Hasil untuk "Production of electric energy or power. Powerplants. Central stations"

Ge Liang, Aowei Zheng, Sheng Huang et al.

Model predictive current control (MPCC) for permanent magnet synchronous motors (PMSMs) relies heavily on the accuracy of the system model and parameters. To enhance parameter robustness, model-free predictive current control (MFPCC) has been developed, which depends solely on current sampling without the need for motor parameters. Conventional MFPCC stores current difference information in a lookup table (LUT), but only one LUT element can be updated per control period, leaving the remaining entries stagnant. The stagnation of current difference updating (SCDU) is the core issue of MFPCC. It degrades the reliability of current differences, further deteriorating control performance and increasing current ripple and harmonics. Thus, this paper proposes a novel MFPCC with stagnation-free current difference update scheme to overcome SCDU and improve performance. First, an effective and fast calculation of inductances is developed using historic sampling information. Then, a dynamic update mechanism of current difference is presented. This mechanism ensures complete SCDU elimination by updating all current differences within each control period, thus guaranteeing the reliability of current differences. Moreover, the proposed method is independent of any initial parameters with excellent parameter robustness. Finally, the proposed method is compared with conventional MPCC and previous MFPCC methods. The superiority of the proposed method is experimentally verified in an interior PMSM.

Long Nan

Michele Garau, Markus Löschenbrand, Bendik Nybakk Torsæter

Hekun Zhang, Zhendong Zhang, Yelin Deng et al.

Due to their advantages, such as abundant raw material reserves, excellent thermal stability, and superior low-temperature performance, sodium-ion batteries (SIBs) exhibit significant potential for future applications in energy storage and electric vehicles. Therefore, in this study, a commercial pouch-type SIB with sodium iron sulfate cathode material was investigated. Firstly, a second-order RC equivalent circuit model was established through parameter identification using multi-rate hybrid pulse power characterization (M-HPPC) tests at various temperatures. Then, both the specific heat capacity and entropy coefficient of the sodium-ion battery were measured through experiments. Building upon this, an electro-thermal coupling model was developed by incorporating a lumped-parameter thermal model that accounts for the heat generation of the tabs. Finally, the prediction performance of this model was validated through discharge tests under different temperature conditions. The results demonstrate that the proposed electro-thermal coupling model can achieve the simultaneous prediction of both temperature and voltage, providing valuable references for the future development of thermal management systems for SIBs.

Nabeeha Qayyum, Umar Jamil, Anzar Mahmood

Worldwide energy demand is increasing exponentially, presenting significant challenges for existing power generation systems to meet this demand. Enhancing energy efficiency has become critical for reducing consumption and addressing the ongoing environmental crisis. Consequently, there is a need for smart control systems that optimize system costs and improve efficiency. Because of the introduction of smart grids, customers can now participate in demand-side management and integrate renewable energy sources (RESs). Electricity consumption during peak hours often leads to increased grid demand and higher costs. However, the integration of RESs enables consumers to operate appliances during peak hours, thereby reducing reliance on grid power. Therefore, residential load management seeks to reduce power peaks and electrical energy costs. In home energy management systems (HEMS), appliance scheduling is crucial because it continually monitors appliance usage, ensuring that energy supply and demand are balanced. This research aims to optimize power usage by reducing peak loads and electricity costs through the integration of RESs, such as solar or photovoltaic (PV) systems, while considering grid limitations, PV capacity, appliance ON/OFF schedules, and time-of-use tariffs. A genetic algorithm (GA) based optimization technique was employed to evaluate the performance of a HEMS and validated with particle swarm optimization (PSO) technique under identical initial conditions for each appliance and their corresponding energy pricing over different periods. The results show that GA achieved a 48% cost reduction compared to PSO, with significant peak load reduction and improved energy optimization when integrated with PV systems. GA also demonstrated better appliance scheduling, with appliances in the “ON” state for 82% of the time, compared to 52% with PSO.

YANG Bo, ZHANG Zijian

ObjectivesAs an essential sustainable energy technology, renewable energy-powered water electrolysis for hydrogen production has attracted widespread attention due to its advantages in environmental protection and low carbon emissions. However, conventional water electrolysis technologies for hydrogen production face challenges in terms of efficiency and cost, the rapid development of artificial intelligence (AI) provides an effective way to solve the difficult problems of hydrogen production technology through electrolysis of water. To address this, this study aims to explore the key applications and development prospects of AI for optimizing the efficiency and economic performance of water electrolysis systems for hydrogen production.MethodsCommon AI tools such as MATLAB, Python, and SimuNPS are employed for algorithm development, deep learning model training, and multi-physics simulation in water electrolysis systems for hydrogen production. By integrating AI technologies, applications such as output prediction, system capacity optimization and scheduling, and fault diagnosis are implemented to improve system performance and stability. A comparative analysis of performance of different AI models in various real-world scenarios is conducted to explore their specific roles and implementation methods in enhancing system performance and controllability.ConclusionsAI technology offers new avenues for enhancing the efficiency and intelligent scheduling of renewable energy-powered water electrolysis hydrogen production systems. Future research should focus on the application of AI in output forecasting, scheduling optimization, and fault diagnosis, promoting deep integration between AI and system operation. Moreover, innovative applications of AI in intelligent monitoring, automatic control, and multi-source coordination should be explored to provide strong support for the development of efficient, stable, and low-carbon hydrogen energy systems.

Canchen Jiang, Ariel Liebman, Bo Jie et al.

Electric vehicle (EV) coordination can provide significant benefits through vehicle-to-everything (V2X) by interacting with the grid, buildings, and other EVs. This work aims to develop a V2X value-stacking framework, including vehicle-to-building (V2B), vehicle-to-grid (V2G), and energy trading, to maximize economic benefits for residential communities while maintaining distribution voltage. This work also seeks to quantify the impact of prediction errors related to building load, renewable energy, and EV arrivals. A dynamic rolling-horizon optimization (RHO) method is employed to leverage multiple revenue streams and maximize the potential of EV coordination. To address energy uncertainties, including hourly local building load, local photovoltaic (PV) generation, and EV arrivals, this work develops a Transformer-based forecasting model named Gated Recurrent Units-Encoder-Temporal Fusion Decoder (GRU-EN-TFD). The simulation results, using real data from Australia's National Electricity Market, and the Independent System Operators in New England and New York in the US, reveal that V2X value stacking can significantly reduce energy costs. The proposed GRU-EN-TFD model outperforms the benchmark forecast model. Uncertainties in EV arrivals have a more substantial impact on value-stacking performance, highlighting the significance of its accurate forecast. This work provides new insights into the dynamic interactions among residential communities, unlocking the full potential of EV batteries.

Angel Pan Du, Miguel Arana-Catania, Enric Grustan Gutiérrez

Artificial Intelligence algorithms are introduced in this work as a tool to predict the performance of new chemical compounds as alternative propellants for electric propulsion, focusing on predicting their ionisation characteristics and fragmentation patterns. The chemical properties and structure of the compounds are encoded using a chemical fingerprint, and the training datasets are extracted from the NIST WebBook. The AI-predicted ionisation energy and minimum appearance energy have a mean relative error of 6.87% and 7.99%, respectively, and a predicted ion mass with a 23.89% relative error. In the cases of full mass spectra due to electron ionisation, the predictions have a cosine similarity of 0.6395 and align with the top 10 most similar mass spectra in 78% of instances within a 30 Da range.

Chuanshen Wu, Yan Wang, Qianyun Shi et al.

Zesen Li, Xiaoyan Hu, Jing Shi et al.

T. Bogaraj, S. Karthikeyan, P. Sweety Jose et al.

Junxian Li, Jiang Guo, Youhan Deng

In today’s power systems operation, the dual challenge of optimizing economic load distribution while minimizing power plant emissions is pivotal. This challenge is accentuated by the pressing environmental concerns and the finite nature of fossil fuel reserves. In this context, renewable energy sources, notably wind power, have emerged as indispensable alternatives due to their cost-effectiveness and environmental compatibility. However, the inherent variability of wind velocity introduces uncertainty into power output, necessitating innovative approaches to address this complexity. To tackle this issue, we propose a scenario-based probabilistic approach that dynamically considers the slope rate of power output. By leveraging the Blue Whale multi-objective algorithm and employing the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) criterion, we identify significant solutions from the Pareto set across a spectrum of scenarios. Our method is rigorously evaluated across various systems and operational contexts, revealing its superiority over alternative algorithms. Specifically, our approach achieves lower objective function values, reduced standard deviation, and superior overall performance. These findings underscore the critical importance of efficient power system management in balancing environmental sustainability and economic viability. By embracing innovative methodologies, we can navigate the evolving energy landscape and contribute towards a more sustainable future.

Nicolò Zatta, Bernardo De Cesaro, Enrico Dal Cin et al.

Reduced-order electrothermal models play a key role in the design and control of lithium-ion cell stacks, calling for accurate model parameter calibration. This paper presents a complete electrical and thermal experimental characterization procedure for the coupled modeling of cylindrical lithium-ion cells in order to implement them in a prototype Formula SAE hybrid racing car. The main goal of the tests is to determine how the cell capacity varies with the temperature and the discharge current to predict the open-circuit voltage of the cell and its entropic component. A simple approach for the characterization of the battery equivalent electrical circuit and a two-step thermal characterization method are also shown. The investigations are carried out on four commercial 18650 NMC lithium cells. The model was shown to predict the battery voltage with an RMS error lower than 20 mV and the temperature with an RMS error equal to 0.5 °C. The authors hope that this manuscript can contribute to the development of standardized characterization techniques for such cells while offering experimental data and validated models that can be used by researchers and BMS designers in different applications.

Jean-Michel Clairand, Mario González-Rodríguez, Rajesh Kumar et al.

A. Bracale, P. Caramia, P. De Falco et al.

Federico Carpi, Michele C. Valles, Gabriele Frediani et al.

Multisensory human–machine interfaces for virtual- or augmented-reality systems are lacking wearable actuated devices that can provide users with tactile feedback on the softness of virtual objects. They are needed for a variety of uses, such as medical simulators, tele-operation systems and tele-presence environments. Such interfaces require actuators that can generate proper tactile feedback, by stimulating the fingertips via quasi-static (non-vibratory) forces, delivered through a deformable surface, so as to control both the contact area and the indentation depth. The actuators should combine a compact and lightweight structure with ease and safety of use, as well as low costs. Among the few actuation technologies that can comply with such requirements, pneumatic driving appears to be one of the most promising. Here, we present an investigation on a new type of pneumatic wearable tactile displays of softness, recently described by our group, which consist of small inflatable chambers arranged at the fingertips. In order to objectively assess the perceptual response that they can elicit, a systematic electroencephalographic study was conducted on ten healthy subjects. Somatosensory evoked potentials (SEPs) were recorded from eight sites above the somatosensory cortex (Fc2, Fc4, C2 and C4, and Fc1, Fc3, C1 and C3), in response to nine conditions of tactile stimulation delivered by the displays: stimulation of either only the thumb, the thumb and index finger simultaneously, or the thumb, index and middle finger simultaneously, each repeated at tactile pressures of 10, 20 and 30 kPa. An analysis of the latency and amplitude of the six components of SEP signals that typically characterise tactile sensing (P50, N100, P200, N300, P300 and N450) showed that this wearable pneumatic device is able to elicit predictable perceptual responses, consistent with the stimulation conditions. This proved that the device is capable of adequate actuation performance, which enables adequate tactile perceptual performance. Moreover, this shows that SEPs may effectively be used with this technology in the future, to assess variable perceptual experiences (especially with combinations of visual and tactile stimuli), in objective terms, complementing subjective information gathered from psychophysical tests.

Junling Fu, Alberto Rota, Shufei Li et al.

Robots are expanding from industrial applications to daily life, in areas such as medical robotics, rehabilitative robotics, social robotics, and mobile/aerial robotics systems. In recent years, augmented reality (AR) has been integrated into many robotic applications, including medical, industrial, human–robot interactions, and collaboration scenarios. In this work, AR for both medical and industrial robot applications is reviewed and summarized. For medical robot applications, we investigated the integration of AR in (1) preoperative and surgical task planning; (2) image-guided robotic surgery; (3) surgical training and simulation; and (4) telesurgery. AR for industrial scenarios is reviewed in (1) human–robot interactions and collaborations; (2) path planning and task allocation; (3) training and simulation; and (4) teleoperation control/assistance. In addition, the limitations and challenges are discussed. Overall, this article serves as a valuable resource for working in the field of AR and robotic research, offering insights into the recent state of the art and prospects for improvement.

Peng Wang, Yiwei Ma, Zhiqi Ling et al.

Abstract Peak‐valley period partition of load curve is a key aspect of time‐of‐use (ToU) tariff to improve power load characteristics, such as shifting peak loads towards valley time periods. Fuzzy clustering algorithm is an effective and popular method commonly used to solve the peak‐valley period partition of load curves, but it still encounters the difficulty of dividing some data within the boundary regions of different time periods. Therefore, this paper presents a peak‐valley period partition and abnormal time correction scheme for ToU tariffs under typical daily load curves based on improved fuzzy C‐means (FCM) clustering algorithm. In order to improve the accuracy of peak‐valley period partition, modified fuzzy membership functions are proposed to improve the initialization of FCM clustering, and a loss function‐based method is presented for calculating the fuzzy parameters of those membership functions. To resolve the problem of abnormal time partitioning within the boundaries of different time periods, an abnormal time period recognition model and a correction model based on fuzzy subsethood are proposed to obtain the final corrected peak‐valley time period partitioning results. On the MATLAB R2020b platform, the effectiveness of the proposed method is verified through two real daily load curves with a time resolution of 5 min.

Guoming QIAN, Jie MENG, Haidong ZHU et al.

In order to take full advantages of the new PV-Storage power station participating in power grid multi-time scale frequency modulation while considering the operation economy. First of all, according to the characteristics of photovoltaic modules, flywheel energy storage and lithium iron phosphate energy storage, their life cycle models are established respectively, and a coordinated operation strategy is proposed to reduce energy storage battery attenuation and improve frequency modulation performance. Second, on the basis of the primary and secondary frequency modulation mechanism, the model of PV-Storage power station participating in power grid frequency modulation capacity planning is established with the maximization of the frequency modulation revenue as the optimization objective function. Finally, a simulation model of PV-Storage system is built by virtue of Matlab software. The simulation results show that, as it is guaranteed that the requirements are fully met for second-level primary frequency modulation and minute-level secondary frequency modulation, the hybrid energy storage combined with photovoltaic reserve capacity can make resource planning more reasonable and improve both the economy and reliability of frequency modulation. Moreover, the reduction of flywheel cost will allow more capacity allocation so as to increase the frequency modulation income, hence the investment return period can be shortened.

Xiaoxiang Ji, Jianghong Li, Jiao Ren et al.

Aero-engine control systems generally adopt centralized or distributed control schemes, in which all or most of the tasks of the control system are mapped to a specific processor for processing. The performance and reliability of this processor have a significant impact on the control system. Based on the aero-engine distributed control system (DCS), we propose a decentralized controller scheme. The characteristic of this scheme is that a network composed of a group of nodes acts as the controller of the system, so that there is no core control processor in the system, and the computation is distributed throughout the entire network. An LQR output feedback control is constructed using system input and output, and the control tasks executed on each node in the decentralized controller are obtained. The constructed LQR output feedback is equivalent to the optimal LQR state feedback. The primal-dual principle is used to tune the parameters of each decentralized controller. The parameter tuning algorithm is simple to calculate, making it conducive for engineering applications. Finally, the proposed scheme was verified by simulation. The simulation results show that a high-precision feedback gain matrix can be obtained with a maximum of eight iterations. The parameter tuning algorithm proposed in this paper converges quickly during the calculation process, and the constructed output feedback scheme achieves equivalent performance to the state feedback scheme, demonstrating the effectiveness of the design scheme proposed in this paper.