Operation Of Systems Research Articles

Intelligent mission planning for autonomous distributed satellite systems

System autonomy plays a critical role in the success of next generation Distributed Satellite Systems (DSS) mission architectures, enabling the inference of external and internal environment conditions to support system adaptation in unexpected and unfamiliar situations. Artificial Intelligence (AI) based techniques show promise in achieving this desired self-adaptive capability by offering on-board predictive real time analytics, supporting an evolution towards Trusted Autonomous Satellite Operations (TASO). In parallel, TASO requires an evolution of the control and coordination of increasingly autonomous large scale DSS, achieved in the design and operation of intelligent Mission Planning Systems (MPS). In this paper, we focus on intelligent mission planning functionality problem utilising a distributed ant colony optimisation approach to achieve local task allocation and platform coordination. The approach is implemented in the context of a Space-Based Space Surveillance (SBSS) mission architecture that considers self-adaptive capabilities, global coordination and supervisory control aspects. The effectiveness of the approach is shown, demonstrating that the proposed solution based on metaheuristics supports an efficient and pervasive SBSS capability.

Physical model and Bayesian theory based hydraulic component fault diagnosis method

Hydraulic components are integral parts of hydraulic systems, and their failures directly impact the normal operation of hydraulic systems. Due to the dynamic variation of working pressure in hydraulic components with load, it is challenging to diagnose faults of hydraulic components under dynamic pressure conditions using existing methods. This paper focuses on the research of internal leakage faults in hydraulic directional control valves and proposes a fault diagnosis method based on physical model and Bayesian theory for hydraulic components. Firstly, this method investigates the working fault mechanism of hydraulic directional control valves and constructs an internal leakage model. Then, correlation analysis is introduced to select fault feature signals, and principal component analysis is adopted to construct internal leakage features. Finally, based on Bayesian model, Markov chain Monte Carlo sampling, and internal leakage features, the parameters of the internal leakage model are estimated to achieve fault diagnosis of internal leakage in hydraulic directional control valves. Experimental results demonstrate that compared to several traditional fault diagnosis methods, the proposed method exhibits higher adaptability and accuracy under dynamic pressure conditions. This method, based on the internal leakage model and Bayesian theory, realizes fault diagnosis of hydraulic components under dynamic pressure conditions, avoiding the shortcomings of deep learning methods that rely on a large amount of fault data to train fault models.

Energy and environmental performance from field operation of commercial-scale SOFC systems

Solid Oxide Fuel Cells (SOFCs) are capable of generating electrical and thermal power with very high conversion efficiency and almost no pollutant emissions into the atmosphere. Despite extensive literature on SOFC-based energy system models and experimental testing at the cell and short-stack level, there is currently a lack of performance data for SOFC modules under actual field conditions. To fill this gap, the present work investigates the energy and environmental performance of six SOFC modules, ranging in size from 10 to 60 kW, over thousands of hours of operation. These systems, supplied by the three leading SOFC manufacturers in Europe, have been installed and operated in different non-residential buildings worldwide, as part of the European Comsos project. The aim of this study is to establish a comprehensive set of field operation data to characterise commercial-scale SOFC systems. Specifically, raw data are processed to derived electrical and thermal efficiency maps, degradation rates and pollutant emissions, including particulate matter (PM), nitrogen oxides (NOx) and carbon monoxide (CO). A comparison with competing technologies is also provided to better highlight the potential benefits of adopting SOFC-based cogeneration systems. Values in the range of 51–61% were found for the system-level electrical efficiency under rated conditions. The electrical efficiency also remained consistently high across a wide modulation range (between 50% and 100% of rated power), with peak values reaching 65%. In addition, promising results were obtained for the average percentage loss in electrical efficiency, with a minimum value of 0.7%/1000 h. Regarding the environmental analysis, NOx and CO emissions were analysed at both constant and variable power output, proving to be impressively low across the entire modulation range. The same applies to PM concentrations, which were below ambient level. Overall, SOFCs demonstrated to be one of the best cogeneration solutions for commercial-scale systems (tens to hundreds of kW in size), from both an energy and environmental perspective. However, further reductions in costs and dedicated financial schemes are necessary for a widespread market penetration.

Independent operations of appetitive and aversive conditioning systems lead to simultaneous production of conflicting memories in an insect.

Pavlovian conditioning is a ubiquitous form of associative learning that enables animals to remember appetitive and aversive experiences. Animals possess appetitive and aversive conditioning systems that memorize and retrieve appetitive and aversive experiences. Here, we addressed a question of whether integration of competing appetitive and aversive information takes place during the encoding of the experience or during memory retrieval. We developed novel experimental procedures to address this question using crickets (Gryllus bimaculatus), which allowed selective blockade of the expression of appetitive and aversive memories by injecting octopamine and dopamine receptor antagonists. We conditioned an odour (conditioned stimulus 1, CS1) with water and then with sodium chloride solution. At 24 h after conditioning, crickets retained both appetitive and aversive memories, and the memories were integrated to produce a conditioned response (CR). Importantly, when a visual pattern (CS2) was conditioned with CS1, appetitive and aversive memories formed simultaneously. This indicates that appetitive and aversive second-order conditionings are achieved at the same time. The memories were integrated for producing a conditioned response. We conclude that appetitive and aversive conditioning systems operate independently to form parallel appetitive and aversive memories, which compete to produce learned behaviour in crickets.

Optimizing BHF/PVDF Composites via Compression Molding for High-Frequency Applications and Electromagnetic Shielding

Electromagnetic interference (EMI) poses significant challenges to the reliable operation of communication systems, medical devices, and electronic equipment, often resulting in signal degradation, data loss, and equipment failure. To mitigate these issues, this study investigates the development of barium hexaferrite (BHF)/polyvinylidene fluoride (PVDF) composites, synthesized via compression molding, to optimize their electrical and magnetic properties for high-frequency EMI shielding applications. Through comprehensive characterization, including X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), vibrating sample magnetometry (VSM), and vector network analysis (VNA), we demonstrate that increasing the BHF content within the composites enhances their complex permittivity, impedance matching, and attenuation coefficients, making them highly effective for EMI shielding. Notably, the composite containing 20 wt.% PVDF achieved a reflection loss (RL) of -42 dB at a thickness of 2 mm, indicating superior shielding effectiveness. These results underscore the potential of BHF/PVDF composites as a viable solution for advanced EMI suppression in high-frequency electronic and military applications, combining economic feasibility with robust performance.

Sustainable inventory, pricing, and marketing strategies for imperfect quality perishable items with inspection errors and advertisement-, expiration date- and price-dependent demand under carbon emissions policies

The demand for perishable products is affected by freshness levels, advertising efforts and selling prices, among other factors. By virtue of being consumed mostly by humans, quality control is also an important factor in perishable production systems. Moreover, inventory management related operations in such production systems release sizeable quantities of carbon emissions that are often regulated by carbon policies. To study the interactions of all these attributes in the context of a perishable inventory system, this paper proposes four sustainable inventory models for a perishable product with imperfect quality, inspection errors and whose demand depends on the advertising frequency, expiration date and selling price. The emissions released are assumed to be governed by carbon tax and carbon cap policies. Two of the models are developed under the assumption that the quality inspection process is 100% effective while the other two models consider the possibility of committing inspection errors, and additionally, for each pair of models, one is developed under a carbon tax policy and the other under a carbon cap policy. All the models are aimed at jointly optimising the perishable product’s lot-size, advertising frequency and selling price. The numerical results show that the presence of inspection errors leads to 28% and 23% lower profits under carbon tax and cap policies, respectively. Moreover, profit can be maximised by either stocking perishable products with longer shelf lives or targeting customers that engage with advertising mediums and those that are not price-conscious, while emissions can be minimised via the enforcement of carbon policies.

A waste reduction strategy through autonomation under a closed-loop supply chain management

Currently, apart from manufacturing processes, the remanufacturing of products is considerably important. Appropriate remanufacturing requires the operation of long-run manufacturing systems. However, in long-run processes, the production system may convert to an out-of-control state due to machine breakdowns. Then, defective products are frequently produced; this increases wastage and disrupts environmental sustainability. In this model, a smart autonomation policy is deliberated for an error-free inspection in separating defective products during production. The autonomation policy facilitates waste reduction through remanufacturing. This paper concentrates on customer awareness and service-dependent demand, which directly improves the overall profitability of the system. A discrete investment to reduce setup cost, continuous investment to collect used goods, and cap-and-trade strategy to limit carbon emission are considered to obtain a more realistic model. Classical optimization method is applied for global maximum profit test of the profit function with respect to cycle length, customer awareness, service investment, discrete investment to reduce setup cost, number of shipments, and container capacities. Numerical testing, sensitivity to total profit in different cost parameters, and comparisons with previous research are explained. Some special scenarios including graphical representations are discussed to prove that a large investment is more beneficial than the cost of specific setup and collection.

Снижение числа несчастных случаев и аварий при управлении поездом в автоматическом режиме

Railroad transport is the main element of economic development of Russian regions; it ensures reliable communication between industrial enterprises and raw material supply bases in Russia. Transportation of enterprise employees and freights is an integral part of the successful functioning of any industrial company. Ensuring safe conditions of train traffic is a matter of high priority for all participants in the transportation process. The article provides external and internal parameters affecting the safety of rolling stock. The impact of external factors, such as weather conditions, optical visibility, illumination, and air transparency on the operation of technical vision systems when driving a train in automatic mode have been analyzed. Taking the impact of these factors into account is necessary to limit the speed of rolling stock in the sectors of low visibility and sharp reduction of visibility range of obstacles, which is crucial to ensure safe train movement when calculating the braking curve. Such a dependence requires the introduction of correcting coefficients into algorithms of the train movement model and considering the correction when calibrating and configuring computers and devices of the technical vision system. In order to determine the internal parameters of rolling stock, an algorithm for the calculation of the coordinates of a technical object has been proposed. For a moving object, the coordinates are calculated on the orthodromic trajectory using the existing dependence between the current longitude and latitude of the object and the speed projections measurements of the rolling stock on the axis of the geographic coordinate system. The proposed options to determine the rolling stock parameters improve the quality and accuracy of determining speed and coordinates and reduce computing and hardware costs aboard a locomotive, which, in turn, affects the time of decision-making to ensure optimal control of rolling stock and prevention of accidents and emergencies.

Theoretical and experimental considerations regarding the airbag system operation

Abstract The concept of Passive Safety aims to reduce the harmful effects on vehicle occupants and pedestrians in the event of an accident. Intelligent car bodies and restraint systems have been designed. Thus, side, front, knee and head airbag systems have been introduced, as well as pre-strained seat belts and force limiters. The objectives of car manufacturers are not only to reduce injuries that can cause death, but also a significant reduction of those that can result in permanent damage such as a whiplash. Advanced airbag systems are currently introduced, which offer protection to the occupants at the knees, torso, hips and abdomen. The topic proposed in the paper concerns both theoretical research on the operation of passive security systems, respectively the airbag system, and experimental research. Regarding the theoretical research, this paper proposes a virtual prototyping method by means of the LS Dyna software package of the airbag system functioning in order to protect the driver in the event of a frontal collision. For the analysis of the airbag system functioning, a complex assembly which contains of a dummy, seat, steering wheel, passive safety systems was used. For the experimental research a stand was designed for the analysis and testing of the airbag system, which has the role to protect the driver in the event of a frontal collision of the vehicle. In order to carry out the experimental tests, it was proposed to create a sledge type stand, which simulates frontal collisions.

Wideband circularly polarized high accuracy survey active antenna for global navigation satellite systems applications

AbstractThis letter presents a new circularly polarized (CP) active antenna for global navigation satellite systems (GNSS) operating in the L1‐band (1.164–1.28 GHz) and L2‐band (1.525–1.615 GHz). The active antenna consists of a passive antenna and a two‐channel low‐noise amplifier (LNA) circuit. To achieve wideband coverage of the L1/L2 bands while maintaining a low profile, a new electroplating process is utilized in fabricating the passive antenna. Additionally, to realize CP performance, the passive antenna is sequentially fed by four ports with a wideband feeding network. To minimize phase variations, a 90° phase shifter loaded with open‐short‐stub is integrated into the feeding network. Furthermore, to improve noise immunity, out‐of‐band rejection, and amplify GNSS signals, the LNA circuit cascades two‐stage dielectric bandpass filters and three‐stage LNAs. Consequently, the circuit features a low noise figure of below 1.65 dB, a maximum out‐of‐band rejection level of 60 dB, and a stable active gain of 30 dB. The performance of the active antenna is evaluated in an outdoor open field using a GNSS receiver.

Heat pipe type double-containment vessel as passive safety system for small modular reactors

This paper proposes a new concept of a passive safety system for small modular reactors as a heat pipe type double-containment vessel. The proposed system configures an additional vessel outside the existing one and functions as a heat pipe that utilizes phase change heat transfer to reduce the requirement of a large pool design. The double-containment vessel features a design consisting solely of the evaporation and condensation sections for heat pipe function with compactness. The ultimate heat sink only serves as a condenser part for the double-containment vessel, therefore significantly reducing the volume of stored fluid. The study numerically evaluates several postulated accidental cases to compare the performance of the double-containment vessel using the thermal hydraulic system code, MARS-KS. Even for serious conditions such as multiple failure accidents without safety systems operation, the double-containment can secure extra golden time to core damage by more than 2,090 sec compared to the original design. This study highlights the feasibility and potential of the heat pipe-based double-containment vessel as a passive safety system, enhancing the safety and reliability of small modular reactors. This kind of design can provide the advantages of safety and economics in the construction of small modular reactors.

Consensus of Networked Multiagent Systems Operating Over Multiple Communication Channels

Consensus of Networked Multiagent Systems Operating Over Multiple Communication Channels

SCADA Systems in Oil and Gas: Driving Innovation and Efficiency in the Digital Age

Abstract: In the oil and gas sector, supervisory control and data acquisition (SCADA) systems have become a revolutionary force that are transforming operations throughout the whole value chain. This in-depth analysis looks at the development, design, and various uses of SCADA systems in the upstream, middle, and downstream domains. It projects that the global SCADA market for oil and gas will grow to $4.52 billion by 2026. Real-time well monitoring, production optimization, and remote operations are made possible by SCADA systems in upstream operations, which greatly increase output and efficiency. The goal of midstream applications is to increase pipeline efficiency and safety by using predictive maintenance, enhanced leak detection, and flow optimization. By streamlining quality assurance, energy management, and process control, SCADA systems can completely transform refinery operations. SCADA deployments are not without difficulties, though, including data management complications, cybersecurity threats, and legacy system interoperability. In addition to addressing these problems, the study looks at new developments that could improve SCADA capabilities, such as edge computing, digital twins, AI and machine learning integration, and 5G technology. According to the research, SCADA is essential for promoting efficiency, safety, and innovation in the oil and gas industry. Businesses that successfully integrate SCADA technologies while overcoming implementation obstacles will be best positioned to prosper in the complicated and fiercely competitive energy market.

Infrared Image Object Detection Algorithm for Substation Equipment Based on Improved YOLOv8

Substations play a crucial role in the proper operation of power systems. Online fault diagnosis of substation equipment is critical for improving the safety and intelligence of power systems. Detecting the target equipment from an infrared image of substation equipment constitutes a pivotal step in online fault diagnosis. To address the challenges of missed detection, false detection, and low detection accuracy in the infrared image object detection in substation equipment, this paper proposes an infrared image object detection algorithm for substation equipment based on an improved YOLOv8n. Firstly, the DCNC2f module is built by combining deformable convolution with the C2f module, and the C2f module in the backbone is replaced by the DCNC2f module to enhance the ability of the model to extract relevant equipment features. Subsequently, the multi-scale convolutional attention module is introduced to improve the ability of the model to capture multi-scale information and enhance detection accuracy. The experimental results on the infrared image dataset of the substation equipment demonstrate that the improved YOLOv8n model achieves mAP@0.5 and mAP@0.5:0.95 of 92.7% and 68.5%, respectively, representing a 2.6% and 3.9% improvement over the baseline model. The improved model significantly enhances object detection accuracy and exhibits superior performance in infrared image object detection in substation equipment.

Q-EDR Hybrid Boost Converter Operation For High Gain With Low Device Stress

Abstract: The hybrid continuous-discontinuous mode (CCM-DCM) operation of non-isolated systems is proposed in this paper. boost converter with quadratic extended duty ratio (Q-EDR) for applications with high gain and low power. The inductors at the input while the EDR was in continuous conduction mode (CCM) The mode of operation of stage inductors is discontinuous conduction. (DCM) in order to reap the advantages of CCM and DCM operation. Interleaving's ripple in the low input current and low voltage The EDR stage switches' stress remains. With what is being half and half method of activity, the inductance worth of EDR stage significant decrease in the inductor. an additional decrease in switch voltage is accomplished during turn-on progress in the proposed activity, prompting huge decrease thus on exchanging losses. This lets the converter run at higher switching speeds. frequency, thereby reducing EDR inductors' size by 45 percent in this instance an experiment confirms the proposed idea. Simulink prototype for a two-phase Q-EDR operating at 30 V-40 V to 400 V output, a gain of more than 12 at duty close to 0.54. The converter has a peak efficiency of 95.5 percent. at a switching frequency of 135 kHz and 300 W for all silicon switches.

Data-driven seasonal scenario generation-based static operation of hybrid energy systems

Integrating intermittent wind and solar energy into hybrid energy system has introduced significant operational uncertainties. This paper develops a static operation model incorporating biomass gasification-based combined heat and power as a coupling center based on conceptual utility grid-connected real data in Sacramento, California. This study involves generating typical scenarios with seasonal characteristics and demand correlations to capture key input parameters accurately. Subsequently, the Newton-Raphson method was developed to calculate the energy flow within these scenarios. Simulation results demonstrate the proposed method achieves over 70 % construction accuracy across different seasonal scenarios. The economic results that the winter electricity loads increased by 44.8 % compared to summer, with corresponding rises in gas and heat loads by 360.6 % and 372.3 %, respectively, resulting in the hybrid energy system economic cost increase of 58.9 %. These results confirm the model's robustness in effectively managing intermittent energy sources and addressing the economic impacts of seasonal demand variations.

Forecasting Wind Speed Using Clustering of Trend-Based Time Series Data

Accurate forecasting of wind speed is crucial for the efficient operation of wind energy systems. As a time-series concern, wind forecasting may help determine how much electricity a proposed wind farm might produce annually. The majority of forecasting techniques perform differently depending on seasonal and trends variation. For this reason, time series data frequently have seasonal and nonlinear trend components eliminated in order to simplify wind forecasting approach. The application function used to remove the seasonality and trend determines accuracy. The proposed method begins by identifying and extracting underlying trends from historical wind speed data, segmenting the time series into distinct trend-based components. This paper proposes a hybrid method for predicting time series. A method for clustering data has been designed that identifies clusters of time series data with similar trend components. Statistical procedures, such as generalized autoregressive and autoregressive integrated moving average scoring approaches, are used to each individual cluster after the appropriate clusters of related trend components have been identified. Ultimately, the components that are made are combined. The datasets collected from the NREL site. The experiment demonstrates that when compared to current statistical approaches, the cluster-based forecasting approach performs better. This research makes contribution towards the field of renewable energy forecasting by providing a robust and scalable method for wind speed prediction, which can be integrated into existing energy management systems for improving the efficiency and stability of wind energy generation. The research paper examines the trend features of time series data of wind employing the suggested hybrid technique on wind forecasting. Performance calculated in terms of RMSE and MAE shows that the proposed technique succeeds as compared to other state of the art techniques.

Advanced feature engineering in microgrid PV forecasting: A fast computing and data-driven hybrid modeling framework

This study introduces an innovative framework designed to forecast the fluctuating short-term generation of photovoltaic (PV) energy in isolated microgrids. The framework relies entirely on solar irradiance data obtained from weather stations located far from the target microgrid. It implements a sophisticated decomposition approach to effectively extract an enriched collection of features from the dataset. Subsequently, a machine learning-based clustering method further enhances the forecasting process by accurately separating the relevant data points. The approach utilizes an advanced two-stage Hybrid Data Linked Model (HDLM) architecture, integrating a Layered Recurrent Neural Framework (LRNF) for prediction and a pattern identification network unit for pattern extraction. This paper demonstrates significant improvements in both the accuracy and effectiveness of estimating PV generation, achieving a mean absolute error of 1.02, a root mean square error of 2.176, and an R-squared score of 0.991. Additionally, the method reduces computing time by 15% after finalizing the input features. A comparative analysis evaluates the superior forecasting capabilities of the HDLM in remote microgrids by benchmarking it against other advanced hybrid deep learning models. The findings highlight the HDLM’s potential to greatly enhance and revolutionize the management and operation of renewable energy systems in remote microgrids.

Optimal sizing and operation of a hybrid energy systems via response surface methodology (RSM)

Hybrid energy systems (HESs) are the most important sources of energy demand-supply, have developed significantly around the world. Microgrids, renewable energy sources, remote telecommunications stations, greenhouses, etc., are being considered as HESs applications. Optimal sizing of these systems is considered as one of the important issues related to energy management. In this paper, the Response Surface Methodology (RSM) is proposed for the optimal sizing of a Photovoltaic (PV) system in a HESs. The suggested procedure solves the optimization problem by considering the factors affecting PV output power about the environmental conditions of the HESs. Providing a mathematical model for each of the input parameters and the ability to assessment the sensitivity of each of the input variables are the most important advantages of the proposed technique. In this paper, the RSM provides the most optimal sizing related to the PV system by considering climatic and geographical factors in the study site, and technical and economic issues related to the HESs. The optimal model obtained is evaluated by the Analysis of Variance (ANOVA) evaluation method, which is one of the important techniques of statistical evaluation. It should be noted that the RSM technique can be utilized to optimize all components of any HES.

Large-scale hydropower dispatching system based on cloud platform and its key technologies

In the past decade, the rapid expansion of hydropower capacity in China brings challenges to hydropower dispatching in data management and operation scheduling. In data management, the scale of hydropower operation data increases explosively, presenting typical big data features. In operation scheduling, the optimization of hydropower system operations is a high-dimension, nonconvex, and nonlinear problem. Conventional optimization methods encounter the “dimensionality disaster”, and cannot solve the scheduling problem efficiently. To deal with these challenges, by analyzing the data characteristics and operation scheduling requirements, this paper develops a large-scale hydropower dispatching system based on the cloud platform. The dispatching system consists of four layers: data access layer, cloud platform layer, data sharing layer, and application layer, which has good scalability and flexibility in data management and operation scheduling. In the application layer, key technologies based on the big data and cloud computing resources of the dispatching system are proposed to carry out the optimal operation of hydropower systems, including distributed parallel dynamic programming (DPDP) for long-/medium-term scheduling and data mining-based method (DMM) for short-term scheduling. Finally, the dispatching system is applied to the operation of hydropower systems in southwest China. Results indicate that: 1) The developed dispatching system is capable of processing 18 million data records per day, with a peak processing speed of 10,000 data requests per second, fulfilling the requirements for hydropower dispatching. 2) In long-term generation scheduling, under varying levels of runoff and plant scales, DPDP has demonstrated a remarkable reduction in computing time compared to dynamic programming, obtaining the same optimal solution with over 90 % less computational time. 3) In short-term peak shaving operation, on typical dry and wet days, the computing time of DMM is 7.2 s and 7.5 s, respectively, far less than the time of mixed-integer linear programming, and the maximum residual load obtained by DMM is lower than historical observations, indicating its effectiveness.