Monitoring methods, systems, devices, storage media, and electronic equipment for power transmission lines.

Abstract

This application relates to the field of power transmission line monitoring technology, and provides a method, system, device, storage medium, and electronic device for monitoring power transmission lines. The method provided in this application acquires a power transmission line event stream, which characterizes the position change information of the power transmission line; reconstructs the motion trajectory of the power transmission line based on the event stream; identifies abnormal motion information of the power transmission line based on the motion trajectory; and outputs an early warning message when the abnormal motion information of the power transmission line meets the early warning conditions. This method can accurately identify abnormal motion information of the power transmission line and quickly respond to changes in the position of the line to issue an early warning.

Description

Technical Field

[0001] This application belongs to the field of power transmission line monitoring technology, and more specifically, relates to a monitoring method, system, device, storage medium and electronic equipment for power transmission lines. Background Technology

[0002] In the operation of high-voltage transmission lines, the galloping and shifting of conductors due to wind are significant factors affecting the safe operation of the equipment. Therefore, in the field of transmission line monitoring, conductor galloping and shifting can lead to contact between conductors and other equipment, causing accidents such as short circuits.

[0003] Currently, traditional methods for monitoring power transmission lines mostly rely on mechanical sensors, which suffer from problems such as slow response speed and insufficient data accuracy. Summary of the Invention

[0004] The purpose of this application is to provide a method, system, device, storage medium, and electronic device for monitoring power transmission lines, aiming to solve the technical problems of traditional power transmission line monitoring methods in the prior art, which rely heavily on mechanical sensors and have slow response speed and insufficient data accuracy.

[0005] To achieve the above objectives, according to the first aspect of this application, a method for monitoring power transmission lines is provided, the method comprising:

[0006] Acquire the transmission line event stream, wherein the transmission line event stream is used to characterize the position change information of the transmission line;

[0007] The motion trajectory of the transmission line is reconstructed based on the event flow of the transmission line.

[0008] Based on the motion trajectory, abnormal motion information of the power transmission line is identified;

[0009] If the abnormal movement information of the power transmission line meets the early warning conditions, an early warning message will be output.

[0010] Optionally, in one possible implementation of the first aspect, reconstructing the motion trajectory of the transmission line based on the event flow of the transmission line includes:

[0011] Multiple consecutive events in the event stream of the power transmission line are clustered into a group of events, wherein each group of events is used to represent the trajectory of the power transmission line within a certain period of time, and any two consecutive events are continuous in spatial location and temporal order.

[0012] By using a polynomial fitting algorithm, curve fitting is performed on each group of events obtained from clustering to obtain the motion trajectory of the transmission line.

[0013] Optionally, in one possible implementation of the first aspect, identifying the abnormal motion information of the transmission line based on the motion trajectory includes:

[0014] The frequency components of the motion trajectory are extracted using the Fast Fourier Transform algorithm;

[0015] Based on the frequency components of the motion trajectory, it is determined whether the power transmission line exhibits conductor galloping or conductor deviation.

[0016] In the event of conductor galloping, the maximum galloping distance of the transmission conductor at different time points is determined, and the frequency of the transmission conductor's movement is determined by analyzing the periodic changes in the trajectory.

[0017] Optionally, in one possible implementation of the first aspect, the step of outputting a warning message when the abnormal movement information of the transmission line meets the warning conditions includes:

[0018] Based on the galloping distance and the movement frequency, the predicted wind speed at the location of the power transmission line is determined;

[0019] If the predicted wind speed exceeds a preset first wind speed threshold, the abnormal movement information of the transmission line is determined to meet the warning conditions, thereby triggering the output of the warning information, wherein the warning information includes: the galloping distance, the movement frequency, the predicted wind speed, and the movement trajectory of the transmission line.

[0020] Optionally, in one possible implementation of the first aspect, identifying the abnormal motion information of the transmission line based on the motion trajectory includes:

[0021] The frequency components of the motion trajectory are extracted using the Fast Fourier Transform algorithm;

[0022] Based on the frequency components of the motion trajectory, it is determined whether the power transmission line exhibits conductor galloping or conductor deviation.

[0023] In the event of conductor offset, the conductor offset distance is determined based on the movement trend of the conductor.

[0024] Optionally, in one possible implementation of the first aspect, the step of outputting a warning message when the abnormal movement information of the transmission line meets the warning conditions includes:

[0025] Based on the conductor offset distance, the predicted wind speed at the location of the transmission conductor is determined;

[0026] If the predicted wind speed exceeds a pre-set second wind speed threshold, the abnormal movement information of the transmission line is determined to meet the warning conditions, thereby triggering the output of the warning information, wherein the warning information includes: the offset distance of the transmission line, the predicted wind speed, and the movement trajectory of the transmission line.

[0027] Optionally, in one possible implementation of the first aspect, the method further includes:

[0028] The historical galloping distance, historical movement frequency, and historical conductor offset distance of the transmission line monitored within a historical period are obtained.

[0029] Based on the historical dancing distance and the historical movement frequency, a first wind speed threshold is pre-set;

[0030] Based on the historical conductor offset distance, a second wind speed threshold is preset;

[0031] Using a multivariate regression analysis algorithm, a mapping relationship is established between the conductor galloping of the transmission line and the first wind speed threshold, and a mapping relationship is established between the conductor offset of the transmission line and the second wind speed threshold.

[0032] According to a second aspect of this application, a monitoring system for power transmission lines is provided, comprising:

[0033] A dynamic vision sensor, installed on a transmission tower, is used to monitor changes in the position of the transmission lines and generate a transmission line event stream based on the changes in position.

[0034] A data processor, connected to the dynamic vision sensor, is used to reconstruct the motion trajectory of the power transmission line based on the event flow of the power transmission line, and to identify abnormal motion information of the power transmission line based on the motion trajectory. If the abnormal motion information of the power transmission line meets the warning conditions, a warning message is output.

[0035] The second aspect and any implementation thereof correspond to the first aspect and any implementation thereof, respectively. The technical effects of the second aspect and any implementation thereof can be found in the technical effects of the first aspect and any implementation thereof, as described above, and will not be repeated here.

[0036] According to a third aspect of this application, a monitoring device for power transmission lines is provided, comprising:

[0037] An acquisition unit is used to acquire a transmission line event stream, wherein the transmission line event stream is used to characterize the position change information of the transmission line;

[0038] A reconstruction unit is used to reconstruct the motion trajectory of the transmission line based on the event flow of the transmission line;

[0039] The identification unit is used to identify abnormal movement information of the power transmission line based on the movement trajectory;

[0040] The output unit is used to output a warning message when the abnormal movement information of the power transmission line meets the warning conditions.

[0041] Fourthly, embodiments of this application provide an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method as described in any one of the above.

[0042] Fifthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method as described in any of the above.

[0043] In a sixth aspect, embodiments of this application provide a computer program product that, when run on an electronic device, causes the electronic device to perform the method described in any one of the first aspects.

[0044] It is understood that the beneficial effects of the second to sixth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here.

[0045] The transmission line monitoring method provided in this application acquires a transmission line event stream, which characterizes the positional changes of the transmission line; reconstructs the transmission line's trajectory based on the event stream; identifies abnormal movement information of the transmission line based on the trajectory; and outputs an early warning message when the abnormal movement information meets the warning conditions. Using the method provided in this application, advanced data processing algorithms and dynamic visual sensor technology are employed to accurately identify abnormal movement information of the transmission line, achieving high frame rate and low latency monitoring, and enabling rapid response to changes in the transmission line's position and the issuance of early warnings. Compared to traditional monitoring methods, this method has significant advantages in response speed, data accuracy, and timely warning. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 This is a schematic diagram of the structure of a monitoring system for power transmission lines provided in an embodiment of this application;

[0048] Figure 2 It is an optional flowchart for early warning analysis based on transmission line event flow;

[0049] Figure 3 This is a schematic flowchart of a monitoring method for power transmission lines provided in this application;

[0050] Figure 4 This is a schematic flowchart of an optional monitoring method for transmission lines provided in this application;

[0051] Figure 5 This is a schematic flowchart of an optional monitoring method for transmission lines provided in this application;

[0052] Figure 6 This is a schematic diagram of the structure of a monitoring device for a power transmission line provided in an embodiment of this application;

[0053] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0054] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0055] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0056] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0057] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0058] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0059] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0060] Conductor galloping is a low-frequency, large-amplitude self-excited vibration generated by eccentrically iced conductors under wind excitation. Conductor drift refers to the lateral or longitudinal movement of conductors under wind force, which is usually slow and small in amplitude. Conductor galloping and drift can lead to contact between conductors and other equipment, causing accidents such as short circuits. In the operation of high-voltage transmission lines, conductor galloping and drift caused by wind force are important factors affecting the safe operation of equipment.

[0061] Currently, the main methods for monitoring the galloping and offset of power transmission lines include installing mechanical sensors, video surveillance, and drone inspections. Mechanical sensors can monitor the vibration frequency and amplitude of the lines in real time, and determine whether the lines have galloped or offset based on the vibration data; video surveillance uses high-resolution cameras to monitor the status of the lines in real time and detect galloping and offset; drones can periodically inspect the lines, acquiring status data through high-resolution cameras and other sensing devices, and conducting large-area inspections of the power transmission lines.

[0062] First, some terms used in the embodiments of this application will be explained to facilitate understanding by those skilled in the art.

[0063] DBSCAN, a density-based clustering algorithm, defines the radius of a neighborhood as eps. Two points are considered density-connected if the distance between them is less than or equal to eps. It defines the minimum number of points a point's neighborhood must contain. DBSCAN iterates through each point in the dataset. If a point's neighborhood contains the minimum number of points, it is marked as a core point. Core points and all their neighbors are then grouped into a cluster. For each core point, its neighborhood is further examined. If these points also satisfy the core point condition, their neighbors are included, forming a density-reachable cluster. This process is repeated until all core points have been processed.

[0064] Polynomial fitting algorithm: First, a suitable polynomial is selected to fit the data. Then, a system of linear equations is constructed based on the data points. Next, optimization techniques such as least squares are used to solve the polynomial coefficients so that the polynomial function approximates the data points as closely as possible.

[0065] Fast Fourier Transform (FFT): An efficient algorithm used to transform a signal from the time domain to the frequency domain in order to analyze the frequency components of the signal.

[0066] Multiple regression analysis is a statistical technique used to study the influence of multiple independent variables on a single dependent variable. By establishing a regression model, changes in the dependent variable can be predicted, and the degree of influence of each independent variable can be assessed.

[0067] The above is a brief introduction to the terms used in the embodiments of this application, and will not be repeated below.

[0068] According to an embodiment of this application, an example of a monitoring system for power transmission lines is also provided; please refer to... Figure 1 As shown, Figure 1 A schematic diagram of a monitoring system for a power transmission line provided in this application is shown. The monitoring system for the power transmission line includes:

[0069] A dynamic vision sensor 100 is installed on the transmission tower to monitor the position change information of the transmission line and generate a transmission line event stream based on the position change information.

[0070] The data processor 102 is connected to the aforementioned dynamic vision sensor and is used to reconstruct the motion trajectory of the power transmission line based on the aforementioned power transmission line event flow, and to identify abnormal motion information of the power transmission line based on the aforementioned motion trajectory. When the abnormal motion information of the power transmission line meets the warning conditions, the processor outputs a warning message.

[0071] In this example, a high-sensitivity dynamic vision sensor is first installed at a suitable location on the transmission tower to ensure it can monitor the dynamic behavior of the conductors from all angles. The dynamic vision sensor captures changes in light energy based on energy "differentiation" to perceive the visual content of the scene. Each pixel can independently detect the intensity of ambient light. When a change in light intensity is detected to exceed a specific threshold, the camera outputs an event. This event includes the time of the change, the pixel's position, and information on whether the light intensity increased (ON) or decreased (OFF). All pixels perform light intensity detection asynchronously and continuously output events, forming a series of asynchronous event streams.

[0072] Since the dynamic vision sensor monitors the state of the wires, the camera parameters need to be set appropriately beforehand. The pixel unit hardware circuit of the dynamic vision sensor includes: a logarithmic photoreceptor, a differential amplifier circuit, and two comparators. The voltage signal Vd iff after passing through the differential amplifier circuit is transmitted to the input terminals of the two comparators. The other input terminals of the two comparators are the upper threshold voltage and the lower threshold voltage, respectively. When Vd iff exceeds either threshold voltage, an ON or OFF event is output accordingly.

[0073] Furthermore, since it is necessary to monitor minute displacements of transmission lines, this application example enhances the sensitivity of the dynamic vision sensor, forming a dynamic vision sensing technology suitable for monitoring scenarios involving conductor galloping and displacement. The dynamic vision sensor monitors the state of the transmission line in real time, generating a series of event streams, which are then processed by a data processor to reconstruct a real-time image of the transmission line and generate early warning information. The data processor uses the transmission line event stream generated by the dynamic vision sensor to perform image reconstruction, that is, to reconstruct the motion trajectory of the transmission line based on the transmission line event stream, thereby obtaining the location and morphology of the discharge channel.

[0074] Please refer to Figure 2 As shown, Figure 2 This is a flowchart for early warning analysis based on power transmission line event flow. By employing the DBSCAN clustering algorithm, multiple consecutive events in the power transmission line event flow are clustered into a group of events. Each group of events represents the trajectory of the power transmission line within a certain period of time, and any two consecutive events are continuous in both spatial location and temporal order. Through a polynomial fitting algorithm, curve fitting is performed on each group of events obtained from clustering to capture the overall trend of the conductor trajectory and obtain the trajectory of the power transmission line.

[0075] Still Figure 2As shown, based on the conductor's trajectory, the frequency components of the trajectory are extracted using Fast Fourier Transform (FFT) to identify whether the conductor exhibits periodic galloping or overall offset. In one example, if conductor galloping is determined, the maximum galloping distance at different time points is calculated to obtain galloping amplitude data; the frequency of conductor movement is calculated by analyzing the periodic changes in the trajectory. In another example, if conductor offset is determined, the offset distance is calculated based on the overall trend of the conductor's movement using constant and linear terms from a polynomial fitting.

[0076] It should be understood that conductor galloping is a low-frequency, large-amplitude self-excited vibration generated by eccentric ice-covered conductors under wind excitation; conductor deflection refers to the lateral or longitudinal movement of the conductor under the action of wind force, which is usually slow and has a small amplitude.

[0077] The predicted wind speed is estimated based on the amplitude and frequency of the transmission line's galloping, or based on the distance the transmission line deviates from its conductor. If the predicted wind speed exceeds a pre-set first wind speed threshold, the abnormal movement information of the transmission line is determined to meet the aforementioned warning conditions, triggering the output of the aforementioned warning information. The warning information includes: the galloping distance, the movement frequency, the predicted wind speed, and the trajectory of the transmission line.

[0078] If the predicted wind speed exceeds a pre-set second wind speed threshold, the abnormal movement information of the transmission line is determined to meet the warning conditions, thereby triggering the output of the warning information, which includes: the offset distance of the transmission line, the predicted wind speed, and the movement trajectory of the transmission line.

[0079] The first wind speed threshold is set based on historical data and actual operating experience, and is a wind speed threshold that maps or corresponds to conductor galloping. The second wind speed threshold is set based on historical data and actual operating experience, and is a wind speed threshold that maps or corresponds to conductor offset distance.

[0080] Through multiple regression analysis, mapping relationships were established between conductor galloping F(m,f) (where m is the amplitude of conductor galloping and f is the frequency of conductor galloping) and wind speed W, and between conductor offset S(d) (where d is the distance of conductor offset) and wind speed W. Wind speed W includes: light wind (1.6-3.3 m / s), gentle breeze (3.4-5.4 m / s), moderate breeze (5.5-7.9 m / s), strong wind (8.0-10.7 m / s), gale (10.8-13.8 m / s), strong wind (13.9-17.1 m / s), and strong wind (17.2-20.7 m / s).

[0081] This application provides an example of a method for monitoring power transmission lines. Please refer to [the example]. Figure 3 As shown, Figure 3 A schematic flowchart of a method for monitoring transmission lines provided in this application is shown. It is provided as an example and not as a limitation. This method can be applied to a monitoring system for transmission lines.

[0082] S101, Obtain the transmission line event stream, wherein the transmission line event stream is used to characterize the position change information of the transmission line.

[0083] S102, the motion trajectory of the transmission line is reconstructed based on the above-mentioned transmission line event flow.

[0084] S103, based on the above motion trajectory, identify the abnormal motion information of the above power transmission line.

[0085] S104, If the abnormal movement information of the above-mentioned transmission line meets the early warning conditions, output early warning information.

[0086] Optionally, the transmission line event stream may contain key information that reflects changes in the position of the transmission line, such as timestamps and changes in position coordinates.

[0087] The execution entity in this application example is a processor in a power transmission line monitoring system, which can also be called a data processor. In one example, the power transmission line can be monitored using dynamic visual sensors installed on the transmission tower. When the position information of the power transmission line changes, the event stream of the power transmission line caused by the position change is collected in real time, ensuring that the dynamic behavior of the power transmission line can be monitored in all aspects.

[0088] Optionally, in this application example, the power transmission line event stream generated by the dynamic visual sensor is obtained through a data processor, and the image is restored based on the power transmission line event stream. That is, the motion trajectory of the power transmission line is reconstructed based on the power transmission line event stream. The reconstructed motion trajectory can clearly show the motion path and change trend of the power transmission line within a certain time range, providing a basis for subsequent abnormal motion recognition.

[0089] Specifically, early warning conditions can be determined in advance based on the actual conditions and operational experience of the transmission lines. For example, some wind speed thresholds (early warning conditions) can be set as examples rather than limitations. When it is determined that the displacement of the transmission line exceeds a certain threshold, the vibration frequency is abnormal, or the direction of movement suddenly changes, the abnormal movement information of the transmission line is determined to meet the early warning conditions, and an early warning message is triggered.

[0090] As an example, and not a limitation, a model of normal motion patterns can be built by analyzing and statistically processing historical data. Then, the real-time monitored motion trajectories can be compared with the model to identify abnormal motion information of the transmission lines, such as conductor galloping or conductor deflection. Machine learning and artificial intelligence algorithms, such as support vector machines and neural networks, can be used to classify and identify motion trajectories, automatically determining whether abnormal motion exists. These algorithms can improve the accuracy and reliability of their judgments by learning from a large amount of normal and abnormal motion data.

[0091] It should be understood that conductor galloping is a large-amplitude, low-frequency vibration phenomenon that occurs in conductors under specific meteorological conditions, mainly caused by wind excitation; conductor deflection usually refers to the situation where conductors deviate from their normal position under the influence of factors such as wind, gravity, and tension. Both are abnormal motion states of conductors under the influence of external factors, and both fall under the category of abnormal conductor motion. Such abnormal motion may adversely affect the safe and stable operation of transmission lines, such as causing line tripping, damage to towers and hardware, etc.

[0092] The method provided in this application utilizes advanced data processing algorithms and dynamic visual sensor technology to accurately identify abnormal movement information of power transmission lines, achieving high frame rate and low latency monitoring. It can quickly respond to changes in line position and issue early warnings. Compared to traditional monitoring methods, this method has significant advantages in response speed, data accuracy, and timely warning.

[0093] Please refer to Figure 4 As shown, Figure 4 A schematic flowchart of a monitoring method for a power transmission line provided in this application is shown. In one possible implementation, the motion trajectory of the power transmission line is reconstructed based on the event flow of the power transmission line, including:

[0094] S201, cluster multiple consecutive events in the above-mentioned transmission line event stream into a group of events, wherein each group of events is used to represent the trajectory of the above-mentioned transmission line within a certain period of time, and any two of the above-mentioned consecutive events are continuous in spatial location and temporal order.

[0095] S202, using a polynomial fitting algorithm, curve fitting is performed on each group of events obtained from clustering to obtain the motion trajectory of the aforementioned transmission line.

[0096] As an example, and not a limitation, clustering algorithms such as K-Means clustering and hierarchical clustering can be used to cluster multiple consecutive events into a group of events. This simplifies complex event flows into several sets of events with motion characteristics within a specific time period. Each group of events represents the trajectory of a transmission line within a certain time period, allowing for more targeted subsequent analysis and processing, thus improving the accuracy and efficiency of trajectory reconstruction.

[0097] After event clustering, the trajectory of the transmission line represented by each event group over a certain period of time is obtained. Since these trajectories may be discrete sets of points, they can be transformed into continuous curves using curve fitting algorithms to more intuitively represent the trajectory of the transmission line.

[0098] It should be understood that polynomial fitting is a commonly used curve fitting method. It uses optimization algorithms such as least squares to find a polynomial function that approximates a given set of data points as closely as possible. Therefore, a suitable polynomial fitting algorithm can be adopted, and the order of the polynomial in the algorithm should be determined. A higher order results in a more complex fitted curve, but may also lead to overfitting.

[0099] Therefore, it is necessary to select an appropriate order based on the actual situation, which can usually be determined through methods such as cross-validation. Then, each group of events obtained from clustering is used as input data and substituted into a multinomial fitting algorithm for calculation to obtain the trajectory curve of the transmission line. Through the above steps, the trajectory of the transmission line can be effectively reconstructed from the event stream.

[0100] In one possible implementation, the identification of abnormal movement information of the transmission line based on the aforementioned movement trajectory includes:

[0101] S301, the frequency components of the above motion trajectory are extracted using the Fast Fourier Transform algorithm.

[0102] S302, based on the frequency components of the above-mentioned motion trajectory, identify whether the above-mentioned transmission line is exhibiting conductor galloping or conductor deviation.

[0103] S303, when the above-mentioned transmission line gallops, determine the maximum galloping distance of the transmission line at different time points, and determine the movement frequency of the transmission line by analyzing the periodic changes of the movement trajectory.

[0104] It should be understood that the Fast Fourier Transform (FFT) algorithm is an efficient spectral analysis method that can convert time-domain signals into frequency-domain signals, thereby revealing the intensity and distribution of different frequency components in the signal.

[0105] In this implementation, the frequency components of the aforementioned motion trajectory are extracted using a Fast Fourier Transform algorithm. This involves transforming the motion trajectory from the time domain to the frequency domain to analyze its frequency components. By extracting the frequency components of the motion trajectory, the main frequency characteristics of the transmission line's motion can be understood.

[0106] In one example, the trajectory can first be represented as time-series data, i.e., position coordinates at a series of time points. Then, the time-series data is processed using a Fast Fourier Transform (FFT) algorithm to obtain a frequency domain signal. The FFT algorithm can convert a time series of length N into a frequency domain sequence of length N, where each element represents the amplitude and phase of a different frequency component. Afterward, based on the frequency components of the trajectory, it can be determined whether the transmission line exhibits abnormal movement such as conductor galloping or conductor deflection. Different abnormal movement patterns typically have different frequency characteristics; therefore, analyzing the frequency components can help identify whether the transmission line is galloping or deflecting.

[0107] Specifically, conductor galloping is a phenomenon where a conductor swings dramatically due to wind, typically with a low frequency and a large amplitude. Conductor deflection, on the other hand, is the deviation of a conductor from its horizontal or vertical direction, which can be caused by various factors and has more complex frequency characteristics.

[0108] Therefore, specific frequency ranges and amplitude thresholds can be set for identifying conductor galloping. If the frequency components of the trajectory include those within the conductor galloping frequency range, and the amplitude exceeds the set threshold, then conductor galloping can be identified. For identifying conductor deflection, the changes in the trajectory in the horizontal and vertical directions can be analyzed. If a continuous increase or significant fluctuation in the conductor's displacement in a certain direction is observed, then conductor deflection can be identified.

[0109] Furthermore, when conductor galloping is confirmed, it allows for further analysis of characteristic parameters of the conductor, such as the maximum galloping distance and frequency. These parameters can provide a basis for assessing the severity of the galloping and taking appropriate protective measures. For example, by analyzing the position coordinates of the trajectory at different time points, the displacement of the conductor in various directions can be calculated, and the maximum displacement value can be taken as the maximum galloping distance. By analyzing the periodic changes in the trajectory, the period of conductor galloping can be determined. Then, based on the relationship between the period and time, the frequency of conductor galloping can be calculated.

[0110] The above examples can effectively identify abnormal movement information of power transmission lines and provide a basis for subsequent early warning and handling, demonstrating high accuracy and reliability.

[0111] In one possible implementation, when the abnormal movement information of the transmission line meets the early warning conditions, an early warning message is output, including:

[0112] S401, Based on the above-mentioned galloping distance and the above-mentioned movement frequency, determine the predicted wind speed value at the location of the above-mentioned transmission line;

[0113] S402, if the predicted wind speed exceeds a preset first wind speed threshold, determine that the abnormal movement information of the transmission line meets the warning conditions, and trigger the output of the warning information, wherein the warning information includes: the galloping distance, the movement frequency, the predicted wind speed, and the movement trajectory of the transmission line.

[0114] Since the galloping of transmission lines is usually closely related to wind speed, this application example can predict the current wind speed to a certain extent by analyzing the galloping characteristics of the lines. Based on the predicted wind speed values, the operating environment and potential risks of the transmission lines can be comprehensively assessed, providing an important basis for subsequent early warning decisions.

[0115] As an example, and not a limitation, one approach is to establish a mathematical model relating swaying distance, sway frequency, and wind speed using historical measured data. For instance, regression analysis or similar methods can be used to determine the relationship between these three factors based on a large amount of measured data.

[0116] In specific applications, the measured galloping distance and frequency can be substituted into a mathematical model to calculate the predicted wind speed at the location of the transmission line. This allows for the determination of whether the abnormal movement of the transmission line meets the warning conditions, and if so, outputs a warning message to enable timely measures to ensure the safe operation of the transmission line.

[0117] By setting a wind speed threshold, timely warnings can be issued when excessively high wind speeds may lead to more serious abnormalities such as power line galloping, reminding relevant personnel to pay attention and take action. Specifically, the calculated wind speed prediction value can be compared with a pre-set first wind speed threshold. If the wind speed prediction value exceeds the first wind speed threshold, the abnormal movement information of the transmission line is considered to meet the warning conditions.

[0118] Once the warning conditions are met, a warning message can be triggered. As an example, and not a limitation, this message includes information such as the galloping distance, frequency of movement, predicted wind speed, and the trajectory of the transmission line. It should be understood that this information helps relevant personnel fully understand the abnormal conditions and operational status of the transmission line, enabling them to take targeted measures. For example, maintenance personnel can assess the extent of damage to the line based on the galloping distance and frequency, and develop reasonable inspection and maintenance plans based on the predicted wind speed and trajectory.

[0119] The above method demonstrates that when abnormal movement information of transmission lines meets the warning conditions, early warning information can be output in a timely manner, providing strong support for ensuring the safe operation of transmission lines. This implementation method comprehensively considers the galloping characteristics of the conductors and wind speed factors, improving the accuracy and reliability of the early warning.

[0120] In one possible implementation, the identification of abnormal movement information of the transmission line based on the aforementioned movement trajectory includes:

[0121] S501, extract the frequency components of the above motion trajectory using the Fast Fourier Transform algorithm;

[0122] S502, Based on the frequency components of the above-mentioned motion trajectory, identify whether the above-mentioned power transmission line has experienced conductor galloping or conductor deviation.

[0123] S503, when the aforementioned conductor offset occurs, the conductor offset distance of the aforementioned transmission conductor is determined based on the movement trend of the aforementioned transmission conductor.

[0124] In the above implementation, the Fast Fourier Transform algorithm can transform the motion trajectory from the time domain to the frequency domain, thereby revealing the different frequency components in the trajectory. Based on the frequency components of the motion trajectory, it is possible to determine whether the transmission line is experiencing two common abnormal motion conditions: conductor galloping or conductor deflection. This allows for early warning, timely detection of problems, and the implementation of corresponding measures to ensure the safe operation of the transmission line.

[0125] Because conductor galloping and conductor deflection have different frequency characteristics, these two anomalies can be accurately distinguished by analyzing frequency components. As an example, and not a limitation, conductor galloping can be identified based on its typical low-frequency, large-amplitude characteristics. For instance, the presence of low-frequency components with large amplitudes in the frequency composition may indicate conductor galloping. For conductor deflection, attention can be paid to the presence of specific frequency components that differ from the conductor's normal operating state. Furthermore, other characteristics of the trajectory, such as changes in the direction and amplitude of displacement, can be considered for a comprehensive assessment.

[0126] When it is determined that a transmission line has deviated, the distance of the deviation can be further determined to assess the severity of the deviation, and then corresponding remedial measures can be taken. For example, the distance of the deviation can be used as a quantitative indicator to measure the extent of the deviation, providing specific data support for subsequent decision-making.

[0127] Specifically, based on the movement trend of the transmission line, the direction of the line's deflection can be determined by analyzing the positional changes of its trajectory at different points in time. Then, the displacement of the line in the deflection direction, i.e., the distance of the line's deflection, is calculated. For example, the displacement can be determined by comparing the position coordinates of the line in the deflection direction at different points in time.

[0128] Through the above examples, by using the Fast Fourier Transform algorithm and the analysis of motion trends, abnormal motion information of transmission lines can be effectively identified, providing an important basis for subsequent processing and maintenance.

[0129] In one possible implementation, when the abnormal movement information of the transmission line meets the early warning conditions, an early warning message is output, including:

[0130] S601, Based on the aforementioned conductor offset distance, determine the predicted wind speed at the location of the aforementioned transmission conductor;

[0131] S602, if the predicted wind speed exceeds the preset second wind speed threshold, determine that the abnormal movement information of the transmission line meets the warning conditions, and trigger the output of the warning information, wherein the warning information includes: the offset distance of the transmission line, the predicted wind speed, and the movement trajectory of the transmission line.

[0132] Since the deviation of power transmission lines is usually related to wind speed—for example, strong winds can cause the lines to shift—this correlation can be used to estimate the current wind speed using the deviation distance. Furthermore, wind speed forecasts can be used to understand the environmental conditions of the power transmission lines, providing crucial information for determining whether a warning is necessary.

[0133] In specific applications, the currently measured conductor offset distance can be substituted into a mathematical model to calculate the predicted wind speed at the location of the transmission line. For example, a mathematical model between conductor offset distance and wind speed can be established in advance using historical measured data.

[0134] The system determines whether abnormal movement information of the transmission line meets the early warning conditions and triggers the output of an early warning message when the conditions are met. By setting a second wind speed threshold, an early warning message can be issued in a timely manner when excessively high wind speeds may pose a greater risk to the transmission line. This early warning message includes information such as the conductor offset distance, predicted wind speed, and the movement trajectory of the transmission line, which can help relevant personnel fully understand the abnormal situation and operating status of the transmission line so as to take appropriate measures to deal with it.

[0135] For example, by comparing the calculated wind speed prediction with a pre-set second wind speed threshold, if the wind speed prediction exceeds the second wind speed threshold, the abnormal movement information of the transmission line is considered to meet the warning conditions. Once the warning conditions are determined to be met, a warning message is triggered and can be communicated to relevant personnel in various ways, such as display screens, SMS notifications, and emails.

[0136] The above examples demonstrate that when abnormal movement information of transmission lines meets the warning conditions, early warning information can be output in a timely manner, providing strong support for ensuring the safe operation of transmission lines.

[0137] Please refer to Figure 5 As shown, Figure 5 A schematic flowchart of a monitoring method for transmission lines provided in this application is shown. In one possible implementation, the method further includes:

[0138] S701, acquire the historical galloping distance, historical movement frequency and historical conductor offset distance of the above-mentioned transmission conductors monitored within the historical period;

[0139] S702, based on the above-mentioned historical dancing distance and the above-mentioned historical movement frequency, a first wind speed threshold is pre-set;

[0140] S703, based on the aforementioned historical conductor offset distance, a second wind speed threshold is preset;

[0141] S704, using a multivariate regression analysis algorithm, establish the mapping relationship between the conductor galloping of the transmission line and the first wind speed threshold, and the mapping relationship between the conductor offset of the transmission line and the second wind speed threshold.

[0142] By monitoring historical data such as the historical galloping distance, historical movement frequency, and historical conductor offset distance of transmission lines over historical periods, this historical data can reflect the movement of transmission lines under different environmental conditions in the past. By analyzing this historical data, we can better understand the behavioral characteristics of transmission lines and provide a basis for setting reasonable early warning thresholds and establishing mapping relationships.

[0143] In addition, the historical data obtained in this application example may contain noise and outliers, which need to be filtered and cleaned. Data cleaning techniques, such as removing outliers and smoothing, can be used to improve the quality of the data.

[0144] Setting appropriate wind speed thresholds can ensure timely warnings when wind speeds pose a potential risk to transmission lines, while avoiding unnecessary false alarms. Specifically, this application example sets a first wind speed threshold based on historical galloping distance and historical movement frequency, and a second wind speed threshold based on historical conductor offset distance. These thresholds are used to determine whether abnormal movement information of the transmission line meets the warning conditions.

[0145] By using a multiple regression analysis algorithm, a mapping relationship between conductor galloping and a first wind speed threshold, and between conductor offset and a second wind speed threshold, are established. Establishing this mapping relationship enables the system to more quickly determine whether the early warning conditions are met based on the current conductor movement, providing support for timely measures and improving the accuracy of early warnings.

[0146] It should be understood that multiple regression analysis is a statistical method used to establish a relationship model between multiple independent variables and a dependent variable. In this case, the independent variables can be characteristic parameters of conductor galloping (such as galloping distance, frequency of movement) or conductor offset distance, and the dependent variable is the wind speed threshold. Furthermore, historical data can be used to train the multiple regression model, and the accuracy and reliability of the model can be verified through methods such as cross-validation. The model's parameters and structure can also be adjusted to improve its performance.

[0147] The above examples demonstrate how fully utilizing historical data and statistical analysis methods can provide a more scientific and accurate approach to monitoring and early warning of power transmission lines. In practical applications, historical data can be continuously updated and optimized, and the model can be periodically evaluated and adjusted to adapt to constantly changing environments and operating conditions.

[0148] It should be understood that the sequence number of each step in the above embodiments does not imply 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 embodiments of this application.

[0149] Corresponding to the monitoring method for transmission lines described in the above embodiments, Figure 6 This is a schematic diagram of the structure of a monitoring device for power transmission lines provided in an embodiment of this application. The device can be implemented as part or all of a computer device, which can be software, hardware, or a combination of both. Figure 7 The electronic device shown.

[0150] Reference Figure 6 The monitoring device for the transmission line includes:

[0151] The acquisition unit 601 is used to acquire the transmission line event stream, wherein the transmission line event stream is used to characterize the position change information of the transmission line;

[0152] Reconstruction unit 602 is used to reconstruct the motion trajectory of the transmission line based on the event flow of the transmission line.

[0153] The identification unit 603 is used to identify abnormal movement information of the power transmission line based on the above-mentioned movement trajectory;

[0154] The output unit 604 is used to output a warning message when the abnormal movement information of the above-mentioned transmission line meets the warning conditions.

[0155] It should be noted that the power transmission line monitoring device provided in the above embodiments is only an example of the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0156] The functional units and modules in the above embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of the embodiments of this application.

[0157] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0158] This application also provides an electronic device, which includes one or more processors and a memory;

[0159] The memory is coupled to one or more processors. The memory is used to store computer program code, which includes computer instructions. The one or more processors invoke the computer instructions to cause the electronic device to perform the aforementioned method for monitoring power transmission lines.

[0160] Electronic devices can be mobile phones, smart screens, tablets, wearable electronic devices, in-vehicle electronic devices, augmented reality (AR) devices, virtual reality (VR) devices, laptops, ultra-mobile personal computers (UMPCs), netbooks, personal digital assistants (PDAs), projectors, or communication devices such as servers, storage devices, and base stations, or smart cars, etc. This application does not limit the specific type of electronic device.

[0161] This application also provides a computer-readable storage medium storing computer instructions; when the computer-readable storage medium is used on an electronic device, the electronic device performs the aforementioned monitoring method for power transmission lines.

[0162] The aforementioned computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the aforementioned computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The aforementioned computer-readable storage medium can be any available medium that a computer can access or can include one or more data storage devices such as servers or data centers that can be integrated with media. The aforementioned available medium can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media, or semiconductor media (e.g., solid-state drives (SSDs)).

[0163] This application also provides a computer program product containing computer instructions, which, when run on an electronic device, enables the electronic device to execute the aforementioned method for monitoring power transmission lines.

[0164] The computer storage medium and computer program product provided in the embodiments of this application are used to execute the methods provided above. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects corresponding to the methods provided above, and will not be repeated here.

[0165] In the above embodiments, implementation can also be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When these computer instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic cable, Digital Subscriber Line, DSL) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer, or a data storage device such as a server or data center that integrates one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., Digital Versatile Discs (DVDs)), or semiconductor media (e.g., Solid State Drives (SSDs)).

[0166] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device 700 can be a mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet device, medical device, fitness equipment, personal digital assistant, etc.

[0167] The memory 701 can be used to store computer software programs 702 and modules. The processor 703 executes various functional applications and data processing of the electronic device by running the software programs and modules stored in the memory 701. The memory 701 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device (such as audio data, telephone directory, etc.). In addition, the memory 701 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0168] The processor 703 may include one or more processors such as a central processing unit (CPU), an application processor (AP), and a baseband processor. The processor can serve as the nerve center and command center of the wireless router. The processor 703 can generate operation control signals based on instruction opcodes and timing signals to control instruction fetching and execution. The memory 701 can be used to store executable program code, including instructions. The processor 703 executes various functional applications and data processing of the network device by running the instructions stored in the memory. The memory 701 may include a program storage area and a data storage area, such as storing data for audio signals to be played. For example, the memory may be Double Data Rate Synchronous Dynamic Random Access Memory (DDR) or Flash memory.

[0169] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0170] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments claimed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0171] In the embodiments provided in this application, it should be understood that the disclosed apparatus / network devices and methods can be implemented in other ways. For example, the apparatus / network device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0172] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0173] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method of monitoring a power transmission conductor, characterized by, The method comprises: acquiring a power transmission conductor event stream, wherein the power transmission conductor event stream is used to represent position change information of a power transmission conductor; reconstructing a motion trajectory of the power transmission conductor based on the power transmission conductor event stream; identifying abnormal motion information of the power transmission conductor based on the motion trajectory; in a case where the abnormal motion information of the power transmission conductor meets a pre-warning condition, outputting a pre-warning prompt information, wherein the reconstructing the motion trajectory of the power transmission conductor based on the power transmission conductor event stream comprises: clustering a plurality of continuous events in the power transmission conductor event stream into a group of events respectively, wherein each group of events is used to represent a motion trajectory of the power transmission conductor within a certain period of time, and any two continuous events are continuous in spatial position and time sequence; performing curve fitting on each group of events obtained by clustering through a polynomial fitting algorithm to obtain the motion trajectory of the power transmission conductor; wherein the identifying the abnormal motion information of the power transmission conductor based on the motion trajectory comprises: extracting a frequency component of the motion trajectory through a fast Fourier transform algorithm, wherein the fast Fourier transform algorithm is used to convert time series data corresponding to the motion trajectory into a frequency domain signal to analyze the frequency component, and the frequency component is used to represent a frequency feature of the motion of the power transmission conductor; identifying whether the power transmission conductor appears conductor galloping or conductor deviation based on the frequency component of the motion trajectory; in a case where the power transmission conductor appears the conductor galloping, determining a maximum galloping distance of the power transmission conductor at different time points, and determining a motion frequency of the power transmission conductor by analyzing the periodic change of the motion trajectory, wherein the displacement value of the power transmission conductor in each direction is calculated by analyzing the position coordinates of the motion trajectory at different time points, the maximum galloping distance is taken as the maximum displacement value in each direction, and the galloping period of the power transmission conductor is determined by analyzing the periodic change of the motion trajectory, and then the frequency of the galloping of the power transmission conductor is calculated according to the relationship between the galloping period and time.

2. The method of claim 1, wherein, The outputting the pre-warning prompt information in the case where the abnormal motion information of the power transmission conductor meets the pre-warning condition comprises: determining a wind speed prediction value of a position of the power transmission conductor according to the galloping distance and the motion frequency; in a case where the wind speed prediction value exceeds a first wind speed threshold value set in advance, determining that the abnormal motion information of the power transmission conductor meets the pre-warning condition to trigger the outputting of the pre-warning prompt information, wherein the pre-warning prompt information comprises the galloping distance, the motion frequency, the wind speed prediction value, and the motion trajectory of the power transmission conductor.

3. The method of claim 1, wherein, The identifying the abnormal motion information of the power transmission conductor based on the motion trajectory comprises: extracting a frequency component of the motion trajectory through a fast Fourier transform algorithm; identifying whether the power transmission conductor appears conductor galloping or conductor deviation based on the frequency component of the motion trajectory; in a case where the power transmission conductor appears the conductor deviation, determining a conductor deviation distance of the power transmission conductor according to a motion trend of the power transmission conductor.

4. The method of claim 3, wherein, The method further comprises: obtaining historical galloping distance, historical motion frequency and historical conductor offset distance of the power transmission conductor monitored in a historical period; determining the first wind speed threshold based on the historical galloping distance and the historical motion frequency; 5. The method of claim 1, wherein, determining the second wind speed threshold based on the historical conductor offset distance; establishing a mapping relationship between the conductor galloping of the power transmission conductor and the first wind speed threshold, and a mapping relationship between the conductor offset of the power transmission conductor and the second wind speed threshold, respectively, by a multiple regression analysis algorithm. For implementing the method of any one of claims 1 to 5, comprising: a dynamic visual sensor installed on a power transmission tower for monitoring position change information of a power transmission conductor and generating a power transmission conductor event stream based on the position change information; a data processor connected to the dynamic visual sensor for reconstructing a motion trajectory of the power transmission conductor based on the power transmission conductor event stream and identifying abnormal motion information of the power transmission conductor based on the motion trajectory, and outputting a pre-warning prompt information in the case that the abnormal motion information of the power transmission conductor meets a pre-warning condition.

6. A monitoring system of a power transmission conductor, characterized by For implementing the method of any one of claims 1 to 5, comprising: an acquisition unit for acquiring a power transmission conductor event stream, wherein the power transmission conductor event stream is used to represent position change information of a power transmission conductor; a reconstruction unit for reconstructing a motion trajectory of the power transmission conductor based on the power transmission conductor event stream; 7. A monitoring device for a power transmission conductor, characterized in that an identification unit for identifying abnormal motion information of the power transmission conductor based on the motion trajectory; an output unit for outputting a pre-warning prompt information in the case that the abnormal motion information of the power transmission conductor meets a pre-warning condition. The processor executes the computer program to implement the method of any one of claims 1 to 5. The computer program is executed by the processor to implement the method of any one of claims 1 to 5. ​ 8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, ​ 9. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 8. ​

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