CN114219120A - Fault type prediction method and device for power transmission line - Google Patents
Fault type prediction method and device for power transmission line Download PDFInfo
- Publication number
- CN114219120A CN114219120A CN202111262379.XA CN202111262379A CN114219120A CN 114219120 A CN114219120 A CN 114219120A CN 202111262379 A CN202111262379 A CN 202111262379A CN 114219120 A CN114219120 A CN 114219120A
- Authority
- CN
- China
- Prior art keywords
- fault
- transmission line
- power transmission
- established
- machine learning
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 230000005540 biological transmission Effects 0.000 title claims abstract description 149
- 238000000034 method Methods 0.000 title claims abstract description 43
- 238000010801 machine learning Methods 0.000 claims abstract description 49
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 9
- 230000006378 damage Effects 0.000 claims description 32
- 239000004020 conductor Substances 0.000 claims description 18
- 238000012549 training Methods 0.000 claims description 15
- 238000001556 precipitation Methods 0.000 claims description 14
- 238000003860 storage Methods 0.000 claims description 11
- 239000012212 insulator Substances 0.000 claims description 10
- 230000008569 process Effects 0.000 claims description 8
- 230000002265 prevention Effects 0.000 abstract description 2
- 230000009467 reduction Effects 0.000 abstract description 2
- 238000010586 diagram Methods 0.000 description 9
- 230000006870 function Effects 0.000 description 8
- 238000004590 computer program Methods 0.000 description 7
- 238000012545 processing Methods 0.000 description 4
- 230000004913 activation Effects 0.000 description 2
- 238000007405 data analysis Methods 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 230000007246 mechanism Effects 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 230000009471 action Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000010219 correlation analysis Methods 0.000 description 1
- 230000001808 coupling effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000016507 interphase Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000003062 neural network model Methods 0.000 description 1
- 210000002569 neuron Anatomy 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 125000006850 spacer group Chemical group 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/048—Activation functions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- Health & Medical Sciences (AREA)
- Data Mining & Analysis (AREA)
- Economics (AREA)
- General Engineering & Computer Science (AREA)
- Evolutionary Computation (AREA)
- General Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Biomedical Technology (AREA)
- Computing Systems (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Molecular Biology (AREA)
- Strategic Management (AREA)
- Computational Linguistics (AREA)
- Human Resources & Organizations (AREA)
- Biophysics (AREA)
- Marketing (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Computational Biology (AREA)
- Primary Health Care (AREA)
- Evolutionary Biology (AREA)
- Water Supply & Treatment (AREA)
- Public Health (AREA)
- Development Economics (AREA)
- Game Theory and Decision Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention relates to the technical field of power grid disaster prevention and reduction prediction, and particularly provides a method and a device for predicting fault types of a power transmission line, wherein the method comprises the following steps: acquiring real-time data of fault influence factors of the power transmission line; inputting the real-time data of the fault influence factors into a pre-established machine learning model, acquiring the output of the pre-established machine learning model, and determining the fault type of the power transmission line based on the output of the pre-established machine learning model. According to the technical scheme provided by the invention, the information of the environment where the line is located and the information of the line can be utilized, and the fault prediction model constructed by a machine learning algorithm is used for realizing the accurate prediction of the fault type of the power transmission line.
Description
Technical Field
The invention relates to the technical field of power grid disaster prevention and reduction prediction, in particular to a method and a device for predicting fault types of a power transmission line.
Background
The trend that meteorological disasters increase year by year brings serious threat to the safe operation of a power grid, and power transmission lines are distributed all over the country and span different geographical climate areas, so that line faults are frequent. The harm of meteorological disasters to the power transmission line mainly comprises the following steps: 1) mechanical damage to the line, such as hardware fatigue wear, strand breakage and breakage of the lead, breakage of sub-leads and interphase spacers, twisting of cross arm tower heads and even tower collapse; 2) electrical hazards, such as tripping power outages or causing transmission line burns, and even grid splitting. Because the line fault caused by natural meteorological disasters is a result of coupling action of multiple factors, and the disaster-causing mechanism is complex, the fault result of the power transmission line caused by the meteorological disasters predicted by the traditional theoretical analysis method is not ideal.
At present, related researches on the real-time prediction and judgment of tripping in line faults are lacked, and two main directions of line tripping are mainly predicted through lightning location and line tripping is predicted based on lightning prediction. The method only aims at the prediction of one fault type of the tripping of the power transmission line, and cannot realize the prediction of the multi-type occurrence probability of the power transmission line fault.
Disclosure of Invention
In order to overcome the defects, the invention provides a method and a device for predicting the fault type of a power transmission line.
In a first aspect, a method for predicting a fault type of a power transmission line is provided, where the method for predicting a fault type of a power transmission line includes:
acquiring real-time data of fault influence factors of the power transmission line;
inputting the real-time data of the fault influence factors into a pre-established machine learning model, acquiring the output of the pre-established machine learning model, and determining the fault type of the power transmission line based on the output of the pre-established machine learning model.
Preferably, the process for determining the fault influence factors of the power transmission line includes:
and selecting the established fault influence related to the fault of the power transmission line from the established fault influence factors as the fault influence factors of the power transmission line.
Further, the selecting the predetermined fault influence related to the occurrence of the fault of the power transmission line from the predetermined fault influence factors as the fault influence factors of the power transmission line includes:
calculating a correlation coefficient between the established fault influence factor and the fault of the power transmission line;
and if the correlation coefficient is larger than the preset value, the established fault influence factor is the fault influence factor of the power transmission line, otherwise, the established fault influence factor is not the fault influence factor of the power transmission line.
Further, the calculating a correlation coefficient between the given fault influence factor and the fault occurrence of the power transmission line includes:
and calculating a correlation coefficient between the established fault influence factor and the fault of the power transmission line by adopting a spearman grade algorithm.
Further, the predetermined fault affecting factor includes at least one of: terrain factors, weather factors and structural factors during waving.
Further, the terrain factor includes at least one of the following: the terrain, the altitude and the line trend of the dancing section;
the topographical factors include at least one of: altitude and line trend of the dancing section;
the waving weather factor comprises at least one of the following: included angles between wind speed, wind direction and the line trend of the dancing section, temperature, humidity, precipitation form and wire icing thickness;
the structural factor includes at least one of: span, diameter of the conductor, number of loops of the tower, type of the conductor, arrangement mode of the conductor, number of split conductors and voltage level.
Further, the terrain comprises at least one of the following: mountainous, hilly and plain; the precipitation form comprises at least one of: rime, and snow; the wire arrangement comprises at least one of the following: vertical, horizontal and triangular.
Further, the fault influencing factor of the power transmission line comprises at least one of the following factors: temperature, humidity, wind speed, precipitation pattern, wire diameter, span, number of wire splits, and voltage rating.
Preferably, the fault type is at least one of: ground wire damage, insulator damage, hardware damage and line tripping.
Further, the process of building the pre-built machine learning model includes:
acquiring fault influence factor data of the power transmission line at historical time and fault type codes of the power transmission line at historical time period;
and taking the fault influence factor data of the power transmission line at the historical moment as input layer training data of the initial machine learning model, coding each fault type corresponding to the power transmission line at the historical moment as output layer training data of the initial machine learning model, training the initial machine learning model, and acquiring the pre-established machine learning model.
Further, if the transmission line has a ground wire damage fault in the historical period, the fault type code of the transmission line in the historical period is 1000;
if the insulator damage fault occurs to the power transmission line in the historical period, the fault type code of the power transmission line in the historical period is 0100;
if the hardware damage fault occurs to the transmission line in the historical period, the fault type code of the transmission line in the historical period is 0010;
if the line trip fault occurs to the transmission line in the historical period, the fault type code of the transmission line in the historical period is 0001.
In a second aspect, a failure type prediction apparatus for a power transmission line is provided, where the failure type prediction apparatus for the power transmission line includes:
the acquisition module is used for acquiring real-time data of fault influence factors of the power transmission line;
and the prediction module is used for inputting the real-time data of the fault influence factors into a pre-established machine learning model, acquiring the output of the pre-established machine learning model, and determining the fault type of the power transmission line based on the output of the pre-established machine learning model.
In a third aspect, a storage device is provided, in which a plurality of program codes are stored, and the program codes are suitable for being loaded and executed by a processor to execute the method for predicting the fault type of the power transmission line according to any one of the above technical solutions.
In a fourth aspect, a control device is provided, which includes a processor and a storage device, where the storage device is adapted to store a plurality of program codes, and the program codes are adapted to be loaded and run by the processor to execute the method for predicting the fault type of the power transmission line according to any one of the above technical solutions.
One or more technical schemes of the invention at least have one or more of the following beneficial effects:
the invention provides a method and a device for predicting the fault type of a power transmission line, which comprise the following steps: acquiring real-time data of fault influence factors of the power transmission line; inputting the real-time data of the fault influence factors into a pre-established machine learning model, acquiring the output of the pre-established machine learning model, and determining the fault type of the power transmission line based on the output of the pre-established machine learning model. According to the technical scheme provided by the invention, the information of the environment where the line is located and the information of the line can be utilized, and the fault prediction model constructed by a machine learning algorithm is used for realizing the accurate prediction of the fault type of the power transmission line.
Drawings
Fig. 1 is a schematic flow chart illustrating the main steps of a method for predicting the fault type of a power transmission line according to an embodiment of the present invention;
fig. 2 is a main configuration block diagram of a failure type prediction apparatus of a power transmission line according to an embodiment of the present invention.
Detailed Description
The following describes embodiments of the present invention in further detail with reference to the accompanying drawings.
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, 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 some, but not all, embodiments of the present invention. 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.
With the continuous development of power grids, a large amount of data records of line faults caused by meteorological disasters are accumulated at present, and a large amount of information of the relation between the meteorology and the line faults is hidden in the data. The machine learning algorithm is a method for searching the relation between data by analyzing big data, can analyze the relation between complex data, and overcomes the defect that the traditional method is used for analyzing the relation of each physical quantity by a simple mechanism.
The method for predicting the fault type of the power transmission line can utilize the information of the environment where the line is located and the information of the line, including the information of temperature, humidity, wind speed, precipitation form, conductor span, sectional area and the like, and carry out prediction and judgment on the fault type of the power transmission line through a fault prediction model constructed by a machine learning algorithm, wherein the fault type mainly comprises four types of conductor and ground wire damage, insulator damage, hardware damage and line tripping.
Referring to fig. 1, fig. 1 is a flow chart illustrating main steps of a method for predicting a fault type of a power transmission line according to an embodiment of the present invention. As shown in fig. 1, the method for predicting the fault type of the power transmission line in the embodiment of the present invention mainly includes the following steps:
step S101: acquiring real-time data of fault influence factors of the power transmission line;
step S102: inputting the real-time data of the fault influence factors into a pre-established machine learning model, acquiring the output of the pre-established machine learning model, and determining the fault type of the power transmission line based on the output of the pre-established machine learning model.
In this embodiment, the process of determining the fault influence factor of the power transmission line includes:
and selecting the established fault influence related to the fault of the power transmission line from the established fault influence factors as the fault influence factors of the power transmission line.
In one embodiment, the selecting, as the fault influencing factor of the power transmission line, an established fault influencing factor related to the occurrence of the fault of the power transmission line from the established fault influencing factors includes:
calculating a correlation coefficient between the established fault influence factor and the fault of the power transmission line;
and if the correlation coefficient is larger than the preset value, the established fault influence factor is the fault influence factor of the power transmission line, otherwise, the established fault influence factor is not the fault influence factor of the power transmission line.
In this embodiment, in order to describe the degree of correlation between the transmission line fault influencing factor (generally referred to as a variable in data analysis) and the fault type more accurately, correlation analysis may be performed by calculating a correlation coefficient. And calculating a correlation coefficient between the established fault influence factor and the fault of the power transmission line by adopting a spearman grade algorithm according to the characteristic of non-normal distribution of the fault type data of the power transmission line.
In this embodiment, many faults of the power transmission line are caused by the combined action of multiple factors, and meanwhile, because the environment of the power transmission line is complex and the physical structure information of the power transmission line is more, effective influencing factors need to be screened out from the multiple factors, and irrelevant variables are removed, so that the given fault influencing factors include at least one of the following factors: terrain factors, weather factors and structural factors during waving.
In one embodiment, the terrain factor includes at least one of: the terrain, the altitude and the line trend of the dancing section;
the topographical factors include at least one of: altitude and line trend of the dancing section;
the waving weather factor comprises at least one of the following: included angles between wind speed, wind direction and the line trend of the dancing section, temperature, humidity, precipitation form and wire icing thickness;
the structural factor includes at least one of: span, diameter of the conductor, number of loops of the tower, type of the conductor, arrangement mode of the conductor, number of split conductors and voltage level.
In one embodiment, the terrain comprises at least one of: mountainous, hilly and plain; the precipitation form comprises at least one of: rime, and snow; the wire arrangement comprises at least one of the following: vertical, horizontal and triangular.
Further, the rank used in the calculation is the average value of the positions after the ranking. Calculating the influence factors of the fault types of the power transmission line and the correlation coefficient between the fault types, and screening the fault influence factors of the power transmission line according to the size of the result, wherein the fault influence factors comprise at least one of the following factors: temperature, humidity, wind speed, precipitation pattern, wire diameter, span, number of wire splits, and voltage rating.
In this embodiment, the fault type is at least one of the following: ground wire damage, insulator damage, hardware damage and line tripping.
In one embodiment, the process of building the pre-built machine learning model includes:
acquiring fault influence factor data of the power transmission line at historical time and fault type codes of the power transmission line at historical time period;
and taking the fault influence factor data of the power transmission line at the historical moment as input layer training data of the initial machine learning model, coding each fault type corresponding to the power transmission line at the historical moment as output layer training data of the initial machine learning model, training the initial machine learning model, and acquiring the pre-established machine learning model.
Further, if the transmission line has a ground wire damage fault in the historical period, the fault type code of the transmission line in the historical period is 1000;
if the insulator damage fault occurs to the power transmission line in the historical period, the fault type code of the power transmission line in the historical period is 0100;
if the hardware damage fault occurs to the transmission line in the historical period, the fault type code of the transmission line in the historical period is 0010;
if the line trip fault occurs to the transmission line in the historical period, the fault type code of the transmission line in the historical period is 0001.
Specifically, in the embodiment of the invention, the modeling judgment of the power transmission line galloping prediction machine learning algorithm is carried out, a BP neural network model is selected, and then the sample characteristics and the model generalization problem are solved. The model design adopts four-layer structure, including input layer, two-layer hidden layer, output layer, through preceding data analysis, eight-dimensional input is selected to the model, is respectively: temperature, humidity, wind speed, precipitation form, wire diameter, span, number of wire splits, and voltage rating; the output is four-dimensional, which is respectively damage of a ground wire, damage of an insulator, damage of hardware fittings and circuit tripping. The training set is obtained by randomly extracting 80% of the whole data set, the testing set adopts the rest 20% of the data set, the first three layers of activation functions of the model adopt Relu functions, and the last layer of activation functions adopt sigmoid functions according to actual needs. Model structure: the neural network comprises an input layer, two hidden layers and an output layer, wherein each layer of the middle hidden layer comprises 10 neuron nodes.
And (3) carrying out 100-800 times of unequal training on the model by using training data, continuously optimizing parameters of the model, and judging whether the model reaches a final state by using whether the prediction accuracy of the model tends to be stable or not. And inputting the test data into the trained prediction model, and comparing the prediction result with the actual result to verify the final prediction effect of the model in the aspect of power transmission line fault prediction.
Based on the same inventive concept, the present invention provides a failure type prediction apparatus for a power transmission line, as shown in fig. 2, the failure type prediction apparatus for a power transmission line includes:
the acquisition module is used for acquiring real-time data of fault influence factors of the power transmission line;
and the prediction module is used for inputting the real-time data of the fault influence factors into a pre-established machine learning model, acquiring the output of the pre-established machine learning model, and determining the fault type of the power transmission line based on the output of the pre-established machine learning model.
Preferably, the process for determining the fault influence factors of the power transmission line includes:
and selecting the established fault influence related to the fault of the power transmission line from the established fault influence factors as the fault influence factors of the power transmission line.
Further, the selecting the predetermined fault influence related to the occurrence of the fault of the power transmission line from the predetermined fault influence factors as the fault influence factors of the power transmission line includes:
calculating a correlation coefficient between the established fault influence factor and the fault of the power transmission line;
and if the correlation coefficient is larger than the preset value, the established fault influence factor is the fault influence factor of the power transmission line, otherwise, the established fault influence factor is not the fault influence factor of the power transmission line.
Further, the calculating a correlation coefficient between the given fault influence factor and the fault occurrence of the power transmission line includes:
and calculating a correlation coefficient between the established fault influence factor and the fault of the power transmission line by adopting a spearman grade algorithm.
Further, the predetermined fault affecting factor includes at least one of: terrain factors, weather factors and structural factors during waving.
Further, the terrain factor includes at least one of the following: the terrain, the altitude and the line trend of the dancing section;
the topographical factors include at least one of: altitude and line trend of the dancing section;
the waving weather factor comprises at least one of the following: included angles between wind speed, wind direction and the line trend of the dancing section, temperature, humidity, precipitation form and wire icing thickness;
the structural factor includes at least one of: span, diameter of the conductor, number of loops of the tower, type of the conductor, arrangement mode of the conductor, number of split conductors and voltage level.
Further, the terrain comprises at least one of the following: mountainous, hilly and plain; the precipitation form comprises at least one of: rime, and snow; the wire arrangement comprises at least one of the following: vertical, horizontal and triangular.
Further, the fault influencing factor of the power transmission line comprises at least one of the following factors: temperature, humidity, wind speed, precipitation pattern, wire diameter, span, number of wire splits, and voltage rating.
Preferably, the fault type is at least one of: ground wire damage, insulator damage, hardware damage and line tripping.
Further, the process of building the pre-built machine learning model includes:
acquiring fault influence factor data of the power transmission line at historical time and fault type codes of the power transmission line at historical time period;
and taking the fault influence factor data of the power transmission line at the historical moment as input layer training data of the initial machine learning model, coding each fault type corresponding to the power transmission line at the historical moment as output layer training data of the initial machine learning model, training the initial machine learning model, and acquiring the pre-established machine learning model.
Further, if the transmission line has a ground wire damage fault in the historical period, the fault type code of the transmission line in the historical period is 1000;
if the insulator damage fault occurs to the power transmission line in the historical period, the fault type code of the power transmission line in the historical period is 0100;
if the hardware damage fault occurs to the transmission line in the historical period, the fault type code of the transmission line in the historical period is 0010;
if the line trip fault occurs to the transmission line in the historical period, the fault type code of the transmission line in the historical period is 0001.
Further, the present invention provides a storage device, wherein a plurality of program codes are stored in the storage device, and the program codes are suitable for being loaded and executed by a processor to execute the method for predicting the fault type of the power transmission line according to any one of the above technical solutions.
Further, the present invention provides a control device, which includes a processor and a storage device, where the storage device is adapted to store a plurality of program codes, and the program codes are adapted to be loaded and run by the processor to execute the method for predicting the fault type of the power transmission line according to any one of the above technical solutions.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (14)
1. A method for predicting the fault type of a power transmission line is characterized by comprising the following steps:
acquiring real-time data of fault influence factors of the power transmission line;
inputting the real-time data of the fault influence factors into a pre-established machine learning model, acquiring the output of the pre-established machine learning model, and determining the fault type of the power transmission line based on the output of the pre-established machine learning model.
2. The method of claim 1, wherein the determining of the fault affecting factor of the power transmission line comprises:
and selecting the established fault influence related to the fault of the power transmission line from the established fault influence factors as the fault influence factors of the power transmission line.
3. The method of claim 2, wherein selecting the predetermined fault influence related to the fault of the transmission line from the predetermined fault influence factors as the fault influence factors of the transmission line comprises:
calculating a correlation coefficient between the established fault influence factor and the fault of the power transmission line;
and if the correlation coefficient is larger than the preset value, the established fault influence factor is the fault influence factor of the power transmission line, otherwise, the established fault influence factor is not the fault influence factor of the power transmission line.
4. The method of claim 3, wherein calculating a correlation coefficient between the established fault influencing factor and the transmission line fault comprises:
and calculating a correlation coefficient between the established fault influence factor and the fault of the power transmission line by adopting a spearman grade algorithm.
5. The method of claim 3, wherein the predetermined fault affecting factors comprise at least one of: terrain factors, weather factors and structural factors during waving.
6. The method of claim 5, wherein the terrain factor comprises at least one of: the terrain, the altitude and the line trend of the dancing section;
the topographical factors include at least one of: altitude and line trend of the dancing section;
the waving weather factor comprises at least one of the following: included angles between wind speed, wind direction and the line trend of the dancing section, temperature, humidity, precipitation form and wire icing thickness;
the structural factor includes at least one of: span, diameter of the conductor, number of loops of the tower, type of the conductor, arrangement mode of the conductor, number of split conductors and voltage level.
7. The method of claim 6, wherein the terrain comprises at least one of: mountainous, hilly and plain; the precipitation form comprises at least one of: rime, and snow; the wire arrangement comprises at least one of the following: vertical, horizontal and triangular.
8. The method of claim 6, wherein the fault affecting factors of the power transmission line include at least one of: temperature, humidity, wind speed, precipitation pattern, wire diameter, span, number of wire splits, and voltage rating.
9. The method of claim 1, wherein the fault type is at least one of: ground wire damage, insulator damage, hardware damage and line tripping.
10. The method of claim 9, wherein the pre-established machine learning model building process comprises:
acquiring fault influence factor data of the power transmission line at historical time and fault type codes of the power transmission line at historical time period;
and taking the fault influence factor data of the power transmission line at the historical moment as input layer training data of the initial machine learning model, coding each fault type corresponding to the power transmission line at the historical moment as output layer training data of the initial machine learning model, training the initial machine learning model, and acquiring the pre-established machine learning model.
11. The method of claim 10, wherein if a fault of conducting ground wire damage occurs to the transmission line in the historical period, the fault type code of the transmission line in the historical period is 1000;
if the insulator damage fault occurs to the power transmission line in the historical period, the fault type code of the power transmission line in the historical period is 0100;
if the hardware damage fault occurs to the transmission line in the historical period, the fault type code of the transmission line in the historical period is 0010;
if the line trip fault occurs to the transmission line in the historical period, the fault type code of the transmission line in the historical period is 0001.
12. A failure type prediction apparatus of a power transmission line, the apparatus comprising:
the acquisition module is used for acquiring real-time data of fault influence factors of the power transmission line;
and the prediction module is used for inputting the real-time data of the fault influence factors into a pre-established machine learning model, acquiring the output of the pre-established machine learning model, and determining the fault type of the power transmission line based on the output of the pre-established machine learning model.
13. A storage device having a plurality of program codes stored therein, wherein the program codes are adapted to be loaded and run by a processor to perform the method of fault type prediction of an electric transmission line according to any one of claims 1 to 11.
14. A control apparatus comprising a processor and a memory device, the memory device being adapted to store a plurality of program codes, characterized in that the program codes are adapted to be loaded and run by the processor to perform the method of fault type prediction of an electric power transmission line according to any one of claims 1 to 11.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111262379.XA CN114219120A (en) | 2021-10-28 | 2021-10-28 | Fault type prediction method and device for power transmission line |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111262379.XA CN114219120A (en) | 2021-10-28 | 2021-10-28 | Fault type prediction method and device for power transmission line |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114219120A true CN114219120A (en) | 2022-03-22 |
Family
ID=80696235
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111262379.XA Pending CN114219120A (en) | 2021-10-28 | 2021-10-28 | Fault type prediction method and device for power transmission line |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114219120A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116365520A (en) * | 2023-06-02 | 2023-06-30 | 中国南方电网有限责任公司超高压输电公司广州局 | Power transmission line equipment risk prediction method and device and computer equipment |
-
2021
- 2021-10-28 CN CN202111262379.XA patent/CN114219120A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116365520A (en) * | 2023-06-02 | 2023-06-30 | 中国南方电网有限责任公司超高压输电公司广州局 | Power transmission line equipment risk prediction method and device and computer equipment |
| CN116365520B (en) * | 2023-06-02 | 2023-10-27 | 中国南方电网有限责任公司超高压输电公司广州局 | Power transmission line equipment risk prediction method and device and computer equipment |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107220775B (en) | Active power distribution network multi-view cooperative vulnerability assessment method considering information system effect | |
| CN107633320B (en) | Power grid line importance degree evaluation method based on meteorological prediction and risk evaluation | |
| CN110598726A (en) | Transmission tower bird damage risk prediction method based on random forest | |
| CN107784395A (en) | A kind of electric power line ice-covering thickness Forecasting Methodology based on LSTM artificial neural networks | |
| CN106469356A (en) | Transmission facility state evaluation based on multidimensional data and risk analysis method and device | |
| CN113191687B (en) | Elastic power distribution network panoramic information visualization method and system | |
| CN114219120A (en) | Fault type prediction method and device for power transmission line | |
| CN112668821A (en) | Distribution line risk analysis method based on insulator fault probability of sand blown region | |
| CN114912355A (en) | Method and device for predicting short-term icing of power transmission line and storage medium | |
| CN116050599A (en) | Line icing fault prediction method, system, storage medium and equipment | |
| Billinton et al. | Consideration of multi-state weather models in reliability evaluation of transmission and distribution systems | |
| CN115293584A (en) | Overhead transmission line risk prediction method, system and terminal | |
| CN116089882A (en) | Cable fault prediction processing method and device and electronic equipment | |
| CN109726880B (en) | Power transmission line parameter rationality assessment method and system | |
| CN112016739A (en) | Fault detection method and device, electronic equipment and storage medium | |
| CN117150808A (en) | Method, system and equipment for evaluating toughness of power transmission line in strong convection weather | |
| Bahrami et al. | A novel pre-storm island formation framework to improve distribution system resilience considering tree-caused failures | |
| CN116415724A (en) | Photovoltaic power station operation maintenance time prediction method and device | |
| CN114595966A (en) | Super-huge type urban power grid toughness assessment method considering different disaster types | |
| CN114493245A (en) | Mountain torrent disaster easiness evaluation method based on GIS and integrated learning | |
| CN112001073B (en) | Reliability analysis and research method for traction power supply system | |
| Hou et al. | Modeling the Resilience of Power Distribution Systems Subjected to Extreme Winds Considering Tree Failures: An Integrated Framework | |
| CN112926664A (en) | Feature selection and CART forest short-time strong rainfall forecasting method based on evolutionary algorithm | |
| CN115224684A (en) | Intelligent power distribution network risk state identification method and system based on immune hazard theory | |
| CN106570561B (en) | A kind of insoluble sediment density forecasting system of insulator surface and method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication |