Understanding moisture information ahead of tropical cyclone (TC) convection is very important for predicting TC track, intensity, and precipitation. The advanced Himawari imager onboard the Japanese Himawari‐8/‐9 satellite can provide high spatial and temporal resolution moisture information. Three‐layered precipitable water (LPW) with its three water vapor absorption infrared bands can be assimilated to generate better understanding and prediction of TC evolution. The impacts of LPW assimilation in the Weather Research and Forecasting model with nine combinations of physical parameterization schemes, including three cumulus parameterization (CP) and three microphysics parameterization (MP) schemes on TC prediction, have been comprehensively analyzed using Typhoon Hato as a case study. The results indicate that LPW assimilation reduces the average track error and speed up TC movement by better adjustment of the atmospheric circulation fields via changing the vertical structure of moisture and thermal profile. The track forecasts retain sensitivity to CP schemes after LPW assimilation. Also, LPW assimilation improves TC intensity prediction because the latent heat release process is accurately adjusted. It has been revealed that LPW assimilation can weaken the intensity sensitivity to MP schemes more than to CP schemes. Skill scores were used to evaluate precipitation forecasts after Hato's landfall. The results indicate that heavy precipitation forecasts are more sensitive to the choice of MP schemes. After LPW assimilation, the equitable threat scores among different results become similar and all forecast skills are increased. In addition, group statistic results with different initial time show the same conclusions.
A novel high-sensitivity fiber-optic temperature sensing system based on the optical pulse correlation principle is proposed. The optical pulse correlation state corresponding to the time drift in fiber-optic transmission lines is detected by a second harmonic generation (SHG) crystal. This sensing system is combined with 3- and 100-m-long monitoring fibers using a time-division multiplexer (TDM) combination technique. By using the linear trend-line method to combine the correlation values of short and long monitoring fibers, a high-temperature sensitivity of 0.001 °C/mV and an approximatly 20 °C dynamic measurable range are successfully achieved.
Summary
In recent years, wildfire disasters have occurred frequently in China, with currently more than 70 000 wildfires annually. During the high wildfire disaster period, multiple transmission lines readily trip simultaneously. This poses a serious threat to the safe operation of a large power grid. The probability that multiple transmission lines will trip is far greater than for only a single transmission line. The degree of risk to the power grid needs to be analyzed under multiple tripping combinations of transmission lines caused by wildfire disasters. However, when a large number of wildfire points is involved, the number of such transmission lines tripping combinations become huge, and rapid analysis of the risk to the power grid in these conditions is very difficult. In the present study, the probability distribution characteristics of power grid fault under wildfire disaster were analyzed using historical data on transmission line wildfires and wildfire tripping. A Markov chain Monte Carlo sampling method is proposed to match the probability distribution characteristics of transmission lines tripping under wildfire disasters. The precision of multi‐fault combination sampling is improved effectively. A quantitative power grid risk analysis method is put forward to estimate the sort of transmission lines risk under wildfire disasters efficiently. This gives scientifically based guidelines for reducing the risk to a power grid from widespread wildfire.
With the development of society, electricity has become an indispensable material, and the reliability of power grid has become more and more important. The ice-covered power grid will lead to accidents such as broken poles and other accidents, which seriously threaten the reliability of the power grid and safe operation. Therefore, a simple and efficient detection method of ice-covered power grid is urgently needed. To solve this problem, based on the good performance of convolution neural network, this paper applies it to the detection of power network icing. A classification method of power network icing detection image based on convolution neural network is proposed, which can effectively classify and recognize power network icing image. In addition, in view of the shortcomings of convolution neural network algorithm, this paper proposes a hybrid classification model combining convolution neural network and support vector machine. Firstly, the convolution neural network is used to extract features, and then the support vector machine is used to replace the softmax layer of the convolution neural network to realize the classification of ice-covered detection images. The simulation results show that it is feasible to use convolution neural network to classify the detection images of ice-covered power grid. Compared with convolution neural network, the hybrid classification model of convolution neural network and support vector machine proposed in this paper has better image classification effect, and further improves the classification performance of detection image of ice-covered power grid, and ensures the reliability and safe operation of power grid.
The useful application of optical pulse correlation sensor for wide region quasidistributed fiber strain measurement is investigated. Using region separation techniques of wavelength multiplexing with FBGs and time multiplexing with intensity partial reflectors, the sensor measures the correlations between reference pulses and monitoring pulses from several cascadable selected sensing regions. This novel sensing system can select the regions and obtain the distributed strain information in any desired sensing region.
Summary
Photovoltaic (PV) power forecasting is of great significance to the grid connection and safe operation of PV plants. Problems such as complex weather conditions, numerous weather types, and limited weather classification methods make such forecasting a highly challenging endeavor. The point forecasting model is limited to apply due to the lack of error information. To solve above problems, a novel interval forecasting method based on generalized weather conditions is proposed. The uncertainty of PV power under different weather conditions is first analyzed, then a generalized weather classification method based on solar irradiance reduction index K is performed. Next, a PV power forecasting multi‐model is established based on the extreme learning machine under different generalized weather types. The confidence interval of forecasted PV power is determined by kernel density estimation. Comparative experiments demonstrate the effectiveness of the proposed method in terms of training time, model performance, and interval accuracy.
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