The domination of inverter-based resources has reduced effective inertia in autonomous microgrids (AMGs), leading to steeper rates of change of frequency and deeper frequency nadirs, which causes synchronization and stability issues. AI-assisted controllers offer a promising solution to synthesize inertia and fast frequency response, overcoming limitations of traditional methods. This article proposes an intelligent dynamic voltage regulation (IDVR) scheme tailored for grid-forming inverters (GFMIs) in an AMG with meshed network topologies (AMGMTs). By leveraging conservation voltage reduction (CVR) principles, IDVR dynamically regulates voltage to emulate inertia and provide dynamic frequency support. Therefore, IDVR offers a cost-effective solution that does not require energy storage, and resolves key limitations of CVR, including failure in reactive power control (e.g., inaccurate Q-sharing). To construct a fully decentralized control structure in a complex AMGMT architecture, a multiagent deep reinforcement learning (MA-DRL) framework is employed, which obviates the limitations on GFMIs’ synchronization arising from spatial variations in nodal voltage. Further, a novel 2D convolutional neural network architecture is proposed to address the computational complexity and cost efficiency of training and operating MA-DRL system in dynamic time scales. Simulation results in MATLAB/Simulink demonstrate the effectiveness of the proposed IDVR.

The transient stability of the grid-feeding voltage source converter (GFD-VSC) has been studied in the context of weak-grid connections. This article investigates the vulnerability of the fault ride-through (FRT) transient of the GFD-VSC in inverter-dominated autonomous microgrids, where the GFD-VSC observes different impedance characteristics. The transient impedance model of the GFD-VSC is developed considering the current controller/saturation block and studying the impact of the phase-locked loop (PLL) synchronizing unit. The saturation of the current controller imposes a significant phase shift and the PLL's consequent action drives the GFD-VSC to a floating reference frame. The boundedness of the trajectories is evaluated through the nonlinear phase system analysis. It is shown that the system is susceptible to instability depending on its operating conditions such as power factor and the X/R ratio of feeder impendence. A state feedback control is proposed to bound the FRT trajectories of the GFD-VSC. The robust performance of the proposed method is reinforced by utilizing the intelligent deep reinforcement learning (DRL) method to adjust the feedback gain. A convolutional neural network based architecture is proposed for the DRL agent to solve the computational issue related to training and operating the DRL agent in a dynamic time scale of power converters. Numerical simulations validate the proposed method.

Unmanned aerial vehicles (UAVs) are utilized to improve the performance of wireless communication networks (WCNs), notably, in the context of Internet of Things (IoT). However, the application of UAVs, as active aerial base stations (BSs)/relays, is questionable in the fifth-generation (5G) WCNs with quasi-optic millimeter wave (mmWave) and beyond in 6G (visible light) WCNs. Because path loss is high in 5G/6G networks that attenuate, even, the Line-of-Sight (LoS) communicating signals propagated by UAVs. Besides, the limited energy/size/weight of UAVs makes it cost-deficient to design aerial multi-input/output BSs for active beamforming to strengthen the signals. Equipping UAVs with the reconfigurable intelligent surface (RIS), a passive component, can help to address the problems with UAV-assisted communication in 5G and optical 6G networks. We propose adopting the RIS-equipped UAV (RISeUAV) to provide aerial LoS service and facilitate communication for mobile Internet-of-Vehicles (IoVs) in an obstructed dense urban area covered by 5G/6G. RISeUAV-aided wireless communication facilitates vehicle-to-vehicle/everything communication for IoVs for updating IoT information required for sensor fusion and autonomous driving. However, autonomous navigation of RISeUAV for this purpose is a multilateral problem and is computationally challenging for being optimally implemented in real time. We intelligently automated RISeUAV navigation using deep reinforcement learning to address the optimality and time complexity issues. Simulation results show the effectiveness of the method.

The reconfigurable intelligent surface (RIS) technology has gained increased attention for improving the performance and efficiency of the fifth-generation (5G) millimeter-wave (mmWave) wireless communication by obviating the propagation and blockage issues. On the other hand, the great flexibility of unmanned aerial vehicles (UAVs) has made them effective gadgets to enhance the coverage of wireless communication networks. Combining these two emerging technologies, the RIS-outfitted UAV (RISoUAV) is a promising solution for providing line-of-sight (LoS) wireless links for mobile targets (MTs) in obstructed high-dense urban areas. In this light, high-speed real-time communication is essential for some vital municipal services like ambulances, fire engines, security guards, police, etc. This important goal is achievable thanks to the RISoUAV-assisted 5G/quasi-optic wireless communication. This paper develops a framework for optimal navigation of a team of RISoUAVs for maintaining LoS links with a team of ground vehicles in a dense urban area. The trajectories of the RISoUAVs are optimized considering the energy efficiency, communication channel gains, and constraints associated with RISoUAVs motion and LoS service. A consensus-based coordinating approach is adopted to coordinate the RISoUAVs navigation to cover all MTs under a good quality of service. Simulation results show the effectiveness of the method. Note to Practitioners—In this paper, we consider a scenario where vehicles need to have high-speed, uninterrupted data links in obstructed, highly dense urban environments. Due to spectrum crunch, the data links are increasingly likely to rely on a high-frequency spectrum, including mmWave with quasi-optic nature, visible light communications, or even laser. However, the obstructed LoS and propagation are critical issues with the 5G and beyond as they rely on the availability of an unobstructed path (e.g., the LoS or a quality reflective path). On the other hand, the RIS performs as a passive reflective element that provides an indirect LoS link, a one-bounce channel, to improve the performance and efficiency of mmWave, and beyond, wireless communication networks. UAVs equipped with RISs are suggested in this paper to be adopted as aerial transponders to reflect signals and facilitate communication in high-density environments. Therefore, the problem of UAV navigation and 3D trajectory planning should be addressed regarding the application, particularly, for providing LoS service for mobile vehicles with arbitrary directions. Autonomous navigation of RISoUAVs for LoS wireless communication is a natural multi-dimensional extension of autonomous navigation with obstacle avoidance where regions in which LoS communication is lost are viewed as obstacles to avoid. However, the environment and valid LoS links can change dynamically due to moving vehicles in the obstructed environment. This makes the navigation design an NP-hard problem that is tackled in this paper by developing an effective navigation program.

A grid-feeding voltage source converter (GFD-VSC) requires a phase-locked loop (PLL) synchronization unit to be connected to the grid. The PLL critically affects the dynamic performance and stability of the GFD-VSC. In particular, a PLL with pre/in-loop filtering, for working under distorted/polluted conditions, possesses a narrow stability margin and deficient performance in weak grid connections and fault ride-through (FRT) transients, also poor performance in frequency estimation. To address these problems, a robust PLL with several enhanced characteristics is proposed in this article. The robust PLL with a dynamic state feedback controller is designed using an${{\bm{H}}}_\infty $robust control. The feedback controller is designed to improve the dynamic stability/response of the PLL, exposed to control uncertainties and exogenous disturbances, weak-grid connection, FRT transients and to improve its performance in frequency estimation. Numerical simulations validate the effectiveness of the proposed PLL.

It is explored in this article that theinteraction of droop controllers through the power network(IDCPN) is the dominant factor affecting the dynamic stability of an autonomous networked microgrid (ANMG) and new models are developed to support the IDCPN. The models are developed to analyze the impacts of three parts on the IDCPN: 1) the X/R ratio of the power network impedance; 2) the droop controllers including low-pass filters, and 3) the impedance characteristics of the grid-forming voltage source inverters (VSIs). The low-frequency oscillations (LFO), excited by IDCPN, is identified and quantified by modeling the first and second parts. The critical impact of the low X/R ratio on amplifying the LFO is clarified. Then, the output impedance of the grid-forming VSI, including the virtual inductance loop (VIL), is developed and rigorously observed that reveals inefficient performance. It is shown that the VIL improves dynamic performance by providing sufficient damping to suppress LFOs and not by effectively boosting the X/R of the VSI output impedance. This, however, is problematic in the current limiting that puts the ANMG at instability risk because of the resistive–capacitive impedance characteristics of the VSIs. A${{\boldsymbol{H}}_\infty }$robust controller is proposed to replace VIL for suppressing LFO and stabilizing ANMG. Numerical/simulation results are provided to prove the accuracy of the models.

The increasing number of electric vehicles (EVs) demands management solutions to deal with the impacts of EV charging on the efficiency of distribution grids. Many suggested methods are derived from analysis on laboratory-scale systems with declared data, which cannot be implemented for real networks. In this article, a two-step scheduling model is developed that effectively guides a large-scale EV fleet in microgrids without demanding a dynamic monetary scheme. The first step corresponds to prediction-based day-ahead optimal scheduling for large scale EVs, which minimizes the costs of electricity supply and EVs' battery degradation. To avoid dimensional problems in calculations, an improved K-means clustering algorithm is presented to divide vehicles into different clusters. In the second step, online coordination is deployed based on an effective scoring system to encourage drivers to follow the first-step provided model. The proposed model is analyzed on a grid-connected microgrid with photovoltaic system integration. The problem (real) data are derived based on an estimate of the development process on the Ontario energy network over the next ten years. Results show that the introduced model can guarantee the accurate deployment of optimal charging/discharging schedules in large-scale systems.

A current limiting scheme is employed for Inverter-Interfaced Distributed Generation (IIDG) units, mostly in the control loops, to limit the current within the tolerable range and to facilitate the IIDG units being connected to the grid during the transient. However, current-limiting/saturation of the droop-controlled grid-forming inverters affects their transient stability and may make them unstable, which yet has not been explored in the literature in the context of autonomous microgrids (MGs). It is disclosed in this paper that the sharp phase angle variation and the arbitrary resistive output impedance of grid-forming inverters in the current limiting mode make the autonomous MGs unstable. Time-domain simulations prove the idea.

The interaction of frequency and voltage droop controllers through power network is the dominant factor affecting dynamic stability of autonomous networked microgrids (ANMGs). The X/R ratio of power lines is low in the low-voltage microgrids (MGs) which makes the interaction/cross-coupling of droop controllers high and raises low/subsynchronous frequency oscillations and thus stability concerns. This issue is investigated in this work by developing new models to support the idea of interaction of droop controllers. To this end, the impacts of power network (mainly the X/R ratio) and the droop rules including the low-pass filter (LPF) are evaluated. The developed models can be used to design sufficient damping to the system by considering the interaction of droop controllers and power network requirements to improve stability margins of ANMGs.

This letter considers the problem of casual heart rate tracking during intensive physical exercise using simultaneous 2 channel photoplethysmographic (PPG) and 3 dimensional (3D) acceleration signals recorded from wrist. This is a challenging problem because the PPG signals recorded from wrist during exercise are contaminated by strong Motion Artifacts (MAs). In this work, a novel algorithm is proposed which consists of two main steps of MA Cancellation and Spectral Analysis. The MA cancellation step cleanses the MA-contaminated PPG signals utilizing the acceleration data and the spectral analysis step estimates a higher resolution spectrum of the signal and selects the spectral peaks corresponding to HR. Experimental results on datasets recorded from 12 subjects during fast running at the peak speed of 15 km/hour showed that the proposed algorithm achieves an average absolute error of 1.25 beat per minute (BPM). These experimental results also confirm that the proposed algorithm keeps high estimation accuracies even in strong MA conditions.