In the scenario of a large-scale power outage after an extreme disaster,such as a severe ice storm,the distribution system with multiple distributed generations(DGs)is of great value for post-disaster load restoration...In the scenario of a large-scale power outage after an extreme disaster,such as a severe ice storm,the distribution system with multiple distributed generations(DGs)is of great value for post-disaster load restoration.However,due to the uncertainty of renewable energy output and the controllability of different DGs,effective utilization of these DGs becomes an urgent issue.To address the uncertainty of renewable energy output under disasters,this paper proposes a multi-stage optimization restoration strategy for a distribution system with distributed resources,such as a mobile energy storage system(MESS),integrated energy system(IES),and photovoltaic(PV).In particular,this study extracts historical data features by utilizing improved conditional generative adversarial nets(CGAN)to generate PV output scenarios.Subsequently,according to the dynamic and static characteristics of the power supply,the time sequence model of each distributed resource is established.On the premise of meeting the constraints of emergency microgrids and load characteristics,the optimal MESS configuration scheme and controllable DGs output are achieved to maximize the restoration of the power supply for critical loads.Finally,the case studies of IEEE 33-bus and PE&G 69-bus systems demonstrate the effectiveness of the proposed model in enhancing the resilience of the distribution system.展开更多
Flame front structure is one of the most fundamental characteristics and, hence, vital for understanding combustion processes. Measuring flame front structure in turbulent flames usually needs laser-based diagnostic t...Flame front structure is one of the most fundamental characteristics and, hence, vital for understanding combustion processes. Measuring flame front structure in turbulent flames usually needs laser-based diagnostic techniques, mostly planar laser-induced fluorescence (PLIF). The equipment of PLIF, burdened with lasers, is often too sophisticated to be configured in harsh environments. Here, to shed the burden, we propose a deep neural network-based method to generate the structures of flame fronts using line-of-sight CH* chemiluminescence that can be obtained without the use of lasers. A conditional generative adversarial network (CGAN) was trained by simultaneously recording CH-PLIF and chemiluminescence images of turbulent premixed methane/air flames. Two distinct generators of the C-GAN, namely Resnet and U-net, were evaluated. The former net performs better in this study in terms of both generating snap-shot images and statistics over multiple images. For chemiluminescence imaging, the selection of the camera’s gate width produces a trade-off between the signal-to-noise (SNR) ratio and the temporal resolution. The trained C-GAN model can generate CH-PLIF images from the chemiluminescence images with an accuracy of over 91% at a Reynolds number of 5000, and the flame surface density at a higher Reynolds number of 10,000 can also be effectively estimated by the model. This new method has the potential to achieve the flame characteristics without the use of laser and significantly simplify the diagnosing system, also with the potential for high-speed flame diagnostics.展开更多
基金supported by the Fundamental Research Funds for the Central Universities 2023JC008.
文摘In the scenario of a large-scale power outage after an extreme disaster,such as a severe ice storm,the distribution system with multiple distributed generations(DGs)is of great value for post-disaster load restoration.However,due to the uncertainty of renewable energy output and the controllability of different DGs,effective utilization of these DGs becomes an urgent issue.To address the uncertainty of renewable energy output under disasters,this paper proposes a multi-stage optimization restoration strategy for a distribution system with distributed resources,such as a mobile energy storage system(MESS),integrated energy system(IES),and photovoltaic(PV).In particular,this study extracts historical data features by utilizing improved conditional generative adversarial nets(CGAN)to generate PV output scenarios.Subsequently,according to the dynamic and static characteristics of the power supply,the time sequence model of each distributed resource is established.On the premise of meeting the constraints of emergency microgrids and load characteristics,the optimal MESS configuration scheme and controllable DGs output are achieved to maximize the restoration of the power supply for critical loads.Finally,the case studies of IEEE 33-bus and PE&G 69-bus systems demonstrate the effectiveness of the proposed model in enhancing the resilience of the distribution system.
基金supported by the National Natural Science Foundation of China(Grant No.52176169,52276164)。
文摘Flame front structure is one of the most fundamental characteristics and, hence, vital for understanding combustion processes. Measuring flame front structure in turbulent flames usually needs laser-based diagnostic techniques, mostly planar laser-induced fluorescence (PLIF). The equipment of PLIF, burdened with lasers, is often too sophisticated to be configured in harsh environments. Here, to shed the burden, we propose a deep neural network-based method to generate the structures of flame fronts using line-of-sight CH* chemiluminescence that can be obtained without the use of lasers. A conditional generative adversarial network (CGAN) was trained by simultaneously recording CH-PLIF and chemiluminescence images of turbulent premixed methane/air flames. Two distinct generators of the C-GAN, namely Resnet and U-net, were evaluated. The former net performs better in this study in terms of both generating snap-shot images and statistics over multiple images. For chemiluminescence imaging, the selection of the camera’s gate width produces a trade-off between the signal-to-noise (SNR) ratio and the temporal resolution. The trained C-GAN model can generate CH-PLIF images from the chemiluminescence images with an accuracy of over 91% at a Reynolds number of 5000, and the flame surface density at a higher Reynolds number of 10,000 can also be effectively estimated by the model. This new method has the potential to achieve the flame characteristics without the use of laser and significantly simplify the diagnosing system, also with the potential for high-speed flame diagnostics.
