CN112541636A - Power transmission line icing risk early warning method and device, medium and electronic equipment - Google Patents

Power transmission line icing risk early warning method and device, medium and electronic equipment Download PDF

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CN112541636A
CN112541636A CN202011502734.1A CN202011502734A CN112541636A CN 112541636 A CN112541636 A CN 112541636A CN 202011502734 A CN202011502734 A CN 202011502734A CN 112541636 A CN112541636 A CN 112541636A
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transmission line
power transmission
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icing
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叶钰
冯涛
蔡泽林
郭俊
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State Grid Corp of China SGCC
State Grid Hunan Electric Power Co Ltd
Disaster Prevention and Mitigation Center of State Grid Hunan Electric Power Co Ltd
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State Grid Hunan Electric Power Co Ltd
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Abstract

The invention relates to a method, a device, a medium and electronic equipment for early warning of icing risk of a power transmission line, wherein the method comprises the following steps: acquiring meteorological forecast data of different sections of the power transmission line; processing the meteorological forecast data of the different sections based on the power transmission line icing disaster classifier to obtain icing disaster early warning results and corresponding confidence coefficients of the different sections; determining the icing disaster early warning result and the corresponding confidence coefficient of the whole power transmission line based on the icing disaster early warning result and the corresponding confidence coefficient of the different sections and the weight values of the different sections; and determining the early warning grade of the icing disaster of the power transmission line based on the early warning result of the icing disaster of the whole power transmission line and the corresponding confidence coefficient. According to the embodiment of the invention, the reliability and accuracy of the early warning result of the icing risk of the power transmission line are greatly improved.

Description

Power transmission line icing risk early warning method and device, medium and electronic equipment
Technical Field
The embodiment of the disclosure relates to the technical field of power transmission lines, in particular to a power transmission line icing risk early warning method, a power transmission line icing risk early warning device, a computer-readable storage medium and electronic equipment for realizing the power transmission line icing risk early warning method.
Background
The ice coating of the power transmission line is easy to occur in winter, so that accidents such as ice flash tripping and tower collapse disconnection of the power transmission line are easy to occur, and the safe and stable operation of a power grid is seriously influenced. Therefore, the influence range and degree of the icing of the power transmission line are predicted and early warned in advance, the icing disaster accident of the power transmission line can be effectively prevented, and the safe and stable operation of the power transmission line is supported vigorously.
At present, researches on an icing prediction method of a power transmission line are carried out in the related technology, for example, the icing prediction method of the power transmission line based on a multivariate physical quantity (wind power, temperature, humidity, raindrops, air pressure and the like) mathematical model is used for realizing effective prediction on the icing thickness of the power transmission line by analyzing the stress influence of a power line and combining factors (a wire structure, material surface properties and the like) of the icing of a wire; for example, a rapid analysis model of the icing thickness and the icing quality of the power transmission line based on the induced ordered weighted average operator combination prediction model is established, factors such as the suspension height of the electric wire, the terrain, the diameter of the electric wire, the torsion of the electric wire, the temperature of the electric wire, the wind speed, the windage yaw, the temperature, the humidity, the illumination intensity, the rainfall intensity, the snowfall intensity and the like are fully considered, and the icing thickness and the icing quality are predicted; and for example, a real-time monitoring and rolling forecasting method for icing disasters of power transmission and transformation equipment based on meteorological and topographic factors is provided, so that quantitative prediction of icing thickness distribution in different areas is realized.
However, the parameters in the models in the current schemes are not acquired accurately enough, and artificial experience factors dominate. Therefore, the method does not have strong theoretical rationality, and the accuracy of the prediction result of the icing disaster of the power transmission line is low.
Disclosure of Invention
In order to solve the technical problem or at least partially solve the technical problem, embodiments of the present disclosure provide a power transmission line icing risk early warning method, a power transmission line icing risk early warning device, and a computer-readable storage medium and an electronic device for implementing the power transmission line icing risk early warning method.
In a first aspect, an embodiment of the present disclosure provides a power transmission line icing risk early warning method, including:
acquiring meteorological forecast data of different sections of the power transmission line;
processing the meteorological forecast data of the different sections based on the power transmission line icing disaster classifier to obtain icing disaster early warning results and corresponding confidence coefficients of the different sections;
determining the icing disaster early warning result and the corresponding confidence coefficient of the whole power transmission line based on the icing disaster early warning result and the corresponding confidence coefficient of the different sections and the weight values of the different sections;
and determining the early warning grade of the icing disaster of the power transmission line based on the early warning result of the icing disaster of the whole power transmission line and the corresponding confidence coefficient.
In some embodiments of the present disclosure, the power transmission line icing disaster classifier is determined by:
classifying internal causes of the power transmission line with icing disasters, and counting historical meteorological data of historical icing disasters of the power transmission line under the internal causes of each category according to classification results;
sequentially dividing the power transmission line into a plurality of different sections, determining the corresponding category of each section in the classification result, and selecting historical meteorological data of the historical icing disaster under the corresponding category of each section to form a training sample set;
and forming the electric transmission line icing disaster classifier based on the training sample set and an Adaboost ensemble learning algorithm.
In some embodiments of the present disclosure, the internal cause comprises: the foundation characteristics of each base tower of the power transmission line comprise an independent foundation, a pile foundation and a digging foundation; the wire diameter, the wire torsion and the wire suspension height of the power transmission line; topographic and geomorphic information at the power transmission line;
the meteorological data comprises any one or more of: precipitation, wind speed, relative humidity, temperature, air pressure, windage yaw, illumination intensity and snowfall intensity.
In some embodiments of the present disclosure, the forming the power transmission line icing disaster classifier based on the training sample set and an Adaboost ensemble learning algorithm includes:
given training sample set
Figure BDA00028440701400000317
Sample class label x for icing disaster accident of power transmission lineiSample class label x of accident sample with specification of 1 and without occurrence of icing disasteriIs-1; i is the training sample index number, ΩiIs a section i, and N is the number of training samples; t is the number of weak classifiers, i.e. training times, wherein the classification algorithm of the weak classifiers is recorded as
Figure BDA0002844070140000031
Weight distribution omega of initialization training samplet(j)＝1/N,j＝1,2,...,N；t＝1,2,...,T；
According to the t-th sample weight distribution omegat(j) From the original sample collection
Figure BDA0002844070140000032
Middle sampling to generate new sample set
Figure BDA0002844070140000033
(t＝1,2,...,T)；
According to
Figure BDA0002844070140000034
Training weak classifier
Figure BDA0002844070140000035
And according to the weak classifier, the original sample set is processed
Figure BDA0002844070140000036
Classifying;
computing the weak classifier
Figure BDA0002844070140000037
Classification error rate of (2):
Figure BDA0002844070140000038
in the above formula, the first and second carbon atoms are,
Figure BDA0002844070140000039
computing the weak classifier
Figure BDA00028440701400000310
Weight coefficient of
Figure BDA00028440701400000311
Updating the sample weight distribution:
Figure BDA00028440701400000312
wherein the content of the first and second substances,
Figure BDA00028440701400000313
is a normalization factor such that
Figure BDA00028440701400000314
Determining the section omega of the transmission line by the following formulaiAn icing disaster final classifier, namely a power transmission line icing disaster classifier:
Figure BDA00028440701400000315
wherein the function
Figure BDA00028440701400000316
Is a symbolic function.
In some embodiments of the present disclosure, processing the weather forecast data of different segments to obtain icing disaster early warning results and corresponding confidence levels of different segments based on the power transmission line icing disaster classifier includes:
calculating to obtain the section omega of the power transmission line based on the following formulaiIcing disaster early warning result xi：
Figure BDA0002844070140000041
Calculating to obtain the section omega of the power transmission line based on the following formulaiCorresponding confidence margin (Ω)i,xi)：
Figure BDA0002844070140000042
In some embodiments of the present disclosure, the determining the icing disaster early warning result and the corresponding confidence degree of the whole power transmission line based on the icing disaster early warning result and the corresponding confidence degree of the different sections and the weight values corresponding to the different sections includes:
the icing disaster early warning result X of the whole power transmission line is determined by the following formula:
Figure BDA0002844070140000043
the confidence coefficient margin (omega, X) of the icing disaster of the whole power transmission line is determined by the following formula:
Figure BDA0002844070140000044
wherein, margin (omega, X) E [ -1,1 [ ]]Different section omegaiCorresponding weight value is thetaΩi。
In some embodiments of the present disclosure, the early warning level of the icing disaster of the power transmission line is specifically determined in the following manner:
when margin (omega, X) >0.9, red early warning is carried out;
when 0.6< margin (omega, X) ≦ 0.9, an orange early warning is given;
early warning of yellow when 0.3< margin (omega, X) ≦ 0.6
When margin (omega, X) is less than or equal to 0.3, the alarm is blue.
In a second aspect, an embodiment of the present disclosure provides a power transmission line icing risk early warning device, including:
the meteorological data acquisition module is used for acquiring meteorological forecast data of different sections of the power transmission line;
the power transmission line section early warning module is used for processing the meteorological forecast data of the different sections based on the power transmission line icing disaster classifier to obtain icing disaster early warning results and corresponding confidence coefficients of the different sections;
the power transmission line early warning module is used for determining the icing disaster early warning result and the corresponding confidence coefficient of the whole power transmission line based on the icing disaster early warning result and the corresponding confidence coefficient of the different sections and the weight values of the different sections;
and the early warning grade determining module is used for determining the early warning grade of the icing disaster of the power transmission line based on the early warning result of the icing disaster of the whole power transmission line and the corresponding confidence coefficient.
In a third aspect, the present disclosure provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the method for warning an icing risk of a power transmission line according to any one of the embodiments.
In a fourth aspect, an embodiment of the present disclosure provides an electronic device, including:
a processor; and
a memory for storing executable instructions of the processor;
wherein the processor is configured to execute the steps of the power transmission line icing risk early warning method according to any one of the above embodiments through executing the executable instructions.
Compared with the prior art, the technical scheme provided by the embodiment of the disclosure has the following advantages:
in the embodiment of the disclosure, meteorological forecast data of different sections of a power transmission line are acquired, the meteorological forecast data of the different sections are processed based on a power transmission line icing disaster classifier to obtain icing disaster early warning results and corresponding confidence degrees of the different sections, the icing disaster early warning results and the corresponding confidence degrees of the whole power transmission line are determined based on the icing disaster early warning results and the corresponding confidence degrees of the different sections and weight values of the different sections, and the power transmission line icing disaster early warning grade is determined based on the icing disaster early warning results and the corresponding confidence degrees of the whole power transmission line. According to the scheme, the meteorological forecast data of different sections of the power transmission line are acquired, the early warning analysis is carried out by combining the icing disaster classifier, the icing disaster early warning result and the corresponding confidence coefficient of the whole power transmission line are determined based on the weight values of the different sections, and then the icing disaster early warning grade of the power transmission line is determined, so that the accuracy of the icing disaster prediction result of the power transmission line is greatly improved, and an important technical support is provided for safe and stable operation of an important power transmission channel.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure.
In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present disclosure, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
Fig. 1 is a flowchart of an icing risk early warning method for a power transmission line according to an embodiment of the disclosure;
fig. 2 is a schematic diagram of an icing risk early warning process of a power transmission line according to an embodiment of the disclosure;
fig. 3 is a schematic diagram of an icing risk early warning device of a power transmission line according to an embodiment of the disclosure;
fig. 4 is a schematic diagram of an electronic device for implementing an early warning method for an icing risk of a power transmission line according to an embodiment of the present disclosure.
Detailed Description
In order that the above objects, features and advantages of the present disclosure may be more clearly understood, aspects of the present disclosure will be further described below. It should be noted that the embodiments and features of the embodiments of the present disclosure may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure, but the present disclosure may be practiced in other ways than those described herein; it is to be understood that the embodiments disclosed in the specification are only a few embodiments of the present disclosure, and not all embodiments.
It is to be understood that, hereinafter, "at least one" means one or more, "a plurality" means two or more. "and/or" is used to describe the association relationship of the associated objects, meaning that there may be three relationships, for example, "a and/or B" may mean: only A, only B and both A and B are present, wherein A and B may be singular or plural. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "at least one of the following" or similar expressions refer to any combination of these items, including any combination of single item(s) or plural items. For example, at least one (one) of a, b, or c, may represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b, c may be single or plural.
Fig. 1 is a flowchart of a power transmission line icing risk early warning method shown in an embodiment of the present disclosure, where the power transmission line icing risk early warning method may include the following steps:
step S101: and acquiring weather forecast data of different sections of the power transmission line.
Step S102: and processing the meteorological forecast data of different sections based on the power transmission line icing disaster classifier to obtain icing disaster early warning results and corresponding confidence coefficients of different sections.
Step S103: and determining the icing disaster early warning result and the corresponding confidence coefficient of the whole power transmission line based on the icing disaster early warning result and the corresponding confidence coefficient of the different sections and the weight values of the different sections.
Step S104: and determining the early warning grade of the icing disaster of the power transmission line based on the early warning result of the icing disaster of the whole power transmission line and the corresponding confidence coefficient.
According to the scheme, the meteorological forecast data of different sections of the power transmission line are acquired, the early warning analysis is carried out by combining the icing disaster classifier, the icing disaster early warning result and the corresponding confidence coefficient of the whole power transmission line are determined based on the weight values of the different sections, the icing disaster early warning grade of the power transmission line is further determined, and the accuracy of the power transmission line icing disaster prediction result is greatly improved.
Specifically, as an example, in some embodiments of the present disclosure, the power transmission line icing disaster classifier in step S102 may be determined by, but is not limited to, the following manners:
classifying internal causes of the power transmission line with icing disasters, and counting historical meteorological data of historical icing disasters of the power transmission line under the internal causes of each category according to classification results;
sequentially dividing the power transmission line into a plurality of different sections, determining the corresponding category of each section in the classification result, and selecting historical meteorological data of the historical icing disaster under the corresponding category of each section to form a training sample set;
and forming the electric transmission line icing disaster classifier based on the training sample set and an Adaboost ensemble learning algorithm.
The scheme of the embodiment is based on the machine learning theory, obtains accurate prediction through the past observation data, provides a rule which is obtained from the observation data and cannot be obtained through physical principle analysis at present, and the rule can predict future data, and provides a more feasible operation mode for accurate prediction and early warning of the icing disaster of the power transmission line.
Specifically, in some embodiments of the present disclosure, the internal cause may include: the foundation characteristics of each base tower of the power transmission line comprise an independent foundation, a pile foundation and a digging foundation; the wire diameter, the wire torsion and the wire suspension height of the power transmission line. And (3) topographic information of terrains (such as watersheds, beaks and the like) at the power transmission line. The meteorological data may include, but is not limited to, any one or more of: precipitation, wind speed, relative humidity, temperature, air pressure, windage yaw, illumination intensity and snowfall intensity.
In some embodiments of the disclosure, the forming the power transmission line icing disaster classifier based on the training sample set and the Adaboost ensemble learning algorithm may specifically include the following steps:
1) given training sample set
Figure BDA0002844070140000081
Sample class label x for icing disaster accident of power transmission lineiSample class label x of accident sample with specification of 1 and without occurrence of icing disasteriIs-1; i is the training sample index number, ΩiIs a section i, and N is the number of training samples; t is the number of weak classifiers, i.e. training times, wherein the classification algorithm of the weak classifiers is recorded as
Figure BDA0002844070140000082
2) Weight distribution omega of initialization training samplet(j)＝1/N,j＝1,2,...,N；t＝1,2,...,T；
3) According to the t-th sample weight distribution omegat(j) From the original sample collection
Figure BDA0002844070140000083
Middle sampling to generate new sample set
Figure BDA0002844070140000084
(t＝1,2,...,T)；
4) According to
Figure BDA0002844070140000085
Training weak classifier
Figure BDA0002844070140000086
And according to the weak classifier, the original sample set is processed
Figure BDA0002844070140000087
Classifying;
5) computing the weak classifier
Figure BDA0002844070140000088
Classification error rate of (2):
Figure BDA0002844070140000089
in the above formula, the first and second carbon atoms are,
Figure BDA00028440701400000810
6) computing the weak classifier
Figure BDA0002844070140000091
Weight coefficient of
Figure BDA0002844070140000092
7) Updating the sample weight distribution:
Figure BDA0002844070140000093
wherein the content of the first and second substances,
Figure BDA0002844070140000094
is a normalization factor such that
Figure BDA0002844070140000095
8) Determining the section omega of the transmission line by the following formulaiAn icing disaster final classifier, namely a power transmission line icing disaster classifier:
Figure BDA0002844070140000096
wherein the function
Figure BDA0002844070140000097
Is a symbolic function.
In some embodiments of the present disclosure, the processing, in step S102, the processing, based on the power transmission line icing disaster classifier, the meteorological forecast data of different sections to obtain icing disaster early warning results and corresponding confidence degrees of different sections includes:
calculating to obtain the section omega of the power transmission line based on the following formulaiIcing disaster early warning result xi：
Figure BDA0002844070140000098
Calculating to obtain the section omega of the power transmission line based on the following formulaiCorresponding confidence margin (Ω)i,xi)：
Figure BDA0002844070140000099
In some embodiments of the present disclosure, the determining, in step S103, the icing disaster early warning result and the corresponding confidence degree of the whole power transmission line based on the icing disaster early warning result and the corresponding confidence degree of the different sections and the weight values corresponding to the different sections includes the following steps:
the icing disaster early warning result X of the whole power transmission line is determined by the following formula:
Figure BDA0002844070140000101
the confidence coefficient margin (omega, X) of the icing disaster of the whole power transmission line is determined by the following formula:
Figure BDA0002844070140000102
wherein, margin (omega, X) E [ -1,1 [ ]]Different section omegaiCorresponding weight value of
Figure BDA0002844070140000103
In the present embodiment, according to each section ΩiWeight value of
Figure BDA0002844070140000104
Proportional linear combination calculation of early warning output result X of icing disaster in whole section of power transmission line and confidence coefficient margin (omega, X), wherein margin (omega, X) belongs to [ -1,1]The larger positive boundary in the middle indicates that the reliability of predicting the icing disaster of the line is high, the larger negative boundary indicates that the reliability of predicting the icing disaster accident of the line is high, and the smaller boundary indicates that the reliability of the prediction result is low.
According to the power transmission line icing risk early warning method based on the improved Adaboost in the embodiment, the strong learning classifier for evaluating the whole power transmission line icing risk is formed by performing integrated learning on the weak classifier for evaluating the power transmission line icing risk under each section, comprehensive calculation processing on related data such as power transmission line icing weather forecast data, power transmission line structural parameters and terrain and features can be achieved, the power transmission line icing disaster early warning analysis result of the region where the power transmission line is output is more accurate, and technical support is provided for safe and stable operation of an important power transmission channel.
In some embodiments of the present disclosure, the early warning level of the icing disaster of the power transmission line in step S104 may be specifically determined by, but not limited to, the following manners: when margin (omega, X) >0.9, the early warning is red, which indicates that ice is particularly seriously coated; when 0.6< margin (omega, X) ≦ 0.9, the early warning is orange, indicating severe icing; at 0.3< margin (Ω, X) ≦ 0.6, a yellow warning indicating moderate icing. When margin (Ω, X) ≦ 0.3, a blue warning is indicated, indicating mild icing.
According to the scheme of the embodiment, the most important external factors influencing the icing disaster of the power transmission line are meteorological data elements, topographic feature element data and the like, and when the internal factors are relatively stable and unchanged, the occurrence of the icing disaster accident of the power grid is determined by the external factors. In order to overcome the defects of poor initiative, low accuracy and the like of the existing power grid icing disaster early warning, the scheme of the embodiment of the disclosure provides a power transmission line icing disaster risk early warning method based on improved Adaboost from the actual occurrence condition and operation angle of the power grid icing disaster, the power transmission line icing disaster risk early warning problem is essentially summarized into a classification prediction problem under supervised learning, a strong classifier is established by improving an Adaboost integrated learning algorithm, element data such as power transmission line structure data parameters, meteorological data and topographic information are comprehensively calculated and processed, the power transmission line icing disaster risk grade is determined, the power transmission line icing disaster prediction early warning under high precision is finally realized to a certain extent, and the accuracy of an early warning result is high.
The technical scheme of the embodiment of the disclosure at least comprises the following technical advantages:
1) the internal cause and the external cause which influence the icing disaster of the power transmission line are considered, historical icing disaster information of the power grid is fully utilized, the actual situation of the icing disaster of the power transmission line is more met, and the accuracy of the early warning result is greatly improved.
2) The improved Adaboost ensemble learning algorithm is adopted, the ability of being suitable for new data is favorably strengthened in the rules learned from sample data, and the Adaboost ensemble learning algorithm has the characteristics of strong generalization ability, easiness in coding and the like, so that the reliability of the prediction early warning result is high.
3) The method disclosed by the embodiment of the disclosure has the advantages of relatively detailed flow, strong operability and higher practicability.
Aspects of particular embodiments of the present disclosure are illustrated below with reference to fig. 2. Taking a sample data set of a 500kV certain transmission line of a Hunan power grid of 28 groups in 2010-2017 as an example, the specific implementation operation steps are as follows:
step S1, analyzing the internal cause of the icing disaster of the power transmission line, and counting the meteorological data of the historical icing disaster accident of the power transmission line in each category according to the classification result; wherein the values are classified according to 9 internal factors, and the total combination is 24*35And (4) seed preparation.
Illustratively, the classification result of the internal cause of the icing disaster of the power transmission line comprises the following steps: the foundation characteristics of each base tower of the power transmission line comprise an independent foundation, a pile foundation and a digging foundation; transmission line characteristics such as wire diameter, wire twist, wire suspension height, etc.; topographic information, such as watershed, puerto, etc.; and (5) landform information. The meteorological data includes: precipitation, wind speed, relative humidity, temperature, air pressure, windage yaw, illumination intensity and snowfall intensity.
Step S2, the whole predicted power transmission line is divided into 20 sections in sequence, and each section omega is collected and sorted respectivelyi(i is not less than 1 and not more than 20) and is recorded as omega-omega1,Ω2,...Ω20}，；
Step S3, for each section ΩiInner transmission line information, selection and section omegaiAnd obtaining corresponding categories in the classification result of the internal factors of the medium power transmission line icing disasters, obtaining historical meteorological data records under the condition of historical icing disaster accidents in the categories to form a training sample set, and forming a classifier by using an Adaboost ensemble learning algorithm. A specific example implementation of step S3 is as follows:
(1) inputting: the training sample set specifically comprises sample class labels, wherein the sample class label x for the occurrence of the icing disaster accident of the power transmission lineiThe rule is 1, and if no icing disaster accident occurs, the rule is-1; i is a training sample index number, and N is the number of training samples; t is the number of weak classifiers, i.e. training times, wherein the classification algorithm of the weak classifiers is marked as CΩi(ii) a Here, the weak classifier algorithm employed may employ branchesA support vector machine.
(2) Initialization: sample weight distribution omegat(j)＝1/N,j＝1,2,...,N；t＝1,2,...,T；
(3) Training sample evolution and weak classifiers:
(3.1) sample weight distribution ω according to the t-th timet(j) From the original sample collection
Figure BDA0002844070140000121
Sampling with put back, generating a new sample set
Figure BDA0002844070140000122
(t＝1,2,...,T)；
(3.2) according to
Figure BDA0002844070140000123
Training weak classifier
Figure BDA0002844070140000124
And according to the classifier, the original sample set is subjected to
Figure BDA0002844070140000125
Classifying;
(3.3) calculating the Weak classifier
Figure BDA0002844070140000126
Classification error rate of (2):
Figure BDA0002844070140000127
in the above formula, the first and second carbon atoms are,
Figure BDA0002844070140000128
(3.4) calculating the Weak classifier
Figure BDA0002844070140000129
Weight coefficient of
Figure BDA00028440701400001210
(3.5) updating the weight distribution
Figure BDA00028440701400001211
Wherein
Figure BDA00028440701400001212
Is a normalization factor such that
Figure BDA00028440701400001213
(3.6) determining the section omega of the power transmission lineiAnd (3) an icing disaster final classifier:
Figure BDA0002844070140000131
here, function
Figure BDA0002844070140000132
Is a symbolic function.
Step S4, obtaining section omega from meteorological departmentiThe weather forecast data U of the icing disaster of the medium power transmission line is used as input, and the section omega is obtained through the classifieriEarly warning output result x of icing disaster of medium power transmission lineiAnd confidence margin value margin (Ω)i,xi)。
The specific calculation formula is as follows:
Figure BDA0002844070140000133
Figure BDA0002844070140000134
step S5, obtaining section omega according to step S4iEarly warning output result x of icing disaster of medium power transmission lineiAnd confidence margin value margin (Ω)i,xi) According to each section omegaiWeight of (2)
Figure BDA0002844070140000135
And calculating an icing disaster early warning output result X and a confidence coefficient margin value margin (omega, X) of the whole section of the power transmission line by proportional linear combination.
The specific calculation formula is as follows:
Figure BDA0002844070140000136
Figure BDA0002844070140000137
the exemplary weight values of the 20 sections of the power transmission line in this embodiment are as follows:
Figure BDA0002844070140000138
in the above formula, margin (Ω, X) is [ -1,1], where a larger positive boundary indicates a high reliability in predicting that the line has an icing disaster, a larger negative boundary indicates a high reliability in predicting that the line does not have an icing disaster, and a smaller boundary indicates a low reliability of the prediction result.
And S6, judging to obtain the risk early warning level of the icing disaster of the power transmission line according to the confidence coefficient margin value margin (omega, X) corresponding to the early warning output result X of the icing disaster of the whole section of the power transmission line obtained in the step S5.
Exemplary transmission line icing disaster risk early warning levels are shown in the following table:
condition Early warning level Remarks for note
margin(Ω,X)>0.9 Red early warning Particularly severe icing
0.6<margin(Ω,X)≦0.9 Orange early warning Severe icing
0.3<margin(Ω,X)≦0.6 Yellow early warning Moderate degree of icing
margin(Ω,X)≦0.3 Blue warning Light icing
It should be noted that although the various steps of the methods of the present disclosure are depicted in the drawings in a particular order, this does not require or imply that these steps must be performed in this particular order, or that all of the depicted steps must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions, etc. Additionally, it will also be readily appreciated that the steps may be performed synchronously or asynchronously, e.g., among multiple modules/processes/threads.
Based on the same concept, the embodiment of the present disclosure provides a transmission line icing risk early warning device, including:
the meteorological data acquisition module 301 is configured to acquire meteorological forecast data of different sections of the power transmission line;
the electric transmission line section early warning module 302 is configured to process the weather forecast data of the different sections based on the electric transmission line icing disaster classifier to obtain icing disaster early warning results and corresponding confidence degrees of the different sections;
the power transmission line early warning module 303 is configured to determine an icing disaster early warning result and a corresponding confidence level of the whole power transmission line based on the icing disaster early warning result and the corresponding confidence level of the different sections and the weight values of the different sections;
and an early warning level determination module 304, configured to determine an early warning level of the icing disaster of the power transmission line based on the icing disaster early warning result and the corresponding confidence of the whole power transmission line.
In some embodiments of the present disclosure, the power transmission line icing disaster classifier is determined by:
classifying internal causes of the power transmission line with icing disasters, and counting historical meteorological data of historical icing disasters of the power transmission line under the internal causes of each category according to classification results;
sequentially dividing the power transmission line into a plurality of different sections, determining the corresponding category of each section in the classification result, and selecting historical meteorological data of the historical icing disaster under the corresponding category of each section to form a training sample set;
and forming the electric transmission line icing disaster classifier based on the training sample set and an Adaboost ensemble learning algorithm.
In some embodiments of the present disclosure, the internal cause comprises: the foundation characteristics of each base tower of the power transmission line comprise an independent foundation, a pile foundation and a digging foundation; the wire diameter, the wire torsion and the wire suspension height of the power transmission line; topographic and geomorphic information at the power transmission line;
the meteorological data comprises any one or more of: precipitation, wind speed, relative humidity, temperature, air pressure, windage yaw, illumination intensity and snowfall intensity.
In some embodiments of the present disclosure, the power transmission line icing disaster classifier is formed based on the training sample set and an Adaboost ensemble learning algorithm, and is specifically determined in the following manner:
giving a training sample set U-ΩiWherein, the sample class label x of the ice coating disaster accident of the transmission lineiSample class label x of accident sample with specification of 1 and without occurrence of icing disasteriIs-1; i is the training sample index number, ΩiIs a section i, and N is the number of training samples; t is the number of weak classifiers, i.e. training times, wherein the classification algorithm of the weak classifiers is recorded as
Figure BDA0002844070140000151
Weight distribution omega of initialization training samplet(j)＝1/N,j＝1,2,...,N；t＝1,2,...,T；
According to the t-th sample weight distribution omegat(j) From the original sample collection
Figure BDA0002844070140000152
Middle sampling to generate new sample set
Figure BDA0002844070140000153
According to
Figure BDA0002844070140000154
Training weak classifier
Figure BDA0002844070140000155
And according to the weak classifier, the original sample set is processed
Figure BDA0002844070140000161
Classifying;
computing the weak classifier
Figure BDA0002844070140000162
Classification error rate of (2):
Figure BDA0002844070140000163
in the above formula, the first and second carbon atoms are,
Figure BDA0002844070140000164
computing the weak classifier
Figure BDA0002844070140000165
Weight coefficient of
Figure BDA0002844070140000166
Updating the sample weight distribution:
Figure BDA0002844070140000167
wherein the content of the first and second substances,
Figure BDA0002844070140000168
is a normalization factor such that
Figure BDA0002844070140000169
Determining the section omega of the transmission line by the following formulaiAn icing disaster final classifier, namely a power transmission line icing disaster classifier:
Figure BDA00028440701400001610
wherein the function
Figure BDA00028440701400001611
Is a symbolic function.
In some embodiments of the present disclosure, the power transmission line section early warning module 302 processes the weather forecast data of different sections based on the power transmission line icing disaster classifier to obtain icing disaster early warning results and corresponding confidence levels of different sections, which may specifically include the following manners:
calculating to obtain the section omega of the power transmission line based on the following formulaiIcing disaster early warning result xi：
Figure BDA00028440701400001612
Calculating to obtain the section omega of the power transmission line based on the following formulaiCorresponding confidence margin (Ω)i,xi)：
Figure BDA0002844070140000171
In some embodiments of the present disclosure, the power transmission line early warning module 303 determines the icing disaster early warning result and the corresponding confidence of the whole power transmission line based on the icing disaster early warning result and the corresponding confidence of the different sections and the weight values corresponding to the different sections, specifically including the following modes:
the icing disaster early warning result X of the whole power transmission line is determined by the following formula:
Figure BDA0002844070140000172
the confidence coefficient margin (omega, X) of the icing disaster of the whole power transmission line is determined by the following formula:
Figure BDA0002844070140000173
wherein, margin (omega, X) E [ -1,1 [ ]]Different section omegaiCorresponding weight value is thetaΩi。
In some embodiments of the present disclosure, the early warning level of the icing disaster of the power transmission line is specifically determined in the following manner:
when margin (omega, X) >0.9, red early warning is carried out;
when 0.6< margin (omega, X) ≦ 0.9, an orange early warning is given;
early warning of yellow when 0.3< margin (omega, X) ≦ 0.6
When margin (omega, X) is less than or equal to 0.3, the alarm is blue.
The specific manner in which the above-mentioned embodiments of the apparatus, and the corresponding technical effects brought about by the operations performed by the respective modules, have been described in detail in the embodiments related to the method, and will not be described in detail herein.
It should be noted that although in the above detailed description several modules or units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units. The components shown as modules or units may or may not be physical units, i.e. may be located in one place or may also be distributed over a plurality of network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the wood-disclosed scheme. One of ordinary skill in the art can understand and implement it without inventive effort.
The embodiment of the disclosure also provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the power transmission line icing risk early warning method according to any one of the embodiments.
By way of example, and not limitation, such readable storage media can be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable storage medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable storage medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
The embodiment of the disclosure also provides an electronic device, which includes a processor and a memory, wherein the memory is used for storing the executable instruction of the processor. Wherein the processor is configured to execute the steps of the method for warning the risk of icing on the power transmission line in any one of the above embodiments through executing the executable instructions.
An electronic device 600 according to this embodiment of the invention is described below with reference to fig. 4. The electronic device 600 shown in fig. 4 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 4, the electronic device 600 is embodied in the form of a general purpose computing device. The components of the electronic device 600 may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 that connects the various system components (including the storage unit 620 and the processing unit 610), a display unit 640, and the like.
Wherein the storage unit stores program codes, which can be executed by the processing unit 610, so that the processing unit 610 executes the steps according to various exemplary embodiments of the present invention described in the aforementioned power transmission line icing risk pre-warning method section of this specification. For example, the processing unit 610 may perform the steps of the power transmission line icing risk pre-warning method as shown in fig. 1.
The storage unit 620 may include readable media in the form of volatile memory units, such as a random access memory unit (RAM)6201 and/or a cache memory unit 6202, and may further include a read-only memory unit (ROM) 6203.
The memory unit 620 may also include a program/utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
Bus 630 may be one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.
The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 600, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 600 to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 650. Also, the electronic device 600 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet) via the network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 via the bus 630. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiment of the present disclosure may be embodied in the form of a software product, where the software product may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a mobile hard disk, or the like) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, or a network device, or the like) to execute the method for warning the risk of icing on the power transmission line according to the embodiment of the present disclosure.
It is noted that, in this document, relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The foregoing are merely exemplary embodiments of the present disclosure, which enable those skilled in the art to understand or practice the present disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the disclosure. Thus, the present disclosure is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (10)

1. A power transmission line icing risk early warning method is characterized by comprising the following steps:
acquiring meteorological forecast data of different sections of the power transmission line;
processing the meteorological forecast data of the different sections based on the power transmission line icing disaster classifier to obtain icing disaster early warning results and corresponding confidence coefficients of the different sections;
determining the icing disaster early warning result and the corresponding confidence coefficient of the whole power transmission line based on the icing disaster early warning result and the corresponding confidence coefficient of the different sections and the weight values of the different sections;
and determining the early warning grade of the icing disaster of the power transmission line based on the early warning result of the icing disaster of the whole power transmission line and the corresponding confidence coefficient.
2. The method for early warning of the icing risk of the power transmission line according to claim 1, wherein the classifier of the icing disaster of the power transmission line is determined by the following method:
classifying internal causes of the power transmission line with icing disasters, and counting historical meteorological data of historical icing disasters of the power transmission line under the internal causes of each category according to classification results;
sequentially dividing the power transmission line into a plurality of different sections, determining the corresponding category of each section in the classification result, and selecting historical meteorological data of the historical icing disaster under the corresponding category of each section to form a training sample set;
and forming the electric transmission line icing disaster classifier based on the training sample set and an Adaboost ensemble learning algorithm.
3. The pre-warning method for the icing risk of the power transmission line according to claim 2, wherein the internal cause comprises: the foundation characteristics of each base tower of the power transmission line comprise an independent foundation, a pile foundation and a digging foundation; the wire diameter, the wire torsion and the wire suspension height of the power transmission line; topographic and geomorphic information at the power transmission line;
the meteorological data comprises any one or more of: precipitation, wind speed, relative humidity, temperature, air pressure, windage yaw, illumination intensity and snowfall intensity.
4. The method for early warning of icing risk of power transmission line according to claim 2 or 3, wherein the step of forming the power transmission line icing disaster classifier based on the training sample set and Adaboost ensemble learning algorithm comprises the following steps:
given training sample set
Figure FDA0002844070130000029
Sample class label x for icing disaster accident of power transmission lineiSample class label x of accident sample with specification of 1 and without occurrence of icing disasteriIs-1; i is the training sample index number, ΩiIs a section i, and N is the number of training samples; t is the number of weak classifiers, i.e. training times, wherein the classification algorithm of the weak classifiers is recorded as
Figure FDA00028440701300000210
Weight distribution omega of initialization training samplet(j)＝1/N,j＝1,2,...,N；t＝1,2,...,T；
According to the t-th sample weight distribution omegat(j) From the original sample collection
Figure FDA00028440701300000211
Middle sampling to generate new sample set
Figure FDA00028440701300000212
According to
Figure FDA00028440701300000213
Training weak classifier
Figure FDA00028440701300000214
And according to the weak classifier, the original sample set is processed
Figure FDA00028440701300000215
Classifying;
computing the weak classifier
Figure FDA00028440701300000216
Classification error rate of (2):
Figure FDA0002844070130000021
in the above formula, the first and second carbon atoms are,
Figure FDA0002844070130000022
computing the weak classifier
Figure FDA00028440701300000217
Weight coefficient of
Figure FDA0002844070130000023
Updating the sample weight distribution:
Figure FDA0002844070130000024
wherein the content of the first and second substances,
Figure FDA0002844070130000025
is a normalization factor such that
Figure FDA0002844070130000026
Determining the section omega of the transmission line by the following formulaiAn icing disaster final classifier, namely a power transmission line icing disaster classifier:
Figure FDA0002844070130000027
wherein the function
Figure FDA0002844070130000028
Is a symbolic function.
5. The method for early warning of icing risk of power transmission line according to claim 4, wherein the step of processing the weather forecast data of different sections based on the classifier of icing disasters of power transmission line to obtain early warning results of icing disasters of different sections and corresponding confidence degrees comprises the steps of:
calculating to obtain the section omega of the power transmission line based on the following formulaiIcing disaster early warning result xi：
Figure FDA0002844070130000034
Calculating to obtain the section omega of the power transmission line based on the following formulaiCorresponding confidence margin (Ω)i,xi)：
Figure FDA0002844070130000031
6. The method for early warning of icing risk of power transmission line according to claim 5, wherein the step of determining the early warning result of icing disaster and the corresponding confidence degree of the whole power transmission line based on the early warning result of icing disaster and the corresponding confidence degree of different sections and the corresponding weight values of different sections comprises the steps of:
the icing disaster early warning result X of the whole power transmission line is determined by the following formula:
Figure FDA0002844070130000032
the confidence coefficient margin (omega, X) of the icing disaster of the whole power transmission line is determined by the following formula:
Figure FDA0002844070130000033
wherein, margin (omega, X) E [ -1,1 [ ]]Different section omegaiCorresponding weight value is thetaΩi。
7. The method for early warning of the icing risk of the power transmission line according to claim 6, wherein the early warning level of the icing disaster of the power transmission line is determined by the following method:
when margin (omega, X) >0.9, red early warning is carried out;
when 0.6< margin (omega, X) ≦ 0.9, an orange early warning is given;
early warning of yellow when 0.3< margin (omega, X) ≦ 0.6
When margin (omega, X) is less than or equal to 0.3, the alarm is blue.
8. The utility model provides a transmission line icing risk early warning device which characterized in that includes:
the meteorological data acquisition module is used for acquiring meteorological forecast data of different sections of the power transmission line;
the power transmission line section early warning module is used for processing the meteorological forecast data of the different sections based on the power transmission line icing disaster classifier to obtain icing disaster early warning results and corresponding confidence coefficients of the different sections;
the power transmission line early warning module is used for determining the icing disaster early warning result and the corresponding confidence coefficient of the whole power transmission line based on the icing disaster early warning result and the corresponding confidence coefficient of the different sections and the weight values of the different sections;
and the early warning grade determining module is used for determining the early warning grade of the icing disaster of the power transmission line based on the early warning result of the icing disaster of the whole power transmission line and the corresponding confidence coefficient.
9. A computer-readable storage medium, on which a computer program is stored, wherein the program, when executed by a processor, implements the steps of the method for warning a risk of icing for a power transmission line according to any one of claims 1 to 7.
10. An electronic device, comprising:
a processor; and
a memory for storing executable instructions of the processor;
wherein the processor is configured to execute the steps of the power transmission line icing risk early warning method according to any one of claims 1 to 7 through executing the executable instructions.
CN202011502734.1A 2020-12-17 2020-12-17 Power transmission line icing risk early warning method and device, medium and electronic equipment Pending CN112541636A (en)

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