Method and device for determining line loss rate of power distribution network and computer program product

Abstract

The invention provides a method and device for determining the line loss rate of a power distribution network and a computer program product, and the method comprises the steps: obtaining a plurality of pieces of line loss important index data which are index data affecting the line loss rate of the power distribution network; inputting the multiple pieces of line loss important index data into a line loss rate determination model, performing empirical mode decomposition on the line loss important index data by using the line loss rate determination model to obtain multiple time sequence mode components, performing superposition reconstruction on the line loss rate component corresponding to each time sequence mode component, and outputting to obtain a target line loss rate of the power distribution network; and under the condition that the target line loss rate is greater than the line loss rate threshold, executing a loss reduction operation which is an operation of reducing the line loss rate of the power distribution network. According to the method, the problem of inaccurate prediction of the power distribution network line loss rate in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the field of distribution network line loss rate prediction, and in particular to a method for determining a distribution network line loss rate, a determining device, a computer-readable storage medium and a computer program product. Background Art

[0002] Line loss rate is one of the important factors affecting the economic benefits of power companies. A large number of distributed generation (DG) are connected to the distribution network, but affected by factors such as weather, their unique timing characteristics change the operation mode of the distribution network. The power generation situation in different time periods may have an impact on the line loss. Therefore, considering the timing characteristics of DG can improve the accuracy of the line loss rate prediction of the distribution network, which is of great significance to improving the economic benefits of power companies.

[0003] At present, the line loss rate prediction methods of distribution networks with DG are mainly divided into two categories: mathematical modeling and machine learning. Mathematical modeling is the most traditional line loss rate prediction method. It is usually based on power system theory and empirical formulas, and analyzes and models factors such as the physical structure of the power grid, load characteristics, and transmission lines to predict the line loss rate. The machine learning method uses a data-driven approach to build a neural network model and train the algorithm to identify the relationship between the line loss rate and its corresponding indicator data, thereby predicting the line loss rate. The mathematical modeling method predicts the line loss rate based on power system theory and empirical formulas, which has certain transparency and interpretability. However, in the DG system, it may show obvious time series changes due to factors such as weather conditions and load changes. The model often cannot capture these changes, and its accuracy is limited. The machine learning method can handle complex data and the nonlinear relationships between them without building a mathematical model. The common BP neural network model has a simple structure, but it cannot predict line losses based on the DG time series characteristics; the LSTM network neural network model has a relatively complex structure, usually requires a lot of computing resources and time, and may also fail to effectively capture the sequence characteristics because the data set is too small and too short. Summary of the invention

[0004] The main purpose of the present application is to provide a method for determining a distribution network line loss rate, a determination device, a computer-readable storage medium and a computer program product, so as to at least solve the problem of inaccurate prediction of the distribution network line loss rate in the prior art.

[0005] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a method for determining a line loss rate of a distribution network is provided, comprising: obtaining a plurality of important line loss indicator data, wherein the important line loss indicator data are indicator data that affect the line loss rate of the distribution network; inputting the plurality of important line loss indicator data into a line loss rate determination model, so as to use the line loss rate determination model to perform empirical mode decomposition on the important line loss indicator data to obtain a plurality of time series modal components, and superimposing and reconstructing the line loss rate components corresponding to each of the time series modal components, and outputting a target line loss rate of the distribution network, wherein the line loss rate determination model is a convergence model obtained by performing modal decomposition on a first sample training set and inputting the resultant to a neural network for training, wherein the first sample training set includes a plurality of groups of first training data, and each group of the first training data includes sample important indicator data and a first sample line loss rate corresponding to the sample important indicator data; when the target line loss rate is greater than a line loss rate threshold, performing a loss reduction operation, wherein the loss reduction operation is an operation of reducing the line loss rate of the distribution network.

[0006] Optionally, obtaining a plurality of important line loss indicator data includes: obtaining all indicator data of the distribution network, the indicator data being data corresponding to various indicator types, the indicator types including at least feeder power supply radius, daily load rate fluctuation coefficient, maximum temperature, minimum temperature and weather type; performing noise reduction processing on all the indicator data to obtain first indicator data corresponding to each indicator type; performing normalization processing on all the first indicator data to obtain second indicator data corresponding to each indicator type; inputting the second indicator data into a linear regression model in sequence to obtain feature weights corresponding to each second indicator data, the linear regression model being a convergence model obtained by inputting a second sample training set into a linear regression equation for training, the second sample training set including multiple groups of second training data, each group of the second training data including sample second indicator data, and second sample line loss rate and sample feature weights corresponding to the sample second indicator data; sorting all the feature weights from large to small to obtain a weight sequence; selecting the second indicator data corresponding to the feature weights whose sequence numbers in the weight sequence are less than or equal to a set number, and determining them as the important line loss indicator data.

[0007] Optionally, performing noise reduction processing on all the indicator data to obtain first indicator data corresponding to each indicator type includes: removing null values ​​in all the indicator data to obtain first noise reduction indicator data corresponding to each indicator type; calculating the deviation degree of each indicator type corresponding to the first noise reduction indicator data according to a first formula, wherein the first formula is The determination of the line loss rate of the distribution network represents the deviation degree, i represents the first

[0008] A number of noise reduction index data, X irepresents the i-th first noise reduction index data corresponding to the current index type, μ represents the mean of all the first noise reduction index data corresponding to the current index type, and σ represents the standard deviation of the first noise reduction index data corresponding to the current index type; when the absolute value of the deviation degree is greater than or equal to the deviation threshold, the first noise reduction index data is eliminated; when the absolute value of the deviation degree is less than the deviation threshold, the first noise reduction index data is determined as the first index data.

[0009] Optionally, before the second indicator data is sequentially input into the linear regression model, the method further includes: constructing an initial linear regression model, the initial linear regression model is the linear regression model to be trained; an analysis step, inputting the sample second indicator data in multiple groups of current second training data into the initial linear regression model for linear regression analysis to obtain the corresponding current first sample line loss rate, the current second training data is the second training data of any group in the second sample training set; an optimization step, taking the mean square error as the first loss function, and optimizing the first loss function by the least squares method, the first loss function is used to measure the difference between the current first sample line loss rate and the second sample line loss rate of the sample second indicator data; repeating the analysis step and the optimization step in sequence until the first loss value of the first loss function is minimized, obtaining the linear regression model, and determining the regression coefficient of the linear regression model as the sample feature weight, the first loss value is the difference between the current first sample line loss rate and the second sample line loss rate of the sample second indicator data.

[0010] Optionally, before inputting the plurality of the line loss important indicator data into the line loss rate determination model, the method further includes: constructing an initial line loss rate determination model, the initial line loss rate determination model including a modal decomposition module and a neural network module, the modal decomposition module is used to perform modal decomposition on the input data, and the neural network module is used to calculate the modal decomposed data to obtain output data; a first input step, inputting the sample important indicator data in multiple groups of current first training data into the initial line loss rate determination model, the current first training data is any group of the first training data in the first sample training set; a decomposition step, performing empirical mode decomposition on the sample important indicator data to obtain multiple sample modal components; a second input step, inputting the multiple groups of current first training data into the initial line loss rate determination model. The sample modal components are input from the input layer of the neural network, calculated through multiple hidden layers of the neural network, and the sample line loss rate components corresponding to each of the sample modal components are obtained; a reconstruction step is performed, in which the sample line loss rate components corresponding to each of the sample modal components are superimposed and reconstructed to obtain the current second sample line loss rate; the first input step, the decomposition step, the second input step and the reconstruction step are repeated at least once in sequence until the number of iterations reaches the maximum number of iterations or the second loss value of the second loss function is less than the second loss threshold, to obtain the line loss rate determination model, the second loss function is the mean square error function between the first sample line loss rate and the current second sample line loss rate, and the second loss value is the value of the mean square error function.

[0011] Optionally, multiple sample modal components are input from the input layer of the neural network and calculated through multiple hidden layers of the neural network to obtain sample line loss rate components corresponding to each sample modal component, including: jump connecting the hidden layers, passing the first output information of the first hidden layer to the second hidden layer for calculation, and obtaining the second output information of the second hidden layer, the first hidden layer is the hidden layer currently being calculated, the second hidden layer is the hidden layer not adjacent to the first hidden layer, the first output information is the output of the first hidden layer and the input of the second hidden layer; the first output information and the second output information are added to obtain the sample line loss rate components corresponding to each sample modal component.

[0012] Optionally, after outputting the target line loss rate of the distribution network, the method further includes: when the target line loss rate is less than or equal to the line loss rate threshold, continuously monitoring the operation of the distribution network, and updating the line loss rate determination model periodically or in real time.

[0013] According to another aspect of the present application, a device for determining a line loss rate of a distribution network is provided, the device comprising: an acquisition unit, for acquiring a plurality of important line loss indicator data, the important line loss indicator data being indicator data affecting the line loss rate of the distribution network; an input-output unit, for inputting the plurality of important line loss indicator data into a line loss rate determination model, so as to use the line loss rate determination model to perform empirical mode decomposition on the important line loss indicator data to obtain a plurality of time series modal components, and superimpose and reconstruct the line loss rate components corresponding to each of the time series modal components, and output a target line loss rate of the distribution network, the line loss rate determination model being a convergence model obtained by performing mode decomposition on a first sample training set and inputting it into a neural network for training, the first sample training set comprising a plurality of groups of first training data, each group of the first training data comprising sample important indicator data and a first sample line loss rate corresponding to the sample important indicator data; an execution unit, for executing a loss reduction operation when the target line loss rate is greater than a line loss rate threshold, the loss reduction operation being an operation of reducing the line loss rate of the distribution network.

[0014] According to another aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute any one of the methods described.

[0015] According to another aspect of the present application, a computer program product is provided, comprising computer instructions, wherein when the computer instructions are executed by a processor, any one of the methods described above is implemented.

[0016] Applying the technical solution of the present application, in a method for determining the line loss rate of a distribution network, first, a plurality of important line loss indicator data are obtained, and the above-mentioned important line loss indicator data are indicator data that affect the line loss rate of the distribution network; then, the plurality of the above-mentioned important line loss indicator data are input into a line loss rate determination model, so as to use the above-mentioned line loss rate determination model to perform empirical mode decomposition on the above-mentioned important line loss indicator data to obtain a plurality of time series modal components, and the line loss rate components corresponding to each of the above-mentioned time series modal components are superimposed and reconstructed, and the target line loss rate of the above-mentioned distribution network is output, and the above-mentioned line loss rate determination model is a convergence model obtained by performing modal decomposition on a first sample training set and inputting it into a neural network for training, and the above-mentioned first sample training set includes multiple groups of first training data, and each group of the above-mentioned first training data includes sample important indicator data and a first sample line loss rate corresponding to the above-mentioned sample important indicator data; finally, when the above-mentioned target line loss rate is greater than the line loss rate threshold, a loss reduction operation is performed, and the above-mentioned loss reduction operation is an operation of reducing the above-mentioned line loss rate of the above-mentioned distribution network. The present application selects multiple important line loss index data that have a greater impact on the line loss rate as input data for the line loss rate determination model; uses the empirical mode decomposition method to decompose each important line loss index data into multiple modal components, uses a neural network to predict the line loss rate of each modal component of the important line loss index data, obtains the line loss rate component of each index data, and superimposes and reconstructs all line loss rate components to obtain the target line loss rate, thereby realizing the prediction of the distribution network line loss rate considering the DG timing characteristics, and can more flexibly adapt to the changes of the DG system, making the prediction result more accurate and reliable. The present application solves the problem of inaccurate prediction of the distribution network line loss rate in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A hardware structure block diagram of a mobile terminal for executing a method for determining a line loss rate of a distribution network provided in an embodiment of the present application is shown;

[0018] Figure 2 A schematic flow chart of a method for determining a line loss rate of a distribution network provided in accordance with an embodiment of the present application is shown;

[0019] Figure 3 A schematic flow chart of a method for determining a line loss rate of a distribution network according to an embodiment of the present application is shown;

[0020] Figure 4 A flow chart of an empirical mode decomposition method provided according to an embodiment of the present application is shown;

[0021] Figure 5 A structure diagram of a line loss rate determination model for a distribution network provided according to an embodiment of the present application is shown;

[0022] Figure 6A graph showing an actual daily line loss rate and a predicted value provided according to an embodiment of the present application is shown;

[0023] Figure 7 A structural block diagram of a device for determining a line loss rate of a distribution network provided according to an embodiment of the present application is shown.

[0024] The above drawings include the following reference numerals:

[0025] 102, processor; 104, memory; 106, transmission device; 108, input and output devices. DETAILED DESCRIPTION

[0026] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0027] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0028] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0029] As introduced in the background technology, the BP neural network model commonly used in the prior art has a simple structure, but cannot predict line loss based on the DG timing characteristics; the LSTM network neural network model has a relatively complex structure, usually requires a large amount of computing resources and time, and may not be able to effectively capture sequence characteristics because the data set is too small or too short. In order to solve the problem of inaccurate prediction of the distribution network line loss rate, the embodiments of the present application provide a method for determining the distribution network line loss rate, a determination device, a computer-readable storage medium and a computer program product.

[0030] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0031] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 FIG. 1 is a hardware structure block diagram of a mobile terminal of a method for determining a line loss rate of a distribution network according to an embodiment of the present invention. Figure 1 As shown, the mobile terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the mobile terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown.

[0032] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the method for determining the line loss rate of the power distribution network in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, the above method is implemented. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the above networks include but are not limited to the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof. The transmission device 106 is used to receive or send data via a network. The specific examples of the above networks may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0033] In this embodiment, a method for determining the line loss rate of a distribution network running on a mobile terminal, a computer terminal or a similar computing device is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0034] Figure 2 FIG. 1 is a flow chart of a method for determining a line loss rate of a distribution network according to an embodiment of the present application. Figure 2 As shown, the method comprises the following steps:

[0035] Step S201, obtaining a plurality of important line loss indicator data, wherein the important line loss indicator data is indicator data that affects the line loss rate of the distribution network.

[0036] Specifically, multiple indicators that have an impact on the line loss rate are selected, and after data preprocessing, the daily line loss rate and indicator data related thereto are obtained, and then multiple important indicators that have the greatest impact on the line loss rate are further selected, namely the above-mentioned important line loss indicator data.

[0037] Step S202, input the multiple important line loss indicator data into the line loss rate determination model, use the line loss rate determination model to perform empirical mode decomposition on the important line loss indicator data to obtain multiple time series modal components, and superimpose and reconstruct the line loss rate components corresponding to the time series modal components, and output the target line loss rate of the distribution network. The line loss rate determination model is a convergence model obtained by performing modal decomposition on the first sample training set and inputting it into the neural network for training. The first sample training set includes multiple groups of first training data, and each group of the first training data includes sample important indicator data and the first sample line loss rate corresponding to the sample important indicator data.

[0038] Specifically, multiple important line loss indicator data are input into the line loss rate determination model to determine the line loss rate. In the line loss rate determination model, each important line loss indicator data is subjected to empirical mode decomposition. The empirical mode decomposition of the line loss rate component can effectively separate different time series modal components, so as to better analyze the influencing factors and change rules of the line loss rate, and obtain n time series modal components corresponding to each important line loss indicator data. Each time series modal component is predicted using a neural network to obtain the line loss rate component corresponding to each time series modal component, and then the line loss rate components corresponding to all the time series modal components are superimposed and reconstructed to obtain the target line loss rate, and output.

[0039] Step S203: When the target line loss rate is greater than a line loss rate threshold, a loss reduction operation is performed, wherein the loss reduction operation is an operation of reducing the line loss rate of the distribution network.

[0040] Specifically, when the above-mentioned target line loss rate is greater than the line loss rate threshold, it indicates that the line loss rate is too high, and quick measures need to be taken to repair and optimize the line loss rate in order to reduce the line loss rate.

[0041] In this embodiment, first, a plurality of important line loss indicator data are obtained, and the above-mentioned important line loss indicator data are indicator data that affect the line loss rate of the distribution network; then, the plurality of the above-mentioned important line loss indicator data are input into the line loss rate determination model, so as to use the above-mentioned line loss rate determination model to perform empirical mode decomposition on the above-mentioned important line loss indicator data to obtain a plurality of time series modal components, and the line loss rate components corresponding to each of the above-mentioned time series modal components are superimposed and reconstructed, and the target line loss rate of the above-mentioned distribution network is output. The above-mentioned line loss rate determination model is a convergence model obtained by performing modal decomposition on the first sample training set and inputting it into the neural network for training. The above-mentioned first sample training set includes multiple groups of first training data, and each group of the above-mentioned first training data includes sample important indicator data and the first sample line loss rate corresponding to the above-mentioned sample important indicator data; finally, when the above-mentioned target line loss rate is greater than the line loss rate threshold, a loss reduction operation is performed, and the above-mentioned loss reduction operation is an operation to reduce the above-mentioned line loss rate of the above-mentioned distribution network. The present application selects multiple important line loss index data that have a greater impact on the line loss rate as input data for the line loss rate determination model; uses the empirical mode decomposition method to decompose each important line loss index data into multiple modal components, uses a neural network to predict the line loss rate of each modal component of the important line loss index data, obtains the line loss rate component of each index data, and superimposes and reconstructs all line loss rate components to obtain the target line loss rate, thereby realizing the prediction of the distribution network line loss rate considering the DG timing characteristics, and can more flexibly adapt to the changes of the DG system, making the prediction result more accurate and reliable. The present application solves the problem of inaccurate prediction of the distribution network line loss rate in the prior art.

[0042] In order to enable those skilled in the art to more clearly understand the technical solution of the present application, the implementation process of the method for determining the line loss rate of the distribution network of the present application will be described in detail below in combination with specific embodiments.

[0043] In order to improve data quality and thus improve the accuracy of the line loss rate determination model, in an optional implementation manner, the above step S201 includes:

[0044] Step S2011, obtaining all indicator data of the above distribution network, the above indicator data is data corresponding to various indicator types, and the above indicator types at least include feeder power supply radius, daily load factor fluctuation coefficient, maximum temperature, minimum temperature and weather type;

[0045] Step S2012, performing noise reduction processing on all the above indicator data to obtain first indicator data corresponding to each of the above indicator types;

[0046] Step S2013, normalizing all the first indicator data to obtain second indicator data corresponding to each of the indicator types;

[0047] Step S2014, inputting the second indicator data into a linear regression model in sequence to obtain feature weights corresponding to the second indicator data, wherein the linear regression model is a convergence model obtained by inputting the second sample training set into a linear regression equation for training, and the second sample training set includes multiple groups of second training data, each group of the second training data includes sample second indicator data, and second sample line loss rate and sample feature weight corresponding to the sample second indicator data;

[0048] Step S2015, sorting all the above feature weights from large to small to obtain a weight sequence;

[0049] Step S2016, selecting the second indicator data corresponding to the feature weights whose sequence numbers of the weight sequence are less than or equal to a set number, and determining them as the important indicator data of line loss.

[0050] In the above embodiment, if Figure 3 As shown in the figure, first obtain the daily line loss rate and historical indicator data, such as the feeder power supply radius, total length, number of special transformers, capacity of special transformers, cable rate, proportion of energy-saving distribution transformers, integrated power, area power, special transformer power, distributed online active power, distributed online reactive power, proportion of distributed online power, number of distributed power sources, voltage qualification rate, proportion of public transformers with low power factor, proportion of public transformers with three-phase unbalance, average distribution load rate, proportion of electricity consumption of large industrial electricity consumption (only special transformers are counted), peak reverse active power of local power plants, flat reverse active power of local power plants, valley reverse active power of local power plants, peak reverse active power of local power plants, total reverse active power of line gateway table, peak reverse active power of line gateway table, daily load rate fluctuation coefficient, maximum temperature, minimum temperature, and weather type as indicator types affecting the line loss rate, and then obtain the indicator data of these indicator types for preprocessing. Due to measurement, collection, transmission failures and other reasons, null values ​​or partial deviations may appear in the data, which will have an adverse effect on model training, so noise removal is required to obtain the above-mentioned first indicator data. Different types of data indicators usually have different dimensions, which may affect the model training results. In order to eliminate such effects, data normalization is required. The min-max normalization method is used to map the original indicator data of each item to a value in the interval [0, 1]. The min-max normalization conversion function is defined as: Where: are the maximum and minimum values ​​of the first indicator data corresponding to the indicator type, respectively. i is the initial value of the data, is the standard value of the data. The second indicator data is obtained after normalization. Then the indicator selection is carried out. The coefficient (weight) of each feature in the linear regression analysis can reflect the influence of the feature on the target variable. A larger absolute coefficient usually indicates that the feature has a greater impact on the prediction result, so this analysis can evaluate the influence of each indicator on the line loss rate. The top 10 indicator types (which can be set according to actual conditions) with the greatest impact on the line loss rate are selected, and the data corresponding to the top 10 indicator types are used as important indicator data for line loss.

[0051] In order to reduce data deviation, in an optional implementation manner, the above step S2021 includes:

[0052] Step S20211, removing null values ​​in all the above indicator data to obtain first noise reduction indicator data corresponding to each of the above indicator types;

[0053] Step S20212, calculating the deviation of each indicator type corresponding to the first noise reduction indicator data according to the first formula, the first formula is: Z represents the above deviation, i represents the number of the above first noise reduction index data, X i represents the i-th first noise reduction index data corresponding to the current index type, represents the mean value of all the first noise reduction index data corresponding to the current index type, and σ represents the standard deviation of the first noise reduction index data corresponding to the current index type;

[0054] Step S20213, when the absolute value of the deviation is greater than or equal to the deviation threshold, the first noise reduction index data is removed;

[0055] Step S20214: when the absolute value of the deviation is less than the deviation threshold, the first noise reduction index data is determined as the first index data.

[0056] In the above embodiment, the null value data is first removed to obtain the first noise reduction index data, and then the Z-score method is used to test the deviation of the first noise reduction index data: Z is the Z-score (i.e. the above-mentioned deviation degree), which is used to indicate the degree of deviation of the first noise reduction index data. When |Z|≥3, it is considered that the deviation of the first noise reduction index data is large and needs to be eliminated. Therefore, the first noise reduction index data that do not meet the requirements are eliminated, and the remaining first noise reduction index data that meet the requirements are retained and determined as the first index data.

[0057] In order to improve the data validity when predicting the line loss rate, in an optional implementation manner, before the above step S2014, the method further includes:

[0058] Step S301, constructing an initial linear regression model, where the initial linear regression model is a linear regression model to be trained;

[0059] Step S302, an analysis step, inputting the sample second indicator data in multiple groups of current second training data into the initial linear regression model for linear regression analysis to obtain the corresponding current first sample line loss rate, wherein the current second training data is any group of the second training data in the second sample training set;

[0060] Step S303, an optimization step, taking the mean square error as a first loss function, and optimizing the first loss function by using the least square method, wherein the first loss function is used to measure the difference between the current first sample line loss rate and the second sample line loss rate of the sample second indicator data;

[0061] Step S304, repeat the above analysis step and the above optimization step in sequence until the first loss value of the above first loss function reaches the minimum, obtain the above linear regression model, and determine the regression coefficient of the above linear regression model as the above sample feature weight, and the above first loss value is the difference between the above current first sample line loss rate and the above second sample line loss rate of the above sample second indicator data.

[0062] In the above embodiment, the regression coefficient (weight) of each indicator type in the linear regression analysis can reflect the influence of the indicator type on the target variable (i.e., line loss rate). A larger absolute coefficient usually indicates that the indicator type has a greater impact on the prediction result, so the linear regression analysis can evaluate the influence of each indicator type on the line loss rate. First, a linear regression model is built to analyze the relationship between the original indicator data and the line loss rate. There are many options for linear regression models, such as simple linear regression model, multivariate linear regression model, polynomial regression model, ridge regression model, etc. As long as linear regression analysis can be implemented, the mean square error (MSE) is used as the loss function, that is, the first loss function mentioned above, to measure the actual line loss rate y in the sample. i (i.e. the second sample line loss rate mentioned above) and the predicted line loss rate y i (i.e. the current first sample line loss rate mentioned above), where: Then, the Ordinary Least Squares (OLS) method is used to optimize the mean square error MSE and find the regression coefficient that minimizes the first loss function. By optimizing the loss function, the difference between the current first sample line loss rate and the second sample line loss rate can be reduced, and the model fitting effect and prediction accuracy can be improved. Until the loss value of the loss function reaches the minimum, the final linear regression model is obtained, and the regression coefficient is determined as the sample feature weight.

[0063] In order to improve the prediction accuracy and stability of the line loss rate, in an optional implementation manner, before the above step S202, the method further includes:

[0064] Step S401, constructing an initial line loss rate determination model, the initial line loss rate determination model comprising a modal decomposition module and a neural network module, the modal decomposition module is used to perform modal decomposition on input data, and the neural network module is used to calculate the modal decomposed data to obtain output data;

[0065] Step S402, a first input step, inputting the sample important indicator data in a plurality of sets of current first training data into the initial line loss rate determination model, wherein the current first training data is any set of the first training data in the first sample training set;

[0066] Step S403, a decomposition step, performing empirical mode decomposition on the above sample important indicator data to obtain multiple sample modal components;

[0067] Step S404, a second input step, inputting the plurality of sample modal components from the input layer of the neural network, and performing calculations through the plurality of hidden layers of the neural network to obtain sample line loss rate components corresponding to the plurality of sample modal components;

[0068] Step S405, a reconstruction step, superimposing and reconstructing the sample line loss rate components corresponding to the above sample modal components to obtain the current second sample line loss rate;

[0069] Step S406, repeatedly execute the above-mentioned first input step, the above-mentioned decomposition step, the above-mentioned second input step and the above-mentioned reconstruction step at least once in sequence, until the number of iterations reaches the maximum number of iterations or the second loss value of the second loss function is less than the second loss threshold, and obtain the above-mentioned line loss rate determination model, the above-mentioned second loss function is the mean square error function between the above-mentioned first sample line loss rate and the above-mentioned current second sample line loss rate, and the above-mentioned second loss value is the numerical value of the above-mentioned mean square error function.

[0070] In the above embodiment, an initial line loss rate determination model is first constructed. The initial line loss rate determination model consists of two parts: empirical mode decomposition and a neural network. The parameter settings of the neural network are shown in Table 1.

[0071] Table 1

[0072]

[0073] The neural network forward propagates the learned data features, the sample modal components pass through the hidden layer, and finally the current second sample line loss rate is obtained from the output layer. Then, the mean square error between the current second sample line loss rate output by the output layer and the true value (i.e., the first sample line loss rate mentioned above) is used as the loss function, and the result of the loss function is used to evaluate the degree of fit of the model, and back propagation is performed to promote network optimization training accuracy. The gradient descent method is used to iteratively optimize the loss function to find the minimum value. In this process, parameters such as weights and bias values ​​are constantly updated, and the value of the loss function is constantly changing, with an overall decreasing trend. When the set value is reached, the learning ends. The way to adjust the weight is: w ij (t+1)=w ij (t)+ηδ p y p , where η is the gain phase, δ p is the output error (that is, the second loss value), which can be expressed as y p (i) is the current second sample line loss rate, y m (i) is the first sample line loss rate. By iteratively executing the input, decomposition, calculation and reconstruction steps for multiple times, the line loss rate determination model can be gradually optimized to obtain the final line loss rate determination model.

[0074] Empirical Mode Decomposition (EMD) is a new method for processing non-stationary signals. It decomposes the signal into multiple intrinsic mode functions (IMFs) based on the time scale characteristics of the data itself and then superimposes them. It does not need to set any basis function in advance and adaptively performs the main component analysis of the signal. It is suitable for the analysis of nonlinear and non-stationary signals as well as linear and stationary signals. For a given signal, EMD first extracts the local characteristics of the signal by finding its local maximum and local minimum. Using the local maximum and minimum, the envelope of the local maximum (upper envelope) and the envelope of the local minimum (lower envelope) can be gradually constructed, and the average value of the upper envelope and the lower envelope can be calculated to obtain the local mean function of the signal. The local mean function is subtracted from the original signal to obtain the one-dimensional first component, which is an IMF. Then, a similar operation is performed on the IMF to obtain the second IMF. This process is iterated until the remaining components become residuals or monotonic functions approaching zero. Adding the obtained IMFs can reconstruct the original signal. Process of EMD decomposition algorithm Figure 4As shown. IMF must meet the following two conditions: (1) The number of local extreme points and zero crossing points of the function must be equal or differ by at most one in the entire time range; (2) At any time point, the average of the upper and lower envelopes must be zero. In actual situations, the mean of the upper and lower envelopes cannot be zero. Usually, when the following formula is satisfied, it is considered that the mean of the envelope meets the IMF mean zero condition 1. In the formula, h k (t) is the kth IMF, ε is the screening threshold, which is generally between 0.2 and 0.3.

[0075] For example, a distribution network containing DG in a certain area is selected, and the feeder power supply radius, total length, number of special variables, distributed online reactive power, proportion of distributed online power, number of distributed power sources, average load rate of distribution lines, maximum temperature, minimum temperature, and weather type are used as line loss rate prediction indicators. The daily line loss rate and historical data of 29 indicators from November 2023 to February 2024 are collected. After data preprocessing, the final total number of samples is 13,650. 80% of the samples are randomly selected as training sets and 20% of the samples are used as test sets. The processed data set is input into the line loss rate determination model, and the number of model training times is set to 1000. The training set is used for model training, and the test set is used to test the daily line loss rate of the BG distribution network. Finally, the predicted daily line loss rate obtained from the test set is compared with the original line loss rate data. Figure 6 Based on the above line loss rate determination model and the traditional BP neural network model, the daily line loss rate of the same data set is predicted. The comparison of the mean square error (MSE) and root mean square error (RMSE) of the two models is shown in Table 2.

[0076] Table 2

[0077]

[0078] By comparison, the prediction ability of the improved line loss rate determination model has been significantly improved. It can be concluded that EMD-BP can better learn the timing characteristics of DG, flexibly adapt to the changes of DG system, and effectively predict the daily line loss rate of the distribution network containing DG.

[0079] In order to reduce the difficulty of model training, in an optional implementation, the above step S404 includes:

[0080] Step S4041, performing jump connection on the hidden layers, passing the first output information of the first hidden layer to the second hidden layer for calculation, and obtaining the second output information of the second hidden layer, wherein the first hidden layer is the hidden layer currently being calculated, the second hidden layer is the hidden layer not adjacent to the first hidden layer, and the first output information is the output of the first hidden layer and the input of the second hidden layer;

[0081] Step S4042: Add the first output information and the second output information to obtain sample line loss rate components corresponding to the sample modal components.

[0082] In the above embodiments, generally speaking, the neural network consists of an input layer, a hidden layer and an output layer, and the layers are often fully connected, that is, any neuron in each layer is connected to all neurons in the next layer, and there is no connection between neurons in the layer. However, due to the limitations of the model structure, the model not only cannot learn time series well, but also has gradient disappearance and training difficulties during the training process. Therefore, in order to solve the above problems and improve the prediction accuracy of the line loss rate of the distribution network containing DG, network optimization is performed on the basis of the traditional BP neural network, and jump connections are introduced. Figure 5 The schematic diagram of the line loss rate determination model is shown in Figure 1. In order to reduce the gradient disappearance, the output information F(x1) of hidden layer 1 is directly transmitted to hidden layer n for skip connection to ensure that the original information will not be lost due to nonlinear transformation. Finally, the output layer information F(xn) is added to F(x1) to output the expected data. For example, for any modal component, through the network skip connection, the output vector y of hidden layer 1 is p1 , pass hidden layer 1 directly to hidden layer 3, and the output vector of hidden layer 3 is y p2 , so the final output vector y of this modal component is p for y p =y p1 +y p2 ,in, x i is the i-th modal component, w i represents the feature weight, and n represents the total number of modal components.

[0083] In order to avoid abnormal line loss rate of the distribution network, in an optional implementation manner, after the above step S202, the method further includes:

[0084] Step S501, when the target line loss rate is less than or equal to the line loss rate threshold, continuously monitor the operation of the distribution network and update the line loss rate determination model periodically or in real time.

[0085] In the above embodiment, when the above target line loss rate is less than or equal to the above line loss rate threshold, it means that the current line loss rate of the distribution network is normal, but it is still necessary to continuously monitor the operation of the above distribution network to prevent abnormal conditions in the distribution network, and it is also necessary to update the above line loss rate determination model regularly or in real time, continuously optimize the operation strategy and control parameters of the distribution network, improve the operation efficiency and performance of the system, reduce energy waste, and reduce operating costs.

[0086] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0087] The embodiment of the present application also provides a device for determining the line loss rate of a distribution network. It should be noted that the device for determining the line loss rate of a distribution network in the embodiment of the present application can be used to execute the method for determining the line loss rate of a distribution network provided in the embodiment of the present application. The device is used to implement the above-mentioned embodiments and preferred implementation modes, and those that have been described will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware for a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0088] The following introduces the device for determining the line loss rate of the distribution network provided in an embodiment of the present application.

[0089] Figure 7 is a structural block diagram of a device for determining a line loss rate of a distribution network according to an embodiment of the present application. Figure 7 As shown, the device comprises:

[0090] The acquisition unit 10 is used to acquire a plurality of important line loss indicator data, where the important line loss indicator data are indicator data that affect the line loss rate of the distribution network.

[0091] Specifically, multiple indicators that have an impact on the line loss rate are selected, and after data preprocessing, the daily line loss rate and indicator data related thereto are obtained, and then multiple important indicators that have the greatest impact on the line loss rate are further selected, namely the above-mentioned important line loss indicator data.

[0092] The input-output unit 20 is used to input the above-mentioned multiple important line loss indicator data into the line loss rate determination model, so as to use the above-mentioned line loss rate determination model to perform empirical mode decomposition on the above-mentioned important line loss indicator data to obtain multiple time series modal components, and superimpose and reconstruct the line loss rate components corresponding to each of the above-mentioned time series modal components, and output the target line loss rate of the above-mentioned distribution network. The above-mentioned line loss rate determination model is a convergence model obtained by performing modal decomposition on the first sample training set and inputting it into the neural network for training. The above-mentioned first sample training set includes multiple groups of first training data, and each group of the above-mentioned first training data includes sample important indicator data and the first sample line loss rate corresponding to the above-mentioned sample important indicator data.

[0093] Specifically, multiple important line loss indicator data are input into the line loss rate determination model to determine the line loss rate. In the line loss rate determination model, each important line loss indicator data is subjected to empirical mode decomposition. The empirical mode decomposition of the line loss rate component can effectively separate different time series modal components, so as to better analyze the influencing factors and change rules of the line loss rate, and obtain n time series modal components corresponding to each important line loss indicator data. Each time series modal component is predicted using a neural network to obtain the line loss rate component corresponding to each time series modal component, and then the line loss rate components corresponding to all the time series modal components are superimposed and reconstructed to obtain the target line loss rate, and output.

[0094] The execution unit 30 is used to execute a loss reduction operation when the target line loss rate is greater than a line loss rate threshold, wherein the loss reduction operation is an operation of reducing the line loss rate of the distribution network.

[0095] Specifically, when the above-mentioned target line loss rate is greater than the line loss rate threshold, it indicates that the line loss rate is too high, and quick measures need to be taken to repair and optimize the line loss rate in order to reduce the line loss rate.

[0096] In this embodiment, an acquisition unit is used to acquire a plurality of important line loss indicator data, wherein the important line loss indicator data are indicator data that affect the line loss rate of the distribution network; an input-output unit is used to input the plurality of important line loss indicator data into a line loss rate determination model, so as to use the line loss rate determination model to perform empirical mode decomposition on the important line loss indicator data to obtain a plurality of time series modal components, and superimpose and reconstruct the line loss rate components corresponding to the time series modal components, and output the target line loss rate of the distribution network, wherein the line loss rate determination model is a convergence model obtained by performing modal decomposition on a first sample training set and inputting the resultant to a neural network for training, wherein the first sample training set includes a plurality of groups of first training data, and each group of the first training data includes sample important indicator data and a first sample line loss rate corresponding to the sample important indicator data; an execution unit is used to execute a loss reduction operation when the target line loss rate is greater than a line loss rate threshold, wherein the loss reduction operation is an operation for reducing the line loss rate of the distribution network. The present application selects multiple important line loss index data that have a greater impact on the line loss rate as input data for the line loss rate determination model; uses the empirical mode decomposition method to decompose each important line loss index data into multiple modal components, uses a neural network to predict the line loss rate of each modal component of the important line loss index data, obtains the line loss rate component of each index data, and superimposes and reconstructs all line loss rate components to obtain the target line loss rate, thereby realizing the prediction of the distribution network line loss rate considering the DG timing characteristics, and can more flexibly adapt to the changes of the DG system, making the prediction result more accurate and reliable. The present application solves the problem of inaccurate prediction of the distribution network line loss rate in the prior art.

[0097] In order to improve data quality and thus improve the accuracy of the line loss rate determination model, in an optional implementation manner, the acquisition unit includes:

[0098] An acquisition module is used to acquire all indicator data of the distribution network, where the indicator data is data corresponding to various indicator types, and the indicator types at least include feeder power supply radius, daily load factor fluctuation coefficient, maximum temperature, minimum temperature and weather type;

[0099] A noise reduction module performs noise reduction processing on all the above indicator data to obtain first indicator data corresponding to each of the above indicator types;

[0100] A normalization module normalizes all the first indicator data to obtain second indicator data corresponding to each of the indicator types;

[0101] An input module, which sequentially inputs the second indicator data into a linear regression model to obtain feature weights corresponding to the second indicator data. The linear regression model is a convergence model obtained by inputting the second sample training set into a linear regression equation for training. The second sample training set includes multiple groups of second training data, each group of the second training data includes sample second indicator data, and second sample line loss rate and sample feature weights corresponding to the sample second indicator data.

[0102] The sorting module sorts all the above feature weights from large to small to obtain a weight sequence;

[0103] A selection module is provided to select the second indicator data corresponding to the feature weights whose sequence numbers of the weight sequence are less than or equal to a set number, and determine them as the important indicator data of line loss.

[0104] In the above embodiment, if Figure 3As shown in the figure, first obtain the daily line loss rate and historical indicator data, such as the feeder power supply radius, total length, number of special transformers, capacity of special transformers, cable rate, proportion of energy-saving distribution transformers, integrated power, area power, special transformer power, distributed online active power, distributed online reactive power, proportion of distributed online power, number of distributed power sources, voltage qualification rate, proportion of public transformers with low power factor, proportion of public transformers with three-phase unbalance, average distribution load rate, proportion of electricity consumption of large industrial electricity consumption (only special transformers are counted), peak reverse active power of local power plants, flat reverse active power of local power plants, valley reverse active power of local power plants, peak reverse active power of local power plants, total reverse active power of line gateway table, peak reverse active power of line gateway table, daily load rate fluctuation coefficient, maximum temperature, minimum temperature, and weather type as indicator types affecting the line loss rate, and then obtain the indicator data of these indicator types for preprocessing. Due to measurement, collection, transmission failures and other reasons, null values ​​or partial deviations may appear in the data, which will have an adverse effect on model training, so noise removal is required to obtain the above-mentioned first indicator data. Different types of data indicators usually have different dimensions, which may affect the model training results. In order to eliminate such effects, data normalization is required. The min-max normalization method is used to map the original indicator data of each item to a value in the interval [0, 1]. The min-max normalization conversion function is defined as: Where: are the maximum and minimum values ​​of the first indicator data corresponding to the indicator type, respectively. i is the initial value of the data, is the standard value of the data. The second indicator data is obtained after normalization. Then the indicator selection is carried out. The coefficient (weight) of each feature in the linear regression analysis can reflect the influence of the feature on the target variable. A larger absolute coefficient usually indicates that the feature has a greater impact on the prediction result, so this analysis can evaluate the influence of each indicator on the line loss rate. The top 10 indicator types (which can be set according to actual conditions) with the greatest impact on the line loss rate are selected, and the data corresponding to the top 10 indicator types are used as important indicator data for line loss.

[0105] In order to reduce data deviation, in an optional implementation manner, the noise reduction module includes:

[0106] A removal submodule is used to remove null values ​​in all the above indicator data to obtain first noise reduction indicator data corresponding to each of the above indicator types;

[0107] The calculation submodule calculates the deviation of each of the above index types corresponding to the above first noise reduction index data according to the first formula. The above first formula is: Z represents the above deviation, i represents the number of the above first noise reduction index data, X irepresents the i-th first noise reduction index data corresponding to the current index type, represents the mean value of all the first noise reduction index data corresponding to the current index type, and σ represents the standard deviation of the first noise reduction index data corresponding to the current index type;

[0108] A removal submodule, when the absolute value of the deviation is greater than or equal to the deviation threshold, removes the first noise reduction index data;

[0109] The determination submodule determines the first noise reduction index data as the first index data when the absolute value of the deviation is less than the deviation threshold.

[0110] In the above embodiment, the null value data is first removed to obtain the first noise reduction index data, and then the Z-score method is used to test the deviation of the first noise reduction index data: Z is the Z-score (i.e. the above-mentioned deviation degree), which is used to indicate the degree of deviation of the first noise reduction index data. When |Z|≥3, it is considered that the deviation of the first noise reduction index data is large and needs to be eliminated. Therefore, the first noise reduction index data that do not meet the requirements are eliminated, and the remaining first noise reduction index data that meet the requirements are retained and determined as the first index data.

[0111] In order to improve the data validity when predicting the line loss rate, in an optional implementation manner, the device further includes:

[0112] A first construction unit is used to construct an initial linear regression model before sequentially inputting the second indicator data into the linear regression model, wherein the initial linear regression model is a linear regression model to be trained;

[0113] An analysis unit, configured to perform an analysis step, input the sample second indicator data in a plurality of sets of current second training data into the initial linear regression model for linear regression analysis, and obtain a corresponding current first sample line loss rate, wherein the current second training data is any set of the second training data in the second sample training set;

[0114] an optimization unit, configured to execute an optimization step, use a mean square error as a first loss function, and optimize the first loss function using a least square method, wherein the first loss function is used to measure a difference between the current first sample line loss rate and the second sample line loss rate of the sample second indicator data;

[0115] The first repeating unit is used to repeat the above-mentioned analysis step and the above-mentioned optimization step in sequence until the first loss value of the above-mentioned first loss function reaches the minimum, thereby obtaining the above-mentioned linear regression model, and determining the regression coefficient of the above-mentioned linear regression model as the above-mentioned sample feature weight. The above-mentioned first loss value is the difference between the above-mentioned current first sample line loss rate and the above-mentioned second sample line loss rate of the above-mentioned sample second indicator data.

[0116] In the above embodiment, the regression coefficient (weight) of each indicator type in the linear regression analysis can reflect the influence of the indicator type on the target variable (i.e., line loss rate). A larger absolute coefficient usually indicates that the indicator type has a greater impact on the prediction result, so the linear regression analysis can evaluate the influence of each indicator type on the line loss rate. First, a linear regression model is built to analyze the relationship between the original indicator data and the line loss rate. There are many options for linear regression models, such as simple linear regression model, multivariate linear regression model, polynomial regression model, ridge regression model, etc. As long as linear regression analysis can be implemented, the mean square error (MSE) is used as the loss function, that is, the first loss function mentioned above, to measure the actual line loss rate y in the sample. i (i.e. the second sample line loss rate mentioned above) and the predicted line loss rate y i (i.e. the current first sample line loss rate mentioned above), where: Then, the Ordinary Least Squares (OLS) method is used to optimize the mean square error MSE and find the regression coefficient that minimizes the first loss function. By optimizing the loss function, the difference between the current first sample line loss rate and the second sample line loss rate can be reduced, and the model fitting effect and prediction accuracy can be improved. Until the loss value of the loss function reaches the minimum, the final linear regression model is obtained, and the regression coefficient is determined as the sample feature weight.

[0117] In order to improve the prediction accuracy and stability of the line loss rate, in an optional implementation manner, the device further includes:

[0118] a second construction unit, configured to construct an initial line loss rate determination model before inputting the plurality of important line loss index data into the line loss rate determination model, wherein the initial line loss rate determination model comprises a modal decomposition module and a neural network module, wherein the modal decomposition module is configured to perform modal decomposition on the input data, and the neural network module is configured to calculate the modal decomposed data to obtain output data;

[0119] A first input unit, used to execute a first input step, inputting the sample important indicator data in a plurality of groups of current first training data into the initial line loss rate determination model, wherein the current first training data is any group of the first training data in the first sample training set;

[0120] A decomposition unit, used to execute the decomposition step, perform empirical mode decomposition on the important indicator data of the sample, and obtain multiple sample modal components;

[0121] A second input unit is used to perform a second input step, input the plurality of sample modal components from the input layer of the neural network, and calculate through the plurality of hidden layers of the neural network to obtain a sample line loss rate component corresponding to each of the sample modal components;

[0122] A reconstruction unit, used to perform a reconstruction step, superimpose and reconstruct the sample line loss rate components corresponding to the above-mentioned sample modal components to obtain a current second sample line loss rate;

[0123] The second repetition unit is used to repeatedly execute the above-mentioned first input step, the above-mentioned decomposition step, the above-mentioned second input step and the above-mentioned reconstruction step in sequence at least once, until the number of iterations reaches the maximum number of iterations or the second loss value of the second loss function is less than the second loss threshold, to obtain the above-mentioned line loss rate determination model, the above-mentioned second loss function is the mean square error function between the above-mentioned first sample line loss rate and the above-mentioned current second sample line loss rate, and the above-mentioned second loss value is the numerical value of the above-mentioned mean square error function.

[0124] In the above embodiment, an initial line loss rate determination model is first constructed. The initial line loss rate determination model consists of two parts: empirical mode decomposition and a neural network. The parameter settings of the neural network are shown in Table 1.

[0125] Table 1

[0126]

[0127] The neural network forward propagates the learned data features, the sample modal components pass through the hidden layer, and finally the current second sample line loss rate is obtained from the output layer. Then, the mean square error between the current second sample line loss rate output by the output layer and the true value (i.e., the first sample line loss rate mentioned above) is used as the loss function, and the result of the loss function is used to evaluate the degree of fit of the model, and back propagation is performed to promote network optimization training accuracy. The gradient descent method is used to iteratively optimize the loss function to find the minimum value. In this process, parameters such as weights and bias values ​​are constantly updated, and the value of the loss function is constantly changing, with an overall decreasing trend. When the set value is reached, the learning ends. The way to adjust the weight is: w ij (t+1)=w ij (t)+ηδ p y p , where η is the gain phase, δ p is the output error (that is, the second loss value), which can be expressed as y p (i) is the current second sample line loss rate, y m(i) is the first sample line loss rate. By iteratively executing the input, decomposition, calculation and reconstruction steps for multiple times, the line loss rate determination model can be gradually optimized to obtain the final line loss rate determination model.

[0128] Empirical Mode Decomposition (EMD) is a new method for processing non-stationary signals. It decomposes the signal into multiple intrinsic mode functions (IMFs) based on the time scale characteristics of the data itself and then superimposes them. It does not need to set any basis function in advance and adaptively performs the main component analysis of the signal. It is suitable for the analysis of nonlinear and non-stationary signals as well as linear and stationary signals. For a given signal, EMD first extracts the local characteristics of the signal by finding its local maximum and local minimum. Using the local maximum and minimum, the envelope of the local maximum (upper envelope) and the envelope of the local minimum (lower envelope) can be gradually constructed, and the average value of the upper envelope and the lower envelope can be calculated to obtain the local mean function of the signal. The local mean function is subtracted from the original signal to obtain the one-dimensional first component, which is an IMF. Then, a similar operation is performed on the IMF to obtain the second IMF. This process is iterated until the remaining components become residuals or monotonic functions approaching zero. Adding the obtained IMFs can reconstruct the original signal. Process of EMD decomposition algorithm Figure 4 As shown. IMF must meet the following two conditions: (1) The number of local extreme points and zero crossing points of the function must be equal or differ by at most one in the entire time range; (2) At any time point, the average of the upper and lower envelopes must be zero. In actual situations, the mean of the upper and lower envelopes cannot be zero. Usually, when the following formula is satisfied, it is considered that the mean of the envelope meets the IMF mean zero condition 1. In the formula, h k (t) is the kth IMF, ε is the screening threshold, which is generally between 0.2 and 0.3.

[0129] In order to reduce the difficulty of model training, in an optional implementation manner, the second input unit includes:

[0130] A first calculation module performs a jump connection on the hidden layer, transfers the first output information of the first hidden layer to the second hidden layer for calculation, and obtains the second output information of the second hidden layer, wherein the first hidden layer is the hidden layer currently being calculated, the second hidden layer is the hidden layer not adjacent to the first hidden layer, and the first output information is the output of the first hidden layer and the input of the second hidden layer;

[0131] The second calculation module adds the first output information and the second output information to obtain the sample line loss rate components corresponding to the sample modal components.

[0132] In the above embodiments, generally speaking, the neural network consists of an input layer, a hidden layer and an output layer, and the layers are often fully connected, that is, any neuron in each layer is connected to all neurons in the next layer, and there is no connection between neurons in the layer. However, due to the limitations of the model structure, the model not only cannot learn time series well, but also has gradient disappearance and training difficulties during the training process. Therefore, in order to solve the above problems and improve the prediction accuracy of the line loss rate of the distribution network containing DG, network optimization is performed on the basis of the traditional BP neural network, and jump connections are introduced. Figure 5 The schematic diagram of the line loss rate determination model is shown in Figure 1. In order to reduce the gradient disappearance, the output information F(x1) of hidden layer 1 is directly transmitted to hidden layer n for skip connection to ensure that the original information will not be lost due to nonlinear transformation. Finally, the output layer information F(xn) is added to F(x1) to output the expected data. For example, for any modal component, through the network skip connection, the output vector y of hidden layer 1 is p1 , pass hidden layer 1 directly to hidden layer 3, and the output vector of hidden layer 3 is y p2 , so the final output vector y of this modal component is p for y p =y p1 +y p2 ,in, x i is the i-th modal component, w i represents the feature weight, and n represents the total number of modal components.

[0133] In order to avoid abnormal line loss rate of the distribution network, in an optional implementation manner, the device further includes:

[0134] The monitoring unit is used to continuously monitor the operation of the distribution network after outputting the target line loss rate of the distribution network, and to update the line loss rate determination model periodically or in real time when the target line loss rate is less than or equal to the line loss rate threshold.

[0135] In the above embodiment, when the above target line loss rate is less than or equal to the above line loss rate threshold, it means that the current line loss rate of the distribution network is normal, but it is still necessary to continuously monitor the operation of the above distribution network to prevent abnormal conditions in the distribution network, and it is also necessary to update the above line loss rate determination model regularly or in real time, continuously optimize the operation strategy and control parameters of the distribution network, improve the operation efficiency and performance of the system, reduce energy waste, and reduce operating costs.

[0136] The above-mentioned device for determining the line loss rate of the distribution network includes a processor and a memory. The above-mentioned acquisition unit, input and output unit, and execution unit are all stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to realize the corresponding functions. The above-mentioned modules are all located in the same processor; or, the above-mentioned modules are located in different processors in the form of any combination.

[0137] The processor includes a kernel, and the kernel retrieves the corresponding program unit from the memory. One or more kernels can be set, and the problem of inaccurate prediction of the line loss rate of the distribution network in the prior art can be solved by adjusting the kernel parameters.

[0138] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0139] An embodiment of the present invention provides a computer-readable storage medium, which includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute the method for determining the line loss rate of the distribution network.

[0140] Specifically, the method for determining the line loss rate of the distribution network includes:

[0141] Step S201, obtaining a plurality of important line loss indicator data, wherein the important line loss indicator data is indicator data affecting the line loss rate of the distribution network;

[0142] Step S202, inputting the plurality of the above-mentioned important line loss indicator data into a line loss rate determination model, so as to use the above-mentioned line loss rate determination model to perform empirical mode decomposition on the above-mentioned important line loss indicator data to obtain a plurality of time series modal components, and superimposing and reconstructing the line loss rate components corresponding to the above-mentioned time series modal components, and outputting the target line loss rate of the above-mentioned distribution network, the above-mentioned line loss rate determination model is a convergence model obtained by performing modal decomposition on the first sample training set and inputting it into a neural network for training, the above-mentioned first sample training set includes a plurality of groups of first training data, and each group of the above-mentioned first training data includes sample important indicator data and the first sample line loss rate corresponding to the above-mentioned sample important indicator data;

[0143] Step S203: When the target line loss rate is greater than a line loss rate threshold, a loss reduction operation is performed, wherein the loss reduction operation is an operation of reducing the line loss rate of the distribution network.

[0144] An embodiment of the present invention provides a processor, and the processor is used to run a program, wherein the method for determining the line loss rate of the distribution network is executed when the program is run.

[0145] Specifically, the method for determining the line loss rate of the distribution network includes:

[0146] Step S201, obtaining a plurality of important line loss indicator data, wherein the important line loss indicator data is indicator data affecting the line loss rate of the distribution network;

[0147] Step S202, inputting the plurality of the above-mentioned important line loss indicator data into a line loss rate determination model, so as to use the above-mentioned line loss rate determination model to perform empirical mode decomposition on the above-mentioned important line loss indicator data to obtain a plurality of time series modal components, and superimposing and reconstructing the line loss rate components corresponding to the above-mentioned time series modal components, and outputting the target line loss rate of the above-mentioned distribution network, the above-mentioned line loss rate determination model is a convergence model obtained by performing modal decomposition on the first sample training set and inputting it into a neural network for training, the above-mentioned first sample training set includes a plurality of groups of first training data, and each group of the above-mentioned first training data includes sample important indicator data and the first sample line loss rate corresponding to the above-mentioned sample important indicator data;

[0148] Step S203: When the target line loss rate is greater than a line loss rate threshold, a loss reduction operation is performed, wherein the loss reduction operation is an operation of reducing the line loss rate of the distribution network.

[0149] An embodiment of the present invention provides a distribution network line loss rate determination system, the distribution network line loss rate determination system includes a processor, a memory, and a program stored in the memory and executable on the processor, and when the processor executes the program, at least the following steps are implemented:

[0150] Step S201, obtaining a plurality of important line loss indicator data, wherein the important line loss indicator data is indicator data affecting the line loss rate of the distribution network;

[0151] Step S202, inputting the plurality of the above-mentioned important line loss indicator data into a line loss rate determination model, so as to use the above-mentioned line loss rate determination model to perform empirical mode decomposition on the above-mentioned important line loss indicator data to obtain a plurality of time series modal components, and superimposing and reconstructing the line loss rate components corresponding to the above-mentioned time series modal components, and outputting the target line loss rate of the above-mentioned distribution network, the above-mentioned line loss rate determination model is a convergence model obtained by performing modal decomposition on the first sample training set and inputting it into a neural network for training, the above-mentioned first sample training set includes a plurality of groups of first training data, and each group of the above-mentioned first training data includes sample important indicator data and the first sample line loss rate corresponding to the above-mentioned sample important indicator data;

[0152] Step S203: When the target line loss rate is greater than a line loss rate threshold, a loss reduction operation is performed, wherein the loss reduction operation is an operation of reducing the line loss rate of the distribution network.

[0153] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program for initializing at least the following method steps:

[0154] Step S201, obtaining a plurality of important line loss indicator data, wherein the important line loss indicator data is indicator data affecting the line loss rate of the distribution network;

[0155] Step S202, inputting the plurality of the above-mentioned important line loss indicator data into a line loss rate determination model, so as to use the above-mentioned line loss rate determination model to perform empirical mode decomposition on the above-mentioned important line loss indicator data to obtain a plurality of time series modal components, and superimposing and reconstructing the line loss rate components corresponding to the above-mentioned time series modal components, and outputting the target line loss rate of the above-mentioned distribution network, the above-mentioned line loss rate determination model is a convergence model obtained by performing modal decomposition on the first sample training set and inputting it into a neural network for training, the above-mentioned first sample training set includes a plurality of groups of first training data, and each group of the above-mentioned first training data includes sample important indicator data and the first sample line loss rate corresponding to the above-mentioned sample important indicator data;

[0156] Step S203: When the target line loss rate is greater than a line loss rate threshold, a loss reduction operation is performed, wherein the loss reduction operation is an operation of reducing the line loss rate of the distribution network.

[0157] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0158] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0159] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0160] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0161] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0162] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0163] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0164] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0165] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0166] From the above description, it can be seen that the above embodiments of the present application achieve the following technical effects:

[0167] 1) The method for determining the line loss rate of a distribution network of the present application, first, obtain a plurality of important line loss indicator data, the above-mentioned important line loss indicator data are indicator data that affect the line loss rate of the distribution network; then, input the plurality of the above-mentioned important line loss indicator data into a line loss rate determination model, so as to use the above-mentioned line loss rate determination model to perform empirical mode decomposition on the above-mentioned important line loss indicator data to obtain a plurality of time series modal components, and superimpose and reconstruct the line loss rate components corresponding to each of the above-mentioned time series modal components, and output the target line loss rate of the above-mentioned distribution network, the above-mentioned line loss rate determination model is a convergence model obtained by performing modal decomposition on a first sample training set and inputting it into a neural network for training, the above-mentioned first sample training set includes a plurality of groups of first training data, each group of the above-mentioned first training data includes sample important indicator data and a first sample line loss rate corresponding to the above-mentioned sample important indicator data; finally, when the above-mentioned target line loss rate is greater than the line loss rate threshold, a loss reduction operation is performed, and the above-mentioned loss reduction operation is an operation to reduce the above-mentioned line loss rate of the above-mentioned distribution network. The present application selects multiple important line loss index data that have a greater impact on the line loss rate as input data for the line loss rate determination model; uses the empirical mode decomposition method to decompose each important line loss index data into multiple modal components, uses a neural network to predict the line loss rate of each modal component of the important line loss index data, obtains the line loss rate component of each index data, and superimposes and reconstructs all line loss rate components to obtain the target line loss rate, thereby realizing the prediction of the distribution network line loss rate considering the DG timing characteristics, and can more flexibly adapt to the changes of the DG system, making the prediction result more accurate and reliable. The present application solves the problem of inaccurate prediction of the distribution network line loss rate in the prior art.

[0168] 2) The device for determining the line loss rate of the distribution network of the present application comprises an acquisition unit for acquiring a plurality of important line loss indicator data, wherein the important line loss indicator data are indicator data affecting the line loss rate of the distribution network; an input-output unit for inputting the plurality of important line loss indicator data into a line loss rate determination model, so as to use the line loss rate determination model to perform empirical mode decomposition on the important line loss indicator data to obtain a plurality of time series modal components, and superimpose and reconstruct the line loss rate components corresponding to the time series modal components, and output the target line loss rate of the distribution network, wherein the line loss rate determination model is a convergence model obtained by performing modal decomposition on a first sample training set and inputting the resultant to a neural network for training, wherein the first sample training set comprises a plurality of groups of first training data, and each group of the first training data comprises sample important indicator data and a first sample line loss rate corresponding to the sample important indicator data; an execution unit for executing a loss reduction operation when the target line loss rate is greater than a line loss rate threshold, wherein the loss reduction operation is an operation for reducing the line loss rate of the distribution network. The present application selects multiple important line loss index data that have a greater impact on the line loss rate as input data for the line loss rate determination model; uses the empirical mode decomposition method to decompose each important line loss index data into multiple modal components, uses a neural network to predict the line loss rate of each modal component of the important line loss index data, obtains the line loss rate component of each index data, and superimposes and reconstructs all line loss rate components to obtain the target line loss rate, thereby realizing the prediction of the distribution network line loss rate considering the DG timing characteristics, and can more flexibly adapt to the changes of the DG system, making the prediction result more accurate and reliable. The present application solves the problem of inaccurate prediction of the distribution network line loss rate in the prior art.

[0169] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for determining a line loss rate of a distribution network, characterized in that: include: Acquire a plurality of important line loss indicator data, wherein the important line loss indicator data is indicator data affecting the line loss rate of the distribution network; Input a plurality of the important line loss indicator data into a line loss rate determination model, use the line loss rate determination model to perform empirical mode decomposition on the important line loss indicator data to obtain a plurality of time series modal components, and superimpose and reconstruct the line loss rate components corresponding to each of the time series modal components, and output a target line loss rate of the distribution network, wherein the line loss rate determination model is a convergence model obtained by performing modal decomposition on a first sample training set and inputting it into a neural network for training, wherein the first sample training set includes a plurality of groups of first training data, and each group of the first training data includes sample important indicator data and a first sample line loss rate corresponding to the sample important indicator data; When the target line loss rate is greater than a line loss rate threshold, a loss reduction operation is performed, where the loss reduction operation is an operation of reducing the line loss rate of the distribution network.

2. The method according to claim 1, characterized in that: Obtain multiple important line loss indicator data, including: Acquire all indicator data of the distribution network, where the indicator data is data corresponding to various indicator types, and the indicator types at least include feeder power supply radius, daily load rate fluctuation coefficient, maximum temperature, minimum temperature and weather type; Performing noise reduction processing on all the indicator data to obtain first indicator data corresponding to each indicator type; Normalizing all the first indicator data to obtain second indicator data corresponding to each indicator type; The second indicator data are sequentially input into a linear regression model to obtain feature weights corresponding to each second indicator data, wherein the linear regression model is a convergence model obtained by inputting the second sample training set into a linear regression equation for training, and the second sample training set includes multiple groups of second training data, each group of the second training data includes sample second indicator data, and second sample line loss rate and sample feature weight corresponding to the sample second indicator data; Sort all the feature weights from large to small to obtain a weight sequence; The second indicator data corresponding to the feature weights whose sequence numbers of the weight sequence are less than or equal to a set number are selected and determined as the important indicator data of line loss.

3. The method according to claim 2, characterized in that Performing noise reduction processing on all the indicator data to obtain first indicator data corresponding to each indicator type includes: Removing null values ​​from all the indicator data to obtain first noise reduction indicator data corresponding to each indicator type; The deviation of each indicator type corresponding to the first noise reduction indicator data is calculated according to the first formula, and the first formula is Z= X i -m , the determination of the line loss rate of the distribution network represents the deviation degree, i represents the first σ The number of the noise reduction index data, X i represents the i-th first noise reduction index data corresponding to the current index type, μ represents the mean of all the first noise reduction index data corresponding to the current index type, and σ represents the standard deviation of the first noise reduction index data corresponding to the current index type; When the absolute value of the deviation is greater than or equal to the deviation threshold, the first noise reduction index data is eliminated; When the absolute value of the deviation degree is smaller than the deviation threshold, the first noise reduction index data is determined as the first index data.

4. The method according to claim 2, characterized in that: Before sequentially inputting the second indicator data into the linear regression model, the method further includes: Constructing an initial linear regression model, wherein the initial linear regression model is a linear regression model to be trained; An analysis step, inputting the sample second indicator data in multiple groups of current second training data into the initial linear regression model for linear regression analysis to obtain the corresponding current first sample line loss rate, wherein the current second training data is any group of the second training data in the second sample training set; an optimization step, taking the mean square error as a first loss function, and optimizing the first loss function by using a least square method, wherein the first loss function is used to measure the difference between the current first sample line loss rate and the second sample line loss rate of the sample second indicator data; Repeat the analysis step and the optimization step in sequence until the first loss value of the first loss function reaches the minimum, obtain the linear regression model, and determine the regression coefficient of the linear regression model as the sample feature weight, and the first loss value is the difference between the current first sample line loss rate and the second sample line loss rate of the sample second indicator data.

5. The method according to claim 1, characterized in that Before inputting the plurality of important line loss index data into the line loss rate determination model, the method further includes: Constructing an initial line loss rate determination model, the initial line loss rate determination model comprising a modal decomposition module and a neural network module, the modal decomposition module is used to perform modal decomposition on input data, and the neural network module is used to calculate the modal decomposed data to obtain output data; A first input step, inputting the sample important indicator data in a plurality of groups of current first training data into the initial line loss rate determination model, wherein the current first training data is any group of the first training data in the first sample training set; A decomposition step, performing empirical mode decomposition on the sample important indicator data to obtain multiple sample modal components; The second input step is to input the plurality of sample modal components from the input layer of the neural network, and calculate through the plurality of hidden layers of the neural network to obtain a sample line loss rate component corresponding to each of the sample modal components; A reconstruction step of superimposing and reconstructing the sample line loss rate components corresponding to the sample modal components to obtain a current second sample line loss rate; Repeat the first input step, the decomposition step, the second input step and the reconstruction step at least once in sequence until the number of iterations reaches the maximum number of iterations or the second loss value of the second loss function is less than the second loss threshold, to obtain the line loss rate determination model, the second loss function is the mean square error function between the first sample line loss rate and the current second sample line loss rate, and the second loss value is the numerical value of the mean square error function.

6. The method according to claim 5, characterized in that Inputting a plurality of the sample modal components from the input layer of the neural network, and performing calculations through a plurality of hidden layers of the neural network, to obtain a sample line loss rate component corresponding to each of the sample modal components, including: Performing a jump connection on the hidden layer, passing the first output information of the first hidden layer to the second hidden layer for calculation, and obtaining the second output information of the second hidden layer, wherein the first hidden layer is the hidden layer currently being calculated, the second hidden layer is the hidden layer not adjacent to the first hidden layer, and the first output information is the output of the first hidden layer and the input of the second hidden layer; The first output information and the second output information are added together to obtain sample line loss rate components corresponding to each of the sample modal components.

7. The method according to claim 1, characterized in that After outputting the target line loss rate of the distribution network, the method further includes: When the target line loss rate is less than or equal to the line loss rate threshold, the operation of the distribution network is continuously monitored, and the line loss rate determination model is updated periodically or in real time.

8. A device for determining line loss rate of a distribution network, characterized in that: The device comprises: An acquisition unit, used to acquire a plurality of important line loss index data, wherein the important line loss index data is index data affecting the line loss rate of the distribution network; An input-output unit, used for inputting a plurality of the important line loss indicator data into a line loss rate determination model, so as to use the line loss rate determination model to perform empirical mode decomposition on the important line loss indicator data to obtain a plurality of time series modal components, and superimpose and reconstruct the line loss rate components corresponding to each of the time series modal components, and output a target line loss rate of the distribution network, wherein the line loss rate determination model is a convergence model obtained by performing modal decomposition on a first sample training set and inputting it into a neural network for training, wherein the first sample training set includes a plurality of groups of first training data, and each group of the first training data includes sample important indicator data and a first sample line loss rate corresponding to the sample important indicator data; An execution unit is used to perform a loss reduction operation when the target line loss rate is greater than a line loss rate threshold, wherein the loss reduction operation is an operation of reducing the line loss rate of the distribution network.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the method according to any one of claims 1 to 7.

10. A computer program product comprising computer instructions, characterized in that: When the computer instructions are executed by a processor, the method according to any one of claims 1 to 3 is implemented.

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