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The connected and autonomous vehicle (CAV) is promised to ease congestion in the future with the rapid development of related technologies in recent years. To explore the characteristics of mixed-traffic flow and the dynamic transmission mechanism, this paper firstly detailed the car-following model of different vehicle types, establishing the fundamental diagram of the mixed-traffic flow through considering the different penetration rates and fleet size of CAV. Secondly, this paper constructed the lane-changing judgment mechanism based on the random utility theory. Finally, the paper proposed a lane-level dynamic cell transmission process, combined with a lane-changing strategy and cell transmission model. The effectiveness and feasibility of the model are verified using simulation analysis. This model makes a systematic, theoretical analysis from the perspective of the internal operation mechanism of traffic flow, and the lane-level traffic strategy provides a theoretical basis for balancing urban lane distribution and intelligent traffic management and control. [ABSTRACT FROM AUTHOR]

The consequent-pole (CP) Vernier permanent-magnet (VPM) machine has been developed over the last decade. In VPM machine, the CP structure can produce considerable torque with a half volumn of the PMs compared with the regular structure, so the cost is reduced and the mechanical strength is increased. In this paper, an improvement of chamfering structure on a CPVPM machine is proposed to alleviate the flux leakage and increase the torque density. The chamfered structure is easily machined and will not influence the robustness of the rotor. The comparison results show that under the same volume and copper loss constraint, the proposed structure has smaller cogging torque, smaller torque ripple, larger torque density and larger power factor. [ABSTRACT FROM AUTHOR]

The high-frequency electromagnetic noise caused by a frequency converter power supply has become the main composition of the vibration and noise of frequency-converter-driven PMSMs. Determining how to reduce this kind of noise is very important to improve motor performance. This paper analyzes the frequency characteristics of the high-frequency noise generated by an inverter, using the magnetic circuit analysis and Maxwell tensor methods. The switching frequency and the natural frequencies of the main modes are optimized according to the modal characteristics of the motor in order to reduce the vibration and noise of the motor. The results show that the high-frequency electromagnetic vibration and noise generated by the inverter is mainly caused by the high-frequency switching harmonic current. The frequencies of the vibration and noise are related to the switching frequency and the modulation wave frequency. At the same time, the simulation calculation of the natural frequencies of the main modes and the noise spectrum obtained from the experiment provide direction for the optimization of the vibration noise near the switching frequency. The switching frequency optimization and the natural frequency optimization based on the main modes of the motor can effectively reduce vibration and noise. This work has certain reference significance for the design of low-noise PMSMs. [ABSTRACT FROM AUTHOR]

Vernier permanent-magnet machines have been attracted more and more attention because of their high torque density. In this paper, the sensorless control strategy of the novel axially magnetized Vernier permanent-magnet (AMVPM) machine is presented. First, the inductance non-linearity is investigated under different load conditions. Second, the mathematical model is established in cooperation with the finite element method. After that, the back electromotive force based sensorless control strategy is developed according to the state equation of the motor. In the sensorless drive, the model reference adaptive system (MRAS) technique incorporated with the inductance non-linearity is used for the speed estimation. The modified control strategy not only increases the stability but also improves the dynamic response of the system. Finally, the simulation results show that the modified MRAS is of high estimation precision, and the AMVPM machine can be well controlled, and the experimental results validated the theoretical design process. [ABSTRACT FROM AUTHOR]

The low-carbon martensitic stainless steel CA6NM is widely used in the impellers of hydroelectric and nuclear power units due to its advantages of high hardness, corrosion fatigue strength, and good fracture toughness. In order to analyze the number of repair welding times on the properties of heat-affected zone (HAZ, the most unstable area of welded joint property) in CA6NM, the microstructure and mechanical properties of HAZ with once-repaired welds (1R) and twice-repaired welds (2R) were tested. Through data analysis, the following conclusions are drawn for welded joints: (1) the grain size of HAZ with a different number of repair welding processes is smaller than that of the base material; (2) the density of texture concentration is reduced and the directionality is not obvious; (3) the density of twins and dislocations are increased; (4) the hardness and impact energy of HAZ of the 1R and 2R specimens are higher than that of the base material. [ABSTRACT FROM AUTHOR]

The subcutaneous implant, which can realize on-demand drug release, is used for the treatments of some chronic diseases such as chronic pain, diabetes, and osteoporosis. This paper proposes a new type of wireless actuator for potential use in active implantable drug delivery systems. The proposed structure suits both wireless power well, which requires far-field flux traveling and receiving, and actuator design, which should take advantage of strong local magnetic fields. The mover in the actuator generates mechanical movement when power is transferred wirelessly to the actuator, and the mover restores to its original position when the wireless power stops. The maximum oscillation amplitude of the mover can be achieved when the ON and OFF frequency of the wireless power transfer is equal to the mechanical resonant frequency of the mover. Experimental results on a prototype actuator with 20 mm in diameter and 10.3 mm in thickness are given for validating the proposed new idea. [ABSTRACT FROM AUTHOR]

This article investigates the influences of end turn length on consequent-pole Vernier permanent-magnet (CPVPM) machines. Four structures are compared to discuss the tradeoff between the end turn length and the gear ratio, and nine structures are compared to discuss the tradeoff between the end turn length and the winding factor. It is found that, when the end turn length is taken into account, the CPVPM machines with concentrated winding have the potential to produce larger torque than those with the distributed winding. Motivated by the above observations, an integral slot concentrated winding CPVPM machine is proposed to be a promising candidate in VPM machine family, which has high torque capability, small end turn length, good fault tolerance, and desired power factor. The influences of key design parameters are investigated. The machine prototype is manufactured, and the electromagnetic performances are experimentally validated. [ABSTRACT FROM AUTHOR]

To solve the problem of a low signal-to-noise ratio of fault signals and the difficulty in effectively and accurately identifying the fault state in the early stage of motor bearing fault occurrence, this paper proposes an early fault diagnosis method for bearings based on the Differential Local Mean Decomposition (DLMD) and fusion of current–vibration signals. This method uses DLMD to decompose the current signal and vibration signal, respectively, and weights the decomposed product function (PF) according to the kurtosis value to reconstruct the signal, and then fuses the reconstructed signals to obtain the current–vibration fusion signal after normalization, and then analyzes the fusion signal spectrally through the Hilbert envelope spectrum. Finally, the fusion signal is analyzed by the Hilbert envelope spectrum, and a clear fault characteristic frequency is obtained. The experimental results demonstrate that compared to traditional bearing fault diagnosis methods, the proposed method significantly improves the signal-to-noise ratio of fault signals, effectively enhances the sensitivity of early-stage fault detection in motor bearings, and improves the accuracy of fault identification. [ABSTRACT FROM AUTHOR]

This article proposes a novel axially magnetized Vernier permanent-magnet (AMVPM) machine which has permanent magnets (PMs) clamped on both ends of the rotor with alternately magnetized directions. The topology improves the mechanical strength of the structure without weakening the electromagnetic performances. The detailed operating principles are described and the design parameters are optimized. The open-circuit back electromotive force (EMF) is calculated analytically by using the harmonic series expressions. Then, a comparison between the AMVPM machine and regular Vernier permanent-magnet (VPM) machine is analyzed by finite-element analysis (FEA) method. Finally, the machine prototype is manufactured and the electromagnetic performances are experimentally validated. [ABSTRACT FROM AUTHOR]

Gene expression programming (GEP) is one of the most prominent algorithms in function mining. In order to obtain a more accurate function model in configuration parameters-execution efficiency (CP-EE) of map-reduce job in the high-speed railway catenary monitoring system, this paper proposes a novel algorithm, called GEP based on multi-strategy (MS-GEP). Compared to traditional GEP, the proposed algorithm can escape premature convergence and jump out of local optimum. First, an adaptive mutation rate is designed according to the evolutionary generations, population diversity, and individual fitness values. A manual intervention strategy is then proposed to determine whether the algorithm enters the dilemma of local optimum based on the generations of population evolutionary stagnation. Finally, the average quality of the population is changed by randomly replacing individuals, and the ancestral population is traced to change the evolutionary direction. The experimental results on the benchmarks of function mining show that the proposed MS-GEP has better solution quality and higher population diversity than other GEP algorithms. Furthermore, the proposed MS-GEP has higher accuracy on the function model of CP-EE of high-speed railway catenary monitoring system than other commonly used algorithms in the field of function mining. [ABSTRACT FROM AUTHOR]

This paper presents the first approach to a current control problem for the direct matrix converter (DMC), which makes use of the deep reinforcement learning algorithm. The main objective of this paper is to solve the real-time capability issues of traditional control schemes (e.g., finite-set model predictive control) while maintaining feasible control performance. Firstly, a deep Q-network (DQN) algorithm is utilized to train an agent, which learns the optimal control policy through interaction with the DMC system without any plant-specific knowledge. Next, the trained agent is used to make computationally efficient online control decisions since the optimization process has been carried out in the training phase in advance. The novelty of this paper lies in presenting the first proof of concept by means of controlling the load phase currents of the DMC via the DQN algorithm to deal with the excessive computational burden. Finally, simulation and experimental results are given to demonstrate the effectiveness and feasibility of the proposed methodology for DMCs. [ABSTRACT FROM AUTHOR]

An event-triggered control technique has been developed recently. This technique explicitly reduced the signal transmission by introducing a flexible design of threshold inequalities. It was later extended to event-triggered model-predictive control for power converter systems. In this letter, by incorporating this control technique into an extended state-observer-based finite-control-set model-predictive control framework, we have developed a new model-predictive control architecture for power converter systems with parametric uncertainties. Meanwhile, a novel cost function with respect to the angle minimization term is embedded into this proposal. The novelty of our development lies not only in integrating the event-triggered mechanism with the suggested finite-control-set model-predictive control architecture for facilitating the alleviation of performance deterioration caused by parameter variations and model uncertainties, but also in a multiobjective optimization design that allows the switching frequency in a low value. Finally, extensive simulative and experimental investigations for a modular multilevel converter confirm the interest and the viability of the proposed design methodology. [ABSTRACT FROM AUTHOR]

In this letter, a neural predictor-based dynamic surface predictive control framework, endowed with the merits of adaptive dynamic surface control and finite control-set model predictive control, is proposed where the estimation of neural predictor is incorporated to identify the system dynamics and lumped unknown uncertainties. The key features of the proposal are that, first, the issue of “explosion of complexity” inherent in the classical back-stepping control is avoided, second, the model uncertainties and disturbances are explicitly dealt with, and, third, the tedious determination procedure of weighting factors is removed. These features lead to a much simpler adaptive predictive control solution, which is convenient to implement in applications. Furthermore, a Lyapunov function is constructed, and the stability analysis is given. It demonstrates that all signals in the closed-loop system are uniformly ultimately bounded. Finally, this proposal is experimentally assessed, where the performance evaluation of steady-state and transient-state confirms the availability of the proposed solution for modular multilevel converter. [ABSTRACT FROM AUTHOR]

This letter proposes a data-driven iterative learning predictive control architecture for power converters. The main objectives of this letter are to enhance the robustness and remain the high performance of finite control-set model predictive control (FCS-MPC) under unmodeled dynamics and parameter mismatch conditions. More specifically, an iterative dynamic linearization technique is utilized to equivalently reformulate the nonlinear power converter system at each operating point. Based on this, a model-free adaptive control scheme is presented to iteratively determine the optimal control actions. Due to the incorporation of iterative learning control and data-driven concept into the FCS-MPC framework, the effect of parameter perturbations can be alleviated in the proposed method, while creating a positive effect on the tracking error. Finally, a convergence analysis is provided and experimental investigations on a three-level neutral-point-clamped (NPC) converter confirm the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

As an important equipment for high-speed railway (HSR) to obtain electric power from outside, the state of the pantograph will directly affect the operation safety of HSR. In order to solve the problems that the current pantograph detection method is easily affected by the environment, cannot effectively deal with the interference of external scenes, has a low accuracy rate and can hardly meet the actual operation requirements of HSR, this study proposes a pantograph detection algorithm. The algorithm mainly includes three parts: the first is to use you only look once (YOLO) V4 to detect and locate the pantograph region in real-time; the second is the blur and dirt detection algorithm for the external interference directly affecting the high-speed camera (HSC), which leads to the pantograph not being detected; the last is the complex background detection algorithm for the external complex scene "overlapping" with the pantograph when imaging, which leads to the pantograph not being recognized effectively. The dirt and blur detection algorithm combined with blob detection and improved Brenner method can accurately evaluate the dirt or blur of HSC, and the complex background detection algorithm based on grayscale and vertical projection can greatly reduce the external scene interference during HSR operation. The algorithm proposed in this study was analyzed and studied on a large number of video samples of HSR operation, and the precision on three different test samples reached 99.92%, 99.90% and 99.98%, respectively. Experimental results show that the algorithm proposed in this study has strong environmental adaptability and can effectively overcome the effects of complex background and external interference on pantograph detection, and has high practical application value. [ABSTRACT FROM AUTHOR]

This article proposes a novel data-driven neural predictors-based robust finite control-set model predictive control (FCS-MPC) methodology for power converters, which aims to enhance the robustness of the control system and the flexibility of multiple control objectives. To be specific, the data-driven predictors-based neural network solution, which has a good potential to estimate the unknown nonlinear system dynamics by deploying real-time and historical data, is incorporated into the proposed design with a cost function derived intuitively from Lyapunov’s control theory. The key feature of this work is that the undesired effects of the uncertainties and the complex multiobjective optimization procedure can be explicitly dealt with, without involving accurate modeling information and weighting factor combinations, while guaranteeing adaptability to unknown nonlinear system dynamics, model variations, and environment changes. Finally, the stability analysis is given, and the effectiveness of the proposed FCS-MPC methodology is verified analytically and confirmed by comprehensive results that prove the theoretical investigations for active front-end modular multilevel converter. [ABSTRACT FROM AUTHOR]

Traditional predictive control schemes dominated the research field of numerous power electronic applications over the past few years, since they usually lead to control solutions with good dynamic and steady results. However, these solutions strongly depend on the available knowledge of the control system (e.g., accurate modeling information), which often results in the lack of robustness in the presence of parametric uncertainties. Furthermore, unnecessary energy loss heavily correlates with high switching frequency, which directly affects efficiency. Aiming to cumbersome aforementioned scarcities, this letter is concerned with a novel approach to the controller design for a modular multilevel converter, which makes use of event-triggered neural-predictor-based finite-control-set model-predictive control methodology under low-switching-frequency operation. The salient feature of the proposed controller is that the uncertainties and unnecessary energy loss in practical systems can be explicitly dealt with, while keeping the switching frequency in a low value. Finally, steady-state and transient-state performance tests together with the analysis of the simulation and experimental results confirm the interest of the proposal, and the results found are promising and motivate further research in this field. [ABSTRACT FROM AUTHOR]

Electricity theft behaviors have caused great harm to the economic benefits of power companies and the secure operation of power systems, thus electricity theft detection is paid much attention in the actual power supply management. In this work, a two-step electricity theft detection strategy is proposed to identify electricity theft users and predict potentially stolen electricity (PSE) for maximizing economic return. In the first step, a neural network model called convolutional autoencoder (CAE) is proposed for electricity theft identification, and the convolutional layer is adopted in CAE to extract and identify the abnormalities of electricity theft users against the uniformity and periodicity of normal power consumption features. In the second step, the PSE of each identified electricity theft user is predicted by the improved regression algorithm named Tr-XGBoost, which combines the extreme gradient boosting (XGBoost) algorithm and transfer adaptive boosting (TrAdaBoost) training strategy. The propsoed Tr-XGBoost could learn the relationship between the extracted electricity features and the PSE of each electricity theft user, and then the predicted PSE can be used to determine the list of electricity theft users to be inspected for maximizing economic return. Case studies on both the IEEE 33-bus test system and a low-voltage distribution system of a province in China show that the proposed two-step electricity theft detection strategy can improve the accuracy of electricity theft identification, and obtain a larger economic return because of a more accurate result of PSE prediction than other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

This letter investigates the possibility of deploying a novel finite control-set model predictive control solution for solving the ongoing research challenges in predictive control regulated modular multilevel converter, i.e., model parameter sensitiveness and excessive computational burden as well as weighting factors selection. Specifically, it is realized by cascading a predictor-based neural network design, which enables a smooth and fast identification of system dynamics, and a computationally efficient finite-set predictive control, which is responsible for simplifying the rolling optimization and reducing the computational complexity. The main contribution of the proposed methodology relies on the fact that no knowledge of any model parameters and weighting factors in whole control process are required, which leads to a significant enhancement in the robustness and reliability of the control system in the presence of parametric uncertainties, while remaining computationally feasible. Finally, the stability analysis is given, and the proposed methodology is experimentally assessed for modular multilevel converter, where steady-state and transient-state performance tests confirm the interest of the proposal. [ABSTRACT FROM AUTHOR]

With the adoption of gas‐fired units (GFU), the interaction between the electricity and gas systems has been intensified. The failure of the gas sources may lead to the insufficiency of the gas supply to the GFUs, and further result in the electricity supply shortage, threatening reliabilities of electricity and gas systems. However, compared with the electric power flow, the dynamics of the gas flow are much slower. Most of the existing studies evaluated the reliabilities of integrated electricity and gas systems (IEGS) without considering the slower dynamics of gas flow, which are not fully accurate in the short‐term. This paper proposes a short‐term reliability evaluation technique for IEGS considering the gas flow dynamics. Firstly, the short‐term reliability models of gas sources, GFUs, and gas compressors are developed. Then, the multi‐stage contingency management scheme is proposed, where gas flow dynamics are analysed for determining the time‐varying load curtailments of electricity and gas. Moreover, a time‐sequential Monte Carlo simulation technique is developed with the finite‐difference scheme to tackle the gas flow dynamics during the short‐term reliability evaluation. Finally, the proposed reliability evaluation technique is validated using an integrated IEEE reliability test system and the practical Belgium gas transmission system. [ABSTRACT FROM AUTHOR]

Summary: In this paper, a novel valley V2 control scheme for interleaved boost converter is presented to improve the transient response performance of system and realize the current sensorless control, and its stability is analyzed by a nonlinear analysis technique. In this proposed methodology, the current sensor is saved by the proposed control signals sharing algorithm, and the nonlinear dynamics is investigated by employing a nonlinear analysis method based on the Monodromy matrix. Meanwhile, the information of the converter parameters' influence on the system stability can be obtained by calculating the Monodromy matrix of switching event. It is worth mentioning that the location of eigenvalues of this matrix can be utilized to analyze the relationship between converter parameters and stability margin. Furthermore, by adopting the knowledge gained from this analysis, an auto‐tuning resonant parametric perturbation is proposed to ensure an excellent performance of converter with an extended range of stable operation. Finally, the simulation and experimental results are presented to verify the effectiveness and theoretical analysis of the proposed method. [ABSTRACT FROM AUTHOR]

This article compares the electromagnetic performances of doubly salient permanent magnet (DSPM) machines with different stator iron core segments. Since the permanent magnets (PMs) of the DSPM machines studied in this article exist in pairs, the feasible numbers of PMs in the 12/8-pole DSPM machine are 12, 6, 4, and 2, and thus, the corresponding stator tooth number between two adjacent PMs is 1, 2, 3, and 6, respectively. Therefore, the stator iron core segments of the four DSPM machines have different shapes. Furthermore, to improve the average torque, PMs are inserted into the slots, and flux focusing teeth are adopted. All four machines are designed to achieve larger average torque under the fixed copper loss and envelope size. The comparison shows that the DSPM machine with two stator teeth between adjacent PMs exhibits the largest average torque and the smallest torque ripple. Moreover, the DSPM machine with six stator teeth between two adjacent PMs has the optimal PM utilization. To verify the predicted results, a 12/8-pole DSPM prototype with two stator teeth between adjacent PMs is manufactured and tested. [ABSTRACT FROM AUTHOR]

The dropper is one of the core components of the high speed railway catenary and dropper failure will lead to serious transportation accidents. It is very important to carry out dropper failure and defect detection. There are a large number of droppers installed in the catenary. The dropper images are collected by the high-definition camera installed on the top of the moving catenary inspection vehicles. The image quality and image consistency are poor, and it is very difficult to identify dropper defects automatically. The railway department and companies lack efficient and intelligent detection methods. This article innovatively proposes a multialgorithm fusion image processing technology, and builds a dropper recognition and failure–defect detection model based on a deep learning algorithm and subpixel level dropper defect detection model, and achieves high accuracy dropper failure and defect detection. The detection model based on the Faster R-CNN algorithm is studied to realize the positioning and recognition of the dropper and the failure detection of bending slack and broken dropper. The subpixel level dropper defect detection algorithm is based on the fusion of the image preprocessing, dropper fine positioning algorithm, edge fitting and bending zoom algorithms, the Hough transform algorithm, and so on. These can be used to realize detection of defects, such as microdeformation, dropper-strands loosened, dropper-strands broken, and foreign body adhesions. The test is verified by the catenary images taken from a practical high speed railway. The detection accuracy, real-time performance, and stability of the algorithm meet the needs of inspection and maintenance for a high speed railway. [ABSTRACT FROM AUTHOR]

This paper proposes a coupled finite element analysis of magnetic-thermal fields using an adaptive single-mesh method. To avoid errors from the data transfer and the mesh reconstruction due to different mesh requirements for the magnetic and thermal analysis, this proposed method built single mesh for both analyses. The results of the coupled parameters were transmitted through the single mesh between magnetic and thermal fields. According to the characteristics of the magnetic and the thermal distribution, the master-slave node method was employed to simplify the original element matrix into the magnetic and the thermal matrices, where slave nodes were no longer solved in the solving process. Results of the slave nodes were expressed by their mater nodes using weight coefficients to regain the results of the original element matrix. A permanent magnet machine was introduced to validate the proposed method with both the commercial software and the experimental tests. Compared to the commercial software, the time cost was reduced by more than 8.0% when a higher accuracy of the thermal analysis was obtained. [ABSTRACT FROM AUTHOR]

This article addresses a novel finite control-set model predictive control solution for nested neutral point-clamped converter. More specifically, it is formed by incorporating a finite-time extended state observer design into a cascade-free finite control-set model predictive control architecture. In this architecture, a finite-time extended state observer is developed to estimate the total disturbances, and an integral error term and a dynamic reference design point of view are embedded into the proposed design so as to improve the performance. The main features of this proposal are: first of all, the detailed knowledge of the physical structure of the controlled plant is not required; second, a cascade-free structure is achieved in finite control-set model predictive control solution without weighting factors and steady-state error; and third, the proposed methodology can operate with different systems not just power converters, while keeping the fast transient response. In contrast to existing finite control-set model predictive control approaches, it can be found that our modification not only can track fast to their references but also can have superior robustness performance. Finally, the proposed solution is verified by comprehensive results that prove the theoretical researches for nested neutral point-clamped converter, while meeting the harmonic standards. [ABSTRACT FROM AUTHOR]

To address the requirement of improved power capacity, reliability and operation, parallel converters have been widely applied in micro-grid. However, the connections between DC and AC sides of different parallel converters may lead to the surge of zero-sequence circulating current (ZSCC), which would result in the deterioration of system functionality. Regarding the stated problem above, this paper proposes an improved data-driven based model predictive control for parallel converters. Specifically, a model-free adaptive control (MFAC), which is based on the measured input/output data, is incorporated into the finite control-set model predictive control (FCS-MPC) framework with multi-objective constraints, thus allowing for the suppression of ZSCC and the improvement of robustness performance. Furthermore, to avoid the tuning of weighting factors, a novel cost function is constructed based on the multi-objective decision making method. The main contribution of the proposed methodology relies on the fact that no knowledge of any model parameters and weighting factors in whole control process are required, which leads to a significant enhancement in the robustness and reliability of the control system in the presence of parametric uncertainties. Furthermore, by this proposal, the accurate current tracking and ZSCC suppression can be achieved. Finally, the validity of the proposed FCS-MPC scheme is verified by comprehensive case studies. • Parallel converters may lead to the surge of zero-sequence circulating current. • An improved data-driven based model predictive control is proposed. • No need for model and parameters, only based on the measured input/output data. • Accurate current tracking and zero-sequence circulating current can be achieved. • Avoidance of the weighting factors tuning and enhanced robustness is realized. [ABSTRACT FROM AUTHOR]

The prediction of iron losses in soft magnetic materials is difficult and has not yet been entirely solved. Although much work has been performed for loss prediction under dc bias conditions, little work evaluates the behavior of iron losses with saturation involved. In this paper, measurements of iron losses have been performed to gain information on how losses are affected by a dc bias when saturation occurs. The Steinmetz parameters are calculated together with the Steinmetz premagnetization graph (SPG) method. The SPG is improved by expressing the parameter \beta as a function of both H_{\textrm {dc}} and \Delta B$ to take the saturation effect into consideration. The calculated losses are compared with the measured ones. The accuracy has always been ≤±10%. [ABSTRACT FROM PUBLISHER]

Current-controlled variable inductors adjust the inductance of an alternating current (ac) coil by applying a controlled dc current to saturate the iron cores of the ac coil. The controlled dc current has to be maintained during operation, which results in increased power losses. This paper presents a new stress-based variable inductor to control inductance using the inverse magnetostrictive effect of a magnetostrictive material. The stress can be applied by a piezoelectrical material, and thus a voltage-controlled variable inductor can be realized with zero-power consumption. The new stress-based variable inductor concept is validated using a 3-D finite-element analysis. A prototype was manufactured, and the experimental results are presented. A linear relationship between inductance and applied stress can be achieved. [ABSTRACT FROM AUTHOR]

Cu 2 O nanoparticles and CuO nanowires were prepared by a thermal oxidation method. The high-energy e-beam in a transmission electron microscope was used to study the anti-radiation properties of the Cu 2 O and CuO nanomaterials. The morphology of Cu 2 O nanoparticles greatly changed during irradiating. Notable mass transmission was observed and new grains formed away the irradiated region. By contrast, neither mass transmission was detected nor new grains were formed when CuO nanowires were irradiated at similar situation. No phase transition was detected whether for Cu 2 O nanoparticles or CuO nanowires during irradiation. The chemical and mechanical stabilities of CuO and Cu 2 O under e-beam irradiation were discussed. [ABSTRACT FROM AUTHOR]

A typical giant magnetostrictive actuator (GMA) expands a magnetostrictive rod to generate strain by varying the current in the coil that surrounds the magnetostrictive rod. The heat generated by the current deteriorates the GMA performance. In particular, a constant current in the coil is required to produce the desired magnetic field when an output strain should be maintained. The GMA does not produce any mechanical power in this condition, but constant power is being consumed by the excitation coil. This paper presents a new type of motor-driven GMA (MDGMA), which works in a coil-free driven manner. The magnetic field in the iron–gallium alloy (Galfenol), which is a type of magnetostrictive material, is periodically altered by rotating the permanent magnets instead of varying the coil current in the traditional GMA. The proposed MDGMA not only achieves continuous adjustment of the output strain, but can also maintain a constant output strain without consuming any power. In addition, the coil-free design releases the new MDGMA from the heat generated by the excitation coil, which allows the MDGMA to work more stably. [ABSTRACT FROM AUTHOR]

This paper proposes a novel rotor structure for high-speed interior permanent magnet motors to overcome huge centrifugal forces under high-speed operation. Instead of the conventional axial stacking of silicon-steel laminations, the retaining shield rotor is inter-stacked by high-strength stainless-steel plates to enhance the rotor strength against the huge centrifugal force. Both mechanical characteristics and electromagnetic behaviors of the retaining shield rotor are analyzed using finite-element method in this paper. Prototypes and experimental results are demonstrated to evaluate the performance. The analysis and test results show that the proposed retaining shield rotor could effectively enhance the rotor strength without a significant impact on the electromagnetic performance, while some design constraints should be compromised. [ABSTRACT FROM PUBLISHER]

Highlights: [•] Interface diffusion with a width of 2–4nm was detected in Cu–Cr alloy. [•] Interface mixing is responsible to more rapid increase of electrical resistivity. [ABSTRACT FROM AUTHOR]

• A distributionally robust day-ahead scheduling model of PIES is proposed. • Wasserstein metric-based DRO is introduced to deal with the uncertainty problem. • Strong duality theory is utilized to linearize the proposed non-convex model. • The model of generalized energy storages is proposed. The optimal scheduling of park-level integrated energy system can improve the efficiency of energy utilization and promote the consumption level of renewable energy. However, the uncertainty of renewable energy sources' output power may lead to negative impacts on the scheduling of park-level integrated energy system. Therefore, a distributionally robust day-ahead scheduling model of PIES considering generalized energy storages is proposed in this paper, aiming to reduce the operating cost, renewable energy curtailment, and carbon emission of park-level integrated energy system. In the proposed model, the actual multi-energy storage devices, integrated demand response and pipeline energy storages are synergistically modeled as generalized energy storages to improve the operating flexibility of park-level integrated energy system; the Wasserstein metric-based distributionally robust optimization method is utilized to handle the uncertainty problems in the scheduling of park-level integrated energy system, which can obtain the expected operating costs of park-level integrated energy system under the worst-case probability distribution restricted in an ambiguity set; the strong duality theory and reformulation–linearization technique are utilized to linearize the proposed non-convex model and make it easier to be solved by the commercial solver. Case studies are performed on a park-level integrated energy system that consists of an IEEE 33-bus distribution network, a 44-node district heating network and a 20-node natural gas network for verifying the effectiveness and advantages of the proposed model. [ABSTRACT FROM AUTHOR]

• The main contribution of the proposed solution relies on the fact that no online optimization and weighting factors as well as parameters information in whole control process are required, while guaranteeing adaptability to different conditions. • Compared with the state-of-the art predictive control strategies, the novelty of the proposed solution is that no online optimization and weighting factors tuning as well as parameters information in whole control process are required. • A KF-based SM voltage online estimation technique along with a currentless sorting strategy is developed, resulting in a simpler implementation and a lower associated costs. Alternatively, the stability of the closed-loop system is analyzed. In this paper, we propose a simple and efficient model-free predictive power control for active front-end modular multilevel converter. The main contribution of the proposed solution relies on the fact that no online optimization and weighting factors as well as parameters information in whole control process are required, while guaranteeing adaptability to different conditions. To be specific, it is realized by cascading an efficient model-free predictive power control based on extended state observer and a Kalman filter-based submodule voltage estimation technique, which is responsible for enhancing the robustness and reliability of the control system in the presence of parametric uncertainties. As such, unlike the conventional scheme that needs to evaluate the cost function, the philosophy behind the proposed methodology is to reduce the control complexity by eliminating the online optimization and weighting factors, which leads to a significant reduction in the calculation effort without sacrificing the performance. Alternatively, the stability of the closed-loop system is analyzed. Finally, the proposed methodology is experimentally assessed for active front-end modular multilevel converter, where steady-state and transient-state performance tests confirm the interest of the proposal. [ABSTRACT FROM AUTHOR]

Phase change heat storage material is a preferred material in solar building heating or off-peak electric-heat storage heating technology and is the research focus. A compact phase change thermal storage device has been designed and experimentally studied for improving heating system load in this work. A new type, magnesium nitrate hexahydrate-based phase change material has been studied to improve the cooling degree and crystallization difficulty. The focus of this study is on the heat charging and discharging characteristics of this new phase change material. The heat storage device has two groups of coils, the inner side which carries water and the outer side which is the phase change material. A testing system was built up to value the thermal cycling performance of the heat storage device. The measurement data include phase change material temperature field, water inlet and water outlet mean temperature, heat charging and heat discharging depth, and flow rates over the operating period. The results show the phase change material has a quick response with the operating temperature range of 20–99 °C. Its latent heat is 151.3 J/g at 91.8 °C. The heat storage density of this phase change material is about 420 MJ/m3. The thermal performance degradation is about 1.8% after 800 operation cycles. The phase change thermal storage device shows flexibility and a great potential to improve the capacity and economy of heating systems. [ABSTRACT FROM AUTHOR]

• For the first time, a Lyapunov-based finite control-set model predictive control (FCS-MPC) is proposed for nested neutral point-clamped converters (NNPCs) without online weighting factors tuning. • The contribution of the proposed approach is twofold. The first one is the elimination of the weighting factors by combining the proposed methodology with the Lyapunov control theory while guaranteeing adaptability to different conditions. The second one is the integration of a dynamic reference design technique with a computationally efficient FCS-MPC scheme which achieves the satisfactory regulation performance, while maintaining fast dynamic performance and flying capacitor voltages balancing. • The novelty of this paper lies not only in combining the proposed methodology with the Lyapunov control theory for avoiding the tedious tuning work of weighting factors, but also in a compatible reference technique that directly allows formulating the optimal control problem using a cascade-free structure without the intervention of a PWM block. This paper presents a novel finite control-set model predictive control (FCS-MPC) strategy for solving the well-known challenges in predictive control regulated NNPCs, which aims to avoid the tedious online adjustment of weighting factors and to achieve the flexible power regulation. Firstly, a modified cost function, which is inspired by Lyapunov control theory, is formulated from system stability point of view. As such, the designed Lyapunov-based cost function has a good potential to avoid the selection of weighting factors for the conventional FCS-MPC scheme, especially in the presence of multiple control objectives. Next, in order to achieve satisfactory regulation performance, a dynamic reference design technique is introduced as an alternative solution to serve this purpose. Worth to note that the computational complexity will be increased while applying the proposed design methodology to the control of NNPCs. In this sense, a computationally efficient FCS-MPC strategy based on Lyapunov theory is presented here. Therefore, the overall computational burden is significantly decreased while maintaining fast transient performance and flying capacitor voltages balancing. Finally, compared with the state-of-the-art FCS-MPC strategies, the effectiveness and feasibility of the proposed methodology for NNPCs are confirmed through comprehensive simulation and experimental results. [ABSTRACT FROM AUTHOR]