Sag measuring and calculating method and device of distribution line, medium and equipment
By constructing an initial sag model based on the conductor length and force equation, and combining it with Kalman filtering and improved least squares method, the problem of low sag calculation accuracy in the existing technology is solved, and more accurate sag prediction is achieved.
Patent Information
- Application Number
- CN202510512338.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-23
- Publication Date
- 2025-09-12
AI Technical Summary
The existing distribution line sag calculation method has the problem of low accuracy and cannot accurately reflect the measured sag state of the transmission line.
Based on the conductor length equation, the force equation at the conductor center point, the conductor tension and the conductor load, an initial sag model is constructed. The objective function is constructed using the measured sag of the sample conductor and real-time environmental parameters. The Kalman filtering technology and the improved least squares method are used to solve the problem, and the optimal solutions for the conductor tension and load are obtained, and then the target sag is calculated.
The accuracy of sag prediction is improved, the influence of environmental parameters on sag can be reflected more accurately, and the accuracy of sag prediction method is improved.
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Figure CN120632248A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of sag measurement, and in particular to a sag measurement method, device, medium and equipment for a distribution line. Background Art
[0002] Sag refers to the vertical distance between the lowest point of a distribution line conductor and the line connecting the two suspension points (such as poles or towers) when the conductor sags due to its own weight and external forces between them. Excessive sag can result in insufficient clearance between the conductor and the ground below, increasing the risk of electrical discharges. Excessive sag can place excessive tension on the conductor, accelerating wear. Therefore, sag detection for distribution lines is crucial and directly impacts the safety and reliability of transmission circuits.
[0003] Currently, sag calculations for distribution lines primarily rely on catenary or parabola models. When using the catenary model, the necessary parameters are first obtained. Based on the properties of the catenary and known conditions, the lowest point of the conductor is determined by solving equations. Based on this lowest point, the sag at the center of the span or other specific points is then calculated. When using the parabola model, the necessary parameters are obtained and substituted into the parabola model to calculate the sag at that point.
[0004] Because the catenary equation involves parameters that are difficult to accurately obtain, such as the conductor's mass per unit length and elastic modulus, measurement errors and variations in these parameters under varying environmental conditions can affect the accuracy of sag calculations. The parabola model calculates sag under hypothetical conditions, and because these assumptions differ from actual conditions, the calculated results can contain significant errors. Consequently, these traditional sag calculation methods cannot accurately reflect the measured sag state of transmission lines. Summary of the Invention
[0005] In view of this, the present invention provides a method, device, medium and equipment for measuring the sag of a distribution line, the main purpose of which is to solve the problem that the sag of a transmission line predicted by the existing sag calculation method of a distribution line has relatively low accuracy.
[0006] According to one aspect of the present application, a method for calculating sag of a distribution line is provided, the method comprising:
[0007] According to the arc length calculation method of the distribution line, a conductor length equation between adjacent tower suspension points is constructed, and according to the stress state of the conductor between the adjacent tower suspension points, a force equation of the conductor center point is constructed;
[0008] Constructing an initial sag model of the conductor based on the conductor length equation, the force equation at the conductor center point, the conductor tension, and the conductor load;
[0009] Obtaining a measured sag of a sample conductor and actual environmental parameters corresponding to the measured sag, constructing an objective function based on the measured sag of the sample conductor and the initial sag model, and solving the objective function based on the measured sag of the sample conductor and the real-time environmental parameters to obtain an optimal solution for the conductor tension and an optimal solution for the conductor load;
[0010] The optimal solution of the conductor tension and the optimal solution of the conductor load are substituted into the initial sag model to obtain a target sag model, the horizontal coordinates of the lowest point of the target conductor are obtained, and the horizontal coordinates of the lowest point of the target conductor are substituted into the target sag model to obtain the predicted sag of the target conductor.
[0011] Optionally, constructing an objective function based on the initial sag model includes:
[0012] Performing maximum likelihood estimation on the initial sag model to obtain an intermediate function;
[0013] An L2 regularization term is introduced into the intermediate function to obtain the objective function.
[0014] Optionally, the objective function is:
[0015]
[0016] Where x is the horizontal coordinate of the lowest point of the sample wire, f(x i ) is the predicted sag of the i-th sample conductor calculated according to the initial sag model, n is the number of sample conductors, F i is the measured sag of the i-th sample conductor, g(x) is the sum of the differences between the measured sag and the predicted sag of all sample conductors, λ is the constraint strength of model parameters and environmental parameters, σ is the conductor tension, ρ is the conductor load, and δ is the threshold parameter.
[0017] Optionally, solving the objective function based on the measured sag of the sample conductor and the real-time environmental parameters to obtain the optimal solution for the conductor tension and the optimal solution for the conductor load includes:
[0018] Setting an initial wire tension, an initial wire load, and an initial step length, correcting the tension based on the initial wire tension and the initial step length to obtain a first corrected wire tension, correcting the load based on the initial wire load and the initial step length to obtain a first corrected wire load, and modifying the step length based on the initial step length to obtain a modified step length;
[0019] Based on the measured sag of the sample conductor and actual environmental parameters, the first corrected conductor tension and the first corrected conductor load are corrected using a Kalman filter technique to obtain a second corrected conductor tension and a second corrected conductor load;
[0020] Based on the second revised wire tension and the revised step length, re-correcting the tension to obtain a new first revised wire tension; based on the second revised wire load and the revised step length, re-correcting the load to obtain a new first revised wire load; based on the revised step length, re-modifying the step length to obtain a new revised step length;
[0021] Based on the new first revised wire tension and the new first revised wire load, a second revised wire tension and a second revised wire load are recalculated until the number of iterations is reached.
[0022] Optionally, the step of correcting the first corrected conductor tension and the first corrected conductor load using a Kalman filter technique based on the measured sag of the sample conductor and actual environmental parameters to obtain a second corrected conductor tension and a second corrected conductor load includes:
[0023] Substituting the actual environmental parameters of the sample conductor, the first corrected conductor tension, and the first corrected conductor load into a preset state equation to obtain vector expressions of a second corrected conductor tension and a second corrected conductor load;
[0024] Substituting the measured sag of the sample conductor, the second corrected conductor tension, and the second corrected conductor load into a preset observation equation, solving the observation equation to obtain state noise;
[0025] Substituting the state noise into the vector expressions of the second corrected wire tension and the second corrected wire load, the second corrected wire tension and the second corrected wire load are obtained.
[0026] Optionally, the correcting the tension based on the initial wire tension and the initial step length to obtain a first corrected wire tension, and correcting the load based on the initial wire load and the initial step length to obtain a first corrected wire load, comprises:
[0027] Substituting the measured sag, predicted sag, and initial conductor tension of the sample conductor into a conductor tension gradient formula to obtain a conductor tension gradient; substituting the measured sag, predicted sag, and initial conductor load of the sample conductor into a conductor load gradient formula to obtain a conductor load gradient;
[0028] Substituting the initial wire tension, the initial step size, and the gradient of the wire tension into a correction formula for wire tension to obtain a first corrected wire tension, and substituting the initial wire load, the initial step size, and the gradient of the wire load into a correction formula for wire load to obtain a first corrected wire load.
[0029] Optionally, the modifying the step length based on the initial step length to obtain a modified step length includes:
[0030] Adding the gradient of the wire tension and the gradient of the wire load to obtain the gradient of the objective function;
[0031] Substitute the gradient of the objective function and the initial step length into the step length modification formula to obtain a modified step length.
[0032] According to another aspect of the present application, a sag measurement device for a distribution line is provided, comprising:
[0033] The first construction module is used to construct a conductor length equation between adjacent tower suspension points based on the arc length calculation method of the distribution line, and to construct a force equation for the conductor center point based on the force state of the conductor between the adjacent tower suspension points;
[0034] A second construction module is used to construct an initial sag model of the conductor based on the conductor length equation, the force equation of the conductor center point, the conductor tension and the conductor load;
[0035] an optimal solution module, configured to obtain a measured sag of a sample conductor and actual environmental parameters corresponding to the measured sag, construct an objective function based on the measured sag of the sample conductor and the initial sag model, and solve the objective function based on the measured sag of the sample conductor and the real-time environmental parameters to obtain an optimal solution for the conductor tension and an optimal solution for the conductor load;
[0036] The sag prediction module is used to substitute the optimal solution of the conductor tension and the optimal solution of the conductor load into the initial sag model to obtain a target sag model, obtain the horizontal coordinates of the lowest point of the target conductor, substitute the horizontal coordinates of the lowest point of the target conductor into the target sag model, and obtain the predicted sag of the target conductor.
[0037] Optionally, the optimal solution module is further used to:
[0038] Performing maximum likelihood estimation on the initial sag model to obtain an intermediate function;
[0039] An L2 regularization term is introduced into the intermediate function to obtain the objective function.
[0040] Optionally, the objective function is:
[0041]
[0042] Where x is the horizontal coordinate of the lowest point of the sample wire, f(x i ) is the predicted sag of the i-th sample conductor calculated according to the initial sag model, n is the number of sample conductors, Fi is the measured sag of the i-th sample conductor, g(x) is the sum of the differences between the measured sag and the predicted sag of all sample conductors, λ is the constraint strength of model parameters and environmental parameters, σ is the conductor tension, ρ is the conductor load, and δ is the threshold parameter.
[0043] Optionally, the optimal solution module is further used to:
[0044] Setting an initial wire tension, an initial wire load, and an initial step length, correcting the tension based on the initial wire tension and the initial step length to obtain a first corrected wire tension, correcting the load based on the initial wire load and the initial step length to obtain a first corrected wire load, and modifying the step length based on the initial step length to obtain a modified step length;
[0045] Based on the measured sag of the sample conductor and actual environmental parameters, the first corrected conductor tension and the first corrected conductor load are corrected using a Kalman filter technique to obtain a second corrected conductor tension and a second corrected conductor load;
[0046] Based on the second revised wire tension and the revised step length, re-correcting the tension to obtain a new first revised wire tension; based on the second revised wire load and the revised step length, re-correcting the load to obtain a new first revised wire load; based on the revised step length, re-modifying the step length to obtain a new revised step length;
[0047] Based on the new first revised wire tension and the new first revised wire load, a second revised wire tension and a second revised wire load are recalculated until the number of iterations is reached.
[0048] Optionally, the optimal solution module is further used to:
[0049] Substituting the actual environmental parameters of the sample conductor, the first corrected conductor tension, and the first corrected conductor load into a preset state equation to obtain vector expressions of a second corrected conductor tension and a second corrected conductor load;
[0050] Substituting the measured sag of the sample conductor, the second corrected conductor tension, and the second corrected conductor load into a preset observation equation, solving the observation equation to obtain state noise;
[0051] Substituting the state noise into the vector expressions of the second corrected wire tension and the second corrected wire load, the second corrected wire tension and the second corrected wire load are obtained.
[0052] Optionally, the optimal solution module is further used to:
[0053] Substituting the measured sag, predicted sag, and initial conductor tension of the sample conductor into a conductor tension gradient formula to obtain a conductor tension gradient; substituting the measured sag, predicted sag, and initial conductor load of the sample conductor into a conductor load gradient formula to obtain a conductor load gradient;
[0054] Substituting the initial wire tension, the initial step size, and the gradient of the wire tension into a correction formula for wire tension to obtain a first corrected wire tension, and substituting the initial wire load, the initial step size, and the gradient of the wire load into a correction formula for wire load to obtain a first corrected wire load.
[0055] Optionally, the optimal solution module is further used to:
[0056] Adding the gradient of the wire tension and the gradient of the wire load to obtain the gradient of the objective function;
[0057] Substitute the gradient of the objective function and the initial step length into the step length modification formula to obtain a modified step length.
[0058] According to another aspect of the present application, a storage medium is provided, in which at least one executable instruction is stored. The executable instruction enables a processor to execute operations corresponding to the above-mentioned method for measuring the sag of a distribution line.
[0059] According to another aspect of the present application, a computer device is provided, comprising: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other via the communication bus;
[0060] The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute operations corresponding to the above-mentioned sag measurement method for the distribution line.
[0061] By means of the above technical solution, the technical solution provided by the embodiment of the present invention has at least the following advantages:
[0062] The present application provides a sag measurement method, apparatus, medium, and equipment for a distribution line. An initial sag model of the conductor is constructed based on a conductor length equation, a force equation at the conductor center point, conductor tension, and conductor load. A target function is constructed based on the measured sag of a sample conductor and the initial sag model. The target function is solved based on the measured sag of the sample conductor and real-time environmental parameters corresponding to the measured sag to obtain an optimal solution for the conductor tension and an optimal solution for the conductor load. The optimal solutions for the conductor tension and the conductor load are substituted into the initial sag model to obtain a target sag model. The horizontal coordinates of the lowest point of the target conductor to be predicted are substituted into the target sag model to obtain a predicted sag of the target conductor. By introducing the measured sag of the sample conductor and real-time environmental parameters to solve the target function, the target sag model obtained can reflect the actual impact of environmental parameters on sag. The predicted sag of the target conductor calculated using the target sag model is relatively accurate, thereby improving the accuracy of the sag prediction method.
[0063] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are specifically listed below. BRIEF DESCRIPTION OF THE DRAWINGS
[0064] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present invention. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:
[0065] Figure 1 A flow chart of a method for calculating sag of a distribution line provided in an embodiment of the present application is shown;
[0066] Figure 2 A schematic diagram showing a distribution line and sag height within a span of a distribution line sag measurement method provided by an embodiment of the present application is shown;
[0067] Figure 3 A schematic diagram of the forces acting on a distribution line is shown, illustrating a method for calculating the sag of a distribution line provided in an embodiment of the present application;
[0068] Figure 4 Another flow chart of a method for calculating sag of a power distribution line provided in an embodiment of the present application is shown;
[0069] Figure 5 A block diagram showing the composition of a sag measurement device for a distribution line provided in an embodiment of the present application is shown;
[0070] Figure 6 A schematic structural diagram of a computer device provided by an embodiment of the present invention is shown.
[0071] in,
[0072] Figure 5 In the middle: 502 - first construction module; 504 - second construction module; 506 - optimal solution module; 508 - sag prediction module;
[0073] Figure 6 In the figure: 602 - processor; 604 - communication interface; 606 - memory; 608 - communication bus; 610 - program. DETAILED DESCRIPTION
[0074] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that, in the absence of conflict, the embodiments and features of the embodiments of the present invention can be combined with each other.
[0075] To further illustrate the technical means and effects employed by the present invention to achieve its intended objectives, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention is provided in conjunction with the accompanying drawings and preferred embodiments. In the following description, different references to "one embodiment" or "embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
[0076] In order to solve the problem that the sag of transmission lines predicted by the existing sag calculation method of distribution lines is relatively low in accuracy, the embodiment of the present application provides a sag measurement method for distribution lines, such as Figure 1 As shown, the method includes:
[0077] 102: Based on the arc length calculation method of the distribution line, the conductor length equation between adjacent tower suspension points is constructed. Based on the stress state of the conductor between adjacent tower suspension points, the force equation of the conductor center point is constructed.
[0078] 104: Construct the initial sag model of the conductor based on the conductor length equation, the force equation at the conductor center point, the conductor tension and the conductor load;
[0079] 106: Obtaining a measured sag of the sample conductor and actual environmental parameters corresponding to the measured sag, constructing an objective function based on the measured sag of the sample conductor and the initial sag model, solving the objective function based on the measured sag of the sample conductor and the real-time environmental parameters, and obtaining an optimal solution for conductor tension and an optimal solution for conductor load;
[0080] 108: Substitute the optimal solutions for conductor tension and conductor load into the initial sag model to obtain a target sag model, obtain the horizontal coordinates of the lowest point of the target conductor, and substitute the horizontal coordinates of the lowest point of the target conductor into the target sag model to obtain the predicted sag of the target conductor.
[0081] In this embodiment, first, according to the arc length calculation method of the distribution line, the conductor length equation between the adjacent tower suspension points is constructed, such as Figure 2 As shown in the figure, points A and B are the suspension points of the transmission line at both ends of the transmission tower, O is the lowest point of the transmission line, d is the distance between the transmission towers, and H is the height difference between the tower suspension points. Based on the distance d between the transmission line suspension points and the height difference H between points A and B, we can get the equation of the line between points A and B, that is, the conductor length equation:
[0082]
[0083] Let ρ be the uniformly distributed load on the transmission line. Through the analysis of the stress at each point on the transmission line, it is found that the horizontal component σ0 should be equal. Figure 3 As shown in the figure, based on the assumption and differential principle, it is found that the transmission line can be extracted into a small unit with a length of l0 for differential processing, and the force condition of the transmission line at the assumed center point M is obtained:
[0084]
[0085] Where l0 is the small unit length of the transmission line, ρ·l0 is the load at any point on the transmission line, σ x is the direction of the resultant force at any point M on the transmission line, and θ is the angle to the horizontal.
[0086] According to the arc length formula:
[0087]
[0088] Among them, x and y are the horizontal coordinates and height coordinates of the transmission line, and we can get:
[0089]
[0090] make: You can get:
[0091]
[0092] Derivative of both sides:
[0093]
[0094] Then let: y′=β, get:
[0095]
[0096] Integrating both sides simultaneously gives:
[0097]
[0098] but:
[0099]
[0100] Since the sag coordinate is placed at the origin, we can get: C1 = 0, and further:
[0101]
[0102] Integrating both sides simultaneously gives:
[0103]
[0104] Also because The force equation at the center point of the wire is obtained:
[0105]
[0106] Considering the influence of environmental parameters, such as temperature and wind speed, which indirectly affect sag through tension, an initial sag model of the conductor is constructed based on the conductor length equation, the force equation at the conductor center point, conductor tension, and conductor load:
[0107]
[0108] The measured sag and actual environmental parameters of the sample conductor are obtained. Based on the measured sag of the sample conductor and the initial sag model, an objective function is constructed. Based on the framework of the traditional least squares method, a variable step-size iterative optimization strategy is combined to accelerate convergence. At the same time, the Kalman filtering technology is combined to correct the optimized parameters to improve the optimization accuracy, and the optimal solutions for the conductor tension and the conductor load are obtained. The optimal solutions for the conductor tension and the conductor load are then substituted into the initial sag model to obtain the target sag model. The horizontal coordinates of the lowest point of the target conductor are substituted into the target sag model to obtain the predicted sag of the target conductor.
[0109] The present application provides a sag measurement method for a distribution line. Compared with the prior art, an initial sag model of the conductor is constructed based on a conductor length equation, a force equation at the conductor center point, conductor tension, and conductor load. A target function is constructed based on the measured sag of a sample conductor and the initial sag model. The target function is solved based on the measured sag of the sample conductor and real-time environmental parameters corresponding to the measured sag to obtain an optimal solution for the conductor tension and an optimal solution for the conductor load. The optimal solutions for the conductor tension and the optimal solutions for the conductor load are substituted into the initial sag model to obtain a target sag model. The horizontal coordinates of the lowest point of the target conductor to be predicted are substituted into the target sag model to obtain a predicted sag of the target conductor. By introducing the measured sag of the sample conductor and real-time environmental parameters to solve the target function, the target sag model obtained can reflect the actual impact of environmental parameters on sag. The predicted sag of the target conductor calculated using the target sag model is relatively accurate, thereby improving the accuracy of the sag prediction method.
[0110] In one embodiment, based on the initial sag model, an objective function is constructed, including:
[0111] Perform maximum likelihood estimation on the initial sag model to obtain the intermediate function;
[0112] Introduce the L2 regularization term into the intermediate function to obtain the objective function.
[0113] Specifically, in order to obtain the best fitting effect and increase the accuracy of the sag formula, an improved least squares method is introduced to reduce the influence of discrete points. That is, the function to be minimized is obtained by using maximum likelihood estimation, which is called the intermediate function here:
[0114]
[0115] Where x is the horizontal coordinate of the lowest point on the sample wire, f(x i ) is the predicted sag of the i-th sample conductor calculated based on the initial sag model, n is the number of sample conductors, F i is the measured sag of the sample conductor, g1(x) is the difference between the measured sag and the predicted sag summed over all applied loss functions, and L(·) is the loss function.
[0116] By accumulating the error losses of all samples as the model optimization target, the parameters are adjusted to minimize the total, so that the model fits the overall data better.
[0117] At the same time, in order to suppress overfitting and enhance the generalization ability of the model, capture the dynamic relationship between environmental variables and sag, and achieve accurate prediction under multi-physics field coupling, the L2 regularization term is introduced in the objective function:
[0118]
[0119] Among them, λ is the constraint strength between model parameters and environmental parameters, σ and ρ are the parameters to be optimized in the model: conductor tension and conductor load;
[0120] Among them, L is a robust loss function;
[0121]
[0122] Among them, F i is the measured sag of the sample conductor, f(x) is the predicted sag of the i-th sample conductor calculated based on the initial sag model, and δ is a pre-set threshold. When the error is small, it is a quadratic function, and when the error is large, it is a linear function. This discrimination method reduces the influence of discrete points to a certain extent, thereby obtaining more accurate sag information. δ is used to distinguish the nature of the error, balancing the model's fit to normal data with its robustness to discrete points. The difference (between the predicted and actual values) is compared with the threshold. When the error is less than the threshold, it indicates a small error. The loss function uses a quadratic function to retain sensitivity to small errors and ensure a high-precision fit of the sag formula. When the error is greater than the threshold, it indicates a large error (an outlier data point). To avoid error amplification, a linear calculation is used, which both retains the error measurement and weakens the weight of outliers, achieving the dual goals of "fine fitting of normal data and robust processing of abnormal data."
[0123] Substitute L into the intermediate function and the objective function is:
[0124]
[0125] Where x is the horizontal coordinate of the lowest point of the sample wire, f(x i ) is the predicted sag of the i-th sample conductor calculated according to the initial sag model, n is the number of sample conductors, F i is the measured sag of the i-th sample conductor, g(x) is the sum of the differences between the measured sag and the predicted sag of all sample conductors, λ is the constraint strength of model parameters and environmental parameters, σ is the conductor tension, ρ is the conductor load, and δ is the threshold parameter.
[0126] In one embodiment, Figure 4 As shown in the figure, the objective function is solved based on the measured sag of the sample conductor and the real-time environmental parameters to obtain the optimal solutions for the conductor tension and the conductor load, including:
[0127] 402: Setting an initial conductor tension, an initial conductor load, and an initial step length, correcting the tension based on the initial conductor tension and the initial step length to obtain a first corrected conductor tension, correcting the load based on the initial conductor load and the initial step length to obtain a first corrected conductor load, and modifying the step length based on the initial step length to obtain a modified step length;
[0128] 404: Based on the measured sag of the sample conductor and actual environmental parameters, the first corrected conductor tension and the first corrected conductor load are corrected using a Kalman filter technique to obtain a second corrected conductor tension and a second corrected conductor load.
[0129] 406: Based on the second revised wire tension and the revised step length, the tension is re-revised to obtain a new first revised wire tension; based on the second revised wire load and the revised step length, the load is re-revised to obtain a new first revised wire load; based on the revised step length, the step length is re-revised to obtain a new revised step length;
[0130] 408 : Recalculate the second revised wire tension and the second revised wire load based on the new first revised wire tension and the new first revised wire load, until the number of iterations is reached.
[0131] In this embodiment, the measured sag, predicted sag, and initial conductor tension of the sample conductor are substituted into the conductor tension gradient formula to obtain the conductor tension gradient. The measured sag, predicted sag, and initial conductor load of the sample conductor are substituted into the conductor load gradient formula to obtain the conductor load gradient.
[0132] Substitute the initial wire tension, initial step length, and gradient of the wire tension into the correction formula of the wire tension to obtain a first corrected wire tension; substitute the initial wire load, initial step length, and gradient of the wire load into the correction formula of the wire load to obtain a first corrected wire load.
[0133] The gradient formulas for wire tension and wire load are:
[0134]
[0135] The correction formula for the first correction wire tension and the correction formula for the first correction wire load are:
[0136]
[0137] Among them, σ k+1 is the wire tension after the k+1th correction, σ k is the wire tension after the kth correction, η k is the step size after the kth correction, ρ k+1 is the conductor load after the k+1th correction, ρ k is the wire load after the kth correction.
[0138] When correcting for the initial wire load and initial wire tension, σ k is the initial wire tension, ρ kis the initial wire load. Substituting these two parameters into the above formula, the first corrected wire tension and the first corrected wire load are calculated.
[0139] In this embodiment, the step length is modified based on the initial step length to obtain the modified step length, including:
[0140] The gradient of the objective function is obtained by adding the gradient of the wire tension and the gradient of the wire load;
[0141] Substitute the gradient of the objective function and the initial step size into the modified step size formula to obtain the modified step size.
[0142] The optimization process of the least squares method is to decompose the objective function into each part and calculate the gradient separately, adjust the parameters through the iterative algorithm, and stop the algorithm iteration based on the gradient change of the objective function as the termination condition. In the optimization process of the improved least squares method, a variable step size gradient descent strategy based on the adaptive gradient descent algorithm is used to balance the convergence speed and stability and improve the optimization efficiency. In the initial iteration, the step size η is large, and the optimal solution is approached quickly; as the gradient of the objective function decreases, the step size is gradually reduced to ensure that the step size is finely adjusted in the later stages of the iteration to avoid oscillation. The step size adjustment formula is designed as:
[0143]
[0144] Where τ is the attenuation coefficient, The gradient norm of the current objective function is the modulus of the vector formed by taking the partial derivative of the objective function with respect to all parameters. A larger gradient norm indicates a steeper local slope of the objective function at the current point, and a larger parameter adjustment is required to approach the extreme point. Conversely, a smaller gradient norm indicates a closer approach to the extreme point. Adjust the step size based on the gradient norm to bring the parameter update result closer to the optimal solution.
[0145] In this embodiment, based on the measured sag of the sample conductor and actual environmental parameters, the first corrected conductor tension and the first corrected conductor load are corrected using Kalman filtering technology to obtain the second corrected conductor tension and the second corrected conductor load, including:
[0146] Substituting the actual environmental parameters of the sample conductor, the first corrected conductor tension, and the first corrected conductor load into a preset state equation to obtain vector expressions of the second corrected conductor tension and the second corrected conductor load;
[0147] Substituting the measured sag of the sample conductor and the vector expressions of the second corrected conductor tension and the second corrected conductor load into the preset observation equation, solving the observation equation to obtain the state noise;
[0148] Substituting the state noise into the vector expressions of the second corrected wire tension and the second corrected wire load, the second corrected wire tension and the second corrected wire load are obtained.
[0149] Taking the first corrected conductor tension and the first corrected conductor load obtained by solving the least squares method as the basic parameters, the influence of dynamic environmental parameters on the sag model is further considered. The Kalman technique is used to introduce the real-time environmental parameters and measured sag corresponding to the sample conductor to correct the predicted estimation of tension and load.
[0150] Specifically, the Kalman filter technology uses the coordination of state equations and observation equations to estimate the optimal values of conductor tension and conductor load in real time, thereby initializing the sag model. In each iteration, after receiving the actual sag of the sample point and the actual environmental data, the following process is executed:
[0151] S1: Define the state equation and observation equation;
[0152] State equation (describing the evolution of tension and load with environmental parameters): s k =Cs k-1 +Du k +w k
[0153] in, is the state vector of the current state, which is the tension and load used in the kth iteration. To obtain the actual sag of the current state, the corresponding environmental parameter vector includes temperature, wind speed, w k is the state noise, C is the preset state transition matrix, which describes how the state evolves from the previous state to the current state, and D is the control input matrix, which quantifies the impact of environmental parameters (temperature, wind speed) on the state;
[0154] Observation equation (establishes the relationship between state and sag observation): z k =Es k +v k
[0155] Among them, z k is the observation vector of the current state, that is, the measured value of sag, E is the observation matrix, which maps the state vector to the observation space, v k To observe noise and characterize environmental interference.
[0156] S2: Real-time iterative process of Kalman filtering (performing a “prediction-update-output” cycle once for each state);
[0157] After receiving new actual sag and actual environmental data at the current iteration, for example, the kth iteration, first, in the state equation, based on the state (tension, load) and environmental parameter changes corresponding to the previous iteration, these parameters are incorporated into the state equation to obtain expressions of the tension and load predicted for the current iteration; then, the expressions of the tension and load predicted for the current iteration are substituted into the observation equation, the state noise and the observation noise are solved, and then the observation noise is calculated based on the relationship between the state noise and the observation noise, and the observation noise is substituted into the expressions of the tension and load predicted for the current iteration to obtain the tension and load corresponding to the current iteration, that is, the second corrected wire tension and the second corrected wire load.
[0158] Substitute the second corrected wire tension and the second corrected wire load into the gradient formula of the wire tension and the gradient formula of the wire load to obtain the new gradient of the wire tension and the new gradient of the wire load. Substitute the new gradient of the wire tension and the new gradient of the wire load into the correction formula of the first corrected wire tension and the correction formula of the first corrected wire load respectively to obtain a new first corrected wire tension and a new second corrected wire load. Update the step size according to the step size formula. Use Kalman filtering technology to correct the new first corrected wire tension and the first corrected wire load again to obtain a new second corrected wire tension and a new second corrected wire load. Correct the tension and load of the new second corrected wire tension and the new corrected wire load again to obtain a new first corrected wire tension and a new first corrected wire load. Repeat the above process until the number of iterations is reached.
[0159] Furthermore, as a response to the above Figure 1 The embodiment of the present invention provides a sag measuring device for a distribution line, such as Figure 5 As shown, the device includes:
[0160] The first construction module 502 is used to construct a conductor length equation between adjacent tower suspension points based on the arc length calculation method of the distribution line, and to construct a force equation at the center point of the conductor based on the force state of the conductor between the adjacent tower suspension points;
[0161] The second construction module 504 is used to construct an initial sag model of the conductor based on the conductor length equation, the force equation of the conductor center point, the conductor tension and the conductor load;
[0162] The optimal solution module 506 is configured to obtain the measured sag of the sample conductor and the actual environmental parameters corresponding to the measured sag, construct an objective function based on the measured sag of the sample conductor and the initial sag model, and solve the objective function based on the measured sag of the sample conductor and the real-time environmental parameters to obtain the optimal solution for the conductor tension and the optimal solution for the conductor load.
[0163] The sag prediction module 508 is used to substitute the optimal solution of the conductor tension and the optimal solution of the conductor load into the initial sag model to obtain the target sag model, obtain the horizontal coordinates of the lowest point of the target conductor, substitute the horizontal coordinates of the lowest point of the target conductor into the target sag model, and obtain the predicted sag of the target conductor.
[0164] The present application provides a sag measurement device for a distribution line. Compared with the prior art, the device constructs an initial sag model of the conductor based on a conductor length equation, a force equation at the conductor center point, conductor tension, and conductor load. A target function is constructed based on the measured sag of a sample conductor and the initial sag model. The target function is solved based on the measured sag of the sample conductor and real-time environmental parameters corresponding to the measured sag to obtain an optimal solution for the conductor tension and an optimal solution for the conductor load. The optimal solutions for the conductor tension and the optimal solutions for the conductor load are substituted into the initial sag model to obtain a target sag model. The horizontal coordinates of the lowest point of the target conductor to be predicted are substituted into the target sag model to obtain a predicted sag of the target conductor. By introducing the measured sag of the sample conductor and real-time environmental parameters to solve the target function, the target sag model obtained can reflect the actual impact of environmental parameters on sag. The predicted sag of the target conductor calculated using the target sag model is relatively accurate, thereby improving the accuracy of the sag prediction method.
[0165] In one embodiment, the optimal solution module is further configured to:
[0166] Perform maximum likelihood estimation on the initial sag model to obtain the intermediate function;
[0167] Introduce the L2 regularization term into the intermediate function to obtain the objective function.
[0168] In one embodiment, the objective function is:
[0169]
[0170] Where x is the horizontal coordinate of the lowest point of the sample wire, f(x i ) is the predicted sag of the i-th sample conductor calculated according to the initial sag model, n is the number of sample conductors, F i is the measured sag of the i-th sample conductor, g(x) is the sum of the differences between the measured sag and the predicted sag of all sample conductors, λ is the constraint strength of model parameters and environmental parameters, σ is the conductor tension, ρ is the conductor load, and δ is the threshold parameter.
[0171] In one embodiment, the optimal solution module is further configured to:
[0172] Setting an initial conductor tension, an initial conductor load, and an initial step length, correcting the tension based on the initial conductor tension and the initial step length to obtain a first corrected conductor tension, correcting the load based on the initial conductor load and the initial step length to obtain a first corrected conductor load, and modifying the step length based on the initial step length to obtain a modified step length;
[0173] Based on the measured sag of the sample conductor and actual environmental parameters, the first corrected conductor tension and the first corrected conductor load are corrected using Kalman filtering technology to obtain the second corrected conductor tension and the second corrected conductor load;
[0174] Based on the second revised wire tension and the revised step length, the tension is re-revised to obtain a new first revised wire tension; based on the second revised wire load and the revised step length, the load is re-revised to obtain a new first revised wire load; based on the revised step length, the step length is re-revised to obtain a new revised step length;
[0175] Based on the new first revised wire tension and the new first revised wire load, the second revised wire tension and the second revised wire load are recalculated until the number of iterations is reached.
[0176] In one embodiment, the optimal solution module is further configured to:
[0177] Substituting the actual environmental parameters of the sample conductor, the first corrected conductor tension, and the first corrected conductor load into a preset state equation to obtain vector expressions of the second corrected conductor tension and the second corrected conductor load;
[0178] Substituting the measured sag of the sample conductor and the vector expressions of the second corrected conductor tension and the second corrected conductor load into the preset observation equation, solving the observation equation to obtain the state noise;
[0179] Substituting the state noise into the vector expressions of the second corrected wire tension and the second corrected wire load, the second corrected wire tension and the second corrected wire load are obtained.
[0180] In one embodiment, the optimal solution module is further configured to:
[0181] Substituting the measured sag, predicted sag and initial conductor tension of the sample conductor into the gradient formula of conductor tension to obtain the gradient of conductor tension; substituting the measured sag, predicted sag and initial conductor load of the sample conductor into the gradient formula of conductor load to obtain the gradient of conductor load;
[0182] Substitute the initial wire tension, initial step length, and gradient of the wire tension into the correction formula of the wire tension to obtain a first corrected wire tension; substitute the initial wire load, initial step length, and gradient of the wire load into the correction formula of the wire load to obtain a first corrected wire load.
[0183] In one embodiment, the optimal solution module is further configured to:
[0184] The gradient of the objective function is obtained by adding the gradient of the wire tension and the gradient of the wire load;
[0185] Substitute the gradient of the objective function and the initial step size into the modified step size formula to obtain the modified step size.
[0186] According to one embodiment of the present invention, a storage medium is provided. The storage medium stores at least one executable instruction. The computer-executable instruction can execute the sag measurement method for a distribution line in any of the above method embodiments.
[0187] Figure 6 A schematic structural diagram of a computer device provided according to an embodiment of the present invention is shown. The specific embodiment of the present invention does not limit the specific implementation of the computer device.
[0188] like Figure 6 As shown, the computer device may include: a processor (processor) 602 , a communication interface (Communications Interface) 604 , a memory (memory) 606 , and a communication bus 608 .
[0189] The processor 602 , the communication interface 604 , and the memory 606 communicate with each other via a communication bus 608 .
[0190] The communication interface 604 is used to communicate with other devices such as clients or other servers.
[0191] The processor 602 is configured to execute the program 610 , and specifically to execute the relevant steps in the embodiment of the sag measurement method for a power distribution line.
[0192] Specifically, the program 610 may include program codes, which include computer operation instructions.
[0193] Processor 602 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. A computer device includes one or more processors, which may be of the same type, such as one or more CPUs, or different types, such as one or more CPUs and one or more ASICs.
[0194] The memory 606 is used to store the program 610. The memory 606 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.
[0195] The program 610 may be specifically configured to enable the processor 602 to perform the following operations:
[0196] Based on the arc length calculation method of the distribution line, the conductor length equation between adjacent tower suspension points is constructed. Based on the stress state of the conductor between adjacent tower suspension points, the force equation of the conductor center point is constructed.
[0197] Construct an initial sag model of the conductor based on the conductor length equation, the force equation at the conductor center point, the conductor tension, and the conductor load;
[0198] Obtain the measured sag of the sample conductor and the actual environmental parameters corresponding to the measured sag, construct an objective function based on the measured sag of the sample conductor and the initial sag model, and solve the objective function based on the measured sag of the sample conductor and the real-time environmental parameters to obtain the optimal solutions for the conductor tension and the conductor load;
[0199] The optimal solutions of conductor tension and conductor load are substituted into the initial sag model to obtain the target sag model. The horizontal coordinates of the lowest point of the target conductor are obtained and substituted into the target sag model to obtain the predicted sag of the target conductor.
[0200] Obviously, those skilled in the art will appreciate that the various modules or steps of the present invention described above can be implemented using a general-purpose computing device, and can be centralized on a single computing device or distributed across a network of multiple computing devices. In one embodiment, they can be implemented using program code executable by a computing device, and thus, can be stored in a storage device and executed by the computing device. In some cases, the steps shown or described herein can be performed in a different order than that shown, or can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.
[0201] The above embodiments are merely exemplary embodiments of the present application and are not intended to limit the scope of the present application. The scope of protection of the present application is defined by the claims. Those skilled in the art may make various modifications or equivalent substitutions to the present application within the essence and scope of protection of the present application, and such modifications or equivalent substitutions shall also be deemed to fall within the scope of protection of the present application.
Claims
1. A method for measuring sag of a distribution line, characterized in that: include: According to the arc length calculation method of the distribution line, a conductor length equation between adjacent tower suspension points is constructed, and according to the stress state of the conductor between the adjacent tower suspension points, a force equation for the conductor center point is constructed; Constructing an initial sag model of the conductor based on the conductor length equation, the force equation at the conductor center point, the conductor tension, and the conductor load; Obtaining a measured sag of a sample conductor and actual environmental parameters corresponding to the measured sag, constructing an objective function based on the measured sag of the sample conductor and the initial sag model, and solving the objective function based on the measured sag of the sample conductor and the real-time environmental parameters to obtain an optimal solution for the conductor tension and an optimal solution for the conductor load; The optimal solution of the conductor tension and the optimal solution of the conductor load are substituted into the initial sag model to obtain a target sag model, the horizontal coordinates of the lowest point of the target conductor are obtained, and the horizontal coordinates of the lowest point of the target conductor are substituted into the target sag model to obtain the predicted sag of the target conductor.
2. The method for calculating the sag of a distribution line according to claim 1, wherein: The objective function is constructed based on the initial sag model, comprising: Performing maximum likelihood estimation on the initial sag model to obtain an intermediate function; The L2 regularization term is introduced into the intermediate function to obtain the objective function.
3. The method for calculating the sag of a distribution line according to claim 2, wherein: The objective function is: Where x is the horizontal coordinate of the lowest point of the sample wire, f(x i ) is the predicted sag of the i-th sample conductor calculated according to the initial sag model, n is the number of sample conductors, F i is the measured sag of the i-th sample conductor, g(x) is the sum of the differences between the measured sag and the predicted sag of all sample conductors, λ is the constraint strength of model parameters and environmental parameters, σ is the conductor tension, ρ is the conductor load, and δ is the threshold parameter.
4. The method for calculating the sag of a distribution line according to claim 1, wherein: Solving the objective function based on the measured sag of the sample conductor and the real-time environmental parameters to obtain the optimal solution for the conductor tension and the optimal solution for the conductor load includes: Setting an initial wire tension, an initial wire load, and an initial step length, correcting the tension based on the initial wire tension and the initial step length to obtain a first corrected wire tension, correcting the load based on the initial wire load and the initial step length to obtain a first corrected wire load, and modifying the step length based on the initial step length to obtain a modified step length; Based on the measured sag of the sample conductor and actual environmental parameters, the first corrected conductor tension and the first corrected conductor load are corrected using a Kalman filter technique to obtain a second corrected conductor tension and a second corrected conductor load; Based on the second revised wire tension and the revised step length, re-correcting the tension to obtain a new first revised wire tension; based on the second revised wire load and the revised step length, re-correcting the load to obtain a new first revised wire load; based on the revised step length, re-modifying the step length to obtain a new revised step length; Based on the new first revised wire tension and the new first revised wire load, a second revised wire tension and a second revised wire load are recalculated until the number of iterations is reached.
5. The method for calculating the sag of a distribution line according to claim 4, wherein: The method of correcting the first corrected conductor tension and the first corrected conductor load by using Kalman filtering technology based on the measured sag of the sample conductor and actual environmental parameters to obtain a second corrected conductor tension and a second corrected conductor load includes: Substituting the actual environmental parameters of the sample conductor, the first corrected conductor tension, and the first corrected conductor load into a preset state equation to obtain vector expressions of a second corrected conductor tension and a second corrected conductor load; Substituting the measured sag of the sample conductor, the second corrected conductor tension, and the second corrected conductor load into a preset observation equation, solving the observation equation to obtain state noise; Substituting the state noise into the vector expressions of the second corrected wire tension and the second corrected wire load, the second corrected wire tension and the second corrected wire load are obtained.
6. The method for calculating the sag of a distribution line according to claim 4, wherein: The method of correcting the tension based on the initial conductor tension and the initial step length to obtain a first corrected conductor tension, and correcting the load based on the initial conductor load and the initial step length to obtain a first corrected conductor load, comprises: Substituting the measured sag, predicted sag, and initial conductor tension of the sample conductor into a conductor tension gradient formula to obtain a conductor tension gradient; substituting the measured sag, predicted sag, and initial conductor load of the sample conductor into a conductor load gradient formula to obtain a conductor load gradient; Substituting the initial wire tension, the initial step size, and the gradient of the wire tension into a correction formula for wire tension to obtain a first corrected wire tension, and substituting the initial wire load, the initial step size, and the gradient of the wire load into a correction formula for wire load to obtain a first corrected wire load.
7. The method for calculating the sag of a distribution line according to claim 6, wherein: The step length is modified based on the initial step length to obtain a modified step length, comprising: Adding the gradient of the wire tension and the gradient of the wire load to obtain the gradient of the objective function; Substitute the gradient of the objective function and the initial step length into the step length modification formula to obtain a modified step length.
8. A sag measuring device for a distribution line, characterized in that: include: The first construction module is used to construct a conductor length equation between adjacent tower suspension points based on the arc length calculation method of the distribution line, and to construct a force equation for the conductor center point based on the force state of the conductor between the adjacent tower suspension points; A second construction module is used to construct an initial sag model of the conductor based on the conductor length equation, the force equation of the conductor center point, the conductor tension and the conductor load; an optimal solution module, configured to obtain a measured sag of a sample conductor and actual environmental parameters corresponding to the measured sag, construct an objective function based on the measured sag of the sample conductor and the initial sag model, and solve the objective function based on the measured sag of the sample conductor and the real-time environmental parameters to obtain an optimal solution for the conductor tension and an optimal solution for the conductor load; The sag prediction module is used to substitute the optimal solution of the conductor tension and the optimal solution of the conductor load into the initial sag model to obtain a target sag model, obtain the horizontal coordinates of the lowest point of the target conductor, substitute the horizontal coordinates of the lowest point of the target conductor into the target sag model, and obtain the predicted sag of the target conductor.
9. A storage medium storing at least one executable instruction, wherein the executable instruction enables a processor to execute operations corresponding to the sag measurement method for a distribution line according to any one of claims 1 to 7.
10. A computer device comprising: A processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform operations corresponding to the sag measurement method for a distribution line according to any one of claims 1 to 7.