The abnormal 2020 Meiyu season caused the worst disasters over the Yangtze River Valley in recent decades. Of these, the Sichuan Basin (SCB) and its surrounding regions were one of the most severely affected areas. Disastrous weather frequently occurs in these regions, with a large proportion of it closely related to the southwest vortices (SWVs). In order to further the understanding of SWV generation, this study investigated the formation mechanisms of a quasi-stationary SWV (by using two sets of vorticity budgets), which caused torrential rainfall (resulting in flash floods in Sichuan and Chongqing), lightning activities (causing tripping incidents of transmission lines in Sichuan) and strong winds (leading to shutting down of wind turbines in Hubei). Results showed that the SWV was generated in a favorable background environment, during which an upper-tropospheric divergence and a middle-tropospheric warm advection appeared over the SCB. Trajectory analyses and vorticity budget showed that the air particles that came from the lower troposphere of the regions south of the Tibetan Plateau dominated the SWV formation. These air particles experienced notable ascending during which an increase in their cyclonic vorticity occurred mainly due to convergence-related stretching, whereas, tilting mainly decelerated this increase. The air particles sourced from the areas within the key region of the SWV and areas northeast of the key region were the second dominant factor for the vortex formation. Overall, for the air particles that formed the SWV, their most rapid changes of vorticity and divergence appeared in the period 24 h before SWV formation, implying that this was the critical period for the SWV generation.
TDFA-band (2-µm waveband) has been considered as a promising optical window for the next generation of optical communication and computing.Absorption modulation, one of the fundamental reconfigurable manipulations, is essential for large scale photonic integrated circuits.However, few efforts have been involved in exploring absorption modulation at TDFA-band.In this work, variable optical attenuators (VOAs) for TDFA-band wavelengths were designed and fabricated based on a silicon-on-insulator (SOI) platform.By embedding a short PIN junction length of 200 µm into the waveguide, the fabricated VOA exhibits a high modulation depth of 40.49dB at 2.2 V and has a fast response time (10 ns) induced by the plasma dispersion effect.Combining the Fabry-Perot cavity effect and plasma dispersion effect of silicon, the attenuator could achieve a maximum attenuation of more than 50 dB.These results promote the 2-µm waveband silicon photonic integration and are expected to the future use of photonic attenuators in crosstalk suppression, optical modulation, and optical channel equalization.
Cold wave events (CWEs) often cause major economic losses and serious casualties in the cold seasons, making CWEs among the most significant types of disastrous weather. Previous studies have mainly focused on disasters due to abrupt drops in surface temperatures, with less discussion of the strong winds associated with CWEs. Based on an intense CWE that occurred in late December 2020, we investigated the evolutionary mechanisms of the associated strong winds in terms of kinetic energy (KE) budget and evaluated the effects of this CWE on wind power production based on quantitative comparisons with the mean state. The results showed that the CWE occurred under favorable background conditions, which were characterized by a southward-moving transversal trough and a southeastward-moving shortwave trough in the middle troposphere. The surface high ridge that formed around Lake Baikal and the cold front around the southern periphery of the ridge were key factors related to the CWE evolution. The positive work carried out on the horizontal wind by the pressure gradient force that linked a lower tropospheric high-pressure ridge inland and a low-pressure trough offshore and the downward momentum transportation due to the descending motions behind the cold front dominated the enhancement and sustainment of the CWE-associated strong winds. The CWE contributed to wind power production by 1) increasing the wind power density (by an average of ∼1.05-fold) and 2) improving the availability of the wind to generate power, as it reduced the percentage of zero wind power generation by ∼6.4%, while maintaining the high-wind-velocity cut-out percentage.
Power fault detection is a method of detecting the presence of abnormal faults in a transmission line. Due to the yearly increase in electricity consumption and the complexity of transmission lines, line maintenance cannot be done efficiently by manual labor. In order to solve this technical problem, a new fault detection method based on YOLO series models for power transmission line is proposed in this paper, introducing drone inspection technology into the field of electric power inspection to help inspectors complete complex inspection tasks. In this paper, the data set collected by UAV is annotated, and three YOLO series target detection algorithms, YOLOv3, YOLOv5, and YOLOx are used to recognize the image and text of pylons, insulator pollution, and corrosion of insulator steel pin and steel cap. The recognition results are compared among the three algorithms. The experimental results show that YOLOv5 has the best fault detection performance on the dataset, and it achieves a metric mAP50 of 90.59% the proposed method is effective and feasible.
The non-stationarity and stochastic nature of wind power bring difficult challenges to large-scale grid-connected of wind power. The ultra-short-term forecasting of wind power is used for balancing load and the optimal optimization of spinning reserve, which has high requirements for prediction accuracy. The neural network can solve the problem of feature selection, but in the task of wind power prediction, it is of great concern to find the optimal input features and model structure by mining physical correlation among features. Inspired by the physical formula for wind power, an uncertain factor is calculated, which caused by both environmental disturbance and wind turbine state changes. This paper proposes a method to predict ultra-short-term wind power, which using the features associated with wind power and the uncertain factors. Time series features are predicted through the Gated Recurrent Unit (GRU) Neural Network, and finally all the features were fused to form a hybrid neural network. The effectiveness of the proposed method has been confirmed on the real datasets derived from a wind field. Compared with the conventional time series dependent methods, our proposed method shows more reasonable results in terms of accuracy and availability.
Abstract Explosive extratropical cyclone (EEC) is the main disastrous weather system over the ocean and offshore areas in the cold season. As a type of vertically deep system, after decades of studies, key features of EECs' vertical extents still remain vague. Based on a reasonably simulated entire‐troposphere‐thick EEC, this study analyzes variation of the EEC's vertical extent and investigates governing mechanisms for its vertical extending. Main findings are as follows: (a) the EEC's vertical extent showed consistent variation features with its central sea level pressure and lower‐level vorticity (correlation coefficients were ~0.9), whereas its relationship with EEC's maximum surface wind was not significant; (b) EEC's upward extending featured strong ascending motion and rapid cyclonic‐vorticity enhancement at the top level of the cyclone and obvious inflow (convergence) in the lower troposphere. (c) vorticity budget at the EEC's top level shows that net import transport of cyclonic vorticity (by westerly and northwesterly winds) from the trough west of the cyclone dominated its upward extending, and upward transport of cyclonic vorticity from levels below the cyclone's top level acted as the second dominant factor. In contrast, divergence‐related vertical shrinking around the EEC's top level was the most detrimental factor for the cyclone's upward extending.
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