Explore the vast range of titles available.

One of the most important requirements of microgrid operation is to maintain a constant frequency such as 50 Hz or 60 Hz, which is closely related to a power balance between supply and demand. In general, microgrids are connected to power grids and surplus/shortage power of microgrids is traded with power grids. Since islanded microgrids are isolated from any power grids, the decrease in generation or load-shedding can be used to maintain the frequency when a power imbalance between supply and demand occurs. The power imbalance restricts the electricity use of consumers in the case of supply shortage and the power supply of suppliers in the case of supply surplus. Therefore, the islanded microgrid should be operated to reduce power imbalance conditions. Meanwhile, the microgrid is a small-scale power system and the employment of skillful operators for effective operation of its components requires high costs. Therefore, automatic operation of the components is effective realistically. In addition, the components are distributed in the microgrid and their operation should consider their owners’ profits. For these reasons, a multiagent system application can be a good alternative for microgrid operation. In this paper, we present a multiagent system for autonomous operation of the islanded microgrid on a power market environment. The proposed multiagent system is designed based on a cooperative operation scheme. We show the functionality and the feasibility of the proposed multiagent system through several tests.

A microgrid is an eco-friendly power system because renewable sources such as solar and wind power are used as the main power sources. For this reason, much research, development, and demonstration projects have recently taken place in many countries. Operation is one of the important research topics for microgrids. For efficient and economical microgrid operation, a human operator is required as in other power systems, but it is difficult because there are some restrictions related to operation costs and privacy issues. To overcome the restriction, autonomous operation for microgrids is required. Recently, an intelligent agent system for autonomous microgrid operation has been studied as a potential solution. This paper proposes a multiagent system for autonomous microgrid operation. To build the multiagent system, the functionalities of agents, interactions among agents, and an effective agent protocol have been designed. The proposed system has been implemented by using an ADIPS/DASH framework as an agent platform. The intelligent multiagent system for microgrid operation based on the proposed scheme is tested to show the functionality and feasibility on a distributed environment through the Internet.

The introduction of autonomous vehicles in underground mine trackless transportation systems can significantly reduce safety risks for personnel in production operations and improve transportation efficiency. Current autonomous mining vehicle technology is characterized by complex algorithms and high deployment costs, which limit its widespread application in underground mines. This paper proposes a light-band-guided autonomous driving method for trackless mining vehicles, where a continuous, digitally controllable light band is installed at the tunnel ceiling to provide uninterrupted vehicle guidance. The light band is controlled by an independent hardware system and uses different colors to indicate vehicle movement status, enabling vehicles to navigate simply by following the designated light trajectory. We designed the necessary hardware and software systems and built a physical model for validation. The system enabled multiple vehicles to be guided simultaneously within the same area to perform diverse transportation tasks according to operational requirements. The model vehicles maintained a safe distance from tunnel walls. In GPS-denied environments, positioning was achieved using dead reckoning and periodic location updates at designated points based on the known light-band trajectory. The proposed method demonstrates high potential for practical applications.

Many industries apply traditional controllers to automate manual control. In recent years, artificial intelligence controllers applied with deep-learning techniques have been suggested as advanced controllers that can achieve goals from many industrial domains, such as humans. Deep reinforcement learning (DRL) is a powerful method for these controllers to learn how to achieve their specific operational goals. As DRL controllers learn through sampling from a target system, they can overcome the limitations of traditional controllers, such as proportional-integral-derivative (PID) controllers. In nuclear power plants (NPPs), automatic systems can manage components during full-power operation. In contrast, startup and shutdown operations are less automated and are typically performed by operators. This study suggests DRL-based and PID-based controllers for cold shutdown operations, which are a part of startup operations. By comparing the suggested controllers, this study aims to verify that learning-based controllers can overcome the limitations of traditional controllers and achieve operational goals with minimal manipulation. First, to identify the required components, operational goals, and inputs/outputs of operations, this study analyzed the general operating procedures for cold shutdown operations. Then, PID- and DRL-based controllers are designed. The PID-based controller consists of PID controllers that are well-tuned using the Ziegler–Nichols rule. The DRL-based controller with long short-term memory (LSTM) is trained with a soft actor-critic algorithm that can reduce the training time by using distributed prioritized experience replay and distributed learning. The LSTM can process a plant time-series data to generate control signals. Subsequently, the suggested controllers were validated using an NPP simulator during the cold shutdown operation. Finally, this study discusses the operational performance by comparing PID- and DRL-based controllers.

With the rapid increase in the use of drones in various applications, including commercial and governmental, and the increasing probability of communication failures and contingencies, research becomes critical to ensure the safety and efficiency of their operations. The aim of this research is to develop adaptive drone flight control algorithms capable of operating effectively under conditions of limited communication and incomplete information to ensure reliable and safe autonomous operation of these systems. The applied methods include computer modelling and simulation, analytical, statistical, functional, deductive and descriptive methods. The study found that the use of performance evaluation methods for complex systems enables the identification of safety and performance criteria for drones, and drone flight control provides basic principles and methods that can be adapted for drones, including autopiloting and navigation. In addition, analyses of satellite communication and navigation prove the need to consider the limitations of this technology when developing drone control algorithms. The combination of these techniques allows for more robust and adaptive drone control systems that can function effectively in complex environments such as communication limitations and incomplete information. Additionally, it was found that the integration of adaptive control algorithms based on these methods allows drones to effectively adapt to variable environmental conditions and make decisions quickly even when communication is lost or information is limited.

This paper deals with an electromechanical system of the trolleybus which is fed by low voltage on-board battery using boost DC/DC converter. Peculiarities of the use of the boost converter to power up field-oriented controlled traction electric drive were studied. It is shown that already existing primary stage of the traction electric drive can be used to implement boost DC/DC converter. Control algorithms for stabilizing the voltage of the direct current link and direct field-oriented control of the torque and the flux of induction motor are presented, which can be applied to implement the maneuvering operation mode of trolleybus. Proposed electromechanical system was tested by the mathematical modeling, taking into account the pulse width modulation in the DC/DC converter, as well as the parameters of the single-section trolleybus and the forces of resistance to its movement. It is shown that when the battery current is lim-ited to 200 A and the voltage of the direct current link (the output voltage of the DC/DC converter) is stabilized at 250 V, unloaded trolleybus can reach a speed of 12 km/h on a flat asphalt surface. The results of the study of the achievable speed of the vehicle at different values of the battery current limit and the level of the DC link voltage task are also presented in the paper. The results of the performed research can be used in the development of new and modernization of existing electromechanical systems of trolleybuses. References 11, figures 8.

Microgrids (MG) are small-scale electric grids with local voltage control and power management systems to facilitate the high penetration and grid integration of renewable energy resources (RES). The distributed generation units (DGs), including RESs, are connected to (micro) grids through power electronics-based inverters. Therefore, new paradigms are required for voltage and frequency regulation by inverter-interfaced DGs (IIDGs). Notably, employing effective voltage and frequency regulation methods for establishing power-sharing among parallel inverters in MGs is the most critical issue. This paper provides a comprehensive study, comparison, and classification of control methods including communication-based, decentralized, and construction and compensation control techniques. The development of inverter-dominated MGs has caused limitations in employing classical control techniques due to their defective performance in handling non-linear models of IIDGs. To this end, this article reviews and illustrates advanced controllers that can deal with the challenges that are created due to the uncertain and arbitrary impedance characteristics of IIDGs in dynamics/transients.

Wind energy plays a crucial role as a renewable source for electricity generation, especially in remote or isolated regions without access to the main power grid. The intermittent characteristics of wind energy make it essential to incorporate energy storage solutions to guarantee a consistent power supply. This study introduces the design, modeling, and control mechanisms of a self-sufficient wind energy conversion system (WECS) that utilizes a Permanent magnet synchronous generator (PMSG) in conjunction with a Water pumping storage station (WPS). The system employs Optimal torque control (OTC) to maximize power extraction from the wind turbine, achieving a peak power coefficient ( C p ) of 0.43. A vector control strategy is applied to the PMSG, maintaining the DC bus voltage at a regulated 465 V for stable system operation. The integrated WPS operates in both motor and generator modes, depending on the excess or shortfall of generated wind energy relative to load demand. In generator mode, the WPS supplements power when wind speeds are insufficient, while in motor mode, it stores excess energy by pumping water to an upper reservoir. Simulation results, conducted in MATLAB/Simulink, show that the system efficiently tracks maximum power points and regulates key parameters. For instance, the PMSG successfully maintains the reference quadrature current, achieving optimal torque and power output. The system’s response under varying wind speeds, with an average wind speed of 8 m/s, demonstrates that the generator speed closely follows turbine speed without a gearbox, leading to efficient power conversion. The results confirm the flexibility and robustness of the control strategies, ensuring continuous power delivery to the load. This makes the system a feasible solution for isolated, off-grid applications, contributing to advancements in renewable energy technologies and autonomous power generation systems.

As the operation mode of construction machinery is being transformed from traditional manual operation to unmanned autonomous operation, a trajectory planning scheme for obstacle avoidance is proposed in this paper to meet the requirements of unmanned autonomous operation of excavators in complex environments and confined spaces. First, a simulation model is established to represent the working mechanism of the excavator and its surrounding obstacle environment. Next, considering the spatial constraints and environmental obstacles present during excavation and swing-loading operations, an optimal trajectory planning strategy is developed for autonomous excavator operation under restricted conditions. The proposed approach enhances the conventional RRT* algorithm through the incorporation of an environmental parameter-based heuristic search and an adaptive goal-biased strategy featuring dynamic step size adjustment. These improvements collectively augment path search efficiency and trajectory quality. For trajectory optimization, a quintic Non-Uniform Rational B-Spline (NURBS) curve is employed to plan the motion path of the bucket tip. The method concurrently optimizes operation duration and motion smoothness, producing a multi-objective optimal trajectory. Results show the enhanced RRT* reduces path length, iteration count, and computation time by 3.65%, 64.15%, and 67.9%, while improving trajectory smoothness by 33.4%. The approach reliably generates collision-free, smooth, and energy-efficient trajectories, ensuring high efficiency and mechanical reliability for autonomous excavator operations.

This paper delves into the application of vibration-based energy harvesting to power environmental sensor nodes, a critical component of modern data collection systems. These sensor nodes play a crucial role in structural health monitoring, providing essential data on external conditions that can affect the health and performance of structures. We investigate the feasibility and efficiency of utilizing piezoelectric vibration energy harvesters to sustainably power environmental wireless sensor nodes on the one hand. On the other hand, we exploit different approaches to minimize the sensor node’s power consumption and maximize its efficiency. The investigations consider various sensor node platforms and assess their performance under different voltage levels and broadcast frequencies. The findings reveal that optimized harvester designs enable real-time data broadcasting with short intervals, ranging from 1 to 3 s, expanding the horizons of environmental monitoring, and show that in case the system includes a battery as a backup plan, the battery’s lifetime can be extended up to 9 times. This work underscores the potential of vibration energy harvesting as a viable solution for powering sensor nodes, enhancing their autonomy, and reducing maintenance costs in remote and challenging environments. It opens doors to broader applications of sustainable energy sources in environmental monitoring and data collection systems.