Low-voltage transformer area load prediction method based on data mining

Abstract

The invention discloses a low-voltage transformer area load prediction method based on data mining, and particularly relates to the technical field of load prediction, which comprises the steps of determining load characteristic data of adjacent nodes in a low-voltage transformer area network topology, determining the sensitivity of reactive power between the adjacent nodes, and determining the load characteristic data of the adjacent nodes; the method comprises the steps of obtaining load characteristic information and topological attribute information of adjacent topological nodes of a fixed low-voltage transformer area network, evaluating the influence of reactive power between the adjacent topological nodes of the network, collecting the reactive power generated by each node according to the historical record of the reactive power generated by each node, and establishing a regression model. According to the method, the direct influence of reactive power generated by each node on load prediction is determined, the uncertainty information and influence information of the nodes are obtained, and the nodes with poor load prediction accuracy performance in the low-voltage transformer area are determined, so that accurate positioning and reactive power optimization control of the key nodes in the low-voltage transformer area are realized; the possible inaccuracy of reactive power on load prediction is reduced, and nodes with low load prediction accuracy are identified.

Description

Technical Field

[0001] The present invention relates to the technical field of load forecasting, and more specifically, to a low-voltage area load forecasting method based on data mining. Background Art

[0002] In low-voltage distribution networks, load forecasting is the basis for key decisions such as power dispatching, operation optimization, and reactive power compensation. However, the load characteristics of low-voltage distribution areas are highly time-varying and uncertain, especially the volatility of reactive power, which has a significant impact on the accuracy of load forecasting. Traditional load forecasting methods mainly rely on active power characteristics. However, since reactive power is closely related to factors such as voltage fluctuations, load characteristics, and network topology, relying solely on active power may not accurately describe the load change trend. In addition, there are differences in the reactive power sensitivity of different nodes in the low-voltage distribution network, and the reactive power changes of adjacent nodes will affect each other, resulting in low load forecasting accuracy for some nodes.

[0003] In order to solve the above defects, a technical solution is now provided. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a low-voltage area load forecasting method based on data mining to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: The low voltage area load forecasting method based on data mining specifically includes the following steps: S1: According to the load characteristic data of adjacent nodes in the low-voltage area network topology, the sensitivity of reactive power between adjacent nodes is determined, and the load characteristic information and topological attribute information of adjacent nodes in the low-voltage area network topology are obtained; S2: Comprehensively analyze the load characteristic information and topology attribute information of adjacent nodes in the low-voltage area network topology to evaluate the impact of reactive power between adjacent nodes in the network topology; S3: According to the historical records of reactive power generated by each node, the reactive power generated by each node is collected, and by building a regression model, the direct impact of the reactive power generated by each node on the load forecast is determined, and the uncertainty information and impact information of the node are determined; S4: Combine the uncertainty information and impact information of the node for analysis, build a prediction and evaluation model, and obtain the nodes with poor load prediction accuracy in the low-voltage area.

[0006] In a preferred embodiment, the load characteristic information of adjacent nodes in the low voltage area network topology includes: The load characteristic information of adjacent nodes in the low-voltage area network topology is represented by the dynamic reactive power stock sensitivity coefficient; The acquisition logic of the dynamic reactive stock sensitivity coefficient is: obtaining the actual reactive load and rated reactive capacity of the adjacent nodes in the low-voltage area network topology, determining the reactive load rate of the adjacent nodes, and introducing a time weight factor based on the dynamic characteristics; The power flow equation is solved by the Newton-Raphson method to obtain the voltage and phase angle of the system in steady state. The sub-matrix of the Jacobian matrix is ​​calculated at the steady-state operating point to determine the sensitivity of the node reactive power to the voltage of its adjacent nodes. Traverse the maximum reactive power-voltage sensitivity of adjacent nodes in the low-voltage area network topology and perform normalization processing; The calculation formula of the reactive load rate of the adjacent nodes is: ; ; The sensitivity calculation formula of the node reactive power to its adjacent node voltage is: ; The calculation formula of the dynamic reactive power stock sensitivity coefficient is: ; Where i, j are the numbers of adjacent nodes in the low voltage area network topology. is the node reactive load rate of node i, is the node reactive load rate of node j, is the time weight factor, is the reactive load of node i at time t, is the reactive load stock of node i at time t, is the reactive load of node j at time t, is the reactive load stock of node j at time t, is the reactive power-voltage sensitivity between node i and node j, J is the Jacobian matrix, which represents the partial derivative matrix of the power flow equation, is the dynamic reactive power stock sensitivity coefficient, It is the maximum value of reactive power-voltage sensitivity of all node pairs in the system.

[0007] In a preferred embodiment, the topological attribute information of the adjacent nodes of the low voltage area network topology includes: The topological attribute information of the adjacent nodes of the low-voltage area network topology is represented by the dynamic reactive energy stock sensitivity coefficient; The acquisition logic of the reactive coupling response coefficient is: by determining the reactive disturbance amount of node i and the reactive response peak value of node j, the reactive floating sensitivity coefficient is determined; By analyzing the waveform timestamp, the time from the occurrence of reactive disturbance at node i to the reactive response peak at node j is determined, the delay attenuation is determined, and the equivalent impedance is introduced to reflect the impact of the physical characteristics of the line on reactive power transmission; The calculation formula of reactive floating sensitivity coefficient is: ; The calculation formula for equivalent impedance is: ; The calculation formula of reactive coupling response coefficient is: ; In the formula, is the reactive floating sensitivity coefficient, is the peak value of the reactive power corresponding to node j, is the reactive disturbance of node i, is the equivalent impedance of node ij, R is the resistance of ij, X is the reactance of ij, Response delay is the reactive coupling response coefficient.

[0008] In a preferred embodiment, evaluating the impact of reactive power between adjacent nodes in a network topology includes: The load characteristic information and topological attribute information of adjacent nodes in the low-voltage area network topology are comprehensively analyzed, and a node evaluation model is constructed based on the BP neural network algorithm to generate a node evaluation coefficient. The calculation formula of the node evaluation coefficient is: ; In the formula, is the node evaluation coefficient, , is the proportional coefficient of the dynamic reactive stock sensitivity coefficient and reactive coupling response coefficient, , They are both greater than 0; Set the node evaluation coefficient threshold, obtain the node evaluation coefficient of each adjacent node in the low-voltage substation network topology, compare the node evaluation coefficient of each adjacent node in the low-voltage substation network topology with the node evaluation coefficient threshold, if the node evaluation coefficient is greater than the node evaluation coefficient threshold, then the adjacent node is marked as a key influencing node, if the node evaluation coefficient is less than the node evaluation coefficient threshold, then it is not marked.

[0009] In a preferred embodiment, the uncertainty information of the node includes: The uncertainty information of the node is represented by the distribution entropy value change coefficient; The acquisition logic of the distribution entropy value variation coefficient is as follows: acquiring the historical time series data of reactive power of each node, setting the time granularity to divide the unit time period, extracting the reactive power of each unit time period according to the time granularity, and forming a sub-data set; The probability density function is estimated using the Gaussian kernel function for the reactive power data of each unit time period, and the distribution entropy value variation coefficient is determined by entropy value calculation; The calculation formula of the probability density function of reactive power of different nodes in each unit time period is: ; The calculation formula of the distribution entropy value change coefficient is: ; In the formula, is the probability density function of the kth unit time period, k is the number of different unit time periods, Unit time period The total number of samples in, n is the sample index, Q is the specified value of reactive power, Unit time period The reactive power inside is the coefficient of change of distribution entropy, and h is the bandwidth.

[0010] In a preferred implementation, the node's impact information includes: The influence information of the node is represented by the joint influence assessment coefficient; The acquisition logic of the joint impact assessment coefficient is: different nodes are divided into different types according to the size of the node assessment coefficient, and the node assessment coefficient of the node adjacent to the node in a unit time period is obtained; Determine the types of adjacent nodes of a node within a unit time period, determine the accuracy deviation of different types of adjacent nodes on the load prediction of low-voltage area nodes through historical records, and perform accuracy scoring on different types of adjacent nodes based on the accuracy deviation of different types of adjacent nodes on the load prediction of low-voltage area nodes; According to the scores of different types of adjacent nodes, the number of different types of adjacent nodes, and the proportion of different types of duration of adjacent nodes, a regression model is constructed to generate a joint impact assessment coefficient; The calculation formula of the joint impact assessment coefficient is: ; In the formula, is the joint impact assessment coefficient, 1, 2, 3, ..., m are the numbers of different types of nodes, , , ……、 Scoring different types of neighboring nodes, , , ……、 is the number of adjacent nodes of different types, , , ……、 is the duration ratio of different types of adjacent nodes, , , ……、 are weights of adjacent nodes of different types, and e is a natural number.

[0011] In a preferred embodiment, determining a node with poor load forecasting accuracy in a low-voltage area includes: Through comprehensive analysis of the uncertainty information and impact information of the nodes, the distribution entropy value change coefficient and the joint impact assessment coefficient are weighted and calculated to build a prediction and assessment model and generate a prediction and assessment coefficient. The calculation formula of the prediction and assessment coefficient is: ; In the formula, is the prediction evaluation coefficient, , are the proportional coefficients of the distribution entropy change coefficient and the joint impact assessment coefficient, respectively. , They are both greater than 0; Set the prediction evaluation coefficient threshold, obtain the prediction evaluation coefficients of nodes in different time periods, and compare the prediction evaluation coefficients of nodes in different time periods with the prediction evaluation coefficient threshold. If the prediction evaluation coefficient is greater than the prediction evaluation coefficient threshold, a warning signal is generated. If the node still has continuous warnings and the reactive power fluctuates greatly, no warning signal is generated.

[0012] Technical effects and advantages of the present invention: 1. The present invention collects real-time reactive load, voltage, and current data of adjacent nodes in low-voltage substations through smart meters, micro PMUs and other equipment, and through physical-data fusion analysis of dynamic reactive stock sensitivity coefficients and reactive coupling response coefficients, combined with intelligent evaluation of BP neural networks, it achieves precise positioning and reactive optimization control of key nodes in low-voltage substations, thereby reducing the inaccuracy of reactive power in load prediction.

[0013] 2. The present invention quantifies the fluctuation of reactive power by analyzing the historical reactive power records of each node, and quantifies the impact of reactive power on load accuracy by analyzing the mutual influence between the reactive powers of each node, thereby identifying nodes with low load prediction accuracy. This helps to take targeted optimization measures after identifying nodes with low prediction accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings; Figure 1 The figure is a flow chart of the low voltage area load forecasting method based on data mining of the present invention. DETAILED DESCRIPTION

[0015] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0016] Example 1 Figure 1 The present invention is a flow chart of a method for predicting low-voltage load in a substation based on data mining, which specifically includes the following steps: S1: According to the load characteristic data of adjacent nodes in the low-voltage area network topology, the sensitivity of reactive power between adjacent nodes is determined, and the load characteristic information and topological attribute information of adjacent nodes in the low-voltage area network topology are obtained; S2: Comprehensively analyze the load characteristic information and topology attribute information of adjacent nodes in the low-voltage area network topology to evaluate the impact of reactive power between adjacent nodes in the network topology; S3: According to the historical records of reactive power generated by each node, the reactive power generated by each node is collected, and by building a regression model, the direct impact of the reactive power generated by each node on the load forecast is determined to obtain the uncertainty information and impact information of the node; S4: Combine the uncertainty information and impact information of the nodes for analysis, build a prediction and evaluation model, and identify the nodes with poor load prediction accuracy in the low-voltage substation area.

[0017] In the network topology of the low-voltage substation, a tree-like or radial structure is formed through cables and wires, and electric energy flows from the transformer to each user device. Among them, the flow of reactive power will directly affect the voltage level in the power grid, and then affect the power quality of the user. By considering the influence of reactive power between adjacent nodes in the network topology of the low-voltage substation, the change trend of the load can be more accurately reflected. By reasonably configuring and dynamically controlling reactive compensation equipment, the reactive power distribution of the low-voltage substation can be significantly improved, thereby reducing the mutual influence of reactive power between nodes and providing more accurate data support for load forecasting.

[0018] By collecting the load characteristic information and topology attribute information of adjacent nodes in the low-voltage substation network topology, the impact of reactive power between adjacent nodes in the network topology is evaluated, and the load characteristic information of adjacent nodes in the low-voltage substation network topology is expressed through the dynamic reactive stock sensitivity coefficient, and the topology attribute information of adjacent nodes in the low-voltage substation network topology is expressed through the reactive coupling response coefficient.

[0019] Among them, the advantage of the dynamic reactive power stock sensitivity coefficient is that it can accurately capture the actual changes in reactive power demand of each node by quantifying the reactive load of each node, avoiding deviations under simple assumptions, and by calculating the sensitivity between nodes, it can determine that some nodes have a greater impact on reactive power changes and identify node pairs with strong electrical coupling. For these nodes, adjusting reactive power is particularly important for the overall stability of the system.

[0020] The acquisition logic of the dynamic reactive stock sensitivity coefficient is: obtaining the actual reactive load and rated reactive capacity of the adjacent nodes in the low-voltage area network topology, determining the reactive load rate of the adjacent nodes, and introducing a time weight factor based on the dynamic characteristics; The power flow equation is solved by the Newton-Raphson method to obtain the voltage and phase angle of the system in steady state. The sub-matrix of the Jacobian matrix is ​​calculated at the steady-state operating point to determine the sensitivity of the node reactive power to the voltage of its adjacent nodes. Traverse the maximum reactive power-voltage sensitivity of adjacent nodes in the low-voltage area network topology and perform normalization processing; The calculation formula of the reactive load rate of the adjacent nodes is: ; ; The sensitivity calculation formula of the node reactive power to its adjacent node voltage is: ; The calculation formula of the dynamic reactive power stock sensitivity coefficient is: ; Where i, j are the numbers of adjacent nodes in the low voltage area network topology. is the node reactive load rate of node i, is the node reactive load rate of node j, is the time weight factor, is the reactive load of node i at time t, is the reactive load stock of node i at time t, is the reactive load of node j at time t, is the reactive load stock of node j at time t, is the reactive power-voltage sensitivity between node i and node j, J is the Jacobian matrix, which represents the partial derivative matrix of the power flow equation, is the dynamic reactive power stock sensitivity coefficient, It is the maximum value of reactive power-voltage sensitivity of all node pairs in the system.

[0021] Need to explain, The dynamic weights that reflect load fluctuations or urgency, such as increases during peak hours and decreases during low-load hours, are larger. The larger the dynamic reactive stock sensitivity coefficient, the more coordinated control of nodes is required in real-time scheduling. By deploying dynamic reactive compensation devices, the accuracy of load prediction can be improved.

[0022] The advantages of the reactive coupling response coefficient are: Reactive coupling response coefficient In low-voltage lines, due to the large resistance, reactive power flow is not only affected by the voltage amplitude difference, but also related to the phase angle difference. Therefore, the reactive coupling response coefficient can take into account the influence of resistance; Load fluctuations in low-voltage substations may lead to frequent reactive power changes. In practical applications, the reactive coupling response coefficient is extracted from a large amount of real-time data through statistical methods or machine learning models. By combining data-driven methods and hierarchical control strategies, the reactive coupling response coefficient can be effectively utilized to optimize reactive power management and improve voltage stability.

[0023] The acquisition logic of the reactive coupling response coefficient is: by determining the reactive disturbance amount of node i and the reactive response peak value of node j, the reactive floating sensitivity coefficient is determined; By analyzing the waveform timestamp, the time from the occurrence of reactive disturbance at node i to the reactive response peak at node j is determined, the delay attenuation is determined, and the equivalent impedance is introduced to reflect the impact of the physical characteristics of the line on reactive power transmission; The calculation formula of reactive floating sensitivity coefficient is: ; The calculation formula for equivalent impedance is: ; The calculation formula of reactive coupling response coefficient is: ; In the formula, is the reactive floating sensitivity coefficient, is the peak value of the reactive power corresponding to node j, is the reactive disturbance of node i, is the equivalent impedance of node ij, R is the resistance of ij, X is the reactance of ij, Response delay is the reactive coupling response coefficient.

[0024] It should be noted that data can be collected through simulation experiments using high-precision smart meters or micro PMUs, or programmable load simulators. The larger the reactive coupling response coefficient, the stronger the reactive influence of node i on node j, and the easier it is to transmit the influence. By deploying dynamic reactive compensation devices at key nodes, voltage fluctuations can be effectively suppressed and coupling effects can be weakened, thereby improving the accuracy of the load forecasting model.

[0025] The load characteristic information and topological attribute information of adjacent nodes in the low-voltage area network topology are comprehensively analyzed, and a node evaluation model is constructed based on the BP neural network algorithm to generate a node evaluation coefficient. The calculation formula of the node evaluation coefficient is: ; In the formula, is the node evaluation coefficient, , is the proportional coefficient of the dynamic reactive stock sensitivity coefficient and reactive coupling response coefficient, , Both are greater than 0.

[0026] Set the node evaluation coefficient threshold, obtain the node evaluation coefficient of each adjacent node in the low-voltage substation network topology, compare the node evaluation coefficient of each adjacent node in the low-voltage substation network topology with the node evaluation coefficient threshold, if the node evaluation coefficient is greater than the node evaluation coefficient threshold, then the adjacent node is marked as a key influencing node, and a dynamic reactive power compensation device is deployed at the key node, if the node evaluation coefficient is less than the node evaluation coefficient threshold, then no marking is performed.

[0027] This embodiment collects real-time reactive load, voltage, and current data of adjacent nodes in the low-voltage substation through smart meters, micro PMUs and other equipment, and through physical-data fusion analysis of dynamic reactive stock sensitivity coefficients and reactive coupling response coefficients, combined with intelligent evaluation of BP neural networks, it achieves precise positioning and reactive optimization control of key nodes in the low-voltage substation, thereby reducing the inaccuracy of reactive power in load prediction.

[0028] Example 2 According to the network topology of the low-voltage substation, the nodes in the low-voltage substation network topology that affect the accuracy of load forecasting due to the reactive power of the nodes are determined. According to the historical records of reactive power generated by each node, the reactive power generated by each node is collected, and the uncertainty information of the node is determined. The uncertainty information of the node is represented by the distribution entropy value change coefficient. By constructing a regression model, the direct impact of the reactive power generated by each node on the load forecast is determined, and the impact information of the node is determined. The impact information of the node is represented by the joint impact assessment coefficient.

[0029] The advantages of the distribution entropy value variation coefficient are: The coefficient of variation of the distribution entropy value can identify the uncertainty of the node and help identify the nodes with unstable reactive power generation in different power consumption periods, thereby improving the accuracy of load forecasting in different power consumption periods.

[0030] The acquisition logic of the distribution entropy value variation coefficient is as follows: acquiring the historical time series data of reactive power of each node, setting the time granularity to divide the unit time period, extracting the reactive power of each unit time period according to the time granularity, and forming a sub-data set; The probability density function is estimated using the Gaussian kernel function for the reactive power data of each unit time period, and the distribution entropy value variation coefficient is determined by entropy value calculation; The calculation formula of the probability density function of reactive power of different nodes in each unit time period is: ; The calculation formula of the distribution entropy value change coefficient is: ; In the formula, is the probability density function of the kth unit time period, k is the number of different unit time periods, Unit time period The total number of samples in, n is the sample index, Q is the specified value of reactive power, Unit time period The reactive power inside is the coefficient of change of distribution entropy, and h is the bandwidth.

[0031] It should be noted that k is determined by the time resolution, which is determined by professionals in the field, and n represents the unit time period. For the nth data point in the distribution entropy, the larger the change coefficient of the distribution entropy value is, the greater the change of the reactive power at the node in the unit time period is, and the more irregular it may be. Therefore, in load forecasting, the error will usually increase with the increase of the forecast time, and the complex change of reactive power will aggravate the error accumulation, resulting in a decrease in the accuracy of long-term forecasting.

[0032] The advantages of the joint impact assessment coefficient are: By combining the impact assessment coefficient, we can quantify the impact of different types of adjacent nodes on the accuracy of target node load prediction. By dividing the node types, we can dynamically adapt to different power consumption patterns, avoid using fixed models for evaluation, and improve the ability to adapt to complex load changes. By analyzing the deviations of load forecasts of different types of adjacent nodes through historical records, a data-driven accuracy scoring system is established to reduce reliance on human experience. Combined with multi-time period data analysis, long-term impact patterns can be identified, which helps to more accurately evaluate the difficulty of load forecasting at nodes.

[0033] The acquisition logic of the joint impact assessment coefficient is: different nodes are divided into different types according to the size of the node assessment coefficient, and the node assessment coefficient of the node adjacent to the node in a unit time period is obtained; Determine the types of adjacent nodes of a node within a unit time period, determine the accuracy deviation of different types of adjacent nodes on the load prediction of low-voltage area nodes through historical records, and perform accuracy scoring on different types of adjacent nodes based on the accuracy deviation of different types of adjacent nodes on the load prediction of low-voltage area nodes; According to the scores of different types of adjacent nodes, the number of different types of adjacent nodes, and the proportion of different types of duration of adjacent nodes, a regression model is constructed to generate a joint impact assessment coefficient; The calculation formula of the joint impact assessment coefficient is: ; In the formula, is the joint impact assessment coefficient, 1, 2, 3, ..., m are the numbers of different types of nodes, , , ……、 Scoring different types of neighboring nodes, , , ……、 is the number of adjacent nodes of different types, , , ……、 is the duration ratio of different types of adjacent nodes, , , ……、 are weights of adjacent nodes of different types, and e is a natural number.

[0034] It should be noted that there may be multiple nodes adjacent to a node, that is, users using the same distribution box. Based on the users' electricity consumption in a unit time period, adjacent nodes may have different patterns of electricity consumption changes. Therefore, nodes may be divided into different node types in a unit time period. The larger the joint impact assessment coefficient, the stronger the interference of adjacent nodes on the node load forecast, the more it needs to be monitored and compensated, and the lower the accuracy of the forecast.

[0035] Through comprehensive analysis of the uncertainty information and impact information of the nodes, the distribution entropy value change coefficient and the joint impact assessment coefficient are weighted and calculated to build a prediction and assessment model and generate a prediction and assessment coefficient. The calculation formula of the prediction and assessment coefficient is: ; In the formula, is the prediction evaluation coefficient, , are the proportional coefficients of the distribution entropy change coefficient and the joint impact assessment coefficient, respectively. , Both are greater than 0.

[0036] Set a prediction evaluation coefficient threshold, obtain the prediction evaluation coefficients of nodes in different time periods, and compare the prediction evaluation coefficients of nodes in different time periods with the prediction evaluation coefficient threshold. If the prediction evaluation coefficient is greater than the prediction evaluation coefficient threshold, a warning signal is generated to notify relevant staff to optimize the load forecasting algorithm for the warning node and time period. If the node still has continuous warnings and the reactive power fluctuates greatly, the power grid operation strategy is appropriately adjusted, such as adding reactive compensation equipment or adjusting the topology structure. If the prediction evaluation coefficient is less than the prediction evaluation coefficient threshold, no warning signal is generated.

[0037] This embodiment quantifies the fluctuation of reactive power by analyzing the historical reactive power records of each node, and quantifies the impact of reactive power on load accuracy by analyzing the mutual influence between the reactive power of each node, thereby identifying nodes with low load prediction accuracy. This helps to take targeted optimization measures after identifying nodes with low prediction accuracy.

[0038] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0039] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0040] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0041] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0042] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0043] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0044] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A low voltage area load forecasting method based on data mining, characterized in that: The specific steps include: S1: According to the load characteristic data of adjacent nodes in the low-voltage area network topology, the sensitivity of reactive power between adjacent nodes is determined, and the load characteristic information and topological attribute information of adjacent nodes in the low-voltage area network topology are obtained; S2: Comprehensively analyze the load characteristic information and topology attribute information of adjacent nodes in the low-voltage area network topology to evaluate the impact of reactive power between adjacent nodes in the network topology; S3: According to the historical records of reactive power generated by each node, the reactive power generated by each node is collected, and by building a regression model, the direct impact of the reactive power generated by each node on the load forecast is determined to obtain the uncertainty information and impact information of the node; S4: Combine the uncertainty information and impact information of the nodes for analysis, build a prediction and evaluation model, and identify the nodes with poor load prediction accuracy in the low-voltage substation area.

2. The low voltage area load forecasting method based on data mining according to claim 1 is characterized in that: Load characteristic information of adjacent nodes in the low-voltage area network topology, including: The load characteristic information of adjacent nodes in the low-voltage area network topology is represented by the dynamic reactive power stock sensitivity coefficient; The acquisition logic of the dynamic reactive stock sensitivity coefficient is: obtaining the actual reactive load and rated reactive capacity of the adjacent nodes in the low-voltage area network topology, determining the reactive load rate of the adjacent nodes, and introducing a time weight factor based on the dynamic characteristics; The power flow equation is solved by the Newton-Raphson method to obtain the voltage and phase angle of the system in steady state. The sub-matrix of the Jacobian matrix is ​​calculated at the steady-state operating point to determine the sensitivity of the node reactive power to the voltage of its adjacent nodes. Traverse the maximum reactive power-voltage sensitivity of adjacent nodes in the low-voltage area network topology and perform normalization processing; The calculation formula of the reactive load rate of the adjacent nodes is: ； ； The sensitivity calculation formula of the node reactive power to its adjacent node voltage is: ； The calculation formula of the dynamic reactive power stock sensitivity coefficient is: ； Where i, j are the numbers of adjacent nodes in the low voltage area network topology. is the node reactive load rate of node i, is the node reactive load rate of node j, is the time weight factor, is the reactive load of node i at time t, is the reactive load stock of node i at time t, is the reactive load of node j at time t, is the reactive load stock of node j at time t, is the reactive power-voltage sensitivity between node i and node j, J is the Jacobian matrix, which represents the partial derivative matrix of the power flow equation, is the dynamic reactive power stock sensitivity coefficient, It is the maximum value of reactive power-voltage sensitivity of all node pairs in the system.

3. The low voltage area load forecasting method based on data mining according to claim 1 is characterized in that: The topological attribute information of adjacent nodes in the low-voltage area network topology includes: The topological attribute information of the adjacent nodes of the low-voltage area network topology is represented by the dynamic reactive energy stock sensitivity coefficient; The acquisition logic of the reactive coupling response coefficient is: by determining the reactive disturbance amount of node i and the reactive response peak value of node j, the reactive floating sensitivity coefficient is determined; By analyzing the waveform timestamp, the time from the occurrence of reactive disturbance at node i to the reactive response peak at node j is determined, the delay attenuation is determined, and the equivalent impedance is introduced to reflect the impact of the physical characteristics of the line on reactive power transmission; The calculation formula of reactive floating sensitivity coefficient is: ； The calculation formula for equivalent impedance is: ； The calculation formula of reactive coupling response coefficient is: ； In the formula, is the reactive floating sensitivity coefficient, is the peak value of the reactive power corresponding to node j, is the reactive disturbance of node i, is the equivalent impedance of node ij, R is the resistance of ij, X is the reactance of ij, Response delay is the reactive coupling response coefficient.

4. The low voltage area load forecasting method based on data mining according to claim 3 is characterized in that: Evaluate the impact of reactive power between adjacent nodes in the network topology, including: The load characteristic information and topological attribute information of adjacent nodes in the low-voltage area network topology are comprehensively analyzed, and a node evaluation model is constructed based on the BP neural network algorithm to generate a node evaluation coefficient. The calculation formula of the node evaluation coefficient is: ； In the formula, is the node evaluation coefficient, , is the proportional coefficient of the dynamic reactive stock sensitivity coefficient and reactive coupling response coefficient, , They are both greater than 0; Set the node evaluation coefficient threshold, obtain the node evaluation coefficient of each adjacent node in the low-voltage substation network topology, compare the node evaluation coefficient of each adjacent node in the low-voltage substation network topology with the node evaluation coefficient threshold, if the node evaluation coefficient is greater than the node evaluation coefficient threshold, then the adjacent node is marked as a key influencing node, if the node evaluation coefficient is less than the node evaluation coefficient threshold, then it is not marked.

5. The method for low voltage area load forecasting based on data mining according to claim 4 is characterized in that: Uncertainty information of the node, including: The uncertainty information of the node is represented by the distribution entropy value change coefficient; The acquisition logic of the distribution entropy value variation coefficient is as follows: acquiring the historical time series data of reactive power of each node, setting the time granularity to divide the unit time period, extracting the reactive power of each unit time period according to the time granularity, and forming a sub-data set; The probability density function is estimated using the Gaussian kernel function for the reactive power data of each unit time period, and the distribution entropy value variation coefficient is determined by entropy value calculation; The calculation formula of the probability density function of reactive power of different nodes in each unit time period is: ； The calculation formula of the distribution entropy value change coefficient is: ； In the formula, is the probability density function of the kth unit time period, k is the number of different unit time periods, Unit time period The total number of samples in, n is the sample index, Q is the specified value of reactive power, Unit time period The reactive power inside is the coefficient of change of distribution entropy value, and h is the bandwidth.

6. The method for low voltage area load forecasting based on data mining according to claim 5 is characterized in that: Node impact information, including: The influence information of the node is represented by the joint influence assessment coefficient; The acquisition logic of the joint impact assessment coefficient is: different nodes are divided into different types according to the size of the node assessment coefficient, and the node assessment coefficient of the node adjacent to the node in a unit time period is obtained; Determine the types of adjacent nodes of a node within a unit time period, determine the accuracy deviation of different types of adjacent nodes on the load prediction of low-voltage area nodes through historical records, and perform accuracy scoring on different types of adjacent nodes based on the accuracy deviation of different types of adjacent nodes on the load prediction of low-voltage area nodes; According to the scores of different types of adjacent nodes, the number of different types of adjacent nodes, and the proportion of different types of duration of adjacent nodes, a regression model is constructed to generate a joint impact assessment coefficient; The calculation formula of the joint impact assessment coefficient is: ； In the formula, is the joint impact assessment coefficient, 1, 2, 3, ..., m are the numbers of different types of nodes, , , ……、 Scoring different types of neighboring nodes, , , ……、 is the number of adjacent nodes of different types, , , ……、 is the duration ratio of different types of adjacent nodes, , , ……、 are weights of adjacent nodes of different types, and e is a natural number.

7. The method for predicting low voltage load based on data mining according to claim 6 is characterized in that: Identify nodes with poor load forecasting accuracy in low-voltage areas, including: Through comprehensive analysis of the uncertainty information and impact information of the nodes, the distribution entropy value change coefficient and the joint impact assessment coefficient are weighted and calculated to build a prediction and assessment model and generate a prediction and assessment coefficient. The calculation formula of the prediction and assessment coefficient is: ； In the formula, is the prediction evaluation coefficient, , are the proportional coefficients of the distribution entropy change coefficient and the joint impact assessment coefficient, respectively. , They are both greater than 0; Set the prediction evaluation coefficient threshold, obtain the prediction evaluation coefficients of nodes in different time periods, and compare the prediction evaluation coefficients of nodes in different time periods with the prediction evaluation coefficient threshold. If the prediction evaluation coefficient is greater than the prediction evaluation coefficient threshold, a warning signal is generated. If the node still has continuous warnings and the reactive power fluctuates greatly, no warning signal is generated.

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