CN112785138A - Method for carrying out three-span line monitoring analysis early warning based on numerical weather - Google Patents
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Abstract
The invention discloses a method for monitoring, analyzing and early warning three-span lines based on numerical weather, which comprises the steps of collecting power information and meteorological information; carrying out data preprocessing, and carrying out data exploration analysis and preprocessing on a data set formed by data acquisition; carrying out data numerical modeling, and analyzing the data numerical modeling by adopting a numerical algorithm; after the numerical modeling is completed, the model result is applied, weather data is judged and analyzed through the correlation result, and early warning information is issued according to the judgment result to remind a user to complete early warning work; the method improves the timeliness of the three-span line meteorological hidden danger investigation, shortens the time required by treatment, and advances the meteorological hidden danger early warning treatment period; the method comprises the steps of providing more accurate and reliable early warning information by using numerical model analysis, trying to analyze defects and meteorological data by collecting weather data and transmission line defect data to obtain defect types related to meteorological factors, and analyzing the incidence relation between the meteorological factors and the transmission line defects by using a data mining algorithm to ensure result accuracy.
Description
Technical Field
The invention belongs to the technical field of power transmission line inspection, and particularly relates to a method for monitoring, analyzing and early warning a three-span line based on numerical weather.
Background
With the rapid development of the industrialization process and the electric power industry, the electric power system plays an irreplaceable role in an energy supply and supply system, and the power transmission line is an important link for ensuring the long-distance energy transmission of the electric power system. Because the components of the power transmission line, such as wires, overhead ground wires, insulators, hardware fittings, towers, grounding devices and the like, are mostly exposed outside, the power transmission line is greatly damaged due to the occurrence of extremely severe natural disasters along with the alternation of seasons. The extremely severe weather of storm snow seriously threatens the safe operation of the power grid in China, and the local collapse of the power grid also occurs in serious areas. Although the probability of occurrence of severe weather is low, the probability of failure of the power transmission line is obviously increased under the severe weather condition. Once a transmission line fails, a large-area power failure phenomenon of a power grid can be caused, and great influence, even irreparable economic loss, is caused on modern life and production. And the faults on the transmission line are based on the premise of defects. Once the defect is not found or processed in time, the defect will be changed into a fault under the influence of the external environment, resulting in serious influence and loss.
Under the large background of climate change, the intensity and the occurrence frequency of severe weather and extreme weather events tend to be enhanced, how to deal with the meteorological disasters is one of the key problems which need to be solved urgently in the three-span power system, and the theory and the technology related to meteorological disaster early warning and risk prevention and control become long-term research hotspots in the field of electrical engineering. In the development process of the three-span power transmission system, abundant meteorological safety guarantee technologies are already accumulated. By utilizing years of observation data and meteorological disaster-causing mechanism analysis, more perfect defense measures have been considered in the aspects of power transmission line structure, insulation design and the like. However, power transmission line faults and line tripping events caused by weather still occur frequently, and the risk of system blackout still exists. The traditional safety protection theory and technology based on static state and equipment level can not completely meet the requirement of safe operation of the power system, and the knowledge of the meteorological risk of the three-span power system is not enough.
For the relationship between the defect occurrence of the three-span transmission line and meteorological factors, the conventional method is to judge by years of experience. The probability of certain defects on certain lines is relatively high when the specific weather is judged, and the defects on the lines need to be paid attention to in daily inspection.
According to the traditional technology, the risk level of the power transmission line is described by adopting parameters such as constant fault rate, outage time and the like which are counted for a long time according to the existing power grid reliability/risk analysis theory, and the change conditions of the risk level of the power transmission line along with time, external meteorological environment and the like cannot be truly reflected. In the running process of the power grid, the consideration of the external meteorological environment is only a single factor or a small amount of states, and the power transmission lines in most regions are subjected to the action of various meteorological environment factors all the year round, so that the conventional method can only reflect the average risk level of the long-term running of the power transmission lines.
Therefore, it is urgently needed to develop a method for monitoring, analyzing and early warning three-span lines based on numerical weather to solve the above problems.
Disclosure of Invention
In order to solve the problems in the background art, the invention provides a method for monitoring, analyzing and early warning three-span lines based on numerical weather.
In order to achieve the purpose, the invention provides the following technical scheme:
the method for monitoring, analyzing and early warning the three-span line based on numerical weather comprises the following steps:
s1, selectively collecting historical data from the power system and the weather station, and collecting the historical data from the weather station at regular time to form timing judgment data;
s2, preprocessing data, and performing data exploration analysis and preprocessing on a data set formed by data acquisition;
s3, carrying out data numerical modeling, analyzing the data numerical modeling by adopting a numerical algorithm, constructing a complete power grid meteorological risk index, rapidly identifying power grid equipment meteorological disasters by utilizing mutually-associated refined power meteorological data, and carrying out association analysis on each different defect and meteorological factor by evaluating power equipment level and power system level risks by combining an equipment fault probability prediction early warning model to obtain the relationship between the meteorological factor and the defect;
and S4, after the numerical modeling is completed, applying the model result, judging and analyzing the weather data through the correlation result, issuing early warning information to the judged result, reminding a user, and completing the early warning work.
Specifically, in step S2, the data exploration analysis and preprocessing includes data distribution analysis, data attribute reduction, data cleansing and attribute construction.
Compared with the prior art, the invention has the beneficial effects that:
by the aid of the method and the device, timeliness of three-span line meteorological hidden danger troubleshooting is improved, processing time is shortened, and early warning and processing periods of meteorological hidden dangers are advanced.
The method and the device provide more accurate and reliable early warning information by using numerical model analysis, try to analyze the defects and the meteorological data by acquiring the weather data and the defect data of the power transmission line, acquire defect types related to meteorological factors, analyze the incidence relation between the meteorological factors and the defects of the power transmission line by using a data mining algorithm, and guarantee the accuracy of results.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides the following technical scheme:
a method for monitoring, analyzing and early warning three-span lines based on numerical weather comprises the following steps:
1. data acquisition
Before analyzing the three-span line hidden danger based on numerical weather, firstly, acquiring service data. The historical data is selectively collected from the power system and a certain meteorological station, and the timing judgment data is collected from the meteorological station at regular time.
2. Data pre-processing
After the data acquisition process is finished, the sample classification of the meteorological data of the three-span line is actually finished, and the data preprocessing is to carry out deep screening processing on all monitoring data. And carrying out data exploration analysis and pretreatment on a data set formed by data acquisition, wherein the data exploration analysis and the pretreatment comprise data distribution analysis, data attribute stipulation, data cleaning and attribute construction.
3. Numerical modeling
After data preprocessing is completed, data numerical modeling is carried out by utilizing formed preprocessing results, a numerical algorithm is adopted to analyze the data, a complete power grid meteorological risk index needs to be constructed, power grid equipment meteorological disasters are rapidly identified by utilizing mutually-associated refined power meteorological data, an equipment fault probability prediction early warning model is combined, power equipment level and power system level risks are evaluated to carry out correlation analysis on different defects and meteorological factors, and the relation between the meteorological factors and the defects is obtained.
4. Analysis and study judgment
And after the numerical modeling is completed, applying a model result, judging and analyzing weather data through the correlation result, issuing early warning information on the judged result, reminding a user of key closing, and completing the numerical analysis and early warning service work of the three-span line.
The key points of the application are as follows:
analyzing a numerical meteorological model:
the traditional mode confirms the problem of lacking a time-varying fault characteristic mathematical model of the power transmission line based on manual observation, and the numerical analysis forecasting technology of the patent pertinently provides a weather-related three-span line continuous time fault rate model. The historical synchronous fault rate is taken as a sample, and a Fourier function, a Gaussian function and a Weibull function are respectively used for simulating a fault rate time function, so that a three-span line time-dependent fault rate model and a line forced outage time probability distribution model which reflect different meteorological disaster characteristics are constructed, and better conditions are created for implementing meteorological risk early warning and prevention and control of the power transmission line.
The speed of patrolling and examining to three-span meteorological hidden danger is promoted greatly, and after the numerical meteorological model analysis method is used, complete power grid meteorological risk indexes are established, and the power grid equipment meteorological disasters are identified rapidly by using the correlated refined power meteorological data, and the forecasting and early warning model is combined with the equipment fault probability, so that the predictability is improved by 65% compared with the traditional measurement and estimation mode.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (2)
1. A method for monitoring, analyzing and early warning three-span lines based on numerical weather is characterized by comprising the following steps:
s1, selectively collecting historical data from the power system and the weather station, and collecting the historical data from the weather station at regular time to form timing judgment data;
s2, preprocessing data, and performing data exploration analysis and preprocessing on a data set formed by data acquisition;
s3, carrying out data numerical modeling, analyzing the data numerical modeling by adopting a numerical algorithm, constructing a complete power grid meteorological risk index, rapidly identifying power grid equipment meteorological disasters by utilizing mutually-associated refined power meteorological data, and carrying out association analysis on each different defect and meteorological factor by evaluating power equipment level and power system level risks by combining an equipment fault probability prediction early warning model to obtain the relationship between the meteorological factor and the defect;
and S4, after the numerical modeling is completed, applying the model result, judging and analyzing the weather data through the correlation result, issuing early warning information to the judged result, reminding a user, and completing the early warning work.
2. The method for three-span line monitoring, analyzing and early warning based on numerical weather of claim 1, wherein in step S2, the data exploration analysis and preprocessing comprises data distribution analysis, data attribute reduction, data cleaning and attribute construction.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113642986A (en) * | 2021-08-02 | 2021-11-12 | 上海示右智能科技有限公司 | Method for constructing digital notarization |
| CN115797708A (en) * | 2023-02-06 | 2023-03-14 | 南京博纳威电子科技有限公司 | Power transmission and distribution synchronous data acquisition method |
| CN116153019A (en) * | 2023-02-13 | 2023-05-23 | 深圳崎点数据有限公司 | Cloud computing-based power grid disaster early warning system |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105631578A (en) * | 2015-12-10 | 2016-06-01 | 浙江大学 | Risk assessment-orientated modeling method of power transmission and transformation equipment failure probability model |
| CN109785289A (en) * | 2018-12-18 | 2019-05-21 | 中国科学院深圳先进技术研究院 | A kind of transmission line of electricity defect inspection method, system and electronic equipment |
| CN111191887A (en) * | 2019-12-17 | 2020-05-22 | 国网湖南省电力有限公司 | Fitting method and system for time distribution characteristics of power transmission line meteorological disaster faults |
| CN111444240A (en) * | 2020-03-26 | 2020-07-24 | 北京国网富达科技发展有限责任公司 | Power transmission line fault early warning method and device |
| CN111488896A (en) * | 2019-01-28 | 2020-08-04 | 国网能源研究院有限公司 | Distribution line time-varying fault probability calculation method based on multi-source data mining |
| CN111860943A (en) * | 2020-06-08 | 2020-10-30 | 清华大学 | Power grid fault prediction method and system based on numerical meteorological data and machine learning |
-
2021
- 2021-01-18 CN CN202110062564.8A patent/CN112785138A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105631578A (en) * | 2015-12-10 | 2016-06-01 | 浙江大学 | Risk assessment-orientated modeling method of power transmission and transformation equipment failure probability model |
| CN109785289A (en) * | 2018-12-18 | 2019-05-21 | 中国科学院深圳先进技术研究院 | A kind of transmission line of electricity defect inspection method, system and electronic equipment |
| CN111488896A (en) * | 2019-01-28 | 2020-08-04 | 国网能源研究院有限公司 | Distribution line time-varying fault probability calculation method based on multi-source data mining |
| CN111191887A (en) * | 2019-12-17 | 2020-05-22 | 国网湖南省电力有限公司 | Fitting method and system for time distribution characteristics of power transmission line meteorological disaster faults |
| CN111444240A (en) * | 2020-03-26 | 2020-07-24 | 北京国网富达科技发展有限责任公司 | Power transmission line fault early warning method and device |
| CN111860943A (en) * | 2020-06-08 | 2020-10-30 | 清华大学 | Power grid fault prediction method and system based on numerical meteorological data and machine learning |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113642986A (en) * | 2021-08-02 | 2021-11-12 | 上海示右智能科技有限公司 | Method for constructing digital notarization |
| CN113642986B (en) * | 2021-08-02 | 2024-04-16 | 上海示右智能科技有限公司 | Method for constructing digital notarization |
| CN115797708A (en) * | 2023-02-06 | 2023-03-14 | 南京博纳威电子科技有限公司 | Power transmission and distribution synchronous data acquisition method |
| CN116153019A (en) * | 2023-02-13 | 2023-05-23 | 深圳崎点数据有限公司 | Cloud computing-based power grid disaster early warning system |
| CN116153019B (en) * | 2023-02-13 | 2023-08-22 | 深圳崎点数据有限公司 | Cloud computing-based power grid disaster early warning system |
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