A cable crosstalk accurate prediction method based on improved sparrow search algorithm

Abstract

The application discloses a cable crosstalk accurate prediction method based on an improved sparrow search algorithm, and first establishes a parallel cable crosstalk model, calculates a cable unit inductance and a capacitance matrix, combines different terminal conditions, and obtains terminal voltage and current values of a disturbed cable by solving a chain parameter matrix method; an error correction model between the parallel cable crosstalk model and a cable simulation model is established; an initialization method of a sparrow population in the sparrow search algorithm is optimized based on a good point set theory, a smooth moving strategy of a sparrow follower population moving strategy in the sparrow search algorithm is improved by using a double-optimal learning strategy, and a fitness function of the improved sparrow search algorithm is determined; and the fitness function is used as an optimization evaluation standard to obtain crosstalk model parameters meeting requirements by correcting the cable crosstalk model parameters. The application solves problems of low prediction accuracy and complex model parameter debugging in single model cable crosstalk prediction, and realizes fast and accurate crosstalk prediction.

Description

Technical Field

[0001] This invention belongs to the field of electromagnetic compatibility, specifically relating to an accurate prediction method for cable crosstalk based on an improved sparrow search algorithm. Background Technology

[0002] In large electrical equipment systems such as aircraft, automobiles, and ships, transmission lines are the main network components carrying information exchange, and their operating status directly affects the normal operation of the system. Simultaneously, transmission lines are also the transmission paths for electrical circuits, capable of both external radiation and highly susceptible to interference from other devices within the system. Therefore, crosstalk in transmission lines cannot be ignored; it has become one of the most critical issues in the field of electromagnetic compatibility. To predict or suppress crosstalk in the electromagnetic environment, it is necessary to employ reasonable and effective methods to predict the electromagnetic coupling of transmission lines.

[0003] Transmission line crosstalk occurs when the electromagnetic energy of an interfering transmission line couples with the signal, causing an additional transient response in the affected line. There are generally three methods for predicting crosstalk: First, a specific cable crosstalk experimental platform is built in a microwave anechoic chamber to predict the magnitude and severity of crosstalk through simulation experiments. However, this method is difficult to implement due to site limitations and the difficulty of platform construction. Second, a specific cable simulation model is created in simulation software to predict the radiated crosstalk value. However, the accuracy of the simulation model's crosstalk prediction is low due to the numerous and difficult-to-measure parameters of actual cables, such as the inability to measure the reactance characteristics of the terminal load. Third, a mathematical model of cable crosstalk is established based on transmission line theory to predict crosstalk. However, this model is overly idealized and contains errors compared to reality, resulting in low prediction accuracy. Summary of the Invention

[0004] Purpose of the invention: This invention provides an accurate prediction method for cable crosstalk based on an improved sparrow search algorithm, which solves the problems of low prediction accuracy and complex model parameter debugging in the prediction of cable crosstalk by a single model.

[0005] Technical solution: The present invention provides an accurate prediction method for cable crosstalk based on an improved sparrow search algorithm, comprising the following steps:

[0006] (1) Establish a cable crosstalk model, calculate the unit inductance and capacitance matrix of the cable, and combine different terminal conditions to solve the terminal voltage and current value of the disturbed cable by using the chain parameter matrix method.

[0007] (2) Establish an error correction model between the crosstalk model of parallel cables and the cable simulation model;

[0008] (3) Based on the theory of optimal point set, optimize the initialization method of sparrow population in sparrow search algorithm, and use the smooth movement strategy of double optimal learning to improve the movement strategy of sparrow follower population in sparrow search algorithm, so as to improve the ability of sparrow search algorithm to find optimization and escape local optima.

[0009] (4) Determine the fitness function of the improved sparrow search algorithm;

[0010] (5) The parameters of the cable crosstalk model after the error correction model are optimized by using the fitness function as the evaluation criterion and the improved sparrow search algorithm to obtain the crosstalk model parameters that meet the requirements.

[0011] Furthermore, the implementation process of step (1) is as follows:

[0012] The unit inductance and capacitance matrix of the cable is as follows:

[0013]

[0014]

[0015] Where μ is the permeability in vacuum, h A h B These are the heights of cables A and B above ground, r. A r B These are the radii of the cable's metal inner cores A and B, respectively, and Δr. A , △r B These are the cable insulation layer thicknesses, and S is the center-to-center distance between cables A and B. The main diagonal elements represent the self-inductance per unit length of the cable, and the diagonal elements represent the mutual inductance per unit length of the cable; ε r It is the relative permittivity of the insulating layer medium, ε e =(ε r -1) / ε r ;

[0016] The chain parameter matrix is ​​as follows:

[0017]

[0018] When the termination condition is only voltage source excitation, the termination voltage and current response of the disturbed transmission line can be obtained by the following equation:

[0019] (φ 12 -φ1Z S -Z L φ 22 +Z L φ 21 Z S I(0)=V L -(φ 11-Z L φ 21 V S

[0020] I(L)=φ 21 V S +(φ 22 -φ 21 Z S )I(0)

[0021] When the termination condition is only current source excitation, the termination voltage and current response of the disturbed transmission line can be obtained analytically as follows:

[0022] (φ 21 -φ 22 Y S -Y L φ 11 +Y L φ 12 Y S V(0)=-I L -(φ 22 -Y L φ 21 )I S

[0023] V(L)=φ 21 I s +(φ 11 -φ 12 Y s V(0)

[0024] Where V(L) and I(L) are the terminal voltage and current values, and V(0) and I(0) are the cable input voltage and current values. S It is driven by the terminal voltage source, I S It is a terminal current source excitation, φ ij It is the chain parameter matrix parameter, Z S Z L It is the internal resistance of the cable termination and the load impedance, Y S Y L It refers to the electrical conductivity and load admittance of the cable terminal.

[0025] Furthermore, the error correction model between the crosstalk model of the parallel cable and the cable simulation model described in step (2) is as follows:

[0026] W 误差 =abs(X1-X2)

[0027] Where abs refers to taking the absolute value of the two numbers, X1 is the crosstalk prediction result of the simulation model under the same conditions, and X2 is the crosstalk prediction result of the theoretical mathematical model under the same conditions.

[0028] Furthermore, the initialization of the sparrow population in the optimal point set theory-optimized sparrow search algorithm described in step (3) is as follows:

[0029] r j ={2cos(2πk / p), 1≤k≤s}

[0030] Among them, ub j , lb j r is the upper and lower bounds of the j-th dimension vector. j ={2cos(2πk / p),1≤k≤s}; where P is the smallest prime number satisfying (p-3) / 2≤s, and {r j} represents r j The decimal part.

[0031] Furthermore, the implementation process of the sparrow follower population movement strategy in step (3) using the smooth movement strategy of double optimal learning to improve the sparrow search algorithm is as follows:

[0032]

[0033] in, and Let represent the two optimal positions in the t-th iteration, dim represent the dimension of the sparrow's position vector, and S represent a random number with a value in the range [-1, 1].

[0034] when When, it means that the i-th follower will gradually forage towards the middle direction between the two optimal positions. When, it means that the i-th follower will gradually move towards the optimal position to forage; when This indicates that the followers with relatively poor adaptability have not obtained food and need to fly to other areas to forage.

[0035] Furthermore, the fitness function described in step (4) is:

[0036] fitness = abs(XY)

[0037] Where abs refers to taking the absolute value of the two numbers, X is the crosstalk prediction result of the prediction model, and Y is the target value range of the actual crosstalk result.

[0038] Furthermore, when the dimension of the sparrow's position vector dim = 2, coordinate rotation is introduced to enhance its ability to escape local optima and expand its capabilities.

[0039]

[0040]

[0041] Where, x k Indicates when The x-coordinate of the follower's position coordinates, y k Indicates when The y-coordinate of the follower's position coordinates, where y0 is the optimal value when two optimal conditions exist. When there exists a unique optimal case, y0 is

[0042] Furthermore, the error-corrected cable crosstalk model parameters described in step (5) include the spacing between the interfering cable and the affected cable, their height above the ground, the terminal load, and the reactance value present in the terminal load.

[0043] Beneficial Effects: Compared with the prior art, the beneficial effects of this invention are as follows: This invention's accurate prediction method for cable crosstalk based on the improved sparrow search algorithm takes into account the non-ideal parameters of the prediction model. Combining the advantages of the fast running speed of theoretical mathematical prediction models and the high prediction accuracy of simulation models, it uses the improved sparrow search algorithm to optimize the non-ideal parameters, obtaining non-ideal parameter settings of the prediction model that conform to the actual situation, thereby obtaining more accurate crosstalk prediction results. Compared with the single simulation model prediction of cable crosstalk, this invention combines the advantages of the fast prediction speed of theoretical mathematical models and uses the improved sparrow search algorithm to optimize the non-ideal parameters, avoiding the process of repeatedly adjusting parameter settings in simulation models, resulting in faster prediction speed. Attached Figure Description

[0044] Figure 1 This is a flowchart of the present invention;

[0045] Figure 2 This is a schematic diagram of the cable crosstalk model established in this invention;

[0046] Figure 3 This is a schematic diagram of the cross-section of the cable crosstalk model established in this invention;

[0047] Figure 4 This is a schematic diagram of the equivalent circuit of the cable crosstalk model established in this invention;

[0048] Figure 5 This is a diagram showing the prediction results of voltage crosstalk at the far end of the disturbed cable when the terminal condition is voltage source excitation according to the present invention.

[0049] Figure 6 This is a diagram showing the prediction results of the far-end current crosstalk of the disturbed cable when the terminal condition is current source excitation according to the present invention. Detailed Implementation

[0050] The invention will now be further described with reference to the accompanying drawings.

[0051] like Figure 1As shown, this invention proposes an accurate prediction method for cable crosstalk based on an improved sparrow search algorithm. First, it utilizes the optimal point set theory and a smooth movement strategy based on double-optimal learning to optimize the initialization method of the sparrow population and the movement strategy of followers within the sparrow population, thereby improving the algorithm's optimization ability and avoiding getting trapped in local optima. Second, it establishes a cable crosstalk model using the chain parameter matrix method for different terminal conditions. Due to the complexity of the actual electromagnetic environment, this invention establishes an error correction model between the cable crosstalk model and the simulation model. Finally, it establishes a fitness function (fitness) for the cable crosstalk model as an iterative evaluation function for the improved sparrow search algorithm, using the improved sparrow search algorithm to optimize the parameters in the cable crosstalk model. By setting the optimization parameters in the simulation model, accurate crosstalk prediction results are quickly obtained. Specifically, the method includes the following steps:

[0052] Step 1: Establish as follows Figure 2 The crosstalk model of the parallel cables shown is used to calculate the unit inductance and capacitance matrix of the cables. By combining different terminal conditions and solving the chain parameter matrix method, the terminal voltage and current values ​​of the disturbed cables are obtained.

[0053] The analytical expressions for the unit inductance and capacitance matrix of a cable are:

[0054]

[0055]

[0056] like Figure 3 As shown, where μ is the permeability in vacuum, and h A h B These are the heights of cables A and B above ground, r. A r B These are the radii of the cable's metal inner cores A and B, respectively, and Δr. A , △r B These are the cable insulation layer thicknesses, and S is the center-to-center distance between cables A and B. The elements on the main diagonal represent the self-inductance per unit length of the cable, and the elements on the opposite diagonal represent the mutual inductance per unit length of the cable. ε r It is the relative permittivity of the insulating layer medium, ε e =(ε r -1) / ε r .

[0057] like Figure 4 As shown, where V S1 and V S2 It is a voltage source for cable terminations, Z S1 Z S2 Z L1 and Z L2 It is a cable termination load. L ji and C ji(j=1,2) represent the inductance and capacitance per unit length of the cable, respectively, and L i C i These are the mutual inductance and mutual capacitance per unit length of cable, respectively.

[0058] The analytical expression for the chain parameter matrix is:

[0059]

[0060] When the termination condition is only voltage source excitation, the termination voltage and current response of the disturbed transmission line can be obtained by solving the following analytical equations:

[0061] (φ 12 -φ1Z S -Z L φ 22 +Z L φ 21 Z S I(0)=V L -(φ 11 -Z L φ 21 V S

[0062] I(L)=φ 21 V S +(φ 22 -φ 21 Z S )I(0)

[0063] When the termination condition is only current source excitation, the termination voltage and current response of the disturbed transmission line can be obtained by solving the following analytical equations:

[0064] (φ 21 -φ 22 Y S -Y L φ 11 +Y L φ 12 Y S V(0)=-I L -(φ 22 -Y L φ 21 )I S

[0065] V(L)=φ 21 I s +(φ 11 -φ 12 Y s V(0)

[0066] Where V(L) and I(L) are the terminal voltage and current values, and V(0) and I(0) are the cable input voltage and current values. S It is driven by the terminal voltage source, I S It is a terminal current source excitation, φ ij It is the chain parameter matrix parameter, Z S Z L It is the internal resistance of the cable termination and the load impedance, Y S Y L It refers to the electrical conductivity and load admittance of the cable terminal.

[0067] Step 2: Establish an error correction model between the crosstalk model of parallel cables and the cable simulation model:

[0068] W 误差 =abs(X1-X2)

[0069] Where abs refers to taking the absolute value of the two numbers, X1 is the crosstalk prediction result of the simulation model under the same conditions, and X2 is the crosstalk prediction result of the theoretical mathematical model under the same conditions.

[0070] Step 3: Optimize the initialization method of the sparrow population in the sparrow search algorithm based on the optimal point set theory, and improve the sparrow follower population movement strategy in the sparrow search algorithm by using the smooth movement strategy of double optimal learning, thereby improving the ability of the sparrow search algorithm to find the best and escape local optima.

[0071] The analytical expression for the initialization method of the optimal point set theory is:

[0072] r j ={2cos(2πk / p), 1≤k≤s}

[0073] Among them, ub j , lb j r is the upper and lower bounds of the j-th dimension vector. j ={2cos(2πk / p),1≤k≤s} where P is the smallest prime number satisfying (p-3) / 2≤s, and {r j} represents r j The decimal part.

[0074] The analytical expression for the movement strategy of the sparrow follower population in the improved sparrow search algorithm based on the smooth movement strategy of dual-optimal learning is as follows:

[0075]

[0076] in, and Let represent the two optimal positions in the t-th iteration, dim represent the dimension of the position vector of the sparrow individual, and S represent a random number with a value in the range [-1, 1].

[0077] when When, it means that the i-th follower will gradually forage towards the middle direction between the two optimal positions. When, it means that the i-th follower will gradually move towards the optimal position to forage. When This indicates that the followers with relatively poor adaptability have not obtained food and need to fly to other areas to forage.

[0078] Furthermore, when the dimension of the position vector dim = 2, coordinate rotation can be introduced to enhance the ability to escape local optima and develop new foes. The analytical expression is as follows:

[0079]

[0080]

[0081] Where, x k Indicates when The x-coordinate of the follower's position coordinates, y k Indicates when The y-coordinate of the follower's position coordinates, where y0 is the optimal value when two optimal conditions exist. When there exists a unique optimal case, y0 is

[0082] Step 4: Establish the fitness function of the improved sparrow search algorithm for the crosstalk target amplitude and the crosstalk model after error correction.

[0083] fitness = abs(XY)

[0084] Where X is the crosstalk prediction result of the prediction model, and Y is the target value range of the actual crosstalk result.

[0085] Step 5: For the model parameters within a certain range after error correction, use the fitness function as the evaluation criterion and use the improved sparrow search algorithm to optimize and obtain the crosstalk model parameters that meet the requirements.

[0086] The model parameters after error correction within a certain range include parameters such as the spacing between interfering and disturbed cables, their height above the ground, the terminal load, and the reactance value present in the terminal load.

[0087] To address the problem of cable crosstalk prediction, and in order to obtain more accurate prediction results, a mathematical model and a simulation model of cable crosstalk are combined, and an improved sparrow search algorithm is used to optimize the simulation parameter settings to achieve a more realistic result. In this embodiment, as shown... Figure 5As shown, when the terminal condition is voltage source excitation, the crosstalk simulation model prediction results do not reach the amplitude standard in actual testing. Considering that the terminal load may have reactive characteristics and is not an ideal load, an improved sparrow search algorithm is used to optimize the parameters for the terminal loads of interfering and disturbed cables. The optimization parameters are set in the simulation model to quickly obtain accurate crosstalk prediction results. For example... Figure 6 As shown, when the terminal condition is current source excitation, the crosstalk simulation model prediction results do not reach the amplitude standard in actual testing. Considering that the terminal load may have reactance characteristics and is not an ideal load, an improved sparrow search algorithm is used to optimize the parameters of the terminal loads of interfering and disturbed cables. The optimization parameters are set in the simulation model to quickly obtain accurate crosstalk prediction results.

[0088] The specific embodiments described herein are merely illustrative of the spirit of the invention. Those skilled in the art to which this invention pertains may make various modifications or additions to the described specific embodiments or use similar methods to substitute them, without departing from the spirit of the invention or exceeding the scope defined by the appended claims.

Claims

1. A method for accurate prediction of cable crosstalk based on an improved sparrow search algorithm, characterized in that, Includes the following steps: (1) Establish a cable crosstalk model, calculate the unit inductance and capacitance matrix of the cable, and combine different terminal conditions to solve the terminal voltage and current values ​​of the disturbed cable by using the chain parameter matrix method; (2) Establish an error correction model between the crosstalk model of parallel cables and the cable simulation model; (3) Based on the theory of optimal point set, optimize the initialization method of sparrow population in sparrow search algorithm, and use the smooth movement strategy of double optimal learning to improve the movement strategy of sparrow follower population in sparrow search algorithm, so as to improve the ability of sparrow search algorithm to find the best and escape from local optima. (4) Determine the fitness function of the improved sparrow search algorithm; (5) The parameters of the cable crosstalk model after the error correction model are optimized by using the fitness function as the evaluation criterion and the improved sparrow search algorithm to obtain the crosstalk model parameters that meet the requirements. The implementation process of the improved sparrow follower population movement strategy in the sparrow search algorithm using the smooth movement strategy of double optimal learning in step (3) is as follows: in, and This represents the two optimal positions in the t-th iteration. The dimension of the position vector of an individual sparrow is represented by S, and S represents a random number with a value in the range [-1, 1]. when When, it means that the i-th follower will gradually forage towards the middle direction between the two optimal positions. When, it means that the i-th follower will gradually move towards the optimal position to forage; when This indicates that the followers with relatively poor adaptability have not obtained food and need to fly to other areas to forage.

2. The method for accurate prediction of cable crosstalk based on an improved sparrow search algorithm according to claim 1, characterized in that, The implementation process of step (1) is as follows: The unit inductance and capacitance matrix of the cable is as follows: in, It is the magnetic permeability in a vacuum. These are the heights of cables A and B relative to ground. These are the radii of the cable's metal inner cores A and B, respectively. These are the thickness of the cable insulation layer, It is the center-to-center distance between cables A and B; The main diagonal element represents the self-inductance per unit length of the cable, and the diagonal element represents the mutual inductance per unit length of the cable. It is the relative permittivity of the insulating layer medium. ; The chain parameter matrix is ​​as follows: When the termination condition is only voltage source excitation, the termination voltage and current response of the disturbed transmission line can be obtained by the following equation: When the termination condition is only current source excitation, the termination voltage and current response of the disturbed transmission line can be obtained analytically as follows: in, , These are the terminal voltage and current values. , These are the cable input voltage and current values. It is driven by the terminal voltage source. It is driven by a terminal current source. It is the chain parameter matrix parameter. , These are the internal resistance of the cable termination and the load impedance. , It refers to the electrical conductivity and load admittance of the cable terminal.

3. The method for accurate prediction of cable crosstalk based on an improved sparrow search algorithm according to claim 1, characterized in that, The error correction model between the crosstalk model of the parallel cable and the cable simulation model in step (2) is as follows: in, The absolute values ​​of the two numbers are taken. X1 is the crosstalk prediction result of the simulation model under the same conditions, and X2 is the crosstalk prediction result of the theoretical mathematical model under the same conditions.

4. The method for accurate prediction of cable crosstalk based on an improved sparrow search algorithm as described in claim 1, characterized in that, The initialization of the sparrow population in the optimal point set theory-optimized sparrow search algorithm in step (3) is as follows: in, , It is the upper and lower bounds of the j-th dimension vector. Where P satisfies The smallest prime number, and express The decimal part.

5. The method for accurate prediction of cable crosstalk based on an improved sparrow search algorithm according to claim 1, characterized in that, The fitness function, fitness, mentioned in step (4), is: in, The absolute values ​​of two numbers are taken, where X is the crosstalk prediction result of the prediction model and Y is the target range of the actual crosstalk result.

6. The method for accurate prediction of cable crosstalk based on an improved sparrow search algorithm according to claim 1, characterized in that, The dimension of the position vector of the individual sparrow At the same time, coordinate rotation is introduced to enhance the ability to escape local optima and develop new features: in, Indicates when The x-coordinate of the follower's position coordinates. Indicates when The ordinate of the follower's position coordinates, when there is a double optimal case. for When there exists a unique optimal case for .

7. The method for accurate prediction of cable crosstalk based on an improved sparrow search algorithm as described in claim 1, characterized in that, The cable crosstalk model parameters after error correction mentioned in step (5) include the spacing between the interfering cable and the affected cable, the height above the ground, the terminal load, and the reactance value present in the terminal load.

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