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State grid lightning stroke prediction method

A prediction method and power grid technology, applied in the field of machine learning, can solve problems that affect the normal operation of the power system, high power tower heights, and inconvenience to residents' lives, so as to facilitate computer identification, reduce the loss of manpower and material resources, and achieve excellent performance and robustness. sticky effect

Pending Publication Date: 2020-06-12
杭州华网信息技术有限公司
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AI Technical Summary

Problems solved by technology

However, because many transmission lines are laid in the wild, and the height of the power tower itself is high, it is often struck by lightning, causing the line to trip and be damaged, which seriously affects the normal operation of the power system and causes inconvenience to residents' lives.
At present, in the face of the lightning strike risk that the State Grid may suffer, the main measures are still to strengthen lightning protection measures, strengthen the strength of the transmission line itself, and take some emergency measures to deal with it. Early warning of the possibility of lightning strike risks in power transmission channels

Method used

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Embodiment

[0048] Such as figure 1 As shown, this embodiment provides a lightning strike prediction method for the State Grid, which is used to predict the lightning strike risk for the power towers, power transmission lines and power transmission channels of the State Grid, including the following steps:

[0049] Collection of historical information, including historical deployment information of the on-site maintenance department, historical information of lightning, historical information of terrain and landform, historical information of tower poles and line ledgers, and historical weather information. Information, fault sub-information; Meteorological history information includes weather condition sub-information, temperature sub-information, humidity sub-information, wind speed sub-information, wind direction sub-information.

[0050] Organize historical information to form complete historical data. Complete historical data has several dimensions, and dimensions are data characterist...

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Abstract

The invention discloses a national power grid lightning stroke prediction method, and relates to the field of machine learning, wherein the method is used for performing lightning stroke risk prediction on a power transmission line and a power tower pole of a state grid. The method comprises the following steps that historical information is collected, complete historical data is formed, the dimension of the complete historical data is a data feature, the data feature is selected, a Catboost model is trained, current information is collected, the complete current data is formed, and lightningstroke risk prediction is conducted through the trained Catboost model. The method provided by the invention can predict the probability and risk level of lightning stroke risks of the power tower pole and the power transmission line, and deploy countermeasures in advance.

Description

【Technical field】 [0001] The invention relates to the field of machine learning, in particular to a lightning strike prediction method for the State Grid. 【Background technique】 [0002] With the continuous development and expansion of my country's power industry, the scale of transmission lines of the State Grid is also expanding. However, because many power transmission lines are laid in the wild, and the height of the power tower itself is high, it is often struck by lightning, causing tripping and damage to the line, seriously affecting the normal operation of the power system, and causing inconvenience to residents' lives. At present, in the face of the lightning strike risk that the State Grid may suffer, the main measures are still to strengthen lightning protection measures, strengthen the strength of the transmission line itself, and take some emergency measures to deal with it. Early warning of the possibility of lightning strike risk in the power transmission cha...

Claims

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Application Information

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IPC IPC(8): G06N3/12G06N20/00G06Q10/04G06Q10/06G06Q50/06G06F16/174
CPCG06Q10/0635G06Q10/04G06N3/126G06F16/174G06N20/00G06Q50/06
Inventor 吴和俊熊志刚王敏康陆宇宁程田宝胡驰远
Owner 杭州华网信息技术有限公司
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