Power grid strong wind disaster early warning method and device based on deep learning
A deep learning and high wind technology, applied in neural learning methods, forecasting, biological neural network models, etc., can solve the problems of classification and lack of systematicness, improve pertinence, improve the accuracy of early warning and forecast, and improve the level of safe operation of power grids. The effect of electricity reliability in society
Inactive Publication Date: 2021-06-22
STATE GRID HENAN ELECTRIC POWER ELECTRIC POWER SCI RES INST +2
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Problems solved by technology
Existing strong wind forecasting and early warning technologies are mostly aimed at public services, and there are few professional strong wind disaster early warning services for power grid production, and there is no classification for wind damage at different production stages of the power grid, and there is no systematic strong wind early warning and forecasting method and graded forecasting products
Method used
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[0050] The application will be further described below in conjunction with the accompanying drawings. The following examples are only used to illustrate the technical solutions of the present invention more clearly, but not to limit the protection scope of the present application.
[0051] Such as figure 1 , a deep learning-based power grid disaster early warning method steps are as follows:
[0052] Step 1, collecting radar data to form input data.
[0053] Specifically, in step 1, the radar data includes, but is not limited to, the maximum echo intensity obtained from the current body sweep echo, the height corresponding to the maximum echo intensity, the height of the convective cell echo top, the time-varying echo intensity, Radial velocity, geometric center position, cloud water content, cloud shape, wind shear, near-earth humidity, wind speed corresponding to the automatic station, mesocyclone, albedo, latitude and longitude.
[0054] In the wind speed prediction stag...
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The invention discloses a power grid strong wind disaster early warning method and device based on deep learning, and the method comprises the steps: collecting radar data as input data, inputting the input data into a pre-trained wind speed prediction model, and outputting wind speed information, wherein the wind speed information is used as an input element of a pre-trained wind disaster early warning model; enabling the wind disaster early warning model to combine the automatic meteorological station data in the same period, correcting the wind speed information, and outputting the wind speed revision information after the error generated by the micro-topographic information is eliminated, carrying out graded early warning on the strong wind disaster, and providing graded strong wind position early warning in real time, thereby solving the problem of refined forecasting of the power grid wind disaster, wherein strong convection strong wind forms a 1km * 1km spatial resolution, and a forecasting result is obtained within 0-120 minutes; and forming a business operation software system, so the early warning and forecasting precision of strong wind disasters is further improved, the pertinence and emergency rescue efficiency of disaster prevention and reduction of a power grid and equipment are improved, and the safe operation level of the power grid and the social power utilization reliability are improved.
Description
technical field [0001] The invention relates to the technical field of weather forecasting, and more specifically, to a method and device for early warning of strong wind disasters in power grids based on deep learning. Background technique [0002] The normal operation of the power grid is closely related to meteorological conditions. Meteorological changes have a significant impact on the peak time and peak load of the power grid, and meteorological disasters will also threaten the safe operation of the power grid. The stability of the power grid system due to natural disasters is second only to Therefore, effective and early prediction and early warning of the meteorological environment and meteorological disasters of power grid operation are an effective way to achieve disaster prevention and mitigation, and are of great significance. [0003] Among the meteorological disasters, strong winds have the most serious impact on the transmission lines in the power grid system....
Claims
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Login to View More IPC IPC(8): G06Q10/04G06Q50/06G06Q50/26G06N3/04G06N3/08
CPCG06Q10/04G06Q50/06G06Q50/26G06N3/08G06N3/047Y02A90/10
Inventor 梁允姚德贵李哲郭志民卢明王超刘善峰王津宇王磊李帅苑司坤高阳
Owner STATE GRID HENAN ELECTRIC POWER ELECTRIC POWER SCI RES INST