Showing papers in "IEEE transactions on industrial electronics in 2023"

Embedded Distributed Temperature Sensing Enabled Multistate Joint Observation of Smart Lithium-Ion Battery

Abstract: Accurate monitoring of the internal statuses is highly valuable for the management of the lithium-ion battery (LIB). This article proposes a thermal-model-based method for multistate joint observation, enabled by a novel smart battery design with an embedded and distributed temperature sensor. In particular, a novel smart battery is designed by implanting the distributed fiber optical sensor internally and externally. This promises a real-time distributed measurement of LIB internal and surface temperature with a high space resolution. Following this endeavor, a low-order joint observer is proposed to coestimate the thermal parameters, heat generation rate, state of charge, and maximum capacity. Experimental results disclose that the smart battery has space-resolved self-monitoring capability with high reproducibility. With the new sensing data, the heat generation rate, state of charge, and maximum capacity of LIB can be observed precisely in real time. The proposed method validates to outperform the commonly-used electrical-model-based method regarding the accuracy and the robustness to battery aging.

Multiscale Deep Feature Learning for Human Activity Recognition Using Wearable Sensors

Abstract: Deep convolutional neural networks (CNNs) achieve state-of-the-art performance in wearable human activity recognition (HAR), which has become a new research trend in ubiquitous computing scenario. Increasing network depth or width can further improve accuracy. However, in order to obtain the optimal HAR performance on mobile platform, it has to consider a reasonable tradeoff between recognition accuracy and resource consumption. Improving the performance of CNNs without increasing memory and computational burden is more beneficial for HAR. In this article, we first propose a new CNN that uses hierarchical-split (HS) idea for a large variety of HAR tasks, which is able to enhance multiscale feature representation ability via capturing a wider range of receptive fields of human activities within one feature layer. Experiments conducted on benchmarks demonstrate that the proposed HS module is an impressive alternative to baseline models with similar model complexity, and can achieve higher recognition performance (e.g., 97.28%, 93.75%, 99.02%, and 79.02% classification accuracies) on UCI-HAR, PAMAP2, WISDM, and UNIMIB-SHAR. Extensive ablation studies are performed to evaluate the effect of the variations of receptive fields on classification performance. Finally, we demonstrate that multiscale receptive fields can help to learn more discriminative features (achieving 94.10% SOTA accuracy) in weakly labeled HAR dataset.

Feature Mode Decomposition: New Decomposition Theory for Rotating Machinery Fault Diagnosis

Abstract: In this article, a new decomposition theory, feature mode decomposition (FMD), is tailored for the feature extraction of machinery fault. The proposed FMD is essentially for the purpose of decomposing the different modes by the designed adaptive finite-impulse response (FIR) filters. Benefitting from the superiority of correlated Kurtosis, FMD takes the impulsiveness and periodicity of fault signal into consideration simultaneously. First, a designed FIR filter bank by Hanning window initialization is used to provide a direction for the decomposition. The period estimation and updating process are then used to lock the fault information. Finally, the redundant and mixing modes are removed in the process of mode selection. The superiority of the FMD is demonstrated to adaptively and accurately decompose the fault mode as well as robust to other interferences and noise using simulated and experimental data collected from bearing single and compound fault. Moreover, it has been demonstrated that FMD has superiority in feature extraction of machinery fault compared with the most popular variational mode decomposition.

Bias-Correction Errors-in-Variables Hammerstein Model Identification

Abstract: In this paper, a bias-correction least-squares (LS) algorithm is proposed for identifying block- oriented errors-in-variables nonlinear Hammerstein (EIV- Hammerstein) systems. Because both the input and output of the EIV-Hammerstein system are observed with additive white noises, the estimation bias of traditional LS algorithm is introduced. The estimation bias is derived from a consistency point of view, which is a function about noise variances and monomial of noiseless system input–output measurements. A bias-estimation scheme based only on the available noisy measurements is then proposed for consistent identification of the monomial of noiseless system input–output measurements in a recursive form. In particular, a specific algorithm based on minimizing the output prediction error is given to find out the unknown noise variances for practical applications, such that the noise effect can be eliminated and the consistent estimated parameters are obtained. The effectiveness of the proposed method is demonstrated by a simulation example and an experimental prototype of wireless power transfer system.

Impedance Characteristic Analysis and Stability Improvement Method for DFIG System Within PLL Bandwidth Based on Different Reference Frames

Abstract: The phase-locked loop (PLL) will have a negative impact on the stability of doubly fed induction generators (DFIG) system under inductive weak grid. Some PLL-less methods have been presented to remove the PLL such as switching the synchronous reference frame to the virtual reference frame or αβ -frame. Based on the equivalent single-input single-output impedance model, it is found that the DFIG systems implemented in the virtual reference frame or αβ -frame are more stable than synchronous reference frame. The reason is that the stator voltage will disturb the reference current calculation of the virtual reference frame and αβ -frame, then introduce a reference calculation matrix G _cal . This matrix will keep the phase of DFIG system always −90° around fundamental frequency, then narrow the negative resistance band and enhance the phase margin (PM). In order to further illustrate the stability improvement caused by G _cal , this article proposes a stability improvement method by emulating the G _cal in synchronous reference frame, so as to improve the PM while retaining the advantages of PLL under unbalanced or harmonically distorted voltage, and fault ride through. The experimental results validate the aforementioned conclusions.

A Self-Tuning <i>LCC/LCC</i> System Based on Switch-Controlled Capacitors for Constant-Power Wireless Electric Vehicle Charging

Abstract: In this article, a self-tuning LCC/LCC wireless power transfer system based on switch-controlled capacitors is proposed to maintain a high power factor on the primary inverter side and fixed output power on the secondary side against self or mutual inductance variation of the magnetic coupler. The PI control on the primary side and the method of gradient descent on the secondary side are proposed based on the proposed normalized mistuned LCC/LCC circuit model. Only two switch-controlled capacitors on each primary and secondary side are used to implement the control scheme without the Wi-Fi communication or parameter identification. A 3 kW experiment setup was built in the lab and two different magnetic shields, namely ferrite and nanocrystalline, were tested on the secondary pad. According to the experiment results, the proposed system is proven to be able to maintain a high power factor (>0.9) and the desired dc output power against 52% mutual inductance variation and 12% self-inductance variation of the secondary pad. The proposed system can be applied as a wireless charger for electric vehicles or a high-power wireless charger test bench.

Coestimation of SOC and Three-Dimensional SOT for Lithium-Ion Batteries Based on Distributed Spatial–Temporal Online Correction

Abstract: Energy storage system based on batteries is a key to achieve a green industrial economy and the online estimation of its status is critical for the battery management system. Therefore, this article proposed a distributed spatial–temporal online correction algorithm for the state of charge (SOC) three-dimensional (3-D) state of temperature (SOT) coestimation of battery. First, the internal resistance is identified, and SOC is estimated based on the adaptive Kalman filter. Then, to improve the fidelity of electrical status estimation under the dynamic operation condition, the SOC estimation is coupled with an online restoration algorithm of distributed temperature. An improved fractal growth process is used to achieve the self-organization and convergence during the restoration of 3-D temperature distribution. Finally, to validate the fidelity of online coestimation algorithm for electrical and thermal parameters, dynamic current profiles are used. The coestimation method raises the fidelity of SOC estimation by 1.5% at most, compared with the SOC estimation algorithm without the SOT estimation. It also keeps the mean relative error of SOT estimation within 8%. Additionally, the robustness of the spatial–temporal online correction method with dual adaptive Kalman filters is validated. The result shows that the coestimation algorithm still has a good convergence performance with disturbance added.

Incorporating Harmonic-Analysis-Based Loss Minimization Into MPTC for Efficiency Improvement of FCFMPM Motor

Abstract: According to air-gap field-modulation theory, multiple field harmonics are responsible for high torque density of field modulation (FM) motors. However, abundant field harmonics will undoubtedly increase motor losses, especially iron loss and PM eddy-current loss. Therefore, a harmonic-analysis-based loss minimization (HLM) method incorporated into model predictive torque control (MPTC) for efficiency improvement of FM motors is proposed in this article. First, by analyzing the loss generation mechanism, a harmonic-based online loss calculation model is built to serve for loss minimization control. Then, the reference flux for MPTC can be derived from the proposed HLM method, which avoids complexity of equivalent resistance tuning and improves accuracy of loss calculation compared to the conventional model-based loss minimization method which lacks attention on the harmonic-based loss analysis. Consequently, the HLM-MPTC strategy is finally determined and then applied in a flux-concentrating field-modulation permanent-magnet prototype to verify its validity. The experimental results evidence that the proposed HLM-MPTC can effectively achieve loss reduction and efficiency improvement.

Observer-Based Backstepping Sliding Mode Control Design for Microgrids Feeding a Constant Power Load

Abstract: This article deals with the problem of controller design for dc microgrids that feed constant power loads. To design the proposed controller, first by the use of the exact feedback linearization approach, the linear model of Brunovsky's canonical representation of the system has been obtained to address the nonlinearity problem of the system. Then, the desired control technique is developed by a combination of sliding mode and backstepping control approaches in which a nonlinear disturbance observer is utilized to estimate the disturbance. The overall stability of the system is analyzed based on the Lyapunov approach. A suitable and practical sliding surface is one of the controller strengths that allow the bus voltage to track the reference voltage with high accuracy and fast transient response. Finally, to prove the mentioned claims, an experimental setup has been constructed and the proposed controller is implemented. The experimental results have been analyzed and error analysis is performed. The results confirm the superiority of the proposed controller compared to state-of-the-art controllers.

Integration of Onboard Charger and Wireless Charging System For Electric Vehicles With Shared Coupler, Compensation, and Rectifier

Abstract: For electric vehicles (EVs) equipped with an onboard charger (OBC) and a wireless power transfer (WPT) system, there will be two charging systems in the EVs, which increases the cost, weight, and complexity. Based on the proposed design of magnetic couplers, this letter proposes a novel integration method for OBC and WPT systems of EV charging. The isolation transformer of the OBC system can be regarded as two strongly coupled coils. The secondary-side coil, namely the receiving coil, can also be loosely coupled with the transmitting coil of the WPT system. Thus, the receiving coil, the compensation, and the rectifier of both the OBC and WPT systems can be shared. In this way, the EV charging system can be capable of conductive charging and wireless charging while still having the advantages of cost effectiveness and high power density. An experimental prototype is implemented to validate the proposal.

Practical Terminal Sliding-Mode Control and Its Applications in Servo Systems

Abstract: This article’s primary motivation is to propose a nonsingular and continuous terminal sliding-mode (TSM) control with reduced chattering, which is able to rapidly stabilize the second-order plant with high precision. To this end, a novel sliding-mode manifold, coined as practical TSM (PTSM) one, is first constructed. Once the proposed sliding surface is reached, a Lipschitz-continuous but slope-steep generalized velocity will be established at the origin such that the global nonsingularity of the equivalent control is ensured. The fast dynamic response with local high gain characterizes the sliding behavior inside the unit neighborhood of the origin. Importantly, the analytical solution of the proposed sliding-mode reduced-order system is deduced, which indicates that the finite time taken to slide into a preset small neighborhood of the origin can be calculated. The above designability of convergence time is necessary for control practices with accurate time sequence planning. Further, the corresponding globally singular-free PTSM reaching law with reduced chattering is designed based on the super-twisting algorithm. Finally, several groups of linear-motor-based control experiments verify the superiorities of proposed controllers.

B-Spline Wavelet Neural Network-Based Adaptive Control for Linear Motor-Driven Systems Via a Novel Gradient Descent Algorithm

Abstract: This paper proposes a gradient descent (GD) algorithm-based B-Spline wavelet neural network (GDBSWNN) learning adaptive controller for linear motor (LM) systems under system uncertainties and actuator saturation constraints. A recursive least squares (RLS) algorithm-based indirect adaptive strategy is used to effectively estimate model parameters, which can guarantee they converge to true values. A novel GDBSWNN compensator is proposed to estimate the remaining complex uncertainties, where weights are updated by online GD training. An auxiliary system is integrated into the control scheme to address the saturation problem, which guarantees stability and satisfactory control performance when saturation occurs. In addition, a stability analysis is presented to prove that all signals of the closed-loop system are bounded using the Lyapunov theory. Experiments have been conducted on an LM-driven motion platform, where different controllers have been tested, demonstrating the effectiveness and advantages of the proposed approach.

A Novel Model Predictive Torque Control of SRMs With Low Measurement Effort

Abstract: This article presents a low-measurement effort and less storage space but an effective method to reduce the torque ripple. First, a torque-balanced measurement method is presented to obtain the flux-linkage characteristics at four torque-balanced positions. Then, a four-order Fourier series model is proposed to describe the entire flux-linkage and torque characteristics. To reduce the storage space, a polynomial-Fourier series model is proposed to describe the torque and current model from the rotor position and flux linkage. Based on the proposed polynomial-Fourier series model, a novel model predictive torque control (MPTC) is implemented to minimize the torque ripple with flux-linkage-based torque estimation. Experimental results show that the proposed method can effectively reduce torque ripple with lower measurement effort and less storage space compared with the traditional MPTC method. The proposed method provides a low effort and convenient way to implement the advanced control of SRMs in the industry application.

A New High Efficiency High Step-Up DC/DC Converter for Renewable Energy Applications

Abstract: This article introduces a new nonisolated soft-switching coupled-inductor (CI) step-up dc/dc converter. The presented topology utilizes a three-winding CI (TWCI) along with a voltage multiplier circuit to increase the voltage conversion ratio without needing a high duty cycle. Using this circuit, a high voltage gain can be achieved without requiring a large number of turns ratio in the CI. The input current ripple of the introduced converter is very low, which is very desirable for renewable energy sources applications. The TWCI also creates an additional design freedom to increase the voltage gain, which indicates more circuit flexibility. Additionally, the voltage stress across the single power switch is limited with the help of a regenerative clamp capacitor. The leakage inductor of the CI is used to create a resonant tank to reduce power losses further. The leakage inductances help provide the zero-current switching conditions for the single power switch and decrease the reverse-recovery issues for all diodes, leading to an efficiency improvement. The operation principle, steady-state analysis, and design considerations are discussed thoroughly. Finally, the theoretical analysis is validated through experimental results obtained from a 200 W prototype with 250 V output voltage.

Lithium-Ion Battery Calendar Health Prognostics Based on Knowledge-Data-Driven Attention

Abstract: In real industrial electronic applications that involve batteries, the inevitable health degradation of batteries would result in both the shorter battery service life and decreased performance. In this article, an attention-based model is proposed for Li-ion battery calendar health prognostics, i.e., the capacity forecaster based on knowledge-data-driven attention (CFKDA), which will be the first work that applies attention mechanism to benefit battery calendar health monitor and management. By taking the battery empirical knowledge as the foundation of its crucial part, i.e., the knowledge-driven attention module, the CFKDA has realized a satisfactory combination of the complementary domain knowledge and data , which has improved both its theoretic strength and prognostic performance significantly. Experimental studies on practical battery calendar ageing demonstrate the superiority of CFKDA in forecasting and generalizing to unwitnessed conditions over both state-of-the-art knowledge-driven and data-driven calendar health prognostic models, implying that the introduction of domain knowledge in CFKDA has brought a significant performance improvement.

Tuning-Free Bayesian Estimation Algorithms for Faulty Sensor Signals in State-Space

Abstract: Sensors provide insights into the industrial processes, while misleading sensor outputs may result in inappropriate decisions or even catastrophic accidents. In this article, the Bayesian estimation algorithms are developed to estimate unforeseen signals in sensor outputs without tuning. The optimal Bayesian estimation method is first derived by incorporating a Gaussian distribution specifying potential unmodeled dynamics into the measurement equation. Since its performance depends on tuning parameters, an iterative Bayesian estimation algorithm is developed using the variational inference technique. Specifically, an inverse Wishart distribution is introduced to describe the predicted covariance of abnormal signals. We then estimate it together with the other independent Gaussian distributions to conditionally approximate the joint posterior distribution, by which the effects of tuning parameters can be replaced adaptively. Testing the proposed algorithms through a simulated electromechanical brake model and a real experimental system shows that the proposed algorithm can satisfactorily estimate additive sensor faults online and services as a sensor monitor that simultaneously provides the locations and magnitudes of faulty signals without tuning.

Null-Space-Based Time-Varying Formation Control of Uncertain Nonlinear Second-Order Multiagent Systems With Collision Avoidance

Abstract: In this paper, the time-varying formation control problem with collision avoidance is addressed for uncertain nonlinear second-order multi-agent systems in a null-space-based behavioral control architecture. To guarantee the tracking and coordination performance simultaneously, a novel and flexible time-varying formation task strategy is designed where only neighborhood information is necessary. Moreover, the agent radius and a sine function are introduced such that the collision avoidance task function describes collision risk more accurately in contrast to existing results. Then, two fixed-time sliding mode controllers with constant and variable exponent coefficients, respectively, are proposed to track the desired trajectory generated by null space projection. Also, the theoretical results for the task design and trajectory tracking are obtained by using the Lyapunov stability theory. Numerical simulation and practical experiments are finally conducted to illustrate the effectiveness and superiority of the proposed method.

Constant Current and Constant Voltage Charging of Wireless Power Transfer System Based on Three-Coil Structure

Abstract: Constant current (CC) and constant voltage (CV) charging are two charging stages for li-ion batteries in an electric vehicle wireless charging system. Based on the three-coil structure, this letter proposes a novel reconfigurable topology to achieve CC and CV charging. The transmitting coil is split into two windings with one winding having a turn number much smaller than the other. The two windings are fully compensated and connected to the corresponding inverter phase. The system can be reconfigured to perform as the two-coil structure with a CC output or as the three-coil structure with CV output. Unlike existing methods, the working frequency is fixed and only one relay is utilized to achieve the shift of CC and CV charging, which has the advantages of simpleness and low cost. An experimental prototype with 94.4% maximum efficiency is implemented to validate the proposal.

Resilient Load Frequency Control of Power Systems to Compensate Random Time Delays and Time-Delay Attacks

Abstract: Load frequency control (LFC) of modern power systems tends to employ open communication networks to transmit measurement/control signals, which makes the LFC scheme more vulnerable to random time delays and time-delay attacks (TDAs). In this article, a resilient and active time-delay-compensation-based LFC scheme is proposed to compensate the random time delays and TDAs. At first, a state observer is employed to estimate the state of the LFC system. Then, a networked predictive control method is used to predict the control signals of the system in future moments. Next, an evaluation and compensation scheme for random time delays and TDAs is constructed in the actuator side of the LFC scheme based on the updating period of the actuator and the timestamp technique. Due to the stochastic characteristics of the random time delays or TDAs, the stability condition of the proposed scheme is developed with the aid of the mean-square stability theory. Moreover, a dual-loop open communication is employed in the proposed scheme to improve the reliability and resilience. At last, simulation and experiment tests are undertaken to demonstrate the effectiveness of the proposed scheme.

PMSM Stator Winding Fault Detection and Classification Based on Bispectrum Analysis and Convolutional Neural Network

Abstract: The diagnosis of permanent magnet synchronous motor (PMSM) faults has been the subject of much research in recent years, due to the growing reliability and safety requirements for drive systems. This article concerns PMSM stator winding fault detection and classification. A novel intelligent diagnosis approach is proposed, based on the bispectrum analysis of a stator phase current and the convolutional neural network (CNN). Rather than using raw phase current signals, bispectrum is applied for symptom extraction and utilized as the input for a pretrained CNN model. The CNN model is used for automatic inference on the winding condition of the PMSM stator. Experimental results are presented to validate the proposed algorithm. The classification effectiveness of the developed CNN is as high as 99.4%. This article also presents the possibility of improving the accuracy of the CNN model and reducing the training time by properly tuning the training parameters. The CNN model learning time is only about one minute. The fault classifier model is developed in Python programming language, avoiding the cost of purchasing additional software.

Analytical Fractional-Order PID Controller Design With Bode’s Ideal Cutoff Filter for PMSM Speed Servo System

Abstract: In this article, a speed control scheme based on an analytically designed fractional-order PID with Bode’s ideal cutoff (FOPID-BICO) filter is proposed. The FOPID controller is designed to track the speed references and the BICO suppress is applied to filter the high-frequency noise. Considering the difficulty of parameters tuning of the FOPID controller, a comprehensive analytical design method based on frequency specifications-defined loop-shaping for the FOPID-BICO controller is first proposed for the permanent magnet synchronous motor speed control. The designed speed servo system satisfies five frequency-domain specifications: gain crossover frequency, phase margin, phase crossover frequency, gain margin, and a “flat phase” constraint. Moreover, the influence of phase crossover frequency on control system is fully exposed through the frequency-domain analysis. The proposed designed method ensures that the control system is robust to loop gain variations and guarantees the optimal performance on rejecting noise in high-frequency and disturbance in low-frequency. The effectiveness of the proposed FOPID-BICO controller has been verified by experimental results.

Modeling, Analysis and Mitigation of Radiated EMI Due to PCB Ground Impedance in a 65W High-density Active-clamp Flyback Converter

Abstract: This paper first reviewed the modeling and prediction techniques for the radiated electromagnetic interference (EMI) in active-clamp Flyback (ACF) power converters. An improved radiated EMI model was then developed including the PCB ground impedance based on the ACF converter under investigation. Next, the improved radiated EMI model was quantified and experimentally verified. Two mitigation techniques were developed for the radiated EMI based on the improved model. Experiments were conducted to verify the developed analysis and mitigation techniques for the radiated EMI. The proposed modeling and mitigation techniques were finally extended to other isolated power converter topologies.

Error-Summation Enhanced Newton Algorithm for Model Predictive Control of Redundant Manipulators

Abstract: Redundant manipulators have been investigated and employed in various fields, and its trajectory tracking is of much importance in the field of robotic control. In this article, a model predictive control (MPC) scheme for the trajectory tracking of redundant manipulators is constructed, which minimizes the tracking error, velocity norm, and acceleration norm simultaneously. The commonly used trajectory tracking schemes for redundant manipulators, such as the minimum-velocity-norm scheme and minimum-acceleration-norm scheme, handle joint limits at different levels by introducing additional parameters, which reduces the feasible region of decision variables. In contrast, the proposed scheme directly considers these limits at three different levels, without reducing the feasible region of decision variables. In addition, to compensate for the deficiencies of most existing algorithms in noise environments, an error-summation enhanced Newton (ESEN) algorithm is proposed for solving the MPC scheme. Through theoretical analysis, it is determined that the proposed ESEN algorithm has a small steady-state error under noise conditions. Finally, in contrast with the comparative trajectory tracking schemes, as determined through computer simulations and experiments, the proposed MPC scheme solved by the ESEN algorithm not only enables the redundant manipulator to perform the trajectory tracking task in excellent fashion, but also offers advantages of high efficiency, fast responsiveness, and strong noise tolerance.

<i>K</i>-Means Clustering-Based Kernel Canonical Correlation Analysis for Multimodal Emotion Recognition in Human–Robot Interaction

Abstract: In this article, K -meansclustering-based Kernel canonical correlation analysis algorithm is proposed for multimodal emotion recognition in human–robot interaction (HRI). The multimodal features (gray pixels; time and frequency domain) extracted from facial expression and speech are fused based on Kernel canonical correlation analysis. K -means clustering is used to select features from multiple modalities and reduce dimensionality. The proposed approach can improve the heterogenicity among different modalities and make multiple modalities complementary to promote multimodal emotion recognition. Experiments on two datasets, namely SAVEE and eNTERFACE‘05, are conducted to evaluate the accuracy of the proposed method. The results show that the proposed method produces good recognition rates that are higher than the ones produced by the methods without K -means clustering; more specifically, they are 2.77% higher in SAVEE and 4.7% higher in eNTERFACE‘05.

Truncation Number Selection of Harmonic State-Space Model Based on the Floquet Characteristic Exponent

Abstract: Harmonic state-space (HSS) theory has been an effective method for studying system stability with time-periodic characteristics, such as modular multilevel converters (MMCs) and renewable power generation under unbalanced grid conditions. However, the original state-space formulation of the HSS model comprises infinite-dimensional matrices. Truncation is necessary before practical computing application. Therefore, this letter proposes a truncation number selection method for the HSS model. The theoretical basis of the proposed method is based on the Floquet characteristic exponent of the linear time-periodic model, which has a straightforward physical meaning. Finally, a case study of an MMC grid-tied system with tests in RT-LAB illustrates the validity of the proposed method.

An Accurate Model of Magnetic Energy Harvester in the Saturated Region for Harvesting Maximum Power: Analysis, Design, and Experimental Verification

Abstract: To reduce the size and weight of the magnetic energy harvester (MEH), the magnetic core should be designed to work in the maximum power region, i.e., in the saturated region. However, the conventional analysis model of the MEH, which ignores the phase difference between the primary and secondary currents, is only suitable for the unsaturated region. When the core is in the saturated region, the results of the numerical calculation severely deviate from the experimental observations due to the non-negligible phase difference. To address this vital problem, this article establishes an excitation current model (ECM) to calculate the phase difference caused by the magnetizing inductance. An accurate model of the MEH is presented based on the ECM in this article, which can calculate the harvesting power of the MEH in the saturated region. Besides, a strategy for finding the maximum power point is proposed, which can be used for the designing of the MEH. Finally, an experimental prototype is constructed to verify the effectiveness of the proposed analysis model and the design method of the MEH. The experimental results show that the proposed analysis model can maintain high accuracy when the magnetic core enters the saturated region, and the MEH designed by the proposed model can reach the expected power with a compact size. Compared with the theoretical value, the output voltage deviation of the MEH is only 0.4%, and the output power deviation is only 0.8%.

Fault Coordination Control for Converter-Interfaced Sources Compatible With Distance Protection During Asymmetrical Faults

Abstract: In this article, distinctive fault characteristics of converter-interfaced renewable energy sources (CIRESs) will result in the misoperation of traditional distance relays. To handle this issue, a new fault coordination control method is proposed to ensure the correct operation of distance relays. Combined with sequence boundary conditions, the relationship between the apparent reactance and the CIRES positive-sequence current angle is revealed. Based on this, a reasonable current angle is generated by adjusting current references of the controller to make the apparent reactance equal to the actual line replica reactance, so distance relays can detect the fault distance correctly. The proposed control method shows good performance for high resistance faults and operation in weak grids. Meanwhile, the proposed method is economical because it does not require revising the controller structure, and only local measured data are used. Furthermore, fault ride through requirements such as current limitation and reactive current injection are still being realized. Simulation analysis is performed in PSCAD and real-time digital simulator to validate the recommended scheme.

Position Sensorless Control for PMSM Drives With Single Current Sensor

Abstract: The aim of this article is an attempt to provide a more credible control mode or a fault-tolerant control strategy for permanent magnet synchronous motor (PMSM) drives when an unexpected failure occurs to the current or position sensor. Considering that, a position sensorless control method that uses only one current sensor is proposed. Different from a traditional position sensorless control scheme that utilizes additional current sensors to obtain information of multiple phase currents, only a single current sensor (SCS) arranged on one of the current branches is included among the whole control circuit. Three-phase currents are reconstructed by sequentially sampling the SCS under specific voltage vectors, and directly employed for motor position estimation. A nonlinear position observer established according to the gradient descent is adopted. Focusing on the special current-acquisition pattern in this article, a step-size optimization method based on golden section search is proposed to minimize position estimation error. The experimental results reveal that the method in this article is comparable to the traditional full-current-sensor method in terms of position estimation, which confirms the fault-tolerant capability as one certain sensor of the control structure malfunctions.

Structural Coupled Electromagnetic Sensing of Defects Diagnostic System

Abstract: Magnetic flux leakage (MFL) detection methods are widely used to detect pipeline defects. However, it is limited by the detection orientation and magnetization. Besides, bulky excitation systems are incapable of adapting to the complex detection environments. This article proposes a new electromagnetic structured coupling sensing of merging alternating current field measurement and MFL within a multiparameter system for different types of pipeline defects detection. In particular, a novel electromagnetic coupling sensor structure is proposed, which enables simultaneous interaction between the excitation modes of yoke and coil. Magnetic yoke is integrated to magnetizing the axial pipeline to detect the circumferential surface and subsurface defects while the coil excites the circumferential uniform alternating current field and recognizes the axial defect. The novel structured sensing is highly sensitive to the detection of both surface and subsurface defects. Simulation and experiments on defects in several samples have been conducted to validate the reliability and efficiency of the proposed system.