The integration of renewable energy sources into modern power systems necessitates efficient and robust control strategies to address challenges such as power quality,stability,and dynamic environmental variations.Thi...The integration of renewable energy sources into modern power systems necessitates efficient and robust control strategies to address challenges such as power quality,stability,and dynamic environmental variations.This paper presents a novel sparrow search algorithm(SSA)-tuned proportional-integral(PI)controller for grid-connected photovoltaic(PV)systems,designed to optimize dynamic perfor-mance,energy extraction,and power quality.Key contributions include the development of a systematic SSA-based optimization frame-work for real-time PI parameter tuning,ensuring precise voltage and current regulation,improved maximum power point tracking(MPPT)efficiency,and minimized total harmonic distortion(THD).The proposed approach is evaluated against conventional PSO-based and P&O controllers through comprehensive simulations,demonstrating its superior performance across key metrics:a 39.47%faster response time compared to PSO,a 12.06%increase in peak active power relative to P&O,and a 52.38%reduction in THD,ensuring compliance with IEEE grid standards.Moreover,the SSA-tuned PI controller exhibits enhanced adaptability to dynamic irradiancefluc-tuations,rapid response time,and robust grid integration under varying conditions,making it highly suitable for real-time smart grid applications.This work establishes the SSA-tuned PI controller as a reliable and efficient solution for improving PV system performance in grid-connected scenarios,while also setting the foundation for future research into multi-objective optimization,experimental valida-tion,and hybrid renewable energy systems.展开更多
Concrete-filled steel tubes(CFST)are widely utilized in civil engineering due to their superior load-bearing capacity,ductility,and seismic resistance.However,existing design codes,such as AISC and Eurocode 4,tend to ...Concrete-filled steel tubes(CFST)are widely utilized in civil engineering due to their superior load-bearing capacity,ductility,and seismic resistance.However,existing design codes,such as AISC and Eurocode 4,tend to be excessively conservative as they fail to account for the composite action between the steel tube and the concrete core.To address this limitation,this study proposes a hybrid model that integrates XGBoost with the Pied Kingfisher Optimizer(PKO),a nature-inspired algorithm,to enhance the accuracy of shear strength prediction for CFST columns.Additionally,quantile regression is employed to construct prediction intervals for the ultimate shear force,while the Asymmetric Squared Error Loss(ASEL)function is incorporated to mitigate overestimation errors.The computational results demonstrate that the PKO-XGBoost model delivers superior predictive accuracy,achieving a Mean Absolute Percentage Error(MAPE)of 4.431%and R2 of 0.9925 on the test set.Furthermore,the ASEL-PKO-XGBoost model substantially reduces overestimation errors to 28.26%,with negligible impact on predictive performance.Additionally,based on the Genetic Algorithm(GA)and existing equation models,a strength equation model is developed,achieving markedly higher accuracy than existing models(R^(2)=0.934).Lastly,web-based Graphical User Interfaces(GUIs)were developed to enable real-time prediction.展开更多
随着养宠需求增长,现有智能猫砂盆在猫咪健康监测方面存在局限,为此设计一款智能猫砂盆健康监测系统,以解决这些问题。系统以Raspberry Pi 4B为主控芯片,集成空气质量监测、自动清理、称重等多个硬件模块,运用YOLOv9算法进行排泄物识别...随着养宠需求增长,现有智能猫砂盆在猫咪健康监测方面存在局限,为此设计一款智能猫砂盆健康监测系统,以解决这些问题。系统以Raspberry Pi 4B为主控芯片,集成空气质量监测、自动清理、称重等多个硬件模块,运用YOLOv9算法进行排泄物识别,并通过手机App实现数据查看与远程控制。经测试,该系统的空气质量监测系统准确性极高,与手动测量相比,平均误差小于5%,在检测到氨气浓度超标后,平均响应时间仅1.5 s,自动启动清理程序后,氨气浓度平均降低75%。自动清理功能效率测试显示,智能猫砂盆清理频率比常规猫砂盆高50%,且清理后清洁度更高,90%的猫表现出对其明显偏好。设计的系统实现了对猫咪健康状况的精准监测与猫砂盆的智能管理,为宠物主人提供了便利,提升了对猫咪健康的关注度。后续仍有优化空间,如优化图像识别算法、拓展空气质量监测种类、增加App健康数据分析功能等。展开更多
A simple control structure in servo system is occasionally needed for simple industrial application which precise and high control performance is not exessively important so that the cost production can be reduced eff...A simple control structure in servo system is occasionally needed for simple industrial application which precise and high control performance is not exessively important so that the cost production can be reduced efficiently. Simplified vector control, which has simple control structure, is utilized as the permanent magnet synchronous motor control algorithm and genetic algorithm is used to tune three PI controllers used in simplified vector control. The control performance is obtained from simulation and investigated to verify the feasibility of the algorithm to be applied in the real application. Simulation results show that the speed and torque responses of the system in both continuous time and discrete time can achieve good performances. Furthermore, simplified vector control combined with genetic algorithm has a similar perfofmance with conventional field oriented control algorithm and possible to be realized into the real simple application in the future.展开更多
Aiming at the problems of slow dynamic response and weak robustness of integer-order proportional integral(PI)controller in double closed loop vector control system of permanent magnet synchronous motor(PMSM),a method...Aiming at the problems of slow dynamic response and weak robustness of integer-order proportional integral(PI)controller in double closed loop vector control system of permanent magnet synchronous motor(PMSM),a method of combining dragonfly algorithm with fractional order PI control is proposed for off-line parameter tuning for the outer loop of speed of the system.The parameter to be optimized is used as the spatial position of the optimal individual searching for food sources in the search space,and the error performance index integrated time and absolute error(ITAE)is used as its target fitness function.The motor speed regulation performances of traditional engineering experience setting integer order PI,particle swarm optimization algorithm tuning fractional order PI and dragonfly algorithm tuning fractional order PI are compared,respectively.Results show that the fractional order PI controller optimized by dragonfly algorithm can improve the dynamic response performance of the system,reduce overshoot and enhance robustness,which proves the feasibility and superiority of the optimization strategy.展开更多
Background Interconnection of different power systems has a major effect on system stability.This study aims to design an optimal load frequency control(LFC)system based on a proportional-integral(PI)controller for a ...Background Interconnection of different power systems has a major effect on system stability.This study aims to design an optimal load frequency control(LFC)system based on a proportional-integral(PI)controller for a two-area power system.Methods Two areas were connected through an AC tie line in parallel with a DC link to stabilize the frequency of oscillations in both areas.The PI parameters were tuned using the cuckoo search algorithm(CSA)to minimize the integral absolute error(IAE).A state matrix was provided,and the stability of the system was verified by calculating the eigenvalues.The frequency response was investigated for load variation,changes in the generator rate constraint,the turbine time constant,and the governor time constant.Results The CSA was compared with particle swarm optimization algorithm(PSO)under identical conditions.The system was modeled based on a state-space mathematical representation and simulated using MATLAB.The results demonstrated the effectiveness of the proposed controller based on both algorithms and,it is clear that CSA is superior to PSO.Conclusion The CSA algorithm smoothens the system response,reduces ripples,decreases overshooting and settling time,and improves the overall system performance under different disturbances.展开更多
针对采用直接电流控制策略的电压源换流器(voltage source converter,VSC)控制系统比例积分(PI)参数难以选取的问题,提出了一种优化外环PI控制器参数的方法。首先建立解耦后的外环参数整定模型,然后基于时间乘绝对误差积分(integral of ...针对采用直接电流控制策略的电压源换流器(voltage source converter,VSC)控制系统比例积分(PI)参数难以选取的问题,提出了一种优化外环PI控制器参数的方法。首先建立解耦后的外环参数整定模型,然后基于时间乘绝对误差积分(integral of time multiplied by the absolute value of error,ITAE)准则构造PI参数优化的性能泛函,针对此最优控制模型的特点,论文采用遗传算法进行求解,在PSCAD搭建VSC-HVDC模型进行仿真验证。展开更多
文摘The integration of renewable energy sources into modern power systems necessitates efficient and robust control strategies to address challenges such as power quality,stability,and dynamic environmental variations.This paper presents a novel sparrow search algorithm(SSA)-tuned proportional-integral(PI)controller for grid-connected photovoltaic(PV)systems,designed to optimize dynamic perfor-mance,energy extraction,and power quality.Key contributions include the development of a systematic SSA-based optimization frame-work for real-time PI parameter tuning,ensuring precise voltage and current regulation,improved maximum power point tracking(MPPT)efficiency,and minimized total harmonic distortion(THD).The proposed approach is evaluated against conventional PSO-based and P&O controllers through comprehensive simulations,demonstrating its superior performance across key metrics:a 39.47%faster response time compared to PSO,a 12.06%increase in peak active power relative to P&O,and a 52.38%reduction in THD,ensuring compliance with IEEE grid standards.Moreover,the SSA-tuned PI controller exhibits enhanced adaptability to dynamic irradiancefluc-tuations,rapid response time,and robust grid integration under varying conditions,making it highly suitable for real-time smart grid applications.This work establishes the SSA-tuned PI controller as a reliable and efficient solution for improving PV system performance in grid-connected scenarios,while also setting the foundation for future research into multi-objective optimization,experimental valida-tion,and hybrid renewable energy systems.
基金funded by United Arab Emirates University(UAEU)under the UAEU-AUA grant number G00004577(12N145)with the corresponding grant at Universiti Malaya(UM)under grant number IF019-2024.
文摘Concrete-filled steel tubes(CFST)are widely utilized in civil engineering due to their superior load-bearing capacity,ductility,and seismic resistance.However,existing design codes,such as AISC and Eurocode 4,tend to be excessively conservative as they fail to account for the composite action between the steel tube and the concrete core.To address this limitation,this study proposes a hybrid model that integrates XGBoost with the Pied Kingfisher Optimizer(PKO),a nature-inspired algorithm,to enhance the accuracy of shear strength prediction for CFST columns.Additionally,quantile regression is employed to construct prediction intervals for the ultimate shear force,while the Asymmetric Squared Error Loss(ASEL)function is incorporated to mitigate overestimation errors.The computational results demonstrate that the PKO-XGBoost model delivers superior predictive accuracy,achieving a Mean Absolute Percentage Error(MAPE)of 4.431%and R2 of 0.9925 on the test set.Furthermore,the ASEL-PKO-XGBoost model substantially reduces overestimation errors to 28.26%,with negligible impact on predictive performance.Additionally,based on the Genetic Algorithm(GA)and existing equation models,a strength equation model is developed,achieving markedly higher accuracy than existing models(R^(2)=0.934).Lastly,web-based Graphical User Interfaces(GUIs)were developed to enable real-time prediction.
文摘随着养宠需求增长,现有智能猫砂盆在猫咪健康监测方面存在局限,为此设计一款智能猫砂盆健康监测系统,以解决这些问题。系统以Raspberry Pi 4B为主控芯片,集成空气质量监测、自动清理、称重等多个硬件模块,运用YOLOv9算法进行排泄物识别,并通过手机App实现数据查看与远程控制。经测试,该系统的空气质量监测系统准确性极高,与手动测量相比,平均误差小于5%,在检测到氨气浓度超标后,平均响应时间仅1.5 s,自动启动清理程序后,氨气浓度平均降低75%。自动清理功能效率测试显示,智能猫砂盆清理频率比常规猫砂盆高50%,且清理后清洁度更高,90%的猫表现出对其明显偏好。设计的系统实现了对猫咪健康状况的精准监测与猫砂盆的智能管理,为宠物主人提供了便利,提升了对猫咪健康的关注度。后续仍有优化空间,如优化图像识别算法、拓展空气质量监测种类、增加App健康数据分析功能等。
文摘A simple control structure in servo system is occasionally needed for simple industrial application which precise and high control performance is not exessively important so that the cost production can be reduced efficiently. Simplified vector control, which has simple control structure, is utilized as the permanent magnet synchronous motor control algorithm and genetic algorithm is used to tune three PI controllers used in simplified vector control. The control performance is obtained from simulation and investigated to verify the feasibility of the algorithm to be applied in the real application. Simulation results show that the speed and torque responses of the system in both continuous time and discrete time can achieve good performances. Furthermore, simplified vector control combined with genetic algorithm has a similar perfofmance with conventional field oriented control algorithm and possible to be realized into the real simple application in the future.
基金Supported by the National Natural Science Foundation of China(61603242)。
文摘Aiming at the problems of slow dynamic response and weak robustness of integer-order proportional integral(PI)controller in double closed loop vector control system of permanent magnet synchronous motor(PMSM),a method of combining dragonfly algorithm with fractional order PI control is proposed for off-line parameter tuning for the outer loop of speed of the system.The parameter to be optimized is used as the spatial position of the optimal individual searching for food sources in the search space,and the error performance index integrated time and absolute error(ITAE)is used as its target fitness function.The motor speed regulation performances of traditional engineering experience setting integer order PI,particle swarm optimization algorithm tuning fractional order PI and dragonfly algorithm tuning fractional order PI are compared,respectively.Results show that the fractional order PI controller optimized by dragonfly algorithm can improve the dynamic response performance of the system,reduce overshoot and enhance robustness,which proves the feasibility and superiority of the optimization strategy.
基金Supported by the Russian Science Foundation(Agreement 23-41-10001,https://rscf.ru/project/23-41-10001/).
文摘Background Interconnection of different power systems has a major effect on system stability.This study aims to design an optimal load frequency control(LFC)system based on a proportional-integral(PI)controller for a two-area power system.Methods Two areas were connected through an AC tie line in parallel with a DC link to stabilize the frequency of oscillations in both areas.The PI parameters were tuned using the cuckoo search algorithm(CSA)to minimize the integral absolute error(IAE).A state matrix was provided,and the stability of the system was verified by calculating the eigenvalues.The frequency response was investigated for load variation,changes in the generator rate constraint,the turbine time constant,and the governor time constant.Results The CSA was compared with particle swarm optimization algorithm(PSO)under identical conditions.The system was modeled based on a state-space mathematical representation and simulated using MATLAB.The results demonstrated the effectiveness of the proposed controller based on both algorithms and,it is clear that CSA is superior to PSO.Conclusion The CSA algorithm smoothens the system response,reduces ripples,decreases overshooting and settling time,and improves the overall system performance under different disturbances.
文摘针对采用直接电流控制策略的电压源换流器(voltage source converter,VSC)控制系统比例积分(PI)参数难以选取的问题,提出了一种优化外环PI控制器参数的方法。首先建立解耦后的外环参数整定模型,然后基于时间乘绝对误差积分(integral of time multiplied by the absolute value of error,ITAE)准则构造PI参数优化的性能泛函,针对此最优控制模型的特点,论文采用遗传算法进行求解,在PSCAD搭建VSC-HVDC模型进行仿真验证。
