CN115293584A - Overhead transmission line risk prediction method, system and terminal - Google Patents
Overhead transmission line risk prediction method, system and terminal Download PDFInfo
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Abstract
The invention relates to the field of intelligent operation and detection of overhead transmission lines, in particular to a risk prediction method for an overhead transmission line. The method comprises the following steps: dividing overhead transmission lines into different importance levels; determining the health state of the overhead transmission line channel according to the hidden danger condition in the overhead transmission line channel; setting a model to evaluate the comprehensive state of the overhead transmission line; counting the comprehensive state data of each overhead transmission line in multiple time periods, and screening out the comprehensive state data of each overhead transmission line in multiple time periods to have periodic hidden dangers; and predicting the hidden danger condition by utilizing regression analysis. The invention can carry out classification management and risk hidden danger prediction analysis on the overhead transmission line based on the comprehensive state, provides auxiliary decision for maintainers, strengthens the inspection force on the high-risk overhead transmission line, more pertinently controls the safety of the overhead transmission line and ensures the stable operation of the overhead transmission line.
Description
Technical Field
The invention relates to the field of intelligent operation and detection of overhead transmission lines, in particular to a method, a system and a terminal for predicting risk of an overhead transmission line.
Background
As an important national infrastructure, the safety and stability of the overhead transmission line are related to the production and life of people. Overhead transmission line bears high load and works in uncertain natural environment in the long-distance electric energy transmission process, needs to carry out a large amount of overhauls and maintains. Because overhead transmission line is huge, the mode of regularly patrolling and examining consumes a large amount of manpowers and can't carry out real-time supervision and processing to the anomaly.
With the upgrading of the overhead transmission line maintenance technology in recent years, the visual remote inspection of the overhead transmission line channel is widely applied, has the capability of acquiring the state of the transmission channel in real time, but currently only stays in the abnormal state, lacks necessary auxiliary decision data in the aspects of global management and control and early defense,
disclosure of Invention
The invention provides a risk prediction method for an overhead transmission line, aiming at realizing global management and control and early defense of a transmission channel, and the method comprises the following steps:
s101, dividing overhead transmission lines into different importance levels;
s102, determining the health state of the overhead transmission line channel according to the hidden danger condition in the overhead transmission line channel;
s103, setting a model to evaluate the comprehensive state of the overhead transmission line;
s104, counting the comprehensive state data of each overhead transmission line in multiple time periods, and screening out the comprehensive state data of each overhead transmission line in multiple time periods to have a periodic hidden danger;
and S105, predicting the hidden danger situation by utilizing regression analysis.
Preferably, the dividing the overhead transmission line into different importance levels in S101 includes:
according to construction data of the overhead transmission line, the overhead transmission line is divided into a key grade, an important grade and a general grade;
the construction data includes: the voltage class of the overhead transmission line, the environment in which the overhead transmission line is located, and the type of the overhead transmission line.
Preferably, the health status includes a severe status, an abnormal status, and a normal status;
the hidden danger conditions comprise hidden danger data and hidden danger types;
the hidden danger types comprise: external potential safety hazards and periodic potential hazards;
the hidden danger data is hidden danger type information and position information obtained by carrying out image analysis on an image shot by the channel monitoring device.
Preferably, the setting model divides the comprehensive state of the overhead transmission line into three risk levels according to the importance level of the overhead transmission line and the health state of the overhead transmission line channel;
the first-level risk grade is all overhead transmission lines with the overhead transmission line channel health state being a serious state, or the overhead transmission lines with the overhead transmission line channel health state being a key grade of an abnormal state;
the secondary risk grade is an important grade and a general grade of the overhead transmission line with the abnormal state of the overhead transmission line channel health state, or a key overhead transmission line with the normal state of the overhead transmission line channel health state;
the third-level risk level is an important level and a general level of the overhead transmission line with the normal state of the health state of the overhead transmission line channel.
Preferably, the comprehensive state data comprises hidden danger conditions, hidden danger occurrence time, accidents caused by the hidden dangers and accident occurrence time;
and generating time sequence data according to the time of occurrence of the hidden danger and the time of occurrence of the accident.
Preferably, the regression analysis is to analyze the time series data by using an ARMA model to determine the possibility of a certain type of risk potential in a certain period of time in the future.
Preferably, the key hierarchy includes: a national grid important power transmission channel and a provincial company important power transmission channel;
the importance levels include: the system comprises an important passage at the grade of the city, 500kV or more overhead transmission lines outside various important transmission passages, a direct current near-zone 220kV overhead transmission line, a 35-110kV overhead transmission line with a single passage for supplying power to important users, an old overhead transmission line defined by the state network, a three-span section, other important lines or line sections, and the like, wherein the sections are different according to the overhead transmission line.
Preferably, the external safety hazards include: crane pump truck construction, violation construction, foreign matter short circuit, tree line discharge and smoke and fire short circuit;
the periodic hidden danger comprises the following steps: bird hidden danger and tree ultrahigh hidden danger.
The application also provides an overhead transmission line risk prediction system, and the system includes: the system comprises an overhead power transmission line channel, an image monitoring device and a risk prediction terminal;
the risk prediction terminal acquires the hidden danger condition in the overhead transmission line channel through an image monitoring device;
a grade division module, a comprehensive state evaluation module, a screening module and a calculation module are arranged in the risk prediction terminal;
the grading module is used for grading the overhead transmission line into different important grades;
the comprehensive state evaluation module is used for setting a model to evaluate the comprehensive state of the overhead transmission line;
the screening module is used for screening out the periodic hidden danger from the comprehensive state data of each overhead transmission line in a plurality of time periods;
and the calculation module is used for predicting the hidden danger conditions by utilizing regression analysis.
The application also provides an electronic device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, and is characterized in that the processor implements the steps of the overhead transmission line risk prediction method when executing the program.
According to the technical scheme, the invention has the following advantages:
according to the invention, risk prediction is realized by carrying out graded management on the overhead transmission line, so that the safe operation capacity and the maintenance efficiency of the overhead transmission line are improved. Through analyzing external potential safety hazards and periodic potential hazards, auxiliary decisions can be provided for maintainers, the inspection force of the high-risk overhead transmission line is enhanced, safety control is carried out on the overhead transmission line in a more targeted mode, the working efficiency is improved, and stable operation of the overhead transmission line is guaranteed.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings used in the description will be briefly introduced, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
Fig. 1 is a schematic diagram of a risk prediction method for an overhead transmission line.
Fig. 2 is a schematic diagram of an overhead transmission line risk prediction system.
Fig. 3 is a flowchart of a risk prediction method for an overhead transmission line.
In the figure: 1. the system comprises an overhead power transmission line channel, 2, an image monitoring device, 3, a risk prediction terminal, 30, a screening module, 31, a calculation module, 32, a comprehensive state evaluation module and 33, and a grading module.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the 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.
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the application. One skilled in the relevant art will recognize, however, that the subject matter of the present application can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and so forth. In other instances, well-known methods, devices, implementations, or operations have not been shown or described in detail to avoid obscuring aspects of the application.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
The embodiment of the application provides a method, a system and a terminal for predicting the risk of an overhead transmission line, which can effectively improve the interpretability of a screened historical time sequence and provide decision reference for prediction. An exemplary application of the time-series prediction apparatus provided in the embodiments of the present application is described below, and the apparatus provided in the embodiments of the present application may be implemented as various types of user terminals such as a notebook computer, a tablet computer, a desktop computer, a set-top box, a mobile device (e.g., a mobile phone, a portable music player, a personal digital assistant, a dedicated messaging device, and a portable game device), and may also be implemented as a server.
In some embodiments, the electronic device may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a network service, cloud communication, a middleware service, a domain name service, a security service, a CDN, and a big data and artificial intelligence platform. The processor may be, but is not limited to, a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, a smart voice interaction device, a smart home appliance, a vehicle-mounted terminal, and the like. The processor and the electronic device may be directly or indirectly connected through wired or wireless communication, which is not limited in the embodiments of the present application.
Before describing the overhead transmission line risk prediction method provided by the embodiment of the present application in detail, a priori knowledge, pearson correlation coefficients, an ARMA model, and regression analysis related in the embodiments of the present application are briefly introduced.
A priori knowledge is a form of rationality that precedes the demonstration and does not rely on sensory or other types of experience.
The Pearson correlation coefficient is used for measuring the degree of correlation between two variables X and Y, and different correlation coefficients can be selected for calculation and analysis according to different conditions met by data. The formulas include a pearson correlation coefficient formula, a covariance formula, and a standard deviation formula.
The Pearson correlation coefficient formula is P (X.Y) = E [ (X-E (X)) (Y-E (Y)) ]/SXSY.
The covariance formula is cov (X, Y) = E { (X-E (X)) (Y-E (Y)) } = E [ XY ] -E [ X ] E [ Y ].
The standard deviation equation s = sqrt (((x 1-x) 2+ (x 2-x) 2+. -% (xn-x) 2)/n).
X is the mean of X1 to xn (also called the expectation E [ X ]).
The short circuit or open circuit fault of 5 overhead transmission lines is taken as an example and calculated through a Pearson correlation coefficient formula.
The number X of short circuit or open circuit faults of the 5 overhead transmission lines is respectively 10, 20, 30, 50 and 80, and the percentage Y of illegal construction probability near the 5 overhead transmission lines is respectively 11%, 12%, 13%, 15% and 18% (using 0.11, 0.12, 0.13, 0.15 and 0.18) in the period of June. The Pearson correlation coefficient calculation process is as follows:
the covariance of the X, Y variables of the molecule is first calculated using the formula E [ XY ] -E [ X ] E [ Y ]:
10*0.11＝1.1
20*0.12＝2.4
30*0.13＝3.9
50*0.15＝7.5
80*0.18＝14.4
E[XY]＝(1.1+2.4+3.9+7.5+14.4)/5＝5.86
E[X]＝(10+20+30+50+80)/5＝38
E[Y]＝(0.11+0.12+0.13+0.15+0.18)/5＝0.138
the covariance cov (X, Y) was calculated to result in 5.86-38X 0.138=0.616.
The standard deviations of the denominators X and Y are then calculated, which has resulted in the mean values of X and Y (expected values E [ X ], E [ Y ]) being 38 and 0.138, respectively.
Calculating the X standard deviation:
(10-38)^2＝784
(20-38)^2＝324
(30-38)^2＝64
(50-38)^2＝144
(80-38)^2＝1764
SX＝sqrt((784+324+64+144+1764)/5)＝24.81935...
calculating the standard deviation of Y:
(0.11-0.138)^2＝0.000784
(0.12-0.138)^2＝0.000324
(0.13-0.138)^2＝0.000064
(0.15-0.138)^2＝0.000144
(0.18-0.138)^2＝0.001764
SY＝sqrt((0.000784+0.000324+0.000064+0.000144+0.001764)/5)＝0.024819...
the SXSY result is calculated to be 24.81935.. 0.024819. =0.616
Finally, the Pearson correlation coefficient of 0.616/0.616=1,X, Y positive correlation is obtained. Namely, the violation construction near the overhead transmission line has positive correlation with the short circuit or open circuit fault of the overhead transmission line.
The ARMA model is used for modeling a stable time sequence, particularly constructing a mathematical model with parameters according to an observed value X of the time sequence, reflecting a dynamic system rule for generating the time sequence X, and providing a basis for forecasting, controlling and feature extraction of the time sequence.
The time sequence refers to time sequence data formed by arranging numerical values of certain statistical index of a certain phenomenon in different time according to time sequence.
Regression analysis is a statistical analysis method for determining the interdependent quantitative relationship between two or more variables, and is mainly characterized by establishing a regression model between a dependent variable Y and an independent variable X influencing the dependent variable Y, measuring the influence capacity of the independent variable X on the dependent variable Y and further predicting the development trend of the dependent variable Y.
Unless defined otherwise, all technical and scientific terms used in the examples of this application have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the embodiments of the present application is for the purpose of describing the embodiments of the present application only and is not intended to be limiting of the present application.
The method for predicting the risk of the overhead transmission line provided by the present application is described in detail below with reference to specific embodiments.
In one embodiment, a site has a large number of overhead transmission lines, including: 2 1000kV, 2 800kV, 16 500kV, 122 220kV, 154 110kV and 28 kV, and 324 in total. Except for important channels of national grid, provincial company and city level, 3 overhead transmission lines of 35kV are lines for supplying power to important users, and 23 three-span sections and 2 old lines are arranged. Overhead transmission lines in the area range relate to 2 ten thousand poles and towers, wherein part of the towers are provided with transmission channel image monitoring devices 2 (14000 poles in total and cover 7000 poles and towers), each tower is provided with two devices for acquiring channel data in opposite directions, and the coverage rate is about 35%. The collected image data can be subjected to image analysis at the cloud based on an artificial intelligence algorithm, various hidden dangers such as machinery, smoke and fire, foreign matters and trees can be effectively identified, and 40 months of data are accumulated at present.
The embodiment performs classification management and risk potential prediction analysis on the overhead transmission line based on the data, determines classification of each line, and predicts the risk potential.
S101, according to the construction data of the overhead transmission lines, performing importance level division on 324 overhead transmission lines.
The key ranks include: the national grid important power transmission channel and the provincial company important power transmission channel.
The importance levels include: the system comprises an important passage at the grade of the city, 500kV or more overhead transmission lines outside various important transmission passages, a direct current near-zone 220kV overhead transmission line, a 35-110kV overhead transmission line with a single passage for supplying power to important users, an old overhead transmission line defined by the state network, a three-span section and other important lines or line sections.
The general grade is other overhead transmission lines.
The important grading principle is as shown above, and 4 key overhead transmission lines, 164 important overhead transmission lines and 156 common lines are determined in total.
And S102, judging the health state of the overhead transmission line channel 1 according to the hidden danger data condition in the image of the overhead transmission line channel 1 identified by the image analysis model.
The severe states include: and a crane and a pump truck are arranged in the protection area for construction.
The abnormal state includes: violation of regulations, foreign matter short circuit, tree line discharge, smoke and fire short circuit.
The normal state includes: there was no abnormality.
The division basis is as shown above, taking data of 11 am on a certain day of a certain month in a certain year as an example, as long as a hidden danger that the above conditions are met appears in a certain section, the health state of the power transmission channel of the overhead power transmission line is a corresponding state, it is determined in total that 86 lines with serious health states of the channel 1 of the overhead power transmission line are present, 21 abnormal data of the health states of the channel 1 of the overhead power transmission line are present, and 217 lines are present normally.
S103, determining the comprehensive state of the overhead transmission line according to the importance level of the overhead transmission line, the channel health state and the set model.
The division is based on the data obtained in step S101 and step S102, and it is determined that 89 lines, 20 lines and 217 lines of the overhead transmission line with the integrated state of 1 level, 2 levels and 3 levels are determined.
Data distribution is as above shown, this application shoots the transmission channel through installation image monitoring device 2, carries out image analysis to the image that the channel monitoring device was shot through artificial intelligence technique, obtains hidden danger type and position automatically, and then realizes whether normally to the passageway health grade. According to the method and the device, the power transmission channels with the hidden dangers are counted, so that the high-risk areas of the power transmission channels and the low-risk power transmission channels with the lower hidden dangers are determined. The image monitoring device 2 and the corresponding power transmission channel protection equipment are preferentially installed in the high-risk areas.
S104, collecting the comprehensive state data of a certain overhead transmission line in two years, classifying according to the channel hidden danger, and screening out the hidden danger with periodicity.
In the following description of the present application, the terms "first \ second" merely distinguish similar objects and do not represent specific ordering for the objects, and it is understood that "first \ second" may be interchanged with specific order or sequence where permitted, so that the embodiments of the present application described herein can be implemented in an order other than that shown or described herein.
In the region, a first power transmission line has 61-grade poles and towers, comprehensive state data of the line in about 40 months is stored in a line comprehensive state monitoring system, 96 pieces of data exist every day, data of about two years are taken for analysis, namely 68725 data of 2018.11.3-2020.11.2 in total 731 days are analyzed (about 1.8% of data is lost due to a network or other reasons), and the analysis results show that the comprehensive state monitoring system relates to various hidden dangers such as fireworks, bird hidden dangers, cranes, foreign conductor materials and the like.
Based on prior knowledge, the smoke and fire under the line are mainly caused by burning the straws and have periodicity; bird hidden dangers are related to seasons, foreign matters of the wires are related to wind, and both of the bird hidden dangers and the foreign matters of the wires have periodicity. For the potential hazards of a crane, two groups of data 2018.11.3-2019.11.2 and 2019.11.3-2020.11.2 are respectively taken, and data with missing corresponding time is deleted, if 2018.12.17:
sequence 1:10010000000010000 … …
Sequence 2:00000000000000100 … …
Partial data is shown as above, data of 2020.2.29 is removed from the sequence 2, wherein 1 represents that a crane is present at the moment, 0 represents that no crane hidden danger exists, and the pearson coefficient, r ≈ 0.13, is calculated and has no periodicity.
S105, carrying out regression analysis on the periodic hidden danger data of the first overhead transmission line, namely smoke and fire, bird hidden dangers and lead foreign matters, and predicting the time of high occurrence of the hidden dangers. Through the analysis of an ARMA model, the hidden danger and high-rise time of the first overhead transmission line is determined as follows: the time of easy fire and smoke is 12 months to 3 months in the next year, the season of high risk of bird hidden danger is 5 months to 9 months, and the season of high risk of foreign matter in the wire is 3 months and 10 months.
The second overhead transmission line is taken as an example to further specifically explain that the second overhead transmission line and the first overhead transmission line are in different environments and different geographical positions. When the second overhead transmission line is analyzed, the crane is found to have periodicity, and after confirmation, a large number of greenhouses are found below the line, intensive crane operation can be carried out in autumn, and activities such as greenhouse construction, renovation, greenhouse film replacement and the like can be carried out. And (4) performing predictive analysis on the crane type of the second overhead transmission line, and determining that the time for easily generating the crane hidden danger is 10 and 11 months.
In the embodiment, the overhead transmission line is subjected to classification management and risk potential prediction analysis based on the overhead transmission line construction data and the channel potential data, classification of each line is determined, the risk potential is predicted, a maintainer can perform real-time abnormal handling and serve as an auxiliary decision according to the classification and prediction result of each line, and advance layout and manpower arrangement are performed on long-term maintenance.
Aiming at the defects that necessary auxiliary decision data are lacked in the aspects of overhead transmission line global management and control and early defense, the invention aims to provide a method for overhead transmission line comprehensive state evaluation and management and control of hierarchical management and hidden danger risk prediction. In order to achieve the technical effects of the present application, the present application provides specific embodiments for further description.
The specific implementation mode is as follows:
s201, dividing the importance of the overhead transmission line according to the construction data of the overhead transmission line;
step S201, the overhead transmission line construction data refers to: the grade of the overhead transmission line, the environment, the power supply of important users, the crossing of railways, the crossing of highways, the crossing of other transmission channels, the old overhead transmission line and other related data.
The division of the transmission importance in step S201 refers to dividing the voltage level, the environment, the state of the overhead transmission line, and the like into three different levels, namely, key, important, and general, specifically as follows: the key overhead transmission line refers to a national grid important transmission channel and a provincial company important transmission channel; the important overhead transmission line refers to an important passage of a city grade, 500kV or more overhead transmission lines outside various important transmission passages, a direct-current near-zone 220kV overhead transmission line, a 35-110kV overhead transmission line with a single passage for supplying power to important users, an old overhead transmission line defined by a national network, a three-span section (spanning railways, spanning highways and spanning other transmission passages), other important overhead transmission lines or overhead transmission line sections; the general overhead power transmission line refers to other overhead power transmission lines excluding the above-described case.
S202, judging the health state of the overhead transmission line channel 1 according to the hidden danger data condition in the overhead transmission line channel 1;
step S202, the hidden danger data in the overhead transmission line channel 1 refers to that the image shot by the channel monitoring device is subjected to image analysis through an artificial intelligence technology, and the type and the position of the hidden danger are automatically obtained, wherein the related hidden danger data comprise external potential safety hazards and the like, wherein the external potential safety hazards can cause faults and short circuits of the overhead transmission line, such as crane pump truck construction, illegal construction, foreign matter of wires, tree wire discharge (high-voltage wires and trees are in contact discharge), smoke and fire and the like.
The step S202 of determining the health status of the overhead transmission line channel 1 means that the health status is classified into three different levels of serious, abnormal and normal according to the information of the type and position of the hidden danger in the channel, which is specifically as follows: the serious condition is that a crane and a pump truck are constructed in the protection area; the abnormity refers to illegal construction, foreign matter short circuit, tree line discharge and smoke and fire short circuit; normal means no abnormality.
S203, determining the comprehensive state of the overhead transmission line according to the importance of the overhead transmission line, the channel health state and the specific model;
the specific model in step S203 is determined by combining the total degree of overhead transmission lines and the channel health degree, and is divided into three levels of 1 to 3, where the 1-level risk condition is the most serious, specifically as follows: the first-level risk grades are all overhead transmission lines with the health state of the overhead transmission line channel 1 being in a serious state, or the key-level overhead transmission lines with the health state of the overhead transmission line channel 1 being in an abnormal state;
the secondary risk grade is an important grade and a general grade of the overhead transmission line with the health state of the overhead transmission line channel 1 being an abnormal state, or a key overhead transmission line with the health state of the overhead transmission line channel 1 being a normal state;
the third-level risk level is an important level and a general level of the overhead transmission line with the health state of the overhead transmission line channel 1 being a normal state.
S204, collecting comprehensive state data of a certain overhead transmission line in two years, classifying according to the channel hidden danger, and screening out the hidden danger with periodicity;
the last two years of comprehensive state data of a certain overhead transmission line in step S204 refers to data recorded after comprehensive state monitoring is performed on the certain overhead transmission line, specifically, the data is recorded every 30 minutes at least once, and the data is recorded every 60 minutes at least at other time periods.
And S204, classifying the hidden danger of the channel refers to classifying the hidden danger according to cranes, pump trucks, other machines, smoke and fire, foreign matters of wires, hidden dangers of birds and tree heights.
The step S204 of screening out the hidden danger with periodicity means to perform data mining analysis on the data classified according to the channel hidden danger in the step d to find out the hidden danger with periodicity, and there are two determination methods:
(1) According to the priori knowledge, if the smoke and fire hidden dangers have strong correlation with seasons and climates, and the foreign matters of the conducting wires have strong correlation with seasons, wind and the like, the model is set to be correlated with the natural period;
(2) The method is characterized in that the method is determined by calculating a Pearson coefficient, hidden danger data of a first year and hidden danger data of a second year are converted into two sequences with equal length according to the occurrence time and the occurrence frequency of the mechanical hidden dangers, the Pearson coefficient is calculated, if strong correlation exists, periodicity exists, otherwise, periodicity does not exist, and particularly, for the same hidden danger such as a crane, the periodicity is obviously different in different regions, for example, the periodicity exists based on data analysis of lines crossing greenhouse regions, the periodicity exists, the periodicity is related to the construction of greenhouses and the renovation of greenhouse films in autumn, and the periodicity does not exist in the analysis of the hidden danger data of cranes in other regions.
And S205, carrying out regression analysis on the periodic potential hazard data of the overhead transmission line, predicting the time of high occurrence of the potential hazard, and the like. The regression analysis in step S205 is to analyze time series data by using an ARMA model to determine the possibility of a certain type of risk potential in a certain period of time in the future, and the present invention uses the model to perform data prediction analysis.
Through the above specific embodiments, the present invention has the following advantages and beneficial effects:
(1) The invention can carry out classification management and risk hidden danger prediction analysis on the overhead transmission line based on the comprehensive state, provides auxiliary decision for maintainers, strengthens the inspection force on the high-risk overhead transmission line, more pointedly controls the safety of the overhead transmission line, improves the working efficiency and ensures the stable operation of the overhead transmission line.
(2) According to the invention, the hierarchical management of the overhead transmission line is combined with the health state of the transmission channel, so that the improvement from data to information is realized, and support is provided for assistant decision making.
(3) The invention carries out prediction analysis based on the long-term comprehensive state data of the overhead transmission line, realizes the promotion from information to knowledge, and further enhances the intelligent operation and inspection efficiency and intelligent level of the overhead transmission line.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. 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 invention. Thus, the present invention 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 risk prediction method for an overhead transmission line is characterized by comprising the following steps:
s101, dividing overhead transmission lines into different importance levels;
s102, determining the health state of the overhead transmission line channel according to the hidden danger condition in the overhead transmission line channel;
s103, setting a model to evaluate the comprehensive state of the overhead transmission line;
s104, counting the comprehensive state data of each overhead transmission line in multiple time periods, and screening out the comprehensive state data of each overhead transmission line in multiple time periods to have a periodic hidden danger;
and S105, predicting the hidden danger condition by utilizing regression analysis.
2. The overhead transmission line risk prediction method of claim 1,
in S101, dividing the overhead transmission line into different importance levels includes:
according to the construction data of the overhead transmission line, the overhead transmission line is divided into a key grade, an important grade and a general grade;
the construction data includes: the voltage class of the overhead transmission line, the environment in which the overhead transmission line is located, and the type of the overhead transmission line.
3. The overhead transmission line risk prediction method of claim 1,
the health state comprises a severe state, an abnormal state and a normal state;
the hidden danger conditions comprise hidden danger data and hidden danger types;
the hidden danger types include: external potential safety hazards and periodic potential hazards;
the hidden danger data is hidden danger type information and position information obtained by carrying out image analysis on an image shot by the channel monitoring device.
4. The overhead transmission line risk prediction method of claim 1,
the setting model divides the comprehensive state of the overhead transmission line into three risk levels according to the importance level of the overhead transmission line and the health state of the overhead transmission line channel;
the first-level risk grade is all overhead transmission lines with the overhead transmission line channel health state being a serious state, or the overhead transmission lines with the overhead transmission line channel health state being a key grade of an abnormal state;
the secondary risk grade is an important grade and a general grade of the overhead transmission line with the abnormal state of the overhead transmission line channel health state, or a key overhead transmission line with the normal state of the overhead transmission line channel health state;
the third-level risk level is an important level and a general level of the overhead transmission line with the normal state of the health state of the overhead transmission line channel.
5. The overhead transmission line risk prediction method of claim 1,
the comprehensive state data comprises hidden danger conditions, hidden danger occurrence time, accidents caused by the hidden dangers and accident occurrence time;
and generating time sequence data according to the time of occurrence of the hidden danger and the time of occurrence of the accident.
6. The overhead transmission line risk prediction method of claim 4,
the regression analysis is to analyze time series data by using an ARMA model to determine the possibility of certain risk hidden dangers in a certain period of time in the future.
7. The overhead transmission line risk prediction method of claim 2,
the key ranks include: the national grid important power transmission channel and the provincial and company important power transmission channel;
the importance levels include: the system comprises an important passage at the grade of the city, 500kV or more overhead transmission lines outside various important transmission passages, a direct current near-zone 220kV overhead transmission line, a 35-110kV overhead transmission line with a single passage for supplying power to important users, an old overhead transmission line defined by the state network, a three-span section, other important lines or line sections, and the like, wherein the sections are different according to the overhead transmission line.
8. The overhead transmission line risk prediction method according to claim 3,
external safety hazards include: crane pump truck construction, violation construction, foreign matter short circuit, tree line discharge and smoke and fire short circuit;
the periodic hidden danger comprises the following steps: bird hidden danger and tree ultrahigh hidden danger.
9. An overhead transmission line risk prediction system, characterized in that, the system includes: the system comprises an overhead power transmission line channel, an image monitoring device and a risk prediction terminal;
the risk prediction terminal acquires the hidden danger condition in the overhead transmission line channel through an image monitoring device;
a grade division module, a comprehensive state evaluation module, a screening module and a calculation module are arranged in the risk prediction terminal;
the grading module is used for grading the overhead transmission line into different important grades;
the comprehensive state evaluation module is used for setting a model to evaluate the comprehensive state of the overhead transmission line;
the screening module is used for screening out the comprehensive state data of each overhead transmission line in a plurality of time periods to have the periodic hidden danger;
and the calculation module is used for predicting the hidden danger conditions by utilizing regression analysis.
10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program performs the steps of the overhead transmission line risk prediction method according to any one of claims 1 to 8.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117350548A (en) * | 2023-12-04 | 2024-01-05 | 国网浙江省电力有限公司宁波供电公司 | Power distribution equipment potential safety hazard investigation method |
| CN117572158A (en) * | 2024-01-16 | 2024-02-20 | 武汉邢仪新未来电力科技股份有限公司 | Wave recording positioning fault indication method, system and indicator |
| CN117830032A (en) * | 2024-03-06 | 2024-04-05 | 广州长川科技有限公司 | Method and system for monitoring snapshot and risk assessment of power transmission line network |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117350548A (en) * | 2023-12-04 | 2024-01-05 | 国网浙江省电力有限公司宁波供电公司 | Power distribution equipment potential safety hazard investigation method |
| CN117350548B (en) * | 2023-12-04 | 2024-04-16 | 国网浙江省电力有限公司宁波供电公司 | Power distribution equipment potential safety hazard investigation method |
| CN117572158A (en) * | 2024-01-16 | 2024-02-20 | 武汉邢仪新未来电力科技股份有限公司 | Wave recording positioning fault indication method, system and indicator |
| CN117572158B (en) * | 2024-01-16 | 2024-03-29 | 武汉邢仪新未来电力科技股份有限公司 | Wave recording positioning fault indication method, system and indicator |
| CN117830032A (en) * | 2024-03-06 | 2024-04-05 | 广州长川科技有限公司 | Method and system for monitoring snapshot and risk assessment of power transmission line network |
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