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In past years, LLC resonant converters have become a mainstream topology for dc–dc power conversion due to their advantages, such as the superior efficiency obtained with the soft switching of MOSFETs. In order to further improve the efficiency of the converter, synchronous rectification (SR) can be implemented as an alternative for diode rectification. As a result, the vast majority of the literature related to this field of study presents different LLC SR control algorithms, which aim to improve the operation of the rectification. Unlike prior work on SR controllers, this paper contributes to the area of power loss estimation using rectifier current equations (RCE). The developed method based on time-domain analysis of SR currents provides a new analytical framework to characterize the behavior of SR. Implications in SR power losses of different time delays are investigated using the developed loss estimation method. In addition, different converter design parameters, such as choice of inductance ratio, can be analyzed. The RCE captures the true discontinuous and complex behavior of SR, which is often oversimplified by the conventional first-harmonic approximation (FHA). As a result, the proposed method facilitates the design of LLC resonant converters and provides increased precision in SR power loss estimation when compared to FHA, and in a considerably faster fashion when compared with precise yet computationally intensive simulation software. This paper is validated with simulation and experimental results. [ABSTRACT FROM AUTHOR]

Finite set model predictive torque control (FCSMPTC) of induction machines has received widespread attention in recent years due to its fast dynamic response, intuitive concept, and ability to handle nonlinear constraints. However, FCSMPTC essentially belongs to the open-loop control paradigm, and unmatched parameters inevitably cause electromagnetic torque tracking error. In addition, the outer loop (i.e., the speed loop) based on a proportional-integral (PI) regulator cannot achieve optimal control between speed dynamic response and torque tracking error compensation. The traditional control paradigm is abbreviated as PI-MPTC. In order to solve the aforementioned problem, this paper proposes active disturbance-rejection-based model predictive torque control (ADR-MPTC). First, the influence mechanism of mismatched parameters on torque prediction error in PI-MPTC is studied, and then the performance of a traditional PI regulator used to compensate for torque prediction error is analyzed. Second, this paper introduces several parts of the proposed ADR-MPTC, including the design of the torque prediction error observer, nonlinear prediction error compensation strategies, an enhanced predictive torque control, and a simplified full-order flux observer. Finally, PI-MPTC and ADR-MPTC are studied experimentally. The experimental results show that compared with PI-MPTC, ADR-MPTC performs better in dynamic and steady states, and has stronger robustness. [ABSTRACT FROM AUTHOR]

This paper deals with the high accurate current set-points solution for interior permanent-magnet synchronous motors (IPMSM) in wide-speed range applications. Considering voltage and current constraints, the operating regions can be divided into maximum torque per ampere, maximum current, field weakening, and maximum torque per voltage regions, which requires solving different nonlinear functions in real time to obtain optimal current set-points. Traditional methods including curve-fitting methods and polynomial approximation (PA) methods are not easy to obtain these solutions, especially involving magnetic saturation problems. In this paper, Newton–Raphson algorithm for improving the control accuracy of the current set-points is proposed. Meanwhile, parameters influence including magnetic saturation and resistive voltage drop is fully investigated. Compared with PA method, the proposed method is able to converge to accurate solutions in few numbers of iterations with reduced execution time, which can be easily implemented on an off-the-shelf digital signal processor. Both simulation results and experimental results on an 8-kW IPMSM rig are conducted showing good agreement with the expected results. [ABSTRACT FROM AUTHOR]

In order to suppress the torque ripple of an open-end winding permanent-magnet synchronous machine (OEW-PMSM), a q-axis current injection method is typically employed but the disturbances caused by the third flux linkage parameter mismatch can augment the torque ripple. To solve this problem, in this paper, first, a zero-sequence current (ZSC) and zero-sequence back-EMF observer (ZCBO) are proposed based on an adaptive sliding mode control, which is able to simultaneously estimate ZSC and zero-sequence back EMF under varying conditions. Based on this ZCBO, the estimated ZSC and zero-sequence back EMF could compensate for the one-step control delay and suppress the aforementioned disturbances. Second, although central hexagon modulation is employed in the predictive current control (PCC) scheme, zero-sequence voltage (ZSV) still exists in the zero-sequence path due to the dead time of the inverter, affecting the predictive accuracy of the ZCBO. To accurately establish the ZCBO, the ZSV caused by the dead time of the inverter is considered in the ZCBO. Finally, a comparative study of two types of methods is presented, and simulations established by MATLAB software and experimental confirmations are carried out to verify the effectiveness of the proposed PCC scheme in this paper. [ABSTRACT FROM AUTHOR]

This paper presents an optimization method for selective harmonic elimination in a cascaded multilevel inverter using teaching–learning-based optimization (TLBO). The main objective in selective harmonic elimination (SHE) strategy is to eliminate low-order harmonics by solving nonlinear equations and reaching optimal solution, while the fundamental component is satisfied. In this paper, in one side, the influence of optimal dc sources is investigated to reach the SHE goal, and in another side, comparing optimization methods is considered. In this paper, the TLBO as a recently emerged nature-inspired algorithm is presented to provide better results for the SHE in comparison with genetic algorithm, artificial bee colony, imperialistic competitive algorithm, harmony search, ant colony optimization, particle swarm optimization, and differential evolution. For better comparison of those methods and influence of optimal dc sources, 5-, 9-, and 15-level inverters are chosen and MATLAB software is used for optimization. Simulation results show the superiority of TLBO, higher precision and probability of convergence than other mentioned algorithms. Finally, to validate the influence of optimal dc sources and the accuracy of TLBO results, the experimental setup is conducted for a 5-level cascaded H-bridge inverter with optimal dc sources. [ABSTRACT FROM AUTHOR]

Thanks to the advantages of high power density and the capacity of bidirectional power transfer, the CLLC resonant converter is widely used in the hybrid ac–dc microgrid as a dc transformer to interlink the ac and dc bus. Since the voltages of ac and dc bus are controlled by the energy management system, the CLLC resonant converter operates under open-loop condition, which means the switching frequency and duty cycle are fixed. As a result, in the hybrid ac–dc microgrid applications, for the CLLC converter, the main concern is not the voltage regulation but the conversion efficiency. This paper focuses on the total power loss optimization and the magnetic design of the CLLC resonant converter based on artificial intelligence (AI) algorithm. In order to optimize the total power loss, an AI algorithm-based two-stage optimal design method is proposed. In the first stage, the total power loss, including the driving loss, turn-off loss, conduction loss of the switches, the power loss of the resonant capacitances, and copper and core loss of the transformer are optimized by the proposed AI algorithm, GA+PSO, and the optimal parameters, including the leakage inductances (Lr1 and Lr2), magnetizing inductance (Lm), and resonant capacitances (Cr1 and Cr2) are derived. In the second stage, the optimal leakage inductances and magnetizing inductance are realized by setting proper distance between the primary winding and the secondary winding (dw), and the thickness of the air gap (da). As for the magnetic design, in this paper, the leakage inductances of a planar transformer are used as the resonant inductances. The equations of dw and da to achieve the optimal leakage inductances and magnetizing inductance are derived. Both the proposed optimal design method and the equations of dw and da are validated by simulations and experiments. [ABSTRACT FROM AUTHOR]

Solid state transformer (SST) integrated hybrid dc–ac microgrid (HMG) has wide operating capabilities in presence of dc–ac loads, renewable energy resources, and energy storage. This paper proposes control strategies for localized control of zonal hybrid microgrid, enabled by the SST and centralized energy storage devices (ESD) integrated using dual active bridge (DAB) converter. The control of DAB dc–dc converter is modified for zero circulating power flow (ZCPF) operation in both SST and ESD. Power management and coordination control used in this paper ensures the power supply reliability of the zonal HMG integrated through the SST. Microgrid system, along with its proposed controllers, provides a stabilized low-voltage dc and ac network ports, in both grid feeding and forming modes. The performance of the configured HMG system is evaluated under various power management scenarios using the MATLAB software platform. Further, the SST architecture, with its proposed controller, is tested on FPGA-based hardware platform. [ABSTRACT FROM AUTHOR]

This paper proposes a bioinspired reference model based fuzzy adaptive tracking control for active suspension systems. A general bioinspired nonlinear structure, which can present ideal nonlinear quasi-zero stiffness for vibration isolation, is adopted as tracking reference model. Fuzzy logic systems are used to approximate unknown nonlinear terms in nonlinear suspension systems. Particularly, a nonlinear damping is designed to improve damping characteristics of the bioinspired reference model. With beneficial nonlinear stiffness and improved nonlinear damping of the bioinspired reference model, the proposed fuzzy adaptive controller can effectively suppress vibration of suspension systems with less actuator force and much improved ride comfort, thus energy saving performance can be achieved. Finally, a quarter-vehicle active suspension system with considering payload uncertainties, general disturbance, and actuator saturation is provided for evaluating the validity and superiority of the bioinspired nonlinear dynamics based fuzzy adaptive control approach proposed in this paper. [ABSTRACT FROM AUTHOR]

In this paper, a cost-effective and highly accurate resolver-to-digital conversion method is presented. The core of the idea is to apply a third-order rational fraction polynomial approximation for the conversion of sinusoidal signals into the pseudo linear signals, which are extended to the range 0°–360° in four quadrants. Then, the polynomial least squares method is used to achieve compensation to acquire the final angles. The presented method shows better performance in terms of accuracy and rapidity compared with the commercial available techniques in simulation results. This paper describes the implementation details of the proposed method and the way to incorporate it in digital signal processor-based permanent magnet synchronous motor drive system. Experimental tests under different conditions are carried out to verify the effectiveness for the proposed method. The obtained maximum error is about 0.0014° over 0°–360°, which can usually be ignored in most industrial applications. [ABSTRACT FROM AUTHOR]

In this paper, a new hybrid model is proposed for the prediction of air gap magnetic field distribution (MFD) in interior permanent magnet machines with any rotor configuration. The slotless magnetic field is first predicted by finite-element method (FEM) with automatic scripting in MATLAB to consider saturation in the rotor iron. Afterward, the conformal mapping viz., Schwarz–Christoffel mapping is introduced to take the slotting effect into account. Consequently, the MFD could be calculated. The back electromagnetic forces, cogging torque, and output torque are obtained accordingly. Then a subdomain model is developed to consider the armature reaction. The results show that the proposed hybrid approach agrees well with the FEM. The model is further verified by experiments. The main contribution of this paper is to reduce the computation time remarkably while maintaining the calculation accuracy. [ABSTRACT FROM AUTHOR]

The remaining useful life (RUL) of transformer insulation paper is largely determined by the winding hot-spot temperature (HST). Frequently the HST is not directly monitored and it is inferred from other measurements. However, measurement errors affect prediction models and if uncertain variables are not taken into account this can lead to incorrect maintenance decisions. Additionally, existing analytic models for HST calculation are not always accurate because they cannot generalize the properties of transformers operating in different contexts. In this context, this paper presents a novel transformer condition assessment approach integrating uncertainty modeling, data-driven forecasting models, and model-based experimental models to increase the prediction accuracy and handle uncertainty. The proposed approach quantifies the effect of measurement errors on transformer RUL predictions and confirms that temperature and load measurement errors affect the RUL estimation. Forecasting results show that the extreme gradient boosting (XGB) algorithm best captures the nonlinearities of the thermal model and improves the prediction accuracy among a number of forecasting approaches. Accordingly, the XGB model is integrated with experimental models in a particle filtering framework to improve thermal modeling and RUL prediction tasks. Models are tested and validated using a real dataset from a power transformer operating in a nuclear power plant. [ABSTRACT FROM AUTHOR]

Microgrids (MGs) are often unbalanced due to the integration of single-phase generators, unbalanced loads, and asymmetrical faults. To better analyze such MGs, this paper presents an approach to analyze the transient response for an inverter-based MG under the unbalanced condition. The dynamic phasor (DP) concept is used for the MG modeling under the stationaryabcreference frame. First, the DP model of the inverter-based distributed generation (DG) is developed. The influence of unbalanced conditions on the inverter including oscillations on the dc side is considered in this paper. Then, the model of the network and loads is developed. Finally, all the submodules are combined on a time-variable system frequency to obtain the complete DP model of the unbalanced MG. To validate the proposed approach, the DP method is applied to an MG test system with three-phase and single-phase DGs. Small-signal analysis is carried out to derive the dominant modes and their influence on the system response. Simulation results from the DP model are compared against the detailed model built in MATLAB/SimPowerSystem. The results from load disturbances and asymmetrical faults are used to verify the DP model. [ABSTRACT FROM AUTHOR]

This paper presents the accurate modeling of HVdc links for the dynamic studies of automatic generation control/load frequency control (AGC/LFC) of the multiarea interconnected power system. In earlier AGC studies, HVdc links were simply modeled by using the first-order transfer function with system time constant ${T_{{\rm{dc}}}}$. It was argued that ${T_{{\rm{dc}}}}$ is the time required by HVdc links to establish dc current after following a load disturbance in the power system. However, no mathematical or analytical justification was provided in support to the existing transfer function model of HVdc links. This paper presents the accurate modeling of HVdc links for AGC studies. The comparative analysis has also been performed to demonstrate error being accrued due to the use of the conventional model of HVdc links. Furthermore, this paper also implements the inertia emulation based control strategy in AGC, which allows us to harness the stored energy from the capacitance of HVdc links for AGC operations. [ABSTRACT FROM AUTHOR]

This paper proposes a novel causality analysis approach called the causal Markov Elman network (CMEN) to characterize the interdependence among heterogeneous time series in multinetwork systems. The CMEN performance, which comprises inputs filtered by Markov property, successfully characterizes various multivariate dependencies in an urban environment. This paper also proposes a novel hypothesis of characterizing joint information between interconnected systems such as electricity and transportation networks. The proposed methodology and the hypotheses are then validated by information theory distance-based metrics. For cross validation, the CMEN is applied to the electricity load forecasting problem using actual data from Tallahassee, Florida. [ABSTRACT FROM AUTHOR]

The rising cost of production testing for a system-on-a-chip (SoC) is one of the crucial matters to chip makers, due to long test time and costly automated-test-equipment. This paper proposes a spectral leakage-driven built-in self-test (BIST) scheme to precisely predict the nonlinearity of mixed-signal circuits in the loopback mode, thereby accomplishing cost-effectiveness (compared to previous BIST-based works). A digitally synthesized single-tone sinusoidal stimulus used for conventional harmonic testing is incoherently sampled by a device under test (DUT). The DUT output signal exhibits the correlation between the DUT harmonics and the spectral leakage introduced by the incoherent sampling. The DUT output signal is then fed to another DUT through a loopback path, so that the harmonics of a pair of DUTs are correlated with the spectral leakage on the loopback response; the magnitude of the spectral leakage is considered as a weighting factor on the harmonic magnitude of those DUTs. The correlation is quantitatively modeled as characteristic equations in , and postprocessing predicts the harmonics of the two individual DUTs, by simultaneously solving the characteristic equations using on-chip DSP core available in an SoC. Simulation and hardware measurements validated that this paper can be practically used for production testing by showing less than 0.3 and 0.6 dB of the prediction errors, respectively. [ABSTRACT FROM AUTHOR]

An interacting multiple model (IMM) filter is a recognized method for adaptive estimation of states that is often necessary to characterize the behavior of dynamic systems with a multiple-mode operation. The traditional IMM filter adopts the measurement set to update the information about the active models. However, when this approach is used for fault-prognostic applications, it can lead to misleading state estimation. To overcome this problem, this paper proposes a novel fault-prognostic technique based on a modified IMM filter with fuzzy systems. The main methodological contribution of this paper is to build an approach based on an IMM filter able to make long-term fault predictions without measurements. This is done by incorporating a fuzzy system into the proposed IMM filter with the objective of modeling the system's dynamics and updating the probabilities of the observed modes. Furthermore, the proposed method and the standard IMM filter are compared in a numerical example and a real experimental platform PRONOSTIA for validation. The results analysis indicates a better prediction performance than the conventional IMM filter considering the failure time predictions and a measurement model not properly adjusted. [ABSTRACT FROM AUTHOR]

In this paper, the quadruple active bridge (QAB) dc–dc converter is proposed to be used as a building block to implement the dc–dc stage of a smart transformer (ST). Different configurations (symmetrical, asymmetrical, and rated for voltage/power) for this converter are considered for investigation. Four different architectures of ST, including one based on the dual active bridge converter as a benchmark and three based on the QAB converter, are presented and compared in terms of cost, efficiency, reliability, and implementation complexity. As an additional contribution, different semiconductor technologies (silicon IGBT and silicon-carbide MOSFETs) are evaluated in order to verify their impact on ST application. The design for each architecture is described and the results are compared. In order to validate the theoretical analysis developed in this paper, a 20-kW prototype is built and experimented. [ABSTRACT FROM AUTHOR]

The paper deals with some new results on the averaging theory approach for the analysis of feedback controlled pulsewidth modulation power electronic converters. It develops some considerations regarding the averaging approximation of time-variant differential systems proving an analytical relationship between the accuracy with which the solution of the averaged model converges to the solution of the original time-variant model (i.e., the switched model) and the switching frequency of the converters. This relation is valid both for stable and unstable systems. Moreover, this paper confirms, as corollary, that under suitable conditions (verified during the standard operations of the power electronic converters) the averaged model uniformly converges to the switched model. [ABSTRACT FROM AUTHOR]

This paper investigates the use of large-scale wayside energy storage to reduce operational costs of ac electric traction power networks. This paper also introduces a method of estimating the battery capacity, based on manufacturer's charging and discharging specifications. The battery model is simple and easy to incorporate into load flow simulation algorithms. The concept and the battery model are validated through a feasibility study within Aurizon's heavy haul electric railway network. [ABSTRACT FROM AUTHOR]

This paper describes a control algorithm for a battery energy storage system (BESS) to deliver a charge/discharge power output in response to changes in the grid frequency constrained by the National Grid Electricity Transmission (NGET)—the primary electricity transmission network operator in the U.K.—while managing the state of charge of the BESS to optimize the availability of the system. Furthermore, this paper investigates using the BESS in order to maximize triad avoidance benefit revenues while layering other services. Simulation using a 2 MW/1 MWh lithium–titanate BESS validated model is carried out to explore possible scenarios using the proposed algorithms. Finally, experimental results of the 2 MW/1 MWh Willenhall Energy Storage System verify the performance of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Inclusion of power electronics allows increased controllability and stability in power systems. The simulation of such systems on a large-scale is challenging due to the presence of a large number of switches and nonlinear devices. This paper presents an advanced simulation algorithm to solve the aforementioned problem. The algorithm considers separation of differential algebraic equations (DAEs) on the basis of numerical stiffness and applies hybrid discretization algorithms to simulate the DAEs. The DAEs, in this paper, represent the nonlinear nonautonomous switched system dynamics of power systems. Stability analysis is performed on a general class of nonlinear nonautonomous switched systems to show the constraints under which the proposed algorithm is stable. To show the validity of the proposed algorithm, two case studies are considered: 1) single high-voltage direct current (HVdc) substation based on the modular multilevel converter (MMC); and 2) an example three-terminal MMC-HVdc system. Relaxation techniques are introduced to create a stable interface for the separated DAEs. The developed algorithms are also validated with PSCAD/EMTDC-detailed reference models. [ABSTRACT FROM AUTHOR]

This paper presents a novel two-dimensional real-time modeling approach for a proton exchange membrane fuel cell (PEMFC) based on a tridiagonal matrix algorithm (Thomas algorithm). The Thomas algorithm consists of a forward elimination and a backward substitution, its arithmetic complexity of computations being much lower than the Gaussian elimination. In order to use this advanced numerical solver, the differential equations of reactant gas convection and diffusion phenomena in serpentine channels are transformed into a tridiagonal equations system. In addition, a three-level bisection algorithm has been developed to solve spatial physical quantities distribution for electrochemical domain. The real-time computing methods developed in this paper are then implemented in C language for a fast execution time in a real-time processor. The proposed real-time model is experimentally validated using a 1.2 kW Ballard NEXA fuel cell system, and its practical feasibilities in advanced real-time control for PEMFC systems have been experimentally demonstrated in an RT-LAB real-time simulator. [ABSTRACT FROM PUBLISHER]

In this paper, the model predictive current control is introduced in the semicontrolled open-winding permanent magnet synchronous motor (OW-PMSM) generation system with a common dc bus to suppress the zero-sequence current for the first time. Particularly, the basic voltage vectors of the semicontrolled OW-PMSM, which are synthesized by the three-phase full-controlled converter and the three-phase uncontrollable diode bridge, is analyzed and their influences on the zero-sequence current are investigated for the first time in this paper. Then the current-error-based cost function that includes dq-axis current error and zero-sequence current error is designed to evaluate all the voltage vectors. Moreover, to further improve the steady-state control performance of the whole generation system, a zero-sequence current suppression method based on the uncontrollable side voltage vector adjustment, which has the advantage of low computation burden, is developed. In this method, two voltage vectors of the controllable converter are applied during one control period. Based on the distribution character of the basic voltage vector in the semicontrollable OW-PMSM generation system, the first voltage vector is selected according to the three-phase current direction to adjust equivalent vector of the uncontrollable diode bridge, whereas for the second voltage vector, the selection method based deadbeat voltage vector sector is designed to reduce the range of the candidate voltage vector. In addition, the vector durations are also computed. Finally, simulation and experiment are carried out to validate the proposed control approaches. [ABSTRACT FROM PUBLISHER]

The steady-state performance of modular multilevel converter (MMC) can be optimized by the following methods: circulating current injection, third-harmonic signal injection, and average capacitor voltage control. However, most of the papers, due to the deficiencies of their MMC models, are limited to the study of adopting just one method and only optimizing a single-aspect of the performance. Thus, this article proposes a general MMC model for the comprehensive study of the steady-state performance optimization. The general model is built based on an equilibrium equation between the internal quantities and the external quantities of the MMC. This general model is capable of analyzing the optimization where the optimizing methods are combined and used to improve the multiaspect performance of the MMC. In addition, more influence factors are taken into account; hence, the calculation error can be smaller than 1% and even the minor effect of each optimizing method can be reflected. The proposed model is compared with traditional models; and it is found that both dc and fundamental components in modulation signals will be affected by the injected circulating current, rather than the constant values in the traditional models. Finally, the proposed MMC model is verified by both simulation and experiment. [ABSTRACT FROM AUTHOR]

This paper presents the implementation of a novel vibration amplitude control and resonant frequency tracking for piezoelectric transducers (PTs) and ultrasonic motors (USMs). It is based on a generalization of the vector control method to the PT and the USM, which is explained in the first part. We show that two independent controllers with a similar structure are required: one tracks the resonant frequency and the second controls the amplitude. We then present the implementation into a low-cost digital signal processing controller with a sampling period of 200  $\mu$s. Experimental results on a Langevin transducer achieved a time response of 20 ms approximately, and the generality of the method is further demonstrated on a 2-D tactile stimulator at the end of this paper. [ABSTRACT FROM PUBLISHER]

In order to effectively suppress surplus torque, realizing high precision loading, this paper applies proportional resonant (PR) control to the load torque control of electric load simulator (ELS), and analyzes and optimizes the stability and dynamic performance of ELS. The mathematical model of ELS is built in z-domain first in this paper, to establish foundation for designing and analyzing PR controller. The stability of the ELS with PR controller is analyzed through Nyquist diagrams, and optimized by reasonably designing the phase compensation angle of PR controller. The dynamic performance is analyzed through pole–zero plots, and the resonant gain of PR controller is designed based on the pole–zero plots to optimize the dynamic performance. Experimental results on ELS are provided, and the proposed method is compared with other typical methods, validating the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

In this paper, new half-bridge and full-bridge topologies for a switched-boost inverter are proposed. These topologies have a high boost factor in comparison with conventional types. In addition, the proposed topologies have continuous input current and can generate zero-voltage level in output. In this paper, the operation of the proposed inverters in different operating modes is analyzed. Then, the values of inductors and capacitors are designed. Moreover, the used switching pattern and its logical diagram are introduced. A comprehensive comparison between the proposed and conventional topologies in terms of the boost factor, the number of elements, and the efficiency shows the good features of the proposed inverters. Finally, to verify correct operation of the proposed topologies and derived equations, simulation and experimental results are presented. [ABSTRACT FROM AUTHOR]

The nine-switch converter is a multiport converter having two three-phase terminals and a dc-link, just like a 12-switch back-to-back (BTB) converter, but with 25% reduction of active switch count. However, the reduction from 12-switch to 9-switch may not always be an efficient choice considering losses in the switches. Only the load–source combination for the ac–ac common frequency mode and the source–source combination for the ac–dc different frequency (ac–dc DF) mode have been reported, so far, to yield relatively lower loss for the nine-switch converter. This paper shows that the nine-switch converter can have relatively lower loss even with a load–source combination, instead of only source–source combination in its ac–dc DF mode—when the upper terminal is connected to a dc load and the lower terminal is connected to an ac source. Mathematical proof is presented with derivation of the particular operating parameters for which the nine-switch converter will have comparatively lower losses. The analysis is validated with simulation and experimental results of a 1-kW system. Finally, the benchmark of the application criteria of the nine-switch converter for having lower conduction loss than the BTB converter is updated in this paper. [ABSTRACT FROM AUTHOR]

Precise positioning of a robot plays an very important role in advanced industrial applications, and this paper presents a new kinematic calibration method based on the extended Kalman filter (EKF) and particle filter (PF) algorithm that can significantly improves the positioning accuracy of the robot. Kinematic and its error models of a robot are established, and its kinematic parameters are identified by using the EKF algorithm first. But the EKF algorithm has a kind of linear truncation error and it is useful for the Gauss noise system in general, so its identified accuracy will be affected for the highly nonlinear robot kinematic system with a non-Gauss noise system. The PF algorithm can solve this with non-Gauss noise and a high nonlinear problem well, but its calibration accuracy and efficiency are affected by the prior distribution of the initial values. Therefore, this paper proposes to use the calibration value of the EKF algorithm as the prior value of the PF algorithm, and then, the PF algorithm is used further to calibrate the kinematic parameters of the robot. Enough experiments have been carried out, and the experimental results validated the viability of the proposed method with the robot positioning accuracy improved significantly. [ABSTRACT FROM AUTHOR]

The aim of this paper is to study the dynamics of a permanent magnet direct current (PMDC) brushed motor while two crucial space-domain effects, namely, slotting effect and commutation phenomenon are taken into account. Semi-analytical dynamic time-domain PMDC brushed motor model inclusive of these space-domain effects is used to analyze and investigate the dynamics. Pulse width modulation operated discontinuous current PMDC brushed motor drive with steady duty cycle is employed to acquire the bifurcation patterns. The chaotic nature due to slotting effect and commutation is identified from the bifurcation patterns and reported. It also has to be admired that such nonlinear dynamical behavior with the same set of parameters at similar operating condition has not been observed when a conventional PMDC brushed motor dynamic model is considered. These space-domain effects, namely, slotting effect and commutation also have a prominent impact to make some time-domain states of a PMDC brushed motor drive chaotic. The role of slotting effect and commutation phenomenon to analyze the chaos over time-domain state variables of the PMDC brushed motor drive is investigated and validated experimentally in this paper. [ABSTRACT FROM PUBLISHER]

This paper applies finite control set model predictive control (FCS-MPC) for dynamic reactive power compensation using a hybrid active power filter (HAPF). The FCS-MPC uses a model based on LCL-filter equations to predict the system behavior and optimize the control action. In fact, the application of FCS-MPC in grid-connected converters with LCL-Filter is quite recent. This algorithm is a very promising control technique for power electronics converters and its use for reactive power control of hybrid filter has not been reported in the literature yet. This paper uses the FCS-MPC in a multivariable structure along with an adaptive notch filter to damp resonance. The main purpose is to improve the dynamic response of the HAPF. Simulation as well as practical results prove the feasibility of FCS-MPC application in HAPF reactive power control. The dynamic response of the equipment was significantly improved and represents the main contribution of this paper. As a result, the FCS-MPC allows tracking fluctuations and abrupt changes in load reactive power. [ABSTRACT FROM PUBLISHER]

An important bottleneck in the design, operation, and exploitation of mechatronic powertrains is the lack of accurate knowledge of broadband external loading. This is caused by the intrusive nature of regular torque measurements. This paper proposes a novel nonintrusive approach to obtain torsional load information on mechatronic powertrains. Online coupled state/input estimation is performed through an augmented nonlinear Kalman filter. This estimation approach exploits general lumped-parameter physics-based models in order to create a widely applicable framework. This paper considers both extended (EKF) and unscented Kalman filtering approaches. Contrary to previous works, no considerable difference in accuracy is obtained from experiments, with a considerably lower computational load for the EKF. This paper reveals the benefits of including rotational acceleration measurements from a theoretical perspective, which is demonstrated through the experimental validation. This drastically increases the broadband accuracy. The result of this paper is an accurate and noninvasive virtual torque sensor with a sufficiently broad bandwidth for use in condition monitoring, control, and future design optimization. [ABSTRACT FROM PUBLISHER]

This paper addresses the problem of load identification in nonintrusive monitoring application when using load signatures in transient signals. The study presents a systematic approach to design a set of wavelet filters to be used as matching patterns for load identification in nonintrusive monitoring. The effect of different higher order wavelet filters and the signal length on the classification accuracy are investigated. The concepts of wavelet clustering and wavelet-signal matching are introduced and are developed in this paper. Machine learning classifiers are used to automate the load classification process using co-testing. The introduced concept is evaluated on a real test system and the results of the experimental work have shown that 1.5 cycle may suffice to achieve a 97.25% classification accuracy. Moreover, the results have shown that the proposed approach is effective in detecting loads with more electronic components and not previously categorized in the assembly. [ABSTRACT FROM PUBLISHER]

This paper presents a three-dimensional (3-D) performance analysis and multiobjective design optimization of coreless-type permanent magnet linear synchronous motors. The average of open circuit magnetic field distribution is analytically predicted by solving two Laplace's equations. The winding factor calculation, which requires an approach different from conventional slotted motors, is provided to evaluate the motor performances. As a result, average thrust and thrust ripple can be obtained without loss of accuracy compared with results of 2-D finite element analysis (FEA). The 3-D leakage influences are subsequently incorporated into the analysis using lumped parameter magnetic circuit model because end effects are not neglected due to a relatively large air gap. Consequently, two meaningful contributions of this paper are summarized as follows: first, 3-D electromagnetic performances can be accurately predicted by the proposed analytical method. Second, with no need for the time-consuming 3-D FEA, differential evolution, a practical approach to global optimization, can simply be employed to search for solutions which belong to the Pareto optimal set. Finally, the validity of the analytical results is confirmed with the experimental results. [ABSTRACT FROM PUBLISHER]

The general trend toward more intelligent energy-aware ac drives is driving the development of new motor topologies and advanced model-based control techniques. Among the candidates, pure reluctance and anisotropic permanent magnet motors are gaining popularity, despite their complex structure. The availability of accurate mathematical models that describe these motors is essential to the design of any model-based advanced control. This paper focuses on the relations between currents and flux linkages, which are obtained through innovative radial basis function neural networks. These special drive-oriented neural networks take as inputs the motor voltages and currents, returning as output the motor flux linkages, inclusive of any nonlinearity and cross-coupling effect. The theoretical foundations of the radial basis function networks, the design hints, and a commented series of experimental results on a real laboratory prototype are included in this paper. The simple structure of the neural network fits for implementation on standard drives. The online training and tracking will be the next steps in field programmable gate array based control systems. [ABSTRACT FROM PUBLISHER]

The paper develops a technique for arbitrary power sharing among three-phase winding sets of a multiphase generator. Multiple d − q modeling is commonly used when independent control of the winding sets is required. This paper utilizes instead the vector space decomposition modeling as the starting point and combines it with a multiple d − q approach to preserve the advantages of the vector space decomposition, while still enabling independent control over each winding set. The power sharing is achieved by imposing appropriate x − y currents at the fundamental frequency so that the flux and average torque are not affected. The theory is developed initially for the nine-phase machine. A general expression for arbitrary current sharing is derived further for any multiphase machine with multiple three-phase windings. The obtained equations are valid for any possible machine topology (asymmetrical/symmetrical, with single or multiple neutral points). The theory is validated experimentally using an asymmetrical nine-phase induction generator with indirect rotor field oriented control. [ABSTRACT FROM PUBLISHER]

The reduced chip size and unipolar current conduction mechanism make silicon carbide (SiC) metal–oxide–semiconductor field-effect transistors (MOSFETs) suitable for high-frequency power electronics applications. Modeling the switching process of the SiC power MOSFET with parasitic components is important for achieving higher efficiency and power density system design. Therefore, this paper proposes a new concise yet accurate switching loss model for SiC power MOSFETs. Addressing the limitations in experimental measurements, numerical simulations are conducted to validate the proposed model taking the output capacitance Coss discharge and charge into consideration. The role of the parasitic components in the second-order model is discussed in depth for switching losses. Furthermore, this paper also provides guidelines in designing the gate driver for ultrafast SiC power MOSFETs. [ABSTRACT FROM PUBLISHER]

The series-resonant converter (SRC) has been used in several application and it recently became popular for smart transformers (STs). In this application, the efficiency and reliability are of paramount importance. Although many papers have addressed the design challenges to improve the converter efficiency, discussions about the reliability are still missing in the literature. In this context, this paper presents a design procedure focusing on the efficiency and reliability improvement of the SRC for ST application. High efficiency is achieved through the use of silicon-carbide MOSFETs, reducing conduction and switching losses, and the detail design procedure based on accurate losses modeling. High reliability is achieved through a fault-tolerant topology and reliability-oriented design of the resonant circuit passive components. Experimental results obtained for the optimized 10 kW SRC has shown an efficiency of 98.61%. [ABSTRACT FROM PUBLISHER]

Data-driven techniques such as principal component analysis (PCA) have been widely used to derive predictive models from historical data and applied for quality prediction in industry. Motivated by reducing data collinearity and extracting informative driving forces behind data, latent variable models are explored to facilitate the prediction by regressing data on a set of extracted features. In this paper, a novel learning strategy is proposed to build dynamic features under a full Bayesian framework, incorporating data information and prior knowledge of process dynamics. Unlike the traditional PCA that extracts features based on variances explained, in this paper, the latent features are extracted with the guidance of preferred velocities of nominal variations. By applying Bayesian learning algorithms, parameters are estimated with probability distributions accounting for corresponding uncertainties, and the number of latent features can be automatically determined by the variational Bayesian inference algorithm. The effectiveness and practicability of this Bayesian dynamic feature regression are demonstrated through simulated examples as well as an industrial case study. [ABSTRACT FROM PUBLISHER]

This paper presents the modeling and characterization of the tower shadow effects using a wind turbine emulator in a laboratory environment. In particular, the downwind wind turbines are considered here as their tower shadow effects are more significant compared to the upwind counterpart. Simulation and experimental results have shown that the wind speed deficit due to this nonideal effect is significant. In addition, the tower shadow effects occur typically two to three times per revolution, depending on the number of blades. The modeling of the tower shadow profiles for tubular and four-leg tower configurations is presented. Typically, these towers are used in small wind turbine applications. The tower shadow profiles are emulated experimentally using a wind turbine emulator with its characteristics being explained. The limitations of emulating the tower shadow effects using a wind turbine emulator are demonstrated through the frequency response test performed in this study. In this study, the wind turbine emulator is connected to an isolated grid which is formed by three single-phase inverters. Finally, this paper concludes with a sensitivity analysis of the power oscillations for different widths and magnitudes of the tower shadow profile. [ABSTRACT FROM PUBLISHER]

For the purpose of improving the performances of T-type neutral point clamped (T-NPC) photovoltaic (PV) grid-connected inverter under large grid, a passivity-based decoupling control scheme is proposed in this paper. First, the Euler–Lagrange (EL) mathematical model of a T-NPC PV grid-connected inverter with an LC filter is built based on the power circuit and operating principles. Second, with this EL mathematical model and passivity of the inverter, the passivity-based controller is designed by the damping injecting method. Third, by adopting a proportional integral regulator to directly control direct current (dc) voltage, together with passivity-based indirect control, better performances of the dc voltage control can be further obtained. With the proposed passivity-based controller, the PV grid-connected inverter can achieve both dynamic current decoupling under the synchronous rotating dq coordinate and dc side voltage dynamic decoupling, which can improve the quality of the current connected into the power grid. The passivity-based PV grid-connected inverter is with strong robustness against parameters perturbations of resistors and attenuating the influence of delay in the inverter. Moreover, the passivity-based decoupling control scheme proposed in this paper for the T-NPC PV grid-connected inverter has a simple structure, and is easily debugged and realized in engineering. Experimental results under 10-kW prototype show that the proposed control scheme is effective. [ABSTRACT FROM PUBLISHER]

Considering the limitation of conventional rheometry measurements and the relevance of the study of food viscosity in the study of dysphagia, we commenced developing a soft-bodied esophageal swallowing robot (ESR). Using experimental data, this paper aims to discover the differential equations (DEs) of the ESR that governs the peristaltic deformation in the esophageal conduit at the given time-varying pressure. The deformation data of the conduit are collected from a quarter version of the ESR due to the inaccessibility to the esophageal occlusion. The three-dimensional displacements of the markers placed on the interior surface of the conduit are measured using a Vicon optical motion capturing system. The dimension of the dataset is first reduced using the principal orthogonal decomposition (POD) method, which is then used to discover the ESR's DEs by the sparse identification of nonlinear dynamics (SINDy). The marker displacements are considered as the ESR's states. The ESR's states are reduced from 27 to 2 by initially applying power spectral density (PSD), and then the POD. The identified states essential to the DEs capture 95 $ \%$ of the total variance of the deformation dataset. Finally, the conduit deformation simulated from the DEs are validated by the experimental measurements in the full version of the ESR. [ABSTRACT FROM AUTHOR]

Benefiting from modeling human behavior and the multipath propagation of wireless signals in the environment, passive behavior-sensing systems can effectively identify individual behaviors by model-based or pattern-based methods. The existing methods use an individual sample to model or profile behavior feature, which leads to their failure in multiperson scenarios due to time-varying environments. In this paper, an online method on modeling the position-behavior feature of multiperson scene is presented. First, a position-based model of individual behavioral feature transformation is proposed. The behavior features in other positions are generated by the behavior of the individual in a particular position. Second, a multiperson behavior feature generation method based on noise reduction is proposed to generate the same behavior feature in an online pattern. Finally, taking advantage of the models, a multiperson fitness coaching system is designed and implemented, named multiuser fitness coach. The system can identify the irregular behavior of individuals in the multiperson environment. The performance of the system is evaluated in different scenarios, and the results show that the precision of feature generation can be effectively applied to the decision of irregular behavior in multiperson scenarios. [ABSTRACT FROM AUTHOR]

To effectively find an optimal solution in the development process of electrical machines, it is essential to predict machine iron loss and thermal behavior in the predesign stage. Most existing publications only consider thermal behavior by roughly limiting the maximum current density. However, especially for high-speed application, iron loss can also be an essential part in thermal-behavior estimation. This paper proposes a simplified model to efficiently calculate iron loss and thermal behavior in the design procedure of switched reluctance machines. For this purpose, a maximum coenergy loop control method is implemented to facilitate the estimation of flux waveform and machine torque. Three different iron loss calculation methods are compared in terms of accuracy and time. Besides, a simplified lumped parameter thermal network is applied for machine sizing. Furthermore, the impact of number of turns and peak magnetomotive force on the losses and machine sizing is discussed. The efficiency and power density of various machine geometries are comparatively analyzed. Finally, the accuracy of the simplified model is verified by comparison with finite element simulations and experiment results. [ABSTRACT FROM AUTHOR]

A distributed adaptive controller is proposed in this paper for a class of underactuated Lagrangian systems to control the actuated variables to track a dynamic leader and keep unactuated ones bounded under a directed communication graph. A finite-time observer is introduced to estimate the leader's velocity. Based on two sliding variables defined for the actuated and unactuated channels, adaptive controllers are designed for the underactuated Lagrangian systems subject to uncertain parameters and external disturbances without or with actuator faults. The convergences of the proposed controllers are proven based on the separation principle between the observer and the controller. Finally, simulations and experiments are conducted to verify the effectiveness of the proposed controllers. [ABSTRACT FROM AUTHOR]

Batch-end quality modeling is used to predict the quality by using batch measurements and generally involves a large number of predictor variables. However, not all of the variables are beneficial for the prediction. Conventional multiway partial least squares (PLS) may not function properly for batch-end quality modeling because of many irrelevant predictor variables. This paper proposes an optimized sparse PLS (OSPLS) modeling approach for simultaneous batch-end quality prediction and relevant-variable selection. The effect of irrelevant variables on the quality-prediction performance is analyzed, and the importance of the relevant-variable selection is emphasized. Then, an OSPLS batch-end quality modeling approach is developed by incorporating the variable resolution optimization and sparse PLS modeling. The quality-prediction accuracy and modeling interpretability are improved because only quality-relevant variables are selected, and quality-irrelevant variables are eliminated. Based on the selected quality-relevant variables, a statistic is established for monitoring the quality status. The proposed OSPLS-based modeling and monitoring approach is applied on a fed-batch penicillin fermentation process and an industrial injection molding process. The results are compared with the state-of-the-art methods to verify the effectiveness of the OSPLS approach. [ABSTRACT FROM AUTHOR]

Contouring error has a significant effect on machining accuracy. In this paper, a model predictive contouring error precompensation method is presented that performs model-based error precompensation with feedback and constraints. The proposed method uses the predicted contouring error without compensation obtained by simulation, as the reference trajectory of the model predictive compensator. It then calculates the compensation signal using a constrained model predictive control algorithm and adds it to the original reference position as the new system input. Experiments were conducted on an X–Y table to compare the proposed method with a traditional cascaded proportional-integral (PI) controller and two existing contouring error precompensation methods. The experimental results show that higher contouring accuracy can be achieved with the proposed method for both sharp corners and free-form curves. [ABSTRACT FROM AUTHOR]

This paper presents a hybrid battery parameterization technique for the purpose of battery state-of-charge (SOC) and state-of-power (SOP) monitoring in real time. The proposed technique is centered around an opportunistic initialization of a dual extended Kalman filter (DEKF) algorithm using pseudorandom binary sequence (PRBS) battery excitation. A second-order electrical equivalent-circuit battery model is used whose parameters are identified using a carefully designed 10-bit 10-Hz PRBS signal while the battery is in a zero- or low-current quiescent mode. The PRBS-identified resistive elements of the battery model are then utilized to provide an initial estimate for the battery's SOP. Once in load conditions, the DEKF algorithm is implemented recursively to provide an accurate estimate of the battery's parameters, SOC, and subsequently its SOP in real time. The experimental results obtained from an electrochemical impedance spectroscopy method give confidence to the performance of the proposed hybrid battery parameterization technique. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive neural network backstepping control design for an experimental helicopter with three rotational degrees of freedom. The helicopter system is described by a state-space model with unmodeled dynamics and external disturbances. By resorting to radial basis function neural networks with online gradient descent training, such unmodeled dynamics and external disturbances can be compensated in real time. With the help of Lyapunov stability criteria, it is proved that the desired trajectories can be boundedly tracked by the corresponding attitude angles of the helicopter under the proposed adaptive neural network backstepping controller. Compared with robust adaptive integral backstepping technique, our approach has smaller root mean squared error. Finally, four sets of contrastive experiment results are, respectively, given to show the effectiveness and advantages of the proposed control scheme. [ABSTRACT FROM AUTHOR]

This paper presents a linear-extended-state-observer-based repetitive control (RC) method to enhance the disturbance rejection performance and tracking performance for a nonlinear system with aperiodic uncertainties and disturbances. Both design scenarios, i.e., with and without a detailed mathematical model of the plant, are considered, respectively. The proposed method exhibits the following three attractive features. First, instead of tuning the observer gain via the bandwidth idea by trial and error, available information on the plant model is used to design the observer gain and the state-feedback RC gains. As a result, the disturbance rejection ability is elevated. Second, robust stability and learning efficiency are adjusted preferentially through two tuning parameters, which effectively improve both the transient and steady-state performance. Third, the observer gain and state-feedback RC gains are simultaneously optimized through an optimization algorithm, in which an index is introduced to evaluate the overall system performance. The design procedure of the system is presented in detail. Finally, an application to tracking control of a chuck-workpiece system demonstrates the validity of the proposed method. Comparisons with the conventional RC method, the standard extended-state-observer-based RC method, and a linear active-disturbance-rejection-control-based RC method show that the proposed method can achieve the best disturbance rejection performance. [ABSTRACT FROM AUTHOR]