation of pressure–state–response indicators for managing biodiversity at several scales; and we point out the potential of embracing citizen science and participatory monitoring to quantify some aspects within those schemes. © 2018 Associação Brasileira de Ciência Ecológica e Conservação

RESUMO – O numero especial Biota-MS esta constituido de 40 artigos sobre a biodiversidade da flora de Mato Grosso do Sul (elaborados por 112 pesquisadores de 39 instituicoes brasileiras e internacionais), em que sao listadas 3.911 especies, sendo discriminadas as especies ameacadas,endemicas ou so citadas para o estado. Apesar dos entraves a preparacao desta edicao, tais como a falta de especialistas em diversos taxons e a dificuldade em quantificar a diversidade de especies de Mato Grosso do Sul a partir do material coletado antes de 1977, quando Mato Grosso e Mato Grosso do Sul eram estado uno, os resultados obtidos constituem-se em um dos mais atualizados e sistematizados trabalhos de organizacao de dados biologicos no Brasil.

Frequency-domain multiplexing was proposed to reduce the number of signals to be digitized in vector control using a resolver as position sensor. Signals from resolver and current sensors can be multiplexed through analog adders. This approach has many advantages over using analog switches. On the other hand, the conventional demultiplexing system involves filtering and delay compensation, while the rotor angle is obtained after the signal demultiplexing. However, this conventional approach is difficult to implement because the required filters must have good stopband rejection, without adding a significant phase delay. This paper proposes an improved demultiplexing system whose structure is simpler than the conventional approach. A type-III angle tracking observer estimates the rotor angle and the resolver outputs directly from the multiplexed signals. The estimated resolver outputs and stopband filters are used to get the current signals. Thus, the demultiplexing of resolver signals and the delay compensators used in the conventional approach were eliminated. Experimental results prove that the proposed approach gives accurate estimations of the rotor angle and current signals. Besides, conditions for the resolver excitation frequency, the inverter switching frequency, and for current synchronous sampling were defined for the application of frequency-domain multiplexing.

Direct torque control (DTC) allows having a speed and torque control with good dynamic response. However, DTC uses a square root function to get the motor stator flux. This function is difficult to implement in microcontrollers or FPGAs. Hence, this paper proposes the use of SPLINE interpolation to implement the square root function with low computational cost, in order to get the magnitude of the stator flux. In SPLINE, the function is divided in intervals, and each interval is approximated using polynomial function. Besides, different discretization techniques to implement the integrator used in flux estimation will be tested in order to show their effects in flux and torque estimation. Simulations show that the proposed approach gives good estimations of torque and flux.

Simplification in the acquisition system allows increasing the robustness and the sampling frequency of a motor drive system. Frequency domain multiplexing (FDM) allows reducing the number of signals to be digitized in vector control, simplifying the analog-to-digital system in a motor drive. Nonetheless, the required demultiplexing process is still an open question. This paper proposes a simple demultiplexing technique based on synchronous current sampling. The triangular carrier used in modulation and the resolver excitation signal are synchronized, so that the valley of the carrier happens when the resolver outputs are equal to zero. Thus, the demultiplexed current signals are equal to the samples obtained through synchronous current sampling, eliminating the harmonics in the current signals. The estimated current signals and an angle tracking observer are used to estimate the angular position without the previous demultiplexing of the resolver output signals. Simulation and experimental results show that the demultiplexing process has high accuracy, rejecting the current harmonics and gives a good estimation of the angular position.

Assessing water quality in streams is usually measured at the local scale and often it is spatially restricted. To scale-up water-condition assessment, there is a need to use new tools that enable prediction of large-scale changes in water quality by expanding the analysis to landscape-levels and bioclimatic predictors. In addition, the traditional use of linear models in biomonitoring can be inappropriate in detecting complex relationships, such as changing patterns of aquatic community structure and complex environmental gradients. In this context, Artificial Intelligence (AI) techniques such as Rough Sets Theory (RST) can be particularly useful for dealing with vague, imprecise, inconsistent and uncertain knowledge involving biotic and abiotic data to enable the classification and prediction of changes in stream water. Here, we applied RST to estimate the water quality in streams of the Brazilian Atlantic Forest by analyzing connections between landscape and climate data with the inclusion of up to 15 families of aquatic insect groups from the orders Ephemeroptera, Plecoptera and Trichoptera (usually known as EPT taxa). First, we developed different decision sets which were the arrangements of the response variable (EPT index) and the predictor classifications. Then, we applied the best decision sets to monitor the condition of stream water in the Atlantic Forest on a large-scale. Our results showed the best decision rules were 61% accurate. Depending on the initial stream condition, this approach on a large-scale led to variable accuracy. By combining the development of different decision sets, the application of the best one on a large-scale, and the use of open-access data (landscape and climate predictors), our study approach demonstrated the potential applicability to evaluate streams in an objective with low-cost manner. This method can complement the environmental assessment of streams based only on local variables. Our framework also creates new perspectives in the analysis of water quality to generate scenarios of changes in streams based on landscape measurements to optimize monitoring networks.

The purpose of this study was to measure the temperature rise on the external root surface of filled root canals using Touch'n Heat (Analytic Endodontics, Orange, CA), the TC System (TC; Tanaka de CastroMinatel Ltda, Cascavel, PR, Brazil), and the Tagger technique.Forty-five single-canal mandibular premolar human teeth were used in the 3 experimental groups. The root canals were enlarged to accommodate up to an R40 Reciproc file (VDW, Munich, Germany). Next, the specimens were filled according to the technique evaluated. The measurement of the temperature was performed by K chromium-aluminum thermocouples attached to the coronal, middle, and apical root levels.There was a significant difference among the 3 techniques (P .001) in relation to the temperature variation between the highest temperature and the initial temperature. The highest temperature change was found with the Tagger technique at the middle third root level (11.8°C), and the lowest variation was in TC at the cervical third (2.05°C).There was a rise of temperature on the external root surface for all of the techniques evaluated. TC showed the lowest temperature rise.

This work presents the use of radial basis function artificial neural network to estimate the sensors readings, exploring the analytical redundancy via auto association. However, in order to guarantee good performance of the network the training and optimization process was modified. In the conventional training algorithm, although the stop criteria, such as summed squared error, is reached, one or more of the individual performance metrics, including: i) accuracy; ii) robustness; iii) spillover and iv) filtering matrix of the neural network may not be satisfactory. The paper describes the proposed algorithm including all the mathematical foundation. A dataset of a petroleum refinery is used to train a RBF network using the conventional and the modified algorithm and the performance of both will be evaluated. Furthermore, AAKR model is used to the same dataset. Finally, a comparison study of the developed models will be done for each of the performance metrics, as well as for the overall effectiveness in order to demonstrate the superiority of the proposed approach.

This paper proposes a simple nonlinear technique to control the angular position and d-axis stator current of a permanent magnet synchronous motor. Instead of making a linearization according to an operation point, the control laws, i.e., the dq-axis stator voltages are composed by two terms. The first one is to compensate the nonlinearities of the PMSM. The second component of the control law is determined by a linear controller to get the angular position. Thus, the design of the controller becomes easier. Simulation results prove the accuracy and robustness of the proposed angular position controller.

Resolver is an angular position sensor widely used in electric/hybrid vehicles and robotics. However, estimating the angular position and speed from resolver outputs is as difficult task. Most algorithms in literature use trigonometric functions to estimate the angular position. However, these functions are difficult to implement, especially in FPGAs. On the other hand, in speed vector control of three-phase motors, the angular position is only used to estimate the mechanical speed and Park transformation matrix. This paper presents a simple technique to get the mechanical speed and Park matrix without calculating trigonometric functions and without estimating directly the angular position. Filtering and synchronous demodulation allows obtaining the sine and cosine of the angular position, while the compensation of the delay produced by filtering and the estimation of the Park matrix are done using simple trigonometric properties and taking advantage that the number of pole pairs is an integer number. Experimental results show the accuracy of the proposed approach.

Este artigo sugere o conversor multinivel em ponte H em cascata como uma solucao para reduzir os efeitos do sombreamento parcial em sistemas fotovoltaicos conectados a rede eletrica. Baseia-se na premissa de individualizar o rastreamento do maximo ponto de potencia de cada grupo gerador fotovoltaico, que define o numero de niveis de inversao. Para distribuir a potencia disponivel em cada conjunto fotovoltaico, foi utilizada a modulacao por deslocamento de fase. A sincronizacao a rede eletrica e o rastreamento de energia ocorrem atraves de um controle droop robusto integrado a alguns controladores proporcionais e integrais. O uso de controle droop robusto elimina a necessidade de algoritmos convencionais de sincronizacao, simplificaa modulacao e reduz o numero de controladores a serem ajustados. Os resultados da simulacao confirmaram a estabilidade e o bom desempenho do controle devido as variacoes de irradiância. As limitacoes do sistema proposto sao apresentadas, bem como a discussao de possiveis solucoes sao abordadas.

This paper proposes a strategy to mitigate the effects of partial shading in grid-connected photovoltaic systems. In order to reach this goal, a cascaded H-bridge multilevel (CHB-ML) converter was selected and the energy balance was obtained based on phase-shifted pulse width modulation, which allows individual power tracking of each level. Many constraints have been noted as the research has been evolving. That's why an extensive literature review was done aiming to find solutions to select better ways to compensate shading effects in more severe situations. It was noticed that there is a relation between the photovoltaic strings voltages and the capability of energy transmission, since the output voltage of each level must be summed to obtain the appropriate grid voltage level. Simulation results were obtained through Simulink/MatLab. The results were collected considering three different scenarios of solar irradiance and the limits of operation were addressed in order to evaluate the restrictions of the proposed solution.

With the increase of distributed generation and storage in the electric grid, the need for isolated, bidirectional converters has risen in recent year. Many of these converters are designed to serve multiple sources and loads and contain monolithic transformers specifically designed for the application. This limits the flexibility of the systems, and typically a whole system must be replaced to add additional ports. The proposed concept allows the low-cost expansion of the multi-winding transformer to achieve a configurable, scalable transformer as part of an isolated multiport converter. Design theory and considerations are presented followed by experimental and analytical results.

Rainflow algorithms are one of the popular counting methods used in fatigue and failure analysis in conjunction with semiconductor lifetime estimation models. However, the rainflow algorithm used in power semiconductor reliability does not consider the time-dependent mean temperature calculation. The equivalent temperature calculation proposed by Nagode et al. is applied to semiconductor lifetime estimation in this paper. A month-long arc furnace load profile is used as a test profile to estimate temperatures in insulated-gate bipolar transistors (IGBTs) in a STATCOM for reactive compensation of load. The degradation in the life of the IGBT power device is predicted based on time-dependent temperature calculation.

Space Vector Pulse Width Modulation (SVPWM) is widely used in three-phase inverters. Besides, Field Programmable Gate Array (FPGA) is a powerful tool in the development of digital controllers applied to power electronics. However, the teaching of these topics for undergraduate and postgraduate students is a difficult task. For this reason, this paper presents a new educational platform based on the integration of a Graphical User Interface (GUI) and FPGA for the didactic teaching of SVPWM. The user defines the DC voltage source of the inverter, the magnitude and angle of the reference space vector in the GUI. As a result, the GUI shows the modulation index, working sector, duty cycle and the switching pulses in each phase. Simultaneously, the results of these calculations are sent to the FPGA through serial communication. Thus, the switching patterns are also generated in the FPGA in real time, which can be used for experimental tests. These experimental tests show that the pulses generated for each phase by the FPGA and the GUI are equivalent. Therefore, the proposed didactic platform helps in the teaching process and motivates the use and research of both SVPWM algorithms and FPGAs for undergraduate and postgraduate students.

Conventional drives for switched reluctance motors (SRMs) consists in supplying DC current pulses sequentially in each stator phase according to rotor position. This square current excitation produces a pulsating torque that limits the SRM applications. This paper aims to improve the torque profile of a 8/6 SRM applying an optimized current excitation obtained using the Field Reconstruction Method (FRM). First, the SRM is modeled in a Finite Element (FE) software as a FRM requirement and further the FRM model replaces the FE model in a interactive optimization routine. Simulation results shown that shaping the excitation current appropriately, the torque ripple can be reduced in approximately 80%.