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This paper presents a wideband low-profile dual-polarized patch antenna with helical-shaped L-probe feeding (HLF) for mmWave 5G mobile device applications. Parametric studies on the HLF structure are performed to identify the optimal specifications. As a result, the optimized antenna achieves a wide bandwidth of 5.4 GHz (24.2–29.6 GHz), good isolation > 18 dB between ports, and 5.1 dBi of good peak realized gain, which is experimentally verified with a 10× upscaled antenna. In addition, various one × four phased arrays with different port configurations and beamform capabilities are designed and simulated for the peak realized gain. The designed antenna array shows a high peak realized gain of 10 dBi, high isolation of 15 dB between the ports, and a small substrate thickness of 0.048λ0 (λ0 is the wavelength of 24.25 GHz). Compared to the state-of-the-art antennas, the designed dual-polarized antenna can operate in the frequency ranges of 24.25–29.6 GHz, including n257, n258, and n261 of the 5G new radio frequency range 2. 

As one of the components comprising food-energy-water systems (FEWS), the energy sector, especially electricity production, is intimately connected to water. Climate extremes-related impacts on water resources will directly affect the interdependence of water, food, and energy. A better understanding of the extent of climate impacts on energy sector and the options to improve water-energy security are needed for planning an overall resilient FEWS. Therefore, we are motivated to examine the climate stress on the thermoelectric power supply using the Water Balance Model coupled with Thermoelectric Power & Thermal Pollution Model (WBM-TP2M), which can simulate water-energy linkages at the power plant, river reach, and regional scales. Using the Midwest (MW) and Northeast (NE) regions as our study area, we design a group of single- and multi-factor experiments both for historical climate period (1980–2019) and a case where we create a series of intensified extremes (2010–2019). The results show that power generation over the two regions features a gradually increasing trend in the past four decades, while, in contrast, thermal pollution has been decreasing steadily since 2005. Heat waves created temporary dips in the generation of electricity and peaks of heavily thermal-polluted stream length. The experiments reveal the significant role of cooling towers in reducing thermal pollution without losing much capacity to generate power, one important measure of resilience against climate extremes. Constraints placed on effluent temperature from the Clean Water Act lead to interrupted operations, which reduces (up to 20%) power generation, increases sensitivity to climatic extremes, but only show a small reduction in thermal pollution. Coal, as a fuel source, is subject to low thermal efficiency and high-water demand, which results in clearly decreased power generation. Overall, our findings suggest that replacing a less thermal-efficient fuel mix and environmentally destructive once-through cooling can move the energy sector towards several beneficial outcomes. Chief among these is a more efficient power production system that uses less water and does so while fostering clean, less carbon-intense technologies (e.g., combined gas cycle turbines, cooling towers, renewable energy), thus linking positive outcomes that simultaneously and positively impact aquatic ecosystems, regional airsheds and human health. 

This paper discusses the challenges faced by electric power systems due to the increasing use of inverter-based renewable energy resources (IBRs) operating in grid-following mode (GFL) and the limited support they provide for the grid’s reliability and stability. With increased IBRs connected to the grid, electric utilities are increasingly requiring IBRs to behave like traditional grid-forming (GFM) synchronous generators to provide support for inertia, frequency, voltage, black start capability, and more. The paper focuses on developing GFM inverter technologies with L, LC, and LCL filters and investigates the performance of combined GFM and GFL inverters with different filtering mechanisms when supplying different types of loads. It also emphasizes achieving voltage controllability at the point of common coupling of the GFM with the rest of an AC system. EMT simulation is utilized to investigate the interaction of combined GFM and GFL inverters with different filtering mechanisms. The research results will assist electric utilities in ensuring the reliability and stability of electric power systems in the future. 

With the proliferation of large-scale grid-connected wind farms, subsynchronous oscillation (SSO) incidents associated with Type-4 wind turbines (WTs) with a permanent magnet synchronous generator (PMSG) have occurred frequently. These incidents have caused severe reliability risks to the power grid. Conventionally, P-Q capability charts are utilized to ensure the safety operating region of a synchronous generator. However, a PMSG WT exhibits completely different and dynamic P-Q capability characteristics due to the difference in energy conversion technique and several critical factors related to the WT power converters. This paper presents a comprehensive dynamic P-Q capability study of a PMSG WT with sufficient and accurate considerations of the WT control and operation in the dq reference frame, its power converter constraints, and grid dynamics. Models of a PMSG WT are first developed based on its control principle in the dq reference frame. Then, algorithms for obtaining the P-Q capability charts of the WT are developed with the considerations of complete WT constraints in different aspects. The study analyzes the root cause of many abnormal operations of grid-connected PMSG WTs, reported in the literature, from the dynamic P-Q capability perspectives. The proposed study is verified via an electromagnetic transient (EMT) model of a grid-connected Type-4 WT. 

An interior permanent magnet (IPM) motor is a prime electric motor used in electric vehicles (EVs), robots, and electric drones. In these applications, maximum torque per ampere (MTPA), flux-weakening, and maximum torque per volt (MTPV) techniques play a critical role in the efficient and reliable torque control of an IPM motor. Although several approaches have been proposed and developed for this purpose, each has its specific limitations. The objective of this paper is to develop a neural network (NN) method to determine MTPA, flux-weakening, and MTPV operating points for the most efficient torque control of the motor over its full speed range. The NN is trained offline by using the Levenberg-Marquardt backpropagation algorithm, which avoids the disadvantages associated with online NN training. A cloud computing system is proposed for routine offline NN training, which enables the lifetime adaptivity and learning capabilities of the offline-trained NN and overcomes the computational challenges related to online NN training. In addition, for the proposed NN mechanism, training data are collected and stored in a highly random manner, which makes it much more feasible and efficient to implement lifetime adaptivity than any other methods. The proposed method is evaluated via both simulation and hardware experiments, which shows the great performance of the NN-based MTPA, MTPV, and flux-weakening control for an IPM motor over its full speed range. Overall, the proposed method can achieve a fast and accurate current reference generation with a simple NN structure, for optimal torque control of an IPM motor. 

Hybrid electric vehicles can achieve better fuel economy than conventional vehicles by utilizing multiple power sources. While these power sources have been controlled by rule-based or optimization-based control algorithms, recent studies have shown that machine learning-based control algorithms such as online Deep Reinforcement Learning (DRL) can effectively control the power sources as well. However, the optimization and training processes for the online DRL-based powertrain control strategy can be very time and resource intensive. In this paper, a new offline–online hybrid DRL strategy is presented where offline vehicle data are exploited to build an initial model and an online learning algorithm explores a new control policy to further improve the fuel economy. In this manner, it is expected that the agent can learn an environment consisting of the vehicle dynamics in a given driving condition more quickly compared to the online algorithms, which learn the optimal control policy by interacting with the vehicle model from zero initial knowledge. By incorporating a priori offline knowledge, the simulation results show that the proposed approach not only accelerates the learning process and makes the learning process more stable, but also leads to a better fuel economy compared to online only learning algorithms.