Miniature air quality sensors are widely used in urban grid-based monitoring due to their flexibility in deployment and low cost.However,the raw data collected by these devices often suffer from low accuracy caused by...Miniature air quality sensors are widely used in urban grid-based monitoring due to their flexibility in deployment and low cost.However,the raw data collected by these devices often suffer from low accuracy caused by environmental interference and sensor drift,highlighting the need for effective calibration methods to improve data reliability.This study proposes a data correction method based on Bayesian Optimization Support Vector Regression(BO-SVR),which combines the nonlinear modeling capability of Support Vector Regression(SVR)with the efficient global hyperparameter search of Bayesian Optimization.By introducing cross-validation loss as the optimization objective and using Gaussian process modeling with an Expected Improvement acquisition strategy,the approach automatically determines optimal hyperparameters for accurate pollutant concentration prediction.Experiments on real-world micro-sensor datasets demonstrate that BO-SVR outperforms traditional SVR,grid search SVR,and random forest(RF)models across multiple pollutants,including PM_(2.5),PM_(10),CO,NO_(2),SO_(2),and O_(3).The proposed method achieves lower prediction residuals,higher fitting accuracy,and better generalization,offering an efficient and practical solution for enhancing the quality of micro-sensor air monitoring data.展开更多
Web pillars enduring complex coupled loads are critical for stability in high-wall mining.This study develops a dynamic failure criterion for web pillars under non-uniform loading using catastrophe theory.Through the ...Web pillars enduring complex coupled loads are critical for stability in high-wall mining.This study develops a dynamic failure criterion for web pillars under non-uniform loading using catastrophe theory.Through the analysis of the web pillar-overburden system’s dynamic stress and deformation,a total potential energy function and dynamic failure criterion were established for web pillars.An optimizing method for web pillar parameters was developed in highwall mining.The dynamic criterion established was used to evaluate the dynamic failure and stability of web pillars under static and dynamic loading.Key findings reveal that vertical displacements exhibit exponential-trigonometric variation under static loads and multi-variable power-law behavior under dynamic blasting.Instability risks arise when the roof’s tensile strength-to-stress ratio drops below 1.Using catastrophe theory,the bifurcation setΔ<0 signals sudden instability.The criterion defines failure as when the unstable web pillar section length l1 exceeds the roof’s critical collapse distance l2.Case studies and simulations determine an optimal web pillar width of 4.6 m.This research enhances safety and resource recovery,providing a theoretical framework for advancing highwall mining technology.展开更多
This research systematically investigates urban three-dimensional greening layout optimization and smart ecocity construction using deep learning and remote sensing technology.An improved U-Net++ architecture combined...This research systematically investigates urban three-dimensional greening layout optimization and smart ecocity construction using deep learning and remote sensing technology.An improved U-Net++ architecture combined with multi-source remote sensing data achieved high-precision recognition of urban three-dimensional greening with 92.8% overall accuracy.Analysis of spatiotemporal evolution patterns in Shanghai,Hangzhou,and Nanjing revealed that threedimensional greening shows a development trend from demonstration to popularization,with 16.5% annual growth rate.The study quantitatively assessed ecological benefits of various three-dimensional greening types.Results indicate that modular vertical greening and intensive roof gardens yield highest ecological benefits,while climbing-type vertical greening and extensive roof gardens offer optimal benefit-cost ratios.Integration of multiple forms generates 15-22% synergistic enhancement.Compared with traditional planning,the multi-objective optimization-based layout achieved 27.5% increase in carbon sequestration,32.6% improvement in temperature regulation,35.8% enhancement in stormwater management,and 42.3% rise in biodiversity index.Three pilot projects validated that actual ecological benefits reached 90.3-102.3% of predicted values.Multi-scenario simulations indicate optimized layouts can reduce urban heat island intensity by 15.2-18.7%,increase carbon neutrality contribution to 8.6-10.2%,and decrease stormwater runoff peaks by 25.3-32.6%.The findings provide technical methods for urban three-dimensional greening optimization and smart eco-city construction,promoting sustainable urban development.展开更多
Artificial rabbits optimization(ARO)is a recently proposed biology-based optimization algorithm inspired by the detour foraging and random hiding behavior of rabbits in nature.However,for solving optimization problems...Artificial rabbits optimization(ARO)is a recently proposed biology-based optimization algorithm inspired by the detour foraging and random hiding behavior of rabbits in nature.However,for solving optimization problems,the ARO algorithm shows slow convergence speed and can fall into local minima.To overcome these drawbacks,this paper proposes chaotic opposition-based learning ARO(COARO),an improved version of the ARO algorithm that incorporates opposition-based learning(OBL)and chaotic local search(CLS)techniques.By adding OBL to ARO,the convergence speed of the algorithm increases and it explores the search space better.Chaotic maps in CLS provide rapid convergence by scanning the search space efficiently,since their ergodicity and non-repetitive properties.The proposed COARO algorithm has been tested using thirty-three distinct benchmark functions.The outcomes have been compared with the most recent optimization algorithms.Additionally,the COARO algorithm’s problem-solving capabilities have been evaluated using six different engineering design problems and compared with various other algorithms.This study also introduces a binary variant of the continuous COARO algorithm,named BCOARO.The performance of BCOARO was evaluated on the breast cancer dataset.The effectiveness of BCOARO has been compared with different feature selection algorithms.The proposed BCOARO outperforms alternative algorithms,according to the findings obtained for real applications in terms of accuracy performance,and fitness value.Extensive experiments show that the COARO and BCOARO algorithms achieve promising results compared to other metaheuristic algorithms.展开更多
This study focused on the mechanical behavior of a deep-buried tunnel constructed in horizontally layered limestone,and investigated the effect of a new combined rockboltecable support system on the tunnel response.Th...This study focused on the mechanical behavior of a deep-buried tunnel constructed in horizontally layered limestone,and investigated the effect of a new combined rockboltecable support system on the tunnel response.The Yujingshan Tunnel,excavated through a giant karst cave,was used as a case study.Firstly,a multi-objective optimization model for the rockboltecable support was proposed by using fuzzy mathematics and multi-objective comprehensive decision-making principles.Subsequently,the parameters of the surrounding rock were calibrated by comparing the simulation results obtained by the discrete element method(DEM)with the field monitoring data to obtain an optimized support scheme based on the optimization model.Finally,the optimization scheme was applied to the karst cave section,which was divided into the B-and C-shaped sections.The distribution range of the rockboltecable support in the C-shaped section was larger than that in the B-shaped section.The field monitoring results,including tunnel crown settlement,horizontal convergence,and axial force of the rockboltecable system,were analyzed to assess the effectiveness of the optimization scheme.The maximum crown settlement and horizontal convergence were measured to be 25.9 mm and 35 mm,accounting for 0.1%and 0.2%of the tunnel height and span,respectively.Although the C-shaped section had poorer rock properties than the B-shaped section,the crown settlement and horizontal convergence in the C-shaped section ranged from 46%to 97%of those observed in the B-shaped section.The cable axial force in the Bshaped section was approximately 60%of that in the C-shaped section.The axial force in the crown rockbolt was much smaller than that in the sidewall rockbolt.Field monitoring results demonstrated that the optimized scheme effectively controlled the deformation of the layered surrounding rock,ensuring that it remained within a safe range.These results provide valuable references for the design of support systems in deep-buried tunnels situated in layered rock masses.展开更多
The selection of important factors in machine learning-based susceptibility assessments is crucial to obtain reliable susceptibility results.In this study,metaheuristic optimization and feature selection techniques we...The selection of important factors in machine learning-based susceptibility assessments is crucial to obtain reliable susceptibility results.In this study,metaheuristic optimization and feature selection techniques were applied to identify the most important input parameters for mapping debris flow susceptibility in the southern mountain area of Chengde City in Hebei Province,China,by using machine learning algorithms.In total,133 historical debris flow records and 16 related factors were selected.The support vector machine(SVM)was first used as the base classifier,and then a hybrid model was introduced by a two-step process.First,the particle swarm optimization(PSO)algorithm was employed to select the SVM model hyperparameters.Second,two feature selection algorithms,namely principal component analysis(PCA)and PSO,were integrated into the PSO-based SVM model,which generated the PCA-PSO-SVM and FS-PSO-SVM models,respectively.Three statistical metrics(accuracy,recall,and specificity)and the area under the receiver operating characteristic curve(AUC)were employed to evaluate and validate the performance of the models.The results indicated that the feature selection-based models exhibited the best performance,followed by the PSO-based SVM and SVM models.Moreover,the performance of the FS-PSO-SVM model was better than that of the PCA-PSO-SVM model,showing the highest AUC,accuracy,recall,and specificity values in both the training and testing processes.It was found that the selection of optimal features is crucial to improving the reliability of debris flow susceptibility assessment results.Moreover,the PSO algorithm was found to be not only an effective tool for hyperparameter optimization,but also a useful feature selection algorithm to improve prediction accuracies of debris flow susceptibility by using machine learning algorithms.The high and very high debris flow susceptibility zone appropriately covers 38.01%of the study area,where debris flow may occur under intensive human activities and heavy rainfall events.展开更多
Sinter is the core raw material for blast furnaces.Flue pressure,which is an important state parameter,affects sinter quality.In this paper,flue pressure prediction and optimization were studied based on the shapley a...Sinter is the core raw material for blast furnaces.Flue pressure,which is an important state parameter,affects sinter quality.In this paper,flue pressure prediction and optimization were studied based on the shapley additive explanation(SHAP)to predict the flue pressure and take targeted adjustment measures.First,the sintering process data were collected and processed.A flue pressure prediction model was then constructed after comparing different feature selection methods and model algorithms using SHAP+extremely random-ized trees(ET).The prediction accuracy of the model within the error range of±0.25 kPa was 92.63%.SHAP analysis was employed to improve the interpretability of the prediction model.The effects of various sintering operation parameters on flue pressure,the relation-ship between the numerical range of key operation parameters and flue pressure,the effect of operation parameter combinations on flue pressure,and the prediction process of the flue pressure prediction model on a single sample were analyzed.A flue pressure optimization module was also constructed and analyzed when the prediction satisfied the judgment conditions.The operating parameter combination was then pushed.The flue pressure was increased by 5.87%during the verification process,achieving a good optimization effect.展开更多
为平抑微源半桥变流器串联星型结构微电网HCSY-MG(half-bridge converter series Y-connection micro-grids)并网系统中微源出力的波动,保证各相直流侧电压之和相等,与并网电流三相平衡,提出1种基于改进近端策略优化PPO(proximal policy...为平抑微源半桥变流器串联星型结构微电网HCSY-MG(half-bridge converter series Y-connection micro-grids)并网系统中微源出力的波动,保证各相直流侧电压之和相等,与并网电流三相平衡,提出1种基于改进近端策略优化PPO(proximal policy optimization)的分布式混合储能系统HESS(hybrid energy storage system)充、放电优化控制策略。在考虑HCSY-MG系统并网电流与分布式HESS特性的条件下,确定影响并网电流的主要系统变量,以及HESS接入系统的最佳拓扑结构。然后结合串联系统的特点,将分布式HESS的充、放电问题转换为深度强化学习的Markov决策过程。同时针对PPO算法中熵损失权重难以确定的问题,提出1种改进的PPO算法,兼顾智能体的收敛性和探索性。最后以某新能源发电基地的典型运行数据为算例,验证所提控制策略的可行性和有效性。展开更多
Present of wind power is sporadically and cannot be utilized as the only fundamental load of energy sources.This paper proposes a wind-solar hybrid energy storage system(HESS)to ensure a stable supply grid for a longe...Present of wind power is sporadically and cannot be utilized as the only fundamental load of energy sources.This paper proposes a wind-solar hybrid energy storage system(HESS)to ensure a stable supply grid for a longer period.A multi-objective genetic algorithm(MOGA)and state of charge(SOC)region division for the batteries are introduced to solve the objective function and configuration of the system capacity,respectively.MATLAB/Simulink was used for simulation test.The optimization results show that for a 0.5 MW wind power and 0.5 MW photovoltaic system,with a combination of a 300 Ah lithium battery,a 200 Ah lead-acid battery,and a water storage tank,the proposed strategy reduces the system construction cost by approximately 18,000 yuan.Additionally,the cycle count of the electrochemical energy storage systemincreases from4515 to 4660,while the depth of discharge decreases from 55.37%to 53.65%,achieving shallow charging and discharging,thereby extending battery life and reducing grid voltage fluctuations significantly.The proposed strategy is a guide for stabilizing the grid connection of wind and solar power generation,capability allocation,and energy management of energy conservation systems.展开更多
This paper employs the PPO(Proximal Policy Optimization) algorithm to study the risk hedging problem of the Shanghai Stock Exchange(SSE) 50ETF options. First, the action and state spaces were designed based on the cha...This paper employs the PPO(Proximal Policy Optimization) algorithm to study the risk hedging problem of the Shanghai Stock Exchange(SSE) 50ETF options. First, the action and state spaces were designed based on the characteristics of the hedging task, and a reward function was developed according to the cost function of the options. Second, combining the concept of curriculum learning, the agent was guided to adopt a simulated-to-real learning approach for dynamic hedging tasks, reducing the learning difficulty and addressing the issue of insufficient option data. A dynamic hedging strategy for 50ETF options was constructed. Finally, numerical experiments demonstrate the superiority of the designed algorithm over traditional hedging strategies in terms of hedging effectiveness.展开更多
Bionic gait learning of quadruped robots based on reinforcement learning has become a hot research topic.The proximal policy optimization(PPO)algorithm has a low probability of learning a successful gait from scratch ...Bionic gait learning of quadruped robots based on reinforcement learning has become a hot research topic.The proximal policy optimization(PPO)algorithm has a low probability of learning a successful gait from scratch due to problems such as reward sparsity.To solve the problem,we propose a experience evolution proximal policy optimization(EEPPO)algorithm which integrates PPO with priori knowledge highlighting by evolutionary strategy.We use the successful trained samples as priori knowledge to guide the learning direction in order to increase the success probability of the learning algorithm.To verify the effectiveness of the proposed EEPPO algorithm,we have conducted simulation experiments of the quadruped robot gait learning task on Pybullet.Experimental results show that the central pattern generator based radial basis function(CPG-RBF)network and the policy network are simultaneously updated to achieve the quadruped robot’s bionic diagonal trot gait learning task using key information such as the robot’s speed,posture and joints information.Experimental comparison results with the traditional soft actor-critic(SAC)algorithm validate the superiority of the proposed EEPPO algorithm,which can learn a more stable diagonal trot gait in flat terrain.展开更多
文摘Miniature air quality sensors are widely used in urban grid-based monitoring due to their flexibility in deployment and low cost.However,the raw data collected by these devices often suffer from low accuracy caused by environmental interference and sensor drift,highlighting the need for effective calibration methods to improve data reliability.This study proposes a data correction method based on Bayesian Optimization Support Vector Regression(BO-SVR),which combines the nonlinear modeling capability of Support Vector Regression(SVR)with the efficient global hyperparameter search of Bayesian Optimization.By introducing cross-validation loss as the optimization objective and using Gaussian process modeling with an Expected Improvement acquisition strategy,the approach automatically determines optimal hyperparameters for accurate pollutant concentration prediction.Experiments on real-world micro-sensor datasets demonstrate that BO-SVR outperforms traditional SVR,grid search SVR,and random forest(RF)models across multiple pollutants,including PM_(2.5),PM_(10),CO,NO_(2),SO_(2),and O_(3).The proposed method achieves lower prediction residuals,higher fitting accuracy,and better generalization,offering an efficient and practical solution for enhancing the quality of micro-sensor air monitoring data.
基金supported by the National Natural Science Foundation of China(Nos.52204136,52474100,and 52204092).
文摘Web pillars enduring complex coupled loads are critical for stability in high-wall mining.This study develops a dynamic failure criterion for web pillars under non-uniform loading using catastrophe theory.Through the analysis of the web pillar-overburden system’s dynamic stress and deformation,a total potential energy function and dynamic failure criterion were established for web pillars.An optimizing method for web pillar parameters was developed in highwall mining.The dynamic criterion established was used to evaluate the dynamic failure and stability of web pillars under static and dynamic loading.Key findings reveal that vertical displacements exhibit exponential-trigonometric variation under static loads and multi-variable power-law behavior under dynamic blasting.Instability risks arise when the roof’s tensile strength-to-stress ratio drops below 1.Using catastrophe theory,the bifurcation setΔ<0 signals sudden instability.The criterion defines failure as when the unstable web pillar section length l1 exceeds the roof’s critical collapse distance l2.Case studies and simulations determine an optimal web pillar width of 4.6 m.This research enhances safety and resource recovery,providing a theoretical framework for advancing highwall mining technology.
文摘This research systematically investigates urban three-dimensional greening layout optimization and smart ecocity construction using deep learning and remote sensing technology.An improved U-Net++ architecture combined with multi-source remote sensing data achieved high-precision recognition of urban three-dimensional greening with 92.8% overall accuracy.Analysis of spatiotemporal evolution patterns in Shanghai,Hangzhou,and Nanjing revealed that threedimensional greening shows a development trend from demonstration to popularization,with 16.5% annual growth rate.The study quantitatively assessed ecological benefits of various three-dimensional greening types.Results indicate that modular vertical greening and intensive roof gardens yield highest ecological benefits,while climbing-type vertical greening and extensive roof gardens offer optimal benefit-cost ratios.Integration of multiple forms generates 15-22% synergistic enhancement.Compared with traditional planning,the multi-objective optimization-based layout achieved 27.5% increase in carbon sequestration,32.6% improvement in temperature regulation,35.8% enhancement in stormwater management,and 42.3% rise in biodiversity index.Three pilot projects validated that actual ecological benefits reached 90.3-102.3% of predicted values.Multi-scenario simulations indicate optimized layouts can reduce urban heat island intensity by 15.2-18.7%,increase carbon neutrality contribution to 8.6-10.2%,and decrease stormwater runoff peaks by 25.3-32.6%.The findings provide technical methods for urban three-dimensional greening optimization and smart eco-city construction,promoting sustainable urban development.
基金funded by Firat University Scientific Research Projects Management Unit for the scientific research project of Feyza AltunbeyÖzbay,numbered MF.23.49.
文摘Artificial rabbits optimization(ARO)is a recently proposed biology-based optimization algorithm inspired by the detour foraging and random hiding behavior of rabbits in nature.However,for solving optimization problems,the ARO algorithm shows slow convergence speed and can fall into local minima.To overcome these drawbacks,this paper proposes chaotic opposition-based learning ARO(COARO),an improved version of the ARO algorithm that incorporates opposition-based learning(OBL)and chaotic local search(CLS)techniques.By adding OBL to ARO,the convergence speed of the algorithm increases and it explores the search space better.Chaotic maps in CLS provide rapid convergence by scanning the search space efficiently,since their ergodicity and non-repetitive properties.The proposed COARO algorithm has been tested using thirty-three distinct benchmark functions.The outcomes have been compared with the most recent optimization algorithms.Additionally,the COARO algorithm’s problem-solving capabilities have been evaluated using six different engineering design problems and compared with various other algorithms.This study also introduces a binary variant of the continuous COARO algorithm,named BCOARO.The performance of BCOARO was evaluated on the breast cancer dataset.The effectiveness of BCOARO has been compared with different feature selection algorithms.The proposed BCOARO outperforms alternative algorithms,according to the findings obtained for real applications in terms of accuracy performance,and fitness value.Extensive experiments show that the COARO and BCOARO algorithms achieve promising results compared to other metaheuristic algorithms.
基金supported by the Fundamental Research Funds for the Central Universities (Grant No.2023JBZY024)Beijing Natural Science Foundation (Grant No.9244040)opening Fund of State Key Laboratory of Geohazard Prevention and Geoenvironment Protection,Chengdu University of Technology (Grant No.SKLGP2023K015).
文摘This study focused on the mechanical behavior of a deep-buried tunnel constructed in horizontally layered limestone,and investigated the effect of a new combined rockboltecable support system on the tunnel response.The Yujingshan Tunnel,excavated through a giant karst cave,was used as a case study.Firstly,a multi-objective optimization model for the rockboltecable support was proposed by using fuzzy mathematics and multi-objective comprehensive decision-making principles.Subsequently,the parameters of the surrounding rock were calibrated by comparing the simulation results obtained by the discrete element method(DEM)with the field monitoring data to obtain an optimized support scheme based on the optimization model.Finally,the optimization scheme was applied to the karst cave section,which was divided into the B-and C-shaped sections.The distribution range of the rockboltecable support in the C-shaped section was larger than that in the B-shaped section.The field monitoring results,including tunnel crown settlement,horizontal convergence,and axial force of the rockboltecable system,were analyzed to assess the effectiveness of the optimization scheme.The maximum crown settlement and horizontal convergence were measured to be 25.9 mm and 35 mm,accounting for 0.1%and 0.2%of the tunnel height and span,respectively.Although the C-shaped section had poorer rock properties than the B-shaped section,the crown settlement and horizontal convergence in the C-shaped section ranged from 46%to 97%of those observed in the B-shaped section.The cable axial force in the Bshaped section was approximately 60%of that in the C-shaped section.The axial force in the crown rockbolt was much smaller than that in the sidewall rockbolt.Field monitoring results demonstrated that the optimized scheme effectively controlled the deformation of the layered surrounding rock,ensuring that it remained within a safe range.These results provide valuable references for the design of support systems in deep-buried tunnels situated in layered rock masses.
基金supported by the Second Tibetan Plateau Scientific Expedition and Research Program(Grant no.2019QZKK0904)Natural Science Foundation of Hebei Province(Grant no.D2022403032)S&T Program of Hebei(Grant no.E2021403001).
文摘The selection of important factors in machine learning-based susceptibility assessments is crucial to obtain reliable susceptibility results.In this study,metaheuristic optimization and feature selection techniques were applied to identify the most important input parameters for mapping debris flow susceptibility in the southern mountain area of Chengde City in Hebei Province,China,by using machine learning algorithms.In total,133 historical debris flow records and 16 related factors were selected.The support vector machine(SVM)was first used as the base classifier,and then a hybrid model was introduced by a two-step process.First,the particle swarm optimization(PSO)algorithm was employed to select the SVM model hyperparameters.Second,two feature selection algorithms,namely principal component analysis(PCA)and PSO,were integrated into the PSO-based SVM model,which generated the PCA-PSO-SVM and FS-PSO-SVM models,respectively.Three statistical metrics(accuracy,recall,and specificity)and the area under the receiver operating characteristic curve(AUC)were employed to evaluate and validate the performance of the models.The results indicated that the feature selection-based models exhibited the best performance,followed by the PSO-based SVM and SVM models.Moreover,the performance of the FS-PSO-SVM model was better than that of the PCA-PSO-SVM model,showing the highest AUC,accuracy,recall,and specificity values in both the training and testing processes.It was found that the selection of optimal features is crucial to improving the reliability of debris flow susceptibility assessment results.Moreover,the PSO algorithm was found to be not only an effective tool for hyperparameter optimization,but also a useful feature selection algorithm to improve prediction accuracies of debris flow susceptibility by using machine learning algorithms.The high and very high debris flow susceptibility zone appropriately covers 38.01%of the study area,where debris flow may occur under intensive human activities and heavy rainfall events.
基金supported by the General Program of the National Natural Science Foundation of China(No.52274326)the China Baowu Low Carbon Metallurgy Innovation Foundation(No.BWLCF202109)the Seventh Batch of Ten Thousand Talents Plan of China(No.ZX20220553).
文摘Sinter is the core raw material for blast furnaces.Flue pressure,which is an important state parameter,affects sinter quality.In this paper,flue pressure prediction and optimization were studied based on the shapley additive explanation(SHAP)to predict the flue pressure and take targeted adjustment measures.First,the sintering process data were collected and processed.A flue pressure prediction model was then constructed after comparing different feature selection methods and model algorithms using SHAP+extremely random-ized trees(ET).The prediction accuracy of the model within the error range of±0.25 kPa was 92.63%.SHAP analysis was employed to improve the interpretability of the prediction model.The effects of various sintering operation parameters on flue pressure,the relation-ship between the numerical range of key operation parameters and flue pressure,the effect of operation parameter combinations on flue pressure,and the prediction process of the flue pressure prediction model on a single sample were analyzed.A flue pressure optimization module was also constructed and analyzed when the prediction satisfied the judgment conditions.The operating parameter combination was then pushed.The flue pressure was increased by 5.87%during the verification process,achieving a good optimization effect.
文摘为平抑微源半桥变流器串联星型结构微电网HCSY-MG(half-bridge converter series Y-connection micro-grids)并网系统中微源出力的波动,保证各相直流侧电压之和相等,与并网电流三相平衡,提出1种基于改进近端策略优化PPO(proximal policy optimization)的分布式混合储能系统HESS(hybrid energy storage system)充、放电优化控制策略。在考虑HCSY-MG系统并网电流与分布式HESS特性的条件下,确定影响并网电流的主要系统变量,以及HESS接入系统的最佳拓扑结构。然后结合串联系统的特点,将分布式HESS的充、放电问题转换为深度强化学习的Markov决策过程。同时针对PPO算法中熵损失权重难以确定的问题,提出1种改进的PPO算法,兼顾智能体的收敛性和探索性。最后以某新能源发电基地的典型运行数据为算例,验证所提控制策略的可行性和有效性。
基金supported by a Horizontal Project on the Development of a Hybrid Energy Storage Simulation Model for Wind Power Based on an RT-LAB Simulation System(PH2023000190)the Inner Mongolia Natural Science Foundation Project and the Optimization of Exergy Efficiency of a Hybrid Energy Storage System with Crossover Control for Wind Power(2023JQ04).
文摘Present of wind power is sporadically and cannot be utilized as the only fundamental load of energy sources.This paper proposes a wind-solar hybrid energy storage system(HESS)to ensure a stable supply grid for a longer period.A multi-objective genetic algorithm(MOGA)and state of charge(SOC)region division for the batteries are introduced to solve the objective function and configuration of the system capacity,respectively.MATLAB/Simulink was used for simulation test.The optimization results show that for a 0.5 MW wind power and 0.5 MW photovoltaic system,with a combination of a 300 Ah lithium battery,a 200 Ah lead-acid battery,and a water storage tank,the proposed strategy reduces the system construction cost by approximately 18,000 yuan.Additionally,the cycle count of the electrochemical energy storage systemincreases from4515 to 4660,while the depth of discharge decreases from 55.37%to 53.65%,achieving shallow charging and discharging,thereby extending battery life and reducing grid voltage fluctuations significantly.The proposed strategy is a guide for stabilizing the grid connection of wind and solar power generation,capability allocation,and energy management of energy conservation systems.
基金supported by the Foundation of Key Laboratory of System Control and Information Processing,Ministry of Education,China,Scip20240111Aeronautical Science Foundation of China,Grant 2024Z071108001the Foundation of Key Laboratory of Traffic Information and Safety of Anhui Higher Education Institutes,Anhui Sanlian University,KLAHEI18018.
文摘This paper employs the PPO(Proximal Policy Optimization) algorithm to study the risk hedging problem of the Shanghai Stock Exchange(SSE) 50ETF options. First, the action and state spaces were designed based on the characteristics of the hedging task, and a reward function was developed according to the cost function of the options. Second, combining the concept of curriculum learning, the agent was guided to adopt a simulated-to-real learning approach for dynamic hedging tasks, reducing the learning difficulty and addressing the issue of insufficient option data. A dynamic hedging strategy for 50ETF options was constructed. Finally, numerical experiments demonstrate the superiority of the designed algorithm over traditional hedging strategies in terms of hedging effectiveness.
基金the National Natural Science Foundation of China(No.62103009)。
文摘Bionic gait learning of quadruped robots based on reinforcement learning has become a hot research topic.The proximal policy optimization(PPO)algorithm has a low probability of learning a successful gait from scratch due to problems such as reward sparsity.To solve the problem,we propose a experience evolution proximal policy optimization(EEPPO)algorithm which integrates PPO with priori knowledge highlighting by evolutionary strategy.We use the successful trained samples as priori knowledge to guide the learning direction in order to increase the success probability of the learning algorithm.To verify the effectiveness of the proposed EEPPO algorithm,we have conducted simulation experiments of the quadruped robot gait learning task on Pybullet.Experimental results show that the central pattern generator based radial basis function(CPG-RBF)network and the policy network are simultaneously updated to achieve the quadruped robot’s bionic diagonal trot gait learning task using key information such as the robot’s speed,posture and joints information.Experimental comparison results with the traditional soft actor-critic(SAC)algorithm validate the superiority of the proposed EEPPO algorithm,which can learn a more stable diagonal trot gait in flat terrain.
