Abstract:[Objective]The behavior of stress corrosion cracking(SCC)in super duplex stainless steel S32707 under deep-sea environments is a critical issue that significantly influences its engineering reliability.As a vital alloying element in steel,nitrogen(N)needs in-depth exploration regarding its role in regulating stress corrosion resistance.Revealing how N influences the evolution and mechanisms of stress corrosion in S32707 steel under simulated deep-sea environments,characterized by high pressure and chloride ion levels,will provide theoretical basis for developing better SCC-resistant solutions for deep-sea engineering materials.[Methods]Slow strain rate tension testing was adopted to analyze the tensile strength,yield strength,and elongation after fracture of S32707 with various N contents in air,simulated sea level,and deep-sea environments.Meanwhile,the SCC sensitivity was combined to evaluate the inhibitory influence of N content on SCC sensitivity,with the scanning electron microscope(SEM)adopted to characterize fracture morphologies and examine the crack propagation path.Furthermore,the stability of the passive film and corrosion kinetics were analyzed by employing potentiodynamic polarization curves.[Results]In simulating deep-sea environments,the mechanical properties of S32707 steel gradually improve with the increasing N content.The tensile strength rises significantly,while yield strength shows a slight decrease,and elongation after fracture increases obviously.The SCC sensitivity of S32707 steel drops from 16.8％to 10.3％with a decrease amplitude of 6.5％,which is significantly higher than the decrease amplitude of SCC sensitivity(1.8％)during simulating sea level environment.This means N improves the stress corrosion resistance of S32707 steel.Additionally,as N content increases,the electrochemical behavior of the steel gradually improves,shown by a decrease in pitting current density from 505.0 nA/cm2 to 341.6 nA/cm2 and an increase in the pitting potential from 60.2 mV to 101.0 mV,which helps inhibit cathodic reaction.Furthermore,as the N content increases,the microstructure exhibits gradual improvement,the area of the quasi-cleavage zone at the steel fracture decreases,quasi-cleavage characteristics diminish,and the number of cracks and crack length decrease,the section shrinkage rate increases,and the crack propagation degree gradually diminishes,suggesting that N suppresses crack initiation and propagation by enhancing fracture toughness.[Conclusions]N enhances the deep-sea stress corrosion resistance of super duplex stainless steel via various mechanisms.It reduces SCC sensitivity,boosts fracture toughness,and inhibits stress corrosion cracking.It can increase self-corrosion potential,decrease pitting current density,inhibit cathodic reaction,and slow local corrosion,thus improving the pitting-resistant performance of S32707 steel.Solid-solution N consumes H+in the pitting corrosion pit and generates NH4+,inhibiting pit acidification and hydrogen evolution corrosion.Therefore,the results show that adjusting N content is an effective method to enhance the stress corrosion resistance of S32707 in high-pressure deep-sea environment,thus providing a theoretical basis for the composition design and engineering application of highly corrosion-resistant duplex stainless steel.

Abstract:[Objective]With the increasing importance of power transmission and transformation projects in distribution networks,traditional cost estimation methods face challenges such as large errors and excessive time consumption,making them inadequate for modern engineering management.To enhance the execution efficiency and estimation accuracy of power transmission and transformation projects,this study proposed a novel cost estimation method based on radial basis function neural network(RBFNN)and significance cost theory.This approach aims to address the limitations of traditional methods in complex cost estimation scenarios while enhancing the robustness and adaptability of the model.[Methods]This study applied significance cost theory to screen historical project data and identify the main factors affecting cost estimation for power transmission and transformation projects.These factors were used as input features for the neural network.The method introduced radial basis functions(RBFs)to restructure the traditional artificial neural network(ANN)architecture,creating a cost estimation model specifically for power transmission and substation projects.The model processed input data using Gaussian functions,initialized the hidden layer centers with the K-means clustering algorithm,and used least squares and gradient descent methods to train the output and hidden layers.To validate the model's effectiveness,100 sets of data from power transmission and transformation projects were used to compare the cumulative absolute error rate and average execution time of traditional methods(unit cost method and index estimation method)with the proposed model.Moreover,SHAP value analysis was employed to quantify the impact of key factors on estimation error rates.[Results]Simulation results demonstrate that the RBFNN-based cost estimation method outperforms traditional methods in both cumulative absolute error rate and execution time.When the test sample size increases to 20,the cumulative error rate for the unit cost method reaches 440％,while the index estimation method reaches 180％,and the proposed model maintains an error rate below 110％.In terms of execution time,traditional methods require an average of 5 s,while the proposed model reduces the time to just 0.5 s.In addition,SHAP value analysis reveals that factors such as wire cross-sectional area,steel pipe poles,and the number of circuits have the greatest influence on estimation error rates,with their SHAP values significantly higher than those of other factors.This finding provides critical insights for model optimization and cost control.[Conclusions]The cost estimation method proposed in this paper,based on RBFNN and significance cost theory,effectively improves the accuracy and efficiency of cost estimation for power transmission and transformation projects.Although the method still exhibits some errors in complex construction environments,it outperforms traditional methods in overall performance,making it highly practical with strong potential for widespread application.Future research will focus on integrating regression analysis,support vector machine(SVM),and other machine learning algorithms to further optimize model precision and better handle the complexity and variability of power transmission and transformation projects.

Abstract:[Objective]With the longer service life of oil and gas pipelines,the failure to identify the pipeline material due to data deficiency has become increasingly prominent.Traditional detection methods are unable to meet the engineering requirements for material identification and aging status assessment.A novel non-destructive testing method is expected to be proposed based on the magnetic compression effect.By analyzing the base value variation of magnetic flux leakage(MFL)signals in different steels under an applied external magnetic field,a correlation model between material and magnetic signal was established to provide a theoretical basis and technical approach for achieving rapid,accurate,and non-contact identification of pipeline materials.[Methods]The magnetic charge theory was integrated with the magnetic compression effect to develop a mathematical model describing the MFL field on the surface of steel under the influence of an external magnetic field.A theoretical derivation was performed to establish the functional relationship among the MFL signal base values,the external magnetic field intensity,and the material's magnetization intensity.The degree of the magnetic compression effect was proposed to be quantitatively characterized by the magnetic compression coefficient.Through the systematic experimental design,six representative structural and pipeline steels were selected as test materials,including Q235,Q345,X52,X65,X70,and X80.The specimens were machined into a standardized dimension of 270 mm × 140 mm × 10 mm.On a custom-built high-precision MFL testing platform,the external magnetic field was incrementally increased from 0 to 48 kA/m in steps of 1 kA/m,and the corresponding MFL signal base values were recorded in real time.To further validate the stability of the method,comparative tests were conducted between the signals obtained from original state steel plates and those obtained from polished plates with a surface roughness of 0.8 μm.All experiments were replicated to ensure both repeatability and accuracy of the results.[Results]According to the experimental results,with the enhancement of external magnetic field intensity,the MFL signal base values in all tested steel materials exhibit an initial increase followed by a subsequent decrease.The peak position varies significantly with the magnetic properties of the material.Specifically,the signal base values for Q235 and X65 peak at 24 kA/m,for X70 and X80 at 25 kA/m,while for Q345 and X52 with higher magnetization intensity,the peaks occur at 26 kA/m.This variation pattern highly coincides with the saturation magnetic characteristics of each steel's M-H curve,which indicates the close relation between the critical field strength for the onset of the magnetic compression effect and the material's magnetic properties.Furthermore,tests conducted under varying surface conditions of the steel plates reveal that,although the signal amplitudes differ,the critical point at which the base value begins to decline remains consistent for the same material.This finding confirms that the method is insensitive to surface conditions,with good anti-interference capability and adaptability to diverse operational environments.[Conclusions]A non-destructive testing method was proposed for pipeline material identification based on the magnetic compression effect.By establishing the correspondence between the MFL signal base values and the external magnetic field intensity,an effective differentiation between various structural and pipeline steels was achieved.This method exhibits not only good repeatability but also strong engineering applicability.The detection results are unaffected by complex factors such as internal pipeline surface roughness or corrosion state,making it suitable for complex working conditions such as internal pipeline detection.The research results provide a new technical means for material identification and safety assessment of aging pipelines,holding significant theoretical importance and engineering application value.

Abstract:[Objective]CNC machine tools in the operating state feature high instantaneous power and low energy efficiency,and their machining energy consumption power changes with complex and variable processing tasks in real time,thus making it difficult to predict energy consumption of machine tool machining.The prediction mechanism model of machining energy consumption of CNC machine tools based on information flow and energy flow requires operators to be aware of the operating status of the machine tool and the characteristics of energy consumption changes of the machine tool,which results in difficult prediction of machining energy consumption of machine tools and long cycles.As the testing techniques and computational power of computers significantly improve,data-driven prediction methods have been introduced to the research on predicting the machining energy consumption of machine tools.Therefore,an adaptive incremental machine learning approach that combines stochastic configuration networks(SCNs)with a multi-mechanism-improved sand cat swarm optimization(SCSO)was proposed to achieve efficient and high-precision prediction of the machining energy consumption of machine tools.[Methods]By taking the helical groove CNC milling machine milling screw rotor as an example,based on the process parameters,the machining energy consumption milling experiments and collected machining energy consumption data were designed.Meanwhile,SCNs were optimized by adopting the multi-mechanism-improved SCSO algorithm to build a prediction model for machining energy consumption.The SCN algorithm was employed as the prediction model for machining energy consumption.The SCSO algorithm improved by combining the Tent population initialization strategy,variable helix search strategy and adaptive t-distribution strategy solved the scale factor and regularization parameter during SCN modeling to improve the prediction accuracy and prediction efficiency of SCNs.[Results]To verify the accuracy of the model,the root mean square error(RMSE)and mean absolute percentage error(MAPE)were employed as the evaluation indexes to compare the BP neural networks(SSA-BP)optimized by the SCSO-SCNs,SCNs,and squirrel search algorithm.Comparison results show that compared to SSA-BP and SCNs,SCSO-SCNs shows a decrease of 38.62％and 46.03％in RMSE respectively,while it presents a reduction of 40.47％and 47.33％in MAPE compared to SSA-BP and SCNs respectively,which proves the performance superiority of the SCSO-SCNs model in the prediction of machining energy consumption.[Conclusions]The proposed machine learning method,which integrates SCNs and multi-mechanism-improved SCSO algorithm,shows more obvious performance advantages in terms of machining energy consumption prediction for CNC machine tools.The improved SCSO algorithm enhances the search efficiency and the ability to jump out of local optimal solutions by optimizing the initial population of the algorithm and improving the population position updating strategy in the improvement and exploitation phases.The SCSO algorithm,based on multi-mechanism improvement,greatly improves the prediction accuracy of the model by seeking the optimal scale factor and generalization factor of SCNs.Comparison with existing machine learning algorithms shows that the proposed method has higher prediction accuracy and greatly improves the prediction efficiency of machining energy consumption.

Abstract:[Objective]Alien species invasion has emerged as a global ecological security problem.The resulting biodiversity loss and ecosystem degradation pose a serious threat to the sustainable development of human society.Traditional biological control methods often suffer from limited control accuracy and poor robustness when faced with environmental uncertainties and random disturbances.To address the dynamic characteristics of the stochastic biological system,this study proposed an adaptive fuzzy control(AFC)strategy based on Lyapunov stability theory.By building a stochastic dynamic model incorporating white noise disturbances and unknown nonlinearities,the study focused on solving the dual-objective problem of coordinated native species protection and invasive species suppression under coupled environmental uncertainty and stochastic perturbations.[Methods]Based on stochastic differential equation theory,a stochastic biological system dynamics model for alien species invasion was constructed.A fuzzy logic system(FLS)was employed to approximate the uncertain nonlinear term in the model.The backstepping method was integrated with adaptive fuzzy control and applied to stochastic biological systems.A fuzzy backstepping controller and an adaptive law with parameter self-tuning capabilities were designed using an appropriately chosen Lyapunov function.[Results]The proposed adaptive controller exhibits intelligent adjustment capabilities,allowing the population density of native species to effectively track the desired reference trajectory within a finite time.The tracking error converges to a neighborhood of zero,demonstrating the controller's ability to monitor and analyze errors in real time.By dynamically adjusting control inputs,the system keeps tracking errors within acceptable bounds.Moreover,all system states under alien species invasion are proven to be semi-globally uniform and ultimately bounded.Notably,the system also maintains stable convergence characteristics and demonstrates strong adaptability under varying intensities of stochastic disturbance.[Conclusions]By combining FLS with nonlinear control theory,the proposed AFC strategy effectively addresses uncertainty control in stochastic biological systems.Numerical simulations further verify the strategy's effectiveness in both protecting native species and managing invasive species,offering novel insights for intelligent regulation of complex ecosystems.

Abstract:[Objective]With the high proportion integration of renewable energy sources such as wind and solar power into the grid,their inherent intermittency and volatility pose significant challenges to the voltage stability at the distribution end of the power grid.In particular,at the end of regional power grids,the uncertainty in wind-solar-load output increases the risk of rapid voltage drops,which may lead to equipment damage or even cascading failures.Existing studies have notable shortcomings in areas such as handling prediction errors in wind-solar-load output and multi-objective collaborative optimization.For example,the full-pure embedding sensitivity analysis method fails to adequately consider the influence of prediction errors,while the source-grid-load coordinated control framework ignores the interference of prediction errors on collaborative outcomes.To address these issues,this paper proposed a novel fast regulation algorithm for low voltage.By quantifying the uncertainty in wind-solar-load output,a multi-objective optimization model was developed that balances safety,performance,and cost.The model aims to achieve rapid and stable voltage regulation at the distribution end of the grid,thereby improving the reliability and adaptability of high-proportion renewable energy integration into the power grid.[Methods]The Collaborative Genetic Algorithm(CGA)was used as the core solution method.Firstly,precise probability density function models were established to account for the randomness in the output of wind power,photovoltaics,and load output.The output of wind power was quantified by combining the Weibull distribution of wind speed with the normal distribution of prediction errors.Photovoltaic output was associated with light intensity and photoelectric conversion efficiency,incorporating prediction errors.Load output was represented by a probability density function reflecting its volatility.Based on this,a low-voltage regulation model was developed with optimization objectives balancing safety,performance,and cost.The safety indicator quantified the total power loss at the distribution end of the grid,the performance indicator included the overall network loss and voltage deviation,while the cost indicator calculated the total lifecycle cost.Through integer-based mixed coding schemes and dynamically adjusted crossover and mutation probabilities,the algorithm effectively optimized the population and output the optimal solution that satisfied voltage stability margin requirements.[Results]Based on actual grid data from a region in Guangzhou,simulation experiments validate the effectiveness of the proposed algorithm.In terms of uncertainty handling,the proposed algorithm shows a significantly higher correlation between wind and photovoltaic output predictions and actual data compared to other traditional methods.This is due to the algorithm modeling output power prediction errors as random variables,which more accurately reflects the uncertainties in real-world systems.Regarding voltage regulation,when fluctuations in wind-solar-load output and increased load lead to voltage drops,the algorithm quickly and effectively restores node voltages to normal levels.Its performance outperforms traditional methods,such as those based on steady-state grid models and the double-loop voltage-current control algorithm.In terms of static voltage stability margin,the proposed algorithm maintains a high voltage stability margin of over 0.8 across various test scenarios,demonstrating strong voltage regulation capability.Furthermore,while ensuring voltage stability,the algorithm also considers the economic and performance efficiency of grid operations.[Conclusions]The fast regulation algorithm for low voltage effectively addresses the issue of low-voltage instability at the distribution end of power grids with high integration of renewable energy by deeply combining the wind-solar-load output uncertainty modeling and multi-objective optimization.This algorithm innovatively introduces probability density functions to quantify prediction errors,which significantly improves the accuracy of wind-solar-load output predictions.By using CGA for coordinated optimization of safety,performance,and cost targets,the algorithm achieves rapid dynamic voltage regulation.Experimental results show that the proposed algorithm outperforms traditional methods in terms of regulation speed,stability margin,and economic efficiency,which provides reliable technical support for intelligent grid control with high proportions of renewable energy integration.The research results not only have significant theoretical value but also demonstrate great potential in practical engineering applications.Future exploration will focus on voltage coordination control strategies across multiple time scales to continuously enhance the stability and economic efficiency of grid operations.

Abstract:[Objective]DDoS attacks,as a highly destructive network threat,seriously threaten the stable operation of the power system.Due to the complexity and variability of data traffic in the power monitoring local area network(LAN),DDoS attack traffic and normal traffic have many similarities in their manifestations,making it difficult to effectively distinguish between the two.Although traditional static threshold methods can achieve traffic monitoring to a certain extent,misjudgments often occur due to their inability to adapt to the dynamic changes of traffic.The detection effect of DDoS attacks is thus weakened and reliable security guarantee cannot be provided for power monitoring LANs.Therefore,a DDoS attack matching detection method for power monitoring local area networks was proposed based on dynamic thresholds.[Methods]Real time network traffic data in the power monitoring local area network were collected through network traffic collection devices.The entropy value of these flows was calculated using information entropy theory.The chaos degree in data could be reflected by information entropy.Normal traffic usually had a certain regularity with relatively stable entropy values.Due to the influx of a large number of abnormal packets,however,DDoS attack traffic had significant fluctuations in entropy values.Based on this characteristic,a dynamic threshold was set,and the entropy value of the traffic was determined as abnormal when exceeding this dynamic threshold.The six-tuple feature set of the abnormal traffic was then extracted,including average flow packet count,average byte count,source IP address growth,flow table survival time variation,port growth,and convection ratio,and input into a pre-trained least squares support vector machine(LSSVM)classifier.The LSSVM classifier learned from the existing samples to establish the mapping relationship between features and classes.The abnormal traffic was then classified and judged to determine whether it was DDoS attack traffic.[Results]According to the test result,the proposed method shows significant improvements on both the ROC and PR curves,with higher receiver operating characteristic curve(ROC-AUC)and accuracy recall curve(PR-AUC)values than the traditional method.This fully demonstrates that the method,with higher accuracy and recall rate in detecting DDoS attacks,can effectively identify DDoS attack traffic hidden in normal traffic and reduce the misjudgment rate.[Conclusions]The detection method based on dynamic thresholds and LSSVM classifier can effectively overcome the difficulty in distinguishing DDoS attack traffic from normal traffic in power monitoring local area networks.By improving the accuracy and reliability of DDoS attack detection,it provides a more effective DDoS attack detection method for power monitoring local area networks,helps improve the security and stability of power systems,ensures the reliable operation of the power supply,and has important practical application value for network security protection in the power industry.

Abstract:[Objective]High-voltage circuit breakers are key control and protection devices in the power system,and their reliable operation is crucial for the safety and stability of power grids.However,during long-term operation,high-voltage circuit breakers may trigger various faults due to mechanical wear,component aging,and other problems.Currently,the detection of high-voltage circuit breakers faces challenges such as diverse detection signals,great difficulty in fault detection,and low accuracy.Therefore,studying an efficient and accurate mechanical status detection method for high-voltage circuit breakers is of great significance for ensuring the safe and stable operation of the power system.[Methods]This study proposed a mechanical status detection model for high-voltage circuit breakers based on multi-modal efficient Transformer.In the data acquisition stage,vibration sensors,current sensors,and displacement sensors were comprehensively employed to synchronously acquire vibration signals,current signals,and displacement signals during the operation of high-voltage circuit breakers,thus constructing a multi-modal signal dataset.In the signal preprocessing stage,wavelet transform technology was adopted to process the acquired multi-modal signals and decompose the signals into different frequency scales,thus effectively removing noise components in the signals,enhancing fault feature signals,and significantly improving signal quality.In terms of model building,an efficient Transformer module was introduced.With its powerful self-attention mechanism,the module could effectively capture long-distance dependency relationships in signal sequences and dig deeply into complex features in multi-modal signals.Additionally,by classifying the operation status of high-voltage circuit breakers into six categories,including normal operation,failure to maintain closing,loose soft connection,single-phase contact wear,loose insulating tie rod,and opening spring fracture,accurate diagnosis of the mechanical status of circuit breakers was realized.[Results]In the simulation experiments,simulation models of different fault types of high-voltage circuit breakers were built to simulate various working conditions during actual operation and generate multi-modal signal data.Inputting the data into the proposed detection model for testing shows that the model can accurately identify different fault types.In the actual experiments,multiple high-voltage circuit breakers were selected as test objects,and multi-modal signal data were collected under their normal operation and different fault settings.The experimental results reveal that the proposed method significantly improves the detection accuracy compared with traditional detection methods while ensuring the detection speed.[Conclusions]The proposed mechanical status detection model for high-voltage circuit breakers based on multi-modal efficient Transformer effectively solves the problems of complex detection signals and severe noise interference.By leveraging the powerful feature extraction and classification capabilities of the efficient Transformer model,accurate identification of multiple mechanical faults in high-voltage circuit breakers is realized.Simulation analysis and experimental results fully demonstrate that this method performs well in both detection accuracy and speed,providing reliable technical support for the status monitoring and fault diagnosis of high-voltage circuit breakers in the power system.It helps to timely detect potential faults of the equipment,and holds application significance and broad promotion prospects for ensuring the safe and stable operation of the power system.

Abstract:[Objective]As the power system continues to expand,transmission lines,being a crucial channel for power transmission,require safe and stable operation.However,transmission lines,long exposed to the complex and variable natural environment,face multiple safety risks such as external force damage and equipment aging.To enable high-precision and high-efficiency automatic detection of potential hazards in transmission lines,this study proposed an intelligent identification technology for external damage risks in transmission lines,based on deep learning.[Methods]This study developed an integrated technical framework of"geometric correction-image enhancement-intelligent recognition",systematically addressing key technical challenges in transmission line image recognition.In the geometric correction stage,a polynomial geometric correction model based on the least squares method was employed.By establishing an accurate coordinate mapping,this model effectively eliminated geometric distortions caused by factors such as shooting angles and lens distortion.In the image enhancement stage,a new image processing algorithm,combining bilateral filtering and the maximum between-class variance method,was proposed.This algorithm effectively removed image noise while retaining the edge features of transmission lines,providing high-quality data for subsequent recognition.In the intelligent recognition stage,a dual-optimized convolutional neural network(CNN)model was designed.The feature extraction process was optimized by dynamically adjusting the convolution kernel weights,and sparse constraints were introduced to enhance feature discriminability.Finally,precise recognition was achieved by integrating the support vector machine classifier.This method overcame the limitations of traditional technologies,such as insufficient geometric distortion correction and feature extraction,offering a comprehensive solution for intelligent identification of hidden dangers of transmission lines.[Results]Tested on real datasets containing multiple types of damage,this method demonstrates significantly higher recognition accuracy compared to mainstream algorithms such as YOLOv4 and Mask R-CNN.It shows greater robustness,especially in complex backgrounds.The method achieves an average positional offset of only 0.013 meters,fully meeting engineering application requirements.The floating point operations for processing 1 000 images reduce to 3.24 × 109,significantly enhancing the real-time processing capability.[Conclusions]The proposed intelligent recognition technology for external damage risks in transmission lines has made significant improvements in recognition accuracy,positioning precision,and computational efficiency through innovative technical approaches and systematic optimizations.The theoretical contributions of this research include establishing a complete image processing system for transmission lines,providing a new approach for related studies;introducing a dual optimization mechanism that offers a viable solution for feature extraction in complex environments;adopting the lightweight network design,which serves as an important reference for applying deep learning models in engineering.

Abstract:[Objective]For a long time,energy storage technology has received close attention in the academic and industrial fields.Compared to those made of other dielectric materials,dielectric capacitors using antiferroelectric materials exhibit higher energy storage densities and faster charge discharge rates.As a typical antiferroelectric material,PbZrO3(PZO)has a great potential in practical energy storage applications due to its unique field-induced phase transition behavior and high Curie temperature.[Methods]The energy storage performance of dielectric materials mainly depends on their polarization performance and electrical breakdown strength.To improve the energy storage density of the energy storage capacitor with PZO as the dielectric,a 3-nm-thick Al layer was deposited on the Pt(111)/Ti/SiO2/Si substrate by thermal evaporation,and a PZO amorphous film was deposited on the Al-coated substrate surface by the sol-gel method.Then,PbZrO3-Al2O3(PZO-AO)nanocomposite films were prepared by two processes of microwave annealing(MA)and conventional annealing(CA).[Results]The results show that a nanocomposite film can be prepared by the method of this study,in which the Al2O3 nanoparticles are distributed in layers on the PZO matrix.The shape of the ferroelectric hysteresis loop and polarization performance of the film can be adjusted.The energy storage density of the PZO-AO nanocomposite film prepared by CA(CA PZO-AO film)is 13.52 J/cm3 in the electric field of 950 kV/cm,which is 83％higher than that of the PZO film prepared by CA(CA PZO film).In addition,the experimental data show that MA can reduce the crystallization activation energy of the PZO film,which can not only make the amorphous PZO film crystallized at a low temperature of 650℃ but also shorten the annealing time to only one-third of CA time.Furthermore,MA can also stabilize the antiferroelectric properties of the PZO film,further improving the energy storage density of the film.Therefore,MA is used to further optimize the microstructure of the PZO-AO nanocomposite film,decrease the grain size of the film,and reduce the leakage current density.The leakage current density of the PZO-AO nanocomposite film prepared by MA(MA PZO-AO film)is in the orders of magnitude of about 10-8,which is 1 order of magnitude lower than that of the CA PZO-AO film.This indicates that the grain size of perovskite and the distribution of Al2O3 nanoparticles are regulated by MA,so as to improve the electrical breakdown strength.The MA PZO-AO film finally has an energy storage density of 18.94 J/cm3 in an electric field of 1 550 kV/cm,which is 40.1％higher than that of the CA PZO-AO film.[Conclusions]The experiment shows that high-quality dielectric nanocomposite films can be prepared by thermal evaporation combined with energy-saving and environmentally friendly MA technology.This research provides a new idea for the design of new energy storage capacitor materials by nanocomposite.

Abstract:[Objective]In the operation and management of power system,the medium and low-voltage distribution network serves as a key link between power sources and users.Its operating efficiency and stability are directly related to the safety and reliability of the entire power system.Three-phase line loss,as an important indicator of the distribution network's operational efficiency,not only reflects the energy loss during the power transmission process but also directly affects the voltage quality,power consumption,and safe operation of the power grid.However,the three-phase line loss data in the distribution network exhibit complex distribution characteristics,such as multi-modal and asymmetric features.During dynamic changes,it is difficult to accurately capture the inherent patterns and structures in the data,which reduces the accuracy of anomaly detection.Therefore,this paper proposed an intelligent method for the anomaly detection of three-phase line loss in medium and low-voltage distribution networks.[Methods]During the data collection process of the distribution network's three-phase line loss,the data can be influenced by multiple factors such as electromagnetic interference and equipment errors,leading to the presence of significant noise and outliers.These noises not only reduce the signal-to-noise ratio but also obscure the true features of the data,thereby affecting the accuracy of subsequent analysis.Therefore,a radial basis function(RBF)neural network was used to extract features from the collected three-phase line loss data.By performing nonlinear mapping of the input data,the method effectively suppressed the interference from noise,enhancing the signal-to-noise ratio.The preprocessed data were then normalized,which further improved the completeness and accuracy of the data collection.A loop current-based method was employed to decompose the circuits in the distribution network into multiple independent loops.In each loop,the real and imaginary parts of the voltage and current were calculated.By analyzing the temporal and phase variations of these values in detail,the operating status of the circuit was thoroughly understood,and potential anomaly patterns were accurately identified.Based on the real and imaginary part values of the voltage and current on the three-phase branch circuits,a Gaussian mixture distribution model was constructed.This model used multiple Gaussian distributions to describe the complex distribution features of the three-phase line loss data,allowing for more accurate capture of the inherent patterns and structures in the data.The maximum expectation algorithm was then used to fit the normalized line loss rate and construct a hybrid Gaussian model consisting of multiple Gaussian mixture distributions.The likelihood probability function of the eigenvector was calculated,and based on a preset probability threshold,the data were determined to be anomalous or normal.If the likelihood probability was below the threshold,it was classified as anomalous;otherwise,it was considered normal.This approach enabled the identification of line loss anomaly data.[Results]Experimental results show that the proposed method can accurately identify three-phase line loss buses,reducing the risk of misjudgment and missed detections.[Conclusions]This method can promptly detect and address faults in the distribution network,which is of significant importance in improving the operational efficiency and reliability of power systems.

Abstract:[Objective]The influence of photovoltaics(PV)-assisted current and extraction current on conventional relay protection hinders the effective functioning of relay protection equipment.A multi-objective setting method for relay protection in distribution networks suitable for conditions with high permeability of distributed PV was proposed to address this problem,which is aimed at enhancing the rapidity,sensitivity,and selectivity of protection,and ensuring economic viability and practicality,thus effectively safeguarding the power grid security and supporting the widespread access of distributed PV.[Methods]The influence of PV-assisted current and extraction current on the protection configuration of the distribution networks was analyzed,and the problem of unwanted operation and refuse operation of distribution network protection caused by PV access was avoided by introducing distance protection and instantaneous current protection as the protection criteria.A multi-objective optimization model with the optimal parameters of protection rapidity,sensitivity and selectivity was built,and the particle swarm optimization(PSO)algorithm was improved by adopting the dynamic splitting operator to make the solution of the protection setting meet the practical application requirements.[Results]High-permeability distributed PV results in unwanted operation or refuse operation of distribution network protection,which is effectively avoided by introducing distance protection and instantaneous current protection as the protection criteria.The multi-objective optimization protection configuration model was built,and the evaluation indexes of the overall protection effect of a certain area were formed,with the solution of the protection setting completed based on PSO algorithm.Finally,the overall evaluation of the protection effect under high-permeability PV access was realized,with the rapidity,sensitivity,and selectivity of protection improved.[Conclusions]The results show that the combination of distance protection and instantaneous current protection can effectively avoid the influence of the PV-assisted effect on the conventional instantaneous current protection.The protection performance can be effectively improved by the proposed multi-objective optimization scheme.Under the equilibrium strategy,the rapidity,sensitivity,and selectivity increase by about 82.2％,about 3.8％,and about 33.1％,respectively.The innovation of this study is that the combination of distance protection and instantaneous current protection was adopted to form the protection criteria,thus avoiding the problem of unwanted operation and refuse operation of the distribution network protection due to PV access.Additionally,a multi-objective optimization scheme for protection settings was constructed,and PSO algorithm was improved by employing the dynamic splitting operator,thereby avoiding the limitations of PSO algorithm and improving the reliability and applicability of protection settings.

Abstract:[Objective]With the continuous expansion of oil and gas pipeline transportation,the importance of pipeline safety inspection has become increasingly prominent.Stress concentration at pipeline defects is the main cause of crack propagation and fracture accidents.However,existing detection methods struggle to achieve quantitative stress evaluation.[Methods]This study proposed a pipeline stress detection method based on dual-field stress-magnetic coupling.By incorporating changes in the Jiles-Atherton(J-A)model parameters under different pipeline stress states,a magnetic stress detection model was built.The effects of elastic stress,plastic strain,and external magnetic fields on magnetization intensity and magnetic signal characteristics were systematically analyzed.The study was grounded in the principles of magnetic stress detection,the J-A model,and magnetic charge theory.By examining the influence of stress at different stages and external magnetic fields on magnetization intensity and magnetic signals,the relationship between hysteresis loops and magnetization intensity under varying conditions was established.In addition,the variation patterns of axial and radial signals under different stress and magnetic field conditions were identified.A proportional coefficient was introduced to develop a dual-magnetic field stress detection model,and separate models for elastic and plastic stress detection were built.Finally,experiments were conducted to verify the theory.Equivalent magnetic field strength formulas for the elastic stress and plastic strain stages were derived,clarifying the variation laws of the pinning coefficient k,shape coefficient a,and domain wall coupling coefficient α with stress.Experimental validation was conducted using X80 pipeline steel specimens subjected to tensile loads ranging from 10 to 80 kN and external magnetic fields from 0 to 10 A/m,with magnetic signal characteristics measured.[Results]The axial component of magnetic signals under different magnetic fields and stress levels exhibits distinct peaks,with peak positions remaining stable despite variations in external fields or stress.Tangential peaks increase with the external magnetic field,aligning with theoretical calculations.Experimental data indicate that the model closely matches measured results under high stress,with minimal error,while low-stress scenarios show slight deviations due to parameter fitting limitations.[Conclusion]In the elastic stage,tensile stress causes the hysteresis loop to rotate counterclockwise initially and then clockwise.Magnetization changes significantly under weak magnetic fields,whereas stress effects become negligible under strong fields.During the plastic stage,plastic strain reduces the slope of the magnetization curve,and both the initial magnetization curve and hysteresis loop rotate clockwise.Magnetization intensity is proportional to magnetic signals,with the ratio of strong magnetic signals to magnetization intensity serving as a proportionality coefficient dependent solely on defect size.The dual-magnetic field stress detection model demonstrates high accuracy under high stress,confirming its capability for stress detection.This study innovatively integrates the dual-magnetic field method with J-A theory,proposing a proportional coefficient-based model for separating elastic and plastic stresses.The approach resolves the issue of overlapping defect and stress signals in traditional methods,providing a high-precision,quantifiable technical solution for stress detection at pipeline defects.This advancement holds significant value for preventing pipeline failures and ensuring safe energy transportation.

Abstract:[Objective]As the demand for resource recycling and sustainable development in the construction industry increases,the application of recycled aggregate in concrete structures has become a research hotspot.However,the mechanical properties of recycled aggregate differ from those of natural aggregate.The low tensile strength,elastic modulus,and high brittleness of recycled aggregate inevitably have a significant influence on the bending performance of prestressed self-compacting recycled concrete(PSRC)beams.To clarify the feasibility of employing recycled aggregate in PSRC beams,this study discussed and analyzed the bending performance differences between PSRC beams and prestressed normal concrete(PNC)beams.[Methods]This paper adopted the finite element analysis method for the study.Firstly,the finite element models of PSRC beams and PNC beams were built based on the ABAQUS software.Meanwhile,the correctness and reliability of the built models were verified by comparing the failure modes,load-deflection curves,and limit loads of the simulated specimens with those of the test specimens.Secondly,on the basis of model verification,a systematic comparison and analysis were conducted on the performance indicators such as cracking load,limit deflection,flexural bearing capacity,and tensile reinforcement strain of PSRC beams and PNC beams.Additionally,based on the maximum and average strain of the tensile reinforcement at the cracking point,the coefficient of uniformity of the tensile reinforcement was corrected,and a calculation formula for the maximum crack width of PSRC beams was established.The applicability and accuracy of the formula were verified.[Results]Under the same reinforcement ratio,the cracking load of PSRC beams is smaller than that of PNC beams,and the difference in crack resistance performance between the two types of concrete beams gradually decreases with the increasing reinforcement ratio.Under the reinforcement ratio between 0.10％and 2.24％,the limit deflection of PSRC beams is 4.04％-19.03％higher than that of PNC beams.Then,as the reinforcement ratio continues to increase,the limit deflection difference between the two types of concrete beams gradually decreases until it is basically zero,which means the influence of the material properties of concrete on the deformation capacity of the component gradually decreases with the rising reinforcement ratio.The flexural bearing capacity of PSRC beams and PNC beams differs by no more than 3％,indicating that the existence of recycled aggregate has little effect on the flexural bearing capacity.Under the action of the same load,when there is a crack in concrete,the strain curve of the tensile reinforcement of PSRC beams is slightly lower than that of PNC beams.Due to the earlier cracking of PSRC beams,the tensile stress transmitted by the tensile zone concrete is borne in advance by the longitudinal reinforcement at the crack,resulting in larger tensile reinforcement strain at the cracking point of PSRC beams than that of PNC beams.[Conclusion]This study proposed a new method for determining the maximum crack width of PSRC beams based on the strain simulation data at the cracking point to correct the coefficient of uniformity of the tensile reinforcement.The maximum crack width was calculated by adopting the proposed new calculation method and the formula in GB50010-2010.It is found that the calculated values of the proposed formula are in sound agreement with the measured values,and the predicted maximum crack width by the proposed formula is more accurate than that by the formula in GB50010-2010.This paper provides a reference basis for the revision of subsequent standards.

Abstract:[Objective]With the increasing proportion of new energy,traditional grid-following(GFL)control based on phase-locked loop(PLL)synchronization gradually exhibits inherent stability limitations in weak grid conditions.Meanwhile,grid-forming(GFM)control with self-synchronizing source characteristics has emerged as a hot solution.However,existing research predominantly focuses on the voltage regulation or synchronization stability of GFM control,with less attention to its frequency modulation capability and characteristics.[Methods]This paper systematically reviewed four mainstream GFM control methods,including droop control,virtual synchronous generator(VSG)control,matching control,and virtual oscillator control(VOC),explained their frequency modulation principles,and analyzed their advantages and disadvantages from the aspects of the control loop and application scenarios.On this basis,a grid-connected simulation model for new energy systems was built to conduct a simulation-based analysis of the frequency modulation response characteristics of different kinds of frequency modulation control across diverse scenarios.Finally,this study summarized challenges of GFM control in strategy optimization,parameter tuning,and multi-unit coordination,with the future development prospects pointed out.[Results]Droop control regulates the active power of generating units by responding to system frequency deviations,featuring advantages of the simple structure and strong grid strength adaptability.However,its lack of inertia support results in relatively weaker frequency modulation performance.On the basis of droop control,VSG control simulates the inertia response characteristics of conventional synchronous machines and can better suppress the change performance of system frequency.However,it faces challenges in parameter tuning,fault ride-through,and multi-unit coordination.Matching control utilizes the dynamic characteristics of DC capacitors to simulate the inertia properties of traditional synchronous machines and thus restrain change performance of system frequency,but it fails to provide sustained support in the frequency quasi-steady state.VOC generates frequency responses similar to droop control via oscillator dynamic equations that directly govern amplitude and frequency.However,it is difficult for its high output harmonics to satisfy grid connection requirements.[Conclusion]Virtual synchronous machine control has become the most promising research direction in GFM control due to its technical advantages of balancing frequency modulation performance and strong grid strength adaptability in participating in system frequency modulation.However,technical challenges including synchronization stability,fault ride-through,and coordinated control need to be tackled.In the future,in-depth research should be conducted on control strategies and parameter optimization,and multi-unit collaborated control to facilitate the large-scale application of GFM control.

Abstract:[Objective]The cost of distribution network engineering is influenced by multidimensional factors such as scale and capacity,equipment and material costs,and geographical conditions.Traditional statistical methods(e.g.,linear regression)struggle to handle high-dimensional nonlinear data effectively,while existing machine learning approaches,despite incorporating feature reduction techniques,still exhibit limitations.For instance,principal component analysis(PCA)sacrifices prediction accuracy for dimensionality reduction,and grey relational analysis(GRA)ignores feature interactions.Therefore,there is an urgent need for a prediction method that retains critical feature information while accounting for complex inter-feature relationships.This study integrated recursive feature elimination(RFE)with the random forest(RF)algorithm to develop a RFE-RF prediction model,aiming to resolve feature redundancy and nonlinear modeling challenges.[Methods]A technical framework of"feature selection-model construction-experimental validation"was adopted.For feature selection,the recursive feature elimination(RFE)method was employed,which iterated training models to gradually eliminate features with minimal predictive contributions,retaining an optimal feature subset.For model construction,the RF algorithm was utilized.Based on ensemble learning principles,RF constructed multiple decision trees and averaged their outputs,effectively mitigating overfitting and enhancing model robustness.RF was insensitive to noisy data and quantified feature importance,providing reliable feature ranking criteria for RFE.By embedding RFE into the RF training process,a closed-loop optimization workflow was established.[Results]Experimental validation used data from 190 distribution network engineering projects provided by a power grid company,covering 21 initial features such as voltage level,line length,and equipment costs.Categorical features were numerically encoded while preserving their original distribution characteristics.Through five-fold cross-validation and root mean square error(RMSE)optimization,the optimal feature subset was identified as 12 optimal feature subsets,including such key factors as line length,comprehensive cable price,and voltage level.Compared with traditional linear regression(LR),RF,and mutual information-based RF(MI-RF)algorithms,the RFE-RF algorithm achieves a mean absolute error(MAE)of 8.657 9 and a mean absolute percentage error(MAPE)of 6.97％on the test set,significantly outperforming other algorithms.The MAE of RFE-RF on the test set increases by only about 4.5％compared to the training set,indicating lower overfitting risks and demonstrating that feature selection effectively enhances model stability.[Conclusion]Feature selection is pivotal for improving the accuracy of distribution network cost prediction.RFE dynamically eliminates redundant features through iterative processes,substantially reducing data dimensionality and noise interference.The RFE-RF model combines high precision with strong interpretability,reduces MAE significantly compared to traditional models,and clearly quantifies the impact weights of individual features on costs.This study marks the application of combining RFE and RF in cost prediction for distribution network engineering,addressing challenges in feature interaction and redundancy filtering and providing a new paradigm for data modeling in complex engineering systems.The model serves as a precise cost prediction tool for power grid enterprises,aiding investment decisions and cost control,thus advancing intelligent and refined construction of distribution networks.Moreover,it reveals the impact mechanism of feature selection on the generalization capability of machine learning models,offering practical references for feature optimization in high-dimensional nonlinear datasets.

Abstract:[Objective]With the global energy transition and the rapid development of clean energy,the penetration rate of high-penetration photovoltaic(PV)sources in distribution networks is increasing.However,PV output power exhibits significant fluctuations and uncertainty due to factors such as solar irradiance and temperature.When a large number of such sources are integrated into distribution networks,they can cause voltage fluctuations,frequency variations,and other issues,presenting significant challenges for power outage fault prediction.Traditional fault prediction methods struggle to accurately capture fault characteristics in complex distribution networks with high PV penetration,leading to reduced prediction accuracy and efficiency,which fails to meet the stability requirements for distribution network operation.[Methods]To improve prediction accuracy and efficiency,this study proposed a fault prediction method for distribution networks with high PV penetration.First,a PV-integrated grid model was built to analyze the impact of PV sources on fault current characteristics in distribution networks.This model clarified how PV sources influence fault current magnitude and distribution under different operating conditions,providing a theoretical basis for subsequent fault zone identification.Next,potential outage zones were inferred by combining grid topology and load imbalance features.The grid topology reflected the connectivity of components,while load imbalance indicated regional load variations.By integrating these factors,the method more accurately localized the fault zone.In addition,power flow entropy was introduced to assess whether circuit loads were in a critical state.Key fault-related power flow features were then extracted from the identified zones.These features were fed into an optimized SA-SAE for training,allowing the system to automatically learn underlying patterns from large datasets and achieve precise outage prediction.[Results]Experimental results demonstrate that the proposed method achieves high prediction accuracy in fault localization for distribution networks with high PV penetration,correctly identifying fault zones(sections 3-6 of the K5-K8 lines)and fault types.Moreover,the average prediction time is only 2.236 seconds,significantly outperforming comparative methods in both accuracy and efficiency.[Conclusion]By comprehensively considering PV integration effects,grid topology,load characteristics,and leveraging power flow entropy and SA-SAE,the proposed method enables high-precision and high-efficiency outage prediction in distribution networks.This method not only enhances prediction accuracy and timeliness,reducing outage risks and economic losses,but also provides robust support for grid planning,operation,and maintenance.It ensures stable distribution network operation and facilitates large-scale integration of clean energy.

Abstract:[Objective]Interior permanent magnet(IPM)motors are widely adopted as submersible motors in oil well applications due to their structural stability,high efficiency,and superior power factor.However,the constrained wellbore diameter and elevated ambient temperatures in deep-well environments impose stringent requirements for enhanced torque density and anti-demagnetization capability of IPM motors.To address these challenges,a novel sine-shaped permanent magnet(PM),synthesized from flat and arched PM,was adopted in this study.The adopted PM optimizes rotor space utilization above conventional flat PM,thereby increasing permanent magnet volume and d-axis permanent magnet thickness,improving torque density and anti-demagnetization capability of IPM motors.[Methods]Maintaining constant dimensions of the flat PM,the finite element analysis(FEA)was employed to systematically evaluate the effects of arched PM sagitta on short-circuit current,anti-demagnetization capability,and no-load and on-load electromagnetic performance.Moreover,the equivalent ring method was implemented to quantify the maximum stress of the rotor core under various sagittas,ensuring the mechanical integrity of the optimized rotor structure.[Results]Although the short-circuit current of the IPM motor will increase as the sagitta increases,its permanent magnets exhibit stronger anti-demagnetization capability.While increasing the sagitta will increase the maximum stress of the rotor core,it is much lower than the yield stress of the core material,meeting practical needs sufficiently.Moreover,the effect of a smaller sagitta on the reluctance torque of the IPM motor can be ignored.When the sagitta is greater than 3 mm,the maximum reluctance torque decreases significantly as the sagitta increases,while the total output torque keeps increasing and torque ripple decreases.[Conclusion]Taking into account the overall influence of the sagitta on the motor's performance,a suitable sine-shaped PM size was selected,and a prototype was manufactured and tested.The experimental results are in good agreement with the 2D FEA simulation results,verifying the accuracy of the simulation analysis,which provides a new approach for the rotor design of the IPM motors.

Abstract:[Objective]The anisotropy and isotropy of thermal transport are fundamental properties of materials,which are crucial in practical applications.However,current research on tuning the transition from anisotropic to isotropic thermal transport primarily relies on structural design or material processing.The methods are time-consuming,costly,and irreversible,which severely limits flexibility of the properties in practical applications.Therefore,a scheme was proposed to regulate the thermal transport properties of two-dimensional borophene by using an external electric field,aiming to explore a new method for stable and reversible regulation without altering the atomic structure of the material.[Methods]First-principles calculations were combined with the phonon Boltzmann transport equation to systematically investigate the effect of an external electric field on the thermal transport properties of borophene.The underlying physical mechanisms were revealed systematically by quantifying the regulatory effects of electric field strength on phonon lifetime,thermal conductivity,and anisotropy,and the ratio of thermal conductivities in two in-plane directions(x and y directions)was used as an indicator of the changes in anisotropy.[Results]Under the influence of an external electric field,the lattice thermal conductivity of borophene in both in-plane directions increases significantly and gradually peaks with a maximum enhancement factor of 2.82.Meanwhile,the intrinsic anisotropy ratio is boosted to a maximum value of 2.13.As the electric field strength increases further,the thermal conductivity drops rapidly,and the anisotropy exhibits oscillating decay.When the electric field strength increases to 0.4 V/Å,the thermal conductivity is dramatically reduced.Nearly isotropic thermal transport characteristics are demonstrated when the anisotropy ratio decreases to 1.25.Further analysis reveals that this abnormal transition from anisotropic to isotropic thermal transport is fundamentally due to the large enhancement and suppression of phonon lifetime at moderate and high electric field strengths,respectively,which acts as an amplifying or reducing factor for thermal conductivity.[Conclusion]Phonon lifetime can be modulated by an external electric field,achieving stable and reversible precise regulation of the thermal transport properties of two-dimensional borophene without altering its atomic structure.This approach effectively overcomes the limitations of traditional regulation methods and provides a new theoretical and technical pathway for the precise regulation of phonon thermal transport anisotropy,showing a broad application prospect in such fields as thermal management of nanoelectronics and thermoelectric energy conversion.

Abstract:[Objective]Cracks in aero-engine turbine guide vanes are typically repaired using wide-gap brazing.However,pores usually appear during the formation of wide-gap brazed joints,which can lead to a decline in their high-temperature mechanical properties.To suppress the formation of pores,a certain pressure is applied during brazing.Nevertheless,the effects of brazing pressure on the microstructure and mechanical properties of wide-gap brazed joints remain unclear.[Methods]In this study,wide-gap brazed joints were prepared under different brazing pressures.The effects of brazing pressure on the microstructure and mechanical properties of the joints were investigated through tensile testing,microhardness characterization,fracture morphology observation,energy dispersive spectroscopy(EDS)analysis,and X-ray diffraction(XRD).[Results]The experimental results show that at a constant brazing temperature,the tensile strengths of the wide-gap brazed joints under brazing pressures of 10,20,and 50 kg with the mass ratio of the brazing filler metal to the base metal as 40∶60 are 436.57,411.76,and 381.95 MPa,respectively,namely that the tensile strength of the joints decreases with increasing brazing pressure.Microhardness and EDS analysis of the joint fracture surface reveal that as the brazing pressure increases,the concentrations of melting point depressant elements and active elements at the fracture surface become higher,and the microhardness significantly increases.This indicates that higher brazing pressure leads to uneven microhardness distribution in the joint,inducing significant stress concentration and thereby reducing joint strength.XRD results confirm that the applied brazing pressure causes noticeable lattice distortion in the joint and base material.Higher brazing pressure results in greater lattice distortion,which blocks the diffusion channels of melting point depressant elements and active elements,hindering their diffusion.This leads to the accumulation of these elements in the joint and base material,thereby causing uneven microhardness distribution and a decline in joint strength.Post-welding heat treatment or increasing the brazing temperature can alleviate lattice distortion,enhance element diffusion,and improve the mechanical properties of the joint.[Conclusion]Applying a certain pressure during the preparation of wide-gap brazed joints helps suppress the formation of internal pores.However,the contradictory effects of brazing pressure and temperature on lattice distortion need to be carefully considered.Excessive brazing pressure can hinder element diffusion,while increasing the brazing temperature can enhance element diffusion,reduce the unevenness of microhardness distribution,and ultimately improve the mechanical properties of the joints.