A signal matching pursuit optimization method for distribution network based on improved sine-cosine algorithm

Abstract

The present invention relates to the technical field of power grid signal matching tracking, and in particular to a distribution network signal matching tracking optimization method based on an improved sine-cosine algorithm, comprising: obtaining historical tracking data in the distribution network based on the sine-cosine optimization algorithm to improve the signal matching tracking efficiency; obtaining an initial solution distribution improved by an optimization algorithm population initialization method based on segmented mapping; improving a control parameter update method of the improved sine-cosine optimization algorithm to obtain a control parameter update strategy; obtaining a matching tracking process termination condition based on a decomposition result of a noisy signal data of the distribution network and a matching degree difference component; optimizing the distribution network signal matching tracking process based on the initial solution distribution, the control parameter update strategy and the matching tracking process termination condition. The improved sine-cosine algorithm proposed in the present invention effectively avoids matching tracking from falling into a local optimum, improves computational efficiency, and simultaneously improves signal tracking accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid signal matching tracking, and in particular to a distribution network signal matching tracking optimization method based on an improved sine-cosine algorithm. Background Art

[0002] With the large-scale integration of distributed renewable energy, the operation of new distribution systems is becoming increasingly complex, posing new challenges to the control and protection of distribution networks. This requires real-time, comprehensive monitoring of distribution networks to provide a data foundation for upgrading control and protection solutions. Due to the vast coverage area and dispersed topology of distribution networks, the large amount of measurement data creates significant pressure on communication. By sparsely decomposing distribution network signals, it is possible to achieve full dynamic monitoring with minimal data volume.

[0003] The application of intelligent optimization algorithms in the matching pursuit process is of great significance. By finding a more optimal basis element selection sequence through global search, they avoid the potential pitfalls of greedy algorithms that can lead to local optima. However, due to the suboptimality of matching pursuit algorithms in existing technologies, a higher number of iterations is often required to achieve a satisfactory approximation, resulting in lower computational efficiency. When existing technologies fail to account for noise interference and incomplete data acquisition, the performance of the matching pursuit algorithm can be significantly reduced, thereby reducing the accuracy of signal tracking. Summary of the Invention

[0004] The purpose of the present invention is to provide a distribution network signal matching pursuit optimization method based on an improved sine-cosine algorithm to solve the problems of low algorithm efficiency and low signal tracking accuracy in the prior art.

[0005] To achieve the above objectives, the present invention provides a distribution network signal matching pursuit optimization method based on an improved sine-cosine algorithm, comprising:

[0006] S1. Obtain historical tracking data based on the sine-cosine optimization algorithm to improve the signal matching tracking efficiency in the distribution network, analyze the sine-cosine optimization algorithm and the historical tracking data, and determine the sine-cosine optimization algorithm to be improved corresponding to the defective data in the historical tracking data;

[0007] S2, obtain the initial solution distribution improved by the optimization algorithm population initialization method based on segmented mapping;

[0008] S3. Improve the control parameter update method of the improved sine-cosine optimization algorithm to obtain a control parameter update strategy;

[0009] S4, obtaining the termination condition of the matching pursuit process constructed based on the decomposition result of the noisy signal data of the distribution network and the matching degree difference component;

[0010] S5. Optimize the distribution network signal matching pursuit process based on the initial solution distribution, control parameter update strategy and matching pursuit process termination conditions.

[0011] In some embodiments of the present application, in step S1, analyzing the sine-cosine optimization algorithm and historical tracking data to determine the sine-cosine optimization algorithm to be improved corresponding to the defective data in the historical tracking data includes:

[0012] S11, iteratively select the atom in the history dictionary that best matches the current signal , and obtain the reconstructed signal s t and the residual signal R(s) The calculation formula is:

[0013] ;

[0014] in, For atoms, for The sparse decomposition coefficient of s t To reconstruct the signal, R ( s ) is the residual signal, t is the number of iterations, s Measuring signals for the original distribution network;

[0015] The iterative process of selecting the best matching atom is regarded as a global optimization problem, and the calculation formula is:

[0016] ;

[0017] Where, is the inner product of the residual signal and the atom, For the t Atomic 's index;

[0018] S12, equipped with N individuals, and the dimension of each individual is M , get the t The first iteration The location of an individual The calculation formula is:

[0019] ;

[0020] in, For the t The first iteration The location of an individual, for Middle Mcomponents; S13, calculate the traditional population initialization method of the sine and cosine algorithm, the calculation formula is:

[0021] ;

[0022] Where, For individuals in j The upper bound of the dimension, For individuals in j The lower bound of the dimension, is a random feature variable between 0 and 1, For the Individuals in j The value of the dimension;

[0023] During the iteration process, the fitness of the individual position calculated by the sine-cosine algorithm based on the objective function is obtained, and the global optimal solution is calculated. The calculation formula is:

[0024] ;

[0025] in, For individuals In the j Update position in dimension, For individuals In the j The original position in dimension, 、 、 、 All are random variables;

[0026] The calculation formula for the random variable group is:

[0027] ;

[0028] in, is the maximum number of iterations, is a preset constant;

[0029] The improved sine-cosine optimization algorithm corresponding to the defective data in the historical tracking data is determined based on the random variable group.

[0030] In some embodiments of the present application, in S2, obtaining an improved initial solution distribution of the optimization algorithm population initialization method based on segmented mapping includes:

[0031] S21. Generate a segmented mapping based on the segmented function. The calculation formula is:

[0032] ;

[0033] in, For the The individuals generated by the sub-segmentation, For the +1 The individuals generated by the sub-segmentation, are preset parameters;

[0034] S22. Construct an optimization algorithm population initialization method based on the segmented mapping results, and obtain an improved initial solution distribution based on the optimization algorithm population initialization method.

[0035] In some embodiments of the present application, in S3, the control parameter update method of the improved sine-cosine optimization algorithm is improved, and the control parameter update strategy obtained includes:

[0036] S31. Calculate the nonlinear adjustment parameter based on the number of iterations and the iteration fitness. The calculation formula is:

[0037] ;

[0038] in, is the nonlinear adjustment parameter, is the fitness of the population, is a preset constant, is the number of iterations, is the maximum number of iterations;

[0039] The nonlinear growth characteristics of the natural logarithm function are used to improve the updating method of the control parameters. The expression is:

[0040] ;

[0041] in, To improve the control parameters, is the initial value of the control parameter, is the nonlinear adjustment parameter;

[0042] S32. The expression of the control parameter update strategy based on the improved update method is:

[0043] .

[0044] In some embodiments of the present application, in S4, the termination condition of the matching pursuit process constructed based on the decomposition result of the noisy signal data of the distribution network and the matching difference component includes:

[0045] S41. Obtain noise-free signal data of the distribution network, and decompose the noise-containing signal data into:

[0046] ;

[0047] in, is a noise-free signal, is the noise within the frequency band, is the out-of-band noise, y is the original signal containing noise;

[0048] Get the first t The residual signal after iteration The energy expression is:

[0049] ;

[0050] in, is the residual signal in the tth iteration energy, is the noise-free signal in the tth iteration The energy of the noise in the sum band is, Out-of-band noise energy;

[0051] S42. Obtain the termination condition of the matching pursuit process, which is expressed as:

[0052] ;

[0053] in, and are weighted parameters, for t The fit metric of the iterative process, for t+1 The fit metric of the iterative process, for t-1 The fit metric of the iterative process, is the termination threshold, is the termination condition of the matching pursuit process.

[0054] In some embodiments of the present application, in S5, optimizing the distribution network signal matching pursuit process based on the initial solution distribution, the control parameter update strategy, and the matching pursuit process termination condition includes:

[0055] S51. Initialize the population based on segmented mapping to obtain initial population distribution characteristics;

[0056] S52, optimizing the distribution network signal matching tracking process based on the control parameter update strategy;

[0057] S53, based on improved control parameters Update control parameters - ;

[0058] S54, updating individual positions based on the traditional population initialization method;

[0059] S55, obtaining the fitness of the individual matching pursuit;

[0060] S56, judging the current matching state based on the termination condition of the matching tracking process, if the current matching state does not meet the matching condition, returning to step S52 and re-executing, if the current matching state meets the matching condition, obtaining the number of iterations;

[0061] S57, judging the number of iterations and the maximum number of iterations T max relationship, if the number of iterations is less than the maximum number of iterations T max, then return to step S51 and execute again. If the number of iterations is greater than the maximum number of iterations T max, then output the matching pursuit result.

[0062] The advantages and beneficial effects of the present invention over the prior art are:

[0063] 1. This invention uses a segmented mapping optimization algorithm population initialization method to generate uniformly distributed, high-quality initial solutions. Compared to traditional random initialization methods, this distribution is more reasonable, enhancing population diversity and enabling the algorithm to converge faster to a region close to the optimal solution in the early stages of iteration, thereby reducing the number of iterations and improving computational efficiency.

[0064] 2. Improvements to the control parameter update method for the sine-cosine optimization algorithm, such as adopting a nonlinear decreasing parameter update strategy, can better balance the algorithm's global search capability and local exploitation capabilities. In the early stages of the iteration, larger parameter values ​​help strengthen the global search and avoid being trapped in local optima. In the later stages of the iteration, gradually reducing the parameters enhances local exploitation capabilities, thereby improving the algorithm's signal tracking accuracy.

[0065] 3. By analyzing historical tracking data from distribution networks using the sine-cosine optimization algorithm, we identify the algorithms that need improvement corresponding to the defective data and optimize them accordingly. This real-world data-based improvement approach effectively addresses existing issues such as low computational efficiency and poor accuracy caused by the suboptimality of the matching pursuit algorithm, thereby improving the robustness of the algorithm.

[0066] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 Schematic diagram of the steps of a distribution network signal matching pursuit optimization method based on an improved sine-cosine algorithm in an embodiment of the present invention;

[0068] Figure 2 A distribution diagram of population initialization based on segmented mapping according to an embodiment of the present invention;

[0069] Figure 3 is the control parameter in the embodiment of the present inventionp 1 Comparison chart of linear and nonlinear adjustment effects;

[0070] Figure 4 Schematic diagram of matching termination affected by noise according to an embodiment of the present invention;

[0071] Figure 5 This is a diagram of the overall process framework of the matching pursuit process optimized based on the improved sine-cosine algorithm according to an embodiment of the present invention;

[0072] Figure 6 Schematic diagram of waveform data actually measured in an embodiment of the present invention. DETAILED DESCRIPTION

[0073] In the description of the present invention, it should be noted that the terms "upper", "lower", "inside", "outside" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, or the orientations or positional relationships in which the inventive product is usually placed when in use. These are only for the convenience of describing the present invention and simplifying the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, they cannot be understood as limitations on the present invention. In the description of the present invention, it should also be noted that, unless otherwise expressly specified and limited, the terms "setting", "installation" and "connection" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or an indirect connection through an intermediate medium, or it can be a communication between the internal parts of two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0074] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0075] like Figure 1 As shown, the present invention provides a distribution network signal matching pursuit optimization method based on an improved sine-cosine algorithm, comprising:

[0076] S1. Obtain historical tracking data based on the sine-cosine optimization algorithm to improve the signal matching tracking efficiency in the distribution network, analyze the sine-cosine optimization algorithm and historical tracking data, and determine the sine-cosine optimization algorithm to be improved corresponding to the defective data in the historical tracking data.

[0077] S2. Obtain the initial solution distribution improved by the population initialization method of the optimization algorithm based on segmented mapping.

[0078] S3. Improve the control parameter updating method of the improved sine-cosine optimization algorithm to obtain a control parameter updating strategy.

[0079] S4. Obtain the termination condition of the matching pursuit process constructed based on the decomposition result of the noisy signal data of the distribution network and the matching degree difference component.

[0080] S5. Optimize the distribution network signal matching pursuit process based on the initial solution distribution, control parameter update strategy and matching pursuit process termination conditions.

[0081] The advantages and beneficial effects of the present invention over the prior art are:

[0082] 1. This invention uses a segmented mapping optimization algorithm population initialization method to generate uniformly distributed, high-quality initial solutions. Compared to traditional random initialization methods, this distribution is more reasonable, enhancing population diversity and enabling the algorithm to converge faster to a region close to the optimal solution in the early stages of iteration, thereby reducing the number of iterations and improving computational efficiency.

[0083] 2. Improvements to the control parameter update method for the sine-cosine optimization algorithm, such as adopting a nonlinear decreasing parameter update strategy, can better balance the algorithm's global search capability and local exploitation capabilities. In the early stages of the iteration, larger parameter values ​​help strengthen the global search and avoid being trapped in local optima. In the later stages of the iteration, gradually reducing the parameters enhances local exploitation capabilities, thereby improving the algorithm's signal tracking accuracy.

[0084] 3. By analyzing historical tracking data from distribution networks using the sine-cosine optimization algorithm, we identify the algorithms that need improvement corresponding to the defective data and optimize them accordingly. This real-world data-based improvement approach effectively addresses existing issues such as low computational efficiency and poor accuracy caused by the suboptimality of the matching pursuit algorithm, thereby improving the robustness of the algorithm.

[0085] In some embodiments of the present application, in step S1, analyzing the sine-cosine optimization algorithm and historical tracking data to determine the sine-cosine optimization algorithm to be improved corresponding to the defective data in the historical tracking data includes:

[0086] S11, iteratively select the atom in the history dictionary that best matches the current signal , and obtain the reconstructed signal s t and the residual signal R(s) The calculation formula is:

[0087] ;

[0088] in, For atoms, for The sparse decomposition coefficient of s t To reconstruct the signal, R ( s ) is the residual signal,t is the number of iterations, s Measuring signals for the original distribution network;

[0089] The iterative process of selecting the best matching atom is regarded as a global optimization problem, and the calculation formula is:

[0090] ;

[0091] Where, is the inner product of the residual signal and the atom, For the t Atomic 's index;

[0092] S12, equipped with N individuals, and the dimension of each individual is M , get the t The first iteration k The location of an individual d The calculation formula is:

[0093] ;

[0094] in, For the t The first iteration The location of an individual, for Middle M components; S13, calculate the traditional population initialization method of the sine and cosine algorithm, the calculation formula is:

[0095] ;

[0096] Where, For individuals in j The upper bound of the dimension, For individuals in j The lower bound of the dimension, is a random feature variable between 0 and 1, For the Individuals in j The value of the dimension;

[0097] During the iteration process, the fitness of the individual position calculated by the sine-cosine algorithm based on the objective function is obtained, and the global optimal solution is calculated. The calculation formula is:

[0098] ;

[0099] in, For individuals In the j Update position in dimension, For individuals In the j The original position in dimension, 、 、 、 are all random variables;

[0100] The calculation formula for the random variable group is:

[0101] ;

[0102] in, is the maximum number of iterations, is a preset constant;

[0103] The improved sine-cosine optimization algorithm corresponding to the defective data in the historical tracking data is determined based on the random variable group.

[0104] In some embodiments of the present application, in S2, obtaining an improved initial solution distribution of the optimization algorithm population initialization method based on segmented mapping includes:

[0105] S21. Generate a segmented mapping based on the segmented function. The calculation formula is:

[0106] ;

[0107] in, For the The individuals generated by the sub-segmentation, For the +1 individual generated by segmentation, are preset parameters;

[0108] Specifically, piecewise mapping can produce initial solutions with good distribution characteristics. It generates sequences through piecewise functions, which show better distribution and randomness within a certain range, thereby creating a more diverse initial population.

[0109] S22, construct an optimization algorithm population initialization method based on the segmented mapping results, such as Figure 2 As shown, the improved initial solution distribution based on the optimization algorithm population initialization method is obtained.

[0110] Specifically, the sequences generated by segmented mapping are very evenly distributed across the entire interval. This provides a wider and more uniform distribution of initial solutions, helping intelligent optimization algorithms more efficiently explore the entire solution space, avoid being trapped in local optimal solutions, and accelerate convergence.

[0111] In some embodiments of the present application, in S3, the control parameter update method of the improved sine-cosine optimization algorithm is improved, and the control parameter update strategy obtained includes:

[0112] S31, calculate the nonlinear adjustment parameters based on the number of iterations and the iteration fitness, such as Figure 3 As shown, the calculation formula is:

[0113] ;

[0114] in, is the nonlinear adjustment parameter, is the fitness of the population, is a preset constant, is the number of iterations, is the maximum number of iterations;

[0115] The nonlinear growth characteristics of the natural logarithm function are used to improve the updating method of the control parameters. The expression is:

[0116] ;

[0117] in, To improve the control parameters, is the initial value of the control parameter, is the nonlinear adjustment parameter;

[0118] In the early iterations, p 1 The higher the value of , the more focused the global search is; in the later iterations, p 1 A lower value of focuses on local search.

[0119] It should be understood that simply using the linear reduction method may not improve the optimization efficiency. In the initial stage, p 1 It should be decreased slowly to search for the optimal solution in a larger range. On the contrary, in the later stages, or when the improvement of fitness is significant, p 1 should be decremented more quickly to significantly enhance the local search capability of the algorithm, so it is necessary to implement p 1 The nonlinear decrease of the algorithm is considered, and the above-mentioned process of calculating the nonlinear adjustment parameters based on the number of iterations and iterative fitness is designed.

[0120] S32. The expression of the control parameter update strategy based on the improved update method is:

[0121] .

[0122] like Figure 4 In some embodiments of the present application, in S4, the termination conditions of the matching pursuit process based on the decomposition result of the noisy signal data of the distribution network and the matching difference component include:

[0123] S41. Obtain noise-free signal data of the distribution network, and decompose the noise-containing signal data into:

[0124] ;

[0125] in, is a noise-free signal, is the noise within the frequency band, is the out-of-band noise, y is the original signal containing noise;

[0126] Get the first t The residual signal after iteration The energy expression is:

[0127] ;

[0128] in, For the t The residual signal in the iteration energy, For the t Noise-free signal in iterations The energy of the noise in the sum band is, Out-of-band noise energy;

[0129] It can be seen that the energy of the residual signal includes part of the energy of the out-of-band noise from s. As the iteration proceeds, the proportion of this energy will gradually increase.

[0130] It is important to understand that in order to improve the efficiency of matching pursuit, it is necessary to find a balance between global periodicity and local fluctuations. In the power distribution system, the presence of a large amount of noise in the signal will have a significant negative impact on the sparse decomposition, so it is necessary to perform statistics and analysis on the noise data.

[0131] S42. Obtain the termination condition of the matching pursuit process, which is expressed as:

[0132] ;

[0133] in, and are weighted parameters, for t The fit metric of the iterative process, for t +1 iteration process of the fit measure, for t -1 iteration process of the fit metric, is the termination threshold, is the termination condition of the matching pursuit process.

[0134] Among them, considering the existence of noise energy, which causes the optimization process to wander around the optimal solution, a robust termination condition based on the weighted evaluation of the deterministic fitness difference of the last two iterations is designed.

[0135] In some embodiments of the present application, in S5, the distribution network signal matching pursuit process is optimized based on the initial solution distribution, the control parameter update strategy and the matching pursuit process termination condition. Figure 5 Shown, including:

[0136] S51. Initialize the population based on segmented mapping to obtain initial population distribution characteristics;

[0137] S52, optimizing the distribution network signal matching tracking process based on the control parameter update strategy;

[0138] S53, based on improved control parameters Update control parameters - ;

[0139] S54, updating individual positions based on the traditional population initialization method;

[0140] S55, obtaining the fitness of the individual matching pursuit;

[0141] S56, judging the current matching state based on the termination condition of the matching tracking process, if the current matching state does not meet the matching condition, returning to step S52 and re-executing, if the current matching state meets the matching condition, obtaining the number of iterations;

[0142] S57, judging the number of iterations and the maximum number of iterations T max relationship, if the number of iterations is less than the maximum number of iterations T max, then return to step S51 and execute again. If the number of iterations is greater than the maximum number of iterations T max, then output the matching pursuit result.

[0143] The beneficial effects of the present invention are as follows: the present invention proposes a distribution network signal matching pursuit optimization method based on the improved sine-cosine algorithm. The improved sine-cosine algorithm proposed in the present invention effectively avoids matching pursuit from falling into local optimality, improves computational efficiency, and ensures higher reconstruction accuracy.

[0144] The following describes the implementation of the present invention in detail with reference to specific examples.

[0145] The effectiveness of this method for sparse decomposition of waveform data is evaluated using the measured waveform data of a distribution network in North China. Figure 6The algorithm's execution process was analyzed using waveform data from a tree-line contact fault as an example. The waveform had a sampling rate of 3.2kHz and a duration of 0.16s. For a waveform of this duration, the proposed method requires 80 atoms to effectively perform sparse decomposition of the signal. These atoms are then uploaded as data frames, with each atom occupying two bytes. Compared to directly uploading the waveform data (each instantaneous value occupies two bytes), the data volume is reduced by more than six times. The average error after reconstruction is 0.1458%, and the maximum error is 0.4796%. These accuracy requirements meet the requirements for distribution network disturbance analysis, demonstrating the efficiency of the proposed method for decomposing and reconstructing distribution network signals.

[0146] In this application, unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. In the event of any inconsistency, the meaning described in this specification or the meaning derived from the contents recorded in this specification shall prevail. In addition, the terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0147] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solutions of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A distribution network signal matching pursuit optimization method based on an improved sine-cosine algorithm, characterized in that: The following steps are involved: S1. Obtain historical tracking data based on the sine-cosine optimization algorithm to improve the signal matching tracking efficiency in the distribution network, analyze the sine-cosine optimization algorithm and the historical tracking data, and determine the sine-cosine optimization algorithm to be improved corresponding to the defective data in the historical tracking data; S2, obtain the initial solution distribution improved by the optimization algorithm population initialization method based on segmented mapping; S3. Improve the control parameter update method of the improved sine-cosine optimization algorithm to obtain a control parameter update strategy; S4, obtaining the termination condition of the matching pursuit process constructed based on the decomposition result of the noisy signal data of the distribution network and the matching degree difference component; S5. Optimize the matching pursuit process of distribution network signals based on the initial solution distribution, control parameter update strategy, and matching pursuit process termination condition; In S4, the termination conditions of the matching pursuit process based on the decomposition result of the noisy signal data of the distribution network and the matching difference component include: S41. Obtain noise-free signal data of the distribution network, and decompose the noise-containing signal data into: ； in, is a noise-free signal, is the noise within the frequency band, is the out-of-band noise, is the original signal containing noise; Get the residual signal after the tth iteration The energy expression is: ； in, For the The residual signal in the iteration energy, For the Noise-free signal in iterations The energy of the noise in the sum band is, Out-of-band noise energy; S42. Obtain the termination condition of the matching pursuit process, which is expressed as: ； in, and are weighted parameters, for The fit metric of the iterative process, for The fit metric of the iterative process, for The fit metric of the iterative process, is the termination threshold, is the termination condition of the matching pursuit process.

2. The distribution network signal matching pursuit optimization method based on the improved sine-cosine algorithm according to claim 1 is characterized in that: In step S1, the sine-cosine optimization algorithm and historical tracking data are analyzed to determine the sine-cosine optimization algorithm to be improved corresponding to the defective data in the historical tracking data, including: S11, iteratively select the atom in the history dictionary that best matches the current signal , and obtain the reconstructed signal and the residual signal The calculation formula is: ； in, For atoms, for The sparse decomposition coefficient of To reconstruct the signal, is the residual signal, is the number of iterations, Measuring signals for the original distribution network; The iterative process of selecting the best matching atom is regarded as a global optimization problem, and the calculation formula is: ； Where, is the inner product of the residual signal and the atom, For the Atomic 's index; S12, equipped with N individuals, and the dimension of each individual is M , get the The first iteration The location of an individual The calculation formula is: ； in, For the The first iteration The location of an individual, for Middle M Quantity S13. Calculate the traditional population initialization method of the sine and cosine algorithm. The calculation formula is: ； Where, For individuals in The upper bound of the dimension, For individuals in The lower bound of the dimension, is a random feature variable between 0 and 1, For the Individuals in The value of the dimension; During the iteration process, the fitness of the individual position calculated by the sine-cosine algorithm based on the objective function is obtained, and the global optimal solution is calculated. The calculation formula is: ； in, For individuals In the Update position in dimension, For individuals In the The original position in dimension, 、 、 、 All are random variables; The calculation formula for the random variable group is: ； in, is the maximum number of iterations, is a preset constant; The improved sine-cosine optimization algorithm corresponding to the defective data in the historical tracking data is determined based on the random variable group.

3. The distribution network signal matching pursuit optimization method based on the improved sine-cosine algorithm according to claim 2 is characterized in that: In S2, the improved initial solution distribution of the optimization algorithm population initialization method based on segmented mapping is obtained, including: S21. Generate a segmented mapping based on the segmented function. The calculation formula is: ； in, For the The individuals generated by the sub-segmentation, For the The individuals generated by the sub-segmentation, are preset parameters; S22. Construct an optimization algorithm population initialization method based on the segmented mapping results, and obtain an improved initial solution distribution based on the optimization algorithm population initialization method.

4. The distribution network signal matching pursuit optimization method based on the improved sine-cosine algorithm according to claim 3 is characterized in that: In S3, the control parameter update method of the improved sine-cosine optimization algorithm is improved, and the control parameter update strategy obtained includes: S31. Calculate the nonlinear adjustment parameter based on the number of iterations and the iteration fitness. The calculation formula is: ； in, is the nonlinear adjustment parameter, is the fitness of the population, is a preset constant, is the number of iterations, is the maximum number of iterations; The nonlinear growth characteristics of the natural logarithm function are used to improve the updating method of the control parameters. The expression is: ； in, To improve the control parameters, is the initial value of the control parameter; S32. The expression of the control parameter update strategy based on the improved update method is: 。 5. The distribution network signal matching pursuit optimization method based on the improved sine-cosine algorithm according to claim 4 is characterized in that: In S5, optimizing the distribution network signal matching pursuit process based on the initial solution distribution, the control parameter update strategy, and the matching pursuit process termination condition includes: S51. Initialize the population based on segmented mapping to obtain initial population distribution characteristics; S52, optimizing the distribution network signal matching tracking process based on the control parameter update strategy; S53, based on improved control parameters Update control parameters - ; S54, updating individual positions based on the traditional population initialization method; S55, obtaining the fitness of the individual matching pursuit; S56, judging the current matching state based on the termination condition of the matching tracking process, if the current matching state does not meet the matching condition, returning to step S52 and re-executing, if the current matching state meets the matching condition, obtaining the number of iterations; S57, judging the number of iterations and the maximum number of iterations T max relationship, if the number of iterations is less than the maximum number of iterations T max, then return to step S51 and execute again. If the number of iterations is greater than the maximum number of iterations T max, then output the matching pursuit result.

Images