Operation Of System Research Articles

Insulator defect detection in transmission line based on an improved lightweight YOLOv5s algorithm

As one of the most important components on the transmission line, the insulator is prone to failure, which affects the safe operation of electrical power system. Hence, it is crucial to accurately detect the insulator defects for further maintenance in time. Recently, with the development of artificial intelligence and target detection algorithms, the insulator defect detection has received more and more attention. However, there are still existing some difficulties: insufficient samples and low detection accuracy. To improve the accuracy of insulator defect detection, this paper proposes an auto-detection method based on an improved lightweight YOLOv5s model. First, this paper introduces the basic network frame of YOLOv5s and proposes an improved algorithm by utilizing the GIoU loss function, Mish activation function, and CBAM module. Then, performs data enhancement in the insulator dataset to enhance the robustness of the model. Finally, trains and tests the improved YOLOv5s model, and compares it with traditional target detection algorithms. Compared with traditional target detection algorithms, the AP value of the proposed algorithm in detecting insulator defects can be improved by 5 %. The results demonstrate the improved algorithm proposed in this paper can effectively identify and position the insulator defects.

Optimizing power system operations: integrating FACTS and ESS through advanced algorithms

Optimizing power system operations: integrating FACTS and ESS through advanced algorithms

On the Model Update Strategies for Supervised Learning in AIOps Solutions

AIOps (Artificial Intelligence for IT Operations) solutions leverage the massive data produced during the operation of large-scale systems and machine learning models to assist software engineers in their system operations. As operation data produced in the field are constantly evolving due to factors such as the changing operational environment and user base, the models in AIOps solutions need to be constantly maintained after deployment. While prior works focus on innovative modeling techniques to improve the performance of AIOps models before releasing them into the field, when and how to update AIOps models remain an under-investigated topic. In this work, we performed a case study on three large-scale public operation data: two trace datasets from the cloud computing platforms of Google and Alibaba and one disk stats dataset from the BackBlaze cloud storage data center. We empirically assessed five different types of model update strategies for supervised learning regarding their performance, updating cost, and stability. We observed that active model update strategies (e.g., periodical retraining, concept drift guided retraining, time-based model ensembles, and online learning) achieve better and more stable performance than a stationary model. Particularly, applying sophisticated model update strategies (e.g., concept drift detection, time-based ensembles, and online learning) could provide better performance, efficiency, and stability than simply retraining AIOps models periodically. In addition, we observed that, although some update strategies (e.g., time-based ensemble and online learning) can save model training time, they significantly sacrifice model testing time, which could hinder their applications in AIOps solutions where the operation data arrive at high pace and volume and where immediate inferences are required. Our findings highlight that practitioners should consider the evolution of operation data and actively maintain AIOps models over time. Our observations can also guide researchers and practitioners in investigating more efficient and effective model update strategies that fit in the context of AIOps.

Analysis of distribution method of designed air quantity in coal mine ventilation—a case study

In a coal mine, air leakage exists in some roadways through doors and other ventilation structures inevitably. Based on this opinion, there are different views on whether these roadways must be assigned airflow in coal mine ventilation design. This paper analyses some relevant regulations and criteria on the designed air quantity of coal mines. Then, based on the ventilation design of the Guizhou Yizhong Coal Mine, through the study of the calculation of needed air quantity of every working place and its distribution method in coal mine ventilation design, this paper puts forward that explosion-proof door, safety exit, and other short distance roadways with ventilation structures need not assign airflow in coal mine ventilation design, while some long-distance roadways need. Additionally, it presents the main reason to support this opinion, gives the distribution method of inner air leakage quantity, which comes with the calculation of the designed mine total air quantity, puts forward the remedy method for the air leakage through ventilation structures in a coal mine ventilation system, then offers the mine operator with the basic opinions for the day-to-day planning and effective operation of a coal mine ventilation system.

Game Study on Morning Peak Road Congestion Problem

The morning peak road congestion problem has always been a hot issue in urban traffic management. Road congestion during the morning peak not only affects the travel efficiency of travelers, but also has a serious negative impact on the urban environment and economic development. This article starts from the perspective of game theory and focuses on the problem of road selection. This article considers government supervision and builds a game model from the two game subjects of the driver and the traffic controller. This article analyzes the relationship between the driver and the driver, the driver and the traffic controller. This paper studies the impact of traffic time, whether the driver investigates in advance, and whether the traffic commander conducts road command on the stable strategy of the driver's road choice game. The research results of this article show that drivers and drivers can achieve Nash equilibrium by choosing different roads, and drivers adopting collaborative strategies during the morning peak period can effectively alleviate road congestion. Under the condition of ensuring the cost of arranging traffic commanders for drivers and the government, drivers can investigate the road in advance and traffic commanders can direct road 2, which can effectively promote the smooth operation of the traffic system. At the same time, this article puts forward targeted policy suggestions, aiming to optimize the allocation of transportation resources, improve road traffic efficiency, reduce morning peak road congestion, and provide scientific reference and decision-making support for urban traffic management.

Overvoltage Avoidance Control Strategy for Braking Process of Brushless DC Motor Drives with Small DC-Link Capacitance

Single-phase input rectifier brushless DC motor drives with a small film capacitor have many advantages, such as high power density and high reliability. However, when the motor system operates in regenerative braking mode, the dc-link capacitor with reduced capacitance may suffer from overvoltage without adding additional hardware circuits. At the same time, the braking torque control of the motor will be affected by speed variations. In order to ensure smooth and reliable operation of the motor system, an anti-overvoltage braking torque control method is proposed in this article. The relationship among the dc-link capacitance, the dc-link capacitor voltage, and the speed during regenerative braking is analyzed quantitatively, and the speed at which the regenerative braking is switched to the plug braking is obtained, which in turn consumes the capacitor energy to avoid dc-link overvoltage. Additionally, based on the relationship between the controllability of the braking torque and the speed, a reference value of the braking current that matches the speed is designed. The proposed method makes use of the capacitor’s energy storage during regenerative braking. Meanwhile, it mitigates the impact of motor speed on braking torque. Finally, the effectiveness of the proposed method is verified on a motor platform equipped with the dc-link film capacitor.

Multi-Time-Scale Low-Carbon Economic Dispatch Method for Virtual Power Plants Considering Pumped Storage Coordination

Low carbon operation of power systems is a key way to achieve the goal of energy power carbon peaking and carbon neutrality. In order to promote the low carbon transition of energy and power and the coordinated and optimized operation of distributed energy sources in virtual power plants (VPP), this paper proposes a framework for collaborative utilization of pumped storage–carbon capture–power-to-gas (P2G) technologies. It also constructs a multi-time scale low carbon economic dispatch model for VPP to minimize the internal resource operation cost of VPP in each time period. During the intraday scheduling stage, the day-ahead scheduling results as the planned output and the energy flow is then dynamically corrected at a short-term resolution in the framework. This allows for the exploration of the low-carbon potential of each aggregation unit within the virtual power plant. The results of the simulation indicate that the strategy and model proposed in this paper can effectively encourage the consumption of renewable energy sources, promote the low-carbon operation of power system power, and serve as a valuable reference for the low-carbon economic operation of the power system.

Experimental and Simulation Studies on Stable Polarity Reversal in Aged HVDC Mass-Impregnated Cables

Mass-impregnated (MI) cables have been used for many years as cables in high-voltage direct current (HVDC) systems. In line commutated converter (LCC) HVDC systems, polarity reversal for power flow control can induce significant electrical stress on MI cables. Furthermore, the mass oil and kraft paper comprising the impregnated insulation have significantly different coefficients of thermal expansion. Load fluctuations in the cable lead to expansion and contraction of the mass, creating pressure within the insulation and causing redistribution of the impregnant. During this process, shrinkage cavities can form within the butt gaps. Since the dielectric strength of the cavities is lower than that of the surrounding impregnation, cavitation phenomena in impregnated paper insulation are considered a factor in degrading insulation performance. Consequently, this study analyzes the electrical conductivity of thermally aged materials and investigates the transient electric field characteristics within the cable. Additionally, it closely analyzes the formation and dissolution of cavities in MI cables during polarity reversal based on a numerical model of pressure behavior in porous media. The conductivity of the impregnated paper indicates that it has excellent resistance to thermal degradation. Simulation results for various load conditions highlight that the interval of load-off time and the magnitude of internal pressure significantly influence the cavitation phenomenon. Lastly, the study proposes stable system operation methods to prevent cavitation in MI cables.

High-Voltage Cable Buffer Layer Ablation Fault Identification Based on Artificial Intelligence and Frequency Domain Impedance Spectroscopy

In recent years, the occurrence of high-voltage cable buffer layer ablation faults has become frequent, posing a serious threat to the safe and stable operation of cables. Failure to promptly detect and address such faults may lead to cable breakdowns, impacting the normal operation of the power system. To overcome the limitations of existing methods for identifying buffer layer ablation faults in high-voltage cables, a method for identifying buffer layer ablation faults based on frequency domain impedance spectroscopy and artificial intelligence is proposed. Firstly, based on the cable distributed parameter model and frequency domain impedance spectroscopy, a mathematical model of the input impedance of a cable containing buffer layer ablation faults is derived. Through a simulation, the input impedance spectroscopy at the first end of the cables under normal conditions, buffer layer ablation, local aging, and inductive faults is performed, enabling the identification of inductive and capacitive faults through a comparative analysis. Secondly, the frequency domain amplitude spectroscopy of the buffer layer ablation and local aging faults are used as datasets and are input into a neural network model for training and validation to identify buffer layer ablation and local aging faults. Finally, using multiple evaluation metrics to assess the neural network model validates the superiority of the MLP neural network in cable fault identification models and experimentally confirms the effectiveness of the proposed method.

A Neural Network Forecasting Approach for the Smart Grid Demand Response Management Problem

Demand response management (DRM) plays a crucial role in the prospective development of smart grids. The precise estimation of electricity demand for individual houses is vital for optimizing the operation and planning of the power system. Accurate forecasting of the required components holds significance as it can substantially impact the final cost, mitigate risks, and support informed decision-making. In this paper, a forecasting approach employing neural networks for smart grid demand-side management is proposed. The study explores various enhanced artificial neural network (ANN) architectures for forecasting smart grid consumption. The performance of the ANN approach in predicting energy demands is evaluated through a comparison with three statistical models: a time series model, an auto-regressive model, and a hybrid model. Experimental results demonstrate the ability of the proposed neural network framework to deliver accurate and reliable energy demand forecasts.

Reconfigurable-Intelligent-Surface-Enhanced Dynamic Resource Allocation for the Social Internet of Electric Vehicle Charging Networks with Causal-Structure-Based Reinforcement Learning

Charging stations and electric vehicle (EV) charging networks signify a significant advancement in technology as a frontier application of the Social Internet of Things (SIoT), presenting both challenges and opportunities for current 6G wireless networks. One primary challenge in this integration is limited wireless network resources, particularly when serving a large number of users within distributed EV charging networks in the SIoT. Factors such as congestion during EV travel, varying EV user preferences, and uncertainties in decision-making regarding charging station resources significantly impact system operation and network resource allocation. To address these challenges, this paper develops a novel framework harnessing the potential of emerging technologies, specifically reconfigurable intelligent surfaces (RISs) and causal-structure-enhanced asynchronous advantage actor–critic (A3C) reinforcement learning techniques. This framework aims to optimize resource allocation, thereby enhancing communication support within EV charging networks. Through the integration of RIS technology, which enables control over electromagnetic waves, and the application of causal reinforcement learning algorithms, the framework dynamically adjusts resource allocation strategies to accommodate evolving conditions in EV charging networks. An essential aspect of this framework is its ability to simultaneously meet real-world social requirements, such as ensuring efficient utilization of network resources. Numerical simulation results validate the effectiveness and adaptability of this approach in improving wireless network efficiency and enhancing user experience within the SIoT context. Through these simulations, it becomes evident that the developed framework offers promising solutions to the challenges posed by integrating the SIoT with EV charging networks.

Application of LuTan-1 SAR Data in Railway Subsidence Monitoring

Abstract. InSAR technology is currently a crucial tool for large-scale surface deformation monitoring, particularly excelling in regional subsidence monitoring. In this study, focusing on the Jinan section of the Shandong railway, newly launched LUTAN-1 satellite SAR data was employed. DInSAR and Stacking techniques were applied to analyze the subsidence in response to the long-term operation load of high-speed trains and surrounding human activities. Through the analysis of data from June 2023 to December 2023, regional subsidence was identified in this section, with a subsidence rate reaching 8 cm/year. Comparative analysis between DInSAR technology monitoring results and precise leveling monitoring results showed consistency in the subsidence deformation trend in the region, achieving centimeter-level deformation monitoring in the study area.The LuTan-1 satellite provided robust SAR data support for railway subsidence monitoring, offering substantial reliability and accuracy to deepen the understanding of railway subsidence issues. These research findings hold significant practical implications for adopting timely maintenance and repair measures, ensuring the safety and reliable operation of the railway system.

Design and cooling of permanent magnet generator in MW-class space closed Brayton cycle system

The closed Brayton cycle (CBC) system is a prime energy conversion technology to convert heat from the reactor to electric power for space missions due to its light weight, compact and stable operation. The CBC system incorporates a permanent magnet generator (PMG) mounted on the CBC shaft with the turbine and compressor. To ensure safe operation of the CBC system, a small fraction of the working fluid is used to provide the required cooling. In this study, four different cooling schemes used in CBC system are concluded and the key dimensions of the PMG are obtained through mechanical design theory and electromagnetic theory. Comprehensive research on the electromagnetic field and loss calculation is then performed. Furthermore, the cooling paths of the PMG are designed and the resulting temperature profiles are calculated. Finally, different cooling schemes are compared and the appropriate cooling advice is proposed.

Performance Analysis of a Compact Offset Strip Fin Heat Exchanger for Lubrication System in Aero Engine

Abstract As a typical fuel-oil heat exchanger, an offset strip fin heat exchanger (OSFHX) can ensure the efficient and stable operation of the aero engine lubrication system. The paper establishes 20 kinds of numerical models of OSFHX that are verified by experiments. The effects of different structural parameters on the performance of lubricating oil in OSFHX were systematically studied. Based on dimensional analysis, the prediction model which can describe the overall performance of the OSFHX is obtained. The results show that with the decrease of fin distance (s), fin length (l), fin height (h), fin thickness (t), and fin shape angle (α), the heat transfer performance of the OSFHX is enhanced and the resistance is increased. With the decrease of s and h, and the increase of t and α, the comprehensive performance of the OSFHX is enhanced. The OSFHX exhibits the optimal performance when l = 4 mm. α has the most significant effect on the comprehensive performance of the OSFHX, successively followed by h, s, t, and l. The average error of Colburn factor (j) and friction factor (f) prediction models is 4.09% and 5.31%, respectively, which can realize the theoretical calculation of the performance of the OSFHX for aviation.

Theoretical substantiation of system for organising preventive treatment of asphalt concrete road pavements

Introduction. Over the past 20 years, the fleet of vehicles in the Republic of Belarus has increased significantly, and, consequently, the intensity of traffic on roads with a proportional increase in wear and tear of their surfaces. The need to protect asphalt concrete pavements from premature destruction dictates the search for new technological solutions. To effectively protect asphalt concrete pavements from the complex effects of water and traffic loads in the autumn-winter and spring-winter periods, the author has developed and implemented one of the options for protecting asphalt concrete pavements of a highway – treating them with a hydrophobic preventive Protekt- 01composition.Materials and methods. The research used theoretical and practical results from the implementation of the developed hydrophobic prophylactic composition.Results. The article presents an overview of the state of the problem of preventing destruction of asphalt concrete pavements abroad and in the Republic of Belarus, an analysis of the measures taken to implement the developed hydrophobic preventive Protekt-01composition at transport infrastructure facilities, and also, based on the data obtained from production testing a ‘System for organizing preventive treatment’ has been proposed, which will be aimed at preventing the beginning and future destruction of the asphalt concrete pavement of highways and will be based on monitoring the technical condition of the control object, planning, organizing, implementing and managing preventive and repair measures using navigation equipment and software that makes it possible to optimize the balance of costs and road surface defects.Originality. Thus, based on the theoretical substantiation and the results of practical implementation of the developed composition, the following are proposed:- method for ensuring the operation of a control system for liquid road-building materials which enables to eliminate overconsumption of the distributed substance;- ‘System for organizing preventive treatment’, which enables to increase the service life of asphalt concrete pavement by 1.2–1.5 times.

High-performance internal circulation anaerobic granular sludge reactor for cattle slaughterhouse wastewater treatment and simultaneous biogas production

This research investigates the efficacy of a high-performance pilot-scale Internal Circulation Anaerobic Reactor inoculated with Granular Sludge (ICAGSR) for treating cattle slaughterhouse wastewater while concurrently generating biogas. The primary objective is to assess the efficiency and performance of ICAGSR in terms of organic pollutant removal and biogas production using granular anaerobic sludge. The research methodology entails operating the ICAGSR system under ambient conditions and systematically varying key parameters, including different Hydraulic Retention Times (HRTs) (24, 12, and 8 h) and Organic Loading Rates (OLRs) (3.3, 6.14, and 12.83 kg COD/m³. d). The study focuses on evaluating pollutants’ removal and biogas production rates. Results reveal that the ICAGSR system achieves exceptional removal efficiency for organic pollutants, with Chemical Oxygen Demand (COD) removal exceeding 74%, 67%, and 68% at HRTs of 24, 12, and 8 h, respectively. Furthermore, the system demonstrates stable and sustainable biogas production, maintaining average methane contents of 80%, 76%, and 72% throughout the experimental period. The successful operation of the ICAGSR system underscores its potential as a viable technology for treating cattle slaughterhouse wastewater and generating renewable biogas. In conclusion, this study contributes to wastewater treatment and renewable energy production by providing a comprehensive analysis of the ICAGSR system’s hydrodynamic properties. The research enhances our understanding of the system’s performance optimization under varying conditions, emphasizing the benefits of utilizing ICAGSR reactors with granular sludge as an effective and sustainable approach. Identifying current gaps, future research directions aim to further refine and broaden the application of ICAGSR technology in wastewater treatment and renewable energy initiatives.

Lithium-ion point-of-care ultrasound battery joint state of charge estimation

With recent advances in ultrasound technology, including accessibility and portability, point-of-care ultrasound systems (POCUS) are becoming ubiquitous in urban and rural health services. The efficient use of POCUS battery power is necessary due to the limited access to electricity in rural locations. Accurate estimation of the state-of-charge (SOC) of the onboard lithium-ion battery is crucial in ensuring the safe, efficient, and reliable operation of the battery management system (BMS). This research suggests a novel approach for striking a reasonable balance between the POCUS battery management systems computation time and SOC estimate accuracy. Given that the lithium-ion battery is a non-linear and time-varying system, it is modelled by a second-order RC equivalent circuit model (2-RC ECM). Real-time parametric identification is carried out using the method of recursive least squares with respect to an adaptive forgetting factor (AFFRLS). The joint SOC evaluation technique, ordering the results of real-time identification and the Extended Kalman Filter (EKF) recommended for estimating battery SOC at temperatures between 0 and 45 °C. Simulations were carried out using experimental data from the DST (Dynamic Stress Test), incremental current and, the Federal Urban Driving Program (FUDS) of the CALCE research group to evaluate the proposed approach. Based on the monitoring of dynamic changes in model parameters and the computational complexity of existing algorithms, the results reveal that the joint AFFRLS-EKF estimation proposed for the SOC strategy achieves good accuracy, high adaptability and rapid convergence of SOC estimation.

Design and operation of a load-tolerant ICRH system in Experimental Advanced Superconducting Tokamak

Ion Cyclotron Resonance Heating (ICRH) has been a dependable tool for sturdy plasma heating with high RF power of several megawatts. However, a sudden increase in the reflected power during ICRH heating experiments is a problem that should be solved for future fusion experimental devices. To solve this issue, a load tolerant matching network has been designed for the ICRH system in EAST. The matching network includes a 3-stub tuner impedance matching system with conjugate-T structure, 30 Ω to 50 Ω transmission line and center grounded antenna strap. By maintaining a low reflection ratio in the network for a wide range of resistance, this matching network can allow sturdy high-power operations without fast impedance matching in EAST. In our matching network, the two arms of a conjugate-T are designed to have a λ/2 length difference which could mitigate current imbalance and antenna poloidal phasing outside of the control problem. The T-point corresponds to the maximum point of the standing wave voltage, which could greatly improve the input impedance of the antenna.

Analysis of numerical modeling of steady-state modes of methane–hydrogen mixture transportation through a compressor station to reduce CO2 emissions

This study presents a mathematical model to evaluate the performance of gas pipelines during hydrogen injection in a gas pipeline-compressor station. The developed model presents the calculation of methane–hydrogen mixture (CH4/H2) transportation through the compressor station, where the compensation of pressure drops in the mass and energy balance takes place. Simultaneously, in the operation of the centrifugal blower system of gas compressor stations, the emissions of CO2 are considered, considering the mixing of gas media and the compression of CH4/H2. This mathematical model is realized for the pipeline transportation of hydrogen, at which the principle of mixture expansion occurs. The aim is to solve the problem of CO2 emissions at compressor stations. The optimization procedure has been formulated using a system of nonlinear algebraic equalities. The research focuses on the adaptation of existing gas transportation systems to CH4/H2 transportation and the impact of environmental risks on the operation of compressor station equipment. In this case, it is possible to determine the quantitative amount of hydrogen that can be added to natural gas. By solving the problem of finding the inner point of sets using the system of nonlinear algebraic equalities, it is possible to obtain the control parameters for safety control of technological modes of CH4/H2 mixture transportation. The study findings reveal that the consumption of gas charger and hydrogen was 50.67 and 0.184 kg/s, respectively, and the estimated efficiency resulting from the modified turbine design was 75.1 percent. These results indicate that the equipment operates more efficiently when hydrogen is being transported. The numerical analytical results indicated in this study hold practical significance for design applications. It will assist in identifying and evaluating the restrictions that may develop during the technological, operational, and design stages of decision-making.

USE OF INTELLIGENT FUZZY IMAGE SEGMENTATION SYSTEMS IN THE PROFESSIONAL TRAINING OF FUTURE SPECIALISTS IN ENGINEERING AND PEDAGOGICAL FIELDS

Relevance: The article addresses the critical issue of integrating intelligent image segmentation systems that utilize fuzzy logic into the training processes for future specialists in engineering and pedagogical fields. This integration is a significant aspect of the digitization of higher education. Aim: The goal is to implement intelligent vehicle image segmentation systems using fuzzy logic to train specialists in engineering and pedagogical fields. Methods: The preliminary processing of the images of the studied objects (vehicles) involved digital filtering methods, contour detection, profile analysis, and contrast enhancement. Image segmentation was performed using watershed methods, contour lines, and region growing. After segmentation, the obtained segments were selected based on size. Fuzzy membership functions were then applied to determine the degree of affiliation of the segments to the meaningful parts of the studied objects, ensuring reliable recognition of these parts and stable operation of the intelligent system despite external influences on the acquired images. Results: A computer system has been developed for the segmentation of vehicle images using fuzzy logic, which has been integrated into the training of specialists in engineering and pedagogical fields. The segmentation methods isolate objects within the images, which are then recognized using fuzzy logic. Thanks to the fuzzy membership functions, elements of vehicle images are reliably recognized even when there is some ambiguity in the shapes of the segments. The practical significance of the developed system is demonstrated through the processing of car images. Conclusions: The integration of the developed system into the educational process provides students with both theoretical knowledge and practical skills related to intelligent image processing systems.