Power distribution network line parameter acquisition method and device, equipment and storage medium

Abstract

The invention discloses a power distribution network line parameter acquisition method and device, equipment and a storage medium, and relates to the technical field of circuits, and the method comprises the steps: determining an initial line impedance parameter based on the data of each time section corresponding to a target power distribution network; calculating the initial line impedance parameters by using a line parameter probability distribution model established based on a historical line parameter set to obtain a line parameter distribution result; determining a first parameter based on the line parameter distribution result, and determining a second parameter based on the first parameter by using a target TPE algorithm, a Bayesian theorem and a preset threshold; determining a current line parameter set based on the second parameter, a function value obtained by processing the second parameter through a preset parameter calculation function and a historical line parameter set, updating the current iteration frequency, judging whether the current iteration frequency and the second parameter meet a first preset condition and a second preset condition respectively, and if both are met, executing the step 3; the second parameter is the target parameter. Therefore, the accuracy of the obtained line parameters can be improved.

Description

Technical Field

[0001] The present invention relates to the field of circuit technologies, and particularly to a method, device, equipment, and storage medium for obtaining line parameters of a distribution network. Background Art

[0002] In the rapidly developing power industry today, as a key link connecting thousands of households, the accuracy of the line parameters of the low-voltage distribution network is crucial for the stable operation of the entire power system. However, due to the incomplete historical records and the frequent renovation and expansion of the distribution network, the line information of the low-voltage distribution network in many distribution management systems (DMS) has become unclear. This information chaos not only affects the power flow calculation function, fault location function, and voltage management function of the power grid, but also leads to a reduction in power supply reliability and an increase in operation and maintenance costs.

[0003] Currently, the following three methods are used to determine the line parameters of the low-voltage distribution network: The first method is to assume that the line impedance information is known and make corrections based on this. However, in actual 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 method.

[0004] The second method relies on installing phasor measurement units (PMUs) at both ends of the line and calculating the impedance parameters of the line by back-calculating the angle information measured by the phasor measurement units. However, this method requires additional equipment investment, which is economically unfeasible when deployed on a large scale.

[0005] The third method is to perform an inverse operation of the least squares solution of the line impedance on the data collected by the advanced metering infrastructure (AIM) or smart meters by solving the power flow equation of the line. However, the above method is very sensitive to the quality and integrity of the data, and any inaccurate or missing data may affect the accuracy of the calculation results.

[0006] As can be seen from the above, how to improve the accuracy of the obtained line parameters in the process of obtaining the line parameters of the distribution network is an urgent problem to be solved at present. Summary of the Invention

[0007] In view of this, the purpose of the present invention is to provide a method, device, equipment, and storage medium for obtaining line parameters of a distribution network, which can improve the accuracy of the line parameter values and enhance the speed of obtaining the line parameters. The specific solutions are as follows:

[0008] In a first aspect, the present application provides a method for obtaining distribution network line parameters, including:

[0009] Obtain a number of time-section intervals in the distribution space of the line parameters to be processed of the target distribution network, and calculate each time-section data in the time-section intervals by using a preset line impedance parameter calculation formula to obtain corresponding initial line impedance parameters;

[0010] 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 a line parameter distribution result;

[0011] Use a preset parameter selection function and a preset parameter calculation function and determine a first parameter based on each of the line parameter distribution results, and use a target TPE algorithm, Bayes' theorem and a preset threshold and determine a second parameter based on the first parameter;

[0012] Determine a 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 update the current iteration number, and then determine whether the current iteration number meets a first preset condition. If it meets, determine whether the second parameter meets a second preset condition. If it meets, set the second parameter as the target parameter.

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

[0014] Perform an initialization operation on the line parameter distribution space to be initialized to obtain a line parameter distribution space to be processed, and select a number of time-section intervals in the line parameter distribution space to be processed; the time-section intervals include time-section data;

[0015] Determine the voltage value, active power and reactive power between two user nodes based on the time-section data, and use a number of preset line impedance parameter calculation formulas and determine corresponding initial line impedance parameters based on the quantity of the time-section data, the voltage value, the active power, the reactive power; the initial line impedance includes a resistance value and a reactance value.

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

[0017] Process each of the line parameter distribution results using the preset parameter calculation function to obtain corresponding function values to be processed;

[0018] Use a preset improvement degree determination algorithm and based on each of the function values to be processed, determine the improvement degree of the corresponding line parameter distribution result to obtain corresponding improvement degree results;

[0019] Use a preset parameter selection function to set the parameter corresponding to the improvement degree result with the largest improvement degree among each of the improvement degree results as the first parameter.

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

[0021] Use the target TPE algorithm with a tree structure to model the probability result corresponding to the first parameter to obtain a corresponding first probability result;

[0022] Use the target TPE algorithm and based on 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, the comparison result of whether the function value to be processed is greater than the preset threshold determines the second probability result;

[0023] Use Bayes' theorem to calculate a third probability result corresponding to the first probability result, the second probability result and the corresponding 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 condition of the first parameter;

[0024] Use the preset improvement degree determination algorithm and based on the fourth probability result, preset hyperparameters, first probability density and second probability density to determine the second parameter; 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.

[0025] Optionally, the using the target TPE algorithm and based on 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 includes:

[0026] Set preset hyperparameters based on user requirements, and determine 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, set the preset hyperparameters to the value corresponding to the second probability result.

[0027] Optionally, 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, updating the current iteration count, and then determining whether the current iteration count meets a first preset condition. If it meets, determine whether the second parameter meets a second preset condition. If it meets, set the second parameter as the target parameter, including:

[0028] 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;

[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 function value 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 function value to be processed corresponding to the second parameter is less than the preset value, set the second parameter as the target parameter;

[0031] 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, arrange the function values to be processed in descending order, establish a new distribution space of line parameters to be processed based on the parameter distribution result corresponding to the obtained arrangement result, and trigger the step of obtaining several time-section intervals in the distribution space of line parameters to be processed.

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

[0033] In a second aspect, the present application provides a device for obtaining distribution network line parameters, including:

[0034] A time-section interval obtaining module, configured to obtain several time-section intervals in the distribution space of line parameters to be processed, and calculate the corresponding initial line impedance parameters for each time-section data in the time-section intervals using a preset line impedance parameter calculation formula;

[0035] A distribution result determination module, configured to establish a line parameter probability distribution model based on a 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 a line parameter distribution result;

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

[0037] A target parameter determination module, configured to determine a current line parameter set based on the second parameter, a function value obtained by processing the second parameter by 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 satisfies a first preset condition. If it satisfies, then determine whether the second parameter satisfies a second preset condition. If it satisfies, then set the second parameter as the target parameter.

[0038] In a third aspect, the present application provides an electronic device, including:

[0039] A memory, configured to store a computer program;

[0040] A processor, configured to execute the computer program to implement the foregoing method for obtaining distribution network line parameters.

[0041] In a fourth aspect, the present application provides a computer-readable storage medium, configured to store a computer program, wherein the computer program, when executed by a processor, implements the foregoing method for obtaining distribution network line parameters.

[0042] As can be seen from the above, before obtaining the distribution network line parameters in this application, it is necessary 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 each time-section data in the time-section interval to obtain the corresponding initial line impedance parameters; based on the historical line parameter set, a line parameter probability distribution model is established to 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 a line parameter distribution result; use a preset parameter selection function and a preset parameter calculation function and determine a first parameter based on each of the line parameter distribution results, and use a target TPE algorithm, Bayes' theorem and a preset threshold and determine a second parameter based on the first parameter; 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, determine the current line parameter set, and update the current iteration number, and then determine whether the current iteration number meets a first preset condition. If it meets, then determine whether the second parameter meets a second preset condition. If it meets, then set the second parameter as the target parameter.

[0043] As can be seen, this application first 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 each time-section data in the time-section interval to obtain the corresponding initial line impedance parameters; subsequently, based on the historical line parameter set, a line parameter probability distribution model is established to 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 a line parameter distribution result; then, use a preset parameter selection function and a preset parameter calculation function and determine a first parameter based on each of the line parameter distribution results, and use a target TPE algorithm, Bayes' theorem and a preset threshold and determine a second parameter based on the first parameter; finally, 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, determine the current line parameter set, and update the current iteration number, and then determine whether the current iteration number meets a first preset condition. If it meets, then determine whether the second parameter meets a second preset condition. If it meets, then set the second parameter as the target parameter. In this way, the accuracy of the line parameter value is improved, the speed of obtaining the line parameters is increased, and thus the user experience is enhanced. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained according to the provided drawings without creative efforts.

[0045] Figure 1 Flow chart of a method for obtaining distribution network line parameters disclosed in this application;

[0046] Figure 2 Schematic diagram of calculating low - voltage distribution network line parameters based on an adaptive - adjustment tree - structured Parson estimator disclosed in this application;

[0047] Figure 3 Schematic flow chart of a specific SMBO method disclosed in this application;

[0048] Figure 4 Schematic diagram of the structure of a device for obtaining distribution network line parameters disclosed in this application;

[0049] Figure 5 Schematic diagram of the structure of an electronic device disclosed in this application. Detailed implementation manners

[0050] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0051] At present, as a key link connecting thousands of households, the accuracy of the line parameters of the low - voltage distribution network is crucial for the stable operation of the entire power system. However, due to the incomplete historical records and the frequent renovation and expansion of the distribution network, the line information of the low - voltage distribution network in many distribution network information management systems has become blurred. This information chaos not only affects the power flow calculation function, fault location function, and voltage management function of the power grid, but also leads to a reduction in power supply reliability and an increase in operation and maintenance costs. Therefore, this application provides a method for obtaining distribution network line parameters, which can improve the accuracy rate of line parameter values and the speed of obtaining line parameters.

[0052] See Figure 1 As shown, 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 of the target distribution network, and calculate each time - section data in the time - section intervals by using a preset line impedance parameter calculation formula to obtain the corresponding initial line impedance parameters.

[0054] In this embodiment, the schematic diagram of calculating low - voltage distribution network line parameters based on an adaptive - adjustment tree - structured Parson estimator is as Figure 2 shown:

[0055] First, the embodiments of the present application need to initialize the parameter space of the lines in the network. . Since multiple sets of line parameters of the network need to be obtained to perform the operation of estimating the parameter distribution, the embodiments of the present application need to select multiple time-section intervals:

[0056] ;

[0057] Among them, , and are all time-section intervals, and each time-section interval contains m sets of time-section data. Specifically, several time-section intervals are obtained in the line parameter distribution space to be processed, and the preset line impedance parameter calculation formula is used to calculate each time-section data in the time-section interval to obtain the corresponding initial line impedance parameters, which may include: performing an initialization operation on the line parameter distribution space to be initialized to obtain the line parameter distribution space to be processed, and selecting several time-section intervals in the line parameter distribution space 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 using several preset line impedance parameter calculation formulas and based on the quantity, voltage value, active power, and reactive power of the time-section data to determine the corresponding initial line impedance parameters; the initial line impedance includes a resistance value and a reactance value.

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

[0059] ;

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

[0061] ;

[0062] Among them, is the voltage of the first time section of node 1, is the voltage at the first time section of node 2, is the voltage at the m-th time section of node 1, is the voltage at the m-th time section of node 2, is the active power at the first time section of node 1, is the active power at the m-th time section of node 1, represents the active power at the first time section of node 2, represents the active power at the m-th time section of node 2, is the reactive power at the first time section of node 1, is the reactive power at the m-th time section of node 1, represents the reactive power at the first time section of node 2, represents the reactive power at the m-th time section of node 2.

[0063] ;

[0064] ;

[0065] Among them, , and are all user-defined coefficients, represents the solution of the impedance of two lines [R1, X1, R2, X2]

[0066] ;

[0067] Among them, RSS (Residual Sum of Squares) represents the solution of the line parameters under the residual sum of squares.

[0068] Furthermore, for multiple time section intervals W, the embodiments of the present application need to calculate a corresponding set of network line parameters for each. Assuming that the voltage, active power data and reactive power data are error-free data, according to the principle, the line parameters calculated for each time section are the same. However, due to the existence of on-site measurement errors, there is noise in the data. Therefore, it is necessary to finally select a set of optimal foot impedance parameter sets that conform to the node power flow distribution and minimize the error result through the initial parameter distribution space constructed by multiple sets of line parameter sets.

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

[0070] In this embodiment, SMBO (Sequential model-based optimization, a black-box function optimization method) is a Bayesian optimization method for continuous optimization. Each iteration is a new hyperparameter, and the selection of the above hyperparameters is based on previous results. By continuously iterating and updating the probability density distribution model, the output result of the objective function is selected.

[0071] Furthermore, after calculating the corresponding initial line impedance parameters by using the preset line impedance parameter calculation formula for each time-section data in the time-section interval, the embodiment of the present 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, using the line parameter probability distribution model and calling the preset parameter distribution algorithm to calculate each initial line impedance parameter, so as to obtain the line parameter distribution result, so as to subsequently use the preset parameter selection function and the preset parameter calculation function and determine the first parameter based on each line parameter distribution result. In a specific implementation manner, the preset parameter distribution algorithm is the preset maximum likelihood estimation algorithm, the preset parameter selection function is the expected improvement function, and the preset parameter calculation function is the Loss (Loss Function) loss function.

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

[0073] In this embodiment, the flow schematic diagram of the SMBO method is as Figure 3 shown:

[0074] First, according to the known historical parameter information H, create a parameter probability distribution model; subsequently, it is necessary to use the acquisition function to select the next parameter in the distribution result . In a specific implementation manner, the expected improvement (EI) is used as the acquisition function, and the expression is as follows:

[0075] ;

[0076] Among them, represents the expected improvement degree of the expected loss function value under the given parameter x, x represents a set of all line impedance parameters in a distribution network, y represents the value calculated by the loss function, is the preset threshold, is the probability that y occurs when x occurs in the probability distribution model M. Specifically, using the preset parameter selection function and the preset parameter calculation function and determining the first parameter based on the distribution results of each line parameter may include: using the preset parameter calculation function to process the distribution results of each line parameter respectively to obtain the corresponding function values to be processed; using the preset improvement degree determination algorithm and determining the improvement degree of the corresponding line parameter distribution result based on each function value to be processed to obtain the corresponding improvement degree result; using the preset parameter selection function to set the parameter corresponding to the improvement degree result with the largest improvement degree among each improvement degree result as the first parameter.

[0077] Further, after determining the first parameter by using the preset parameter selection function and the preset parameter calculation function and based on the distribution results of each line parameter, the embodiment of the present application needs to determine the second parameter by using the target TPE algorithm, Bayes' theorem and a preset threshold and based on the first parameter. Specifically, determining the second parameter by using the target TPE algorithm, Bayes' theorem and a preset threshold and based on the first parameter may include: using the target TPE algorithm with a tree structure to model the probability result corresponding to the first parameter to obtain the corresponding first probability result; using the target TPE algorithm and based on 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 the corresponding second probability result; wherein, the comparison result of whether the function value to be processed is greater than the preset threshold determines the second probability result; using Bayes' theorem to calculate the first probability result, the second probability result and the third probability result corresponding to the corresponding function value to be processed 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; using the preset improvement degree determination algorithm and determining 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.

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

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

[0080] ;

[0081] wherein, is the probability that y occurs when x occurs, is the probability that x occurs when y occurs, is the probability that y occurs, The probability of x occurring.

[0082] In this embodiment, different from directly performing modeling, instead, the Tree-structured Parzen Estimator (TPE) is used to as well as model, and based on the y value calculated by the loss function, the following two distributions are generated to define , that is, TPE will set a hyperparameter , and set it to the quantile of, so as to construct different distributions for the observation points x on both sides of the threshold y * , and the expression is as follows:

[0083] ;

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

[0085] In this embodiment, using the target TPE algorithm and based on a preset threshold to model the probability result corresponding to the first parameter under the condition of the corresponding function value to be processed, and obtaining the corresponding second probability result may include: setting a preset hyperparameter based on user requirements, 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, then set the preset hyperparameter to the value corresponding to the second probability result.

[0086] Furthermore, the expression obtained by the above division operation in the embodiment of the present application is as follows:

[0087] ;

[0088] Furthermore, based on the above formula and the expected improvement formula, it can be obtained that:

[0089] ;

[0090] From the above formula, it can be seen that the numerical size of is inversely proportional to the numerical size of the denominator in the above formula, that is, when determining , the size of the denominator only depends on the ratio of the two-end probabilities , so, the parameter to be searched for each iteration is the one that makes this ratio the largest。

[0091] Step S14: 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 count. Then, determine whether the current iteration count 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 the new observation value, add the obtained new observation value to the historical parameter set. At the same time, it is necessary to determine whether the first termination condition is met in the embodiments of the present application. In a specific implementation, the first termination condition is whether it has been iterated K times. Specifically, 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 count, and then determining whether the current iteration count meets the first preset condition. If it does, determine whether the second parameter meets the second preset condition. If it does, setting the second parameter as the target parameter may include: combining the second parameter, the function value to be processed, and the historical line parameter set using the 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 the preset maximum iteration count. If the current iteration count is greater than the preset maximum iteration count, output the second parameter; determining whether 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 less than the preset value. 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 less than the preset value, set the second parameter as the target parameter; 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, arrange the function values to be processed in descending order, and establish a new line parameter distribution space to be processed based on the parameter distribution result corresponding to the obtained arrangement result, and trigger the step of obtaining several time section intervals in the line parameter distribution space to be processed.

[0093] In a specific implementation, the value of K is 500. If the current iteration count is greater than 500, then the line parameters ; if the current iteration count is not greater than 500, perform the next iteration operation until the current iteration count is greater than 500, and output the optimal line impedance parameter set 。

[0094] It should be noted that since the exact value of the line impedance cannot be directly obtained in the embodiments of the present application, it is impossible to directly verify whether the calculated line impedance is accurate. Therefore, in the embodiments of the present application, the calculated line impedance can be applied to the network model to inversely verify whether the power flow calculated under these impedance conditions matches the actual power flow situation. This method can indirectly evaluate the accuracy of line impedance calculation, thereby ensuring that the parameter calculation of the distribution network is closer to the actual situation. That is, in the practice of parameter calculation in the low-voltage distribution network, the parameters mentioned are a set of line parameters, and the loss function can be defined as being used to calculate the line parameters and network topology structure to derive the sum of the deviations between the node voltages and currents, and the sum of the above deviations and the actual measured values.

[0095] ;

[0096] Among them, represents the sum of the absolute values of the voltage deviation values of m sections, represents the sum of the absolute values of the current deviation values of m sections.

[0097] n represents the number of nodes in the network, and m represents the number of time sections for parameter calculation. , , are the user node voltage amplitude, active power consumption, and reactive power consumption; is the node voltage amplitude in the distribution network calculated according to the line impedance x.

[0098] In this embodiment, since there is a large amount of historical data in the embodiments of the present application, 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 in a loop, and it is judged whether the second termination condition is satisfied during the loop processing:

[0099] ;

[0100] Among them, is the current large loop count, and each large loop includes fixed small loops, and after a large number of experiments, .

[0101] If the loss result is less than 50, the current line parameter is set to , if the loss result is not less than 50, a new line parameter distribution space is generated.

[0102] Among them, in the process of generating a new line parameter distribution space, the embodiments of the present application first need to sort the current

[0103] values from large to small, and the expression of the sorting formula is as follows:

[0104] Subsequently, based on the size of the sorting result, a group of new line parameters are determined:

[0105] ;

[0106] Finally, based on the new line parameters, the corresponding parameter distribution is calculated to obtain . And update value.

[0107] It can be seen that the embodiments of the present application first need to obtain several time-section intervals in the line parameter distribution space to be processed, and use a preset line impedance parameter calculation formula to calculate each time-section data in the time-section intervals to obtain the corresponding initial line impedance parameters; subsequently, establish a line parameter probability distribution model based on the historical line parameter set to use the line parameter probability distribution model and call a preset parameter distribution algorithm to calculate each initial line impedance parameter to obtain a line parameter distribution result; then, use a preset parameter selection function and a preset parameter calculation function and determine a first parameter based on each line parameter distribution result, and use a target TPE algorithm, Bayes' theorem and a preset threshold to determine a second parameter based on the first parameter; finally, 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 update the current iteration number, and then determine whether the current iteration number meets the first preset condition. If it meets, determine whether the second parameter meets the second preset condition. If it meets, set the second parameter as the target parameter. In this way, the accuracy of the line parameter value is improved, and the speed of obtaining the line parameter is increased.

[0108] Correspondingly, as shown in Figure 4 , the present application also provides a device for obtaining distribution network line parameters, including:

[0109] A time-section interval obtaining module 11, configured to obtain several time-section intervals in the line parameter distribution space to be processed, and use a preset line impedance parameter calculation formula to calculate each time-section data in the time-section intervals to obtain the corresponding initial line impedance parameters;

[0110] A distribution result determination module 12, configured to establish a line parameter probability distribution model based on a historical line parameter set, so as to calculate each of the initial line impedance parameters by using the line parameter probability distribution model and invoking a preset parameter distribution algorithm, and obtain a line parameter distribution result;

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

[0112] A target parameter determination module 14, configured to determine a current line parameter set based on the second parameter, a function value obtained by processing the second parameter by 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 a first preset condition. If it meets, determine whether the second parameter meets a second preset condition. If it meets, set the second parameter as the target parameter.

[0113] It can be seen that in the embodiment of the present application, first, a plurality of time section intervals need to be obtained in a line parameter distribution space to be processed, and each time section data in the time section intervals is calculated by using a preset line impedance parameter calculation formula to obtain corresponding initial line impedance parameters; subsequently, a line parameter probability distribution model is established based on a historical line parameter set, so as to calculate each initial line impedance parameter by using the line parameter probability distribution model and invoking a preset parameter distribution algorithm, and obtain a line parameter distribution result; then, a first parameter is determined by using a preset parameter selection function and a preset parameter calculation function and based on each line parameter distribution result, and a second parameter is determined by using a target TPE algorithm, Bayes' theorem and a preset threshold and based on the first parameter; finally, a current line parameter set is determined based on the second parameter, a function value obtained by processing the second parameter by using the preset parameter calculation function, and the historical line parameter set, and the current iteration number is updated, and then it is determined whether the current iteration number meets a first preset condition. If it meets, determine whether the second parameter meets a second preset condition. If it meets, set the second parameter as the target parameter. In this way, the accuracy of the line parameter value is improved, and the speed of obtaining the line parameter is increased.

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

[0115] A distribution space initialization unit, configured to perform an initialization operation on a line parameter distribution space to be initialized, obtain a line parameter distribution space to be processed, and select a plurality of time section intervals in the line parameter distribution space to be processed; the time section intervals include time section data;

[0116] A line impedance parameter determination unit, configured to determine the voltage value, active power, and reactive power between two user nodes based on the time-section data, and use a number of preset line impedance parameter calculation formulas to determine corresponding initial line impedance parameters based on the quantity of the time-section data, the voltage value, the active power, and the reactive power; the initial line impedance includes a resistance value and an inductive reactance value.

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

[0118] A function value determination unit, configured to process each of the line parameter distribution results by using the preset parameter calculation function to obtain corresponding function values to be processed;

[0119] An improvement degree result determination unit, configured to determine the improvement degree of the corresponding line parameter distribution result based on each of the function values to be processed by using a preset improvement degree determination algorithm, to obtain a corresponding improvement degree result;

[0120] A first parameter determination unit, configured to set the parameter corresponding to the improvement degree result with the largest improvement degree among each of the improvement degree results as a first parameter by using a preset parameter selection function.

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

[0122] A first probability result determination unit, configured to model the probability result corresponding to the first parameter by using a target TPE algorithm with a tree structure to obtain a corresponding first probability result;

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

[0124] A third probability result determination unit, configured to calculate a fourth probability result of the first probability result, the second probability result, and the third probability result corresponding to the corresponding function value to be processed by using the Bayesian theorem; the fourth probability result is the probability calculation result corresponding to the function value to be processed under the condition of the first parameter;

[0125] A second parameter determination unit, configured to use the preset improvement degree determination algorithm and determine a second parameter based on the fourth probability result, preset hyperparameters, first probability density, and second probability density; the first probability density is a 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 a 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.

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

[0127] A hyperparameter setting unit, configured to set preset hyperparameters based on user requirements, and determine 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, set the preset hyperparameters to the value corresponding to the fourth probability result.

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

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

[0130] An iteration number judgment unit, configured to determine whether the current iteration number is greater than a preset maximum iteration number. If the current iteration number is greater than the preset maximum iteration number, output the second parameter;

[0131] A target parameter determination subunit, configured to determine whether 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 less than a preset value. 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 less than the preset value, set the second parameter as the target parameter;

[0132] A function value sorting unit, configured to, 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, arrange the function values to be processed in descending order, and establish a new to-be-processed line parameter distribution space based on the parameter distribution result corresponding to the obtained arrangement result, and trigger the step of obtaining several time section intervals in the to-be-processed line parameter distribution space.

[0133] Furthermore, an embodiment of the present application also discloses an electronic device, Figure 5It is a structural diagram of an electronic device 20 shown according to an exemplary embodiment. The content in the figure should not be considered as any limitation on the scope of use 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. Among them, the memory 22 is used to store a computer program, and the computer program is loaded and executed by the processor 21 to implement the relevant steps in the method for obtaining distribution network line parameters disclosed in any of the foregoing embodiments. Additionally, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0134] In this embodiment, the power supply 23 is used to provide operating voltages 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 specific limitations are not imposed here; the input / output interface 25 is used to obtain external input data or output data to the outside, and its specific interface type can be selected according to specific application requirements, and no specific limitations are made here.

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

[0136] Among them, the operating system 221 is used to manage and control each hardware device and the computer program 222 on the electronic device 20, and it can be Windows Server, Netware, Unix, Linux, etc. The computer program 222, in addition to including a computer program capable of completing the method for obtaining distribution network line parameters executed by the electronic device 20 disclosed in any of the foregoing embodiments, may further include a computer program capable of completing 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 method for obtaining distribution network line parameters disclosed above. For the specific steps of this method, reference can be made to the corresponding content disclosed in the foregoing embodiments, and details will not be elaborated here.

[0138] In this specification, each embodiment is described in a progressive manner. The key point of each embodiment is to illustrate the differences from other embodiments. The same or similar parts among the embodiments can be referred to each other. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the description of the method part.

[0139] Those skilled in the art may further realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner 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 to exceed the scope of this application.

[0140] The steps of the methods or algorithms described in combination with the embodiments disclosed herein can be directly implemented by hardware, software modules executed by a processor, or a combination of the two. The software modules can be placed in a random access memory (RAM), internal 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 also be noted that in this document, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the existence of additional identical elements in the process, method, article or device comprising the element.

[0142] The technical solutions provided in this application have been introduced in detail above. Specific examples are used herein to elaborate on the principles and implementation manners of this application. The description of the above embodiments is only used to help understand the method and its core idea of this application; at the same time, for those of ordinary skill in the art, according to the idea of this application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to this application.

Claims

1. A method for obtaining distribution network line parameters, characterized in that, it includes: Obtain several time-section intervals in the distribution space of the line parameters to be processed of the target distribution network, and use a preset line impedance parameter calculation formula to calculate each time-section data in the time-section interval to obtain corresponding initial line impedance parameters; Establish a line parameter probability distribution model based on the historical line parameter set, so as to 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 a line parameter distribution result; Use a preset parameter selection function and a preset parameter calculation function and determine a first parameter based on each of the line parameter distribution results, and use a target TPE algorithm, Bayes' theorem and a preset threshold to determine a 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 a first preset condition. If it meets, determine whether the second parameter meets a second preset condition. If it meets, set the second parameter as the target parameter.

2. The method for obtaining distribution network line parameters according to claim 1, characterized in that, The step of obtaining several time-section intervals in the distribution space of the line parameters to be processed and using a preset line impedance parameter calculation formula to calculate each time-section data in the time-section interval to obtain corresponding initial line impedance parameters includes: Perform an initialization operation on the line parameter distribution space to be initialized to obtain the line parameter distribution space to be processed, and select several time-section intervals in the line parameter distribution space to be processed; the time-section interval includes time-section data; Determine the voltage value, active power and reactive power between two user nodes based on the time-section data, and use several preset line impedance parameter calculation formulas and determine corresponding initial line impedance parameters based on the quantity of the time-section data, the voltage value, the active power, and the reactive power; the initial line impedance includes a resistance value and a reactance value.

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

4. The method for obtaining distribution network line parameters according to claim 3, characterized in that, Determining the second parameter by using the target TPE algorithm, Bayes' theorem and a preset threshold and based on the first parameter includes: Modeling the probability result corresponding to the first parameter by using the target TPE algorithm with a tree structure to obtain a corresponding first probability result; Modeling the probability result corresponding to the first parameter under the condition of the corresponding function value to be processed by using the target TPE algorithm and based on the preset threshold to obtain a corresponding second probability result; wherein, the comparison result of whether the function value to be processed is greater than the preset threshold determines the second probability result; Calculating a fourth probability result by using Bayes' theorem for the first probability result, the second probability result and a third probability result corresponding to the corresponding function value to be processed; 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 by using the 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 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.

5. The method for obtaining distribution network line parameters according to claim 4, wherein, the modeling the probability result corresponding to the first parameter under the condition of the corresponding function value to be processed by using the target TPE algorithm and based on the preset threshold to obtain a corresponding second probability result includes: Setting preset hyperparameters based on user requirements, and judging 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, setting the preset hyperparameters as the value corresponding to the second probability result.

6. The method for obtaining distribution network line parameters according to claim 5, wherein, determining the current line parameter set based on the second parameter, the function value obtained by processing the second parameter by using the preset parameter calculation function and the historical line parameter set, updating the current iteration number, and then judging whether the current iteration number meets a first preset condition. If it meets, judging whether the second parameter meets a second preset condition. If it meets, setting the second parameter as the target parameter includes: Combining the second parameter, the function value to be processed and the historical line parameter set by using a preset combination rule to obtain the current line parameter set, and updating the current iteration number; Judging whether the current iteration number is greater than a preset maximum iteration number. If the current iteration number is greater than the preset maximum iteration number, outputting the second parameter; Determine whether 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 less than a preset value. 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 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 function value to be processed corresponding to the second parameter is not less than the preset value, then arrange the function values to be processed in descending order, and establish a new distribution space of the line parameters to be processed based on the parameter distribution result corresponding to the obtained arrangement result, and trigger the step of obtaining several time-section intervals in the distribution space of the line parameters to be processed.

7. The method for obtaining distribution network line parameters according to claim 6, 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 loss function.

8. A device for obtaining distribution network line parameters, characterized in that it includes: a time-section interval obtaining module, configured to obtain several time-section intervals in the distribution space of the line parameters to be processed, and calculate each time-section data in the time-section interval by using a preset line impedance parameter calculation formula to obtain corresponding initial line impedance parameters; a distribution result determining module, configured to establish a line parameter probability distribution model based on a historical line parameter set, and calculate each of the initial line impedance parameters by using the line parameter probability distribution model and calling a preset parameter distribution algorithm to obtain a line parameter distribution result; an intermediate parameter determining module, configured to determine a first parameter by using a preset parameter selection function and a preset parameter calculation function and based on each of the line parameter distribution results, and determine a second parameter by using a target TPE algorithm, Bayes' theorem and a preset threshold and based on the first parameter; a target parameter determining module, configured to determine a current line parameter set based on the second parameter, the function value obtained by processing the second parameter by 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 a first preset condition. If it meets, then determine whether the second parameter meets a second preset condition. If it meets, then set the second parameter as the target parameter.

9. An electronic device, characterized in that it includes: a memory, configured to store a computer program; a processor, configured to execute the computer program to implement the steps of the method for obtaining distribution network line parameters according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that it is used to store a computer program, wherein the computer program, when executed by a processor, implements the steps of the method for obtaining distribution network line parameters according to any one of claims 1 to 7.

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