This paper proposes a voltage source converter (VSC) -based AC-DC hybrid distribution system (HDS) resilient model to mitigate power outages caused by wildfires. Before a wildfire happens, the public-safety power shut...This paper proposes a voltage source converter (VSC) -based AC-DC hybrid distribution system (HDS) resilient model to mitigate power outages caused by wildfires. Before a wildfire happens, the public-safety power shutoff (PSPS) strategy is applied to actively cut some vulnerable lines which may easily cause wildfires, and reinforce some lines that are connected to critical loads. To mitigate load shedding caused by active line disconnection in the PSPS strategy, network reconfiguration is applied before the wildfire occurrence. During the restoration period, repair crews (RCs) repair faulted lines, and network reconfiguration is also taken into consideration in the recovery strategy to pick up critical loads. Since there exists possible errors in the wildfire prediction, several different scenarios of wildfire occurrence have been taken into consideration, leading to the proposition of a stochastic multi-period resilient model for the VSC-based AC-DC HDS. To accelerate the computational performance, a progressive hedging algorithm has been applied to solve the stochastic model which can be written as a mixed-integer linear program. The proposed model is verified on a 106-bus AC-DC HDS under wildfire conditions, and the result shows the proposed model not only can improve the system resilience but also accelerate computational speed.展开更多
Robotic fish actuated by smart materials has attracted extensive attention and has been widely used in many applications.In this study,a robotic fish actuated by dielectric elastomer(DE)films is proposed.The tensile b...Robotic fish actuated by smart materials has attracted extensive attention and has been widely used in many applications.In this study,a robotic fish actuated by dielectric elastomer(DE)films is proposed.The tensile behaviours of DE film VHB4905 are studied,and the Ogden constitutive equation is employed to describe the stress‐strain behaviour of the DE film.The fabrication processes of the robotic fish,including prestretching treatment of the DE films,electrode coating with carbon paste,and waterproof treatment,are illustrated in detail.The dynamic response of the fabricated DE actuators under different excitation voltages is tested based on the experimental setup.Experimental results show that the first‐order natural frequencies of the obtained DE actuator in air is 4.05 Hz.Finally,the swimming performances of the proposed robotic fish at different driving levels are demonstrated,and it achieves an average swimming speed of 20.38 mm/s,with a driving voltage of 5kV at 0.8 Hz.展开更多
The geometric designs of MEMS devices can profoundly impact their physical properties and eventual performances.However,it is challenging for researchers to rationally consider a large number of possible designs,as it...The geometric designs of MEMS devices can profoundly impact their physical properties and eventual performances.However,it is challenging for researchers to rationally consider a large number of possible designs,as it would be very time-and resource-consuming to study all these cases using numerical simulation.In this paper,we report the use of deep learning techniques to accelerate the MEMS design cycle by quickly and accurately predicting the physical properties of numerous design candidates with vastly different geometric features.Design candidates are represented in a nonparameterized,topologically unconstrained form using pixelated black-and-white images.After sufficient training,a deep neural network can quickly calculate the physical properties of interest with good accuracy without using conventional numerical tools such as finite element analysis.As an example,we apply our deep learning approach in the prediction of the modal frequency and quality factor of disk-shaped microscale resonators.With reasonable training,our deep learning neural network becomes a high-speed,high-accuracy calculator:it can identify the flexural mode frequency and the quality factor 4.6×10^(3)times and 2.6×10^(4)times faster,respectively,than conventional numerical simulation packages,with good accuracies of 98.8±1.6%and 96.8±3.1%,respectively.When simultaneously predicting the frequency and the quality factor,up to~96.0%of the total computation time can be saved during the design process.The proposed technique can rapidly screen over thousands of design candidates and promotes experience-free and data-driven MEMS structural designs.展开更多
基金supported in part by National Key Research and Development Program of China(2022YFA1004600)in part by the National Natural Science Foundation of China(51977166,52277123)in part by the Natural Science Foundation of Shaanxi Province(2022JC-19)。
文摘This paper proposes a voltage source converter (VSC) -based AC-DC hybrid distribution system (HDS) resilient model to mitigate power outages caused by wildfires. Before a wildfire happens, the public-safety power shutoff (PSPS) strategy is applied to actively cut some vulnerable lines which may easily cause wildfires, and reinforce some lines that are connected to critical loads. To mitigate load shedding caused by active line disconnection in the PSPS strategy, network reconfiguration is applied before the wildfire occurrence. During the restoration period, repair crews (RCs) repair faulted lines, and network reconfiguration is also taken into consideration in the recovery strategy to pick up critical loads. Since there exists possible errors in the wildfire prediction, several different scenarios of wildfire occurrence have been taken into consideration, leading to the proposition of a stochastic multi-period resilient model for the VSC-based AC-DC HDS. To accelerate the computational performance, a progressive hedging algorithm has been applied to solve the stochastic model which can be written as a mixed-integer linear program. The proposed model is verified on a 106-bus AC-DC HDS under wildfire conditions, and the result shows the proposed model not only can improve the system resilience but also accelerate computational speed.
基金supported by the Zhejiang Provincial Natural Science Foundation of China(Nos.LGF21E050002&LY21F030003)Fundamental Research Funds for the Provincial Universities of Zhejiang(No.SJLY2021014)+2 种基金Open Research Project of the State Key Laboratory of Industrial Control Technology,Zhejiang University(No.ICT2022B22)Projects in Science and Technique Plans of Ningbo City(No.2019B10100)General Scientific Research Project of Education Department of Zhejiang Province(No.Y201839158).
文摘Robotic fish actuated by smart materials has attracted extensive attention and has been widely used in many applications.In this study,a robotic fish actuated by dielectric elastomer(DE)films is proposed.The tensile behaviours of DE film VHB4905 are studied,and the Ogden constitutive equation is employed to describe the stress‐strain behaviour of the DE film.The fabrication processes of the robotic fish,including prestretching treatment of the DE films,electrode coating with carbon paste,and waterproof treatment,are illustrated in detail.The dynamic response of the fabricated DE actuators under different excitation voltages is tested based on the experimental setup.Experimental results show that the first‐order natural frequencies of the obtained DE actuator in air is 4.05 Hz.Finally,the swimming performances of the proposed robotic fish at different driving levels are demonstrated,and it achieves an average swimming speed of 20.38 mm/s,with a driving voltage of 5kV at 0.8 Hz.
文摘The geometric designs of MEMS devices can profoundly impact their physical properties and eventual performances.However,it is challenging for researchers to rationally consider a large number of possible designs,as it would be very time-and resource-consuming to study all these cases using numerical simulation.In this paper,we report the use of deep learning techniques to accelerate the MEMS design cycle by quickly and accurately predicting the physical properties of numerous design candidates with vastly different geometric features.Design candidates are represented in a nonparameterized,topologically unconstrained form using pixelated black-and-white images.After sufficient training,a deep neural network can quickly calculate the physical properties of interest with good accuracy without using conventional numerical tools such as finite element analysis.As an example,we apply our deep learning approach in the prediction of the modal frequency and quality factor of disk-shaped microscale resonators.With reasonable training,our deep learning neural network becomes a high-speed,high-accuracy calculator:it can identify the flexural mode frequency and the quality factor 4.6×10^(3)times and 2.6×10^(4)times faster,respectively,than conventional numerical simulation packages,with good accuracies of 98.8±1.6%and 96.8±3.1%,respectively.When simultaneously predicting the frequency and the quality factor,up to~96.0%of the total computation time can be saved during the design process.The proposed technique can rapidly screen over thousands of design candidates and promotes experience-free and data-driven MEMS structural designs.
