A method, device, equipment and storage medium for obtaining distribution network line parameters

Abstract

This application discloses a method, apparatus, device, and storage medium for acquiring distribution network line parameters, relating to the field of circuit technology. The method includes: determining initial line impedance parameters based on time-section data corresponding to the target distribution network; calculating the initial line impedance parameters using a line parameter probability distribution model established based on historical line parameter sets to obtain line parameter distribution results; determining a first parameter based on the line parameter distribution results, and determining a second parameter based on the first parameter using a target TPE algorithm, Bayes' theorem, and a preset threshold; determining the current line parameter set based on the second parameter, the function value obtained by processing the second parameter using a preset parameter calculation function, and the historical line parameter set; updating the current iteration number; and determining whether the current iteration number and the second parameter satisfy a first preset condition and a second preset condition, respectively. If both are satisfied, the second parameter is the target parameter. This improves the accuracy of the acquired line parameters.

Description

Technical Field

[0001] This invention relates to the field of circuit technology, and in particular to a method, apparatus, device, and storage medium for obtaining parameters of power distribution network lines. Background Technology

[0002] In today's rapidly developing power industry, the accuracy of line parameters in low-voltage distribution networks, as a crucial link connecting countless households, is vital for the stable operation of the entire power system. However, due to incomplete historical records and frequent upgrades and expansions of distribution networks, the line information of low-voltage distribution networks in many distribution management systems (DMS) has become unclear. This information confusion not only affects the power grid's power flow calculation, fault location, and voltage management functions, but also leads to reduced power supply reliability and increased operation and maintenance costs.

[0003] Currently, three methods are used to determine line parameters for low-voltage distribution networks: The first method assumes that the line impedance information is known and makes corrections based on this. However, in practical applications, the impedance information in the distribution network information management system is often inaccurate or even completely missing, thus limiting the practicality of the above methods.

[0004] The second method relies on installing Phaser Measurement Units (PMUs) at both ends of the line and using the angle information measured by the PMUs to calculate the line's impedance parameters. However, this method requires additional equipment investment, making it economically unfeasible for large-scale deployments.

[0005] The third method involves inversely calculating the line impedance using the least-squares solution from data collected by an Advanced Metering Infrastructure (AIM) system or smart meters by solving the power flow equations of the line. However, this method is highly sensitive to the quality and completeness of the data; any inaccuracy or missing data can affect the accuracy of the calculation results.

[0006] As can be seen from the above, improving the accuracy of the obtained line parameters during the acquisition of power distribution network line parameters is an urgent problem to be solved. Summary of the Invention

[0007] In view of this, the purpose of this invention is to provide a method, apparatus, device, and storage medium for acquiring power distribution network line parameters, which can improve the accuracy of line parameter values ​​and increase the speed of acquiring line parameters. The specific solution is as follows:

[0008] Firstly, this application provides a method for obtaining parameters of a power distribution network line, including:

[0009] Several time section intervals are obtained in the distribution space of the line parameters to be processed in the target distribution network, and the data of each time section in the time section interval are calculated using the preset line impedance parameter calculation formula to obtain the corresponding initial line impedance parameters.

[0010] A probability distribution model of line parameters is established based on a set of historical line parameters. The line parameter probability distribution model is used to calculate the impedance parameters of each initial line by calling a preset parameter distribution algorithm, so as to obtain the line parameter distribution results.

[0011] The first parameter is determined by using a preset parameter selection function and a preset parameter calculation function and based on the distribution results of each line parameter; and the second parameter is determined by using the target TPE algorithm, Bayes' theorem, and a preset threshold and based on the first parameter.

[0012] Based on the second parameter, the function value obtained by processing the second parameter using the preset parameter calculation function, and the historical line parameter set, the current line parameter set is determined, and the current iteration number is updated. Then, it is determined whether the current iteration number meets the first preset condition. If it does, it is determined whether the second parameter meets the second preset condition. If it does, the second parameter is set as the target parameter.

[0013] Optionally, the step of obtaining several time-section intervals in the distribution space of the line parameters to be processed, and calculating the data of each time section in the time-section intervals using a preset line impedance parameter calculation formula to obtain the corresponding initial line impedance parameters, includes:

[0014] An initialization operation is performed on the distribution space of line parameters to be initialized to obtain the distribution space of line parameters to be processed, and several time segment intervals are selected in the distribution space of line parameters to be processed; the time segment intervals include time segment data;

[0015] Based on the time-series data, the voltage, active power, and reactive power between the two user nodes are determined. Several preset line impedance parameter calculation formulas are used to determine the corresponding initial line impedance parameters based on the number of time-series data, the voltage, the active power, and the reactive power. The initial line impedance includes resistance and reactance values.

[0016] Optionally, the step of determining the first parameter using a preset parameter selection function and a preset parameter calculation function, and based on the distribution results of each of the line parameters, includes:

[0017] The preset parameter calculation function is used to process the distribution results of each line parameter to obtain the corresponding function value to be processed;

[0018] A preset improvement degree determination algorithm is used to determine the improvement degree of the corresponding line parameter distribution results based on the values ​​of each of the functions to be processed, thereby obtaining the corresponding improvement degree results;

[0019] The parameter corresponding to the improvement result with the greatest improvement among the various improvement results is set as the first parameter using a preset parameter selection function.

[0020] Optionally, the step of using the target TPE algorithm, Bayes' theorem, and a preset threshold to determine the second parameter based on the first parameter includes:

[0021] The probability result corresponding to the first parameter is modeled using the target TPE algorithm with a tree structure to obtain the corresponding first probability result;

[0022] The target TPE algorithm is used to model the probability result corresponding to the first parameter under the condition of the corresponding function value to be processed, based on the preset threshold, to obtain the corresponding second probability result; wherein, whether the function value to be processed is greater than the preset threshold determines the second probability result;

[0023] The first probability result, the second probability result, and the third probability result corresponding to the value of the function to be processed are calculated using the Bayes theorem to obtain a fourth probability result; the fourth probability result is the probability calculation result corresponding to the value of the function to be processed under the conditions of the first parameter.

[0024] The second parameter is determined using the preset improvement degree determination algorithm and based on the fourth probability result, preset hyperparameters, first probability density, and second probability density; the first probability density is the probability density determined based on the initial line impedance parameter corresponding to the function value to be processed when the value of the function to be processed is less than the preset threshold; the second probability density is the probability density determined based on the initial line impedance parameter corresponding to the function value to be processed when the value of the function to be processed is not less than the preset threshold.

[0025] Optionally, the step of using the target TPE algorithm and modeling the probability result corresponding to the first parameter under the condition of the corresponding function value to be processed, based on the preset threshold, to obtain the corresponding second probability result, includes:

[0026] Based on user needs, preset hyperparameters are set, and it is determined whether the value of the function to be processed is greater than the preset threshold. If the value of the function to be processed is not greater than the preset threshold, the preset hyperparameters are set to the value corresponding to the second probability result.

[0027] Optionally, the process of determining the current line parameter set based on the second parameter, the function value obtained by processing the second parameter using the preset parameter calculation function, and the historical line parameter set, and updating the current iteration number, then determining whether the current iteration number satisfies the first preset condition; if so, determining whether the second parameter satisfies the second preset condition; if so, setting the second parameter as the target parameter, includes:

[0028] The second parameter, the function value to be processed, and the historical line parameter set are combined using a preset combination rule to obtain the current line parameter set, and the current iteration number is updated.

[0029] Determine whether the current iteration count is greater than the preset maximum iteration count. If the current iteration count is greater than the preset maximum iteration count, output the second parameter.

[0030] Determine whether the difference between the loss calculation result corresponding to the second parameter and the value of the function to be processed corresponding to the second parameter is less than a preset value. If the difference between the loss calculation result corresponding to the second parameter and the value of the function to be processed corresponding to the second parameter is less than the preset value, then set the second parameter as the target parameter.

[0031] If the difference between the loss calculation result corresponding to the second parameter and the value of the function to be processed corresponding to the second parameter is not less than the preset value, then the values ​​of the function to be processed are arranged in descending order, and a new parameter distribution space of the line to be processed is established based on the parameter distribution result corresponding to the obtained arrangement result, and the step of obtaining several time section intervals in the parameter distribution space of the line to be processed is triggered.

[0032] Optionally, the preset parameter distribution algorithm is a preset maximum likelihood estimation algorithm, the preset parameter selection function is an expected improvement function, and the preset parameter calculation function is a loss function.

[0033] Secondly, this application provides a device for acquiring parameters of a power distribution network line, comprising:

[0034] The time section interval acquisition module is used to acquire several time section intervals in the distribution space of the line parameters to be processed, and to calculate the data of each time section in the time section interval using a preset line impedance parameter calculation formula to obtain the corresponding initial line impedance parameters.

[0035] The distribution result determination module is used to establish a line parameter probability distribution model based on the historical line parameter set, and to use the line parameter probability distribution model and call a preset parameter distribution algorithm to calculate the initial line impedance parameters to obtain the line parameter distribution result;

[0036] The intermediate parameter determination module is used to determine the first parameter by using a preset parameter selection function and a preset parameter calculation function and based on the distribution results of each line parameter, and to determine the second parameter by using the target TPE algorithm, Bayes' theorem and a preset threshold and based on the first parameter.

[0037] The target parameter determination module is used to determine the current line parameter set based on the second parameter, the function value obtained by processing the second parameter by the preset parameter calculation function, and the historical line parameter set, and to update the current iteration number. Then, it is determined whether the current iteration number meets the first preset condition. If it does, it is determined whether the second parameter meets the second preset condition. If it does, the second parameter is set as the target parameter.

[0038] Thirdly, this application provides an electronic device, comprising:

[0039] Memory, used to store computer programs;

[0040] A processor is used to execute the computer program to implement the aforementioned method for obtaining power distribution network line parameters.

[0041] Fourthly, this application provides a computer-readable storage medium for storing a computer program, wherein the computer program, when executed by a processor, implements the aforementioned method for obtaining power distribution network line parameters.

[0042] As can be seen from the above, before obtaining the distribution network line parameters, this application needs to obtain several time-section intervals in the distribution space of the line parameters to be processed, and use a preset line impedance parameter calculation formula to calculate the data of each time section in the time-section interval to obtain the corresponding initial line impedance parameters; establish a line parameter probability distribution model based on the historical line parameter set, and use the line parameter probability distribution model and call a preset parameter distribution algorithm to calculate each of the initial line impedance parameters to obtain the line parameter distribution results; use a preset parameter selection function and a preset parameter calculation function to determine the first parameter based on the distribution results of each of the line parameters, and use the target TPE algorithm, Bayes' theorem and a preset threshold to determine the second parameter based on the first parameter; determine the current line parameter set based on the second parameter, the function value obtained by processing the second parameter using the preset parameter calculation function, and the historical line parameter set, and update the current iteration number, and then determine whether the current iteration number meets the first preset condition. If it does, determine whether the second parameter meets the second preset condition. If it does, set the second parameter as the target parameter.

[0043] Therefore, this application first obtains several time-section intervals from the distribution space of the line parameters to be processed, and calculates the corresponding initial line impedance parameters by using a preset line impedance parameter calculation formula for each time-section data in the time-section intervals. Then, a line parameter probability distribution model is established based on the historical line parameter set. This model is then used to calculate each initial line impedance parameter using a preset parameter distribution algorithm, yielding the line parameter distribution results. Next, a first parameter is determined using a preset parameter selection function and a preset parameter calculation function, based on the distribution results of each line parameter. A second parameter is then determined using the target TPE algorithm, Bayes' theorem, and a preset threshold, based on the first parameter. Finally, the current line parameter set is determined based on the second parameter, the function value obtained by processing the second parameter using the preset parameter calculation function, and the historical line parameter set. The current iteration number is updated, and it is then determined whether the current iteration number meets the first preset condition. If it does, the second parameter is then determined to meet the second preset condition. If it does, the second parameter is set as the target parameter. This improves the accuracy of the line parameter values ​​and increases the speed of obtaining line parameters, thereby enhancing the user experience. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0045] Figure 1 This is a flowchart of a method for obtaining distribution network line parameters disclosed in this application;

[0046] Figure 2 This is a schematic diagram illustrating a specific low-voltage distribution network line parameter calculation method based on an adaptive adjustment tree structure Parsons estimator disclosed in this application;

[0047] Figure 3 This is a flowchart illustrating a specific SMBO method disclosed in this application;

[0048] Figure 4 This is a schematic diagram of the structure of a power distribution network line parameter acquisition device disclosed in this application;

[0049] Figure 5 This is a structural diagram of an electronic device disclosed in this application. Detailed Implementation

[0050] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0051] Currently, low-voltage distribution networks, as a crucial link connecting countless households, rely heavily on the accuracy of their line parameters for the stable operation of the entire power system. However, due to incomplete historical records and frequent upgrades and expansions of distribution networks, the line information of low-voltage distribution networks in many distribution network information management systems has become unclear. This information confusion not only affects the power flow calculation, fault location, and voltage management functions of the power grid but also leads to reduced power supply reliability and increased operation and maintenance costs. Therefore, this application provides a method for obtaining distribution network line parameters, which can improve the accuracy of line parameter values ​​and increase the speed of parameter acquisition.

[0052] See Figure 1 As shown in the figure, an embodiment of the present invention discloses a method for obtaining distribution network line parameters, including:

[0053] Step S11: Obtain several time section intervals in the distribution space of the line parameters to be processed in the target distribution network, and use the preset line impedance parameter calculation formula to calculate the data of each time section in the time section interval to obtain the corresponding initial line impedance parameters.

[0054] In this embodiment, a schematic diagram of low-voltage distribution network line parameter calculation based on an adaptive adjustment tree structure Parsons estimator is shown below. Figure 2 As shown:

[0055] First, this embodiment of the application requires initializing the parameter space of the lines in the network. Since multiple sets of network line parameters are required to perform parameter distribution estimation, this embodiment of the application needs to select multiple time intervals:

[0056] ;

[0057] in, , and All are time-section intervals, and each time-section interval contains m sets of time-section data. Specifically, several time-section intervals are obtained from the distribution space of the line parameters to be processed, and the data of each time-section interval is calculated using a preset line impedance parameter calculation formula to obtain the corresponding initial line impedance parameters. This may include: initializing the distribution space of the line parameters to be initialized to obtain the distribution space of the line parameters to be processed, and selecting several time-section intervals in the distribution space of the line parameters to be processed; the time-section interval includes time-section data; determining the voltage value, active power, and reactive power between two user nodes based on the time-section data, and determining the corresponding initial line impedance parameters using several preset line impedance parameter calculation formulas and based on the number of time-section data, voltage value, active power, and reactive power; the initial line impedance includes resistance value and reactance value.

[0058] In one specific implementation, the embodiments of this application can list m equations to solve for the line impedance parameters corresponding to the time section:

[0059] ;

[0060] in, This indicates the resistance on line 1. This represents the active power on line 1. This indicates the voltage on line 1. This indicates the resistance on line 2. This indicates the active power on line 2. This indicates the voltage on line 2. This indicates the reactive power on line 1. This represents the set of line impedance parameters at node 1. This represents the set of line impedance parameters at node 2. This indicates the reactive power on line 2.

[0061] ;

[0062] in, The voltage at the first time segment of node 1. The voltage at the first time segment of node 2. Let be the voltage at the m-th time segment of node 1. Let be the voltage at the m-th time segment of node 2. This represents the active power at the first time segment of node 1. The active power at the m-th time segment of node 1, This represents the active power at the first time segment of node 2. This represents the active power at the m-th time segment of node 2. This represents the reactive power at the first time segment of node 1. Let m be the reactive power at the m-th time segment of node 1. This represents the reactive power at the first time segment of node 2. This represents the reactive power at the m-th time segment of node 2.

[0063] ;

[0064] ;

[0065] in, , and All are custom coefficients. The solution represents the impedances of two lines [R1, X1, R2, X2].

[0066] ;

[0067] Here, RSS (Residual Sum of Squares) indicates that the line parameters are the solution under the residual sum of squares.

[0068] Furthermore, for multiple time intervals W, this embodiment of the application needs to calculate a set of network line parameters for each corresponding time interval. Assuming that the voltage, active power data and reactive power data are error-free, the line parameters calculated for each time interval are the same in principle. However, due to the existence of field measurement errors, there is noise in the data. Therefore, it is necessary to select an optimal set of impedance parameters that conforms to the node power flow distribution and minimizes the error result by constructing an initial parameter distribution space through multiple sets of line parameters.

[0069] Step S12: Establish a line parameter probability distribution model based on the historical line parameter set, and use the line parameter probability distribution model and call the preset parameter distribution algorithm to calculate the initial line impedance parameters to obtain the line parameter distribution results.

[0070] In this embodiment, SMBO (Sequential model-based optimization, i.e., a black-box function optimization method) is a Bayesian optimization method for continuous optimization. Each iteration introduces new hyperparameters, and the selection of these hyperparameters is based on previous results. By continuously iterating and updating the probability density distribution model, the output of the objective function can be optimized. Selected.

[0071] Furthermore, after calculating the corresponding initial line impedance parameters for each time segment data within the time segment interval using the preset line impedance parameter calculation formula, this embodiment of the application needs to train the initial line parameter probability distribution model and adjust the model parameters based on the pre-collected historical line parameter set to obtain the line parameter probability distribution model. Subsequently, the line parameter probability distribution model is used and a preset parameter distribution algorithm is called to calculate each initial line impedance parameter, thereby obtaining the line parameter distribution results, so that the first parameter can be determined subsequently using the preset parameter selection function and the preset parameter calculation function based on the distribution results of each line parameter. In one specific embodiment, the preset parameter distribution algorithm is a preset maximum likelihood estimation algorithm, the preset parameter selection function is an expected improvement function, and the preset parameter calculation function is a Loss function.

[0072] Step S13: Determine the first parameter using the preset parameter selection function and preset parameter calculation function based on the distribution results of each line parameter, and determine the second parameter using the target TPE algorithm, Bayes' theorem, and preset threshold based on the first parameter.

[0073] In this embodiment, the flowchart of the SMBO method is as follows: Figure 3 As shown:

[0074] First, based on the known historical parameter information H, a parameter probability distribution model is created; then, the acquisition function is used to select the next parameter from the distribution results. In one specific implementation, the expected improvement (EI) is used as the acquisition function, and its expression is as follows:

[0075] ;

[0076] in, This represents the expected improvement in the value of the loss function given parameters x, where x represents the set of impedance parameters for all lines in a distribution network, and y represents the calculated value of the loss function. For the preset threshold, Let y be the probability of y occurring given that x has occurred in the probability distribution model M. Specifically, determining the first parameter using a preset parameter selection function and a preset parameter calculation function, based on the distribution results of each line parameter, may include: processing the distribution results of each line parameter separately using the preset parameter calculation function to obtain the corresponding function value to be processed; determining the degree of improvement of the corresponding line parameter distribution results using a preset improvement degree determination algorithm based on each function value to be processed, obtaining the corresponding improvement degree result; and setting the parameter corresponding to the improvement degree result with the largest improvement degree among the improvement degree results as the first parameter using the preset parameter selection function.

[0077] Furthermore, after determining the first parameter using the preset parameter selection function and preset parameter calculation function and based on the distribution results of each line parameter, this embodiment of the application needs to use the target TPE algorithm, Bayes' theorem and preset threshold and based on the first parameter to determine the second parameter. Specifically, the process of determining the second parameter based on the first parameter using the target TPE algorithm, Bayes' theorem, and a preset threshold can include: modeling the probability result corresponding to the first parameter using the target TPE algorithm with a tree structure to obtain the corresponding first probability result; modeling the probability result corresponding to the first parameter under the condition of the corresponding function value to be processed using the target TPE algorithm and based on the preset threshold to obtain the corresponding second probability result; wherein, the comparison result corresponding to whether the function value to be processed is greater than the preset threshold determines the second probability result; calculating the first probability result, the second probability result, and the third probability result corresponding to the function value to be processed using Bayes' theorem to obtain the fourth probability result; the fourth probability result is the probability calculation result corresponding to the function value to be processed under the condition of the first parameter; determining the second parameter using a preset improvement degree determination algorithm based on the fourth probability result, the preset hyperparameter, the first probability density, and the second probability density; the first probability density is the probability density determined based on the initial line impedance parameter corresponding to the function value to be processed when the function value to be processed is less than the preset threshold; the second probability density is the probability density determined based on the initial line impedance parameter corresponding to the function value to be processed when the function value to be processed is not less than the preset threshold.

[0078] In one specific implementation, the objective function is a loss function during the parameter calculation process in this embodiment.

[0079] It is worth mentioning that the expression for Bayes' theorem is as follows:

[0080] ;

[0081] in, Let x be the probability of y occurring given that x has occurred. Let x be the probability of x occurring given that y has occurred. Let y be the probability of occurrence. Let x be the probability of occurrence.

[0082] In this embodiment, unlike the previous one... Instead of directly modeling, we use the Tree-structured Parzen Estimator (TPE) to... as well as Modeling, and calculating the y-value based on the loss function, yielded the following two parts for definition. In other words, TPE will set a hyperparameter. and set it as The quantiles, to the threshold y * The observation points x on both sides have different distributions, and their expressions are as follows:

[0083] ;

[0084] in, For the corresponding loss function value is less than The probability density is formed by the set of line impedance parameters, and g(x) is the probability density formed by the remaining observations. It is a certain quantile of the observed values, such that .

[0085] In this embodiment, the target TPE algorithm is used to model the probability result corresponding to the first parameter under the condition of the corresponding function value to be processed, based on a preset threshold, to obtain the corresponding second probability result. This may include: setting a preset hyperparameter based on user needs, and determining whether the function value to be processed is greater than the preset threshold. If the function value to be processed is not greater than the preset threshold, the preset hyperparameter is set to the value corresponding to the second probability result.

[0086] Furthermore, the expression obtained by the above partitioning operation in this embodiment is as follows:

[0087] ;

[0088] Furthermore, based on the above formula and the desired improved formula, we can obtain:

[0089] ;

[0090] From the above formula, we can obtain that The value of is inversely proportional to the value of the denominator in the above formula; that is, when is determined After that, the size of the denominator depends only on the ratio of the probabilities on both sides. Therefore, the parameters that need to be found in each iteration That is, to maximize the ratio. .

[0091] Step S14: Based on the second parameter, the function value obtained by processing the second parameter using the preset parameter calculation function, and the historical line parameter set, determine the current line parameter set, update the current iteration number, and then determine whether the current iteration number meets the first preset condition. If it does, determine whether the second parameter meets the second preset condition. If it does, set the second parameter as the target parameter.

[0092] In this embodiment, after obtaining new observations, the new observations are added to the historical parameter set. Simultaneously, this embodiment needs to determine whether a first termination condition is met. In one specific implementation, the first termination condition is whether K iterations have been performed. Specifically, the current line parameter set is determined based on the second parameter, the function value obtained by processing the second parameter using a preset parameter calculation function, and the historical line parameter set. The current iteration count is then updated. Next, it is determined whether the current iteration count meets the first preset condition. If it does, it is determined whether the second parameter meets the second preset condition. If it does, the second parameter is set as the target parameter. This can include: combining the second parameter, the function value to be processed, and the historical line parameter set using a preset combination rule to obtain the current line parameter set, and updating the current iteration count; determining whether the current iteration count is greater than a preset maximum iteration count. If the current iteration count is greater than the preset maximum iteration count, the Kth iteration is output. Two parameters are used to determine whether the difference between the loss calculation result corresponding to the second parameter and the value of the function to be processed corresponding to the second parameter is less than a preset value. If the difference is less than the preset value, the second parameter is set as the target parameter. If the difference is not less than the preset value, the values ​​of the functions to be processed are arranged in descending order, and a new parameter distribution space for the line to be processed is established based on the parameter distribution result corresponding to the arrangement result. This triggers the step of obtaining several time section intervals in the parameter distribution space for the line to be processed.

[0093] In one specific implementation, the value of K is 500. If the current iteration count is greater than 500, then the line parameters are... If the current iteration count is not greater than 500, proceed to the next iteration until the current iteration count is greater than 500, and output the optimal set of line impedance parameters. .

[0094] It is worth noting that, since the exact value of the line impedance cannot be directly obtained in this embodiment, it is impossible to directly verify whether the calculated line impedance is accurate. Therefore, this embodiment can apply the calculated line impedance to the network model to reverse-verify whether the power flow calculated under these impedance conditions matches the actual power flow. This method can indirectly evaluate the accuracy of the line impedance calculation, thereby ensuring that the parameter calculation of the distribution network is closer to the actual situation. That is, the parameters mentioned in the practice of parameter calculation in low-voltage distribution networks are a set of line parameters, and the loss function can be defined as the deviation of node voltage and current derived from the calculation of line parameters and network topology, and the sum of the above deviation and the actual measured value.

[0095] ;

[0096] in, This represents the sum of the absolute values ​​of the voltage deviations at m cross-sections. This represents the sum of the absolute values ​​of the current deviations across m cross-sections.

[0097] n represents the number of nodes in the network, and m represents the number of time segments for parameter calculation. , , For user node voltage amplitude, active power consumption, and reactive power consumption; This refers to the node voltage amplitude in the distribution network calculated based on the line impedance x.

[0098] In this embodiment, since the embodiment includes a large amount of historical data, the maximum likelihood estimation algorithm can be used to perform maximum likelihood estimation on the line parameter x to obtain the parameter distribution result. Subsequently, after obtaining the parameter distribution result, the above parameter distribution result is processed cyclically, and during the cyclic processing, it is determined whether the second termination condition is met.

[0099] ;

[0100] in, This is the current large loop count, and each large loop includes... This is a fixed, unchanging small cycle, and it has been tested extensively. .

[0101] If the loss result is less than 50, then the current line parameters will be adjusted. Set as If the loss result is not less than 50, a new line parameter distribution space is generated. .

[0102] In the process of generating a new line parameter distribution space, this embodiment of the application first needs to convert the current... The values ​​are sorted from largest to smallest, and the sorting formula is expressed as follows:

[0103] ;

[0104] Subsequently, based on the ranking results, a new set of line parameters is determined:

[0105] ;

[0106] Finally, the corresponding parameter distribution is calculated based on the new line parameters, thus obtaining... And update. The value of .

[0107] Therefore, the embodiments of this application first need to obtain several time-section intervals in the distribution space of the line parameters to be processed, and calculate the data of each time section in the time-section interval using a preset line impedance parameter calculation formula to obtain the corresponding initial line impedance parameters. Then, a line parameter probability distribution model is established based on the historical line parameter set. This model is then used to calculate each initial line impedance parameter using a preset parameter distribution algorithm to obtain the line parameter distribution results. Next, a first parameter is determined using a preset parameter selection function and a preset parameter calculation function based on the distribution results of each line parameter. A second parameter is then determined using the target TPE algorithm, Bayes' theorem, and a preset threshold based on the first parameter. Finally, the current line parameter set is determined based on the second parameter, the function value obtained by processing the second parameter using the preset parameter calculation function, and the historical line parameter set. The current iteration number is updated, and it is then determined whether the current iteration number meets the first preset condition. If it does, it is determined whether the second parameter meets the second preset condition. If it does, the second parameter is set as the target parameter. This improves the accuracy of the line parameter values ​​and increases the speed of obtaining the line parameters.

[0108] Accordingly, see Figure 4 As shown, this application also provides a device for obtaining parameters of a power distribution network line, comprising:

[0109] The time section interval acquisition module 11 is used to acquire several time section intervals in the distribution space of the line parameters to be processed, and to calculate the data of each time section in the time section interval using a preset line impedance parameter calculation formula to obtain the corresponding initial line impedance parameters.

[0110] The distribution result determination module 12 is used to establish a line parameter probability distribution model based on the historical line parameter set, and to use the line parameter probability distribution model and call the preset parameter distribution algorithm to calculate each of the initial line impedance parameters to obtain the line parameter distribution result;

[0111] The intermediate parameter determination module 13 is used to determine the first parameter by using a preset parameter selection function and a preset parameter calculation function and based on the distribution results of each line parameter, and to determine the second parameter by using the target TPE algorithm, Bayes' theorem and a preset threshold and based on the first parameter.

[0112] The target parameter determination module 14 is used to determine the current line parameter set based on the second parameter, the function value obtained by processing the second parameter by the preset parameter calculation function, and the historical line parameter set, and to update the current iteration number. Then, it is determined whether the current iteration number meets the first preset condition. If it does, it is determined whether the second parameter meets the second preset condition. If it does, the second parameter is set as the target parameter.

[0113] Therefore, the embodiments of this application first need to obtain several time-section intervals in the distribution space of the line parameters to be processed, and calculate the data of each time section in the time-section interval using a preset line impedance parameter calculation formula to obtain the corresponding initial line impedance parameters. Then, a line parameter probability distribution model is established based on the historical line parameter set. This model is then used to calculate each initial line impedance parameter using a preset parameter distribution algorithm to obtain the line parameter distribution results. Next, a first parameter is determined using a preset parameter selection function and a preset parameter calculation function based on the distribution results of each line parameter. A second parameter is then determined using the target TPE algorithm, Bayes' theorem, and a preset threshold based on the first parameter. Finally, the current line parameter set is determined based on the second parameter, the function value obtained by processing the second parameter using the preset parameter calculation function, and the historical line parameter set. The current iteration number is updated, and it is then determined whether the current iteration number meets the first preset condition. If it does, it is determined whether the second parameter meets the second preset condition. If it does, the second parameter is set as the target parameter. This improves the accuracy of the line parameter values ​​and increases the speed of obtaining the line parameters.

[0114] In some specific embodiments, the time section interval acquisition module 11 may specifically include:

[0115] The distribution space initialization unit is used to initialize the distribution space of line parameters to be initialized, obtain the distribution space of line parameters to be processed, and select several time segment intervals in the distribution space of line parameters to be processed; the time segment intervals include time segment data;

[0116] The line impedance parameter determination unit is used to determine the voltage value, active power and reactive power between two user nodes based on the time cross-sectional data, and to determine the corresponding initial line impedance parameters based on the number of time cross-sectional data, the voltage value, the active power and the reactive power using several preset line impedance parameter calculation formulas; the initial line impedance includes resistance value and reactance value.

[0117] In some specific embodiments, the intermediate parameter determination module 13 may specifically include:

[0118] The function value determination unit is used to process the distribution results of each line parameter using the preset parameter calculation function to obtain the corresponding function value to be processed;

[0119] The improvement degree result determination unit is used to determine the improvement degree of the corresponding line parameter distribution results by using a preset improvement degree determination algorithm and based on the values ​​of each of the functions to be processed, and to obtain the corresponding improvement degree result;

[0120] The first parameter determination unit is used to set the first parameter for the parameter corresponding to the improvement result with the largest improvement among the improvement results using a preset parameter selection function.

[0121] In some specific embodiments, the intermediate parameter determination module 13 may specifically include:

[0122] The first probability result determination unit is used to model the probability result corresponding to the first parameter using the target TPE algorithm with a tree structure, and obtain the corresponding first probability result.

[0123] The second probability result determination unit is used to model the probability result corresponding to the first parameter under the condition of the corresponding function value to be processed using the target TPE algorithm and based on the preset threshold, so as to obtain the corresponding second probability result; wherein, the comparison result corresponding to whether the function value to be processed is greater than the preset threshold determines the second probability result;

[0124] The third probability result determination unit is used to calculate the first probability result, the second probability result, and the third probability result corresponding to the corresponding value of the function to be processed using the Bayes theorem to obtain a fourth probability result; the fourth probability result is the probability calculation result corresponding to the value of the function to be processed under the conditions of the first parameter.

[0125] The second parameter determination unit is used to determine a second parameter based on the preset improvement degree determination algorithm and the fourth probability result, the preset hyperparameter, the first probability density, and the second probability density; the first probability density is the probability density determined based on the initial line impedance parameter corresponding to the function value to be processed when the value of the function to be processed is less than the preset threshold; the second probability density is the probability density determined based on the initial line impedance parameter corresponding to the function value to be processed when the value of the function to be processed is not less than the preset threshold.

[0126] In some specific embodiments, the intermediate parameter determination module 13 may specifically include:

[0127] The hyperparameter setting unit is used to set preset hyperparameters based on user requirements and determine whether the value of the function to be processed is greater than the preset threshold. If the value of the function to be processed is not greater than the preset threshold, the preset hyperparameters are set to the values ​​corresponding to the second probability results.

[0128] In some specific embodiments, the target parameter determination module 14 may specifically include:

[0129] The iteration count update unit is used to combine the second parameter, the function value to be processed, and the historical line parameter set using a preset combination rule to obtain the current line parameter set and update the current iteration count;

[0130] The iteration count determination unit is used to determine whether the current iteration count is greater than the preset maximum iteration count. If the current iteration count is greater than the preset maximum iteration count, the second parameter is output.

[0131] The target parameter determination subunit is used to determine whether the difference between the loss calculation result corresponding to the second parameter and the value of the function to be processed corresponding to the second parameter is less than a preset value. If the difference between the loss calculation result corresponding to the second parameter and the value of the function to be processed corresponding to the second parameter is less than the preset value, then the second parameter is set as the target parameter.

[0132] The function value sorting unit is used to sort the function values ​​to be processed in descending order if the difference between the loss calculation result corresponding to the second parameter and the function value to be processed corresponding to the second parameter is not less than the preset value, and to establish a new parameter distribution space of the line to be processed based on the parameter distribution result corresponding to the sorting result, and to trigger the step of obtaining several time section intervals in the parameter distribution space of the line to be processed.

[0133] Furthermore, embodiments of this application also disclose an electronic device, Figure 5This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content of the diagram should not be construed as limiting the scope of this application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 stores a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the power distribution line parameter acquisition method disclosed in any of the foregoing embodiments. Furthermore, the electronic device 20 in this embodiment may specifically be a computer.

[0134] In this embodiment, the power supply 23 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and external devices, and the communication protocol it follows can be any communication protocol applicable to the technical solution of this application, and is not specifically limited here; the input / output interface 25 is used to acquire external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs, and is not specifically limited here.

[0135] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk or optical disk, etc. The resources stored thereon can include operating system 221, computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0136] The operating system 221 is used to manage and control the various hardware devices on the electronic device 20 and the computer program 222, which may be Windows Server, Netware, Unix, Linux, etc. In addition to including a computer program capable of performing the power distribution line parameter acquisition method executed by the electronic device 20 as disclosed in any of the foregoing embodiments, the computer program 222 may further include a computer program capable of performing other specific tasks.

[0137] Furthermore, this application also discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the aforementioned method for obtaining distribution network line parameters. The specific steps of this method can be found in the corresponding content disclosed in the foregoing embodiments, and will not be repeated here.

[0138] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section.

[0139] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0140] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0141] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0142] The technical solutions provided in this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for obtaining parameters of a power distribution network line, characterized in that, include: Several time section intervals are obtained in the distribution space of the line parameters to be processed in the target distribution network, and the data of each time section in the time section interval are calculated using the preset line impedance parameter calculation formula to obtain the corresponding initial line impedance parameters. A probability distribution model of line parameters is established based on a set of historical line parameters. The line parameter probability distribution model is used to calculate the impedance parameters of each initial line by calling a preset parameter distribution algorithm, so as to obtain the line parameter distribution results. A first parameter is determined using a preset parameter selection function and a preset parameter calculation function, based on the distribution results of each line parameter. A second parameter is then determined using a target TPE algorithm, Bayes' theorem, and a preset threshold, based on the first parameter. Specifically, a tree-structured target TPE algorithm is used to model the probability result corresponding to the first parameter, yielding a first probability result. The target TPE algorithm, based on the preset threshold, is used to model the probability result corresponding to the first parameter under the condition of the corresponding function value to be processed, yielding a second probability result. The comparison result corresponding to whether the function value to be processed is greater than the preset threshold determines the second probability result. Bayes' theorem is used to model the probability result of the first probability result, ... The second probability result is calculated together with the third probability result corresponding to the function value to be processed to obtain a fourth probability result; the fourth probability result is the probability calculation result corresponding to the function value to be processed under the conditions of the first parameter; a preset improvement degree determination algorithm is used to determine the second parameter based on the fourth probability result, the preset hyperparameter, the first probability density, and the second probability density; the first probability density is the probability density determined based on the initial line impedance parameter corresponding to the function value to be processed when the function value to be processed is less than the preset threshold; the second probability density is the probability density determined based on the initial line impedance parameter corresponding to the function value to be processed when the function value to be processed is not less than the preset threshold. Based on the second parameter, the function value obtained by processing the second parameter using the preset parameter calculation function, and the historical line parameter set, the current line parameter set is determined, and the current iteration number is updated. Then, it is determined whether the current iteration number meets the first preset condition. If it does, it is determined whether the second parameter meets the second preset condition. If it does, the second parameter is set as the target parameter.

2. The method for obtaining distribution network line parameters according to claim 1, characterized in that, The process involves obtaining several time-section intervals within the distribution space of the line parameters to be processed in the target distribution network, and calculating the corresponding initial line impedance parameters for each time-section data within the time-section intervals using a preset line impedance parameter calculation formula. This includes: An initialization operation is performed on the distribution space of line parameters to be initialized to obtain the distribution space of line parameters to be processed, and several time segment intervals are selected in the distribution space of line parameters to be processed; the time segment intervals include time segment data; Based on the time-series data, the voltage, active power, and reactive power between the two user nodes are determined. Several preset line impedance parameter calculation formulas are used to determine the corresponding initial line impedance parameters based on the number of time-series data, the voltage, the active power, and the reactive power. The initial line impedance includes resistance and reactance values.

3. The method for obtaining distribution network line parameters according to any one of claims 1 to 2, characterized in that, The step of determining the first parameter using a preset parameter selection function and a preset parameter calculation function, and based on the distribution results of each line parameter, includes: The preset parameter calculation function is used to process the distribution results of each line parameter to obtain the corresponding function value to be processed; A preset improvement degree determination algorithm is used to determine the improvement degree of the corresponding line parameter distribution results based on the values ​​of each of the functions to be processed, thereby obtaining the corresponding improvement degree results; The parameter corresponding to the improvement result with the greatest improvement among the various improvement results is set as the first parameter using a preset parameter selection function.

4. The method for obtaining distribution network line parameters according to claim 3, characterized in that, The step of modeling the probability result corresponding to the first parameter under the condition of the corresponding function value to be processed using the target TPE algorithm and based on the preset threshold to obtain the corresponding second probability result includes: Based on user needs, preset hyperparameters are set, and it is determined whether the value of the function to be processed is greater than the preset threshold. If the value of the function to be processed is not greater than the preset threshold, the preset hyperparameters are set to the value corresponding to the second probability result.

5. The method for obtaining distribution network line parameters according to claim 4, characterized in that, The process involves determining the current line parameter set based on the function value obtained by processing the second parameter using the preset parameter calculation function and the historical line parameter set, updating the current iteration number, and then determining whether the current iteration number satisfies the first preset condition. If it does, the process further determines whether the second parameter satisfies the second preset condition. If it does, the process sets the second parameter as the target parameter, including: The second parameter, the function value to be processed, and the historical line parameter set are combined using a preset combination rule to obtain the current line parameter set, and the current iteration number is updated. Determine whether the current iteration count is greater than the preset maximum iteration count. If the current iteration count is greater than the preset maximum iteration count, output the second parameter. Determine whether the difference between the loss calculation result corresponding to the second parameter and the value of the function to be processed corresponding to the second parameter is less than a preset value. If the difference between the loss calculation result corresponding to the second parameter and the value of the function to be processed corresponding to the second parameter is less than the preset value, then set the second parameter as the target parameter. If the difference between the loss calculation result corresponding to the second parameter and the value of the function to be processed corresponding to the second parameter is not less than the preset value, then the values ​​of the function to be processed are arranged in descending order, and a new distribution space of line parameters to be processed is established based on the parameter distribution result corresponding to the obtained arrangement result, and the step of obtaining several time section intervals in the distribution space of line parameters to be processed in the target distribution network is triggered.

6. The method for obtaining distribution network line parameters according to claim 5, characterized in that, The preset parameter distribution algorithm is a preset maximum likelihood estimation algorithm, the preset parameter selection function is an expected improvement function, and the preset parameter calculation function is a loss function.

7. A device for acquiring parameters of a power distribution network line, characterized in that, include: The time section interval acquisition module is used to acquire several time section intervals in the distribution space of the line parameters to be processed, and to calculate the data of each time section in the time section interval using a preset line impedance parameter calculation formula to obtain the corresponding initial line impedance parameters. The distribution result determination module is used to establish a line parameter probability distribution model based on the historical line parameter set, and to use the line parameter probability distribution model and call a preset parameter distribution algorithm to calculate the initial line impedance parameters to obtain the line parameter distribution result; The intermediate parameter determination module is used to determine a first parameter based on the distribution results of each line parameter using a preset parameter selection function and a preset parameter calculation function, and to determine a second parameter based on the first parameter using a target TPE algorithm, Bayes' theorem, and a preset threshold. Specifically, it uses a tree-structured target TPE algorithm to model the probability result corresponding to the first parameter to obtain a corresponding first probability result; it uses the target TPE algorithm and the preset threshold to model the probability result corresponding to the first parameter under the condition of the corresponding function value to be processed to obtain a corresponding second probability result; wherein, whether the function value to be processed is greater than the preset threshold determines the second probability result; and it uses Bayes' theorem to model the probability result of the first parameter. A fourth probability result is obtained by calculating the probability of the first probability result, the second probability result, and the third probability result corresponding to the value of the function to be processed; the fourth probability result is the probability calculation result corresponding to the value of the function to be processed under the conditions of the first parameter; a second parameter is determined by using a preset improvement degree determination algorithm and based on the fourth probability result, preset hyperparameters, a first probability density, and a second probability density; the first probability density is the probability density determined based on the initial line impedance parameter corresponding to the value of the function to be processed when the value of the function to be processed is less than the preset threshold; the second probability density is the probability density determined based on the initial line impedance parameter corresponding to the value of the function to be processed when the value of the function to be processed is not less than the preset threshold. The target parameter determination module is used to determine the current line parameter set based on the second parameter, the function value obtained by processing the second parameter by the preset parameter calculation function, and the historical line parameter set, and to update the current iteration number. Then, it is determined whether the current iteration number meets the first preset condition. If it does, it is determined whether the second parameter meets the second preset condition. If it does, the second parameter is set as the target parameter.

8. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor is configured to execute the computer program to implement the steps of the method for obtaining power distribution line parameters as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, Used to store a computer program, wherein the computer program, when executed by a processor, implements the steps of the method for obtaining power distribution line parameters as described in any one of claims 1 to 6.

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