Operation Of System Research Articles

STATISTICAL ANALYSIS OF FRICTIONAL PROCESSESOF A SLIDING BEARING

This article addresses the issue of diagnosing friction nodes in sliding bearings, which are among the mostchallenging and complex topics in the tribological research. The article presents a methodology for diagnosingfriction nodes operating under variable loads based on the analysis of vibration acceleration signals from thebearing using statistical methods. In the study, statistical parameters were determined to assess the technicalcondition of sliding bearings operating with variable loads under dry and mixed friction conditions. Usingstatistical tools to interpret selected tribological parameters that occur in the friction process aims to ensure theproper operation of the tribological system under operating conditions. Analysis of the measurement resultspresented in the study using statistical tools will allow the development of a methodology to assess the correctoperation of sliding bearings.

Control the Working Process of the Rotor System with Tilting Pad Bearing

Various processes take place in rotor systems with tilting pad bearings. It is important not only to control but also to manage these processes. Due to the instability of the oil film layer in a bearing with inclined pads, oil whirl/whip can occur. Such whirl/whip destabilize the operation of the rotor system. Additional elastic elements between the tilt pads suppress oil whirl/whip and thus reduce rotor vibration excitation. By identifying the working zones of such bearings where oil whirl and whip occur, the problems of rotor rotation instability can be solved. In order to determine the effectiveness of the elastic elements between the tilting pads, research was conducted. A special stand with diagnostic equipment was used for the tests. The clearance between the rotor and the bearing was 50 μm. During the research, the rotor rotation speed was varied from 0 to 5000 rpm. After conducting the research, stable and unstable rotor working zones were determined (Zone I: 0 to 1938 rpm; Zone II: 1938–3923 rpm; Zone III: 3923–5000 rpm). Based on the obtained research results, it is possible to control the working process of the rotor system.

The Essence of Commercial Bank, principles and functions

The paper describes the relevance of the topic, sets the goal and objectives, specifies the theoretical and methodological foundations, specifies that the banking system guides the country's payments and settlements system, defines the essence, principles and functions of a commercial bank. The banking system is a wide range of diverse services, starting from traditional savings-loan and settlement-cash operations, ending with modern monetary credit and financial instruments. The bank is an intermediary link that attracts the temporarily free resources of economic subjects and supplies them to those subjects of the economy who experience a temporary shortage of these resources. The bank assumes the risk of non-return of resources. The effective operation of the payment system in any country requires the improvement of settlement operations and their technologies. Banks are the centers through which the majority of settlement and payment transactions are carried out. Settlements and payments of an individual or a legal entity, as well as of the entire economy, are carried out through banks. The theoretical and methodological basis of the work is - the researches of Georgian and foreign scientists on the regulation of the banking sector, the analytical works of various authors, the information-statistical and analytical materials of the National Bank of Georgia. Key words: Banking system, active bank transactions, passive operations.

Human Operator Mental Fatigue Assessment Based on Video: ML-Driven Approach and Its Application to HFAVD Dataset

The detection of the human mental fatigue state holds immense significance due to its direct impact on work efficiency, specifically in system operation control. Numerous approaches have been proposed to address the challenge of fatigue detection, aiming to identify signs of fatigue and alert the individual. This paper introduces an approach to human mental fatigue assessment based on the application of machine learning techniques to the video of a working operator. For validation purposes, the approach was applied to a dataset, “Human Fatigue Assessment Based on Video Data” (HFAVD) integrating video data with features computed by using our computer vision deep learning models. The incorporated features encompass head movements represented by Euler angles (roll, pitch, and yaw), vital signs (blood pressure, heart rate, oxygen saturation, and respiratory rate), and eye and mouth states (blinking and yawning). The integration of these features eliminates the need for the manual calculation or detection of these parameters, and it obviates the requirement for sensors and external devices, which are commonly employed in existing datasets. The main objective of our work is to advance research in fatigue detection, particularly in work and academic settings. For this reason, we conducted a series of experiments by utilizing machine learning techniques to analyze the dataset and assess the fatigue state based on the features predicted by our models. The results reveal that the random forest technique consistently achieved the highest accuracy and F1-score across all experiments, predominantly exceeding 90%. These findings suggest that random forest is a highly promising technique for this task and prove the strong connection and association among the predicted features used to annotate the videos and the state of fatigue.

Toward Climate Resilient Asphalt Binder Selection in Canada

Climate change and extreme weather events present a fundamental challenge to pavement engineering and planning practice, given that transportation infrastructure has traditionally been planned and designed using historical climate data under the implicit assumption that climate is stationary and future conditions will resemble past ones. Transportation professionals should consider future risks scenarios in road design, material selection and system operations. This will be challenging given the inherent uncertainties in any climate projections. However, changes might be needed since transportation professionals are expected to deliver cost-effective and climate-resilient transportation infrastructure. The material selection plays an important role in the durability of flexible roads; therefore, it is important to consider the whole road design life cycle in terms of climate change and extreme weather events impact on asphalt selection as well as to consider different traffic speeds. This led to the development of the NRC Climate Adaptation and Asphalt Selection Tool (CAAST).

New operation analysis method for vacuum tube driving a tuned cavity

The rf system of China Spallation Neutron Source (CSNS) Rapid Cycling Synchrotron (RCS) utilizes vacuum tubes to drive the ferrite-loaded cavities. Ensuring stable tube operation in the rf system is crucial for the acceleration of high-intensity beams, as any instability may impede further increases in beam power. Therefore, the operation of the vacuum tube under heavy beam loading has attracted significant attention, particularly in the case of CSNS-II RCS, where the circulating beam current is expected to reach up to 15.25 A, imposing a substantial burden on the vacuum tube for beam loading compensation. Thus, a comprehensive analysis of tube operation is imperative. However, current methods for the tube operation analysis are developed based on wideband rf cavities, which are inadequate when the load of the tube is a narrowband ferrite-loaded cavity equipped with a tuning system. The presence of a tuning system renders the modeling method for wideband rf cavities inapplicable for ferrite-loaded cavities. Therefore, the development of a new method is necessary. This paper introduces a novel method for analyzing operation of vacuum tube driving a tuned cavity. The effectiveness of the method is validated by comparing the analysis results with measurements under beam loading conditions. Furthermore, an estimation of tube operation under 500 kW beam loading for CSNS-II is provided. Published by the American Physical Society 2024

PEMFC Gas-Feeding Control: Critical Insights and Review

Proton exchange membrane fuel cells (PEMFCs) are currently a relatively mature type of hydrogen energy device due to their high efficiency and low noise compared to traditional power devices. However, there are still challenges that hinder the large-scale application of PEMFCs. One key challenge lies in the gas supply system, which is a complex, coupled nonlinear system. Therefore, an effective control strategy is essential for the efficient and stable operation of the gas control system. This paper aims to provide a comprehensive and systematic overview of the control strategies for PEMFC anode and cathode supply systems based on an analysis of 182 papers. The review covers modern control theories and optimization algorithms, including their design, objectives, performance, applications, and so on. Additionally, the advantages and disadvantages of these control methods are thoroughly evaluated and summarized.

Improved Droop Control Strategy for Microgrids Based on Auto Disturbance Rejection Control and LSTM

This thesis proposes an improved droop control strategy design based on active disturbance rejection control and LSTM. This strategy uses the droop control method to coordinately control the distributed generation units (DGs) in a microgrid to achieve stable operation of the microgrid system. Linear-Auto Disturbance Rejection Control (LADRC) is introduced and an improved LADRC is designed based on the error principle. A disturbance compensation link is introduced on the basis of traditional LADRC to form ILADRC and a droop control strategy is used. Instead of improving the PD controller in LADRC, an improved droop control strategy is formed, which not only achieves natural decoupling between powers, but also improves the system’s immunity and transient operation capabilities. At the same time, in order to achieve adaptive parameter tuning in the improved droop control strategy, this article introduces long short-term memory (LSTM) to form an adaptive improved droop control strategy which further improves the system’s immunity and robustness. This article builds a simulation model through the MATLAB/Simulink simulation experiment platform and tests PI control and traditional droop control. The strategy and the improved droop control strategy designed in this thesis are experimentally compared and verified, and simulation analysis and verification are conducted on the two working conditions. The simulation results clearly demonstrate the superiority of the improved droop control strategy over PI control and traditional droop control, indicating that the correctness and reliability under various working conditions are verified.

Temperature stability analysis of the all-fiber current sensor with a loop structure

Abstract All-fiber optical current sensor (AFOCS) is a perfect product formed by the combination of fiber-optic sensing technology and the Faraday effect. It boasts significant advantages such as resistance to electromagnetic interference, a high measurement dynamic range and precision, low power consumption, low cost, and insulation. Additionally, it enables long-distance transmission, making it one of the main devices for current monitoring in smart grids. However, the birefringence within the sensing fiber is highly susceptible to changes in temperature, causing the polarization state of light to be extremely sensitive to temperature variations. This sensitivity significantly impacts the accuracy of current measurements. A novel loop structure AFOCS with a coupled fiber polarization rotator (FPR) is introduced in this paper. The operating principle of this structure is theoretically analyzed, and the Jones matrix is used to analyze the output light intensity signal and derive the error formula. After comparing the measurement accuracy of the basic AFOCS and the loop structure AFOCS, it is demonstrated that this novel structure can improve current sensitivity by enhancing the temperature robustness of the system. Additionally, the error generated by the FPR is controlled below 1%, meeting the requirements for stable system operation. Therefore, this novel structure effectively improves the accuracy of current measurement and exhibits strong temperature stability.

Integration of a solid oxide electrolysis system with solar thermal and electrical energy: A testing campaign for operation and control strategy definition

AbstractThe EU project PROMETEO has the scope of testing a 25 kW solid oxide electrolysis system integrated with a concentrated solar power plant via thermal energy storage in a relevant environment. Given the plant layout and the hydrogen demand characteristics, this work aims to identify how to operate the system effectively when renewable electricity is unavailable and how to modulate the load during hydrogen generation, thus defining the system's operation modes and control strategy. A 5 kWe stack has been tested at the FBK facility. The hot standby tests show that feeding a reducing gas at the negative electrode and air at the positive electrode, without polarizing the stack, effectively keeps the stack hot at 750°C and prevents degradation. Conversely, the electric protection approach leads to significant stack degradation (15% voltage drop in 200 h for one cluster). Regarding modulation of hydrogen generation, with low steam flowrates, the stack current and the flow rate of produced hydrogen mainly depend on the steam flow rate, while it is not affected by the stack temperature; conversely, with high steam flowrates, the current depends only on the stack temperature (from 25 A at 670°C to 65 A at 760°C). Based on the results, two hot standby modes and two load control strategies to be implemented and tested in the PROMETEO prototype are proposed.

Optimization of bus stop layout considering multiple factors including passenger flow direction.

Bus stop layout typically requires consideration of urban population distribution, traffic conditions, and passenger flow demand to establish an efficient foundation for the bus system's operation. Based on the above key factors, this paper introduces a strategic method to optimize the bus stop layout from a macro perspective in order to save passengers' travel time and improve the attractiveness of the bus system. This approach accounts for the matching degree between the Origin-Destination (OD) direction of passengers and their walking direction heading to bus stops. Initially, we take into account factors such as the population and area of traffic districts, and urban road conditions. Utilizing the hypernetwork multidimensional data clustering method along with GIS technology, we construct an alternative set of bus stops based on the hypernetwork framework. This set serves as a reference for the positioning of newly built and moved bus stops. Subsequently, we develop a two-stage model for bus stop layout decision-making. The first stage focuses on determining the bus stop layout at the traffic district level, taking into account multi-factors including the passenger flow matching degree. The second stage is designed to mitigate the negative impact of bus stop optimization on the overall service level of the urban bus system. A case study conducted in XT city demonstrates the effectiveness of our approach. Post-optimization, there is a 15.83% increase in the alignment between passenger flow direction and bus stop layout. Additionally, the average travel time for passengers is reduced by 7.55 minutes.

Adaptive Generalized Extended State Observer for a Single Phase PV Grid-connected System Operating Under the Sudanese-Sahelian Climate of Cameroon

Knowing the health of a system allows to guarante its efficiency and sustainability. The state observer is one of several techniques used by authors to estimate system state. This paper focuses on the problem of simultaneous states estimation of DC (Direct Current) and AC (Alternating Current) sides of a single-phase Photovoltaic (PV) grid-connected operating under the Sudanese-Sahelian climate of Cameroon. A generalized extended state observer (GESO) has been designed to simultaneously estimate the three states and the three disturbances of the system. A good estimation of the state and disturbances is achieved by the appropriate choice of the observer gain and the disturbance compensation gain resulting from the correct pole placement. The GESO robustness has been tested by varying the PV voltage and grid voltage. When there are no input fluctuations, the estimation errors of nominal states and disturbances converge to zero. The fluctuation in PV voltage resulting from partial shading has a significant impact on the boost converter current. The boost converter current varies proportionally with the drop in voltage due to partial shading from 55% to 59%. Under the grid voltage fluctuation, the boost converter current remains stable while the DC bus voltage and inverter current are significantly affected. The proposed GESO prove its robustness to perturbations from the PV array and grid side into the Single-Phase PV Grid-connected System. This paper contributes to the study of observers applied to the PV system and points the way to future work on diagnosing faults in PV systems operating in Cameroon's Sudanese-Sahelian climate.

The Prediction of the Leakage Airflow Rate Using the Supply and Return Airflow Rate in a Variable Air Volume System

Pressure differences in the envelope of a building result in leakage airflow (i.e., the unintended flow of air). This can lead to increased building heating and cooling energy, decreased thermal comfort for occupants, and the spread of moisture. To address this problem, it is necessary to know the leakage airflow in a building. Generally, the leakage airflow in a building is calculated by determining the leakage function through fan pressurization methods, such as the blower door test, and substituting the pressure difference measured by the pressure sensor. However, it is difficult to install continuous pressure sensors in an operating building. Therefore, this study proposes a method to utilize the supply and return airflow of an air conditioning system to predict the variation in the leakage airflow with changing indoor and outdoor airflow, and the efficacy of this approach was verified through experiments. The experiment measured the indoor and outdoor pressure difference of the building with a change in the speed of the supply and return fans and the opening rate of the variable air volume (VAV) damper. As a result of the experiment, the indoor–outdoor pressure difference is proportional to the difference between the indoor supply airflow and the ventilation airflow. In addition, the relationship between the pressure difference and the leakage airflow was derived through the pressurization/decompression method using an air handler, and the leakage airflow from the pressure difference generated by the operation of the air conditioning system was calculated. Lastly, the relationship between the supply and return airflow difference and the leakage airflow was derived based on the experimental results, and the leakage airflow was predicted based on the relationship.

Improving the Classification of Defect Levels in Main Electrical Equipment Through Advanced Prompting on a Large Language Model

The stable operation of the power system is closely related to the national economy and people’s livelihoods. Therefore, the timely detection, qualitative assessment, and handling of major equipment defects are crucial. The classification of defect levels in main electrical equipment is a fundamental task in this process, which is often manually completed, supplemented by knowledge bases or expert systems. However, this approach is time-consuming, labor-intensive, involves challenging human–machine interaction, and relies on expert experience. Conversational large language models, such as ChatGPT, ERNIE Bot, ChatGLM, have garnered widespread recognition in various domains. However, these models may have errors in the reasoning process, resulting in biased or even erroneous outputs, which is referred to as the “hallucinations”. The hallucinations’ problem of large language models poses challenges in specific fields. To mitigate the hallucinations in large language models, researchers often seek to incorporate domain-specific knowledge into these models through methods like fine-tuning or prompt learning. In order to enhance model performance while minimizing computational costs, this study adopts the prompt learning approach. Specifically, we propose a large language model prompt learning framework based on knowledge graphs, aiming to provide the large language model with reasoning support by leveraging specific information stored within the knowledge graph and receive explainable reasoning result. Experimental results demonstrate that our module achieved superior results on the power defect dataset compared to the non-prompt method.

RATIONALE FOR THE USE OF A HYDRATE FORMATION INHIBITOR IN PRODUCTION WELLS IN WESTERN SIBERIA

Operation of the gas transmission system under hydrate conditions leads to the risk of deposits, changes in operating modes and plugging of pipelines and gas transmission equipment. Inhibitors such as methanol and glycols are mainly used to prevent hydrate formation. Less commonly, small amounts of polymer compounds (kinetic inhibitors) are used, which instead of inhibiting formation work thermodynamically, slowing the rate of formation of gas hydrates. However, the problems associated with excessive methanol consumption and the production of products that do not meet the standards for the content of dissolved methanol remain unresolved. These problems can be solved by improving the technological scheme of gas collection and treatment. The article focuses on the use of methanol as a hydrate inhibitor, which effectively prevents this process. It has been shown that in order to optimize the flow rate of methanol, it is necessary to take into account the salinity of formation water. The urgency of the problem is due to the high risks for the normal operation of wells, since gas hydrates can lead to clogging of equipment and pipelines, which in turn can cause emergencies and shutdown of the entire production. The article describes the current status of field development in Western Siberia, including an analysis of reserves and an assessment of production potential. Seven pay zones have been identified in the field, two of which are associated with the Upper Cretaceous deposits, four with the Upper Jurassic and one with the Middle Jurassic. The authors proposed a method for calculating the optimal amount of methanol required to prevent the formation of gas hydrates. The calculations are based on data on the composition of Cenomanian gas, temperature and other conditions in the field under consideration. The study identified critical points where gas hydrates are most likely to form. This made it possible to accurately calculate the required amount of methanol to maintain the hydrate-free state of the wells. The results of testing in the field of Western Siberia showed that the dosing of methanol is an effective means to prevent hydrate formation.

System-level Prognostics and Health Management for Complex Industrial Systems

Prognostics and health management (PHM) has become essential to guarantee aware and safe system operation and to inform economic decision-making. However, due to the nature of detection, diagnostics, and prognostic methods, applications have mainly been limited to the component level. In practice, most industrial systems consist of multiple interacting components whose partial degradation could lead to the system's failure (or subsystems).This research addresses the limitations of traditional component-level PHM techniques by proposing a novel system-level framework. By implementing a hierarchical structure of components and subsystems, we will select an optimal method for each subsystem to aggregate its component health assessments. The overall system health can then be estimated by further combining the obtained estimates. The research considers simplified and holistic modeling techniques, margin-based methods, and hybrid graphical models. This approach aims to provide reliable system health predictions and online components' sensitivity measures to enhance maintenance decision-making. We consider an application in the context of the nuclear industry, characterized by strict safety and economic requirements. Using a SIMULINK model to approximate a Pressurized Water Reactor (PWR) with real industrial inputs, we plan to add component degradation modules and use simulated sensor data and reliability information to test the proposed framework. Initial results on artificial case studies show the feasibility of integrating component-level health predictions.

Mitigating Risk and Emissions in Power Systems: A Two-Stage Robust Dispatch Model with Carbon Trading

The large-scale integration of renewable energy sources is crucial for reducing carbon emissions. Integrating carbon trading mechanisms into electricity markets can further maximize this potential. However, the inherent uncertainty in renewable power generation poses significant challenges to effective decarbonization, renewable energy accommodation, and the security and cost efficiency of power system operations. In response to these challenges, this paper proposes a two-stage robust power dispatch model that incorporates carbon trading. This model is designed to minimize system operating costs, risk costs, and carbon trading costs while fully accounting for uncertainties in renewable energy output and the effects of carbon trading mechanisms. This model is solved using the column-and-constraint generation algorithm. Validation of an improved IEEE 39-bus system demonstrates its effectiveness, ensuring that dispatch decisions are both robust and cost-efficient. Compared to traditional dispatch models, the proposed model significantly reduces system risk costs, enhances the utilization of renewable energy, and, through the introduction of a ladder carbon trading mechanism, achieves substantial reductions in carbon emissions during system operation.

Low-carbon optimization of multi-microgrid system operation considering supply–demand bidirectionality based on Nash bargaining

Low-carbon optimization of multi-microgrid system operation considering supply–demand bidirectionality based on Nash bargaining

Influence of the Building’s Thermal Insulation on Intermittent Heating Mode Efficiency

Abstract The results of mathematical modeling of a building’s thermal insulation influence on the efficiency of intermittent heating mode are presented. This work aim is to assess the influence of thermal insulation on the efficiency of using the intermittent heating mode for the educational building of the Odessa National Polytechnic University’s Heat Engineering Laboratory. The paper analyzes the factors related to improving the efficiency of the programmed heat supply mode. Modeling of the operating modes for heat supply system operated in intermittent heating mode is carried out. There was performed the mathematical modeling of the heating system operation modes for the most unfavorable climatic conditions at low outdoor temperatures, with respective system efficiency indicators obtaining. The potential of energy saving level for administrative, educational, and office buildings depending on the thickness of thermal insulation of external and internal walls is studied. Recommendations have been elaborated for the use of buildings thermal insulation to improve the efficiency of intermittent heating.

Linking Electricity and Air Quality Models by Downscaling: Weather-Informed Hourly Dispatch of Generation Accounting for Renewable and Load Temporal Variability Scenarios.

National models of the electric sector typically consider a handful of generator operating periods per year, while pollutant fate and transport models have an hourly resolution. We bridge that scale gap by introducing a novel fundamental-based temporal downscaling method (TDM) for translating national or regional energy scenarios to hourly emissions. Optimization-based generator dispatch is used to account for variations in emissions stemming from weather-sensitive power demands and wind and solar generation. The TDM is demonstrated by downscaling emissions from the electricity market module in the National Energy Model System. As a case study, we implement the TDM in the Virginia-Carolinas region and compare its results with traditional statistical downscaling used in the Sparse Matrix Operator Kernel Emissions (SMOKE) processing model. We find that the TDM emission profiles respond to weather and that nitrogen oxide emissions are positively correlated with conditions conducive to ozone formation. In contrast, SMOKE emission time series, which are rooted in historical operating patterns, exhibit insensitivity to weather conditions and potential biases, particularly with high renewable penetration and climate change. Relying on SMOKE profiles can also obscure variations in emission patterns across different policy scenarios, potentially downplaying their impacts on power system operations and emissions.