Search Results (18)

The growing demand for low-emission maritime transport and efficient onboard energy management has intensified research into advanced control strategies for hybrid shipboard microgrids. These systems integrate both AC and DC power domains, incorporating renewable energy sources and battery storage to enhance fuel efficiency, reduce greenhouse gas emissions, and support operational flexibility. However, integrating renewable energy into shipboard microgrids introduces challenges, such as power fluctuations, varying line impedances, and disturbances caused by AC/DC load transitions, harmonics, and mismatches in demand and supply. These issues impact system stability and the seamless coordination of multiple distributed generators. To address these challenges, we proposed a hierarchical control strategy that supports sustainable operation by improving the voltage and frequency regulation under dynamic conditions, as demonstrated through both MATLAB/Simulink simulations and real-time hardware validation. Simulation results show that the proposed controller reduces the frequency deviation by up to 25.5% and power variation improved by 20.1% compared with conventional PI-based secondary control during load transition scenarios. Hardware implementation on the NVIDIA Jetson Nano confirms real-time feasibility, maintaining power and frequency tracking errors below 5% under dynamic loading. A comparative analysis of the classical PI and sliding mode control-based designs is conducted under various grid conditions, such as cold ironing mode of the shipboard microgrid, and load variations, considering both the AC and DC loads. The system stability and control law formulation are verified through simulations in MATLAB/SIMULINK and practical implementation. The experimental results demonstrate that the proposed secondary control architecture enhances the system robustness and ensures sustainable operation, making it a viable solution for modern shipboard microgrids transitioning towards green energy. Full article

To address the modeling complexity and multi-objective collaborative optimization challenges in multi-depot and multiple unmanned aerial vehicle (UAV) delivery task planning, this paper proposes a bi-layer planning framework, which comprehensively considers resource constraints, multi-depot coordination, and the coupling characteristics of path execution. The novelty of this work lies in the seamless integration of an enhanced genetic algorithm and tailored swarm optimization within a unified two-tier architecture. The upper layer tackles the task assignment problem by formulating a multi-objective optimization model aimed at minimizing economic costs, delivery delays, and the number of UAVs deployed. The Enhanced Non-Dominated Sorting Genetic Algorithm II (ENSGA-II) is developed, incorporating heuristic initialization, goal-oriented search operators, an adaptive mutation mechanism, and a staged evolution control strategy to improve solution feasibility and distribution quality. The main contributions are threefold: (1) a novel ENSGA-II design for efficient and well-distributed task allocation; (2) an improved PSO-based path planner with chaotic initialization and adaptive parameters; and (3) comprehensive validation demonstrating substantial gains over baseline methods. The lower layer addresses the path planning problem by establishing a multi-objective model that considers path length, flight risk, and altitude variation. An improved particle swarm optimization (PSO) algorithm is proposed by integrating chaotic initialization, linearly adjusted acceleration coefficients and maximum velocity, a stochastic disturbance-based position update mechanism, and an adaptively tuned inertia weight to enhance algorithmic performance and path generation quality. Simulation results under typical task scenarios demonstrate that the proposed model achieves an average reduction of 47.8% in economic costs and 71.4% in UAV deployment quantity while significantly reducing delivery window violations. The framework exhibits excellent capability in multi-objective collaborative optimization. The ENSGA-II algorithm outperforms baseline algorithms significantly across performance metrics, achieving a hypervolume (HV) value of 1.0771 (improving by 72.35% to 109.82%) and an average inverted generational distance (IGD) of 0.0295, markedly better than those of comparison algorithms (ranging from 0.0893 to 0.2714). The algorithm also demonstrates overwhelming superiority in the C-metric, indicating outstanding global optimization capability in terms of distribution, convergence, and the diversity of the solution set. Moreover, the proposed framework and algorithm are both effective and feasible, offering a novel approach to low-altitude urban logistics delivery problems. Full article

Airports are complex environments where efficient localization and intelligent traffic management are essential for ensuring smooth navigation and operational efficiency for both pedestrians and Autonomous Guided Vehicles (AGVs). This study presents an Artificial Intelligence (AI)-driven airport traffic management system that integrates Visible Light Communication (VLC), rerouting techniques, and adaptive reward mechanisms to optimize traffic flow, reduce congestion, and enhance safety. VLC-enabled luminaires serve as transmission points for location-specific guidance, forming a hybrid mesh network based on tetrachromatic LEDs with On-Off Keying (OOK) modulation and SiC optical receivers. AI agents, driven by Deep Reinforcement Learning (DRL), continuously analyze traffic conditions, apply adaptive rewards to improve decision-making, and dynamically reroute agents to balance traffic loads and avoid bottlenecks. Traffic states are encoded and processed through Q-learning algorithms, enabling intelligent phase activation and responsive control strategies. Simulation results confirm that the proposed system enables more balanced green time allocation, with reductions of up to 43% in vehicle-prioritized phases (e.g., Phase 1 at C1) to accommodate pedestrian flows. These adjustments lead to improved route planning, reduced halting times, and enhanced coordination between AGVs and pedestrian traffic across multiple intersections. Additionally, traffic flow responsiveness is preserved, with critical clearance phases maintaining stability or showing slight increases despite pedestrian prioritization. Simulation results confirm improved route planning, reduced halting times, and enhanced coordination between AGVs and pedestrian flows. The system also enables accurate indoor localization without relying on a Global Positioning System (GPS), supporting seamless movement and operational optimization. By combining VLC, adaptive AI models, and rerouting strategies, the proposed approach contributes to safer, more efficient, and human-centered airport mobility. Full article

Power electronics (PE) technology has become integral across various applications, playing a vital role in sectors worldwide. The integration of renewable energy (RE) into modern power grids requires highly efficient and reliable power conversion systems, especially with the increasing demand for grid controllability and flexibility. Advanced control and information technologies have established power electronics converters as essential enablers of large-scale RE generation. However, their widespread use has introduced challenges to conventional power grids, including reduced system inertia and stability issues. This article studies the critical role of power electronics in the grid integration of RE systems, addressing key technical challenges and requirements. A special focus is given to the integration of wind energy, solar photovoltaic, and energy storage systems. This paper reviews essential aspects of energy generation and conversion, including the control strategies for individual power converters and system-level coordination for large-scale energy systems. This article additionally includes grid codes that pertain to wind and photovoltaic systems, as well as power conversion and control technologies. Finally, it outlines the future research directions, aimed at overcoming emerging challenges and advancing the seamless integration of RE systems into the grid, thereby contributing to the development of more sustainable and resilient energy infrastructure. Full article

Harnessing surplus wind and solar energy for water electrolysis boosts the efficiency of renewable energy utilization and supports the development of a low-carbon energy framework. However, the intermittent and unpredictable nature of wind and solar power generation poses significant challenges to the dynamic stability and hydrogen production efficiency of electrolyzers. This study introduces a multi-state rotational control strategy for a hybrid electrolyzer system designed to produce hydrogen. Through a detailed examination of the interplay between electrolyzer power and efficiency—along with operational factors such as load range and startup/shutdown times—six distinct operational states are categorized under three modes. Taking into account the differing dynamic response characteristics of proton exchange membrane electrolyzers (PEMEL) and alkaline electrolyzers (AEL), a power-matching mechanism is developed to optimize the performance of these two electrolyzer types under varied and complex conditions. This mechanism facilitates coordinated scheduling and seamless transitions between operational states within the hybrid system. Simulation results demonstrate that, compared to the traditional sequential startup and shutdown approach, the proposed strategy increases hydrogen production by 10.73% for the same input power. Moreover, it reduces the standard deviation and coefficient of variation in operating duration under rated conditions by 27.71 min and 47.04, respectively, thereby enhancing both hydrogen production efficiency and the dynamic operational stability of the electrolyzer cluster. Full article

To mitigate voltage limit issues in the operation of a novel electromagnetic voltage regulation device, this paper presents a flexible loop-closing control strategy with voltage optimization. The approach uses a two-stage path optimization: in the first stage, the voltage phase at the loop-closing point is adjusted to ensure smooth operation, while in the second stage, the voltage magnitude is optimized to prevent voltage limits and achieve seamless regulation. By integrating phase angle difference calculations with coordinated rotation angle control, the simulation results show that this strategy reduces loop-closing current by approximately 95.87% compared to direct loop closing, decreases voltage fluctuations by around 50.0% compared to traditional methods, and shortens operation time by 40.14%. This approach significantly enhances system stability and response speed, effectively addressing the issue of excessive loop-closing current caused by voltage deviations at distribution network tie switches. Full article

This study investigates the integration of reinforcement learning (RL) with optimal control to enhance precision and energy efficiency in industrial robotic manipulation. A novel framework is proposed, combining Deep Deterministic Policy Gradient (DDPG) with a Linear Quadratic Regulator (LQR) controller, specifically applied to the ABB IRB120, a six-degree-of-freedom (6-DOF) industrial manipulator, for pick-and-place tasks in warehouse automation. The methodology employs an actor–critic RL architecture with a 27-dimensional state input and a 6-dimensional joint action output. The RL agent was trained using MATLAB’s Reinforcement Learning Toolbox and integrated with ABB’s RobotStudio simulation environment via TCP/IP communication. LQR controllers were incorporated to optimize joint-space trajectory tracking, minimizing energy consumption while ensuring precise control. The novelty of this research lies in its synergistic combination of RL and LQR control, addressing energy efficiency and precision simultaneously—an area that has seen limited exploration in industrial robotics. Experimental validation across 100 diverse scenarios confirmed the framework’s effectiveness, achieving a mean positioning accuracy of 2.14 mm (a 28% improvement over traditional methods), a 92.5% success rate in pick-and-place tasks, and a 22.7% reduction in energy consumption. The system demonstrated stable convergence after 458 episodes and maintained a mean joint angle error of 4.30°, validating its robustness and efficiency. These findings highlight the potential of RL for broader industrial applications. The demonstrated accuracy and success rate suggest its applicability to complex tasks such as electronic component assembly, multi-step manufacturing, delicate material handling, precision coordination, and quality inspection tasks like automated visual inspection, surface defect detection, and dimensional verification. Successful implementation in such contexts requires addressing challenges including task complexity, computational efficiency, and adaptability to process variability, alongside ensuring safety, reliability, and seamless system integration. This research builds upon existing advancements in warehouse automation, inverse kinematics, and energy-efficient robotics, contributing to the development of adaptive and sustainable control strategies for industrial manipulators in automated environments. Full article

Distributed energy resources have demonstrated their potential to mitigate the limitations of large, centralized generation systems. This is achieved through the geographical distribution of generation sources that capitalize on the potential of their respective environments to satisfy local demand. In a microgrid, the control problem is inherently distributed, rendering traditional control techniques inefficient due to the impracticality of central governance. Instead, coordination among its components is essential. The challenge involves enabling these components to operate under optimal conditions, such as charging batteries with surplus solar energy or deactivating controllable loads when market prices rise. Consequently, there is a pressing need for innovative distributed strategies like emergent control. Inspired by phenomena such as the environmentally responsive behavior of ants, emergent control involves decentralized coordination schemes. This paper introduces an emergent control strategy for microgrids, grounded in the response threshold model, to establish an autonomous distributed control approach. The results, utilizing our methodology, demonstrate seamless coordination among the diverse components of a microgrid. For instance, system resilience is evident in scenarios where, upon the failure of certain components, others commence operation. Moreover, in dynamic conditions, such as varying weather and economic factors, the microgrid adeptly adapts to meet demand fluctuations. Our emergent control scheme enhances response times, performance, and on/off delay times. In various test scenarios, Integrated Absolute Error (IAE) metrics of approximately 0.01% were achieved, indicating a negligible difference between supplied and demanded energy. Furthermore, our approach prioritizes the utilization of renewable sources, increasing their usage from 59.7% to 86.1%. This shift not only reduces reliance on the public grid but also leads to significant energy cost savings. Full article

Multi-microgrids (MMGs) revolutionize integrating and managing diverse distributed energy resources (DERs), significantly enhancing the overall efficiency of energy systems. Unlike traditional power systems, MMGs comprise interconnected microgrids that operate independently or collaboratively. This innovative concept adeptly addresses challenges posed by pulsed load effects, capitalizing on the cooperative nature of interconnected microgrids. A coordinated MMG system effectively redistributes and shares the impact of pulsed loads, mitigating voltage fluctuations and ensuring sustained system stability. The proposed cooperative MMG scheme optimizes power distribution and load prioritization, facilitating the seamless allocation of surplus energy from neighboring microgrids to meet sudden surges in demand. This study focuses on DC standalone multi-microgrid systems, showcasing their inherent adaptability, resilience, and operational efficiency in managing pulse, variable, and unpredictable generation deficits. Several experiments on a laboratory-scale DC multi-microgrid validate the system’s robust performance. Notably, transient current fluctuations during pulse loads are promptly stabilized through the effective collaboration of microgrids. Variable load experiments reveal distinct behaviors, shedding light on the profound influence of control strategies. This research reveals the transformative potential of MMGs in addressing energy challenges, with a particular focus on DC standalone multi-microgrid systems. The findings underscore the adaptability and resilience of the proposed cooperative scheme, marking a significant stride in the evolution of modern power systems. Full article

The hybrid AC/DC microgrid is an independent and controllable energy system that connects various types of distributed power sources, energy storage, and loads. It offers advantages such as a high power quality, flexibility, and cost effectiveness. The operation states of the microgrid primarily include grid-connected and islanded modes. The smooth switching between these two states is a key technology for ensuring the flexible and efficient operation of the microgrid. In this paper, the typical structure of an AC–DC hybrid microgrid and its coordination control strategy are introduced, and an improved microgrid model is proposed. Secondly, an adaptive current–voltage–frequency integrated control method based on signal compensation is proposed to solve the impulse current and voltage generated during the switching between a grid-connected state and an off-grid state. Finally, in response to unplanned grid-connected scenarios, an improved pre-synchronization control strategy based on BP neural networks is introduced to rapidly restore stable operation. The proposed control strategies enhanced the steady-state and transient stability of the hybrid wind–solar–energy storage AC/DC microgrid, achieving seamless grid-connected and islanded transitions without disturbances. The simulation and experimental results validated the correctness and effectiveness of the proposed theories. Full article

Efficient logistics and transport at the port heavily relies on efficient AGV scheduling and planning for container transshipment. This paper presents a comprehensive approach to address the challenges in AGV path planning and coordination within the domain of intelligent transportation systems. We propose an enhanced graph search method for constructing the global path of a single AGV that mitigates the issues associated with paths closely aligned with obstacle corner points. Moreover, a centralized global planning module is developed to facilitate planning and scheduling. Each individual AGV establishes real-time communication with the upper layers to accurately determine its position at complex intersections. By computing its priority sequence within a coordination circle, the AGV effectively treats the high-priority trajectories of other vehicles as dynamic obstacles for its local trajectory planning. The feasibility of trajectory information is ensured by solving the online real-time Optimal Control Problem (OCP). In the trajectory planning process for a single AGV, we incorporate a linear programming-based obstacle avoidance strategy. This strategy transforms the obstacle avoidance optimization problem into trajectory planning constraints using Karush-Kuhn-Tucker (KKT) conditions. Consequently, seamless and secure AGV movement within the port environment is guaranteed. The global planning module encompasses a global regulatory mechanism that provides each AGV with an initial feasible path. This approach not only facilitates complexity decomposition for large-scale problems, but also maintains path feasibility through continuous real-time communication with the upper layers during AGV travel. A key advantage of our progressive solution lies in its flexibility and scalability. This approach readily accommodates extensions based on the original problem and allows adjustments in the overall problem size in response to varying port cargo throughput, all without requiring a complete system overhaul. Full article

To exhaust the potential of energy efficiency and dynamic performance of the dual-motor four-drive powertrain, this study developed a multi-objective real-time optimal energy management strategy considering energy efficiency and flexible torque response. First, a theoretical analysis of energy loss and operating characteristics was performed to elucidate the energy-saving advantages and control challenges of the dual-motor four-drive powertrain. Second, an economic strategy based on the adaptive nonlinear particle swarm optimization (ANLPSO) and optimization freezing tolerance mechanism was devised to realize real-time optimal power distribution. Then, the pre-shifting recognition schedule and gradient torque recovery strategy were developed to achieve flexible torque response during gear shifting. Finally, smooth switching logic was created to assure a seamless transition between the two strategies. Numerous simulation results indicate that compared with the single-motor drive strategy, the proposed strategy can increase energy efficiency by 8.1%, 4.02%, and 9.49% under NEDC, WLTC, and CLTC, respectively. During shifting, the longitudinal acceleration and jerk of the proposed strategy are significantly superior to those of the original strategy, thereby enhancing the vehicle’s dynamic performance and ride comfort. The results of the drum experiment validate the efficacy of the proposed method for energy consumption optimization and torque coordination control in the actual vehicle environment. Full article

Microgrids (MGs) are capable of playing an important role in the future of intelligent energy systems. This can be achieved by allowing the effective and seamless integration of distributed energy resources (DERs) loads, besides energy-storage systems (ESS) in the local area, so they are gaining attraction worldwide. In this regard, a DC MG is an economical, flexible, and dependable solution requiring a trustworthy control structure such as a hierarchical control strategy to be appropriately coordinated and used to electrify remote areas. Two control layers are involved in the hierarchy control strategy, including local- and global-control levels. However, this research focuses mainly on the issues of DC MG’s local control layer under various load interruptions and power-production fluctuations, including inaccurate power-sharing among sources and unregulated DC-bus voltage of the microgrid, along with a high ripple of battery current. Therefore, this work suggests developing local control levels for the DC MG based on the hybrid particle swarm optimization/grey wolf optimizer (HPSO–GWO) algorithm to address these problems. The key results of the simulation studies reveal that the proposed control scheme has achieved significant improvement in terms of voltage adjustment and power distribution between photovoltaic (PV) and battery technologies accompanied by a supercapacitor, in comparison to the existing control scheme. Moreover, the settling time and overshoot/undershoot are minimized despite the tremendous load and generation variations, which proves the proposed method’s efficiency. Full article

The coordination of clustered microgrids (MGs) needs to be achieved in a seamless manner to tackle generation-load mismatch among MGs. A hierarchical control strategy based on PI controllers for local and global layers has been proposed in the literature to coordinate DC MGs in a cluster. However, this control strategy may not be able to resist significant load disturbances and unexpected generated powers due to the sporadic nature of the renewable energy resources. These issues are inevitable because both layers are highly dependent on PI controllers who cannot fully overcome the abovementioned obstacles. Therefore, Grey Wolf Optimizer (GWO) is proposed to enhance the performance of the global layer by optimizing its PI controller parameters. The simulation studies were conducted using the well-established MATLAB Simulink, and the results reveal that the optimized global layer performs better than the conventional ones. It is noticed that not only accurate power-sharing and proper voltage regulation within ±1% along with fewer power losses are achieved by adopting the modified consensus algorithm for the clustered DC MGs, but also the settling time and overshoot/undershoot are reduced even with the enormous load and generation changes which indicates the effectiveness of the proposed method used in the paper. Full article

The purpose of smart grid architecture as compared to the conventional grid is to ensure more stability, reliability and bi-directional communication between the utility and the consumer. The deployment of the same has succeeded in improving the efficiency of the distribution systems and effective co-ordination and interoperability among the different components of the grid. Smart inverters play a major role in seamless grid integration, control and conversion of power when the renewable energy sources are present. However, they come with several security challenges as well, which are of considerable concern. Certain cyber threats include physical and cyber attacks, natural phenomena which in turn can lead to grid failure, blackouts, commercial energy losses, privacy and safety issues, etc. Therefore, there is a need for critical examination of all these issues which must be considered for designing cyber secure smart inverters at the distribution level. In this comprehensive review, keeping the technological perspective in mind, the existing gaps and the necessity for the same are highlighted. The various topologies, IEEE protocols and the control strategy are presented in detail. This will enable prospective researchers to address the design issues of smart inverters with greater focus on security and reliability aspects. Full article