抽水蓄能作为成熟的商业化储能技术,是目前解决新能源消纳的有效途径。受不同地域条件限制,传统定速抽蓄凸显出水头适应能力差、灵活调节能力不足的问题,这使得可变速抽蓄机组成为未来抽蓄电站的优选,但准确评估存量机组进行变速改造的...抽水蓄能作为成熟的商业化储能技术,是目前解决新能源消纳的有效途径。受不同地域条件限制,传统定速抽蓄凸显出水头适应能力差、灵活调节能力不足的问题,这使得可变速抽蓄机组成为未来抽蓄电站的优选,但准确评估存量机组进行变速改造的经济、社会及环境综合效益是亟待解决的难题。本文基于改进的层次分析法(analytic hierarchy process,AHP)-逼近理想解排序法(technique for order preference by similarity to the ideal solution,TOPSIS),提出了一种可变速抽蓄改造可行性评估方法,从经济效益、社会效益和环境效益三方面,建立了考虑综合效益的改造可行性评价指标体系;进一步对AHP的权重获得方法进行改进,并与TOPSIS结合,对可变速抽蓄改造项目进行评分,得出其改造可行性的综合评分。对某地区三处可变速抽蓄电站改造进行算例分析,验证本文所提方法的有效性与可行性。所提方法对可变速抽蓄改造的发展具有一定的工程实践意义,为可变速抽蓄改造方案的可行性评估提供了理论依据。展开更多
This research proposes an improved Puma optimization algorithm(IPuma)as a novel dynamic recon-figuration tool for a photovoltaic(PV)array linked in total-cross-tied(TCT).The proposed algorithm utilizes the Newton-Raph...This research proposes an improved Puma optimization algorithm(IPuma)as a novel dynamic recon-figuration tool for a photovoltaic(PV)array linked in total-cross-tied(TCT).The proposed algorithm utilizes the Newton-Raphson search rule(NRSR)to boost the exploration process,especially in search spaces with more local regions,and boost the exploitation with adaptive parameters alternating with random parameters in the original Puma.The effectiveness of the introduced IPuma is confirmed through comprehensive evaluations on the CEC’20 benchmark problems.It shows superior performance compared to both established and modern metaheuristic algorithms in terms of effectively navigating the search space and achieving convergence towards near-optimal regions.The findings indicated that the IPuma algorithm demonstrates considerable statistical promise and surpasses the performance of competing algorithms.In addition,the proposed IPuma is utilized to reconfigure a 9×9 PV array that operates under different shade patterns,such as lower triangular(LT),long wide(LW),and short wide(SW).In addition to other programmed approaches,such as the Whale optimization algorithm(WOA),grey wolf optimizer(GWO),Harris Hawks optimization(HHO),particle swarm optimization(PSO),gravitational search algorithm(GSA),biogeography-based optimization(BBO),sine cosine algorithm(SCA),equilibrium optimizer(EO),and original Puma,the indicated method is contrasted to the traditional configurations of TCT and Sudoku.In addition,the metrics of mismatch power loss,maximum efficiency improvement,efficiency improvement ratio,and peak-to-mean ratio are calculated to assess the effectiveness of the indicated approach.The proposed IPuma improved the generated power by 36.72%,28.03%,and 40.97%for SW,LW,and LT,respectively,outperforming the TCT configuration.In addition,it achieved the best maximum efficiency improvement among the algorithms considered,with 26.86%,21.89%,and 29.07%for the examined patterns.The results highlight the superiority and competence of the proposed approach in both convergence rates and stability,as well as applicability to dynamically reconfigure the PV system and enhance its harvested energy.展开更多
Objective:To analyze the impact of improved emergency integrated nursing on the treatment effectiveness and safety of emergency trauma patients.Methods:Study duration:December 2024 to December 2025.Observation target:...Objective:To analyze the impact of improved emergency integrated nursing on the treatment effectiveness and safety of emergency trauma patients.Methods:Study duration:December 2024 to December 2025.Observation target:emergency trauma patients in our hospital.Sample size:92 cases.Using computer-based grouping,the 92 patients were divided into two equally sized groups:a control group of 46 patients who received conventional emergency nursing care,and an observation group of 46 patients who underwent an improved emergency integrated nursing model.The treatment-related indicators,treatment effectiveness,and incidence of adverse events were evaluated in both groups.Results:After intervention,the pre-hospital emergency care time,emergency diagnosis time,total emergency rescue duration,and examination waiting time in the control group were all longer than those in the observation group(p<0.05);the treatment effectiveness in the control group(effective rate:82.61%)was worse than that in the observation group(effective rate:95.65%),p<0.05;compared with the control group,the observation group had a lower incidence of adverse events,p<0.05.Conclusion:Implementing an improved emergency integrated nursing model for emergency trauma patients helps streamline the treatment process,enhance treatment effectiveness,and reduce the incidence of adverse events.展开更多
The internal flow fields within a three-dimensional inward-tunning combined inlet are extremely complex,especially during the engine mode transition,where the tunnel changes may impact the flow fields significantly.To...The internal flow fields within a three-dimensional inward-tunning combined inlet are extremely complex,especially during the engine mode transition,where the tunnel changes may impact the flow fields significantly.To develop an efficient flow field reconstruction model for this,we present an Improved Conditional Denoising Diffusion Generative Adversarial Network(ICDDGAN),which integrates Conditional Denoising Diffusion Probabilistic Models(CDDPMs)with Style GAN,and introduce a reconstruction discrimination mechanism and dynamic loss weight learning strategy.We establish the Mach number flow field dataset by numerical simulation at various backpressures for the mode transition process from turbine mode to ejector ramjet mode at Mach number 2.5.The proposed ICDDGAN model,given only sparse parameter information,can rapidly generate high-quality Mach number flow fields without a large number of samples for training.The results show that ICDDGAN is superior to CDDGAN in terms of training convergence and stability.Moreover,the interpolation and extrapolation test results during backpressure conditions show that ICDDGAN can accurately and quickly reconstruct Mach number fields at various tunnel slice shapes,with a Structural Similarity Index Measure(SSIM)of over 0.96 and a Mean-Square Error(MSE)of 0.035%to actual flow fields,reducing time costs by 7-8 orders of magnitude compared to Computational Fluid Dynamics(CFD)calculations.This can provide an efficient means for rapid computation of complex flow fields.展开更多
An optimized volt-ampere reactive(VAR)control framework is proposed for transmission-level power systems to simultaneously mitigate voltage deviations and active-power losses through coordinated control of large-scale...An optimized volt-ampere reactive(VAR)control framework is proposed for transmission-level power systems to simultaneously mitigate voltage deviations and active-power losses through coordinated control of large-scale wind/solar farms with shunt static var generators(SVGs).The model explicitly represents reactive-power regulation characteristics of doubly-fed wind turbines and PV inverters under real-time meteorological conditions,and quantifies SVG high-speed compensation capability,enabling seamless transition from localized VAR management to a globally coordinated strategy.An enhanced adaptive gain-sharing knowledge optimizer(AGSK-SD)integrates simulated annealing and diversity maintenance to autonomously tune voltage-control actions,renewable source reactive-power set-points,and SVG output.The algorithm adaptively modulates knowledge factors and ratios across search phases,performs SA-based fine-grained local exploitation,and periodically re-injects population diversity to prevent premature convergence.Comprehensive tests on IEEE 9-bus and 39-bus systems demonstrate AGSK-SD’s superiority over NSGA-II and MOPSO in hypervolume(HV),inverse generative distance(IGD),and spread metrics while maintaining acceptable computational burden.The method reduces network losses from 2.7191 to 2.15 MW(20.79%reduction)and from 15.1891 to 11.22 MW(26.16%reduction)in the 9-bus and 39-bus systems respectively.Simultaneously,the cumulative voltage-deviation index decreases from 0.0277 to 3.42×10^(−4) p.u.(98.77%reduction)in the 9-bus system,and from 0.0556 to 0.0107 p.u.(80.76%reduction)in the 39-bus system.These improvements demonstrate significant suppression of line losses and voltage fluctuations.Comparative analysis with traditional heuristic optimization algorithms confirms the superior performance of the proposed approach.展开更多
Due to the lack of accurate data and complex parameterization,the prediction of groundwater depth is a chal-lenge for numerical models.Machine learning can effectively solve this issue and has been proven useful in th...Due to the lack of accurate data and complex parameterization,the prediction of groundwater depth is a chal-lenge for numerical models.Machine learning can effectively solve this issue and has been proven useful in the prediction of groundwater depth in many areas.In this study,two new models are applied to the prediction of groundwater depth in the Ningxia area,China.The two models combine the improved dung beetle optimizer(DBO)algorithm with two deep learning models:The Multi-head Attention-Convolution Neural Network-Long Short Term Memory networks(MH-CNN-LSTM)and the Multi-head Attention-Convolution Neural Network-Gated Recurrent Unit(MH-CNN-GRU).The models with DBO show better prediction performance,with larger R(correlation coefficient),RPD(residual prediction deviation),and lower RMSE(root-mean-square error).Com-pared with the models with the original DBO,the R and RPD of models with the improved DBO increase by over 1.5%,and the RMSE decreases by over 1.8%,indicating better prediction results.In addition,compared with the multiple linear regression model,a traditional statistical model,deep learning models have better prediction performance.展开更多
Soil improvement is one of the most important issues in geotechnical engineering practice.The wide application of traditional improvement techniques(cement/chemical materials)are limited due to damage ecological en-vi...Soil improvement is one of the most important issues in geotechnical engineering practice.The wide application of traditional improvement techniques(cement/chemical materials)are limited due to damage ecological en-vironment and intensify carbon emissions.However,the use of microbially induced calcium carbonate pre-cipitation(MICP)to obtain bio-cement is a novel technique with the potential to induce soil stability,providing a low-carbon,environment-friendly,and sustainable integrated solution for some geotechnical engineering pro-blems in the environment.This paper presents a comprehensive review of the latest progress in soil improvement based on the MICP strategy.It systematically summarizes and overviews the mineralization mechanism,influ-encing factors,improved methods,engineering characteristics,and current field application status of the MICP.Additionally,it also explores the limitations and correspondingly proposes prospective applications via the MICP approach for soil improvement.This review indicates that the utilization of different environmental calcium-based wastes in MICP and combination of materials and MICP are conducive to meeting engineering and market demand.Furthermore,we recommend and encourage global collaborative study and practice with a view to commercializing MICP technique in the future.The current review purports to provide insights for engineers and interdisciplinary researchers,and guidance for future engineering applications.展开更多
为科学评估水电站与抽水蓄能联合运行的综合性能,提出一种基于层次分析法(analytic hierarchy process,AHP)与逼近理想解排序法(technique for order preference by similarity to an ideal solution,TOPSIS)相结合的多指标综合评价模...为科学评估水电站与抽水蓄能联合运行的综合性能,提出一种基于层次分析法(analytic hierarchy process,AHP)与逼近理想解排序法(technique for order preference by similarity to an ideal solution,TOPSIS)相结合的多指标综合评价模型。首先,从经济性、技术性、环境效益、水情及政策5个维度出发,构建包含发电效率、调峰能力、碳排放强度、来水量变化等11项指标的综合评价体系。其次,采用AHP确定各指标权重以体现不同维度的差异性影响,并通过TOPSIS计算各运行方案与理想解的贴近度,实现联合运行模式的优劣排序。最后,以某区域水电站与抽水蓄能联合工程为实例进行验证分析。结果表明:相较于传统单一评价方法,AHP-TOPSIS模型能够有效兼顾主客观因素,量化评价结果,其中调峰能力与动态投资回收期对综合性能影响显著;同时,联合运行方案中储能容量配置与调度策略的协同优化可提升系统综合效益15%以上。研究结果为多能互补系统中水电-抽蓄联合运行的方案优选与决策制定提供了理论依据,对推动清洁能源高效利用具有实际意义。展开更多
文摘抽水蓄能作为成熟的商业化储能技术,是目前解决新能源消纳的有效途径。受不同地域条件限制,传统定速抽蓄凸显出水头适应能力差、灵活调节能力不足的问题,这使得可变速抽蓄机组成为未来抽蓄电站的优选,但准确评估存量机组进行变速改造的经济、社会及环境综合效益是亟待解决的难题。本文基于改进的层次分析法(analytic hierarchy process,AHP)-逼近理想解排序法(technique for order preference by similarity to the ideal solution,TOPSIS),提出了一种可变速抽蓄改造可行性评估方法,从经济效益、社会效益和环境效益三方面,建立了考虑综合效益的改造可行性评价指标体系;进一步对AHP的权重获得方法进行改进,并与TOPSIS结合,对可变速抽蓄改造项目进行评分,得出其改造可行性的综合评分。对某地区三处可变速抽蓄电站改造进行算例分析,验证本文所提方法的有效性与可行性。所提方法对可变速抽蓄改造的发展具有一定的工程实践意义,为可变速抽蓄改造方案的可行性评估提供了理论依据。
基金funded by the Deanship of Scientific Research and Libraries,Princess Nourah bint Abdulrahman University,through the Program of Research Project Funding After Publication,grant No.(RPFAP-82-1445)。
文摘This research proposes an improved Puma optimization algorithm(IPuma)as a novel dynamic recon-figuration tool for a photovoltaic(PV)array linked in total-cross-tied(TCT).The proposed algorithm utilizes the Newton-Raphson search rule(NRSR)to boost the exploration process,especially in search spaces with more local regions,and boost the exploitation with adaptive parameters alternating with random parameters in the original Puma.The effectiveness of the introduced IPuma is confirmed through comprehensive evaluations on the CEC’20 benchmark problems.It shows superior performance compared to both established and modern metaheuristic algorithms in terms of effectively navigating the search space and achieving convergence towards near-optimal regions.The findings indicated that the IPuma algorithm demonstrates considerable statistical promise and surpasses the performance of competing algorithms.In addition,the proposed IPuma is utilized to reconfigure a 9×9 PV array that operates under different shade patterns,such as lower triangular(LT),long wide(LW),and short wide(SW).In addition to other programmed approaches,such as the Whale optimization algorithm(WOA),grey wolf optimizer(GWO),Harris Hawks optimization(HHO),particle swarm optimization(PSO),gravitational search algorithm(GSA),biogeography-based optimization(BBO),sine cosine algorithm(SCA),equilibrium optimizer(EO),and original Puma,the indicated method is contrasted to the traditional configurations of TCT and Sudoku.In addition,the metrics of mismatch power loss,maximum efficiency improvement,efficiency improvement ratio,and peak-to-mean ratio are calculated to assess the effectiveness of the indicated approach.The proposed IPuma improved the generated power by 36.72%,28.03%,and 40.97%for SW,LW,and LT,respectively,outperforming the TCT configuration.In addition,it achieved the best maximum efficiency improvement among the algorithms considered,with 26.86%,21.89%,and 29.07%for the examined patterns.The results highlight the superiority and competence of the proposed approach in both convergence rates and stability,as well as applicability to dynamically reconfigure the PV system and enhance its harvested energy.
文摘Objective:To analyze the impact of improved emergency integrated nursing on the treatment effectiveness and safety of emergency trauma patients.Methods:Study duration:December 2024 to December 2025.Observation target:emergency trauma patients in our hospital.Sample size:92 cases.Using computer-based grouping,the 92 patients were divided into two equally sized groups:a control group of 46 patients who received conventional emergency nursing care,and an observation group of 46 patients who underwent an improved emergency integrated nursing model.The treatment-related indicators,treatment effectiveness,and incidence of adverse events were evaluated in both groups.Results:After intervention,the pre-hospital emergency care time,emergency diagnosis time,total emergency rescue duration,and examination waiting time in the control group were all longer than those in the observation group(p<0.05);the treatment effectiveness in the control group(effective rate:82.61%)was worse than that in the observation group(effective rate:95.65%),p<0.05;compared with the control group,the observation group had a lower incidence of adverse events,p<0.05.Conclusion:Implementing an improved emergency integrated nursing model for emergency trauma patients helps streamline the treatment process,enhance treatment effectiveness,and reduce the incidence of adverse events.
文摘The internal flow fields within a three-dimensional inward-tunning combined inlet are extremely complex,especially during the engine mode transition,where the tunnel changes may impact the flow fields significantly.To develop an efficient flow field reconstruction model for this,we present an Improved Conditional Denoising Diffusion Generative Adversarial Network(ICDDGAN),which integrates Conditional Denoising Diffusion Probabilistic Models(CDDPMs)with Style GAN,and introduce a reconstruction discrimination mechanism and dynamic loss weight learning strategy.We establish the Mach number flow field dataset by numerical simulation at various backpressures for the mode transition process from turbine mode to ejector ramjet mode at Mach number 2.5.The proposed ICDDGAN model,given only sparse parameter information,can rapidly generate high-quality Mach number flow fields without a large number of samples for training.The results show that ICDDGAN is superior to CDDGAN in terms of training convergence and stability.Moreover,the interpolation and extrapolation test results during backpressure conditions show that ICDDGAN can accurately and quickly reconstruct Mach number fields at various tunnel slice shapes,with a Structural Similarity Index Measure(SSIM)of over 0.96 and a Mean-Square Error(MSE)of 0.035%to actual flow fields,reducing time costs by 7-8 orders of magnitude compared to Computational Fluid Dynamics(CFD)calculations.This can provide an efficient means for rapid computation of complex flow fields.
基金supported by Yunnan Power Grid Co.,Ltd.Science and Technology Project:Research and application of key technologies for graphical-based power grid accident reconstruction and simulation(YNKJXM20240333).
文摘An optimized volt-ampere reactive(VAR)control framework is proposed for transmission-level power systems to simultaneously mitigate voltage deviations and active-power losses through coordinated control of large-scale wind/solar farms with shunt static var generators(SVGs).The model explicitly represents reactive-power regulation characteristics of doubly-fed wind turbines and PV inverters under real-time meteorological conditions,and quantifies SVG high-speed compensation capability,enabling seamless transition from localized VAR management to a globally coordinated strategy.An enhanced adaptive gain-sharing knowledge optimizer(AGSK-SD)integrates simulated annealing and diversity maintenance to autonomously tune voltage-control actions,renewable source reactive-power set-points,and SVG output.The algorithm adaptively modulates knowledge factors and ratios across search phases,performs SA-based fine-grained local exploitation,and periodically re-injects population diversity to prevent premature convergence.Comprehensive tests on IEEE 9-bus and 39-bus systems demonstrate AGSK-SD’s superiority over NSGA-II and MOPSO in hypervolume(HV),inverse generative distance(IGD),and spread metrics while maintaining acceptable computational burden.The method reduces network losses from 2.7191 to 2.15 MW(20.79%reduction)and from 15.1891 to 11.22 MW(26.16%reduction)in the 9-bus and 39-bus systems respectively.Simultaneously,the cumulative voltage-deviation index decreases from 0.0277 to 3.42×10^(−4) p.u.(98.77%reduction)in the 9-bus system,and from 0.0556 to 0.0107 p.u.(80.76%reduction)in the 39-bus system.These improvements demonstrate significant suppression of line losses and voltage fluctuations.Comparative analysis with traditional heuristic optimization algorithms confirms the superior performance of the proposed approach.
基金supported by the National Natural Science Foundation of China [grant numbers 42088101 and 42375048]。
文摘Due to the lack of accurate data and complex parameterization,the prediction of groundwater depth is a chal-lenge for numerical models.Machine learning can effectively solve this issue and has been proven useful in the prediction of groundwater depth in many areas.In this study,two new models are applied to the prediction of groundwater depth in the Ningxia area,China.The two models combine the improved dung beetle optimizer(DBO)algorithm with two deep learning models:The Multi-head Attention-Convolution Neural Network-Long Short Term Memory networks(MH-CNN-LSTM)and the Multi-head Attention-Convolution Neural Network-Gated Recurrent Unit(MH-CNN-GRU).The models with DBO show better prediction performance,with larger R(correlation coefficient),RPD(residual prediction deviation),and lower RMSE(root-mean-square error).Com-pared with the models with the original DBO,the R and RPD of models with the improved DBO increase by over 1.5%,and the RMSE decreases by over 1.8%,indicating better prediction results.In addition,compared with the multiple linear regression model,a traditional statistical model,deep learning models have better prediction performance.
基金funded by the National Natural Science Foundation of China(No.41962016)the Natural Science Foundation of NingXia(Nos.2023AAC02023,2023A1218,and 2021AAC02006).
文摘Soil improvement is one of the most important issues in geotechnical engineering practice.The wide application of traditional improvement techniques(cement/chemical materials)are limited due to damage ecological en-vironment and intensify carbon emissions.However,the use of microbially induced calcium carbonate pre-cipitation(MICP)to obtain bio-cement is a novel technique with the potential to induce soil stability,providing a low-carbon,environment-friendly,and sustainable integrated solution for some geotechnical engineering pro-blems in the environment.This paper presents a comprehensive review of the latest progress in soil improvement based on the MICP strategy.It systematically summarizes and overviews the mineralization mechanism,influ-encing factors,improved methods,engineering characteristics,and current field application status of the MICP.Additionally,it also explores the limitations and correspondingly proposes prospective applications via the MICP approach for soil improvement.This review indicates that the utilization of different environmental calcium-based wastes in MICP and combination of materials and MICP are conducive to meeting engineering and market demand.Furthermore,we recommend and encourage global collaborative study and practice with a view to commercializing MICP technique in the future.The current review purports to provide insights for engineers and interdisciplinary researchers,and guidance for future engineering applications.
文摘为科学评估水电站与抽水蓄能联合运行的综合性能,提出一种基于层次分析法(analytic hierarchy process,AHP)与逼近理想解排序法(technique for order preference by similarity to an ideal solution,TOPSIS)相结合的多指标综合评价模型。首先,从经济性、技术性、环境效益、水情及政策5个维度出发,构建包含发电效率、调峰能力、碳排放强度、来水量变化等11项指标的综合评价体系。其次,采用AHP确定各指标权重以体现不同维度的差异性影响,并通过TOPSIS计算各运行方案与理想解的贴近度,实现联合运行模式的优劣排序。最后,以某区域水电站与抽水蓄能联合工程为实例进行验证分析。结果表明:相较于传统单一评价方法,AHP-TOPSIS模型能够有效兼顾主客观因素,量化评价结果,其中调峰能力与动态投资回收期对综合性能影响显著;同时,联合运行方案中储能容量配置与调度策略的协同优化可提升系统综合效益15%以上。研究结果为多能互补系统中水电-抽蓄联合运行的方案优选与决策制定提供了理论依据,对推动清洁能源高效利用具有实际意义。
