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Special Feature that lists the names of people involved in the review of technical papers for the IEEE Transactions on Industry Applications and Industry Applications Magazine. [ABSTRACT FROM AUTHOR]

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication. [ABSTRACT FROM AUTHOR]

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication. [ABSTRACT FROM AUTHOR]

A list of periodical staff is presented including the editor-in-chief, the industrial and commercial power system department, and the industrial power conversion systems department.

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM AUTHOR]

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM AUTHOR]

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication. [ABSTRACT FROM AUTHOR]

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication. [ABSTRACT FROM AUTHOR]

The article offers information on the procedure for submission of a research paper in the fields of electrical and electronics engineering.

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM AUTHOR]

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM AUTHOR]

Provides a listing of current committee members and society officers. [ABSTRACT FROM AUTHOR]

Provides instructions and guidelines to prospective authors who wish to submit manuscripts. [ABSTRACT FROM AUTHOR]

An accurate discrimination on moisture status of oil-impregnated paper (OIP) bushings is crucial for the maintenance and replacement schedule of bushings. Based on frequency-domain spectroscopy (FDS) measurement and Dissado–Hill (DH) relaxation model, this article proposes a hybrid approach of hidden Markov model and gray wolf optimization (GWO-HMM) for MS estimation of bushings subjected to the ununiform moisture distribution and dynamic time-series modeling. First, simulation models of moisture diffusion and FDS of the OIP bushing were constructed using finite element modeling approach. Then, the GWO algorithm was employed to explore dielectric parameters influenced by moisture in the DH model. Then, the GWO-HMMs was adopted as a classification tool to discriminate the MS of the simulation and experimental data of the bushings. Classification results confirm that the average identification accuracies of the proposed method are 97.17% and 96.81% over these two datasets, which demonstrates the effectiveness of the proposed moisture estimate method for OIP bushings. [ABSTRACT FROM AUTHOR]

This paper presents a dual active bridge (DAB) converter with reactive power control capability (Q-capability) for a battery energy storage system (BESS) or an electric vehicle (EV) to deliver vehicle-to-home (V2H) and vehicle-to-grid (V2G) services. The DAB topology is preferable for EV charging systems due to its galvanic isolation and bidirectional power flow capabilities. However, this topology increases reactive current peak and loses zero voltage switching (ZVS) operation at light loads if it is not controlled with improved modulation strategies and proper Q control. This paper proposes a mixed modulation method and a reactive power optimization (RPO) algorithm based on power system load flow concepts. Due to the complexities associated with the analytical solution using time domain analysis, this paper develops detailed universal expressions of a DAB converter using the harmonic analysis method. Analytical identification of ZVS boundaries and DSP implementation of the RPO algorithm become easier with these universal expressions. Multiple modes analysis for different operational states is no longer required. Simulation results have been shown for different modulation strategies of the DAB converters. The theoretical analysis of modulation strategies, RPO algorithm and ZVS operation are validated by experimental results on a DAB prototype. Results show that the proposed control strategy reduces the reactive power and provide ZVS operation over a wide ranges of voltages. [ABSTRACT FROM AUTHOR]

Wireless power transfer (WPT) technology has become a popular research direction in the field of industrial applications, with the advantages of contactless operation, high degree of freedom, and great controllability. However, there are still some challenging issues that need to be resolved. First of all, more advanced circuit topologies and magnetic coupling structures need to be designed to achieve the power conversion goals of diversified applications. Second, novel control methods, optimization algorithms, and modeling methods enable systems to be better controlled and designed. Finally, potential application scenarios need to be discovered so that this novel power transfer method can be applied in more suitable applications. Therefore, this topic is organized by the responsible editor to gather the power of more scholars. With reviewers' hard work and considerations, 32 papers were finally accepted for publication in this Special Issue. [ABSTRACT FROM AUTHOR]

In this paper, a sizing method is proposed for photovoltaic (PV) and battery energy storage systems (BESSs) for buildings with demand side management capability. Three objective functions (OF) are defined, describing the self-sufficiency of the building, the net-present value of the investment in PV and BESS, and their combination in a joint expression, respectively. The latter OF provides cost-effective solutions that are potentially more profitable in case of future high increase of the electricity cost. Two analysis methods are developed to find the optimal solution under a predefined load shifting strategy. The first one applies an exhaustive search by examining all possible combinations of PV and BESS capacity. The drawback of this method is the high computational burden due to unnecessary access to the solution space. The second method is implemented with a particle swarm optimization (PSO) variant, namely the unified-PSO. Under this approach, the analysis algorithm is guided in the solution space toward efficient solutions under lower computational time. The comparative assessment of the two methods in both residential and industrial sites verifies that via the PSO algorithm the optimal solutions are found under lower execution time. [ABSTRACT FROM AUTHOR]

This article presents the calculation procedure of short circuits adopting the characteristic currents method (CCM). In a previous paper by the author G. Parise, the CCM has been upgraded with the introduction of “characteristic” countercurrents and the generalization of the Kirchhoff laws. For illustrating the calculation procedure, the selected case study deals with a low voltage cold ironing system where multiple ships are allowed to operate simultaneously on one shore power transformer and, so, a comparison has been developed between the IEC approach of the short-circuit calculation and the CCM. [ABSTRACT FROM AUTHOR]

The power systems are transitioning to renewables. The power system infrastructure digitalization is the next stage in this evolution. A digitalized future distribution network (DN) has an opportunity to evolve into real-time analysis based on a huge volume of data. Such real-time analysis will be feasible through the integration of the theoretical background of fault analysis and machine learning techniques. Fault location is one of the major issues to improve reliability indices. The voltage sags characterization is used to locate faults in the DN. This article presents a methodology to characterize voltage sags using fault analysis and deep convolutional neural networks. The voltage divider model allows the characterization and the discrete wavelet transform is used in signal processing. The machine learning and deep learning models used allow estimating the sag magnitude, fault location, phases involved, duration, and impact of distributed generation (DG) in each event. The IEEE 13-node test feeder including DG was used to validate the effectiveness of the proposed methodology. This paper provides a way to handle a Big Data stream in the DN control center and to efficiently locate faults in several operational scenarios. [ABSTRACT FROM AUTHOR]

A multi-level boost topology based on a flying capacitor is compared in this work with the hybrid, multi-level, partial power processing topology already presented in the literature for use as a highly efficient and compact dc/dc stage in 1500-V, two-stage, grid-connected PV systems. Different variations of the resonant stage of the hybrid topology are analyzed and compared in terms of losses, volume and complexity. Full multivariable optimization is conducted for the multi-level boost topology with different number of levels and for all the analyzed variations of the resonant stage of the hybrid topology. Appropriately rated new classes of 650-V and 900-V WBG devices are included in the optimization algorithm. It is concluded that due to the dominant conduction losses and gap in the voltage rating of the commercially available WBG devices between 650V and 200V, increase of the number of levels over 4 leads to the deteriorated loss and volume performances. All the analysis related to the resonant stage of the hybrid topology is confirmed by detailed and comprehensive measurements of two prototypes of 4kW of nominal and 7kW of maximal power. Additionally, all the loss models applied in the optimization algorithms presented in this paper are confirmed by detailed measurements, thermal calibration and finite element thermal simulations of the wide bandgap devices and inductor designs employed in this work. [ABSTRACT FROM AUTHOR]

The successful mission of CubeSat require reliable and fault-tolerant electrical power system (EPS) that powers all the other subsystems and payloads. Several studies have shown that EPS failure has been one of the main contributors for the CubeSat mission failure. The main objective of this paper is to enhance the lifetime of CubeSats EPS by proposing a distributed EPS architecture and power-down mode. The distributed architecture consists of independent converters for maximum power point tracking operation of photovoltaic (PV) panels. This is possible by using gallium nitride field-effect transistors to reduce the converter footprint and placing the converters on the back-side of the PV panels. Also, single-inductor based $N$ -1 redundant converters for generation and load-side are proposed to provide uninterrupted operation during component failures. Thus, the proposed distributed architecture not only improves the reliability but also facilitates placement of redundant converters to achieve the fault-tolerant capability. To further reduce the electrical and thermal stresses on the semiconductor devices and thus, improve the EPS life span, a novel power-down mode is proposed for the power converters. In this mode, semiconductor devices are switched-off whenever the power processed is below the threshold level and/or no-load operation. Reliability block diagrams are illustrated to demonstrate the reliability improvement and calculations shows that the proposed distributed architecture has higher reliability than the conventional EPS architecture. All the proposed concepts are validated using a prototype developed based on the specifications of MYSAT-1, which is an 1U CubeSat launched by Khalifa University. [ABSTRACT FROM AUTHOR]

Hybrid ac-dc grids can gain the benefits of both ac and dc grids. The Ac-Dc circuit analysis can be performed using sequential methods that decouple ac and dc systems or unified (simultaneous) methods that solve the ac and dc mismatch equations simultaneously. This paper introduces a unified ac–dc analysis via an improved model for the ac–dc coupling through which the ac and dc load flow analyses can be simultaneously conducted. The ac–dc coupling is modeled by an admittance building block, and thus, the dc grid is converted to an ac-equivalent circuit within the hybrid network. However, unlike past literature, state variables beyond nodal voltages and angles are avoided, leading to a simpler algorithm, and reactive power injection of the coupling mechanism, a requirement for the IEEE 1547 Std, is explicitly modeled. Furthermore, the proposed model can be applied to both voltage-source and line-commuted converters. Accordingly, the conventional ac power flow algorithms can be applied to the hybrid circuit paving the path for seamless adoption of hybrid circuits and significantly saving time and effort in programming, troubleshooting, and upgrading techniques currently used in research and industry as well as a high-level analysis tool regarding the hybrid ac-dc grid is achieved. [ABSTRACT FROM AUTHOR]

Design of radial passive magnetic bearings with active dampers (radial PMB-AD) and their application in a down-hole natural gas compressor (DHGC) are discussed. These compact and reliable bearings allowed noncontact suspension of a high-speed DHGC rotor spinning at 50 kr/min in a highly adverse environment more than 3 km (10 000 ft) below the ground with temperatures reaching 100 °C. The design was further complicated by the space limitations imposed by a well casing. The paper presents the design of PMB-AD modules, as well as their integration and testing as a part of a DHGC. [ABSTRACT FROM AUTHOR]

To Promote and meet the projected levels of electric vehicle (EV) penetration, the development of charging infrastructure, i.e., both slow charging and dc-fast charging, is essential. Slow/onboard chargers pose weight, efficiency, and thermal management challenges to the designers, while the off-board fast/superfast/ultra-superfast chargers pose efficiency, power rating, and control challenges. In particular, the installation of dc fast chargers (including fast/superfast/ultra-superfast) is also dictated by the impact on the utility grid and the existing loads. New installation or retrofitting of the existing infrastructure is another significant investment challenge jointly explored by stakeholders, utility companies, and car/charger manufacturers. Another primary concern is battery life management limiting the charging current intake by the EV battery. [ABSTRACT FROM AUTHOR]

Accurate monthly electricity consumption forecasting (MECF) is important for electricity retailers to mitigate trading risks in the electricity market. Clustering is commonly used to improve the accuracy of MECF. However, in the existing clustering-based forecasting methods, clustering and forecasting are independently performed and lack coordination, which limits the further improvement of forecasting accuracy. To address this issue, an adaptive optimal greedy clustering-based MECF method is proposed in this article. First, a metric of predictability is defined based on the goodness of fit and the cluster's average electricity consumption. Under a predefined number of clusters, the greedy clustering algorithm achieves the optimal division of individuals with the goal of maximizing predictability. Then, an adaptive method is designed to select the optimal number of clusters from a variety of clustering scenarios according to the prediction accuracy on the validation dataset. The effectiveness and superiority of the proposed method have been verified on a real-world dataset. [ABSTRACT FROM AUTHOR]

As the evolution of the Internet of Things (IoT) pushes more devices to become intelligent, infrastructure within facilities must keep pace with the overall burden on the system. As the number of intelligent nodes within systems continues to increase, methods are needed in order to preserve network bandwidth and to provide reliable data sources for various control and reporting strategies. This article serves as an overview of message queuing telemetry transport and its applicability to process industries. [ABSTRACT FROM AUTHOR]

This article aims at developing a multigene symbolic regression model of interfacial tension (IFT) for determining the health index of insulating oil. The oil parameters considered for modeling of IFT are break down voltage, resistivity, acidity, tan delta and water. The unique study undertaken in this article attempts to develop a multigene symbolic regression model for generating and evaluating the relationship between IFT and other oil parameters. Data utilized in this study involves actual oil testing parameters of in-service transformers and reactors of age more than 15 years. A sensitivity analysis has been carried out to investigate the relative contribution of input variables to the model output. The result of sensitivity analysis has further been used to determine the weight of parameters for calculating the health index of insulating oil. Finally, the health index of 400 in-service transformer and reactors are evaluated and analyzed. This research will help in undertaking decisions for the replacement of oil based on health index, thereby increasing the life span of transformer and reactors. The health index so obtained will act as a reference for planning necessary maintenance activities of the equipment and thereby saving unprecedented failure of the oil-filled transformer and reactors. [ABSTRACT FROM AUTHOR]

This article provides the background for and summary of key barriers one should consider when analyzing motor control designs in order to prevent runaway motors, especially runaway motor-driven pumps against closed valves with the potential to lead to fire. A Failure Modes and Effects Analysis (FMEA) is used to analyze typical motor control designs with the intent of determining a means of preventing a runaway condition due to failure of any one component. Following the determination of key barriers, the pipeline industry is surveyed to assess the prevalence of each barrier in similar operations and to look for additional best practices not previously documented. Additional challenges are discussed, and conclusions are presented with final recommendations for consideration to avoid runaway motors, especially those that drive pumps with the potential to run against closed valves, possibly leading to fire. [ABSTRACT FROM AUTHOR]

The issue of a state estimation-based fault-tolerant controller for direct current (dc) microgrids (MGs) is studied in this article. It is considered that the dc MG contains nonlinear constant power load (CPL) and is subjected to actuator faults. Current sensors are not installed and the voltages of the dc MG are measured in the presence of noise and sensor faults. To estimate the system states, a novel dual-Extended Kalman filter is proposed, which simultaneously estimates the states and faults. The fault- and noise-free estimations are then deployed in a nonlinear Takagi–Sugeno fuzzy predictive controller to regulate the dc MG. The proposed method outperforms the exiting results, being robust against faults and noise. Also, the predictive scheme makes it robust against system uncertainties and forces the system states to converge the desired values, precisely. The accuracy and robustness of the developed method are evaluated and compared to advanced state-of-the-art techniques for a typical dc MG with a resistive load, CPL, and energy storage unit. [ABSTRACT FROM AUTHOR]

Tide refers to a phenomenon that causes the change of water level in oceans. Tidal level forecasting plays an important role in many real-world applications especially those related to oceanic and coastal areas. For instance, accurate forecasting of tidal level can significantly increase the vessels’ safety as an excessive level of tidal makes serious problems in the movement of vessels. In this work, we propose a deep learning-based prediction interval framework in order to model the forecasting uncertainties of tidal current datasets. The proposed model develops optimum prediction intervals (PIs) focused on the deep learning-based CNN-LSTM model (CLSTM), and nonparametric approach termed as the lower upper bound estimation (LUBE) model. Moreover, we develop a novel deep neuroevolution algorithm based on a two-stage modification of the gaining-sharing knowledge optimization algorithm to optimize the architecture of the CLSTM automatically without the procedure of trial and error. This leads to a decline in the complexity raises in designing manually the deep learning architectures, as well as an enhancement in the performance of the PIs. We also utilize coverage width criterion to establish an excellent correlation appropriately between both the PI coverage probability and PI normalized average width. We indicate the searching efficiency and high accuracy of our proposed framework named as MGSK-CLSTM-LUBE by examining over the practical collected tidal current datasets from the Bay of Fundy, NS, Canada. [ABSTRACT FROM AUTHOR]

This article proposes a two-stage optimization algorithm to effectively determine the system configuration at one stage, as well as the capacity of components at the other stage in the middle of the former. This algorithm fits in best with the hybrid systems with more possible types of components. The studied system, in this article, includes diesel generators, wind turbines, photovoltaic arrays, and tidal generators as the power generation components, as well as battery banks and flywheels as the energy storage components. It also includes fuel cells and electrolyzers that either work as batteries or generate electricity in the presence of biomass. When the number of components (decision variables) increases, it becomes difficult to find an optimal solution by the conventional methods. Therefore, in this study, a two-stage multiobjective optimization algorithm is applied to design a cost effective and environmental friendly ac minigrid hybrid power system. In each iteration of the proposed algorithm, first, renewable energy sources and energy storage components are selected to form a hybrid power system along with the diesel generator. Then, the capacities of the components are optimized based on the two objective functions, including levelized cost of electricity and emissions. The optimization model uses real annual data in hourly time intervals for the load, solar insolation, ambient temperature, and wind speed. It is found that the proposed algorithm decreases the susceptibility of the solutions to multiple runs compared with the conventional algorithms and achieves a better optimized power system design. [ABSTRACT FROM AUTHOR]

Consumption patterns of electric power systems are important for distribution companies, because of their significant impact on energy losses amount. Therefore, some incentives are suggested by distribution companies to energy consumers for correcting their consumption manner. For a specific load pattern, distribution system reconfiguration (DSR) is an effective way to mitigate energy losses. Hence, some research works have included load variations in the DSR problem to show the importance of consumption patterns in reconfiguration decisions. However, some of the specialized literature has ignored load changes in their reconfiguration models due to the high computational burden and processing time. On the other hand, the energy losses are calculated inaccurately if the consumption pattern is neglected. Consequently, the main goal of this article is to investigate load pattern impact on switching sequences to find out how much is load profile important for minimization of energy losses in DSR. The evaluations were carried out for three well-known distribution systems using a classic optimization tool, the A Mathematical Programming Language. [ABSTRACT FROM AUTHOR]

This article presents a new hybrid three-phase cascaded modular multilevel inverter developed with a reduced switch count concept for harvesting renewable energy. The basic module of the proposed inverter consists of six switches, three asymmetric dc links, and two blocking diodes. The inverter can generate six positive voltage states, which are negatively polarized by the end-side H-bridge unit. As a result, a higher number of voltage levels are obtained with a minimum number of switches that ensure reduced total harmonic distortion in the grid current. Additionally, the proposed inverter can operate at a comparatively lower switching frequency, which decreases the switching losses. Most of the switches in each individual module conduct for a small period resulting in low conduction losses. The reduced switch count with the low switching and conduction losses increases the compactness and the efficiency of the proposed inverter. Multiple high-frequency magnetic links are utilized for the dc sources required for the proposed inverter to achieve inherent dc-link voltage balancing ability and galvanic isolation. The performance of the proposed inverter is validated by simulation and experimental investigations. [ABSTRACT FROM AUTHOR]

Thermal stress of the power semiconductor is one of the most important indicators for the reliability assessment of power electronics-based power systems. The mapping of the Insulated-Gate Bipolar Transistor (IGBT) junction temperature is usually required to analyze the thermal stress and loss dissipation. However, it is difficult to identify inherent mechanisms of the bond-wire and solder layer failure modes in IGBT power modules. In order to solve the problem, an online method to identify inherent mechanisms of the bond-wire and solder layer failure modes in IGBT power modules is proposed. This method can separate the root causes of the bond-wire lift-off and solder layer fatigue by measuring the on-state voltage drop through a sinusoidal loading current based on an H-bridge circuit, together with its corresponding control and measurement. By comparing the on-state voltage drop at the intersection current and the peak current of the converter, the wear-out conditions of the IGBT power modules can be monitored in real-time with the determination of different failure modes. Finally, experimental results are presented in order to verify the effectiveness and feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

This article aims at developing a gradient boosting (GB) model with parameters optimized by the Bayesian optimization (BO) method for detecting various types of fault in an oil-immersed transformer and reactor. The developed model depicts higher accuracy in comparison with other machine learning (ML) techniques, such as XGBoost, AdaBoost, random forest, and support vector machine parameters being optimized by BO. A comparison of the skill score depicts the superiority of the developed Bayesian optimization-based gradient boosting (BO-GB) model with respect to Bayesian optimized other ML techniques. Further comparison of the model with conventional Duval pentagon verifies the accuracy of detecting the faults, which were missed by the former. Also, the software has been developed based on the proposed BO-GB method for the on-site detection of the fault. In addition, actual values of dissolved gases with respect to fault confirmed after the internal inspection of the transformer and reactor are tabulated and compared. The data so presented will help in utilizing the authentic dissolved gas data in addition to the already available IEC TC10 databases for future works. The study undertaken in this article will help utilities in diagnosing the incipient faults more accurately and precisely, thereby preventing hazardous operating conditions along with minimizing the downtime cost. [ABSTRACT FROM AUTHOR]

Applying 0.3–1.25 kV/cm electric potential gradients to cooled water vapor triggers the copious formation of electrified ice needles, as long as 36 mm. This is completely different from the meager ice formation in the same setup but in the absence of the external field. Needle excess electric charge is evidenced by its attraction toward the electrodes and by its shape change, when the field is withdrawn. These observations are explained considering the effect of the electric potential on ice surface tension, thus decreasing or eliminating the energy barrier to ice nucleation; and the elongated ice habit that allows the accumulation of charge sufficient to create fields higher than those initially applied. Applying this information to other crystallization phenomena in various systems can contribute to improve industrial processes while preventing the consequences of undesired electrification, in industrial plants and products. [ABSTRACT FROM AUTHOR]

Emerging technologies are helping to accelerate the ongoing energy transition. At the forefront of these new technologies is blockchain, which has the potential to disrupt energy trading markets. This article explores this potential by presenting an innovative multilevel transactive energy (TE) optimization model for the scheduling of distributed energy resources (DERs) within connected virtual power plants (VPPs). The model allows for energy transactions within a given VPP as well as between connected VPPs. A blockchain-based smart contract layer is applied on top of the TE optimization model to automate and record energy transactions. The model is formulated to adhere to the new regulations for the self-generation and self-consumption of energy in Portugal. This new set of regulations can ease barriers to entry for consumers and increase their active participation in energy markets. Results show a decrease in energy costs for consumers and increased generation of locally produced electricity. This model shows that blockchain-based smart contracts can be successfully integrated into a hierarchical energy trading model, which respects the novel energy regulation. This combination of technologies can be used to increase consumer participation, lower energy bills, and increase the penetration of locally generated electricity from renewable energy sources. [ABSTRACT FROM AUTHOR]

Luminance is the main feature indicating organic light-emitting diode (OLED) degradation; however, this requires delicate photometric measurements that are difficult and costly to realize in situ. In this article, an original luminance decay prediction method not requiring optical measurements will be presented. Analysis of the impedance revealed a strong correlation between the luminance and the time constant of the equivalent circuit. As a result, the time constant is chosen as the main feature to track OLED degradation, the evolution of which is linearly modeled over time at different stress levels using the design of experiments methodology. Simultaneously, a linear relationship between luminance degradation and the evolution of time constants, in terms of the thermal stress applied, enables prediction of the luminance degradation over time. This method is an accurate novel solution for luminance tracking in particular for data taken inside the experimental plan limits. [ABSTRACT FROM AUTHOR]

This article presents an energy management strategy for a microgrid having solar PV arrays and a battery energy storage system (BESS). Most of the energy management strategy used for commercial photovoltaic (PV) inverters and battery inverters do not consider the future load behavior and cannot ensure the energy resiliency for a PV and battery storage based microgrid. This article proposes the use of a model predictive control strategy that considers future load behavior and energy cost profile to determine an optimum power flow trajectory to achieve energy resiliency, minimize the operating cost and maximize the profit of the microgrid. The proposed control strategy of the energy management system (EMS) is validated using both simulations in MATLAB/Simulink environment and by carrying out laboratory experiments on a developed laboratory test platform. For the converters of the test platform, this article develops a magnetically linked seven-level multilevel converter (acting as the solar PV inverter) and a full-bridge inverter with an advanced pulsewidth modulation technique (acting as the BESS inverter). Commercial inverters usually require large and bulky power frequency transformers for the grid integration of renewable energy sources. The high-frequency magnetic link-based power electronic converter can produce multiple balanced dc power sources with reduced size, ensures lower power losses during the energy conversion process and can also ensure galvanic isolation. The results from the simulation and experimental studies of the EMS demonstrate that the developed energy management strategy ensures both the minimization of the operating cost of the system and the increased resiliency of the whole system. [ABSTRACT FROM AUTHOR]

This article presents low-frequency ripple power steering control strategies for a power conversion topology that comprises a high frequency linked three-port converter (HFL-TPC) and a voltage source inverter (VSI) for PV-BESS/EV applications. The proposed topology has three interfaces, which can be connected with a photovoltaic (PV) port, battery energy storage systems (BESS) and/or electric vehicles (EVs) port, and a four-quadrant ac port. The proposed topology inherently exhibits double line frequency (DLF) power fluctuation that needs to be decoupled and steered away from the PV port. In this article, we have developed and demonstrated two control strategies to deal with the DLF ripple. The first control strategy utilizes loop gain of the closed loop control system to decouple the DLF power, however, a residual DLF power still exists at the ac port. To completely decouple the DLF fluctuations, an improved sequence-based control strategy is proposed where the DLF voltage components are converted into positive and negative sequence components and controlled separately on their respective synchronous reference frames. To verify the practical feasibility of the proposed low-frequency ripple power steering control strategies, a 40 V, 400 W HFL-TPC is built and tested. Experimental results show that the proposed methods decouple the DLF power fluctuation and selectively steer it to the BESS/EV port. Comparative results reveal that the sequence-based control strategy shows a higher degree of DLF ripple suppression. [ABSTRACT FROM AUTHOR]

Magnetically levitated motor systems based around bearingless motors and generators and magnetic bearings offer a potential step change improvement in motor system lifetime and efficiency, solve critical challenges related to oil-lubricated contact bearings, and feature prognostic capabilities for condition-based maintenance. Acceptance of this technology has been historically limited by challenges related to cost, design and control complexity, manufacturing issues, and low force density. However, recent advances in supporting technologies enable new approaches to address these challenges. [ABSTRACT FROM AUTHOR]

Presents the table of contents for this issue of the publication. [ABSTRACT FROM AUTHOR]

The article announces the submission of a research paper related to the fields of electrical and electronics engineering.

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM AUTHOR]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM AUTHOR]

A magneto-structural combined dimensional and topology optimization approach for interior permanent magnet synchronous machine (IPMSM) rotor design is proposed using the solid isotropic material with penalization (SIMP) density-based topology optimization method. This method optimizes the location and dimensions of the permanent magnets (PMs) embedded in the rotor, while simultaneously optimizing the lamination flux barriers using topology optimization. To allow for the location and dimensions of the PMs to change a Laplace smoothing mesh deformation technique is used. Three examples of IPMSM rotor optimizations are presented to demonstrate the proposed approach. In addition, a geometric template-based optimization is carried out for a comparison to one of the dimensional and topology optimized examples to better illustrate the difference. [ABSTRACT FROM AUTHOR]

Considering its unique operational characteristics, the autonomous mobility-on-demand (AMoD) system is playing a more and more critical role in the future transportation system. In this article, we consider a joint planning problem of autonomous fleet (i.e., vehicle type and fleet size) and charging infrastructure (i.e., the size of charging piles with various charging rates, distributed renewable resources) while accounting for the spatial–temporal demand-side flexibility of the AMoD system. We adopt an augmented network flow model to capture the autonomous fleet's charging, order serving, and rebalancing decisions. The proposed planning problem is modeled as a two-stage stochastic program and solved by multicut Benders decomposition techniques. Simulation experiments based on real-world data are performed to demonstrate the effectiveness of the proposed planning scheme. [ABSTRACT FROM AUTHOR]

It is now widely recognized that energy storage enables increased integration of renewable resources. One of the uses of storage is to provide synthetic inertia, making up for some of the inertia lost from displaced conventional generation, thereby maintaining frequency stability. However, energy storage systems continue to be very expensive, and this motivates the development of innovative approaches to mitigate some of the challenges posed by renewable resources. This article presents a novel method called “grid-scale virtual energy storage” that harvests free energy storage from properties inherent to control of multiarea power systems, thereby increasing the amount of renewable generation that a system can tolerate before its frequency stability is compromised. The concept of virtual energy storage proposed here is based on the surplus of necessary energy that is required to restore the system frequency to within a safe range of the nominal frequency. In a dynamic sense, virtual energy storage is very responsive and is not limited by the operation time and capacity. Moreover, it can be exchanged easily among control areas and does not require installation and implementation expenses as it takes advantage of the existing diversity of area control errors in interconnected systems and employs an appropriate frequency control method. It also helps in reducing the reserve requirement, regulation capacity, transmission loading, and wear and tear on the power system by functioning as normal energy storage. In this article, the proposed new approach is explained mathematically, and its operation is analyzed using a mathematical model and simulation in order to validate its effectiveness. [ABSTRACT FROM AUTHOR]