CN109460923B - Power transmission line icing probability prediction method - Google Patents
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Abstract
The invention provides a method for predicting the icing probability of a power transmission line, which is characterized in that a prediction model is established according to actual icing accident data of the power transmission line and microclimate station data of stations adjacent to accident points, and four indexes of precipitation, temperature, relative humidity and wind speed which affect the icing of the power transmission line are quantized in the prediction model, namely, clear assignment is given. In this way, the transmission line icing probability grade index P can be calculated only by substituting the precipitation sum r, the average temperature t, the average relative humidity rh and the average wind speed u in the same day in the previous day and the current day in the later period, and the icing probability grade can be divided. According to the power transmission line icing accident data provided by the power department, the power microclimate station observation data near the accident point is utilized to analyze the meteorological element threshold value when the power transmission line is iced, so that the meteorological element index is closer to the real condition, and reliable basis is provided for the new construction and maintenance of the power transmission line and the power transmission line icing probability prediction.
Description
Technical Field
The invention relates to an applied meteorological technique, in particular to a method for predicting icing probability of a power transmission line.
Background
Meteorological conditions and electric power have a close relationship, and meteorological disasters have increasingly obvious influence on the safety of a power grid. Statistics shows that according to the statistics of the operation conditions of the transmission conductors of 330k V nationwide in 2012-2014, meteorological factors influencing the safe operation of a power grid in the northern river region include ice damage, wind damage and lightning damage, and particularly the brake drop rate of the ice damage in the northern river region is third nationwide. The ice coating of the electric wire is the root cause of ice damage, and has great harm to the operation safety of power grids in the north and the river. During 2015, 5-month and 10-day Hebei health and maintenance sources are frozen and rainy, large-area electric wires of a power grid are frozen, power transmission lines are short-circuited, even a single 220k V power transmission line is inverted, normal power transmission cannot be carried out, and power transmission is blocked.
At present, related departments deeply research the icing condition of the power transmission line, and can predict the icing condition of the power transmission line in time and the icing thickness and the like by establishing a model. However, most of the researches are completed based on the transmission line ice accumulation observation data of the meteorological stations in each county and city, and actually, most of the transmission lines are in the wild, the transmission lines are greatly influenced by the external complex environment, and the data measured by the meteorological stations in each county and city inevitably have certain errors.
Disclosure of Invention
The invention aims to provide a method for predicting the icing probability of a power transmission line, which can timely, accurately and reliably predict the icing probability of the power transmission line and provide a reliable basis for power departments.
The invention is realized by the following steps: a method for predicting icing probability of a power transmission line comprises the following steps:
a. establishing a prediction model; the prediction model is as follows:
P＝R×T×RH×U
wherein P is the icing probability grade index of the power transmission line, R represents the intensity index of precipitation, T represents the temperature index, RH represents the relative humidity index, and U represents the average wind speed index;
the formula of the precipitation intensity index R is as follows:
wherein r is the total precipitation amount of the day before and the day of ice coating;
the formula of the temperature index T is as follows:
wherein t is the average temperature of the day;
the formula of the relative humidity index RH is as follows:
wherein rh is the average relative humidity of the day;
the formula of the average wind speed index U is as follows:
wherein u is the average wind speed of the day and the unit is m/s;
b. according to the prediction model in the step a, calculating a transmission line icing probability grade index P according to the precipitation sum r of the day before and the day of icing, the average temperature t of the day, the average relative humidity rh of the day and the average wind speed u of the day; and P & lt0 & gt represents that the transmission line has no icing risk, and P & gt 0 represents that the transmission line has the icing risk.
In the step b, after the transmission line icing probability grade index P is obtained through calculation, the transmission line icing probability grade is divided according to the following method:
when P is 0, the prediction level of the icing probability of the transmission line is first grade, and the icing risk is not existed;
when 0 is present<P＜P15％In the process, the predication level of the icing probability of the power transmission line is two levels, and the icing low risk is represented;
when P is present15％≤P＜P85％Meanwhile, the power transmission line icing probability prediction grade is three grades, and risks in icing are represented;
when P is more than or equal to P85％In the process, the predication level of the icing probability of the power transmission line is four, which represents high risk of icing;
wherein, P15％And P85％The method is obtained by adopting a percentile classification method when a prediction model is established in the step a, namely: when a prediction model is established, the icing probability grade indexes P of the transmission lines corresponding to the samples are sorted from small to large, and P15％Namely the 15 th percent transmission line icing probability grade index or the corresponding transmission line icing probability grade index after the 15 th percent is rounded up in the arranged sequence, P85％Namely the icing probability grade index of the 85% transmission line from small to large in the arranged sequence or the icing probability grade index of the corresponding transmission line after the 85% transmission line is rounded up.
And a, when the prediction model is established in the step a, the selected sample is from observation data of the electric microclimate station near the icing accident point of the power transmission line.
According to the method, the meteorological element threshold indexes of the previous day and the current day of the icing of the transmission line are obtained by utilizing the observation data of the electric microclimate station near the accident point according to the icing accident data of the transmission line provided by the electric power department, the influence degree of the icing probability of the transmission line is assigned according to each index, and a prediction model of the icing probability of the transmission line is established. The icing probability of the power transmission line can be predicted according to the prediction model, and after a prediction result is obtained, the power transmission line can be graded. The grade division is carried out according to a percentile classification method, which specifically comprises the following steps: firstly, calculating the icing probability of a transmission line of a sample when a prediction model is built to obtain a group of transmission line icing probability sequences arranged from small to large; then dividing the power transmission line icing probability sequence into three parts, namely, an icing low risk, an icing medium risk and an icing high risk; the demarcation point between the ice coating low risk and the ice coating in risk and the demarcation point between the ice coating in risk and the ice coating high risk can be obtained by a percentile classification method; and judging the prediction result to fall into which range of the power transmission line icing probability sequence, so as to obtain which level the power transmission line icing probability prediction level is.
The method is based on the observation data of the electric microclimate station when the prediction model is established, and the observation data of the electric microclimate station is closer to the meteorological conditions when an accident occurs than the data measured by the conventional meteorological station, so that the index threshold value obtained by the method is closer to the actual condition and has higher practical value, and the method provides reliable basis for new construction and maintenance of the transmission line and prediction of the icing probability of the transmission line.
Detailed Description
The method for predicting the icing probability of the power transmission line comprises the following steps:
a. establishing a prediction model; the prediction model is as follows:
P＝R×T×RH×U
wherein, P is the index of the icing probability grade of the power transmission line, R represents the index of the intensity of precipitation, T represents the index of the temperature, RH represents the index of the relative humidity, and U represents the index of the average wind speed.
The formula of the precipitation intensity index R is as follows:
wherein r is the total precipitation amount of the day before and the day of ice coating.
The formula of the temperature index T is as follows:
wherein t is the average temperature of the day.
The formula of the relative humidity index RH is as follows:
wherein rh is the average relative humidity of the day.
The formula of the average wind speed index U is as follows:
wherein u is the average wind speed of the day and the unit is m/s.
When the prediction model is established, the sample data is derived from observation data of the electric microclimate station near the icing accident point of the power transmission line. The method utilizes observation data of the electric microclimate station near the accident point to analyze the meteorological element threshold value when the power transmission line is coated with ice, so that the meteorological element index is closer to the real condition.
After the prediction model is established, the samples can be substituted into the prediction model for calculation to obtain a group of power transmission line icing probability sequences arranged from small to large; then, the power transmission line icing probability sequence is divided into three parts, namely, an icing low risk, an icing medium risk and an icing high risk. The icing risk is low, the icing risk is middle and the icing risk is high, and the corresponding transmission line icing probability grade index P is larger than 0. When P is 0, the power transmission line has no icing risk. The demarcation point between the low and high risk of icing, and the demarcation point between the high and low risk of icing can be obtained by percentile classification.
b. According to the prediction model in the step a, calculating a transmission line icing probability grade index P according to the precipitation sum r of the day before and the day of icing, the average temperature t of the day, the average relative humidity rh of the day and the average wind speed u of the day; and P & lt0 & gt represents that the transmission line has no icing risk, and P & gt 0 represents that the transmission line has the icing risk. For the condition that the transmission line has the icing risk, the method can also judge which range (three intervals of icing low risk, icing medium risk and icing high risk) the P falls into the transmission line icing probability sequence, and can know which level the transmission line icing probability prediction level is.
The method comprises the following steps: the power transmission line icing probability prediction grade is divided according to the following method:
when P is 0, the prediction grade of the icing probability of the corresponding transmission line is first grade, and the icing risk is not existed;
when 0 is present<P＜P15％Then, the corresponding power transmission line icing probability prediction grade is two-grade, and the icing low risk is represented;
when P is present15％≤P＜P85％In time, the corresponding power transmission line icing probability prediction grade is three-grade, and the risk in icing is represented;
when P is more than or equal to P85％And in time, the corresponding power transmission line icing probability prediction grade is four grades, and high icing risk is represented.
Here, P15％Is the demarcation point between the low risk of icing and the risk during icing, P85％Is the demarcation point between the risk in icing and the high risk of icing, namely: all greater than 0 and less than P15％Is defined as a low risk of icing, all greater than or equal to P15％Less than P85％Is defined as the risk in icing, all greater than or equal to P85％Is defined as a high risk of icing.
P15％And P85％The method is obtained by adopting a percentile classification method when a prediction model is established in the step a, namely: when a prediction model is established, the icing probability grade indexes P of the transmission lines corresponding to the samples are sorted from small to large, and P15％Is thatThe icing probability grade index of the 15 th% transmission line from small to large in the arranged sequence or the icing probability grade index of the corresponding transmission line after the 15 th% is rounded up, P85％Namely the icing probability grade index of the 85% transmission line from small to large in the arranged sequence or the icing probability grade index of the corresponding transmission line after the 85% transmission line is rounded up.
It should be noted that, different original sample data may exist, and differences may exist between obtained percentiles corresponding to a boundary point between the ice coating low risk and the ice coating medium risk, and a boundary point between the ice coating medium risk and the ice coating high risk. Therefore, 15% and 85% in the present invention are taken as percentage points of two demarcation points, which is a practical example and is not intended to limit the present invention in any way.
Application example:
the method comprises the steps of analyzing the power transmission line icing probability prediction by utilizing ice icing disaster-causing data of north power transmission lines in the river north of 2012-2015 and data of nearby electric microclimate stations. The tests were carried out using the disaster cases of 9 times after 11/22/2015.
Assigning according to the assignment condition of each element, calculating the icing probability grade index P, and classifying the icing probability grade index P by using a percentile method to obtain the following grades:
when P is 0, the prediction grade of the icing probability of the transmission line is first grade, and no icing risk exists;
when P is more than 0 and less than 5, the power transmission line icing probability prediction grade is two-grade, and the risk is low;
when P is more than or equal to 5 and less than 150, the power transmission line icing probability prediction grade is three-grade, and the risk is the risk in icing;
when P is 150, the prediction level of the icing probability of the transmission line is four levels, and the risk of icing is high.
Here, P15％＝5，P85％＝150。
The results of the tests were as follows:
TABLE 1
| Icing probability rating | Number of times | Ratio of occupation of | Accuracy of this level and above |
| Four stages | 3 | 33.3％ | 33.3％ |
| Three-stage | 4 | 44.4％ | 77.7％ |
| Second stage | 2 | 22.2％ | 100％ |
| First level (missing report) | 0 |
As can be seen from Table 1, the method for predicting the icing probability of the power transmission line has no condition of missing report, and can better predict the icing condition of the power transmission line.
According to the method for predicting the icing probability of the power transmission line, the main influence indexes of the icing of the power transmission line are quantized and respectively assigned according to the grades by applying a batching method based on the actual icing accident data of the power transmission line and the microclimate station data of the stations adjacent to the accident point, so that an icing probability prediction model is obtained, and the purpose of calculating and releasing the grade prediction product of the icing probability of the power transmission line day by day is achieved. The power transmission line icing accident frequently occurs in the field, and the prediction model established by the invention is based on the observation data of the electric microclimate station and is closer to the meteorological condition when the accident occurs than the conventional meteorological station, so that the obtained index threshold value is closer to the actual condition, and the accurate prediction of the power transmission line icing probability is realized.
Claims (2)
1. A method for predicting icing probability of a power transmission line is characterized by comprising the following steps:
a. establishing a prediction model; the prediction model is as follows:
P＝R×T×RH×U
wherein P is the icing probability grade index of the power transmission line, R represents the intensity index of precipitation, T represents the temperature index, RH represents the relative humidity index, and U represents the average wind speed index;
the formula of the precipitation intensity index R is as follows:
wherein r is the total precipitation amount of the day before and the day of ice coating;
the formula of the temperature index T is as follows:
wherein t is the average temperature of the day;
the formula of the relative humidity index RH is as follows:
wherein rh is the average relative humidity of the day;
the formula of the average wind speed index U is as follows:
wherein u is the average wind speed of the day and the unit is m/s;
b. according to the prediction model in the step a, calculating a transmission line icing probability grade index P according to the precipitation sum r of the day before and the day of icing, the average temperature t of the day, the average relative humidity rh of the day and the average wind speed u of the day;
after the power transmission line icing probability grade index P is obtained through calculation, the power transmission line icing probability grade is divided according to the following method:
when P is 0, the prediction level of the icing probability of the transmission line is first grade, and the icing risk is not existed;
when 0 is present<P＜P15％In the process, the predication level of the icing probability of the power transmission line is two levels, and the icing low risk is represented;
when P is present15％≤P＜P85％Meanwhile, the power transmission line icing probability prediction grade is three grades, and risks in icing are represented;
when P is more than or equal to P85％In the process, the predication level of the icing probability of the power transmission line is four, which represents high risk of icing;
wherein, P15％And P85％The method is obtained by adopting a percentile classification method when a prediction model is established in the step a, namely: when a prediction model is established, the icing probability grade indexes P of the transmission lines corresponding to the samples are sorted from small to large, and P15％Namely the 15 th percent transmission line icing probability grade index or the corresponding transmission line icing probability grade index after the 15 th percent is rounded up in the arranged sequence, P85％Namely the icing probability grade index of the 85% transmission line from small to large in the arranged sequence or the icing probability grade index of the corresponding transmission line after the 85% transmission line is rounded up.
2. The method of claim 1, wherein the samples selected in the step a of creating the prediction model are derived from power microclimate station observations near the point of power line icing.
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| CN113110653B (en) * | 2021-04-12 | 2022-03-29 | 安徽气象信息有限公司 | Mechanical type wind sensor freeze-proof device based on thing networking |
| CN116451594B (en) * | 2023-06-15 | 2023-08-18 | 北京东润环能科技股份有限公司 | Training method and device of icing prediction model, prediction method and device and electronic equipment |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103605902A (en) * | 2013-12-02 | 2014-02-26 | 国家电网公司 | Method for evaluating and calculating electric transmission line ice coating environment influence factors under micrometeorological condition |
| CN104361535A (en) * | 2014-11-26 | 2015-02-18 | 上海电力学院 | Electric transmission line icing state assessment method |
| CN104615868A (en) * | 2015-01-23 | 2015-05-13 | 云南电网有限责任公司 | Method for judging whether icing of electric transmission line exists or not and predicting icing thickness |
| CN104766143A (en) * | 2015-04-22 | 2015-07-08 | 国家电网公司 | Electric transmission line icing grade long-term prediction method based on support vector classification |
| CN106779301A (en) * | 2016-11-15 | 2017-05-31 | 国网四川省电力公司电力科学研究院 | Coated by ice of overhead power transmission line state judging method |
-
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Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103605902A (en) * | 2013-12-02 | 2014-02-26 | 国家电网公司 | Method for evaluating and calculating electric transmission line ice coating environment influence factors under micrometeorological condition |
| CN104361535A (en) * | 2014-11-26 | 2015-02-18 | 上海电力学院 | Electric transmission line icing state assessment method |
| CN104615868A (en) * | 2015-01-23 | 2015-05-13 | 云南电网有限责任公司 | Method for judging whether icing of electric transmission line exists or not and predicting icing thickness |
| CN104766143A (en) * | 2015-04-22 | 2015-07-08 | 国家电网公司 | Electric transmission line icing grade long-term prediction method based on support vector classification |
| CN106779301A (en) * | 2016-11-15 | 2017-05-31 | 国网四川省电力公司电力科学研究院 | Coated by ice of overhead power transmission line state judging method |
Non-Patent Citations (1)
| Title |
|---|
| 精细化气象要素下输电线路覆冰预测预警研究;李伟 等;《电力大数据》;20180228;第21卷(第2期);第1页至第6页 * |
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