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According to the data of the national transmission line icing trip accidents, this paper uses data mining technology to preprocess the trip data, and uses data analysis technology to mine and analyze the main influencing factors of the line icing trip, including meteorological factors, micro terrain factors and the combination of line parameters and meteorological parameters, etc The main reason and mechanism of transmission line icing trip are analyzed.

Deep-learning based Super-Resolution (SR) methods have exhibited promising performance under non-blind setting where blur kernel is known. However, blur kernels of Low-Resolution (LR) images in different practical applications are usually unknown. It may lead to significant performance drop when degradation process of training images deviates from that of real images. In this paper, we propose a novel blind SR framework to super-resolve LR images degraded by arbitrary blur kernel with accurate kernel estimation in frequency domain. To our best knowledge, this is the first deep learning method which conducts blur kernel estimation in frequency domain. Specifically, we first demonstrate that feature representation in frequency domain is more conducive for blur kernel reconstruction than in spatial domain. Next, we present a Spectrum-to-Kernel (S$2$K) network to estimate general blur kernels in diverse forms. We use a Conditional GAN (CGAN) combined with SR-oriented optimization target to learn the end-to-end translation from degraded images' spectra to unknown kernels. Extensive experiments on both synthetic and real-world images demonstrate that our proposed method sufficiently reduces blur kernel estimation error, thus enables the off-the-shelf non-blind SR methods to work under blind setting effectively, and achieves superior performance over state-of-the-art blind SR methods, averagely by 1.39dB, 0.48dB on commom blind SR setting (with Gaussian kernels) for scales $2\times$ and $4\times$, respectively., Comment: Accepted to NeurIPS 2021

Cascade interfaces for the new energy accommodation can be frequently found in regional power grids. To achieve both maximization goals for the new energy accommodation and the interface utilization, it is necessary to study the multi energy coordination and optimization technology for power grids with complex cascade interfaces. Therefore, in this paper the intelligence grid operation system for regional power grids was built. The modified model based on risk control about ultra-short-term prediction for new energy and the model of multi-objective joint optimization for power grids with complex cascade interfaces were established. The system effectiveness was verified by the application example of one partial grid in Northwest China. The multi energy coordination and optimization control was realized for the grid with complex cascade interfaces. On the premise of ensuring the safety of power grid, the both maximization goals for new energy accommodation and interface utilization were achieved. This paper was important for the development of multi energy coordination and optimization technology in similar power grids with complex cascade interfaces.

The inherent intermittency and fluctuation of new energy bring great challenges to real-time dispatching and the controls in a power system with high penetration of renewable energy. It is difficult for traditional real-time dispatching of the power grid to deal with the conflicts among the consumption of new energy, the efficiency of the power grid and the carrying capacity of dispatchers. By constructing an intelligent trading system, which can realize the automatic adjustment and control of the provincial geographic tie line plan, a multi-source trans-provincial coordinated optimization control technology was proposed to achieve the optimization goal of the maximum utilization rate of new energy in the regional power grid, the maximum utilization rate of new energy transmission sections and the minimum transaction costs among provinces. Results of the operation in Northwest power grid show that this technology can effectively improve the utilization rates of new energy in various provinces and relevant new energy transmission sections, and effectively reduce the pressure on the carrying capacity of dispatchers caused by frequent transaction requirements between provinces as well. In this paper, the operation data of a typical day in Northwest power grid was selected to show the principle and effect of the multi-source trans-provincial coordinated optimization control technology proposed.

Aiming at the “front door” protection problem compatible with electromagnetic pulse protection and signal transmission and reception, an X-band waveguide clipper based on periodic structure is designed. The periodic structure consists of frequency selection surface and semiconductor diodes[1], and loaded into the waveguide. The simulation results show that the waveguide clipper has the characteristics of “low pass and high resistance” in the energy domain.

Aiming at the problem of protection of strong electromagnetic protection compatible with signal transceiver and receiver, the mechanism of energy selective strong electromagnetic protection is analyzed. A new active electromagnetic pulse protection surface is designed and simulated. It has the characteristics of low pass and high resistance in the energy domain. Also, it combined with the design of band-pass frequency selective surface, low insertion loss and high shielding are realized.

Multi-target is a common situation in modern warfare. Extracting the radar characteristic of one of the targets has important theoretical and engineering significance when facing with the multi-target. This paper presents a method of multi-target radar characteristic extraction and rebuilding based on scattering center, which solves the problem of extracting one of the target radar characteristics under multi-target conditions. First, the classical radar imaging algorithm and scattering center extraction algorithm are briefly introduced. Then get the radar image and scattering center of the local target by window function, it depends on the location of two-dimensional radar image and scattering center. At last, rebuild the radar characteristic of the local target through the scattering center algorithm. The simulation results show that the method can effectively solve the problem of multi-target characteristic extraction; also it has a high precision.

The mapping relation between electromagnetic scattering characteristics of different targets has great theoretical and scientific value. This study uses CLEAN algorithm to get targets' scattering centers at first and then builds the mapping relation with the scattering centers. The numerical examples shows that the method has a high accuracy for parameter estimation and can effectively rebuild the scattering characteristics of targets.