In this paper, the state-feedback Nash game based mixed H2/H∞ design^（[1, 2]）has been extended for output feedback case. The algorithm is applied to control bioreactor system with a Laguerre-Wavelet Network（LWN）^...In this paper, the state-feedback Nash game based mixed H2/H∞ design^（[1, 2]）has been extended for output feedback case. The algorithm is applied to control bioreactor system with a Laguerre-Wavelet Network（LWN）^（[3, 4]）model of the bioreactor.This is achieved by using the LWN model as a deviation model and by successively linearising the deviation model along the state trajectory. For reducing the approximation error and to improve the controller performance, symbolic derivation algorithm, viz.,automatic differentiation is employed. A cautionary note is also given on the fragility of the output feedback mixed H2/H∞ model predictive controller^（[4, 5]）due to its sensitivity to its own parametric changes.展开更多
Accurate short-term photovoltaic(PV)output forecasting is beneficial for increasing grid stabil-ity and enhancing the capacity for photovoltaic power absorption.In response to the challenges faced by commonly used pho...Accurate short-term photovoltaic(PV)output forecasting is beneficial for increasing grid stabil-ity and enhancing the capacity for photovoltaic power absorption.In response to the challenges faced by commonly used photovoltaic forecasting methods,which struggle to handle issues such as non-u-niform lengths of time series data for power generation and meteorological conditions,overlapping photovoltaic characteristics,and nonlinear correlations,an improved method that utilizes spectral clustering and dynamic time warping(DTW)for selecting similar days is proposed to optimize the dataset along the temporal dimension.Furthermore,XGBoost is employed for recursive feature selec-tion.On this basis,to address the issue that single forecasting models excel at capturing different data characteristics and tend to exhibit significant prediction errors under adverse meteorological con-ditions,an improved forecasting model based on Stacking and weighted fusion is proposed to reduce the independent bias and variance of individual models and enhance the predictive accuracy.Final-ly,experimental validation is carried out using real data from a photovoltaic power station in the Xi-aoshan District of Hangzhou,China,demonstrating that the proposed method can still achieve accu-rate and robust forecasting results even under conditions of significant meteorological fluctuations.展开更多
Under the support of National Natural Science Foundation of China including international cooperative research project, key project and other project, professor Chen Xikang from Academy of Mathematics and Systems Scie...Under the support of National Natural Science Foundation of China including international cooperative research project, key project and other project, professor Chen Xikang from Academy of Mathematics and Systems Science under the Chinese Academy of Sciences, together with his colleagues, put forward in-put-occupancy-output technique and then used it in national grain output prediction approach. The main achievements are as follows:展开更多
Floating photovoltaic systems provide better land use and higher energy output through water cooling effects,but accurate power forecasting remains challenging due to complex environmental factors and measurement erro...Floating photovoltaic systems provide better land use and higher energy output through water cooling effects,but accurate power forecasting remains challenging due to complex environmental factors and measurement errors.This study presents an improved teaching-learning-based optimization algorithm with extreme learning machine for floating photovoltaic power forecasting.The method uses an adaptive teaching factor that adjusts the balance between exploration and exploitation during optimization,replacing fixed teaching factors with continuous,iteration-based adjustment.The research evaluated the approach using comprehensive real data from a floating photovoltaic installation at Universiti Malaysia Pahang Al-Sultan Abdullah,Malaysia.The proposed method achieved superior forecasting accuracy compared to benchmark algorithms including standard teaching-learningbased optimization with extreme learning machine,manta rays foraging optimization with extreme learning machine,moth flame optimization with extreme learning machine,ant colony optimization with extreme learning machine and salp swarm algorithm with extreme learning machine.The improved teaching-learning-based optimization approach demonstrated a root mean squared error of 7.81 kW and coefficient of determination of 0.9386,outperforming all comparison methods with statistically significant improvements.The algorithm showed faster convergence,enhanced stability,and superior computational efficiency while maintaining accuracy suitable for real-time grid integration applications.Phase current measurements were identified as the most important predictors for floating photovoltaic power forecasting.The system achieved high prediction accuracy with most forecasts falling within acceptable error tolerance,making the proposed approach a reliable solution for floating photovoltaic power forecasting that supports grid integration and renewable energy deployment.The methodology addresses unique characteristics of aquatic solar installations while providing practical implementation viability for operational floating photovoltaic systems.展开更多
To address the limitations of traditional numerical simulation methods in determining the optimal structure parameters of thermoelectric module,such as complex modeling procedures,low computational efficiency,and poor...To address the limitations of traditional numerical simulation methods in determining the optimal structure parameters of thermoelectric module,such as complex modeling procedures,low computational efficiency,and poor adaptability to multi-objective design,this paper introduces an efficient structural optimization approach of segmented annular thermoelectric module that combines the uniformly equivalent element integral method and multi-parameter and multi-objective optimization algorithm under both constant temperature and heat flux boundary conditions.The optimization results show that the optimal resistance ratio is independent of the boundary conditions,and the optimal thermoelectric leg ratios remain approximately 1.2 across all studied cases in this study.Notably,the optimal segment ratios are highly sensitive to the temperatures at the two ends of the optimized segmented annular thermoelectric module under all conditions and can be directly calculated using the proposed fitting formulas.In addition,an optimal total thermoelectric leg angle exists for the segmented annular thermoelectric module to achieve the maximum temperature difference within the operating temperature range of the thermoelectric materials.The output power and efficiency of the optimized segmented annular thermoelectric module can be predicted using the parameter-based fitting formulas,with relative errors below 3%when compared to the direct optimization results.The proposed method in this paper offers significant advantages in terms of modeling simplicity,computational efficiency,and highly compatible with machine learning frameworks,thereby enabling artificial intelligence-assisted design and optimization pipelines for segmented annular thermoelectric modules.展开更多
文摘In this paper, the state-feedback Nash game based mixed H2/H∞ design^（[1, 2]）has been extended for output feedback case. The algorithm is applied to control bioreactor system with a Laguerre-Wavelet Network（LWN）^（[3, 4]）model of the bioreactor.This is achieved by using the LWN model as a deviation model and by successively linearising the deviation model along the state trajectory. For reducing the approximation error and to improve the controller performance, symbolic derivation algorithm, viz.,automatic differentiation is employed. A cautionary note is also given on the fragility of the output feedback mixed H2/H∞ model predictive controller^（[4, 5]）due to its sensitivity to its own parametric changes.
基金Supported by the National Natural Science Foundation of China(No.52005442)the Technology Project of Zhejiang Huayun Information Technology Co.,Ltd.(No.HYJT/JS-2020-004).
文摘Accurate short-term photovoltaic(PV)output forecasting is beneficial for increasing grid stabil-ity and enhancing the capacity for photovoltaic power absorption.In response to the challenges faced by commonly used photovoltaic forecasting methods,which struggle to handle issues such as non-u-niform lengths of time series data for power generation and meteorological conditions,overlapping photovoltaic characteristics,and nonlinear correlations,an improved method that utilizes spectral clustering and dynamic time warping(DTW)for selecting similar days is proposed to optimize the dataset along the temporal dimension.Furthermore,XGBoost is employed for recursive feature selec-tion.On this basis,to address the issue that single forecasting models excel at capturing different data characteristics and tend to exhibit significant prediction errors under adverse meteorological con-ditions,an improved forecasting model based on Stacking and weighted fusion is proposed to reduce the independent bias and variance of individual models and enhance the predictive accuracy.Final-ly,experimental validation is carried out using real data from a photovoltaic power station in the Xi-aoshan District of Hangzhou,China,demonstrating that the proposed method can still achieve accu-rate and robust forecasting results even under conditions of significant meteorological fluctuations.
文摘Under the support of National Natural Science Foundation of China including international cooperative research project, key project and other project, professor Chen Xikang from Academy of Mathematics and Systems Science under the Chinese Academy of Sciences, together with his colleagues, put forward in-put-occupancy-output technique and then used it in national grain output prediction approach. The main achievements are as follows:
基金supported by the Ministry of Higher Education Malaysia(MOHE)under the Fundamental Research Grant Scheme(FRGS/1/2022/ICT04/UMP/02/1).
文摘Floating photovoltaic systems provide better land use and higher energy output through water cooling effects,but accurate power forecasting remains challenging due to complex environmental factors and measurement errors.This study presents an improved teaching-learning-based optimization algorithm with extreme learning machine for floating photovoltaic power forecasting.The method uses an adaptive teaching factor that adjusts the balance between exploration and exploitation during optimization,replacing fixed teaching factors with continuous,iteration-based adjustment.The research evaluated the approach using comprehensive real data from a floating photovoltaic installation at Universiti Malaysia Pahang Al-Sultan Abdullah,Malaysia.The proposed method achieved superior forecasting accuracy compared to benchmark algorithms including standard teaching-learningbased optimization with extreme learning machine,manta rays foraging optimization with extreme learning machine,moth flame optimization with extreme learning machine,ant colony optimization with extreme learning machine and salp swarm algorithm with extreme learning machine.The improved teaching-learning-based optimization approach demonstrated a root mean squared error of 7.81 kW and coefficient of determination of 0.9386,outperforming all comparison methods with statistically significant improvements.The algorithm showed faster convergence,enhanced stability,and superior computational efficiency while maintaining accuracy suitable for real-time grid integration applications.Phase current measurements were identified as the most important predictors for floating photovoltaic power forecasting.The system achieved high prediction accuracy with most forecasts falling within acceptable error tolerance,making the proposed approach a reliable solution for floating photovoltaic power forecasting that supports grid integration and renewable energy deployment.The methodology addresses unique characteristics of aquatic solar installations while providing practical implementation viability for operational floating photovoltaic systems.
基金support by Postdoctoral Fellowship Program of CPSF(GZC20232004)this research was funded by the National Key R&D Program of China(2023YFB4604700).
文摘To address the limitations of traditional numerical simulation methods in determining the optimal structure parameters of thermoelectric module,such as complex modeling procedures,low computational efficiency,and poor adaptability to multi-objective design,this paper introduces an efficient structural optimization approach of segmented annular thermoelectric module that combines the uniformly equivalent element integral method and multi-parameter and multi-objective optimization algorithm under both constant temperature and heat flux boundary conditions.The optimization results show that the optimal resistance ratio is independent of the boundary conditions,and the optimal thermoelectric leg ratios remain approximately 1.2 across all studied cases in this study.Notably,the optimal segment ratios are highly sensitive to the temperatures at the two ends of the optimized segmented annular thermoelectric module under all conditions and can be directly calculated using the proposed fitting formulas.In addition,an optimal total thermoelectric leg angle exists for the segmented annular thermoelectric module to achieve the maximum temperature difference within the operating temperature range of the thermoelectric materials.The output power and efficiency of the optimized segmented annular thermoelectric module can be predicted using the parameter-based fitting formulas,with relative errors below 3%when compared to the direct optimization results.The proposed method in this paper offers significant advantages in terms of modeling simplicity,computational efficiency,and highly compatible with machine learning frameworks,thereby enabling artificial intelligence-assisted design and optimization pipelines for segmented annular thermoelectric modules.
