Due to the climate-dependent nature of renewable energy sources(RESs),solving the optimal power flow(OPF)problem in power systems that integrate RESs,such as photovoltaic(PV)units and wind turbines(WTs),remains a sign...Due to the climate-dependent nature of renewable energy sources(RESs),solving the optimal power flow(OPF)problem in power systems that integrate RESs,such as photovoltaic(PV)units and wind turbines(WTs),remains a significant challenge.To address this problem,this study presents an effective framework that incorporates solar and wind power generation.To manage the nonconvex and nonlinear characteristics of the OPF problem,a modified physics-inspired algorithm termed the Enhanced Coulomb’s and Franklin’s laws Algorithm(ECFA),is deployed.In the proposed OPF model,the power generated from RESs is considered a dependent variable,while voltages at buses equipped with RESs serve as decision variables.Real-time data on solar irradiation and wind speed are used to model the power outputs of PV units and WTs,respectively.Although the Coulomb’s and Franklin’s law algorithm(CFA)offers some advantages,it underperforms on complex optimization tasks compared to SSA,BA,SCA,ABC,and CFA.The enhanced version of the CFA improves the search process across the feasible space by incorporating diverse interaction methods and enhancing exploitation capabilities.The performance of the proposed ECFA is assessed through comprehensive comparisons with state-of-the-art methods for solving the OPF problem.展开更多
The history of computing started with analog computers consisting of physical devices performing specialized functions such as predicting the position of astronomical bodies and the trajectory of cannon balls.In moder...The history of computing started with analog computers consisting of physical devices performing specialized functions such as predicting the position of astronomical bodies and the trajectory of cannon balls.In modern times,this idea has been extended,for example,to ultrafast nonlinear optics serving as a surrogate analog computer to probe the behavior of complex phenomena such as rogue waves.Here we discuss a new paradigm where physical phenomena coded as an algorithm perform computational imaging tasks.Specifically,diffraction followed by coherent detection becomes an image enhancement tool.Vision Enhancement via Virtual diffraction and coherent Detection(VEViD)reimagines a digital image as a spatially varying metaphoric“lightfield”and then subjects the field to the physical processes akin to diffraction and coherent detection.The term“Virtual”captures the deviation from the physical world.The light field is pixelated and the propagation imparts a phase with dependence on frequency which is different from the monotonically-increasing behavior of physical diffraction.Temporal frequencies exist in three bands corresponding to the RGB color channels of a digital image.The phase of the output,not the intensity,represents the output image.VEViD is a high-performance low-light-level and color enhancement tool that emerges from this paradigm.The algorithm is extremely fast,interpretable,and reduces to a compact and intuitively-appealing mathematical expression.We demonstrate image enhancement of 4k video at over 200 frames per second and show the utility of this physical algorithm in improving the accuracy of object detection in low-light conditions by neural networks.The application of VEViD to color enhancement is also demonstrated.展开更多
文摘Due to the climate-dependent nature of renewable energy sources(RESs),solving the optimal power flow(OPF)problem in power systems that integrate RESs,such as photovoltaic(PV)units and wind turbines(WTs),remains a significant challenge.To address this problem,this study presents an effective framework that incorporates solar and wind power generation.To manage the nonconvex and nonlinear characteristics of the OPF problem,a modified physics-inspired algorithm termed the Enhanced Coulomb’s and Franklin’s laws Algorithm(ECFA),is deployed.In the proposed OPF model,the power generated from RESs is considered a dependent variable,while voltages at buses equipped with RESs serve as decision variables.Real-time data on solar irradiation and wind speed are used to model the power outputs of PV units and WTs,respectively.Although the Coulomb’s and Franklin’s law algorithm(CFA)offers some advantages,it underperforms on complex optimization tasks compared to SSA,BA,SCA,ABC,and CFA.The enhanced version of the CFA improves the search process across the feasible space by incorporating diverse interaction methods and enhancing exploitation capabilities.The performance of the proposed ECFA is assessed through comprehensive comparisons with state-of-the-art methods for solving the OPF problem.
基金Parker Center for Cancer Immunotherapy(PICI),Grant No.20163828,and by the Office of Naval Research(ONR)Multi-disciplinary University Research Initiatives(MURI)program on Optical Computing Award Number N00014-14-1-0505.
文摘The history of computing started with analog computers consisting of physical devices performing specialized functions such as predicting the position of astronomical bodies and the trajectory of cannon balls.In modern times,this idea has been extended,for example,to ultrafast nonlinear optics serving as a surrogate analog computer to probe the behavior of complex phenomena such as rogue waves.Here we discuss a new paradigm where physical phenomena coded as an algorithm perform computational imaging tasks.Specifically,diffraction followed by coherent detection becomes an image enhancement tool.Vision Enhancement via Virtual diffraction and coherent Detection(VEViD)reimagines a digital image as a spatially varying metaphoric“lightfield”and then subjects the field to the physical processes akin to diffraction and coherent detection.The term“Virtual”captures the deviation from the physical world.The light field is pixelated and the propagation imparts a phase with dependence on frequency which is different from the monotonically-increasing behavior of physical diffraction.Temporal frequencies exist in three bands corresponding to the RGB color channels of a digital image.The phase of the output,not the intensity,represents the output image.VEViD is a high-performance low-light-level and color enhancement tool that emerges from this paradigm.The algorithm is extremely fast,interpretable,and reduces to a compact and intuitively-appealing mathematical expression.We demonstrate image enhancement of 4k video at over 200 frames per second and show the utility of this physical algorithm in improving the accuracy of object detection in low-light conditions by neural networks.The application of VEViD to color enhancement is also demonstrated.
