Showing papers in "IEEE Transactions on Energy Conversion in 2021"

Composite Super-twisting Sliding Mode Control Design for PMSM Speed Regulation Problem Based on a Novel Disturbance Observer

Abstract: This paper aims to improve the performance of the permanent magnet synchronous motor (PMSM) speed regulation system by combining a novel disturbance observer (DOB) with the super-twisting sliding mode (STSM) technique. First, a STSM controller is constructed to eliminate the adverse effects of the lumped disturbance in the PMSM speed regulation system. A novel DOB is introduced to estimate and compensate the lumped unknown disturbance, which constitutes a composite controller with a feedforward compensation term and a state-feedback control. As a result, the gain of the composite sliding mode controller can be significantly reduced, which will improve the performance of the closed-loop PMSM speed regulation system. The validity and robustness of the proposed composite control scheme are fully verified by simulation and experimental results.

Reduced-Order Aggregate Model for Large-Scale Converters With Inhomogeneous Initial Conditions in DC Microgrids

Abstract: In practical microgrids, the inhomogeneous initial values are widely appeared due to soft-starting operation. If traditional model order reduction approaches are applied, the input-output maps error between the original system and reduced-order system is large. To address this problem, this paper proposes a reduced-order aggregate model based on balanced truncation approach to provide the preprocessing approach for the real-time simulation of large-scale converters with inhomogeneous initial conditions in DC microgrid. Firstly, the standard linear time-invariant model with inhomogeneous initial conditions is established through non-leader multiagents concept. To end this, it is convenient for scholars to build complex system modeling with switched topology. Furthermore, the full system is divided into two components, i.e., the unforced component with nontrivial initial conditions and forced component with null initial conditions. Moreover, this paper presents an aggregated approach that involves independent reducing component responses and combining reducing component responses. Based on this, the input-output maps error is reduced. Then, the approximated error estimate of the reduced-order aggregate model regarding large-scale converters in DC microgrid is first provided, which provides prior knowledge and theoretical basis for DC microgrid designers. Finally, the simulation results illustrate the accuracy of the proposed approach.

Wind Turbine Gearbox Anomaly Detection Based on Adaptive Threshold and Twin Support Vector Machines

Abstract: Data-driven condition monitoring reduces downtime of wind turbines and increases reliability. Wind turbine operation and maintenance (O\&M) cost is a significant factor that calls for automated fault detection systems in wind turbines. In this manuscript, the anomaly detection problem for wind turbine gearbox is formulated based on adaptive threshold and twin support vector machine (TWSVM). In this work, SCADA data from wind farms located in the UK is considered with samples from thirteen months before failure. Gearbox oil and bearing temperatures are used as two univariate time-series for analyzing adaptive threshold. The effectiveness of the proposed method is compared with standard classifiers like support vector machines (SVM), k-nearest neighbors (KNN), multi-layer perceptron neural network (MLPNN), and decision tree (DT). Anomaly detection of wind turbine gearbox using TWSVM and adaptive threshold results in an accurate performance, thus increasing the reliability. The missed failure and false positive rate that indicate the proposed methodology's ability is also investigated to discriminate between false alarms, and comparison with previous studies shows superior performance.

Large-Signal Stability Improvement of DC-DC Converters in DC Microgrid

Abstract: In DC microgrids, constant power loads (CPLs) reduce the effective damping of the DC-DC converter and may induce destabilizing effects into the DC-DC converter. To overcome such problems regarding CPL and ensure large-signal stability of DC-DC converters in DC microgrids, some feedforward terms are added to $V$ - $I$ droop-based dual-loop controller for a DC-DC converter based on the large-signal model. It is proven that the feedforward terms can not only improve the transient response but also guarantee the exponential stability of the closed-loop system in the whole operating range in regards to a large-signal manner, which is verified by using a singular perturbation model. Moreover, a disturbance observer is designed to estimate the output current, thereby enabling the removal of the current measurement sensor. The proposed technique can be easily plugged into a pre-defined $V$ - $I$ droop-based dual-loop controller without an additional sensor being required. Ultimately, both simulation and experimental tests verify the effectiveness of the proposed method.

System-Level Robust Design Optimization of a Switched Reluctance Motor Drive System Considering Multiple Driving Cycles

Abstract: In this article, a novel system-level robust design optimization method is presented to improve the performance of switched reluctance motor (SRM) drive systems under multiple operating conditions. Based on typical driving cycles of electric vehicles (EVs), five typical driving modes of the SRM are determined. The optimization objectives in each driving mode are established. The significant parameters of the motor and controller of each driving mode are selected as the optimization variables by using the sensitivity analysis. In order to simplify the optimization process, correlation analysis is performed to determine the coherence of the objective functions of all driving modes. Then, a sequential Taguchi method is applied to find an optimal design which is less sensitive to the noise factors. To verify the effectiveness of the proposed method, an SRM drive system applied in EVs with a 12/10 SRM and angle position control method is investigated. It is found that the proposed method can significantly reduce the torque ripple and improve the comprehensive performance. Finally, a 12/10 SRM is prototyped and tested to validate the simulation results.

Non-Isolated n -Stage High Step-up DC-DC Converter for Low Voltage DC Source Integration

Abstract: The dc-dc converters with high step-up DC-voltage gain play a vital role in integrating low voltage DC sources. Though several converter topologies are reported in the recent past, attempts have been made to reduce the components, especially the switching devices, passive elements, converter losses, etc., of the converter. A novel DC-DC converter topology, viz., single switched impedance network (SSIN)-based converter with n -stage is proposed in this paper. The operation of the SSIN based converter in continuous and discontinuous conduction modes are discussed. The effect of parasitic elements on DC-voltage gain and efficiency of the SSIN is carried out. The small-signal model of the SSIN converter is derived. The performance of the SSIN converter is compared with the similar converter topologies. The 500 W prototypes of the SSIN with n = 1 and n = 2 are fabricated, and the experimental results are presented in this paper. To improve the converter performance, SiC-based semiconductor devices are used. The SSIN converter with two-stage has low switch loss at 500 W load, improved efficiency during high power and high output voltage operation, the switch stress of Vo /2, etc.

Adaptive Predefined Performance Sliding Mode Control of Motor Driving Systems With Disturbances

Abstract: In this paper, a predefined time sliding mode control with prescribed performance is presented for dual-inertia driving systems with unknown disturbances. A modified prescribed performance function (PPF) without requiring the accurate initial error is designed to guarantee that the tracking error remains within the prescribed boundary. An adaptive law is then constructed to estimate the unknown upper boundary parameters of the lumped dynamics (e.g., parameter uncertainties and external disturbances), so that the prior knowledge of the upper bound of uncertainties is not required. The parameter estimation is incorporated into the control design to eliminate the effects of the unknown dynamics. Using the sliding mode technique, an adaptive predefined time sliding mode control is developed. The proposed control method can achieve fast convergence rate of the tracking error, and the stability of the closed-loop system is proved via the Lyapunov function method. Comparative experiments are carried out based on a dual-inertia driving system to validate the efficacy of the proposed approach.

Multimode Optimization of Switched Reluctance Machines in Hybrid Electric Vehicles

Abstract: The belt-driven starter/generator (BSG), as a cost-effective solution, has been widely employed in hybrid electric vehicles (HEVs) to improve the stability and reduce the fuel consumption of the vehicles. It can provide more than 10% reduction in CO2. Electrical machine is the heart of the BSG system, which is functioned both as motor and generator. In order to optimize both aspects of motor and generator simultaneously, this paper presents a new multimode optimization method for the switched reluctance machines. First, the general multimode concept and optimization method are presented. The switched reluctance motor and the switched reluctance generator are the two operation modes. The optimization models are established based on motor and generator functions. Sensitivity analysis, surrogate models and genetic algorithms are employed to improve the efficiency of the multimode optimization. Then, a design example of a segmented-rotor switched reluctance machine (SSRM) is investigated. Seven design variables and four driving modes are considered in the multiobjective optimization model. The Kriging model is employed to approximate the finite element model (FEM) in the optimization. Finally, the optimization results are depicted, and an optimal solution is selected. The comparison between the initial and optimal designs shows that the proposed method can improve the foremost performance of the SSRM under all driving modes.

Coordinated Control of Networked AC/DC Microgrids With Adaptive Virtual Inertia and Governor-Gain for Stability Enhancement

Abstract: This article proposes an adaptive coordinated control strategy for the networked AC/DC microgrids (MGs) to enhance the frequency and dc voltage stability of the system while keeping proper power sharing. First, a control strategy based on the synchronverter and virtual dc machine (VDCM) for the converters connecting the AC and DC MGs is proposed, which is consisted of an adaptive virtual governor and an adaptive virtual inertia regulator besides the power sharing controller. Following, in order to enhance the system stability performance, the parameter design approach of the adaptive virtual inertia and virtual governor-gain is proposed accordingly, in which the adaptive virtual inertia and virtual governor-gain are comprehensively determined by the frequency and/or dc voltage, virtual rotor speed, and rate of change of the frequency (ROCOF) and/or dc voltage (ROCOV). After that, the small-signal stability analysis of the networked AC/DC MGs with the proposed control strategy is investigated to guide the design and selection of control parameters. Finally, simulation and experimental results demonstrate that the proposed method improves the frequency/dc voltage nadir and dynamic performance of the system.

Modeling and Analysis of Inner Controls Effects on Damping and Synchronizing Torque Components in VSG-Controlled Converter

Abstract: In virtual synchronous generator (VSG)-related discussions, the inner voltage and current controls are always omitted due to their fast dynamics. However, converters require a lower switching frequency as their power rating increases, which constrains the cascaded controls’ bandwidths. Hence, the interactions between the controls at different time-scales should be analyzed. Specifically, the aim is to reveal the effects of inner control loops on the VSG system from the torque perspective. First, this paper derives a linearized model of the internal voltage phase and amplitude, which accurately depicts the VSG system characteristics. Subsequently, based on this linearized model, we derive an equivalent single-input single-output motion-equation- based model. The proposed model can be used to investigate the change in damping and synchronizing torque components under low control bandwidths and weak grid conditions. Finally, simulation results are presented to validate the proposed modeling and effects analysis. Our model is general and can be used for any power ratings, and the study also provides a guide to designing control parameters for VSG systems.

Transient Stability Enhancement Control Strategy Based on Improved PLL for Grid Connected VSC during Severe Grid Fault

Abstract: In this article, the transient synchronization process of the grid-connected voltage source converters (VSC) is studied detailly. Firstly, the phase-locked loop (PLL)-synchronized VSC is modeled according to the rotor motion equation of synchronous generator (SG). Furthermore, the VSC's damping ratio is derived, and the effects of the VSC's control parameters and the operation status on the equivalent power angle (EPA) is investigated. Moreover, the influence of the PLL's parameters and the voltage dips degree on the VSC's transient frequency behavior are analyzed. Analysis result reveals that the VSC's damping ratio decreases once the grid voltage drops, so that the EPA and frequency of the VSC may appear large overshoot and oscillation, which may trigger the frequency protection and deteriorate the VSC's transient stability. In order to ensure that the VSC can smoothly operate to new equilibrium points, an improved PLL is proposed, which can adaptively adjust the VSC's damping ratio for different voltage sags. The proposed method can not only reduce the overshoot of the EPA but also restrain the VSC's frequency dips degree. Finally, the simulations and experimental tests validate the effectiveness of the theoretical analysis.

A Review of Predictive Control Techniques for Switched Reluctance Machine Drives. Part I: Fundamentals and Current Control

Abstract: This two-part paper presents a review of the predictive control techniques applied in switched reluctance machine (SRM) drives. The objective is to promote the applications of predictive control-based strategies in these machines, given its potential to develop high-performance operation and make SRM more suitable for practical scenarios. Part I of this survey presents all fundamental concepts of SRM drives, predictive control and the adopted classification, and a literature review of predictive current control (PCC) strategies. The control techniques are analyzed according to their modelling approach, switching behaviour and calculation of optimal input. A performance comparison is also presented, and the current challenges, improvement opportunities and future trends of PCC in SRM are discussed.

Multiobjective and Multiphysics Design Optimization of a Switched Reluctance Motor for Electric Vehicle Applications

Abstract: Switched reluctance motors (SRMs) have attracted much attention in industry due to the advantages of low cost, robust structure, high fault tolerance and high torque density. However, several disadvantages like high torque ripples and coil temperatures hinder their industrialization for some applications requiring high dynamic performance, like electric vehicles (EVs). In this paper, a multiobjective and multiphysics design optimization method considering both thermal and electromagnetic performance is presented for a 12/10 SRM. First, the topology of the SRM is introduced and the optimal parameters are defined. Then, the electromagnetic finite element model (FEM) is introduced and the improved transient lumped-parameter thermal model (TLPTM), considering both axial and radial heat transfer for the SRM, is proposed. Second, the objectives and constraints of the optimization are determined. To improve the optimization efficiency, the sequential subspace optimization strategy is employed to find the optimal solution of this high-dimensional design optimization problem. Finally, to validate the effectiveness of the proposed method, both simulation and experimental results are given and discussed. Compared with the initial design, the optimal solution exhibits lower temperature, higher torque, lower torque ripple and less loss.

Robust Design Optimization of Electrical Machines: Multi-Objective Approach

Abstract: This article presents a new method for multi-objective robust design optimization of electrical machines and provides a detailed comparison with so far introduced techniques First, two robust design approaches, worst-case design and design for six-sigma, are compared with the conventional deterministic approach for multi-objective optimization Through a case study on a permanent magnet motor, it is found that the reliabilities of motors produced based on robust designs are 100% under the investigated constraints, while the reliabilities of deterministic designs can be lower than 30% A major disadvantage of robust optimization is the huge computation cost, especially for high-dimensional problems To attempt this problem, a new multi-objective sequential optimization method (MSOM) with an orthogonal design technique and hypervolume indicator (as a measure of convergence) is proposed for both deterministic and robust design optimization of electrical machines Through another case study, it is found that the new MSOM can improve motor performance and greatly reduce the computational cost For the robust optimization, the number of required finite element simulations can be reduced by more than 40%, compared with that required by the conventional approach The proposed method can be applied to many-objective (robust) design optimization of electrical machines

Experimental Validation of a Hybrid Storage Framework to Cope With Fluctuating Power of Hybrid Renewable Energy-Based Systems

Abstract: The technology of supercapacitor is vastly examined by researchers to cope with a lower power density of battery. It is validated that a hybrid battery-supercapacitor storage (HBSS) framework can improve the overall efficiency due to taking advantage of battery's higher energy density and supercapacitor's higher power density. Despite the extensive investigations on this hybrid storage scheme in the electric vehicle (EV) industry, its experimental implementation in the power grid to cope with non-dispatchable nature of wind and solar energy is still in its initial stages. As a result, this paper concentrates on analysis of its experimental implementation on a lab-scale hybrid microgrid located at Southern Illinois University, Carbondale, IL, USA containing two wind turbines and two photovoltaic (PV) modules. The objective of this work is to promote the utilization of the experimentally validated lab-scale HBSS in power industry.

Torque Ripple Reduction Method for Permanent Magnet Synchronous Machine Drives With Novel Harmonic Current Control

Abstract: Torque ripple reduction has become an active research area for permanent magnet synchronous machine (PMSM) drives . This paper presents a novel torque ripple reduction method based on harmonic current control. In the proposed method, the optimal harmonic current solution based on the torque ripple model with minimum stator resistive loss is derived in the rotor reference frame (RRF). The optimal harmonic currents are injected by the proposed harmonic current controller (HCC), wherein the magnitude of harmonic currents are defined as controller variables. To estimate the harmonic currents, the proposed method also utilizes the least mean square (LMS) based adaptive filter (AF), wherein the coefficients are defined as feedback to the proposed HCC. The proposed methodology is demonstrated using both simulations and experiments and is verified to reduce torque ripples of PMSM drives over a wide range of speeds.

Variable-Frequency Retuned WPT System for Power Transfer and Efficiency Improvement in Dynamic EV Charging With Fixed Voltage Characteristic

Abstract: A high efficiency and a constant voltage at the output stage are among the major concerns in wireless charging of dynamic electric vehicles. They are usually achieved by additional converters or asymmetric switching which suffer from high cost and hard switching. To overcome these drawbacks, a variable-frequency retuning approach is proposed in this paper with LCL-Series compensation. As opposed to conventional control methods with resonance frequency as target operating frequency, this approach uses a new optimum frequency which enables ZVS. Also, it minimizes the conduction loss of the inverter switches in a wide range of load and coupling coefficients. In addition, it improves the total system efficiency up to 6% together with an increase in the power transfer capability. Using the proposed method, both sides’ currents increases up to 45% depending on the load. To find the optimum frequency in a system without resonant parameter information, an on-line maximum efficiency search method based on Perturb and Observe is employed. Extensive simulation results using PLECS, together with experimental results from a laboratory setup are presented to confirm the effectiveness of the proposed method under varying load and coupling.

Model Predictive Control of PMSM Drives Based on General Discrete Space Vector Modulation

Abstract: Apart from the basic voltage vectors of a inverter, virtual vectors can be generated by discrete space vector modulation (DSVM) technique. Thus, DSVM based-model predictive control (MPC) can achieve reduced torque ripples and current harmonics for a permanent magnet synchronous machine (PMSM) drive. However, the computational burden is also significantly increased due to a large number of candidate voltage vectors. In this article, a novel DSVM-based MPC is proposed, which can select the optimal voltage vector and calculate three-phase duty ratios in an efficient way without enumerating all the candidate voltage vectors, hence achieving good performance with low computational burden. The effectiveness of the proposed method is validated by the presented experimental results.

Data-Driven Permanent Magnet Temperature Estimation in Synchronous Motors With Supervised Machine Learning: A Benchmark

Abstract: Monitoring the magnet temperature in permanent magnet synchronous motors (PMSMs) for automotive applications is a challenging task for several decades now, as signal injection or sensor-based methods still prove unfeasible in a commercial context Overheating results in severe motor deterioration and is thus of high concern for the machine's control strategy and its design Lack of precise temperature estimations leads to lesser device utilization and higher material cost In this work, several machine learning (ML) models are empirically evaluated on their estimation accuracy for the task of predicting latent high-dynamic magnet temperature profiles, specifically, ordinary least squares, support vector regression, $k$ -nearest neighbors, randomized trees, and neural networks Having test bench data available, it is shown that ML approaches relying merely on collected data meet the estimation performance of classical thermal models built on thermodynamic theory Through benchmarking, this work reveals the potential of simpler ML models in terms of regression accuracy, model size, and their data demand in comparison to parameter-heavy deep neural networks, which were investigated in the literature before Especially linear regression and simple feed-forward neural networks with optimized hyperparameters mark strong predictive quality at low to moderate model sizes

Electrochemical Model-Based Fast Charging: Physical Constraint-Triggered PI Control

Abstract: This paper proposes a new fast charging strategy for lithium-ion batteries. The approach relies on an experimentally validated high-fidelity model describing battery electrochemical and thermal dynamics that determine the fast charging capability. Such a high-dimensional nonlinear dynamic model can be intractable to compute in real-time if it is fused with the extended Kalman filter or the unscented Kalman filter that is commonly used in the community of battery management. To significantly save computational efforts and achieve rapid convergence, the ensemble transform Kalman filter (ETKF) is selected and tailored to estimate distributed battery states. Then, a health- and safety-aware charging protocol is proposed based on successively applied proportional-integral (PI) control actions. The controller regulates charging rates using online battery state information and the imposed constraints, in which each PI control action automatically comes into play when its corresponding constraint is triggered. The proposed physical constraint-triggered PI charging control strategy with the ETKF is evaluated and compared with several prevalent alternatives. It shows that the derived controller can achieve close to the optimal solution in terms of charging time and trajectory, as determined by a nonlinear model predictive controller, but at a drastically reduced computational cost.

Convolutional Neural Network-Based False Battery Data Detection and Classification for Battery Energy Storage Systems

Abstract: Battery energy storage systems (BESSs) rely on battery sensor data and communication. It is crucial to evaluate the trustworthiness of battery sensor and communication data in (BESS) since inaccurate battery data caused by sensor faults, communication failures, and even cyber-attacks can not only impose serious damages to BESSs, but also threaten the overall reliability of BESS-based applications (e.g., electric vehicles (EVs), power grids). This paper proposes a battery data trust framework that enables detect and classify false battery sensor data and communication data by using a deep learning algorithm. The proposed convolutional neural network (CNN)-based false battery data detection and classification (FBD2C) model could potentially improve safety and reliability of the BESSs. The proposed algorithm is validated by simulation and experimental results.

Hybrid Model Predictive Control of DC–DC Boost Converters With Constant Power Load

Abstract: This article presents a hybrid model predictive controller to ensure dc microgrid stability and enhance the performance of dc-dc boost converters interfaced with constant power loads (CPLs) in a hybrid system. Hybrid systems are dynamic systems with both continuous current mode and discontinuous current mode states. The main purpose in this article is to develop an advanced control technique for voltage regulation and stabilization of the converters in the presence of CPLs due to serious stability concerns, without considering the accurate modelling information of the system. In this regard, an automatic model, considering different modes of operation induced by semiconductor switches in dc-dc boost converters and highly non-linear nature of CPL is employed to design the proposed control approach. The non-linear CPL connected directly to a dc-dc boost converter is utilized to define an optimal tracking control problem by minimizing a finite-prediction horizon cost function, which is known as a finite control set MPC. The proposed controller, which is implemented in both continuous and discontinuous current modes, accounts for the regulation of output voltage within the predefined range. The effectiveness of the proposed hybrid model predictive control is verified using a comparative evaluation with discrete-time averaged model predictive control, continuous control set MPC, and the conventional PI control under experimental conditions. The results authenticate an improved dynamic performance, which can be applied to practical dc microgrids with CPLs.

Neural Network Based Model Predictive Controllers for Modular Multilevel Converters

Abstract: Modular multilevel converter (MMC) has attracted much attention for years due to its good performance in harmonics reduction and efficiency improvement. Model predictive control (MPC) based controllers are widely adopted for MMC because the control design is straightforward and different control objectives can be simply implemented in a cost function. However, the computational burden of MPC imposes limitations in the control implementation of MMC because of many possible switching states. To solve this, we design machine learning (ML) based controllers for MMC based on the data collection from the MPC algorithm. The ML models are trained to emulate the MPC controllers which can effectively reduce the computation burden of real-time control since the trained models are built with simple math functions that are not correlated with the complexity of the MPC algorithm. The ML method applied in this study is a neural network (NN) and there are two types of establishing ML controllers: NN regression and NN pattern recognition. Both are trained using the sampled data and tested in a real-time MMC system. A comparison of experimental results shows that NN regression has a much better control performance and lower computation burden than the NN pattern recognition.

A New Transformer-Less Common Grounded Three-Level Grid-Tied Inverter With Voltage Boosting Capability

Abstract: A new transformer-less three-level grid-tied inverter is proposed in this article. By employing the switched-capacitor (SC) modules, a new topology is derived. The proposed topology has some features which are high efficiency, the capability of voltage boosting without utilization of any additional boost stage, and suppressing the leakage current by using common grounded strategy. In this article, a peak current control is employed to generate the gate pulses of power semiconductor switches and adjustment of both active and reactive powers. By doing so, the grid injected current is controlled and adjusted to have a proper quality. The operation modes, design consideration, and a comparison with some recently proposed grid-connected topologies are done through this paper. Finally, the performance and feasibility of the proposed inverter are confirmed with a 500 W experimental prototype.

An MPC Based Algorithm for a Multipurpose Grid Integrated Solar PV System With Enhanced Power Quality and PCC Voltage Assist

Abstract: The continuously fluctuating energy output and varying power demands in the renewable energy systems have led to the degradation of power quality. This work presents a model predictive based control for a solar PV system integrated to the grid for optimal management and control of the power transfer. The double stage three-phase configuration is controlled using model predictive control (MPC) strategy, which considers the power converters' switching states to predict the next control variable. The control uses a modified-dual second-order generalized-integrator for estimation of the power requirements based on the continuously varying system parameters. The PCC voltages assist and the ride through operation are performed based on the drops in voltage levels and optimum switching state is selected based on the minimization of the cost function to deliver the required active and reactive powers to the grid. The performance of the controller is validated through simulation and is also shown using hardware implementation. The IEEE-519 standard is followed throughout and a comparative analysis shows the remarkable performance of the presented grid controller.

Active Disturbance Rejection Control Based Single Current Feedback Resonance Damping Strategy for LCL-Type Grid-Connected Inverter

Abstract: The LCL-type grid-connected inverter (GCI) is widely adopted between distributed generation (DG) and power grid to realize DC/AC power conversion However, the underdamped LCL filter will cause a resonance phenomenon near the control stability boundary, which may lead to instability of the GCI system The traditional passive damping method will cause power loss, while the active damping method may have the need for additional sensors or be sensitive to parameter changes In this article, a control scheme based on active disturbance rejection control (ADRC) is proposed for the LCL-type GCI, and the systematic parameter design method is presented through root locus analysis With the proposed strategy, only the grid-injected current is sensed to achieve the objectives of grid-injected current direct control and robust resonance damping for the LCL-type GCI In the design process, the influence of digital control delay and grid impedance uncertainty on system stability is explicitly analyzed in detail Through this, the GCI can work stably over a wide range of the typical inductive-resistive grid impedance In addition, it is found that the proposed strategy also has good adaptability to the filter parameter perturbation Simulation and experimental results validate the theoretical analysis in this paper

Model Predictive Torque Control for Dual Three-Phase PMSMs with Simplified Deadbeat Solution and Discrete Space-Vector Modulation

Abstract: Unprecise voltage vectors applied in conventional model predictive control (MPC) would cause additional ripples in electromagnetic torque. To eliminate the problem, this article proposes an improved model predictive torque control (MPTC) based on deadbeat solution and discrete space-vector modulation (DSVM) for dual three-phase permanent magnet synchronous machines (PMSMs). First, deadbeat-direct torque and flux control (DB-DTFC) algorithm is applied and simplified, so that the computational burden can be reduced. Second, the virtual voltage vectors (VVVs) are adopted in dual three-phase voltage vector space to reduce voltage harmonics. Also, the DSVM is further proposed in this VVVs based vector space to generate more voltage vectors. After that, the simplified DB-DTFC and the DSVM scheme are artfully combined to select suitable voltage vector candidates for the MPTC, and the best one is then chosen to control the dual three-phase PMSMs. Finally, both simulation and experimental results are given to verify the effectiveness of the proposed method.

Rotor Stress Analysis of High-Speed Permanent Magnet Machines With Segmented Magnets Retained by Carbon-Fibre Sleeve

Abstract: In this article, the rotor stress is analysed for a high-speed permanent magnet (PM) machine (HSPMM) with segmented magnets retained by a carbon-fibre sleeve. The influence of rotor PM segmentation is firstly considered in the stress analysis. It is found that when the segmented magnets are under tensile stress, significant sleeve stress concentration will occur due to magnet edging effect. By contrast, the PMs will benefit from the segmentation with reduced tangential stress. This stress reduction effect is enhanced when the number of PM segments is larger. Furthermore, the influence of PM segmentation on the worst operating scenario is determined. In order to avoid stress concentration due to PM segmentation, the design guidelines are then given by investigating the influence of sleeve thickness and interference fit. A new design scheme of sleeve thickness is proposed based on the identified worst case scenarios. Finally, a 6-slot 4-pole high speed PM machine with segmented magnets retained by carbon-fibre sleeve is prototyped and tested at the speed of 90 krpm. The rotor stability indirectly indicts the validity of the theoretical analysis and design scheme.

A Review of Predictive Control Techniques for Switched Reluctance Machine Drives. Part II: Torque Control, Assessment and Challenges

Abstract: This two-part survey presents the recent advances and developments of predictive control in switched reluctance machine drives. The aim is to highlight the potential of using predictive control to enhance the performance of these drives and make them more competitive with respect to conventional AC drives. The second part of this review presents the contributions in predictive torque control. The different approaches adopted in the literature regarding phase torque distribution, modelling and switching behaviour are discussed and analyzed in order to identify the current challenges and suggest future research. Further applications of predictive control are also described and a general conclusion on future applications and potential research based on the literature review are directed.

Robust Design Optimization of Switched Reluctance Motor Drive Systems Based on System-Level Sequential Taguchi Method

Abstract: In this paper, a new system-level sequential Taguchi method (SLSTM) is proposed to achieve the optimal solution with high robustness for switched reluctance motor (SRM) drive systems. An SRM drive system consisting of a segmented-rotor SRM and the angle position controller is investigated as a case study. In the implementation, the optimization function contains torque, loss, and torque ripple. The control factors of the system are selected according to the sensitivity analysis results. Manufacturing tolerances are considered to guarantee that the optimal solution features low sensitiveness to uncertainties. The process of defining the design levels of all the control factors and noise factor is illustrated and the orthogonal array is established. The optimization of the SLSTM is carried out sequentially until the certain convergence condition is satisfied. Finally, the component-level sequential Taguchi method (CLSTM) is carried out for comparison. It appears that the proposed SLSTM is efficient in searching the robust optimal solution for the SRM drive system. Besides, it can achieve better output performance, such as higher average torque and lower torque ripple, and a higher level of robustness compared with the initial design and CLSTM.