Intelligent dynamic sag measurement system for overhead line

Abstract

The application discloses an overhead line sag intelligent dynamic adjustment system and relates to the technical field of overhead line dynamic adjustment; the application acquires line basic parameters of a target line first, then calls meteorological prediction parameters and simulates external meteorological environment based on meteorological basic parameters to obtain a meteorological influence scene, subsequently inputs the line basic parameters and the meteorological influence scene into a pre-trained sag prediction model to output a sag prediction value; when the sag prediction value exceeds a preset sag fluctuation range and is determined as abnormal, the sag prediction value is combined with line layout parameters to formulate an executable line adjustment scheme and implement adjustment. The application combines meteorological influence scene simulation with an artificial intelligence model, significantly improves the accuracy and foresight of sag prediction, overcomes the defects of traditional methods that can only be passively monitored and cannot give early warning, comprehensively improves the operation stability of overhead lines under adverse weather, reduces the line failure probability, and guarantees the continuous, safe and reliable operation of a power transmission system.

Description

Technical Field

[0001] This invention belongs to the field of overhead line dynamic adjustment and testing technology, specifically an intelligent dynamic adjustment and testing system for overhead line sag. Background Technology

[0002] The sag of overhead lines is a key indicator for ensuring the safe operation of transmission lines. Insufficient sag can easily lead to conductor tension overload, line breakage, and tower damage, while excessive sag can cause safety accidents such as insufficient safety distance to the ground and short circuits. Its operating status directly affects the reliability of the transmission system.

[0003] Existing overhead line sag monitoring and control methods mostly adopt the traditional approach of passive monitoring and post-event adjustment. They typically only collect basic line parameters and real-time environmental data, perform static detection and simple calculations on the sag status, and carry out manual or semi-automatic adjustment operations only when the sag exceeds the safe range. They cannot conduct scenario-based simulation and quantitative analysis of external meteorological loads such as strong winds, icing, snow accumulation, and rime, making it difficult to accurately assess the dynamic impact of future meteorological environments on sag.

[0004] This invention provides an intelligent dynamic adjustment system for overhead line sag to solve the above-mentioned technical problems. Summary of the Invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes an intelligent dynamic adjustment system for overhead line sag.

[0006] To achieve the above objectives, a first aspect of the present invention provides an intelligent dynamic adjustment system for overhead line sag, comprising: Intelligent commissioning module: used to acquire the basic parameters of the target line; among which, the basic parameters of the line include the sag foundation value, real-time environmental parameters, line structure parameters and line layout parameters; This is used to retrieve meteorological forecast parameters and combine them with basic line parameters to predict the sag value; the sag prediction value is obtained through a pre-constructed sag prediction model; and... When the predicted sag value is abnormal, a line adjustment plan is formulated based on the predicted sag value and the line layout parameters; the target line is adjusted according to the line adjustment plan to maintain the safe operation of the target line.

[0007] In one possible implementation, the predicted sag value is obtained by combining the prediction of the line's basic parameters, including: Meteorological impact scenarios are simulated based on meteorological forecast parameters; these scenarios are used to assess the impact of the external meteorological environment on the sag foundation value of the target line. The predicted sag value of the target line is predicted based on the line's basic parameters and meteorological impact scenarios.

[0008] In one possible implementation, meteorological impact scenarios are simulated based on meteorological forecast parameters, including: The parameters in the meteorological forecast parameters are aligned to obtain the basic meteorological parameters; the alignment process includes time alignment and sampling alignment. The external meteorological environment is simulated based on basic meteorological parameters, and meteorological impact scenarios are extracted from the simulation results. These meteorological impact scenarios include external meteorological loads, load duration, load intensity, and load direction. External meteorological loads include strong winds, icing, snow accumulation, and rime.

[0009] In one possible implementation, the predicted sag value is predicted based on the line's basic parameters and meteorological impact scenarios, including: A sag prediction model is obtained by training an artificial intelligence model; the artificial intelligence model includes a BP neural network model or an RBF neural network model. The basic parameters of the line and the meteorological impact scenario are input into the sag prediction model to obtain the sag prediction value.

[0010] In one possible implementation, a line adjustment plan is formulated based on the predicted sag value and the line layout parameters, including: The sag adjustment range corresponding to the sag base value is calculated based on the sag prediction value; the sag adjustment range is constrained by the sag fluctuation range. Calculate the sag adjustment value based on the sag adjustment range; formulate a line adjustment plan based on the line layout parameters and the sag adjustment value.

[0011] In one possible implementation, the sag adjustment range corresponding to the sag base value is calculated based on the sag prediction value, including: The sag adjustment direction is determined based on the predicted sag value; based on the sag adjustment direction, a basic adjustment range is generated according to the basic sag value and the upper or lower limit of the sag fluctuation range. The sag adjustment range is extracted from the basic adjustment range using the sag prediction model.

[0012] In one possible implementation, the sag adjustment value is calculated based on the sag adjustment range, including: Determine whether the time interval between adjacent sag prediction values ​​is greater than a preset interval threshold; if yes, calculate the sag adjustment value based on the sag adjustment range; otherwise, integrate the sag adjustment ranges into an adjustment range group. The sag adjustment value is calculated based on the sag adjustment range in the adjustment range group.

[0013] In one possible implementation, the sag adjustment value is jointly calculated based on the sag adjustment ranges in the adjustment range group, including: Determine whether there is an intersection of the sag adjustment ranges in the adjustment range group; if yes, take the intersection of the sag adjustment ranges as the target adjustment range; otherwise, calculate the sag adjustment value based on the sag adjustment range. Calculate the sag adjustment value based on the target adjustment range.

[0014] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention first obtains the basic parameters of the target line, then retrieves meteorological forecast parameters and simulates the external meteorological environment based on the basic meteorological parameters to obtain the meteorological impact scenario. Subsequently, the basic line parameters and the meteorological impact scenario are input into a pre-trained sag prediction model, which outputs the sag prediction value. When the sag prediction value exceeds the preset sag fluctuation range and is determined to be abnormal, the sag prediction value is combined with the line layout parameters to formulate an executable line adjustment plan and implement the adjustment. This invention, by combining meteorological impact scenario simulation with an artificial intelligence model, significantly improves the accuracy and foresight of sag prediction, overcomes the shortcomings of traditional methods that can only passively monitor and cannot provide early warnings, comprehensively improves the operational stability of overhead lines under severe weather conditions, reduces the probability of line failure, and ensures the continuous, safe, and reliable operation of the power transmission system.

[0015] 2. After obtaining the sag adjustment range, this invention first determines whether the time interval between adjacent sag prediction values ​​is greater than a preset interval threshold. If it is greater than the threshold, the sag adjustment value is calculated separately based on the corresponding sag adjustment range. If it is not greater than the threshold, multiple sag adjustment ranges are integrated into an adjustment range group. Then, it determines whether there is an intersection among the sag adjustment ranges within the adjustment range group. If there is an intersection, the intersection is used as the target adjustment range; otherwise, the calculation is performed in a conventional manner. Finally, the sag adjustment value is determined based on the target adjustment range or a single sag adjustment range. This invention effectively avoids the problem of continuous abnormal sag prediction values ​​and frequent line adjustments caused by frequent fluctuations in meteorological forecast parameters by combining time interval judgment with sag adjustment range processing. This significantly reduces the frequency of adjustment operations and lowers the costs of manpower, materials, and equipment wear and tear. By determining the target adjustment range through intersection, a single adjustment can simultaneously adapt to meteorological impact scenarios in multiple time periods, improving adjustment efficiency and coverage, and enhancing the overall intelligence and practicality of the adjustment system. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, 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 the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1This is a schematic diagram illustrating the working steps of the intelligent dynamic adjustment system for overhead line sag in an embodiment of the present invention; Figure 2 This is a schematic diagram of the process for obtaining the sag prediction value in an embodiment of the present invention; Figure 3 This is a schematic diagram illustrating the calculation process for the sag adjustment value in an embodiment of the present invention. Detailed Implementation

[0018] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Please see Figure 1 The first aspect of this invention provides an intelligent dynamic adjustment system for overhead line sag, comprising an intelligent adjustment module: for acquiring basic line parameters of the target line; wherein the basic line parameters include basic sag values, real-time environmental parameters, line structure parameters, and line layout parameters; for retrieving meteorological forecast parameters and combining them with the basic line parameters to predict sag values; wherein the predicted sag values ​​are obtained through a pre-constructed sag prediction model; and, when the predicted sag values ​​are abnormal, for formulating a line adjustment plan based on the predicted sag values ​​and line layout parameters; and for adjusting the target line according to the line adjustment plan to maintain the safe operation of the target line.

[0020] Overhead line sag refers to the vertical distance between the line connecting the two suspension points of an overhead transmission / distribution line conductor, suspended between two towers, and the lowest point of the conductor due to its own weight. Overhead line sag is a crucial indicator of normal operation. Abnormal sag can occur during long-term operation of overhead lines. Therefore, accurate assessment of overhead transmission line sag is of paramount importance for the safe operation of transmission lines.

[0021] If the sag of an overhead line is too small, the conductor will be too taut, leading to excessive tension during sudden drops in temperature, icing, or strong winds, which can easily cause the line to break and damage the tower. If the sag of an overhead line is too large, the conductor will droop excessively, resulting in insufficient safe distances to the ground and crossings, which can easily cause discharges and short circuits, leading to safety accidents.

[0022] The sag of overhead power lines is mainly affected by factors such as conductor parameters, span length, temperature, and external meteorological loads. For example, temperature: rising temperature → thermal expansion of the conductor → increased sag; falling temperature → thermal contraction of the conductor → decreased sag and increased conductor tension; conductor parameters: conductor cross-sectional area, mass per unit length: the greater the self-weight, the greater the sag under the same span; conductor material: aluminum wire and steel-cored aluminum stranded wire have different elasticity and coefficients of linear expansion, resulting in different sag variations; conductor service life: older conductors creep and elongate, causing the sag to increase year by year; span length: the greater the horizontal distance between two towers (span length), the greater the sag value under the same tension and temperature conditions, exhibiting a non-linear growth. External meteorological loads: icing: the self-weight of the conductor increases significantly after icing, resulting in a significant increase in sag; strong winds: horizontal wind pressure causes the conductor to sway, effectively increasing sag and reducing the distance between phases and to the ground; snow accumulation and condensation: additional loads indirectly increase sag.

[0023] As mentioned earlier, external meteorological loads such as strong winds, icing, snow accumulation, and rime can all affect the sag value of overhead lines. For example, icing, snow accumulation, and rime on conductors increase the vertical additional load on the conductors, increasing their self-weight and leading to a significant increase in sag. This reduces the safe distance to the ground and for crossings. Strong winds generate horizontal wind pressure, causing the conductors to shift laterally and form oblique sag, compressing the phase-to-phase and phase-to-ground distances, which can easily trigger line discharge and short-circuit faults. When multiple external meteorological loads are superimposed, the excessive sag and the operational risks of the line will be further aggravated. The implementation process of the intelligent sag adjustment method for overhead lines provided by this invention is as follows: A01: Retrieve the basic parameters of the target line; among which, the basic parameters of the line include the sag foundation value, real-time environmental parameters, line structure parameters, and line layout parameters; The target line refers to the overhead line that needs dynamic monitoring and adjustment of sag. Generally, it refers to the overhead line between two towers. If the entire overhead line needs to be adjusted, it can be divided into several target lines according to the towers.

[0024] The baseline sag value refers to the sag value of the target line under its current state, which can be calculated using existing publicly available methods, such as calculating the sag value of the target line using a preset state equation. Real-time environmental parameters refer to the real-time meteorological parameters corresponding to the target line when the baseline sag value is calculated, including temperature, humidity, wind speed, and wind direction. Line structural parameters refer to the structural parameters of the target line itself, which are closely related to the sag value, including conductor cross-sectional area, mass per unit length, conductor material, and conductor service life. Line layout parameters are the layout parameters of the target line, which are also closely related to the sag value. These parameters include specific load, span, and the height difference between the suspension points at both ends.

[0025] A02: Retrieve meteorological forecast parameters and combine them with basic line parameters to obtain the sag prediction value; the sag prediction value is obtained through a pre-constructed sag prediction model. Meteorological forecast parameters are used to predict external meteorological loads that may affect the target railway line. These parameters may influence the sag foundation value of the target railway line. The meteorological forecast parameters contain the same parameters as the real-time meteorological parameters. Meteorological forecast parameters mainly refer to data related to the formation of strong winds, icing, snow accumulation, and rime, such as temperature, humidity, and wind speed.

[0026] Predicting the impact of meteorological forecast parameters on the baseline sag value based on the basic line parameters essentially involves analyzing whether meteorological forecast parameters will lead to an increase or decrease in the baseline sag value, thereby obtaining the predicted sag value. The predicted sag value is achieved through a pre-constructed sag prediction model, which can be built using polynomial fitting or artificial intelligence models.

[0027] A03: When the sag prediction value is abnormal, formulate a line adjustment plan based on the sag prediction value and line layout parameters; adjust the target line according to the line adjustment plan to maintain the safe operation of the target line.

[0028] The target line has a corresponding sag fluctuation range. When the sag value of the target line is within the sag fluctuation range, it will not affect the safe and stable operation of the target line. Based on this, it is determined whether the sag prediction value is within the sag fluctuation range. If it is within the sag fluctuation range, the sag prediction value is normal, and it is determined that the target line can still operate safely and stably under the influence of meteorological prediction parameters. Otherwise, if the sag prediction value is abnormal, it is determined that the meteorological prediction parameters will affect the safe and stable operation of the target line.

[0029] If the predicted sag value is abnormal, it indicates that the sag value of the target line will exceed the preset sag fluctuation range under the influence of external meteorological loads, affecting the safe and stable operation of the target line. By comparing the predicted sag value and the basic sag value, the sag adjustment range can be determined. Based on the sag adjustment range and the line layout parameters, a line adjustment plan is formulated. The target line is then adjusted according to the line adjustment plan to ensure that the sag value of the target line does not exceed the preset sag fluctuation range under the influence of external meteorological loads.

[0030] This invention obtains the predicted sag value of the target line by combining meteorological forecast parameters and line basic parameters. Based on the predicted sag value, a line adjustment plan is formulated in conjunction with the line layout parameters. The target line is adjusted according to the line adjustment plan to avoid the sag value of the target line being too large or too small under the influence of external meteorological loads, and to ensure that the target line can still operate safely and stably even under the influence of external meteorological loads.

[0031] Meteorological forecast parameters affect the sag value of the target line because the meteorological conditions corresponding to these parameters induce external meteorological loads, which are the primary influencing factor on the sag value. This invention first predicts the meteorological impact scenario based on the meteorological forecast parameters, and then analyzes the impact of the meteorological impact scenario and the line's foundation parameters on the sag value of the target line, thereby obtaining the predicted sag value. Please refer to [link to relevant documentation]. Figure 2 The implementation process of this invention, which combines the prediction of line foundation parameters to obtain the sag prediction value, is as follows: B01: Simulate and obtain meteorological impact scenarios based on meteorological forecast parameters; Meteorological impact scenarios refer to the external meteorological loads and their impact range that may be caused by meteorological forecast parameters. Meteorological impact scenarios are mainly used to assess the impact of the external meteorological environment on the sag foundation value of the target line.

[0032] Meteorological impact scenarios can be obtained by simulating meteorological forecast parameters. That is, simulating the impact of various external meteorological loads on the target line under the action of multiple meteorological forecast parameters. Only after clarifying the external meteorological parameters can the impact of the external environment on the sag value of the target line be assessed.

[0033] B02: Predict the sag value of the target line based on the basic parameters of the line and the meteorological impact scenario.

[0034] Sag values ​​are closely related to basic line parameters and meteorological impact scenarios. By combining these parameters, we predict future changes in sag values. The prediction of sag values ​​is achieved through a pre-established mapping model.

[0035] This invention transforms meteorological forecast parameters into meteorological impact scenarios, and predicts the sag value of the target line based on the line's basic parameters and the meteorological impact scenarios. This invention reduces data processing by transforming meteorological forecast parameters, and improves the prediction accuracy of sag values ​​by clearly identifying the external meteorological loads affecting the target line's sag value.

[0036] Generally, normal external weather changes do not cause significant changes in the sag of the target line. However, when the external weather environment triggers specific external meteorological loads, these loads can have a substantial impact on the sag value of the target line. The implementation process of this invention, which simulates and obtains meteorological impact scenarios based on meteorological forecast parameters, is as follows: C01: Align the parameter items in the meteorological forecast parameters to obtain the basic meteorological parameters; Since meteorological forecast parameters include multiple parameters from various sources, the parameters are first aligned, including time alignment and sampling alignment, and then integrated into the basic meteorological parameters.

[0037] C02: Simulate the external meteorological environment based on basic meteorological parameters, and extract meteorological impact scenarios from the simulation results.

[0038] The external meteorological environment of the target route is simulated using meteorological simulation software. After inputting the basic meteorological parameters into the software, the external meteorological loads in the area where the target route is located can be simulated over time. The external meteorological loads, their duration of action, and their intensity can be extracted from the simulation results. If necessary, the direction of the loads can also be extracted. For example, when the external meteorological load is a strong wind, the influence of the wind direction on the sag value needs to be considered. The extracted data are then integrated into a meteorological impact scenario.

[0039] Meteorological simulation software can be directly selected from existing software, such as LiPowerline and Meteodyn WT. External meteorological loads can be obtained through simulation using multiple meteorological simulation software programs.

[0040] Since the sag value of the target line is affected by the line's basic parameters and meteorological scenarios, it is necessary to establish a mapping relationship between the degree of influence of the line's basic parameters and meteorological scenarios on the sag value in advance. Considering the large number of parameter types involved, this invention can predict the sag value through an artificial intelligence model, and the implementation process is as follows: D01: Obtaining a sag prediction model based on artificial intelligence model training; Since the sag prediction is affected by multiple parameters, choosing an artificial intelligence model can establish an accurate mapping relationship through its nonlinear mapping capability, thereby ensuring the accuracy of the sag prediction.

[0041] Artificial intelligence models include BP neural network models, RBF neural network models, etc. When training an AI model, a training sample set is first constructed using historical or simulated data. This set includes several sample data points, each containing training input data and its corresponding training output data. The parameters included in the training input data are consistent with the basic parameters of the railway line and the meteorological impact scenario. The training output data is the predicted sag value under the corresponding scenario. The AI ​​model is then trained using this training sample set. The trained AI model is labeled as a sag prediction model and then encapsulated and stored for future reference.

[0042] D02: Input the basic parameters of the line and the meteorological impact scenario into the sag prediction model to obtain the sag prediction value.

[0043] The sag prediction model is invoked when predicting the sag value of the target route. The basic parameters of the target route and the meteorological impact scenario are input into the sag prediction model to obtain the sag prediction value. The sag prediction value is not a single value, but rather includes multiple sag prediction values ​​within a time period, which is the same time period corresponding to the external prediction parameters.

[0044] For example, assuming that the meteorological forecast parameters include temperature, humidity, wind speed, etc., the parameters are resampled according to a preset frequency and integrated to obtain the basic meteorological parameters. The basic meteorological parameters are input into the meteorological simulation software, and the existence of external meteorological loads is analyzed based on the meteorological simulation results. If external meteorological loads exist, the external meteorological loads and their action time and intensity are extracted. In this way, it can be determined which external meteorological loads will affect the target line.

[0045] Next, the meteorological impact scenario and the basic parameters of the line are input into the sag prediction model to obtain the predicted sag value under the action of external meteorological loads. If the target line is not affected by external meteorological loads, its predicted sag value is basically consistent with the basic sag value. If the target line is affected by external meteorological loads, its predicted sag value will differ significantly from the basic sag value.

[0046] After obtaining the predicted sag value of the target line based on combined meteorological forecast parameters and line baseline parameters, if the predicted sag value exceeds the preset sag fluctuation range, it indicates that the corresponding target line may not function normally at that time. Therefore, adjustments to the target line are necessary to ensure its safe and stable operation. Please refer to [link / reference]. Figure 3 The implementation process of this invention for formulating a line adjustment scheme based on the predicted sag value and line layout parameters is as follows: E01: Calculate the sag adjustment range corresponding to the sag base value based on the sag prediction value; whereby the sag adjustment range is constrained by the sag fluctuation range. When the predicted sag value exceeds the sag fluctuation range, the target line needs to be adjusted to avoid this situation. Using the sag fluctuation range as a constraint, we can calculate the range to which the basic sag value should be adjusted before the predicted sag value returns to the sag fluctuation range at the corresponding moment. The adjustable range of the basic sag value is taken as the sag adjustment range.

[0047] It is worth noting that within the sag adjustment range, a sag value is selected. This sag value corresponds to a sag prediction value under the meteorological forecast parameters and the corresponding line basic parameters. When determining whether the sag prediction value exceeds the sag fluctuation range, the sag fluctuation range used as the benchmark is not the sag fluctuation range corresponding to the sag basic value, but the sag fluctuation range re-determined based on the updated line basic parameters after adjusting the target line according to the sag value.

[0048] E02: Calculate the sag adjustment value based on the sag adjustment range; formulate a line adjustment plan based on the line layout parameters and the sag adjustment value.

[0049] The sag adjustment range corresponding to the basic sag value can be calculated based on the predicted sag value using the aforementioned sag prediction model. The specific implementation process is as follows: F01: Determine the sag adjustment direction based on the sag prediction value; Based on the sag adjustment direction, generate the basic adjustment range according to the basic sag value and the upper or lower limit of the sag fluctuation range; If the predicted sag value exceeds the lower limit of the sag fluctuation range, the base sag value needs to be adjusted upwards to ensure that the predicted sag value returns to the corresponding sag fluctuation range. If the base sag value needs to be adjusted upwards, the base sag value and the upper limit of the sag fluctuation range are combined into the adjustment range. If the base sag value needs to be adjusted downwards, the base sag value and the lower limit of the sag fluctuation range are combined into the adjustment range.

[0050] F02: Use the sag prediction model to extract the sag adjustment range from the basic adjustment range.

[0051] After determining the basic adjustment range, an adjustment interval value is set. Based on the adjustment interval value, several sag reference values ​​are selected within the basic adjustment range. Simultaneously, the basic line parameters and the corresponding sag fluctuation range are updated based on the sag reference values. The updated basic line parameters and meteorological impact scenarios are input into the sag prediction model to obtain the sag prediction value. If the sag prediction value falls within the updated sag fluctuation range, the sag reference value is marked as the sag target value. The sag range formed by all sag target values ​​is used as the sag adjustment range. In this way, adjusting the basic sag value according to any sag value within the sag adjustment range ensures that the corresponding sag prediction value remains within the sag fluctuation range.

[0052] For example, after obtaining the predicted sag value, it is compared with the preset sag fluctuation range. If it exceeds the sag fluctuation range, it is judged as abnormal. Since the external meteorological load is usually continuous, the predicted sag value may show continuous abnormalities. If the predicted sag value is continuously abnormal, discrete values ​​are extracted from it at fixed intervals (such as every minute, every ten minutes, etc.) to calculate the sag adjustment range of the discrete predicted sag values.

[0053] When calculating the sag adjustment range for the predicted sag value, if the predicted sag value is greater than the baseline sag value, it indicates that the baseline sag value should be reduced. Therefore, the sag adjustment direction is upward. The range between the baseline sag value and the upper limit of the sag fluctuation direction is integrated into the sag adjustment range. This sag adjustment range is used to constrain the adjustment of the baseline sag value.

[0054] Within the sag adjustment range, several sag reference values ​​are taken at preset intervals (e.g., 5cm, 10cm). These sag reference values ​​are used to replace the basic sag values ​​in the line's basic parameters. Simultaneously, the line structure parameters and sag fluctuation range are updated. This is because if the sag value of the target line changes, its line structure parameters and sag fluctuation range will also change. The updated line basic parameters and meteorological impact scenarios are input into the sag prediction model to obtain the sag prediction value. If the sag prediction value does not exceed the updated sag fluctuation range, then the sag prediction value is used as the sag target value.

[0055] After obtaining several target sag values ​​according to the scheme, the range formed by these target sag values ​​is taken as the sag adjustment range. Adjusting the basic sag value to the sag adjustment range can ensure that the sag value of the target line is also in a normal state under the influence of external meteorological loads.

[0056] If the meteorological forecast parameters for the area where the target route is located fluctuate frequently, it will cause drastic fluctuations in the sag forecast value. This may require frequent adjustments to the target route based on the sag forecast value, which not only increases manpower costs but also undermines the safe and stable operation of the target route. To avoid frequent adjustments to the sag of the target route, this invention merges adjacent adjustments that meet certain conditions. The process is as follows: G01: Determine whether the time interval between adjacent sag prediction values ​​is greater than the preset interval threshold; if yes, calculate the sag adjustment value based on the sag adjustment range; if no, integrate the sag adjustment ranges into an adjustment range group. Referring to the aforementioned scheme, each abnormal sag prediction value corresponds to a sag adjustment range. If the time interval between sag prediction values ​​is short, an adjustment can be made once for multiple abnormal sag prediction values, reducing the adjustment frequency and saving manpower and resources. If the time interval between sag prediction values ​​is long, each sag prediction value can be adjusted independently.

[0057] The preset interval threshold can be set based on experience, taking into account both cost and external weather changes. The higher the cost, the longer the interval threshold should be; the more drastic the external weather changes, the shorter the interval threshold should be. The default interval threshold can be set to one week or one month.

[0058] G02: Calculate the sag adjustment value based on the sag adjustment range in the adjustment range group.

[0059] The sag adjustment value is the difference between the sag base value and the sag reference value. The sag reference value is determined from the sag adjustment range or from the adjustment range group.

[0060] The sag adjustment value is calculated based on the sag adjustment ranges in the adjustment range group. The implementation process is as follows: H01: Determine whether there is an intersection of the sag adjustment ranges in the adjustment range group; if yes, take the intersection of the sag adjustment ranges as the target adjustment range; if no, calculate the sag adjustment value based on the sag adjustment range. H02: Calculate the sag adjustment value based on the target adjustment range.

[0061] For example, suppose the sag predictions for both time A and time B are abnormal. Normally, the target route would be adjusted separately for each time. However, if time A and time B are very close, such as three days apart, the cost of adjusting the target route would increase. In this case, considering both cost and external weather changes, a preset interval threshold is used. For example, if the interval threshold is set to one week, the sag adjustment ranges for time A and time B are integrated into an adjustment range group. The intersection of the sag adjustment ranges in the adjustment range group is selected as the target adjustment range. Adjusting the target route according to the target adjustment range can resolve the impact of both times with a single adjustment. If the interval threshold is set to one day, adjustments are made separately for time A and time B.

[0062] When calculating the sag adjustment value based on the sag adjustment range (or target adjustment range), select a value (upper limit, lower limit, or middle value) within the sag adjustment range as the sag reference value, and use the difference between the sag base value and the sag reference value as the sag adjustment value.

[0063] After calculating the sag adjustment value, a line adjustment plan is formulated based on the line layout parameters. The line adjustment plan includes adjusting one or both ends of the target line, as well as the adjustment length. The target line is adjusted according to the line adjustment plan to ensure that its sag value remains within the normal range under the influence of external meteorological conditions, thus guaranteeing the safe and stable operation of the target line.

[0064] It should be understood that the descriptions of technical features, technical solutions, beneficial effects, or similar language in this application do not imply that all features and advantages can be achieved in any single embodiment. Rather, it is understood that the description of a feature or beneficial effect means that a specific technical feature, technical solution, or beneficial effect is included in at least one embodiment. Therefore, the descriptions of technical features, technical solutions, or beneficial effects in this specification do not necessarily refer to the same embodiment. Furthermore, the technical features, technical solutions, and beneficial effects described in this embodiment can be combined in any suitable manner. Those skilled in the art will understand that embodiments can be implemented without one or more specific technical features, technical solutions, or beneficial effects of a particular embodiment. In other embodiments, additional technical features and beneficial effects may be identified in specific embodiments that do not embody all embodiments.

[0065] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any other combination thereof. When implemented using a software program, it can be implemented entirely or partially in the form of a computer program product. This computer program product includes one or more computer instructions. When the computer program 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, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, 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 containing one or more servers, data centers, etc., that can be integrated with the medium. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state disks (SSDs)).

[0066] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. An intelligent dynamic adjustment system for overhead line sag, characterized in that, include: Intelligent commissioning module: used to acquire the basic parameters of the target line; among which, the basic parameters of the line include the sag foundation value, real-time environmental parameters, line structure parameters and line layout parameters; This is used to retrieve meteorological forecast parameters and combine them with basic line parameters to predict the sag value; the sag prediction value is obtained through a pre-constructed sag prediction model; and... When the predicted sag value is abnormal, a line adjustment plan is formulated based on the predicted sag value and the line layout parameters; the target line is adjusted according to the line adjustment plan to maintain the safe operation of the target line.

2. The intelligent dynamic adjustment system for overhead line sag according to claim 1, characterized in that, The predicted sag values ​​are obtained by combining the basic parameters of the line, including: Meteorological impact scenarios are simulated based on meteorological forecast parameters; these scenarios are used to assess the impact of the external meteorological environment on the sag foundation value of the target line. The predicted sag value of the target line is predicted based on the line's basic parameters and meteorological impact scenarios.

3. The intelligent dynamic adjustment system for overhead line sag according to claim 2, characterized in that, Meteorological impact scenarios are simulated based on meteorological forecast parameters, including: The parameters in the meteorological forecast parameters are aligned to obtain the basic meteorological parameters; the alignment process includes time alignment and sampling alignment. The external meteorological environment is simulated based on basic meteorological parameters, and meteorological impact scenarios are extracted from the simulation results. These meteorological impact scenarios include external meteorological loads, load duration, load intensity, and load direction. External meteorological loads include strong winds, icing, snow accumulation, and rime.

4. The intelligent dynamic adjustment system for overhead line sag according to claim 2, characterized in that, Based on the basic parameters of the line and the meteorological impact scenario, the predicted sag value is included: A sag prediction model is obtained by training an artificial intelligence model; the artificial intelligence model includes a BP neural network model or an RBF neural network model. The basic parameters of the line and the meteorological impact scenario are input into the sag prediction model to obtain the sag prediction value.

5. The intelligent dynamic adjustment system for overhead line sag according to claim 1, characterized in that, Based on the predicted sag value and the line layout parameters, a line adjustment plan is formulated, including: The sag adjustment range corresponding to the sag base value is calculated based on the sag prediction value; the sag adjustment range is constrained by the sag fluctuation range. Calculate the sag adjustment value based on the sag adjustment range; formulate a line adjustment plan based on the line layout parameters and the sag adjustment value.

6. The intelligent dynamic adjustment system for overhead line sag according to claim 5, characterized in that, The sag adjustment range corresponding to the basic sag value is calculated based on the predicted sag value, including: The sag adjustment direction is determined based on the predicted sag value; based on the sag adjustment direction, a basic adjustment range is generated according to the basic sag value and the upper or lower limit of the sag fluctuation range. The sag adjustment range is extracted from the basic adjustment range using the sag prediction model.

7. The intelligent dynamic adjustment system for overhead line sag according to claim 5, characterized in that, The sag adjustment value is calculated based on the sag adjustment range, including: Determine whether the time interval between adjacent sag prediction values ​​is greater than a preset interval threshold; if yes, calculate the sag adjustment value based on the sag adjustment range; otherwise, integrate the sag adjustment ranges into an adjustment range group. The sag adjustment value is calculated based on the sag adjustment range in the adjustment range group.

8. The intelligent dynamic adjustment system for overhead line sag according to claim 7, characterized in that, The sag adjustment value is calculated based on the sag adjustment range in the adjustment range group, including: Determine whether there is an intersection of the sag adjustment ranges in the adjustment range group; if yes, take the intersection of the sag adjustment ranges as the target adjustment range; otherwise, calculate the sag adjustment value based on the sag adjustment range. Calculate the sag adjustment value based on the target adjustment range.

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