Prediction analysis model generation method, prediction method and device for power data of transformer area

Abstract

The invention discloses a prediction analysis model generation method, a prediction method and a prediction device for transformer area power data, and relates to the technical field of intelligent power distribution. The method for generating the prediction analysis model of the power data of the transformer area comprises the steps of selecting at least part of model input parameters from model input parameters of a neural network model based on the weight of the neural network model; obtaining an initial element set based on at least part of model input parameters, and obtaining a data calculation mode set; obtaining a target element from the initial element set, and obtaining a target data calculation mode based on the data calculation mode set; obtaining a display analysis formula based on the target element and the target data calculation mode, the display analysis formula representing a prediction value of the to-be-predicted power parameter obtained based on the target element by using the target data calculation mode; and obtaining a prediction analysis model based on the display analysis formula. According to the invention, the prediction accuracy and interpretability of the power data of the transformer area are improved.

Description

Technical Field

[0001] The present application relates to the field of intelligent power distribution technology, and in particular to a prediction and analysis model generation method, prediction method and device for substation power data. Background Art

[0002] The prediction of power data such as current, voltage, and load in power grid substations is a very important basic work in distribution network management. A good prediction method can effectively improve the intelligence level of distribution equipment and the management efficiency of substations. At present, the prediction accuracy of the prediction method for power data such as current, voltage, and load in substations is not high enough, the efficiency is relatively low, and it is difficult to provide good interpretability. Due to the lack of interpretability, it is difficult to further ensure its robustness. Summary of the invention

[0003] The implementation method of the present application aims to solve one of the technical problems in the related technology at least to a certain extent. To this end, the purpose of the implementation method of the present application is to propose a prediction and analysis model generation method for substation power data, a prediction method, a device, a data processing unit, an electronic device, a distribution network system, a cloud-edge system, a storage medium and a program product.

[0004] The embodiment of the present application provides a method for generating a prediction and analytical model for substation power data, the prediction and analytical model generation method comprising: based on the weights of a neural network model, selecting at least part of the model input parameters from the model input parameters of the neural network model, wherein the neural network model is used to predict based on the parameter values ​​of the model input parameters to obtain predicted values ​​of the power parameters to be predicted; obtaining an initial element set based on the at least part of the model input parameters, and acquiring a data calculation method set; obtaining a target element from the initial element set, and obtaining a target data calculation method based on the data calculation method set; obtaining a display analytical expression based on the target element and the target data calculation method, wherein the display analytical expression represents the predicted value of the power parameter to be predicted obtained based on the target element using the target data calculation method; obtaining a prediction analytical model based on the display analytical expression, wherein the prediction analytical model is used to predict the predicted value of the power parameter to be predicted.

[0005] Exemplarily, each model input parameter corresponds to a weight; the weight based on the neural network model selects at least part of the model input parameters from the model input parameters of the neural network model, including: sorting the weights corresponding to each model input parameter, and selecting at least one weight with a front order from the sorted weights; and using the model input parameter corresponding to the selected at least one weight as the selected at least part of the model input parameter.

[0006] Exemplarily, obtaining the initial element set based on at least part of the model input parameters includes: taking the at least part of the model input parameters as the initial element set, wherein the target element includes the target parameter selected from the at least part of the model input parameters, and the display analytical expression represents the predicted value of the power parameter to be predicted obtained by processing the parameter value of the target parameter using the target data calculation method; or obtaining at least one constant, and taking the at least part of the model input parameters and the at least one constant as the initial element set, wherein the target element includes the target parameter selected from the at least part of the model input parameters and the target constant selected from the at least one constant, and the display analytical expression represents the predicted value of the power parameter to be predicted obtained by processing the parameter value of the target parameter and the target constant using the target data calculation method.

[0007] Exemplarily, the acquiring of the data calculation method set includes: constructing the data calculation method set based on operators and / or functional relationships.

[0008] Exemplarily, the method of obtaining a target element from the initial element set and obtaining a target data calculation method based on the data calculation method set includes: constructing an initial population based on the initial element set and the data calculation method set, wherein the initial population includes at least one individual, and each individual includes at least one element selected from the initial element set and at least one data calculation method selected from the data calculation method set; using the initial population or the next generation population obtained by genetic operation as the current population; calculating the fitness score of each individual in the current population, and judging whether a first current condition satisfies a first end condition; if the first current condition does not satisfy a first end condition, When the first end condition is met, based on the fitness score, a genetic operation is performed on the current population to obtain a next generation population, and it is determined whether the second current condition meets the second end condition; if the second current condition does not meet the second end condition, the next generation population is used as the current population for the next round of calculation; if the first current condition meets the first end condition, the current population is used as the last generation population; if the second current condition meets the second end condition, the next generation population is used as the last generation population, and a target individual is determined from the last generation population; an element in the target individual is used as the target element, and a data calculation method in the target individual is used as a target data calculation method.

[0009] Exemplarily, the calculation of the fitness score of each individual in the current group includes: obtaining sample data, wherein the sample data includes the actual parameter value of the parameter and the actual parameter value of the power parameter to be predicted; using the data calculation method in the individual to process the actual parameter value of the parameter to obtain a processing result; and obtaining the fitness score of the individual based on the difference between the processing result and the actual parameter value of the power parameter to be predicted.

[0010] Exemplarily, the genetic operation includes at least one of the following: retaining at least one individual with the highest fitness score;

[0011] Select individuals from the current population; copy at least one individual with the highest fitness score; mutate the individuals in the current population; insert strings into the individuals in the current population; and recombine the individuals in the current population.

[0012] Exemplarily, the power parameter to be predicted includes at least one of the following: current, voltage, and load.

[0013] Another embodiment of the present application provides a method for predicting substation power data, the prediction method comprising: obtaining a prediction analytical model, wherein the prediction analytical model is obtained according to a prediction analytical model generation method for substation power data; inputting a parameter value of a target parameter into the prediction analytical model for prediction, and obtaining a predicted value of the power parameter to be predicted.

[0014] Another embodiment of the present application provides a prediction and analytical model generation device for substation power data, the prediction and analytical model generation device comprising: a selection module, which is used to select at least part of the model input parameters from the model input parameters of the neural network model based on the weights of the neural network model, wherein the neural network model is used to predict based on the parameter values ​​of the model input parameters to obtain the predicted value of the power parameter to be predicted; a first acquisition module, which is used to obtain an initial element set based on at least part of the model input parameters, and to obtain a data calculation method set; a second acquisition module, which is used to obtain a target element from the initial element set, and to obtain a target data calculation method based on the data calculation method set; a third acquisition module, which is used to obtain a display analytical expression based on the target element and the target data calculation method, wherein the display analytical expression represents the predicted value of the power parameter to be predicted obtained based on the target element using the target data calculation method; a fourth acquisition module, which is used to obtain a prediction analytical model based on the display analytical expression, wherein the prediction analytical model is used to predict the predicted value of the power parameter to be predicted.

[0015] Another embodiment of the present application provides a prediction device for substation power data, the prediction device comprising: an acquisition module, used to acquire a prediction analytical model, wherein the prediction analytical model is obtained according to a prediction analytical model generation device for substation power data; a prediction module, used to predict the parameter value of a target parameter, input it into the prediction analytical model for prediction, and obtain a predicted value of the power parameter to be predicted.

[0016] Another embodiment of the present application provides a data processing unit, which includes a board or a chip, and is used to implement the steps of the method described in any of the above embodiments.

[0017] Another embodiment of the present application provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the method described in any one of the above embodiments are implemented.

[0018] Another embodiment of the present application provides a distribution network system, which is used to implement the steps of the method described in any of the above embodiments, or the distribution network system includes the above electronic device.

[0019] Another embodiment of the present application provides a cloud-edge-terminal system, which includes a cloud, an edge, and a terminal. At least one of the cloud, the edge, and the terminal is used to implement the steps of the method described in any of the above embodiments, or at least one of the cloud, the edge, and the terminal includes the above-mentioned electronic device.

[0020] Another embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the method described in any one of the above embodiments are implemented.

[0021] Another embodiment of the present application provides a computer program product, which includes instructions. When the instructions are executed by a processor of a computer device, the computer device can perform the steps of the method of any of the above embodiments.

[0022] In the above implementation, based on the weight of the neural network model, at least part of the model input parameters are selected from the model input parameters of the neural network model; based on at least part of the model input parameters, an initial element set is obtained, and a data calculation method set is obtained; a target element is obtained from the initial element set, and a target data calculation method is obtained based on the data calculation method set; based on the target element and the target data calculation method, a display analytical expression is obtained, and the display analytical expression represents the predicted value of the power parameter to be predicted obtained based on the target element using the target data calculation method; based on the display analytical expression, a prediction analytical model is obtained. This application improves the prediction accuracy and interpretability of the power data in the substation area. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 A flow chart of a method for generating a prediction and analysis model for power data in a substation area provided in an embodiment of the present application;

[0024] Figure 2 A flow chart of a method for predicting power data in a substation area provided for an implementation method of the present application field;

[0025] Figure 3 A schematic diagram of a device for generating a prediction and analysis model for power data in a substation area provided in an embodiment of the present application;

[0026] Figure 4 A schematic diagram of a device for predicting power data in an area provided in an embodiment of the present application. DETAILED DESCRIPTION

[0027] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0028] The prediction of current, voltage, load and other parameters in the power grid area is a very important basic work in distribution network management. A good prediction method can effectively improve the intelligence level of distribution equipment and the management efficiency of the area. Distribution equipment includes FTU, DTU, etc.

[0029] At present, the prediction accuracy of the current, voltage and load prediction methods in the substation area is not high enough, the efficiency is relatively low, and it is difficult to provide good interpretability. Due to the lack of interpretability, it is difficult to further ensure its robustness.

[0030] For example, the prediction methods for current, voltage, and load in the substation area can be divided into two categories:

[0031] The first type is the analytical expression fitting method. This type of method uses polynomial fitting, linear / nonlinear regression and other methods to fit an explicit analytical expression based on sample data to characterize the changes in current, voltage and load.

[0032] The second category is non-analytical methods such as neural networks, deep learning, and traditional genetic algorithms. These methods train and iteratively improve the model based on data, and eventually train a "black box" model that can characterize some characteristics of current, voltage, and load changes, but the model cannot be expressed as an analytical expression with measurable parameters as dependent variables.

[0033] The advantage of the first type of fitting analytical method is that it can more intuitively display the laws of change of current, voltage and load in the substation, but the disadvantage is that the prediction accuracy of this type of method is not high enough and the efficiency is relatively low.

[0034] The second type of non-analytical methods, especially deep learning methods, can better mine the features in the data as the scale of high-quality data continues to grow. However, due to their "black box" characteristics, they are difficult to provide good interpretability, and due to the lack of interpretability, it is difficult to further ensure their robustness.

[0035] In view of this, the present application proposes a method for generating a predictive analytical model for substation power data and a method for predicting substation power data, so as to accurately, efficiently and interpretably characterize the substation power data, generate an explicit analytical expression, and predict future substation power data accordingly.

[0036] Figure 1 A flowchart of a method for generating a predictive analytical model for substation power data provided in an embodiment of the present application.

[0037] like Figure 1 As shown, the prediction and analysis model generation method 100 for substation power data provided in the embodiment of the present application includes, for example, steps S110 - S150 .

[0038] Step S110, based on the weight of the neural network model, at least part of the model input parameters are selected from the model input parameters of the neural network model, and the neural network model is used to make predictions based on the parameter values ​​of the model input parameters to obtain predicted values ​​of the power parameters to be predicted.

[0039] Exemplarily, the model input parameters of the neural network model may include multiple ones, and the model output parameters are the power parameters to be predicted, which may be one or more. The model input parameters may be independent variables, and the power parameters to be predicted may be dependent variables. The neural network model may be trained in advance, and the sample data used to train the neural network model may be real historical data, and the sample data used to train the model may include the true value of the independent variable and the true value of the dependent variable. When training the model, the neural network model predicts based on the parameter value of the model input parameter (the true value of the independent variable) to obtain the predicted value of the power parameter to be predicted (the predicted value of the dependent variable), and adjusts the weight of the model based on the difference between the predicted value of the predicted power parameter and the true value of the dependent variable in the sample data, so that the model converges, thereby training the neural network model.

[0040] After the neural network model is obtained through training, at least part of the model input parameters are selected from the model input parameters of the neural network model based on the weights of the neural network model.

[0041] Step S120, obtaining an initial element set based on at least part of the model input parameters, and acquiring a data calculation method set.

[0042] For example, the initial element set is constructed based on at least part of the model input parameters, and the initial element set may include other data in addition to at least part of the model input parameters.

[0043] The data calculation method set includes multiple calculation methods, and the calculation methods are used to calculate the elements in the initial element set.

[0044] Step S130, obtaining a target element from the initial element set, and obtaining a target data calculation method based on a data calculation method set.

[0045] For example, at least some of the elements are determined from the initial element combination as target elements, and at least some of the calculation methods are determined from the data calculation method set as target data calculation methods.

[0046] Step S140, based on the target element and the target data calculation method, a display analytical expression is obtained, wherein the display analytical expression represents the predicted value of the power parameter to be predicted obtained based on the target element by using the target data calculation method.

[0047] Exemplarily, the target element includes at least independent variable parameters, and the target element may also include other data, and the power parameter to be predicted is used as the dependent variable. The displayed analytical expression can represent the functional relationship between the independent variable and the dependent variable.

[0048] Step S150, obtaining a prediction analysis model based on the displayed analytical expression, wherein the prediction analysis model is used to predict a predicted value of the power parameter to be predicted.

[0049] For example, the display analytical expression can be directly used as the prediction analytical model, or the display analytical expression can be used as the prediction analytical model after relevant optimization adjustments. After obtaining the prediction analytical model, the parameter values ​​of the independent variable parameters related to the power grid (which can be future parameter values) can be input into the prediction analytical model, and the prediction analytical model outputs the predicted value of the power parameter to be predicted, so as to realize the prediction of the future power parameters.

[0050] It can be seen that through the method of the present application, based on the weight of the neural network model, at least part of the model input parameters are selected from the model input parameters of the neural network model; based on at least part of the model input parameters, an initial element set is obtained, and a data calculation method set is obtained; a target element is obtained from the initial element set, and a target data calculation method is obtained from the data calculation method set; based on the target element and the target data calculation method, a display analytical expression is obtained, and the display analytical expression represents the predicted value of the power parameter to be predicted obtained based on the target element using the target data calculation method; based on the display analytical expression, a prediction analytical model is obtained. The prediction analytical model is interpretable and accurate in prediction, thereby improving the prediction accuracy and interpretability of the power data in the substation area.

[0051] In another example, the model input parameters may include multiple ones, each of which may correspond to a weight, and the weight corresponding to each model input parameter may be the original weight in the model, or the weight obtained by summing, weighted averaging, etc. the original weights. For example, a neural network model may include a multi-layer structure, each of which includes multiple nodes, so the weights between nodes can be regarded as original weights, and the original weights are usually more than the number of model input parameters. Therefore, the weight corresponding to each model input parameter may be an important original weight associated with the model input parameter determined from multiple original weights of the model as the weight corresponding to the model input parameter, or multiple original weights associated with the model input parameter may be determined from multiple original weights of the model, and the multiple associated original weights may be summed, weighted averaged, etc. as the weight corresponding to the model input parameter.

[0052] The weights corresponding to each model input parameter are sorted, and at least one weight ranked first is selected from the sorted weights, and then the model input parameters corresponding to the at least one selected weight are used as at least part of the selected model input parameters. For example, k weights ranked first are selected from the sorted weights, where k is an integer greater than 0, and the k model input parameters corresponding to the k weights are used as the selected parameters, and the k model input parameters can be added to the initial element set.

[0053] In one example, the power parameter to be predicted may be at least one of current, voltage, and load, and may also include other parameters.

[0054] When the power parameters to be predicted include current, voltage and load, the model input parameters of the neural network model may include at least one of multiple parameters such as temperature, humidity, date, time, etc.

[0055] When the power parameter to be predicted is current, the model input parameters of the neural network model may include at least one of multiple types of parameters such as voltage, load, temperature, humidity, date, time, etc.

[0056] When the power parameter to be predicted is voltage, the model input parameters of the neural network model may include at least one of multiple types of parameters such as current, load, temperature, humidity, date, time, etc.

[0057] When the power parameter to be predicted is load, the model input parameters of the neural network model may include at least one of multiple types of parameters such as current, voltage, temperature, humidity, date, time, etc.

[0058] When the power parameters to be predicted are current and voltage, the model input parameters of the neural network model may include at least one of multiple parameters such as load, temperature, humidity, date, time, etc. The same is true for other situations, which will not be described here.

[0059] For example, the power parameters to be predicted include current, voltage, and load, and the model input parameters of the neural network model include temperature, humidity, date, and time. Based on the weight of the model, k model input parameters selected from the model input parameters are, for example, temperature, humidity, and time, indicating that temperature, humidity, and time have a greater impact on current, voltage, and load.

[0060] In one example, an initial element set can be constructed based on at least part of the model input parameters and at least one constant. For example, in one case, at least part of the model input parameters can be used as the initial element set. In another case, at least one constant can be obtained, and at least part of the model input parameters and at least one constant can be used as the initial element set. In other words, the initial element set can include only k model input parameters, or the initial element set can include one or more constants in addition to k model input parameters. For example, the initial element set includes temperature, humidity, time, constant 1, constant 2, constant 3, etc.

[0061] In one example, a data calculation method set can be constructed based on operators and / or functional relationships. The data calculation method set can include multiple operators and / or multiple functional relationships, where the operators can be addition, subtraction, multiplication, division, etc., and the functional relationships can be elementary functions.

[0062] When determining the display analytical expression, it is necessary to determine the target element from the initial element set. The target element includes a target parameter selected from at least part of the model input parameters, and the display analytical expression represents the predicted value of the power parameter to be predicted obtained by processing the parameter value of the target parameter using the target data calculation method. Alternatively, the target element includes a target parameter selected from at least part of the model input parameters and a target constant selected from at least one constant, and the display analytical expression represents the predicted value of the power parameter to be predicted obtained by processing the parameter value of the target parameter and the target constant using the target data calculation method. It is also necessary to obtain the target data calculation method from the data calculation method set.

[0063] For example, target parameters (such as temperature, time) and target constants (such as constant 1) can be selected from the initial element set, and a target data calculation method can be selected from the data calculation method set. Based on the target parameters (such as temperature, time) and the target constants (such as constant 1) and the target data calculation method, a display analytical expression can be obtained, which, for example, takes the power parameter to be predicted (current, voltage, load) as the dependent variable, the target parameter (such as temperature, time) as the independent variable, and the target constant (such as constant 1) as the constant of the function.

[0064] For ease of understanding, the analytical expression is shown as y=a*x1 2 -x2+b as an example, y can represent the power parameter to be predicted, and the target data calculation method selected from the data calculation method set includes, for example, the function relationship () 2 , operators *, +, -, x1, x2, for example, represent target parameters (temperature and time) respectively, and a, b, for example, are both target constants.

[0065] In one example, the target element may be obtained from the initial element set based on a genetic algorithm, and the target data calculation method may be obtained based on a data calculation method set.

[0066] For example, based on the initial element set and the data calculation method set, an initial group is constructed, the initial group includes at least one individual, and each individual includes at least one element selected from the initial element set and at least one data calculation method selected from the data calculation method set.

[0067] The genetic algorithm includes multiple rounds of calculations. For one round of calculations, the initial population or the next generation population obtained by genetic operations can be used as the current population.

[0068] Then, the fitness score of each individual in the current group is calculated, and it is determined whether the first current condition satisfies the first end condition. The current first condition may be, for example, the fitness score, and the first end condition may be, for example, a threshold condition. The first current condition satisfies the first end condition, for example, the fitness score satisfies the threshold condition, such as the fitness score is greater than a preset threshold. The current first condition may also be other conditions, such as the number of genetic iteration calculations or others.

[0069] If the first current condition does not meet the first end condition, a genetic operation is performed on the current population based on the fitness score to obtain the next generation population, and it is determined whether the second current condition meets the second end condition. The second current condition is, for example, the number of genetic iterative calculations, and the second end condition is, for example, a preset number of times. The second current condition meets the second end condition, for example, including that the number of genetic iterative calculations exceeds the preset number of times. If the second current condition does not meet the second end condition, the next generation population is used as the current population for the next round of calculations.

[0070] If the first current condition meets the first end condition, the current group is taken as the last generation group. If the second current condition meets the second end condition, the next generation group is taken as the last generation group, and the target individual is determined in the last generation group, the element in the target individual is taken as the target element, and the data calculation method in the target individual is taken as the target data calculation method.

[0071] When calculating the fitness score of each individual in the current group, sample data may be first obtained. The sample data includes actual parameter values ​​of the parameters and actual parameter values ​​of the power parameters to be predicted. The sample data may be historical data.

[0072] Then, the actual parameter value of the parameter is processed by using the data calculation method in each individual to obtain the processing result. The data calculation method in the individual can be, for example, some functional relationship formulas, and the actual parameter value of the parameter is input into the function as an independent variable to obtain the processing result, and the processing result is the dependent variable result. Next, based on the difference between the processing result and the actual parameter value of the power parameter to be predicted, the fitness score of the individual is obtained. The smaller the difference between the processing result and the actual parameter value of the power parameter to be predicted, the higher the fitness score, indicating that the data calculation method of the individual is more accurate and the effect is better.

[0073] In one example, the genetic operation includes at least one of the following:

[0074] Keep at least one individual with the highest fitness score;

[0075] Select individuals from the current population, for example randomly;

[0076] Copy at least one individual with the highest fitness score;

[0077] Mutate individuals in the current group, including changing one or more elements and / or calculation methods within them;

[0078] Perform string insertion operations on individuals in the current group. There are many ways to insert strings, including inserting any fragment in an individual into any position of the individual, inserting any fragment in an individual into any position of another individual, etc.

[0079] The individuals in the current group are reorganized. The reorganization method includes splitting two or more individuals from one point or multiple points and reconnecting and combining them with each other.

[0080] Figure 2 A flowchart of a method for predicting power data in an area provided in an implementation manner of the present application field.

[0081] like Figure 2 As shown, the prediction method 200 of the power data of the substation includes steps S210-S220, for example.

[0082] Step S210, obtaining a prediction analysis model.

[0083] Exemplarily, the prediction analysis model may be a display analysis formula, and the prediction analysis model is obtained according to the above method.

[0084] Step S220: input the parameter value of the target parameter into the prediction analysis model for prediction, and obtain the predicted value of the power parameter to be predicted.

[0085] Exemplarily, the target parameter may be an independent variable parameter, and the parameter value of the target parameter may be a specific value, such as temperature, humidity, date, time, and the parameter value of the target parameter may be a temperature value, a humidity value, a specific date, and a specific time. The parameter value of the target parameter may be a future value, and the predicted value of the power parameter to be predicted obtained thereby may be a future value.

[0086] The present application also proposes a combination of a method for generating a prediction analysis model for substation power data and a method for predicting substation power data.

[0087] (1) Based on the historical current, voltage, load, temperature, humidity, date, time and other data of the substation, a sample set is generated, and the samples are used to train the neural network model.

[0088] (2) The neural network model is trained by the sample set to obtain a neural network model that represents the relationship between the independent variables (such as temperature, humidity, date, time) and the power data to be predicted (such as substation current, voltage, and load).

[0089] (3) According to the weights of the neural network model, select the independent variable set E corresponding to the first k weights from high to low.

[0090] (4) Set the end conditions of the genetic algorithm evolution generation process.

[0091] (5) Create the initial population of the evolution process. Let R be a set of function symbols (operators or other elementary functions), which is the initial element set mentioned above. Let Q be a set of terminal symbols (including variables or constants), where all variables come from the independent variable set E, which is the data calculation method set mentioned above. The initial population includes at least one individual, and each individual is a sequence a1, a2, ..., a n , b1, b2, ..., b m ,a i ∈R,b j ∈Q,m≥n(e-1)+1, where e is the maximum number of parameters processed by operators or other elementary functions in R, such as b1, b2..., b m In the example, b1 processes 3 variables or constants, b2 processes 2 variables or constants, and b m The number of variables or constants processed is 1, and the maximum number of meshes e can be 3.

[0092] (6) Calculate the fitness score of each individual.

[0093] (7) Check whether the termination conditions are met. If not, proceed to the next step; otherwise, terminate.

[0094] (8) Through genetic manipulation, the next generation of population is produced.

[0095] (9) Go to step (6).

[0096] Among them, the fitness score of each individual in step (6) reflects the degree to which the individual is likely to reach or approach the optimal solution during the calculation process. It is necessary to design a suitable fitness function to calculate the fitness score of each individual. Different fitness functions can be designed for different problems. For example, the following fitness function can be designed:

[0097]

[0098] Among them, n' is the number of samples, and the function value f i，j It represents the function value obtained based on the function expression (data calculation method) corresponding to the i-th individual and the parameter value of the independent variable in the j-th sample (the actual parameter value of the independent variable parameter). The function value f i，j is the processing result of the above text; S j It represents the true value (actual parameter value) of the power parameter to be predicted (such as the current, voltage, load and other dependent variables) actually measured in the jth sample.

[0099] The genetic operations in step (8) may include: retaining the individual with the highest current fitness, selection, replication, mutation (including changing one or more elements and / or calculation methods within it, etc.), string insertion (including inserting any fragment in an individual into any position of the individual, inserting any fragment in an individual into any position of another individual, etc.), and recombination (including splitting two or more individuals from one point or multiple points and rejoining and combining them, etc.).

[0100] Through genetic algorithms, it is possible to eventually determine an individual with the highest fitness score from the last generation of the population. This individual can include target parameters, target constants, and target data calculation methods (operators, other elementary functions). The target parameters are used as independent variables, and the power parameters to be predicted are used as dependent variables. The target parameters and target constants are calculated using the target data calculation method to obtain the power parameters to be predicted, thereby obtaining the explicit analytical expression.

[0101] After obtaining the explicit analytical expression through the genetic algorithm, the current, voltage and load can be predicted based on the explicit analytical expression.

[0102] In another example of the present application, the above-mentioned prediction analysis model generation method and / or the substation power data prediction method can be implemented as a module or a complete electronic device. The module or electronic device can read historical data such as substation current, voltage, load, etc., receive prediction parameters for the future, such as future time, type of parameter to be predicted (such as current, voltage, load, etc.), and output the value of the parameter to be predicted at a given future time.

[0103] In another example of the present application, the above-mentioned prediction analysis model generation method and / or the substation power data prediction method can be integrated into a data processing unit, and the data processing unit includes a board or a chip. The board or chip can be plugged into other devices or systems.

[0104] In another example of the present application, the above-mentioned prediction and analysis model generation method and / or the substation power data prediction method, or the electronic equipment of the module or the whole machine, can be embedded in the distribution network system, and the distribution network system includes, for example, FTU and DTU. Many distribution network devices and systems have the demand for prediction of substation current, voltage, load, etc. In these distribution network devices and systems, such as FTU and DTU, the electronic equipment equipped with the above-mentioned method, module or whole machine can support these devices and systems to realize more intelligent functions. For example, in FTU and DTU, the equipment can predict the future values ​​of current, voltage, and load more accurately and efficiently. By embedding the electronic equipment of the above-mentioned method, module or whole machine into the distribution network system, more accurate substation load balancing, power dispatching, high-quality power supply, etc. can be achieved.

[0105] In another example of the present application, the above-mentioned prediction analysis model generation method and / or the substation power data prediction method, or the electronic equipment of the module or the whole machine, can be integrated into the cloud, edge and terminal in the cloud-edge-end system, or at least one of the cloud, edge and terminal. The cloud-edge-end system is used to implement distribution network management.

[0106] The above method proposed in this application can generate explicit analytical expressions that describe parameters such as current, voltage, and load more accurately and quickly, and has high interpretability. By generating explicit analytical expressions, the changes in the current, voltage, load, etc. of the substation area can be described. Based on the displayed analytical expressions, future currents, voltages, loads, etc. can be predicted. The efficiency of generating analytical expressions through data in this application is high, which is more than 2 orders of magnitude faster than traditional evolutionary algorithms, and has higher prediction accuracy. Traditional evolutionary algorithms include genetic algorithms (GA, Genetic Algorithm), genetic programming (GP, Genetic Programming), etc.

[0107] Figure 3 A schematic diagram of a device for generating a predictive analytical model for substation power data provided in an embodiment of the present application.

[0108] like Figure 3 As shown, the prediction and analysis model generating device 300 for substation power data includes: a selection module 310 , a first obtaining module 320 , a second obtaining module 330 , a third obtaining module 340 and a fourth obtaining module 350 .

[0109] The selection module 310 is used to select at least part of the model input parameters from the model input parameters of the neural network model based on the weights of the neural network model, wherein the neural network model is used to make predictions based on the parameter values ​​of the model input parameters to obtain predicted values ​​of the power parameters to be predicted.

[0110] The first obtaining module 320 is used to obtain an initial element set based on at least part of the model input parameters, and to obtain a data calculation method set.

[0111] The second obtaining module 330 is used to obtain a target element from the initial element set, and obtain a target data calculation method based on a data calculation method set.

[0112] The third obtaining module 340 is used to obtain a display analytical expression based on the target element and the target data calculation method, wherein the display analytical expression represents the predicted value of the power parameter to be predicted obtained based on the target element using the target data calculation method.

[0113] The fourth obtaining module 350 is used to obtain a prediction analytical model based on the displayed analytical expression, wherein the prediction analytical model is used to predict a predicted value of the power parameter to be predicted.

[0114] It can be understood that for the specific description of the prediction and analysis model generating device 300 for substation power data, reference can be made to the description of the prediction and analysis model generating method for substation power data in the above text, which will not be repeated here.

[0115] Figure 4 A schematic diagram of a device for predicting power data in an area provided in an embodiment of the present application.

[0116] like Figure 4 As shown, the prediction device 400 for substation power data includes: an acquisition module 410 and a prediction module 420 .

[0117] The acquisition module 410 is used to acquire the prediction analysis model.

[0118] The prediction module 420 is used to predict the parameter value of the target parameter, input it into the prediction analysis model for prediction, and obtain the predicted value of the power parameter to be predicted.

[0119] It can be understood that for the detailed description of the prediction device 400 for the power data of the substation area, reference can be made to the description of the prediction method for the power data of the substation area in the above text, which will not be described in detail here.

[0120] An embodiment of the present application provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the method in any one of the above embodiments are implemented.

[0121] An embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the method in any one of the above embodiments are implemented.

[0122] One embodiment of the present application provides a computer program product, which includes instructions. When the instructions are executed by a processor of a computer device, the computer device can perform the steps of the method of any of the above embodiments.

[0123] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this application, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection portion with one or more wirings (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or otherwise processing in a suitable manner if necessary, and then stored in a computer memory.

[0124] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0125] In the description of the present application, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In the present application, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0126] In the description of the present application, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0127] In addition, the terms "first", "second", etc. used in the embodiments of the present application are only used for descriptive purposes and should not be understood as indicating or implying relative importance, or implicitly indicating the number of technical features indicated in the present embodiment. Therefore, the features defined by the terms "first", "second", etc. in the embodiments of the present application can explicitly or implicitly indicate that at least one of the features is included in the embodiment. In the description of the present application, the word "multiple" means at least two or two or more, such as two, three, four, etc., unless otherwise clearly and specifically defined in the embodiments.

[0128] In this application, unless otherwise clearly specified or limited in the embodiments, the terms "installed", "connected", "connected" and "fixed" etc. appearing in the embodiments should be understood in a broad sense. For example, the connection can be a fixed connection, a detachable connection, or an integrated connection. It can be understood that it can also be a mechanical connection, an electrical connection, etc.; of course, it can also be a direct connection, or an indirect connection through an intermediate medium, or it can be the internal connection of two elements, or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to the specific implementation situation.

[0129] In the present application, unless otherwise clearly specified and limited, a first feature being “above” or “below” a second feature may mean that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. Moreover, a first feature being “above”, “above”, and “above” a second feature may mean that the first feature is directly above or obliquely above the second feature, or simply means that the first feature is higher in level than the second feature. A first feature being “below”, “below”, and “below” a second feature may mean that the first feature is directly below or obliquely below the second feature, or simply means that the first feature is lower in level than the second feature.

[0130] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for generating a prediction and analytical model for power data in a substation area, characterized in that: The prediction and analysis model generation method comprises: Based on the weights of the neural network model, selecting at least some model input parameters from the model input parameters of the neural network model, wherein the neural network model is used to perform prediction based on parameter values ​​of the model input parameters to obtain a predicted value of the power parameter to be predicted; Obtaining an initial element set based on at least part of the model input parameters, and obtaining a data calculation method set; Obtaining a target element from the initial element set, and obtaining a target data calculation method based on the data calculation method set; Based on the target element and the target data calculation method, a display analytical expression is obtained, wherein the display analytical expression represents a predicted value of the power parameter to be predicted obtained based on the target element by using the target data calculation method; Based on the displayed analytical expression, a prediction analytical model is obtained, wherein the prediction analytical model is used to predict the predicted value of the power parameter to be predicted.

2. The prediction analysis model generation method according to claim 1, characterized in that: Each model input parameter corresponds to a weight; the weight based on the neural network model selects at least part of the model input parameters from the model input parameters of the neural network model, including: Sort the weights corresponding to each model input parameter, and select at least one weight that is ranked first from the sorted weights; The model input parameter corresponding to the at least one selected weight is used as at least part of the selected model input parameter.

3. The prediction analysis model generation method according to claim 1, characterized in that: The obtaining of an initial element set based on at least part of the model input parameters comprises: The at least part of the model input parameters is used as the initial element set, wherein the target element includes a target parameter selected from the at least part of the model input parameters, and the explicit analytical expression represents a predicted value of the power parameter to be predicted obtained by processing the parameter value of the target parameter using the target data calculation method; or Acquire at least one constant, and use the at least part of the model input parameters and the at least one constant as the initial element set, wherein the target element includes a target parameter selected from the at least part of the model input parameters and a target constant selected from the at least one constant, and the displayed analytical expression represents the predicted value of the power parameter to be predicted obtained by processing the parameter value of the target parameter and the target constant using the target data calculation method.

4. The prediction analysis model generation method according to claim 1, characterized in that: The set of data acquisition calculation methods includes: Based on the operators and / or functional relationships, the data calculation method set is constructed.

5. The prediction analysis model generation method according to claim 1, characterized in that: The obtaining of the target element from the initial element set and the obtaining of the target data calculation method based on the data calculation method set include: Based on the initial element set and the data calculation method set, construct an initial group, wherein the initial group includes at least one individual, and each individual includes at least one element selected from the initial element set and at least one data calculation method selected from the data calculation method set; Taking the initial population or the next generation population obtained by genetic operation as the current population; Calculating the fitness score of each individual in the current group, and determining whether the first current condition satisfies the first end condition; If the first current condition does not satisfy the first end condition, based on the fitness score, perform a genetic operation on the current population to obtain a next generation population, and determine whether the second current condition satisfies the second end condition; If the second current condition does not satisfy the second end condition, the next generation population is used as the current population for the next round of calculation; If the first current condition satisfies a first end condition, the current group is taken as the last generation group; if the second current condition satisfies a second end condition, the next generation group is taken as the last generation group, and a target individual is determined from the last generation group; The element in the target individual is used as the target element, and the data calculation method in the target individual is used as the target data calculation method.

6. The prediction analysis model generation method according to claim 5, characterized in that: The calculating of the fitness score of each individual in the current population comprises: Acquiring sample data, wherein the sample data includes actual parameter values ​​of the parameters and actual parameter values ​​of the power parameters to be predicted; Using the data calculation method in the individual, the actual parameter value of the processing parameter is processed to obtain the processing result; Based on the difference between the processing result and the actual parameter value of the power parameter to be predicted, the fitness score of the individual is obtained.

7. The prediction analysis model generation method according to claim 5, characterized in that: The genetic operation includes at least one of the following: Keep at least one individual with the highest fitness score; selecting an individual from the current population; Copy at least one individual with the highest fitness score; mutating individuals in the current population; Performing string insertion on individuals in the current group; The individuals in the current population are reorganized.

8. The prediction analysis model generation method according to any one of claims 1 to 7, characterized in that: The power parameter to be predicted includes at least one of the following: current, voltage, and load.

9. A method for predicting power data in a substation, characterized in that: The prediction method comprises: Obtaining a prediction and analysis model, wherein the prediction and analysis model is obtained according to the method according to any one of claims 1 to 8; The parameter value of the target parameter is input into the prediction analysis model for prediction to obtain the predicted value of the power parameter to be predicted.

10. A device for generating a prediction and analysis model for power data in a substation area, characterized in that: The prediction and analysis model generating device comprises: A selection module, configured to select at least part of the model input parameters from the model input parameters of the neural network model based on the weights of the neural network model, wherein the neural network model is configured to perform prediction based on the parameter values ​​of the model input parameters to obtain a predicted value of the power parameter to be predicted; A first obtaining module, configured to obtain an initial element set based on at least part of the model input parameters, and to obtain a data calculation method set; A second obtaining module, used to obtain a target element from the initial element set, and to obtain a target data calculation method based on the data calculation method set; A third obtaining module is used to obtain a display analytical expression based on the target element and the target data calculation method, wherein the display analytical expression represents a predicted value of the power parameter to be predicted obtained based on the target element using the target data calculation method; The fourth obtaining module is used to obtain a prediction analytical model based on the display analytical expression, wherein the prediction analytical model is used to predict and obtain a predicted value of the power parameter to be predicted.

11. A prediction device for power data of a substation, characterized in that: The prediction device comprises: an acquisition module, used for acquiring a prediction and analysis model, wherein the prediction and analysis model is obtained by the device according to claim 10; The prediction module is used to predict the parameter value of the target parameter, input it into the prediction analysis model for prediction, and obtain the predicted value of the power parameter to be predicted.

12. A data processing unit, characterized in that: The data processing unit comprises a board or a chip, and the data processing unit is used to implement the steps of any one of the methods of claims 1-9.

13. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 9 are implemented.

14. A distribution network system, characterized in that: The distribution network system is used to implement the steps of the method described in any one of claims 1 to 9, or the distribution network system includes the electronic device of claim 13.

15. A cloud-edge-end system, characterized in that: The cloud-edge-terminal system includes a cloud, an edge and a terminal, and at least one of the cloud, the edge and the terminal is used to implement the steps of the method described in any one of claims 1 to 9, or at least one of the cloud, the edge and the terminal includes the electronic device of claim 13.

16. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 9 are implemented.

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