Operational Situations Research Articles

Design of Radial Distribution System to Study Load-Flow and Short Circuit Analysis Using ETAP Software

This work's analysis provides information on fault analysis and load flow studies, which help to understand how radial distribution networks operate both in normal and emergency circumstances. The research explores optimization methods, describing capacitor placement, transformer size, and feeder reconfiguration in order to lower losses and increase system efficacy. Moreover, the work looks at reliability-focused maintenance techniques, stressing the need of predictive maintenance and condition monitoring in raising the dependability of radial distribution networks. Modern electrical power networks depend heavily on their radial distribution system, which effectively and dependable distributes electricity to customers. Many radial distribution system-related topics are covered in detail on this website, including analytical methods, optimization processes, and dependability enhancement strategies. Power flow, voltage control, and losses in radial networks are investigated in this paper under a range of operational situations. The last and crucial stage of a power system, the electrical distribution system makes sure that customers have a steady supply of electricity. Regretfully, because it is often neglected and exposed to environmental risks, our distribution network has a number of problems. Our distribution feeder is rigorously analyzed for load flow and short-circuits in order to overcome these challenges and find and fix problems. The main topics of this study are the load flow and short-circuit analysis of a radial distribution feeder, which is mostly constructed with (ETAP). The final and crucial part of a power system, the electrical distribution system ensures that consumers receive an uninterrupted and steady supply of electricity. Our distribution feeder undergoes a rigorous and cautious process to address these issues front on because of the uncontrollable nature and susceptibility of our network to environmental hazards. This paper delves deeply into the issues of short-circuiting and load flow in radial distribution feeders. Both symmetrical and asymmetrical faults are thoroughly investigated by mathematical computations and ETAP simulations; the results of the software simulations are contrasted with those of the mathematical analysis. These discoveries will definitely influence the development and management of our system and point us in the direction of workable and practical solutions.

Data Analysis and Optimization Strategies in Financial Management

In the increasingly fierce market competition, the decision-making level of enterprises can directly affect their future. Once a decision is made incorrectly, it can cause a significant crisis for the company. The information data in enterprise financial management is the most important reference for company leaders when making decisions. In corporate governance, financial analysis can comprehensively reflect the operational situation of a company. Financial analysis can provide strong data support for the production and operation of enterprises, and it is an effective way to improve operational efficiency. This article optimizes asset allocation based on data analysis and optimization strategies to achieve maximum return on investment portfolio. The current value, annual return, and risk level of stocks for different asset classes are 10 million yuan, with an annual return of 15%. The current value of the bond is 8 million yuan, with an annual yield of 6%. This article provides a reliable basis for financial management and business decision-making.

Strategic resilience in human performance in the context of science and education - perspective.

Special situations that jeopardize the internal and external security for communities are increasing in their frequency and complexity. This creates complicated challenges for individuals, governments and humanity. National and international strategies are being developed that focus on the resilience and coping of all first responders during these extreme stress states. The aim of the article is to present the framework of strategic resilience, its multidimensional measurement and the possibilities for training robust resilience to increase operational effectiveness in special operational situations. Research in the area of special operation situations often focuses on the human performance cluster. The Unit of Applied Research 18_RECESS (18_ Research and Education Center for Extraordinary Tactical Situations and Strategically Resilience) at the Department of Disaster Prevention and Crisis Management at Fresenius University of Applied Sciences in Idstein fits into this field. It pursues the adaptation of existing resilience models to special operation situations in line with the definition of strategic resilience. In addition, the focus is on the possibility of training strategic resilience to increase operational effectiveness. There are currently several research projects being conducted in the civilian and civilian-military sectors, as well as with ground and special operations forces. Strategic Resilience with its 3 axes Psychological, Physiological and Cognitive Resilience covers the core domains of various existing models of resilience. This provides an adequate concept to describe different factors leading to personnel perseverance in special operation situations. There exists the possibility to train several domains of strategic resilience via problem-oriented intervention (POH).

Analysis of Macro-economic Operation Situation and Fiscal and Tax Policy Orientation in the Post-epidemic Era

Analysis of Macro-economic Operation Situation and Fiscal and Tax Policy Orientation in the Post-epidemic Era

Safety on automated passenger ships: Exploration of evacuation scenarios for coastal vessels

Many advancements are being made within the domain of autonomous shipping, motivating discussions of corresponding amendments to international safety regulations within the International Maritime Organization. Near-coastal passenger ferries are a form of sea traffic that has been the target of automation trials due to their short voyages and relatively protected waters of operation. This study investigated emergency evacuation from a range of such ships, covering both the current situation (focused on crew tasks, external rescue actors and interactions) and safety aspects that should be considered when automation brings about new work patterns, such as remote supervision and control. The study employed qualitative methods – interviews, field visits and a stakeholder workshop. Results give insight into ferry evacuation processes and challenges in their current form. In addition, results from the application of different automated evacuation scenarios suggest that more detailed studies are needed within the areas of remote operation situation awareness, remote operator and onboard personnel competencies, passenger safety information and communication, simple and robust evacuation equipment, technical means allowing assistance between autonomous and regular ships, and lastly, both procedures and interfaces for collaboration in a changing rescue network.

The optimal multi-stress–strength reliability technique for the progressive first failure in the length-bias exponential model using Bayesian and non-Bayesian methods

In many real-world situations, systems frequently fail in their demanding operational settings. Researchers pay little attention to the fact that systems typically fail to execute their intended activities when it reaches its extreme operating situations as appropriate. In this paper, we try to develop and study inferential aspect of a system reliability having multiple components based on a progressive first failure censoring scheme assuming unit length-bias exponential distribution. Regarding estimation, asymptotic, boot-p, and boot-t approaches under the interval estimation are adopted, while the maximum likelihood method under the point estimation is considered. The MCMC method is used to get the Bayes estimate of the reliability parameter assuming both the symmetric and asymmetric loss functions. The associated confidence intervals are also reported as appropriate. The effectiveness of the various adopted estimation strategies is evaluated and compared using Monte Carlo simulation studies and examples from real-world applications.

DNBR Limit Analysis for the Inner channel of Annular Fuel

The departure from nucleate boiling ratios (DNBR) limit for annular fuel might vary from that of conventional nuclear fuel rod assemblies due to the distinct features of annular fuel assemblies. Precise estimation of DNBR is crucial for ensuring the safe and optimal functioning of nuclear reactors equipped with annular fuel assemblies. This study examines the DNBR limit of inner channel for annular fuel, using the look up table-2006 (LUT-2006).A comprehensive database having data for both normal operation and accident situations in pressurized water reactors (PWRs) using annular fuel was built to analyze the DNBR limit of the inner channel of annular fuel. The critical heat flux (CHF) data in this database were categorized into two groups with the limiting quality as the boundary. The prediction accuracy of LUT-2006 and the DNBR limits were evaluated respectively. Given that the inner diameter of the inner channel of annular fuel in mainstream designs typically ranges from 8-9 mm, and considering the potential occurrence of the limiting quality phenomenon in the inner channel, it would be more suitable to utilize the LUT-2006 based on an 8 mm tube standard to establish the DNBR limit for the inner channel of annular fuel. A conservative DNBR limit of 1.308 is determined for the inner channel of an annular fuel using the LUT-2006. In future thermal-hydraulic optimization designs of annular fuel, it is advisable to utilize LUT-2006 for calculating the DNBR of the inner channel of the fuel.

Investigation of Hydrogen-Blended Natural Gas Pipelines in Utility Tunnel Leakage and Development of an Accident Ventilation Strategy for the Worst Leakage Conditions

The development of hydrogen-blended natural gas (HBNG) increases the risk of gas transportation and presents challenges for pipeline security in utility tunnels. The objective of this study is to investigate the diffusion properties of HBNG in utility tunnels and evaluate the effectiveness of various ventilation mechanisms. The numerical simulation software Fluent 2023 R1 is applied to simulate and analyze the leakage of small holes in a HBNG pipeline in the natural gas compartment. By examining the leaking behavior of HBNG through small holes in different circumstances, we aimed to identify the most unfavorable operational situation for leakage. Subsequently, we analyzed the ventilation strategy for sub-high-pressure pipes at various pressure levels in this unfavorable condition. This study’s findings demonstrate that blending hydrogen improves the gas diffusion capacity and increases the likelihood of explosion. The primary factors that influence the pattern of leakage are the size of the leaking holes and the pressure of the pipeline. The gas compartment experiences the most unfavorable working conditions for natural gas pipeline leaks when there are higher pressures, wider leak openings, higher hydrogen blending ratios (HBRs), and leaks in close proximity to an air inlet. When the HBR is 20%, the minimum accident ventilation rates for pressures of 0.4 MPa and 0.8 MPa are 15 air changes per hour and 21 air changes per hour, respectively. The maximum allowable wind speed for accident ventilation is 5 m/s, as regulated by China’s national standard, GB 50838-2015. This regulation makes it difficult to minimize the risk of leakage in a 1.6 MPa gas pipeline. It is recommended to install a safety interlock device to quickly shut off the pipeline in the event of a leak in order to facilitate the dispersion of the substance.

Ultra-short-term operation situation prediction method of active distribution network based on convolutional neural network long short term memory

The large-scale and high-permeability access of distributed generations (DGs) is making the distribution networks develop into active distribution networks (ADNs). The increasing complexity of the structure of ADNs also increases the vulnerabilities for cyberattacks. To accurately predict the operation situation of ADNs no matter whether cyberattacks occurred, this paper proposes an ultra-short-term operation situation prediction method of ADNs based on CNN-LSTM. Firstly, an ADNs situation prediction index system is established, which can comprehensively evaluate the future operation situation of ADNs from different perspectives. Secondly, a multi-feature data reconstruction method based on the time sliding window algorithm is proposed, which is utilized for constructing 2-dimensional matrix and as input datasets for multiple independent CNN-LSTM models to predict each situation prediction index. Then, there are 8 independent CNN-LSTM models shared same input matrix to predict 8 ADNs situation prediction index value without interfering with each other. Among them, CNN is used to extract the spatial correlation feature vectors of ADNs historical operation data and meteorological factor data in ultra-short term, which are reconstructed in time series and used as the input data of LSTM. LSTM is used to extract long-term historical temporal relationship to obtain ADNs operation situation prediction index value. Finally, we utilize the fuzzy analytic hierarchy process to calculate the future operation situation of ultra-short-term ADNs. Taking a real ADNs with 33-node as an example, we compared our proposed methodology to other existing excellent machine learning-based models, the result demonstrates that our proposed prediction method exhibits outstanding performance in time consumption and prediction accuracy.

Performance Evaluation and Dynamic Characteristics of a Self-Excited Induction Generator for Pico Hydro Power Plants

The dynamic performance of an isolated three-phase squirrel cage self-excited induction generator (SEIG) in a Pico Hydro Power Plant (PHPP) is examined in this work. The investigation is carried out with the help of MATLAB/Simulink for mathematical modeling and simulation of the proposed system under various operational situations. The SEIG model, which was created using the steady-state equivalent circuit approach, included the electrical, magnetic, and mechanical components of the SEIG and PHPP. The dynamic behavior of the SEIG is explored under a variety of operating situations. The effects of load variations, speed fluctuations, and other disturbances on the voltage and frequency of the generator are examined. The experiment results were used to validate the simulation results. This research has implications for the design and optimization of PHPP using SEIGs.

Optimal operation of integrated energy systems considering energy trading and integrated demand response

Currently, a large number of integrated energy systems operate independently, which is not conducive to the efficient utilization of clean energy. To fully utilize the energy resources of multiple integrated energy systems, realize the local energy consumption and efficient utilization, and further improve the flexibility on the demand side, this paper constructs an optimal scheduling framework for integrated energy systems considering multi-energy sharing and integrated demand response. A cooperative operation model of multiple integrated energy systems is established to minimize overall cost based on Nash bargaining theory. The energy-sharing problem is solved distributively via the alternating direction multiplier method and the stakeholders' privacy and security is protected well. The validity of the proposed model is verified by arithmetic analysis. Compared with the situation of independent operation and no comprehensive demand response, the operation scheduling strategy proposed in this article has better results. Implementing multi energy sharing and comprehensive demand response can save 21.45% of costs. The overall cost of the whole system has been reduced from the initial 18033.26RMB to 14164.39RMB. In addition, In the context of fluctuating renewable energy scenarios, the energy sharing strategy proposed in this article can still effectively reduce energy costs. Therefore, the energy scheduling strategy proposed in this article can promote the utilization of clean energy and has high practical value.

Analysis of a multi-energy coupling model for rural energy under the rural digital economy

With the growth of the digital economy, the sustainable growth of rural energy has become crucial. However, traditional rural energy models have the drawback of not considering digital technology and renewable energy. Therefore, there is an urgent need for rational planning and development of rural energy. According to this, a multi-energy coupling model for rural energy systems was established by considering equipment capacity planning and operation scheduling optimization based on a multi-energy coupling structure. At the same time, considering the biomass resources in rural energy systems, an optimized configuration model for biomass coal-fired coupled power generation units was established. The results showed that the energy consumption cost in County A accounted for only 3.3%. County C focused mainly on tourism and emphasized economic efficiency, with investment costs 8.6% and 10.3% lower than other rural areas. The system utilized time of use electricity prices to optimize operation. The low storage stage was from 1:00 to 8:00, while the high incidence stage was from 12:00 to 14:00 and from 7:00 to 21:00. In the actual scenario, the multi-energy coupling model can be combined with intelligent technology to realize the real-time monitoring, prediction and optimal control of the energy system. Through the introduction of advanced digital technology, the model can be more flexible to deal with the diversified energy sources and complex operational scheduling situations involved in rural energy systems. This can improve the response speed and adaptability of the system, making the energy system more resilient and efficient.

A step-by-step guide to include key electroencephalography (EEG) parameters in the study of human performance applied to air traffic control

The study of human performance of air traffic controllers (ATCOs) is an interesting line of research to improve operational safety. In recent years, there has been an increase in the number of techniques available to develop this research based on massive data analysis. This study presents the use of certain electroencephalography (EEG) parameters to study the human performance of ATCOs. Although software and applications are now available to calculate these parameters, there are often problems in understanding the detailed process used to calculate them. The parameters presented in this study are intended to overcome this limitation and are applicable in real air traffic control (ATC) situations. Six parameters are analysed: excitement, stress, relaxation, boredom, engagement, and attention. As an application case for the parameters, a total of 50 data samples obtained during the development of real-time simulations on a highly realistic ATC platform are analysed. From these data, the above-mentioned EEG parameters and their trends are calculated. In addition, the evolution of these parameters is studied in relation to two other variables that characterise the operational situation of the sector during the simulations: the taskload based on ATC events and the number of simultaneous aircraft in the sector per minute.

Ways to implement risk management in the fight against organised crime

The aggravation of the state of operations under martial law poses a threat to a wide range of rights, interests of individuals and society. To prevent further deterioration of the crime situation, it is necessary to find progressive mechanisms to combat organised crime. The purpose of this study was to formulate scientifically sound proposals for the development of mechanisms for combating organised crime and counteracting the establishment of corrupt ties by criminals in institutions, organisations, and enter- prises, especially those belonging to the national security system. According to the purpose and specifics of the subject under study, the historical approach, comparative legal, and systemic-structural methods were employed. The study outlined the content of the processes of organising the management of subsystems involved in ensuring national security. The need to ensure its effective implementation is evidenced by statistical data on the complication of the operational situation in certain regions, the general socio-po- litical situation and threatening trends in the criminalisation of society. The state of stagnation does not correspond to the course of the most secure development of society, and therefore the main task of the state is to actively protect citizens from dangerous anti-social, criminal, and violent manifestations. It was sub- stantiated that the success of counteracting these negative phenomena depends primarily on the unification of certain mechanisms at both the international and domestic levels, including standards. It was proved that legal forms of combating organised crime should be improved towards the betterment of standards in the risk management system, specifically, ensuring information security, and stimulating the protection of information flows. It was argued that the introduction of the term “compliance audit” into certain laws of Ukraine defining the basic principles of implementation of the state financial control, audit of financial statements, and organisation of audit activities will provide a positive effect in combating these negative phenomena. The findings of this study can serve as the basis for the preparation of forecast and programme documents by supreme audit institutions, anti-corruption bodies, and the business community to counteract manifestations of organised crime, identify and eliminate corrupt ties with criminals

Design of Mixed Reality Visualization System for Operational Situation Using Cloud-based Geospatial Information

The importance of geospatial information is increasingly highlighted in the defense domain. Accurate and up-to-date geospatial data is essential for situational awareness, target analysis, and mission planning in millitary operations. The use of high-resolution geospatial data in military operations requires large storage and fast image processing capabilities. Efficient image processing is required for tasks such as extracting useful information from satellite images and creating 3D terrain for mission planning, In this paper, we designed a cloud-based operational situation mixed reality visualization system that utilizes large-scale geospatial information distributed processed on a cloud server based on the container orchestration platform Kubernetes. We implemented a prototype and confirmed the suitability of the design.

Interdepartmental Commission on Countering Extremism in the Russian Federation: scientific and practical aspects of its activities and issues of its optimization

The object of the study is public relations related to counteraction to extremist activities. The subject of the study is the organizational and legal basis for its provision, both in modern Russia and in foreign countries. The purpose of the study is to conduct a comprehensive theoretical study of the fight against extremism and its prevention by the Interdepartmental Commission on Countering Extremism, to study the mechanisms of countering extremism, to analyze the available scientific literature, to develop a theoretical design of the support system. Theoretical and other sources considered together are a common information base that contributes to the achievement of scientific validity and reliability of the formulated provisions. The empirical basis of the study is formed by documents and materials of federal government bodies, public authorities of the subjects of the Russian Federation, information and analytical materials that allow assessing the state and level of protection of the state and society from extremist threats. The methodological basis of the research consists in the use of a set of existing methods of cognition, the basis of which is the dialectical method. General scientific techniques (analysis, synthesis, induction, deduction) are used in the study of individual issues of the topic. Special legal methods of scientific cognition are also widely used: formal legal, historical legal, comparative legal, structural and functional. The use of a set of methods allowed us to explore the theoretical aspects of countering extremism. The scientific novelty of the study is that it is one of the first scientific papers addressing the issues of the Interdepartmental Commission on Countering Extremism in the Russian Federation by analyzing various aspects of its activities and legal regulation. The article proposes the author's own approaches to the systematic study of problems related to legal regulation and the mechanism for exercising the powers of the Interdepartmental Commission on Countering Extremism in the context of analyzing the essence and issues of implementing specific areas of activity, building interaction with public authorities and public organizations, examines the problems of anti-extremist work, their content and relationship with the operational situation, suggests specific directions for improving legislation and directions for new scientific research in this area.

Accurate recognition of rice plants based on visual and tactile sensing.

Crop recognition is the basis of intelligent agricultural machine operations. Visual perception methods have achieved high recognition accuracy. However, the reliability of such methods is difficult to guarantee because of the complex environment of paddy fields. Tactile sensing methods are not affected by background or environmental interference, and have high reliability. However, in an ideal environment, the recognition accuracy is not as high as that of the visual method. To balance the accuracy and reliability of rice plant recognition, a combined visual-tactile method was proposed in this study. A rice plant recognition device was developed with a poly(vinylidene fluoride) sensor embedded inside the device as a tactile perceptron and a graphic designed as a visual perceptron. The primary role of the tactile perceptron is to initially recognize rice plants and provide a time point for image capture for visual perception. The main role of the visual perceptron is to extract features from the captured images and recognize rice plants again. The results of tactile and visual recognition were eventually fused to achieve accurate recognition of rice plants. The contact speed between the recognition perceptron and rice-weed was selected for the field performance test based on the real situation of paddy field operation. The results showed that the accuracy and reliability of rice plant recognition decreased as the travelling speed of the paddy field operation machine increased. The results of this study provide a basis for intelligent farm machinery operations in rice fields. © 2024 Society of Chemical Industry.

Practical Advantages of Multivariable Control Strategy for Off-Grid Variable Speed Variable-Pitch (VS-VP) Wind Turbines

The design of variable speed wind turbine control systems is a hard challenge where a nonlinear multivariable process with high disturbances, restrictions and interaction between variables appears. Under that situation, a reliable and powerful control strategy is needed to achieve a maximum performance. In this paper a multivariable control scheme based on a torque controller and pitch angle controller for a VS-VP small wind turbine connected with a permanent-magnet DC generator (PMG) is proposed. Both controllers are tuned for operating in several wind speed regions. Simulation results show the robustness, effectiveness and benefits of the multivariable control strategy used for different operation situations. The multivariable controller is compared with other baseline control schemes such as LQG controller and standard switched controller.

A flexible energy management for distributed energy supplied smart home in microgrid connection with grid‐connection/islanding mode functionality

AbstractIn this study, a flexible control approach is presented for smart home with DES system in microgrid connection. The smart home with DES has operation capability under GC‐Mode and I‐Mode functions with the proposed control algorithm. The control algorithm is also developed in terms of international standards IEEE 929 and IEC 61727 for grid connection requirements. Additionally, a PLL‐less control algorithm is embedded in the proposed control algorithm for the active/reactive power management and the grid synchronization. Thus, slow dynamic response and calculation burden issues in controller is avoided and the power flow capability of the grid‐connected DES in smart home is enhanced. Furthermore, the coordinate transformation is fulfilled for the power flow control in the control loop via the generation of cosine and sinus functions. Besides, the mathematical formulations of the proposed control algorithm are presented in detail. To show the effectiveness of the proposed control algorithm, a smart home with a photovoltaic DES model is designed and constructed in a simulation environment. The proposed control method is tested under various operation cases such as dynamic environmental conditions, GC‐Mode/I‐Mode, reactive power support and voltage variations. The proposed method shows efficient performance under applications of the different operation situations. Moreover, the international requirements are satisfied for GC‐Mode and I‐Mode transitions.

Recent Developments in Research on the Impact of ESG on Corporate Value

ESG (Environmental, Social, and Governance) has become an increasingly prominent topic in the investment field. Questions such as whether ESG can create value for companies, the credibility of ESG investments, and whether ESG should be incorporated into my long-term investment plan are crucial considerations. Many scholars have conducted research from various perspectives to address these questions. This article summarizes relevant literature and answers the impact of ESG on corporate value and the factors influencing ESG's impact on corporate value. The research findings are as follows: 1.ESG has a generally positive impact on corporate value, with some factors showing inhibitory effects or no significant correlation.2.ESG primarily affects corporate value through aspects such as market valuation, financing costs, innovation capability, and reputation. 3. The impact of ESG on corporate value varies depending on the ownership structure and industry of the company. Based on these findings, companies should decide whether to enhance their ESG performance based on various factors such as their operational situation, industry composition, and ownership structure.