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AbstractThis study focuses on stabilizing a bidirectional inductive wireless power transfer (WPT) system using an event‐triggered approach. It is only assumed the inductor currents on both the primary and pickup sides are measurable, and they are sent synchronously to the controller via a digital channel. To estimate the unmeasured states and maintain plant stability, a full‐order state observer and an observer‐based controller have been developed. The control parameters are optimized through a genetic algorithm to achieve the desired output response. An emulation methodology is then applied to create an output‐based event‐triggering condition. This condition ensures the stability of the closed‐loop system even in the presence of communication constraints. To prevent Zeno sampling, a minimal time interval between two transmissions is enforced using time‐regularization techniques. Furthermore, the performance of the event‐triggered controller is enhanced by solving a linear matrix inequality condition, which further reduces the number of transmission instances. The methodology offers a systematic and optimal design for the bidirectional inductive WPT system. It eliminates the need for manual tuning of control parameters, which is particularly beneficial given the system's complex nature. To address both continuous‐time and discrete‐time dynamics, the entire system is represented as a hybrid dynamical system, making it more intuitive for networked control systems. The efficiency of this approach is assessed through numerical simulations of a WPT system, demonstrating its effectiveness. The results show that the average intertransmission interval has been increased from 0.0799 to 0.1782 s, that is, the proposed event‐triggering strategy reduced the number of transmissions to more than 50% compared with conventional periodic sampling.

Abstract A precise estimate of PV panels temperature is crucial for accurately assessing their electrical performance. Therefore, in this study, one of the main aims has been to significantly improve the prediction accuracy of the PV cell temperature, by using realistic boundary conditions. Unlike previous thermal models in the literature, which usually focus on its mere application, a detailed step by step development and numerical implementation of the complete model has also been provided in great details in this work. The developed model is transient, so it can fully simulate the thermal performance of any PV panel under time-varying field conditions. Once the model is defined for a specific PV panel, the only external inputs it needs are the total incident solar irradiation, wind speed and the ambient temperature. The model has been adequately validated through PV panel’s datasheet provided information, literature data and against a versatile set of experimental data under various weather conditions. After thorough validations, the developed model was compared to various other widely used empirical, analytical and numerical thermal models from the literature. The comparison shows that by using realistic boundary conditions, the developed thermal model has far better prediction accuracy than other models from the literature. The methodology presented in this study is completely generic. That is, though it has been implemented and validated here for a silicon-based PV module the approach may be used to model any free-standing plane PV surface, with appropriate modifications to layer thicknesses and material properties. A range of weather conditions may also be accommodated.

The growing presence of EVs in regional microgrids introduces increased variability and uncertainty in the areas’ load profiles. This paper presents a novel approach for optimizing energy and reserve minimization in a sustainable integrated microgrid with electric vehicles (EVs) by the use of the dynamic and adjustable Manta Ray Foraging (DAMRF) algorithm. The DAMRF algorithm harnesses the inherent flexibility of EVs as controllable loads and develops a comprehensive dispatch model for a large-scale EV response. The model takes into account the management, operational, and environmental costs associated with load fluctuations in the microgrid. Simulation evaluations conducted based on a practical microgrid environment validate the effectiveness of our wind–solar energy storage and management strategy. The results showcase significant improvements in energy and reserve minimization, highlighting the potential advantages of integrating EVs into sustainable microgrid systems. In addition, the DAMRF algorithm achieves lower environmental pollution control costs (USD 8000) compared to the costs associated with the Genetic Algorithm (GA) (USD 8654.639) and PSO (USD 8579.546), emphasizing its ability to effectively control and minimize environmental pollution. In addition, the DAMRF algorithm offers a more cost-effective solution for managing the power grid, and the shorter solution running time of the DAMRF is almost the same as PSO’s quicker decision-making and response times, enhancing the overall responsiveness and adaptability of the power grid management system.

Abstract The increased demand of energy in domestic applications necessitates the development of innovative engineering solutions in building heating, ventilating, and air conditioning (HVAC) systems. As the largest energy intensive sector is domestic buildings, more focus is currently directed to reduce air conditioning energy consumption. Double-pipe heat exchangers are considered one of the practical solutions in today’s HVAC industry. Nevertheless, a few studies focus on using double-pipe heat exchangers in air conditioning applications. This paper experimentally investigates the usage of double-pipe condenser and evaporator in an air conditioning system serving a 45 m 3 balanced calorimeter of 2.24 kW heat load. Deionized water (DIW) was used as the secondary heat transfer working fluid for both the evaporator and condenser units, and R-22 was used as the AC system refrigerant. Experimental results of the double-pipe heat evaporator/condenser setup showed a promising reduction in the compressor work and an increase in the system coefficient of performance (COP). The collected data showed that the system efficiency depends more on the evaporator DIW flowrate than on the condenser DIW flowrate. By increasing the DIW flowrate in the evaporator, the compressor work was shown to decrease, while the COP was shown to increase. In comparison with a standard rated air conditioning unit, using a double-pipe evaporator and condenser units with the maximum DIW flowrates resulted in a decrease of about 53% in the compressor work and a similar percentage of increase in the system COP.

This paper reviews advances in the state of the art of applying three-dimensional finite-element (3-D-FE) magnetic field computation techniques to the analysis and quantification of parameters and performance of permanent magnet (PM) brushless DC motors as components of brushless DC drives. The application of the powerful 3-D-FE formulation based on the coupled magnetic vector potential-magnetic scalar potential (CMVP-MSP) concept to a case study PM brushless DC motor with skewed permanent magnet mounts is reviewed and summarized. This large-scale simulation of such a motor drive system is shown to be successfully implementable using workstation type computer environments.

Designing power aware wearable devices is the main key in building compact size autonomous smart devices to successfully connect health Internet of things solutions. With their ability to perform tasks ranging from simple self-monitoring to complex interactive tasks, these devices hold great promises in providing a large scale cost effective solution to the challenges facing nowadays healthcare systems. Despite the advances in sensing and hardware design, there still remain several technical challenges facing the research community to build devices that meet the computational requirements with a self-powered capability. Overcoming these challenges require major improvements in all the building blocks of wearable devices including sensors, power management, signal processing, computing architectures, and communication. This paper surveys some of the past milestones related to these subsystems and discusses promising research directions addressing their limitations.

Summary Scale formation including those formed by iron sulfides have been a major hassle in the upstream sector of the oil and gas industry for many decades. Iron Sulfide scales including pyrite (FeS2) and troilite (FeS) often form a precipitate in the matrix formation, tubulars and other downhole equipment in the wells resulting in plant shutdown. Herein, a molecular modelling tool known as Density Functional Theory (DFT) is used to study the binding affinity of chelating agents to ferrous ion, which is the state of iron in pyrite scale. The calculated binding affinity of the chelating agents to Fe2+ increased in the order; GLDA < HEDTA < EDTA < DTPA which correlated with what has been reported experimentally. The number of nitrogen atoms in a chelating agent plays a predominant role in its binding ability. This could give insights on how novel chemicals could be designed which would be more effective and environmentally friendly in iron sulfide scale removal.