Safe Operation Research Articles

Calculation of Distribution Network PV Hosting Capacity Considering Source–Load Uncertainty and Active Management

The access of a high proportion of photovoltaic (PV) will change the energy structure of the distribution network (DN), resulting in a series of safety operation risks. This paper proposes a two-stage PV hosting capacity (PVHC) calculation model to assess the maximum PVHC, considering the uncertainty and active management (AM). Firstly, we employ a robust optimization model to characterize the uncertainty of sources and loads in DN with PV and analyze the worst-case scenarios for PVHC. Subsequently, we construct a PVHC calculation model that takes into account AM, and convert the model into a mixed-integer second-order cone (MISOC) model using linearization techniques. Finally, we apply “heuristic optimization + CPLEX solver” to solve the model and introduce overvoltage and overcurrent indices to analyze the safety of the DN under PV limit access. Case studies are carried out on the IEEE 33-bus system and a practical case. Results show that (1) only the uncertainty that reduces the load or increases the output efficiency will affect PVHC; (2) for DN limited by overvoltage, AM can better improve PVHC; however, for DN limited by maximum transmission power, the effect of AM is low; (3) for most DN, SVC can improve PVHC, but the effect is modest. And network reconfiguration can significantly increase PVHC on the system with poor branch network, even reaching 150% of the original PVHC.

Railway Track Irregularity Estimation Using Car Body Vibration: A Data-Driven Approach for Regional Railway

Track and preventive maintenance are necessary for the safe and comfortable operation of railways. Track displacement measured by track inspection vehicles or trolleys has been primarily used for track management. Thus, vibration data measured in in-service vehicles have not been extensively used for track management. In this study, we propose a new technique for estimating track irregularities from measured car body vibration for track management. The correlation between track irregularity and car body vibration was analysed using a multibody dynamics simulation of travelling rail vehicles. Gaussian process regression (GPR) was applied to the track irregularity and car body vibration data obtained from the simulation, and a method was proposed to estimate the track irregularities from the constructed regression model. The longitudinal-level, alignment, and cross-level irregularities were estimated from the measured car body vibrations and travelling speeds on a regional railway, and the results were compared with the actual track irregularity data. The results showed that the proposed method is applicable for track irregularity management in regional railways.

Research on Temperature–Pressure Coupling Model of Gas Storage Well during Injection Production

Periodic changes in wellbore temperature and pressure caused by the cyclic injecting and producing of gas storage wells affect wellbore integrity. To explore the distribution and influencing factors of wellbore temperature and pressure during gas storage well injection-production processes, based on energy conservation, momentum theorem, and the transient heat transfer mechanism of the wellbore, a temperature and pressure coupling model for gas storage injection-production wellbores was established, and a piecewise iterative method was used to solve the model equations. Compared with the field data, the predicted relative errors of the wellhead temperature and pressure were 2.30% and 2.07%, respectively, indicating that the coupling model has a high predictive accuracy. The influences of the injection-production conditions, tubing diameter, and overall heat transfer coefficient on the wellbore temperature and pressure distributions were analyzed through an example. When the gas injection flow rate increased by 1.5 times, the bottomhole temperature decreased by 37%. Doubling the overall heat transfer coefficient resulted in a 10% rise in the bottomhole temperature. An increase of 0.3 times in the gas injection pressure led to a 31% increase in bottomhole pressure. With a 1.5-fold increase in the gas production flow rate, the wellhead temperature rose by 28%, and the wellhead pressure dropped by 20%. The research in this paper can serve as a guide for the optimization design and safe operation of gas storage wells.

Application of Information Link Control in Surgical Specimen Near-Miss Events in a South China Hospital: Nonrandomized Controlled Study.

Information control is a promising approach for managing surgical specimens. However, there is limited research evidence on surgical near misses. This is particularly true in the closed loop of information control for each link. A new model of surgical specimen process management is further constructed, and a safe operating room nursing practice environment is created by intercepting specimen near-miss events through information safety barriers. In a large hospital in China, 84,289 surgical specimens collected in the conventional information specimen management mode from January to December 2021 were selected as the control group, and 99,998 surgical specimens collected in the information safety barrier control surgical specimen management mode from January to December 2022 were selected as the improvement group. The incidence of near misses, the qualified rate of pathological specimen fixation, and the average time required for specimen fixation were compared under the 2 management modes. The causes of 2 groups of near misses were analyzed and the near misses of information safety barrier control surgical specimens were studied. Under the information-based safety barrier control surgical specimen management model, the incidence of adverse events in surgical specimens was reduced, the reporting of near-miss events in surgical specimens was improved by 100%, the quality control quality management of surgical specimens was effectively improved, the pass rate of surgical pathology specimen fixation was improved, and the meantime for surgical specimen fixation was shortened, with differences considered statistically significant at P<.05. Our research has developed a new mode of managing the surgical specimen process. This mode can prevent errors in approaching specimens by implementing information security barriers, thereby enhancing the quality of specimen management, ensuring the safety of medical procedures, and improving the quality of hospital services.

Transient Stability Assessment in Power Systems: A Spatiotemporal Graph Convolutional Network Approach with Graph Simplification

Accurate and fast transient stability assessment (TSA) of power systems is crucial for safe operation. However, deep learning-based methods require long training and fail to simultaneously extract the spatiotemporal characteristics of the transient process in power systems, limiting their performance in prediction. This paper proposes a novel TSA method based on a spatiotemporal graph convolutional network with graph simplification. First, based on the topology and node information entropy of power grids, as well as the power flow of each node, the input characteristic matrix is compressed to accelerate evaluation. Then, a high-performance TSA model combining a graph convolutional network and a Gated Convolutional Network is constructed to extract the spatial features of the power grid and the temporal features of the transient process. This model establishes a mapping relationship between spatiotemporal features and their transient stability. Finally, the focal loss function has been improved to dynamically adjust the influence of samples with different levels of difficulty on model training, adaptively addressing the challenge of sample imbalance. This improvement reduces misclassification rates and enhances overall accuracy. Case studies on the IEEE 39-bus system demonstrate that the proposed method is rapid, reliable, and generalizable.

Complementary Analysis and Performance Improvement of a Hydro-Wind Hybrid Power System

Hydropower as a flexible regulation resource is a rare choice to suppress the ever-increasing penetration of wind power in electrical power systems. The complementary characteristics and performance improvement of a hydro–wind hybrid power system based on a mathematical model of the hybrid power system is studied in this paper. This established model takes into account the stochastic variation in wind speeds in the wind power subsystem and the hydraulic–mechanical–electrical coupling characteristics of the hydropower subsystem. The complementary analysis is conducted based on the evaluation variables outputted by the established model, such as the wind power, hydro-regulation power, hydraulic power, and frequency. To make full use of the regulation capability of the hydropower system, the optimization of parameter settings is also carried out to improve complementary performances of the hybrid power system. The results from the complementary analysis show the detailed characteristics of hydro–wind coordinated operation under different types of real wind speeds. Here, 95% of installed hydro-capacity is used to complement the power shortage of the intermittent wind energy under the low wind speed. Alternatively, only around 66% of the installed hydro-capacity can be utilized to cope with the fluctuation in wind power under the medium and high wind speeds before the optimization of parameter settings. The recommended values and change rules of the control, hydraulic, and electrical parameters for the hydropower system are subsequently revealed from the analysis of parameter settings to contribute to a stable and safe hybrid power system. The results show that the optimized parameter can increase the maximal regulating capacity of the hydropower system by nearly 9 MW, approximately a sixth of the total installed hydropower capacity. The method and results obtained in this paper provide theoretical and technical guidance for the safe and economical operation of power stations.

Efficient Method for Photovoltaic Power Generation Forecasting Based on State Space Modeling and BiTCN

As global carbon reduction initiatives progress and the new energy sector rapidly develops, photovoltaic (PV) power generation is playing an increasingly significant role in renewable energy. Accurate PV output forecasting, influenced by meteorological factors, is essential for efficient energy management. This paper presents an optimal hybrid forecasting strategy, integrating bidirectional temporal convolutional networks (BiTCN), dynamic convolution (DC), bidirectional long short-term memory networks (BiLSTM), and a novel mixed-state space model (Mixed-SSM). The mixed-SSM combines the state space model (SSM), multilayer perceptron (MLP), and multi-head self-attention mechanism (MHSA) to capture complementary temporal, nonlinear, and long-term features. Pearson and Spearman correlation analyses are used to select features strongly correlated with PV output, improving the prediction correlation coefficient (R2) by at least 0.87%. The K-Means++ algorithm further enhances input data features, achieving a maximum R2 of 86.9% and a positive R2 gain of 6.62%. Compared with BiTCN variants such as BiTCN-BiGRU, BiTCN-transformer, and BiTCN-LSTM, the proposed method delivers a mean absolute error (MAE) of 1.1%, root mean squared error (RMSE) of 1.2%, and an R2 of 89.1%. These results demonstrate the model’s effectiveness in forecasting PV power and supporting low-carbon, safe grid operation.

Study on control strategies of the MW-scale supercritical CO2 recompression Brayton cycle for lead-cooled fast reactor

The supercritical CO2 (sCO2) Brayton cycle for the lead-cooled fast reactor (LFR) is an ideal power generation system for the distributed energy supply in remote islands. To figure out the optimal operating modes of the MW-scale sCO2 LFR unit in different load demands, this work performs a dynamic simulation based on the developed control modules. A parametric analysis is conducted about the effects of rotational speed, split ratio, and main gas temperature on the dynamic characteristics. Control strategies are further studied based on the thermodynamic performance and safety under four variable temperature load regulation modes and three fixed temperature rapid load regulation modes. Results show that the tracking αopt mode has the highest average net efficiency (ηnetav) of 19.13 %. The tracking αsafe mode allows compressors to operate in the safest range by sacrificing a little ηnetav. Excellent load change rates are revealed by using the main circuit regulating valve control mode and the turbine bypass control mode, with −12.48 %Pe/min & 11.92 %Pe/min, and −8.25 %Pe/min & 9.70 %Pe/min, respectively. Though the load change rate of the RS control mode is −3.96 %Pe/min & 3.47 %Pe/min, its ηnetav is higher. Our work guides the efficient, flexible, and safe operation of the sCO2 LFR unit.

Active protection scheme based on high‐frequency current for distribution networks with inverter‐interfaced distributed generators

AbstractWith the high penetration and flexible access of inverter‐interfaced distributed generators (IIDGs), it is gradually becoming difficult for traditional protection schemes to meet the requirements for the safe operation of distribution networks (DNs). Active protection schemes based on power electronic equipment provide a new approach. On the basis of the controllability of voltage source converters, a method for active high‐frequency signal injection and a selection principle for the corresponding control parameters are proposed. Considering the impact of T‐connected load branches on the protected line, the high‐frequency current characteristics at the three terminals during internal and external faults are analysed. On this basis, an active protection scheme based on high‐frequency current is proposed for DNs with IIDGs. The performance of the proposed scheme is verified via PSCAD/EMTDC simulation software. The test results show that the proposed scheme can reliably trip during internal faults and identify faulty phases, which has better endurance to fault resistance.

Research on Intelligent Disinfection Robot Based on STC89C52 Microcontroller Control System

An intelligent disinfection robot based on STC89C52 microcontroller as the control system is proposed to address the current problems of high cost, scene limitations, and complex control factors. With the assistance of software development, the peripheral circuit of the chip is designed, and the robot can dynamically adjust its path according to its own and surrounding companions' position and motion status. The research results indicate that the various modules of the system can work in coordination, with high accuracy in tracking routes, precise image recognition, timely obstacle avoidance, fast response speed, and safe and reliable system operation. The disinfection robot carried out disinfection and sterilization test on the most common staphylococcus aureus and mold, and the test results showed that the pathogenic bacteria decreased significantly after 2 min, and all bacteria were removed after 3 min, and the killing rate of bacteria was up to&gt;96.0%. This method improves the obstacle avoidance ability and collaborative efficiency of robot systems in complex environments, and can effectively respond to various unexpected situations.

Signal recognition and prediction system based on random forest model

To accurately predict the occurrence of rock bursts during deep coal mining and ensure the safe and efficient operation of coal mines, a signal recognition and prediction system based on the Random Forest model is proposed. This system utilizes electromagnetic radiation (EMR) and acoustic emission (AE) signal data, employing feature extraction and point biserial correlation analysis to screen out the most relevant feature parameters. A Random Forest binary classification model is constructed to identify interference signals. Subsequently, by introducing new features such as moving average slope and exponentially weighted moving average (EWMA), a time series analysis of precursor feature signals is conducted. A real-time warning model based on the Random Forest algorithm is developed, dynamically calculating the probability of precursor feature signal occurrence by integrating historical data and real-time data changes. This approach improves the accuracy and recall rate of signal recognition and prediction, providing reliable data support for mine safety management.

Features of the use of unmanned aircraft during the inspection of the scene of the incident during the investigation of violations of the road safety rules or vehicle operation

The article highlights the procedural and tactical features of the inspection of the scene of a traffic accident with the use of an unmanned aerial vehicle. To achieve this goal, the forensic features of conducting an inspection of the scene during the pretrial investigation of crimes of violations of road safety rules or vehicle operation at each of the preparatory, working, and final stages are revealed. The current state and prospects of the use of unmanned aerial vehicles by the bodies and units of the National Police during the inspection of the scene of a traffic accident were studied. The advantages of using an unmanned aerial vehicle during the inspection of the scene of a traffic accident are given, the concept of an unmanned aerial vehicle is defined, the methods and tactical methods of using an unmanned aerial vehicle. Scientific and practical recommendations are given on the procedure for recording crimes with the help of an unmanned aerial vehicle regarding violations of the rules of road traffic safety or vehicle operation during the inspection of the scene of the incident. It was concluded that the use of an unmanned aerial vehicle in the activity of the investigator during the investigation of traffic accidents is legitimate and provides much more advantages than the use of other methods of recording, such as measuring with a manual or laser tape measure, taking photos or video recording with cameras or video cameras. The use of UAVs during the inspection of the scene of the incident significantly reduces the time of its inspection, while providing the opportunity to obtain high-quality and sufficient evidentiary material.

Investigation of heat transfer characteristics and optimization design of platen superheaters in the furnace of a 350 MWe supercritical CFB boiler

With the scale-up of circulating fluidized bed (CFB) boilers, the size of the platen heating surface within the furnace increases. Deformation, crack, and tube explosion of the platen heating surface threaten the safe and stable operation of CFB boiler. Large wall temperature deviation and overtemperature of partial tubes of heating surfaces are two of main reasons resulting in above problems. To identify the causes of large wall temperature deviation and decrease them, the heat transfer characteristics and wall temperature distribution uniformity of platen superheaters in the furnace of a 350 MWe supercritical CFB boiler were studied by experimental tests, and the structure of the high temperature superheater was optimized by numerical simulation to improve the temperature distribution uniformity. The steam parameters and wall temperatures of the platen superheaters were measured at boiler loads of 50 % BMCR, 75 % BMCR, and 100 % BMCR. The heat transfer coefficient, heat flux, and wall temperature uniformity of the platen superheaters were analyzed. At 100 % BMCR boiler load, the average heat transfer coefficient and heat flux of the medium temperature superheater IIs were 165.5 W/(m2·°C) and 56 kW/m2, respectively; while those of the high temperature superheaters were 179.7 W/(m2·°C) and 53.2 kW/m2, respectively. The wall temperature distribution uniformity among six medium temperature superheater IIs or six high temperature superheaters was relative good, while the temperature distribution uniformity of a single platen superheater required improvement. An optimized structure of the high temperature superheater was obtained from eight structures by numerical simulation, achieving a steam temperature deviation of less than 8 °C at the boiler load of 100 % BMCR.

Evaluation of Advances in Battery Health Prediction for Electric Vehicles from Traditional Linear Filters to Latest Machine Learning Approaches

In recent years, there has been growing interest in Li-ion battery State-of-Health (SOH) estimation due to its critical role in ensuring the safe and reliable operation of Electric Vehicles (EVs). Effective energy management and accurate SOH prediction are essential for the reliability and sustainability of EVs. This paper presents an in-depth review of SOH estimation techniques, starting with an overview of seminal methods that lay the theoretical groundwork for battery modeling and SOH prediction. The review then evaluates recent advancements in Machine Learning (ML) and Artificial Intelligence (AI) techniques, emphasizing their contributions to improving SOH estimation. Through a rigorous screening process, the paper systematically assesses the evolution of these advanced methods, addressing specific research questions to evaluate their effectiveness and practical implications. Key findings highlight the potential of hybrid models that integrate Equivalent Circuit Models (ECMs) with Deep Learning approaches, offering enhanced accuracy and real-time performance. Additionally, the paper discusses limitations of current methods, such as challenges in translating laboratory-based models to real-world conditions and the computational complexity of some prospective methods. In conclusion, this paper identifies promising future research directions aimed at optimizing hybrid models and overcoming existing constraints to advance SOH estimation and battery management in Electric Vehicles.

An optimization dispatching strategy for integrated electricity and natural gas systems with fast charging stations

With the increasing number of electric vehicles (EVs), a large amount of charging load will affect the safety and economic operation of the distribution network (DN). In this paper, an optimization dispatching strategy for integrated electricity and natural gas systems (IEGS) with fast charging stations (FCSs) is proposed to address the impact of fast charging loads. Firstly, the FCS load model is established based on the simulation of vehicle traveling and vehicle charging. Secondly, a natural gas supply model is established, which includes constraints such as the adjustment range, direction, and total number of supply flow adjustments. Then, an optimization dispatching strategy for IEGS based on energy storage and gas storage is proposed. Finally, the effectiveness of the proposed strategy is verified. The simulation results show that when the proposed strategy is applied, the voltage of FCS node will be stabilized within a good range. And the energy purchase cost will be reduced by more than 50%. The proposed method improves the carrying capacity of DN for FCSs.

Rapid Aircraft Wake Vortex Identification Model Based on Optimized Image Object Recognition Networks

Wake vortices generated by aircraft during near-ground operations have a significant impact on airport safety during takeoffs and landings. Identifying wake vortices in complex airspaces assists air traffic controllers in making informed decisions, ensuring the safety of aircraft operations at airports, and enhancing the intelligence level of air traffic control. Unlike traditional image recognition, identifying wake vortices using airborne LiDAR data demands a higher level of accuracy. This study proposes the IRSN-WAKE network by optimizing the Inception-ResNet-v2 network. To improve the model’s feature representation capability, we introduce the SE module into the Inception-ResNet-v2 network, which adaptively weights feature channels to enhance the network’s focus on key features. Additionally, we design and incorporate a noise suppression module to mitigate noise and enhance the robustness of feature extraction. Ablation experiments demonstrate that the introduction of the noise suppression module and the SE module significantly improves the performance of the IRSN-WAKE network in wake vortex identification tasks, achieving an accuracy rate of 98.60%. Comparative experimental results indicate that the IRSN-WAKE network has higher recognition accuracy and robustness compared to common recognition networks, achieving high-accuracy aircraft wake vortex identification and providing technical support for the safe operation of flights.

Convolutional variational autoencoder and multi-scale attention convolutional neural network based diagnostics on filament current sensors for mass spectrometers

Fault detection of the filament current sensor of the spaceborne mass spectrometer is of great significance to the safe operation of the mass spectrometer. Neglecting sensor failures may affect the normal operation of the mass spectrometer, which in turn affects the health of the astronauts and the space missions. This paper proposes an enhanced intelligent diagnosis method based on filament current sensor for mass spectrometer via improved deep learning network, aiming to improve the accuracy and reliability of the filament current sensor when the fault samples are insufficient. The improved convolutional variational autoencoder (CVAE) model not only enhances the data for four typical sensor fault signals, namely bias, drift, missing and random, but also solves the problem of insufficient samples when spike, Precision degradation and stuck fault occur in sensors. Short-time Fourier transform (STFT) is utilized to transform one-dimensional data into two-dimensional spectrograms, which gives more fault characteristics to the data set. The improved multi-scale attention mechanism convolutional neural network (MSAM-CNN) model is constructed to perform fault diagnosis on the generated spectrogram, which solves the problem of low accuracy of fault identification in traditional convolutional neural network (CNN) models. The results of ablation and comparison experiments show that the samples generated by CVAE have smaller Mean Absolute Error (MAE), Mean Square Error (MSE), and Root Mean Square Error (RMSE), the enhanced samples improve the classification and clustering of MSAM-CNN, and compared to other diagnostic models, MSAM-CNN achieves the highest accuracy, precision, recall, and F1-score of 99.2%, 99.3%, 98.8%, and 0.991.

Modeling of Induction Motor Direct Starting with and without Considering Current Displacement in Slot

This article presents a mathematical model of three-phase induction motor (IM) with a squirrel cage rotor and investigates its starting modes. Specifically, two scenarios are considered: direct starting of an IM and direct starting considering the current displacement effect in the rotor slots. Analyzing the starting modes of an IM without the use of automatic control systems is crucial for ensuring reliable, efficient, and safe operation of equipment across various industrial and commercial sectors. Understanding and accounting for the processes occurring during the starting mode of an IM allows for minimizing risks, enhancing energy efficiency, and reducing operational costs. This article details the mathematical modeling methods used for analyzing these starting modes and the results obtained from the modeling. These results were compared with data obtained experimentally, allowing for the assessment of the accuracy and reliability of the proposed model. The conducted research highlights the importance of considering current displacement in the rotor slots for accurate modeling and analysis of induction motor starting modes, particularly in capturing the differences in the amplitudes of the starting current and the faster transition to steady-state operation. Conclusions drawn from the comparison of modeling and experimental data provide valuable insights for the further development of control and operation methods for induction motors.

Study on the characteristics particles of coarse grain material in high bench dump

Particle shape is an important factor affecting the strength and deformation of coarse materials in dumps. However, conducting research on particle shape in current laboratory experiments, and there exists no classification standard for the particle shape of the dump. Considering the project on high-bench dump in Jiangxi Province, based on the random particle construction method, this study determined the characteristic particles of coarse grain material in the dumping site by combining field investigation results, large-scale direct shear tests, and numerical analysis. Further, the influence mechanism of coarse grain content and characteristic particles on the mechanical characteristics of the dump were analyzed. The results showed that the increase in coarse grain content enhanced the irregularity of particle shape, increased the internal friction angle between particles, and reduced the cohesion of the dump. Moreover, with increase in the normal stress, the shear strength changed from insignificant to gradually enhanced, although the increasing trend slowed down. The constructed random particle model can well solve the effects of occlusion, nesting, and friction between particles that is neglected by the standard spherical particle model, and the fitting degree is better. The particle characteristics of coarse-grain materials in the dump were primarily block, strip, and flake. The three types of shape characteristics under low normal stress did not affect the mechanical properties of the dump. Further, with the increase in normal stress, the stress-strain curve reflected the shape characteristics and content of the dominant characteristics in the dump. Specifically, the block sample exhibited the dominant characteristics and the most content under the same normal stress, followed by the strip sample, with the flake sample exerting the least influence. The research results provide a theoretical basis for long-term safe operation, disaster monitoring, and early warning of high bench dump.

A real-world battery state of charge prediction method based on a lightweight mixer architecture

This paper proposes a lightweight model based on the improved Mixer architecture—TrendFlow-Mixer, designed for efficient prediction of the state of charge (SOC) of lithium-ion batteries. SOC accuracy is crucial for the safe operation, usage, and maintenance of power batteries. When predicting the SOC of real-world batteries, the algorithm's generalization, robustness, and computational efficiency across different battery types must be considered. To this end, TrendFlow-Mixer introduces targeted improvements to battery characteristics, significantly alleviating the Mixer architecture's lag in SOC prediction. The model's core structure integrates the Fast Fourier Transform and arrangement transformation convolutional structures. This allows for efficient extraction of domain-specific spatial features and enables cross-channel correlation interactions. This reduces the model's parameter size while enhancing its adaptability to new domains. The model quickly captures data trend changes and identifies data flow patterns, improving computational efficiency by 70 % compared to Transformer-based architectures. TrendFlow-Mixer employs a dual-prediction head, combining rapid feature change analysis with a nonlinear prediction output to generate SOC predictions. This design facilitates fine-tuning of the model's structure and parameters while enhancing overall flexibility. Experimental results show that in a laboratory dynamic condition dataset, the model achieves a minimum mean absolute error (MAE) of 0.11 %, with the maximum prediction error under extreme temperatures controlled within 4.66 %. In validation using real-world data from the BMW i3, the mean absolute percentage error remains around 1 %, with a minimum MAE of 0.465 %. The results demonstrate that TrendFlow-Mixer possesses high computational efficiency, excellent generalization, and adaptability, effectively predicting SOC trends.