Multi-source data fusion transmission conductor galloping analysis method and device

Abstract

The invention provides a transmission conductor galloping analysis method and device based on multi-source data fusion, and relates to the technical field of power grids. According to the method, the geographic data and the meteorological data of the area where the power transmission conductor is located are analyzed, the meteorological distribution condition of the area where the power transmission conductor is located is determined, and the galloping behavior of each spacer is predicted in combination with the GWR model representing the correlation between the galloping behavior of the spacer and the meteorological distribution condition. And obtaining a galloping analysis result of the transmission conductor. According to the method, the geographic data, the meteorological data and the historical galloping behaviors are combined, multi-source data fusion prediction of the power transmission conductor is realized, the influence of the geographic data of the positions of the spacers of the power transmission conductor on the meteorological data is considered, spatial differentiation prediction of the meteorological data at the spacers is realized, and the prediction accuracy is improved. Therefore, the accuracy of transmission conductor galloping behavior prediction is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power grid, and in particular to a method and device for analyzing the galloping of a transmission line by fusion of multi-source data. Background Art

[0002] Transmission line galloping is a low-frequency, large-amplitude self-excited vibration phenomenon that occurs when the conductor is eccentrically covered with ice and excited by wind. Due to the large amplitude, galloping can cause line fatigue and increased tension, which in turn causes hardware damage, tower collapse accidents, and flashover and tripping caused by close phase distances, posing a serious threat to the safe operation of the power grid. my country's geographical environment and climatic conditions are complex and changeable, and long-distance transmission lines are prone to galloping accidents during use. Therefore, real-time detection and analysis of transmission line galloping to prevent disasters is of great practical significance.

[0003] Existing methods for detecting the galloping of transmission lines mainly rely on contact sensors, such as gyroscope sensors, which are installed on transmission towers to collect data and analyze them. However, this method of detecting galloping can only analyze the galloping of transmission lines, and cannot predict the galloping of transmission lines. Meteorological conditions can be used to predict the galloping of transmission lines, but because meteorological forecasts cover a large range and the span of the area where the transmission lines are located is generally large, meteorological conditions cannot accurately predict the galloping behavior of transmission lines. Summary of the invention

[0004] The present invention provides a multi-source data fusion transmission line galloping analysis method and device, which can combine geographic data, meteorological data and historical galloping behavior to achieve multi-source data fusion prediction of transmission lines and improve the accuracy of transmission line galloping behavior prediction.

[0005] In a first aspect, the present invention provides a method for analyzing the dancing of a power transmission line by fusion of multi-source data, the method comprising: obtaining geographic data of the area where the power transmission line is located, meteorological data of a predicted period, and position data of the power transmission line; the position data comprises the position coordinates of each spacer on the power transmission line; based on the meteorological data of the predicted period, and the geographic data, a meteorological distribution analysis is performed to determine the meteorological distribution of the area where the power transmission line is located; based on the position data of the power transmission line, the meteorological distribution, and a preset GWR model, a predicted dancing behavior of each spacer on the power transmission line is predicted, the predicted dancing behavior comprising dancing frequency and dancing amplitude; the GWR model characterizes the correlation between the dancing behavior of the spacer and the meteorological distribution; based on the predicted dancing behavior of each spacer on the power transmission line, a risk analysis is performed to determine the dancing analysis result of the power transmission line.

[0006] In a possible implementation, a risk analysis is performed based on the predicted dancing behavior of each spacer on the transmission line to determine the dancing analysis result of the transmission line, including: if the dancing frequency is less than the set frequency, and the dancing amplitude is less than the set amplitude, then the dancing analysis result of the transmission line is determined to be normal dancing; if the dancing frequency is greater than or equal to the set frequency, and the dancing amplitude is less than the set amplitude, then the dancing analysis result of the transmission line is determined to be a first-level risk; if the dancing frequency is less than the set frequency, and the dancing amplitude is greater than or equal to the set amplitude, then the dancing analysis result of the transmission line is determined to be a second-level risk; if the dancing frequency is greater than or equal to the set frequency, and the dancing amplitude is greater than or equal to the set amplitude, then the dancing analysis result of the transmission line is determined to be a third-level risk.

[0007] In a possible implementation, after a risk analysis is performed based on the predicted dancing behavior of each spacer on the transmission line and the dancing analysis result of the transmission line is determined, it also includes: if the dancing analysis result is normal dancing, the normal inspection mode is maintained unchanged; if the dancing analysis result is a first-level risk, the anti-dancing plan is determined to increase the inspection frequency; and the inspection frequency in the anti-dancing plan is determined based on the dancing frequency; if the dancing analysis result is a second-level risk, the anti-dancing plan is determined to increase the inspection frequency and add anti-dancing devices; and the inspection frequency in the anti-dancing plan is determined based on the dancing frequency, and the location information of the anti-dancing device to be added is determined based on the dancing amplitude; if the dancing analysis result is a third-level risk, the anti-dancing plan is determined to increase the inspection frequency, add anti-dancing devices and customize backup lines; and the inspection frequency in the anti-dancing plan is determined based on the dancing frequency, and the location information of the anti-dancing device to be added is determined based on the dancing amplitude; the backup line is determined based on the location data of the transmission lines in various areas of the power system.

[0008] In a possible implementation, based on the meteorological data and geographic data of the forecast period, a meteorological distribution analysis is performed to determine the meteorological distribution of the area where the transmission line is located, including: feature analysis and normalization of the meteorological data and geographic data of the forecast period to determine standard meteorological features and standard geographic features; based on the standard geographic features, a preset meteorological geostatistical model is updated to determine an updated meteorological geostatistical model; the meteorological geostatistical model is used to characterize the correlation between the meteorological data at a certain interval bar and the geographic data of the interval bar. Based on the meteorological data and the updated meteorological geostatistical model, the meteorological distribution of the area where the transmission line is located is determined, and the meteorological distribution includes the meteorological data at each interval bar of the transmission line.

[0009] In a possible implementation, before performing a meteorological distribution analysis based on the meteorological data of the forecast period and the geographic data to determine the meteorological distribution of the area where the transmission line is located, it also includes: obtaining the geographic data of the area where the transmission line is located and the meteorological data of the historical period; performing feature analysis and normalization on the geographic data of the area where the transmission line is located and the meteorological data of the historical period to determine the standard meteorological characteristics and standard geographic characteristics in the historical period; using the standard geographic characteristics as independent variables and the standard meteorological characteristics as dependent variables, performing data fitting to obtain a meteorological geostatistical model.

[0010] In a possible implementation, before predicting the predicted dancing behavior of each spacer on the transmission line based on the location data of the transmission line, the meteorological distribution, and the preset GWR model, it also includes: obtaining the geographical data of the area where the transmission line is located, the meteorological data of the historical period, and the historical video data of the transmission line; based on the historical video data of the transmission line, performing trajectory analysis to determine the dancing trajectory data of each spacer of the transmission line in the historical period; based on the geographical data and the meteorological data of the historical period, performing meteorological distribution analysis to determine the meteorological distribution of the area where the transmission line is located at each time in the historical period; the meteorological distribution includes the meteorological data at each spacer on the transmission line; based on the meteorological distribution of the area where the transmission line is located at each time in the historical period, the geographical data, and the dancing trajectory data of each spacer in the historical period, a GWR model is constructed; the GWR model includes local regression coefficients at each spacer, wherein the local regression coefficient at any spacer is used to characterize the correlation between the dancing behavior at the spacer at that location and the meteorological data and the geographical data.

[0011] In a possible implementation, based on the position data of the transmission line, the meteorological distribution, and the preset GWR model, the predicted dancing behavior of each spacer on the transmission line is predicted, including: determining the geographical data at each spacer on the transmission line based on the position data of the transmission line and the geographical data of the area where the transmission line is located; determining the meteorological data at each spacer based on the position data of the transmission line and the meteorological distribution of the area where the transmission line is located; determining the local regression coefficient at each spacer based on the geographical data and meteorological data at each spacer on the transmission line and the preset GWR model; determining the predicted dancing behavior of each spacer on the transmission line based on the geographical data, meteorological data and local regression coefficient at each spacer.

[0012] In one possible implementation, trajectory analysis is performed based on historical video data of a transmission line to determine dancing trajectory data of each spacer bar of the transmission line in a historical period, including: frame processing of the historical video data to obtain multi-frame video images of each spacer bar; target detection of each spacer bar in the multi-frame video images of each spacer bar to determine the position coordinates of each spacer bar at each moment; based on the position coordinates of each spacer bar at each moment, determining the dancing trajectory data of each spacer bar of the transmission line in a historical period.

[0013] In a possible implementation, a GWR model is constructed based on the meteorological distribution conditions and geographical data of the area where the transmission lines are located at each moment in the historical period, as well as the dancing trajectory data of each spacer bar in the historical period, including: based on the meteorological distribution conditions and geographical data of the area where the transmission lines are located at each moment in the historical period, feature analysis and normalization processing are performed to determine the standard meteorological characteristics and standard geographical characteristics of each spacer bar at each moment in the historical period; based on the dancing trajectory data of each spacer bar in the historical period, the dancing behavior of each spacer bar at each moment is determined; with the standard meteorological characteristics and standard geographical characteristics of each spacer bar at each moment as independent variables and the dancing behavior of each spacer bar at each moment as the dependent variable, the least squares method is used for data fitting to determine the local regression coefficient at each spacer bar; based on the local regression coefficient at each spacer bar, as well as the standard meteorological characteristics and standard geographical characteristics of each spacer bar, a GWR model is constructed.

[0014] In a second aspect, an embodiment of the present invention provides a transmission line dancing analysis device with multi-source data fusion, the dancing analysis device comprising: a communication module, used to obtain geographical data of the area where the transmission line is located, meteorological data of the forecast period and the position data of the transmission line; the position data comprises the position coordinates of each spacer on the transmission line; a processing module, used to perform meteorological distribution analysis based on the meteorological data of the forecast period and the geographical data, and determine the meteorological distribution of the area where the transmission line is located; based on the position data of the transmission line, the meteorological distribution, and a preset GWR model, predict the predicted dancing behavior of each spacer on the transmission line, the predicted dancing behavior comprises the dancing frequency and the dancing amplitude; the GWR model characterizes the correlation between the dancing behavior of the spacer and the meteorological distribution; based on the predicted dancing behavior of each spacer on the transmission line, perform risk analysis and determine the dancing analysis result of the transmission line.

[0015] In a third aspect, an embodiment of the present invention provides an electronic device, comprising a memory and a processor, the memory storing a computer program, the processor being used to call and run the computer program stored in the memory to perform the steps of the method described in the first aspect and any possible implementation method of the first aspect.

[0016] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and wherein when the computer program is executed by a processor, the steps of the method described in the first aspect and any possible implementation method of the first aspect are implemented.

[0017] The present invention provides a transmission line dancing analysis method and device with multi-source data fusion. The present invention determines the meteorological distribution of the area where the transmission line is located by analyzing the geographic data and meteorological data of the area where the transmission line is located, and combines the GWR model that characterizes the correlation between the dancing behavior of the spacer and the meteorological distribution to predict the dancing behavior of each spacer and obtain the dancing analysis result of the transmission line. The present invention combines geographic data, meteorological data and historical dancing behavior to realize multi-source data fusion prediction of the transmission line, takes into account the influence of the geographic data of the location of each spacer of the transmission line on the meteorological data, and realizes the spatial differentiation prediction of the meteorological data at each spacer, thereby improving the accuracy of the prediction of the dancing behavior of the transmission line. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0019] Figure 1 It is a flow chart of a transmission line galloping analysis method based on multi-source data fusion provided by an embodiment of the present invention;

[0020] Figure 2 It is a structural schematic diagram of a transmission line dancing analysis device with multi-source data fusion provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0021] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0022] In the description of the present invention, unless otherwise specified, " / " means "or", for example, A / B can mean A or B. "And / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, "at least one" and "plurality" refer to two or more. The words "first", "second", etc. do not limit the quantity and execution order, and the words "first", "second", etc. do not limit them to be different.

[0023] In the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a concrete way for easy understanding.

[0024] In addition, the terms "including" and "having" and any variations thereof mentioned in the description of this application are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or modules is not limited to the listed steps or modules, but may optionally include other steps or modules that are not listed, or may optionally include other steps or modules that are inherent to these processes, methods, products or devices.

[0025] In order to make the purpose, technical solutions and advantages of the present invention more clear, the following will be described through specific embodiments in conjunction with the accompanying drawings of the present invention.

[0026] As described in the background art, the use of single meteorological data to predict the galloping of transmission lines has a technical problem of low accuracy.

[0027] To solve this technical problem, Figure 1 As shown, an embodiment of the present invention provides a method for analyzing the galloping of a transmission line by fusion of multi-source data. The method includes steps S101-S104.

[0028] S101, obtaining geographical data of the area where the power transmission line is located, meteorological data of the forecast period, and location data of the power transmission line.

[0029] In the embodiment of the present application, the position data includes the position coordinates of each spacer rod on the transmission line.

[0030] In some embodiments, the geographic data includes at least one of: topography, landforms, water bodies, and vegetation cover.

[0031] In some embodiments, the meteorological data includes at least one of the following: air temperature, humidity, wind speed, and wind direction.

[0032] S102: Based on the meteorological data of the forecast period and the geographical data, a meteorological distribution analysis is performed to determine the meteorological distribution conditions of the area where the transmission line is located.

[0033] In some embodiments, the weather distribution includes weather data at each spacer of the transmission line.

[0034] As a possible implementation manner, step S102 may be specifically implemented as steps S1021 - S1023 .

[0035] S1021. Perform feature analysis and normalization processing on the meteorological data and geographic data of the forecast period to determine standard meteorological features and standard geographic features.

[0036] In some embodiments, the standard meteorological features include standard features in dimensions such as temperature, humidity, wind speed, and wind direction.

[0037] In some embodiments, the standard geographic features include standard features in dimensions such as terrain, landforms, water bodies, and vegetation cover.

[0038] As a possible implementation method, the embodiment of the present invention can extract features of indicators such as temperature, humidity, wind speed and wind direction in the meteorological data of the forecast period, and normalize the extracted features to obtain standard meteorological features.

[0039] As a possible implementation method, the embodiment of the present invention can extract features of indicators such as terrain, landform, water body and vegetation coverage in geographic data, and normalize the extracted features to obtain standard geographic features.

[0040] S1022. Based on standard geographic features, the preset meteorological and geostatistical model is updated to determine an updated meteorological and geostatistical model.

[0041] In some embodiments, the meteorological geostatistical model is used to characterize the correlation between the meteorological data at a certain interval bar and the geographic data at the interval bar.

[0042] For example, the geographical data of the area where the power transmission line is located may change with the seasons and time. Therefore, the embodiment of the present invention can update the geographical data of the area where the power transmission line is located in the meteorological geostatistical model, so that the updated meteorological geostatistical model conforms to the actual situation of the power transmission line in the current period, thereby improving the accuracy of the prediction of the power transmission line dancing behavior.

[0043] S1023. Based on the meteorological data and the updated meteorological-geophysical model, determine the meteorological distribution of the area where the transmission line is located.

[0044] Exemplarily, the embodiments of the present invention may use an updated meteorological model and meteorological data of the forecast period to analyze the meteorological data at each interval bar to determine the meteorological distribution of the area where the transmission line is located.

[0045] S103: Based on the location data of the transmission line, the meteorological distribution, and the preset GWR model, the predicted dancing behavior of each spacer on the transmission line is predicted.

[0046] In the embodiment of the present application, the predicted dancing behavior includes dancing frequency and dancing amplitude.

[0047] In the embodiment of the present application, the GWR model characterizes the correlation between the dancing behavior of the spacer rod and the meteorological distribution.

[0048] In some embodiments, the GWR model characterizes the correlation between the dancing behavior of the spacer and the meteorological data and the geographical data at the spacer.

[0049] As a possible implementation manner, step S103 may be specifically implemented as steps S1031 - S1034 .

[0050] S1031. Based on the position data of the transmission line and the geographic data of the area where the transmission line is located, determine the geographic data of each spacer rod on the transmission line.

[0051] Exemplarily, the embodiment of the present invention can determine the geographic data at a certain spacer bar according to the position coordinates of the spacer bar on the transmission line and the correspondence between the geographic data and the position coordinates. The geographic data of a certain spacer bar can be a vector composed of data such as terrain, landform, water body and vegetation coverage.

[0052] S1032. Determine the meteorological data at each spacing rod based on the location data of the transmission line and the meteorological distribution conditions in the area where the transmission line is located.

[0053] Exemplarily, the embodiment of the present invention can determine the meteorological data at a certain spacer bar according to the position coordinates of the spacer bar on the transmission line and the corresponding relationship between the meteorological data and the position coordinates in the meteorological distribution. The meteorological data at a certain spacer bar can be a vector composed of data such as temperature, humidity, wind speed and wind direction.

[0054] S1033. Based on the geographic data and meteorological data at each spacer on the transmission line and a preset GWR model, determine the local regression coefficient at each spacer.

[0055] In some embodiments, the local regression coefficient at a certain interval bar is used to characterize the correlation between the dancing behavior at the interval bar and the meteorological data and the geographical data.

[0056] It should be noted that the local regression coefficient will vary depending on the geographical data and meteorological data at each interval bar. This can fully consider the spatial differences in meteorological data, so that when using meteorological data to predict the dancing behavior, the determination of meteorological data at each interval bar takes into account the influence of geographical data on meteorological data, that is, spatial differences, thereby improving the accuracy of meteorological data at each interval bar, and then improving the accuracy of the prediction of the dancing behavior of the transmission line.

[0057] S1034. Determine the predicted dancing behavior of each spacer on the transmission line based on the geographic data, meteorological data and local regression coefficient at each spacer.

[0058] In some embodiments, the local regression coefficients may include a first regression coefficient and a second regression coefficient.

[0059] The embodiment of the present invention can determine the predicted dancing behavior based on a weighted summation with the first regression coefficient as the weight of the geographical data and the second regression coefficient as the weight of the meteorological data.

[0060] Exemplarily, the local regression coefficient can be determined by fitting the data using the least square method according to the dancing trajectory of each spacer bar in the historical period, geographic data and meteorological data, to obtain the local regression coefficient at each spacer bar. In this way, the individual data fitting at each spacer bar ensures the difference between the spacers, that is, the overall spatial difference of the area where the transmission line is located, and improves the accuracy of the transmission line dancing behavior prediction.

[0061] S104. Based on the predicted dancing behavior of each spacer on the transmission line, a risk analysis is performed to determine the dancing analysis result of the transmission line.

[0062] As a possible implementation manner, step S104 may be specifically implemented as steps S1041 - S1044 .

[0063] S1041. If the dancing frequency is less than the set frequency and the dancing amplitude is less than the set amplitude, it is determined that the dancing analysis result of the transmission line is normal dancing.

[0064] S1042. If the dancing frequency is greater than or equal to the set frequency and the dancing amplitude is less than the set amplitude, the dancing analysis result of the transmission line is determined to be a first-level risk.

[0065] S1043. If the dancing frequency is less than the set frequency and the dancing amplitude is greater than or equal to the set amplitude, the dancing analysis result of the transmission line is determined to be a level 2 risk.

[0066] S1044. If the dancing frequency is greater than or equal to the set frequency, and the dancing amplitude is greater than or equal to the set amplitude, then the dancing analysis result of the transmission line is determined to be a third-level risk.

[0067] The present invention provides a transmission line galloping analysis method based on multi-source data fusion, which determines the meteorological distribution of the area where the transmission line is located by analyzing the geographic data and meteorological data of the area where the transmission line is located, and combines the GWR model that characterizes the correlation between the galloping behavior of the spacer and the meteorological distribution to achieve the prediction of the galloping behavior of each spacer and obtain the galloping analysis result of the transmission line. The present invention combines geographic data, meteorological data and historical galloping behavior to achieve multi-source data fusion prediction of the transmission line, takes into account the influence of the geographic data of the location of each spacer of the transmission line on the meteorological data, and achieves spatial differentiation prediction of the meteorological data at each spacer, thereby improving the accuracy of the prediction of the galloping behavior of the transmission line.

[0068] Optionally, the transmission line galloping analysis method based on multi-source data fusion provided in an embodiment of the present invention further includes steps S201-S204 after step S104.

[0069] S201. If the dancing analysis result is normal dancing, the normal inspection mode is maintained unchanged.

[0070] S202: If the dancing analysis result is a first-level risk, the anti-dancing plan is determined to increase the inspection frequency; and the inspection frequency in the anti-dancing plan is determined based on the dancing frequency.

[0071] It should be noted that the first-level risk means that the dancing risk is relatively small. In the embodiment of the present invention, the inspection frequency can be increased to inspect the spacer bars at the first-level risk, thereby improving the risk awareness of the spacer bars at the location and performing continuous monitoring.

[0072] For example, the risk of dancing increases with the increase of dancing frequency. The present invention can perform inspections at a lower inspection frequency when the dancing frequency is lower, and at a higher inspection frequency when the dancing frequency is higher. In this way, the inspection frequency is gradually increased to ensure continuous monitoring of the transmission line and ensure safe operation of the transmission line.

[0073] S203. If the dancing analysis result is a level 2 risk, the anti-dancing plan is determined to increase the inspection frequency and add anti-dancing devices; and the inspection frequency in the anti-dancing plan is determined based on the dancing frequency, and the location information of the anti-dancing device to be added is determined based on the dancing amplitude.

[0074] It should be noted that the second-level risk indicates a greater risk of galloping. In the embodiment of the present invention, an anti-galloping device can be added on the basis of increasing the inspection frequency. For the transmission line at the spacer bar at the second-level risk, the embodiment of the present invention can increase the number of spacers at that location, reduce the galloping amplitude, and keep the transmission line galloping within a safe range.

[0075] S204. If the dancing analysis result is a third-level risk, the anti-dancing plan is determined to increase the inspection frequency, add anti-dancing devices and customize backup lines; and the inspection frequency in the anti-dancing plan is determined based on the dancing frequency, and the location information of the anti-dancing device to be added is determined based on the dancing amplitude; and the backup line is determined based on the location data of the transmission lines in various areas of the power system.

[0076] It should be noted that the third-level risk indicates that the risk of dancing is serious and the power transmission line is prone to breaking. In the embodiment of the present invention, a backup line can be set up on the basis of increasing the inspection frequency and the anti-dancing device. When a power transmission line breaks, the backup line is used to supply power and the breaking accident is repaired.

[0077] Optionally, the transmission line galloping analysis method based on multi-source data fusion provided in an embodiment of the present invention further includes steps S301 - S303 before step S102 .

[0078] S301. Obtain geographic data of the area where the transmission line is located and meteorological data of the historical period.

[0079] S301. Perform feature analysis and normalization on the geographical data of the area where the transmission line is located and the meteorological data of the historical period to determine the standard meteorological characteristics and standard geographical characteristics of the historical period.

[0080] S301. Using standard geographical features as independent variables and standard meteorological features as dependent variables, data fitting is performed to obtain a meteorological geostatistical model.

[0081] In this way, the embodiment of the present invention can perform data fitting on meteorological data and geographic data in the historical period to obtain a meteorological geostatistical model, and statistically obtain the correlation between the meteorological distribution of regional meteorological data at each interval bar and the geographic data at each interval bar, thereby realizing the prediction of meteorological data in the forecast period, taking into account the changes in meteorological data with geographic data, and ensuring the accuracy of meteorological data at each interval bar, thereby improving the accuracy of the prediction of the dancing behavior of the transmission line.

[0082] Optionally, the transmission line galloping analysis method based on multi-source data fusion provided in an embodiment of the present invention further includes steps S401 to S404 before step S103.

[0083] S401, obtaining geographical data of the area where the power transmission line is located, meteorological data of the historical period, and historical video data of the power transmission line;

[0084] S402: Based on the historical video data of the transmission line, trajectory analysis is performed to determine the dancing trajectory data of each spacer rod of the transmission line in the historical period.

[0085] As a possible implementation manner, step S402 may be implemented as steps S4021-S4023.

[0086] S4021, perform frame processing on the historical video data to obtain multiple frames of video images of each interval bar.

[0087] S4022, performing target detection on each spacer bar in the multi-frame video images of each spacer bar, and determining the position coordinates of each spacer bar at each moment.

[0088] S4023. Based on the position coordinates of each spacer at each moment, determine the dancing trajectory data of each spacer of the transmission line in the historical period.

[0089] S403: Based on the geographical data and the meteorological data of the historical period, a meteorological distribution analysis is performed to determine the meteorological distribution conditions of the area where the transmission line is located at each time in the historical period.

[0090] In some embodiments, the meteorological distribution includes meteorological data at each spacer bar on the transmission line;

[0091] S404: construct a GWR model based on the meteorological distribution conditions of the area where the transmission line is located at each time in the historical period, geographical data, and the dancing trajectory data of each spacer rod in the historical period.

[0092] In some embodiments, the GWR model includes a local regression coefficient at each spacer bar, wherein the local regression coefficient at any spacer bar is used to characterize the correlation between the dancing behavior at the spacer bar at that location and the meteorological data and the geographic data.

[0093] As a possible implementation manner, step S404 may be implemented as steps S4041 - S4043 .

[0094] S4041. Based on the meteorological distribution conditions of the area where the transmission lines are located at each moment in the historical period and the geographical data of the area where the transmission lines are located, characteristic analysis and normalization processing are performed to determine the standard meteorological characteristics and standard geographical characteristics of each spacing rod at each moment in the historical period.

[0095] S4042. Determine the dancing behavior of each spacer bar at each moment based on the dancing trajectory data of each spacer bar in the historical period.

[0096] S4043. Taking the standard meteorological characteristics and standard geographical characteristics of each spacer at each time as independent variables, taking the dancing behavior of each spacer at each time as dependent variable, adopting the least square method to fit the data, and determining the local regression coefficient at each spacer.

[0097] S4044. Construct a GWR model based on the local regression coefficient at each interval bar and the standard meteorological characteristics and standard geographical characteristics of each interval bar.

[0098] In this way, the embodiment of the present invention can analyze the dancing trajectory, geographic data and meteorological data of the transmission line in the historical period, perform data fitting, and obtain the correlation between the dancing behavior and the changes in meteorological data and geographic data, that is, the GWR model, to achieve spatial differentiated prediction of meteorological data at each interval bar, thereby improving the accuracy of the prediction of the dancing behavior of the transmission line.

[0099] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0100] The following is an embodiment of the device of the present invention. For details not described in detail therein, reference may be made to the corresponding method embodiment described above.

[0101] Figure 2 The schematic diagram of the structure of a transmission line galloping analysis device with multi-source data fusion provided by an embodiment of the present invention is shown. The galloping analysis device 500 includes a communication module 501 and a processing module 502 .

[0102] The communication module 501 is used to obtain geographical data of the area where the transmission line is located, meteorological data of the forecast period and position data of the transmission line; the position data includes the position coordinates of each spacer on the transmission line;

[0103] Processing module 502 is used to perform meteorological distribution analysis based on meteorological data of the forecast period and geographical data to determine the meteorological distribution of the area where the transmission line is located; based on the location data of the transmission line, the meteorological distribution, and the preset GWR model, predict the predicted dancing behavior of each spacer on the transmission line, and the predicted dancing behavior includes dancing frequency and dancing amplitude; the GWR model characterizes the correlation between the dancing behavior of the spacer and the meteorological distribution; based on the predicted dancing behavior of each spacer on the transmission line, perform risk analysis to determine the dancing analysis results of the transmission line.

[0104] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A transmission line galloping analysis method based on multi-source data fusion, characterized in that: include: Obtaining geographic data of the area where the transmission line is located, meteorological data for the forecast period, and location data of the transmission line; The position data includes the position coordinates of each spacer bar on the transmission line; Based on the meteorological data of the forecast period and the geographic data, a meteorological distribution analysis is performed to determine the meteorological distribution of the area where the transmission line is located; Based on the location data of the transmission line, the meteorological distribution, and the preset GWR model, the predicted dancing behavior of each spacer on the transmission line is predicted, and the predicted dancing behavior includes the dancing frequency and the dancing amplitude; The GWR model characterizes the correlation between the dancing behavior of the spacer bar and the meteorological distribution; Based on the predicted galloping behavior of each spacer on the transmission line, a risk analysis is performed to determine the galloping analysis result of the transmission line.

2. The method for analyzing the galloping of a transmission line based on multi-source data fusion according to claim 1, characterized in that: The step of performing risk analysis based on the predicted galloping behavior of each spacer on the transmission line to determine the galloping analysis result of the transmission line includes: If the dancing frequency is less than the set frequency and the dancing amplitude is less than the set amplitude, then the dancing analysis result of the transmission line is determined to be normal dancing; If the dancing frequency is greater than or equal to the set frequency, and the dancing amplitude is less than the set amplitude, then the dancing analysis result of the transmission line is determined to be a first-level risk; If the dancing frequency is less than the set frequency and the dancing amplitude is greater than or equal to the set amplitude, the dancing analysis result of the transmission line is determined to be a level 2 risk; If the dancing frequency is greater than or equal to the set frequency, and the dancing amplitude is greater than or equal to the set amplitude, then the dancing analysis result of the transmission line is determined to be a third-level risk.

3. The transmission line galloping analysis method based on multi-source data fusion according to claim 2 is characterized in that: After performing risk analysis based on the predicted galloping behavior of each spacer on the transmission line and determining the galloping analysis result of the transmission line, the method further includes: If the dancing analysis result is normal dancing, the normal inspection mode is maintained unchanged; If the dancing analysis result is a first-level risk, the anti-dancing plan is determined to increase the inspection frequency; and the inspection frequency in the anti-dancing plan is determined based on the dancing frequency; If the dancing analysis result is a secondary risk, the anti-dancing plan is determined to increase the inspection frequency and add an anti-dancing device; and the inspection frequency in the anti-dancing plan is determined based on the dancing frequency, and the location information of the anti-dancing device to be added is determined based on the dancing amplitude; If the dancing analysis result is a third-level risk, the anti-dance plan is determined to increase the inspection frequency, add anti-dance devices and customize backup lines; and the inspection frequency in the anti-dance plan is determined based on the dancing frequency, and the location information of the anti-dance device to be added is determined based on the dancing amplitude; the backup line is determined based on the location data of the transmission lines in various areas of the power system.

4. The transmission line galloping analysis method based on multi-source data fusion according to claim 1 is characterized in that: The step of performing a meteorological distribution analysis based on the meteorological data of the forecast period and the geographical data to determine the meteorological distribution of the area where the transmission line is located includes: Performing feature analysis and normalization processing on the meteorological data and the geographic data of the forecast period to determine standard meteorological features and standard geographic features; Based on the standard geographic features, the preset meteorological geostatistical model is updated to determine the updated meteorological geostatistical model; the meteorological geostatistical model is used to characterize the correlation between the meteorological data at a certain interval bar and the geographic data of the interval bar. Based on the meteorological data and the updated meteorological model, the meteorological distribution of the area where the transmission line is located is determined, and the meteorological distribution includes the meteorological data at each spacer of the transmission line.

5. The method for analyzing the galloping of a transmission line based on multi-source data fusion according to claim 4, characterized in that: Before performing meteorological distribution analysis based on the meteorological data of the forecast period and the geographical data to determine the meteorological distribution of the area where the transmission line is located, the method further includes: Obtain geographical data of the area where the transmission lines are located and meteorological data of historical periods; Performing feature analysis and normalization processing on the geographical data of the area where the transmission line is located and the meteorological data of the historical period to determine the standard meteorological features and standard geographical features in the historical period; The standard geographical features are used as independent variables and the standard meteorological features are used as dependent variables, and data fitting is performed to obtain the meteorological geostatistical model.

6. The method for analyzing the galloping of a transmission line by multi-source data fusion according to claim 1, characterized in that: Before predicting the predicted dancing behavior of each spacer on the transmission line based on the position data of the transmission line, the meteorological distribution, and the preset GWR model, the method further includes: Obtaining geographic data of the area where the transmission line is located, meteorological data of historical periods, and historical video data of the transmission line; Based on the historical video data of the transmission line, trajectory analysis is performed to determine the dancing trajectory data of each spacer rod of the transmission line in the historical period; Based on the geographic data and the meteorological data of the historical period, a meteorological distribution analysis is performed to determine the meteorological distribution of the area where the transmission line is located at each time in the historical period; the meteorological distribution includes the meteorological data at each spacer on the transmission line; The GWR model is constructed based on the meteorological distribution conditions in the area where the transmission lines are located at each moment in the historical period, the geographic data, and the dancing trajectory data of each spacer bar in the historical period; the GWR model includes local regression coefficients at each spacer bar, wherein the local regression coefficient at any spacer bar is used to characterize the correlation between the dancing behavior at the spacer bar at that location and the meteorological data and the geographic data.

7. The method for analyzing the galloping of a transmission line by multi-source data fusion according to claim 6, characterized in that: The predicted dancing behavior of each spacer on the transmission line is predicted based on the position data of the transmission line, the meteorological distribution, and the preset GWR model, including: Determine the geographical data of each spacer on the transmission line based on the position data of the transmission line and the geographical data of the area where the transmission line is located; Determine the meteorological data at each spacer based on the position data of the transmission line and the meteorological distribution conditions in the area where the transmission line is located; Determine the local regression coefficient at each spacer bar based on the geographic data and meteorological data at each spacer bar on the transmission line and a preset GWR model; Based on the geographic data, meteorological data and local regression coefficients at each of the spacers, the predicted galloping behavior of each spacer on the transmission line is determined.

8. The method for analyzing the galloping of a transmission line by multi-source data fusion according to claim 6, characterized in that: The performing of trajectory analysis based on the historical video data of the transmission line to determine the dancing trajectory data of each spacer rod of the transmission line in the historical period includes: Performing frame processing on the historical video data to obtain multiple frames of video images of each spacer bar; Performing target detection on each spacer bar in multiple frames of video images of each spacer bar to determine the position coordinates of each spacer bar at each moment; Based on the position coordinates of each spacer bar at each moment, the dancing trajectory data of each spacer bar of the transmission line in the historical period are determined.

9. The method for analyzing the galloping of a power transmission line by fusion of multi-source data according to claim 6, characterized in that: The GWR model is constructed based on the meteorological distribution of the area where the transmission line is located at each time in the historical period, the geographical data, and the dancing trajectory data of each spacer in the historical period, including: Based on the meteorological distribution conditions of the area where the transmission line is located at each moment in the historical period and the geographical data of the area where the transmission line is located, characteristic analysis and normalization processing are performed to determine the standard meteorological characteristics and standard geographical characteristics of each spacer at each moment in the historical period; Determining the dancing behavior of each spacer bar at each moment based on the dancing trajectory data of each spacer bar in the historical period; The standard meteorological characteristics and standard geographical characteristics of each spacer at each time are taken as independent variables, the dancing behavior of each spacer at each time is taken as dependent variable, and the least square method is used for data fitting to determine the local regression coefficient at each spacer; The GWR model is constructed based on the local regression coefficients at each spacer bar and the standard meteorological characteristics and standard geographical characteristics of each spacer bar.

10. A transmission line galloping analysis device with multi-source data fusion, characterized in that: The dancing analysis device comprises: A communication module, used to obtain geographical data of the area where the transmission line is located, meteorological data of the forecast period and position data of the transmission line; the position data includes the position coordinates of each spacer on the transmission line; A processing module is used to perform a meteorological distribution analysis based on the meteorological data of the forecast period and the geographical data to determine the meteorological distribution of the area where the transmission line is located; based on the location data of the transmission line, the meteorological distribution, and a preset GWR model, predict the predicted dancing behavior of each spacer on the transmission line, the predicted dancing behavior including the dancing frequency and the dancing amplitude; the GWR model characterizes the correlation between the dancing behavior of the spacer and the meteorological distribution; based on the predicted dancing behavior of each spacer on the transmission line, a risk analysis is performed to determine the dancing analysis result of the transmission line.

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