Energy storage systems (ESSs) facilitate utility grid operations on various levels, which include power generation, power transmission, and power distribution. The benefits of these systems produce an overall improvement of grid stability, security, and resilience; cost reductions resulting from the need for less expensive reserve equipment; enhanced power quality; and customer satisfaction. In line

Presents the front cover for this issue of the publication.

Presents the table of contents for this issue of the publication.

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

Presents the introductory editorial for this issue of the publication.

Presents the President’s message for this issue of the publication.

Battery electric vehicles (BEVs) have limited driving ranges compared to conventional vehicles (CVs). Therefore, whether BEVs meet highway-trip requirements poses a critical concern for consumers. The real-world driving range is significantly different from laboratory test results due to a lack of real-world factors. Here, a hybrid physical-stochastic model is proposed to assess the real-world performance

Batteries are widely applied to the energy storage and power supply in portable electronics, transportation, power systems, communication networks, and so forth. They are particularly demanded in the emerging technologies of vehicle electrification and renewable energy integration for a green and sustainable society. To meet various voltage, power, and energy requirements in large-scale applications

Wind energy has received considerable attention during the past two decades, owing to its vital role in battling both the energy crisis and global climate change. Wind energy has grown to become the most significant nonconventional renewable energy source. Today, modern wind turbines (WTs) are controlled to offer several functionalities that improve their flexibility at a level similar to conventional

The energy paradigm is making the modern power grid more difficult to study, design, and control. Precisely speaking, the pace of the new energy paradigm involves the high penetration of power electronics systems in the power grid, which becomes a challenge from stability, vulnerability, and power-quality points of views. Therefore, various literature have focused on defining the modern power grid

Detecting inaccurate smart meters and targeting them for replacement can save significant resources. For this purpose, a novel deeplearning method was developed based on long short-term memory (LSTM) and a modified convolutional neural network (CNN) to predict electricity usage trajectories based on historical data. From the significant difference between the predicted trajectory and the observed one

The Energy Internet is proposed to enhance the collaborative utilization of distributed renewable energy resources; enable a flexible, customer-engaged energy transaction network; and achieve real-time balancing of supply and demand. This allows integrating advanced Internet of Things (IoT)-based architectures, information and communications technology (ICT)-based end-to-end digital energy chains;

Electrically propelled ships gained popularity by the early 20th century, with the rapid development of submarines and mediumcapacity container ships, mainly using dc motors [1]. Synchronous ac motors have since been employed for naval propulsion systems, but due to the restricted operation of the available power electronic devices at that time, these configurations were too expensive and unreliable

As an emerging research topic, wireless power transfer (WPT) with a single transmitter for multiple pickups (STMP) shows salient advantages of reducing the number of power inverters, increasing the power density, and so on, which has profound implications for various applications, such as wireless charging for electric vehicles (EVs) and smart homes. First, this article introduces the concept of STMP-WPT

High-performance wireless communication is crucial to the digital transformation of industrial systems, which is driven by Industry 4.0 and Industrial Internet initiatives. Among the candidate industrial wireless technologies, 5G (cellular/mobile) holds significant potential. The operation of private (nonpublic) 5G networks in industrial environments is promising to fully unleash this potential. This

The fourth industrial revolution, also termed Industry 4.0 (I4.0), will digitalize manufacturing operations and decentralize them from the shop floor to the office and across entire enterprise networks [1]. The proposed system-level digitalization solutions for I4.0 include cyber-physical systems [2] and digital twins [3]. Standardized I4.0 technologies are urgently required to change industry and

The limitations of the thermal, vibration, or electrical monitoring of electric machines such as false indications, low sensitivity, and difficulty of fault interpretation have recently been exposed. This has led to a shift in the direction in research toward applying new techniques for improving the reliability of condition monitoring. With the changing environment, the purpose of this article is

Presents the recipients of IES society awards in 2020.

Presents information on the ICPS 2020 Conference.

Presents information on the EFTA 2020 Conference.

Presents information on the Second IEEE International Conference on Industrial Electronics for Sustainable Energy.

Reports on the new IES Technical Committee on Technology, Ethics, and Society.

Reports on the 100th-Anniversary Harbin Institute of Technology-IES Lectures on Industrial Technologies.

Reports on new standards development work completed by te IAS society.

Presents information on various IES Society chapters.

Presents corrections to production errors in the article, “Protection testing for multiterminal high-voltage dc grid: Procedures and assessments,” (Liu, Z., et al), IEEE Ind. Electron. Mag., vol. 14, no. 3, pp. 46–64, 2020. doi: 10.1109/ MIE.2020.2977150.

Reports on IES activities using virtual meetings and events.

Presents information on the 2020 Women in Engineering Workshop.

The author is world-renowned and known for his 27 previous books elated to electrical machines, power electronics, and drives. This new offering offers a third edition of a handbook devoted to induction machines as a response to the significant progress observed in the last decade in technology and design for better precision and performance. Just like the second edition, the new third edition consists

The book is a wonderful collection of peer-reviewed papers on power electronic converters and control for dc microgrid systems. After a preface by Prof. Frede Blaabjerg recommending the book, the chapters review the state of the art in wind energy conversion systems. Then, research outcomes are presented, such as multichannelbased microgrids; large-scale renewable energy integration issues, such as

Presents the IES society calendar of upcoming events and meetings.

Advertisement: TechRxiv is a free preprint server for unpublished research in electrical engineering, computer science, and related technology. Powered by IEEE, TechRxiv provides researchers across a broad range of fields the opportunity to share early results of their work ahead of formal peer review and publication. Researchers are asked to upload their unpublished research.

eAssessment The application of multiterminal (MT), high-voltage dc (HVdc) (MTdc) grid technology requires test procedures for the operation and implementation of the protection solutions. The test procedures are usually derived from experience and from extensive measurement data, which, at present, are still not widely available. Based on a hardware-inthe- loop (HIL) method, advanced dc protection

Microgrids can be isolated from large-scale power transmission/distribution systems (macrogrids) to deliver energy to their local communities using local energy resources and distribution systems when power outages occur in the macrogrids. In such situations, microgrids could be considered the last available resource to provide energy to critical infrastructure.

Industrial agents (IAs) [1] are multiagentbased systems (MASs) [2] that, for many years, have been advocated as a promising and realistic solution for an emerging set of industrial challenges. In the past, MASs fell into the scope of enterprise agility [3]-[8], and now, more than ever, pertain to the industrial digital transformation and sustainability spheres. MAS technology is being applied to several

Currently, the necessity of efficient and reliable power systems is also increasing because of the strict requirements that standards and regulations impose, but still costs have to remain low. The monitoring and control of the components' lifetime can lead to reduce maintenance costs.However, overcoming the related challenges is not a straightforward task, as it involves knowledge of power device

Lithium (Li)-ion batteries have become the mainstream energy storage solution for many applications, such as electric vehicles (EVs) and smart grids. However, various faults in a Li-ion battery system (LIBS) can potentially cause performance degradation and severe safety issues. Developing advanced fault diagnosis technologies is becoming increasingly critical for the safe operation of LIBS. This article

This historical column follows the article on Mario Tchou's ELEA 9003, which was published in the June 2020 issue of IEEE Industrial Electronics Magazine, and recounts events that also took place at the Olivetti Company. We know that much of the early progress in personal computers took place in the United States, and particularly on the West Coast, in the early 1970s. Nevertheless, trailblazers were

Worldwide, the current year has been quite challenging due to the COVID-19 pandemic and its dramatic effects. All of us have had to suddenly change our lives and habits to face such an unexpected global emergency for which we were not prepared. From one day to the next, the luckiest ones, that is, those of us who have not lost our jobs, have had to switch from working on site to smart working. In many

In this article, the model-free robust formation control problem is addressed for cooperative underactuated quadrotors involving unknown nonlinear dynamics and disturbances. Based on the hierarchical control scheme and the reinforcement learning theory, a robust controller is proposed without knowledge of each quadrotor dynamics, consisting of a distributed observer to estimate the position state of

Disturbance, which is generally unknown to the controller, is unavoidable in real-world systems and it may affect the expected system state and output. Existing control methods, like robust model predictive control, can produce robust solutions to maintain the system stability. However, these robust methods trade the solution optimality for stability. In this article, a method called generative adversarial

An enormous and ever-growing volume of data is nowadays becoming available in a sequential fashion in various real-world applications. Learning in nonstationary environments constitutes a major challenge, and this problem becomes orders of magnitude more complex in the presence of class imbalance. We provide new insights into learning from nonstationary and imbalanced data in online learning, a largely

In sparse empirical risk minimization (ERM) models, when sensitive personal data are used, e.g., genetic, healthcare, and financial data, it is crucial to preserve the differential privacy (DP) in training. In many applications, the information (i.e., features) of an individual is held by different organizations, which give rise to the prevalent yet challenging setting of the featurewise distributed

We introduce a gated value network (GVN) for general multilabel classification (MLC) tasks. GVN was motivated by deep value network (DVN) that directly exploits the ``compatibility'' metric as the learning pursuit for MLC. Meanwhile, it further improves traditional DVN on twofold. First, GVN relaxes the complex variable optimization steps in DVN inference by incorporating a feedforward predictor for

In this article, a novel learning methodology is introduced for the problem of classification in the context of high-dimensional data. In particular, the challenges introduced by high-dimensional data sets are addressed by formulating a L₁ regularized zero-sum game where optimal sparsity is estimated through a two-player game between the penalty coefficients/sparsity parameters and the deep neural

We aim at the optimization of the tracking control of a robot to improve the robustness, under the effect of unknown nonlinear perturbations. First, an auxiliary system is introduced, and optimal control of the auxiliary system can be seen as an approximate optimal control of the robot. Then, neural networks (NNs) are employed to approximate the solution of the Hamilton-Jacobi-Isaacs equation under

Optimal control of nonlinear impulsive systems with free impulse instants and the number of impulses is investigated in this study. A scheme based on adaptive dynamic programming is developed, which leads to a feedback (approximate) solution to the defined optimal impulsive control problem. This is done by proposing a learning algorithm for tuning parameters of a function approximator, which, once

This article proposes a neural terminal sliding mode controller (TSMC) with finite-time (FT) convergence for the uncertain MEMS gyroscope dynamics. To address the uncertainty, considering the periodic tracking property of MEMS gyroscopes, a composite learning mechanism driven by the learning performance evaluation signal is applied to learn the system dynamics. By selecting the terminal sliding mode

Visual question answering (VQA) that involves understanding an image and paired questions develops very quickly with the boost of deep learning in relevant research fields, such as natural language processing and computer vision. Existing works highly rely on the knowledge of the data set. However, some questions require more professional cues other than the data set knowledge to answer questions correctly

In the field of computer vision, without sufficient labeled images, it is challenging to train an accurate model. However, through visual adaptation from source to target domains, a relevant labeled dataset can help solve such problem. Many methods apply adversarial learning to diminish cross-domain distribution difference. They are able to greatly enhance the performance on target classification tasks

Attention is an increasingly popular mechanism used in a wide range of neural architectures. The mechanism itself has been realized in a variety of formats. However, because of the fast-paced advances in this domain, a systematic overview of attention is still missing. In this article, we define a unified model for attention architectures in natural language processing, with a focus on those designed

Composite function optimization problem often arises in machine learning known as regularized empirical minimization. We introduce the acceleration technique to the Newton-type proximal method and propose a novel algorithm called accelerated proximal subsampled Newton method (APSSN). APSSN only subsamples a small subset of samples to construct an approximate Hessian that achieves computational efficiency

This article applies a singular perturbation theory to solve an optimal linear quadratic tracker problem for a continuous-time two-time-scale process. Previously, singular perturbation was applied for system regulation. It is shown that the two-time-scale tracking problem can be separated into a linear-quadratic tracker (LQT) problem for the slow system and a linear-quadratic regulator (LQR) problem

This article considers global exponential synchronization almost surely (GES a.s.) for a class of switched discrete-time neural networks (DTNNs). The considered system switches from one mode to another according to transition probability (TP) and evolves with mode-dependent average dwell time (MDADT), i.e., TP-based MDADT switching, which is more practical than classical average dwell time (ADT) switching

A continuous stirred-tank reactor (CSTR) system is widely applied in wastewater treatment processes. Its control is a challenging industrial-process-control problem due to great difficulty to achieve accurate system identification. This work proposes a deep learning-based model predictive control (DeepMPC) to model and control the CSTR system. The proposed DeepMPC consists of a growing deep belief

Semantic-preserving hashing establishes efficient multimedia retrieval by transferring knowledge from original data to hash codes so that the latter can preserve the underlying visual and semantic similarities. However, it becomes a crucial bottleneck: how to effectively bridge the trilateral domain gaps (i.e., the visual, semantic, and hashing spaces) to further improve the retrieval accuracy. In

In this article, the synchronous fault-tolerant near-optimal control strategy design problem is studied for a class of discrete-time nonlinear pursuit-evasion (PE) games. In the studied PE game, the input saturation phenomenon and possible actuator fault are simultaneously taken into consideration. To accelerate the estimation speed, a novel nonlinear fault estimator is designed by introducing a nonlinear

In this article, the exponential stability problem for fractional-order complex multi-links networks with aperiodically intermittent control is considered. Using the graph theory and Lyapunov method, two theorems, including a Lyapunov-type theorem and a coefficient-type theorem, are given to ensure the exponential stability of the underlying networks. The theoretical results show that the exponential

Text segmentation is a fundamental step in natural language processing (NLP) and information retrieval (IR) tasks. Most existing approaches do not explicitly take into account the facet information of documents for segmentation. Text segmentation and facet annotation are often addressed as separate problems, but they operate in a common input space. This article proposes FTS, which is a novel model