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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. 

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. 

Spoke-type PMSMs were designed with commercial permanent magnets and theoretically designed hexaferrite: Nd-Fe-B (NdFe35, G1NH), Alnico (8B, 8H, 9), and La-CoSrM hexaferrite (NMF-15G). It was found that coercivity (Hc) plays a crucial role in determining motor performance. The ANSYS Maxwell software was used to characterize the designed motor performance. Commercial RE-free Alnico 9 holds a 10.5 MGOe of (BH)max, much higher than a 5.5 MGOe of RE-free Alnico 8B/8H and SrM (SrFe12O19) hexaferrite magnets. However, the Alnico 9 motor performance is not better than the other Alnico 8B/8H and hexaferrite motors. The spoke-type PMSM with our theoretically designed SrM hexaferrite simulated motor performance. A motor performs best when the Hc/Br ratio equals one with a high Hc. For instance, the motor torque and peak power increase to 189 Nm and 178 kW, respectively, as the Hc increases to 4.86 kOe from 2.43 kOe. However, the motor performance is not significantly changed with a fixed Hc and various Br. It was found that regardless of (BH)max, coercivity (Hc) plays a dominant role in motor performance.