A power distribution management method and system based on AVC system

Abstract

The present invention is applicable to the technical field of power distribution management, and in particular, relates to a power distribution management method and system based on an AVC system, the method comprising determining the accuracy of a theoretical data relationship based on actual data of two measurement nodes; for any measurement node, calculating the accuracy mean between it and other measurement nodes, and adjusting the application probability of each measurement node according to the accuracy mean; when a power distribution management request is received, randomly selecting a preset number of measurement nodes based on the application probability at the corresponding moment, and performing power distribution management based on the data at the selected measurement nodes; the present invention selects a limited number of instruments to obtain data, and reversely infers all data in the entire line model through the limited data, from point to surface, greatly reducing the pressure of obtaining data while reducing part of the accuracy, and providing support for improving the power distribution frequency.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution management, and in particular to a power distribution management method and system based on an AVC system. Background Art

[0002] The power distribution management method and system based on the AVC (Automatic Voltage Control) system is an advanced management method that uses automation technology to monitor and control the distribution network in the power system in real time to ensure voltage stability and power supply reliability. The application of the AVC system in power distribution management aims to dynamically adjust voltage and reactive power through automated control equipment and optimization algorithms to improve the operating efficiency of the distribution network.

[0003] In the power distribution management process of the AVC system, a very important part is to obtain data from the power grid. The existing data acquisition process basically relies on various instruments with networking functions. The data acquisition process of these instruments is real-time, and all instruments will upload data. This method is suitable for scheduled power distribution processes. For example, power distribution is carried out every hour. If the power distribution frequency is increased, the pressure to obtain data will be very high. If the pressure to obtain data can be alleviated, the power distribution frequency can be increased, making the power distribution management process more sensitive. Summary of the invention

[0004] The object of the present invention is to provide a power distribution management method and system based on an AVC system, so as to solve the problem of "how to improve the smoothness of power distribution management based on an AVC system through interpolation and buffering" raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A power distribution management method based on an AVC system, the method comprising:

[0007] Modeling the power grid line to obtain a line model, obtaining power consumption information of the power supply point of the power grid line, identifying the power consumption information, and selecting a measurement node in the line model; installing a sensor unit at the measurement node;

[0008] At a preset frequency, two measurement nodes are randomly selected to obtain the series-parallel relationship of the measurement nodes in the line model, and the theoretical data relationship at the measurement nodes is determined according to the series-parallel relationship;

[0009] Determine the accuracy of the theoretical data relationship based on the actual data of the two measurement nodes;

[0010] For any measurement node, calculate the accuracy mean of it and other measurement nodes, and adjust the application probability of each measurement node according to the accuracy mean; wherein the application probability is proportional to the accuracy mean;

[0011] When a power distribution management request is received, a preset number of calculation nodes are randomly selected based on the application probability at the corresponding time, and power distribution management is performed based on the data at the selected calculation nodes.

[0012] Furthermore, the steps of modeling the power grid line to obtain a line model, obtaining power consumption information of power supply points of the power grid line, identifying the power consumption information, and selecting a measurement node in the line model include:

[0013] Obtaining the line position of the power grid line, and constructing a line model according to a preset scale; the line model is based on a circuit model of the actual line;

[0014] Querying the power supply point in the power grid line to obtain power consumption information at the power supply point; the power consumption information is instantaneous power consumption with time as an independent variable;

[0015] The power consumption information is analyzed and a measurement node is determined in a line model.

[0016] Furthermore, the step of analyzing the power consumption information and determining the measurement node in the line model includes:

[0017] Obtaining the instantaneous power consumption of each power consumption unit according to a preset time interval;

[0018] Based on the same time axis, the instantaneous power consumption of each power consumption unit is counted and the power consumption curve is fitted;

[0019] Obtaining the integral of each power consumption curve within a time period, and calculating the integral sum as a first value; the first value is used to characterize the total power supply of the power supply point;

[0020] Calculate the derivative curve of each power consumption curve, convert the derivative curve into absolute value and accumulate it, and the obtained curve is called the fluctuation curve; calculate the integral of the fluctuation curve in the time period as the second value; the second value is used to characterize the power supply stability of the power supply point;

[0021] An evaluation value is determined according to the first value and the second value, and a power supply point is selected as a measurement node based on the evaluation value.

[0022] Furthermore, the step of determining an evaluation value according to the first value and the second value, and selecting a power supply point as a measurement node based on the evaluation value includes:

[0023] Adding the first value and the second value according to a preset weight;

[0024] For any power supply point, calculate the distance between the selected measurement node and the power supply point;

[0025] Determine an evaluation value according to the summed distance and the distance, and when the evaluation value reaches a preset evaluation value threshold, select a power supply point as a measurement node;

[0026] The calculation process of the evaluation value is:

[0027] In the formula, x0 represents the transformer to be analyzed, x i is the i-th transformer; Value(x0) is the evaluation value of the transformer to be analyzed, α and β are both preset weights; A is the first value, B is the second value; dis(x0,x i ) is the distance between the transformer to be analyzed and the i-th transformer.

[0028] Furthermore, the step of calculating the accuracy mean of any measurement node and other measurement nodes, and adjusting the application probability of each measurement node according to the accuracy mean includes:

[0029] For any measurement node, query its accuracy with other measurement nodes during the historical calculation process;

[0030] Calculate the average of all accuracies found as the average accuracy;

[0031] The application probability of each measurement node is determined based on the accuracy mean.

[0032] Furthermore, when receiving the power distribution management request, randomly selecting a preset number of measurement nodes based on the application probability at the corresponding moment, and performing power distribution management based on the data at the selected measurement nodes includes:

[0033] When receiving a power distribution management request, query the application probability of each measurement node at the current moment;

[0034] Randomly calculating nodes based on the application probability until the number of selected calculation nodes reaches a preset number;

[0035] Predicting the data at all measurement nodes based on the data at the selected calculation nodes and the theoretical data relationship between the measurement nodes;

[0036] The data at all measurement nodes are input into the trained power distribution management model, and the power distribution management plan is output.

[0037] The technical solution of the present invention also provides a power distribution management system based on the AVC system, the system comprising:

[0038] A calculation node selection module is used to model the power grid line, obtain a line model, obtain power consumption information of the power supply point of the power grid line, identify the power consumption information, and select a calculation node in the line model; a sensor unit is installed at the calculation node;

[0039] A circuit relationship determination module is used to randomly select two measurement nodes at a preset frequency, obtain the series-parallel relationship of the measurement nodes in the circuit model, and determine the theoretical data relationship at the measurement nodes according to the series-parallel relationship;

[0040] An accuracy determination module, used to determine the accuracy of the theoretical data relationship based on the actual data of the two measurement nodes;

[0041] An application probability calculation module is used to calculate the accuracy mean of any measurement node and other measurement nodes, and adjust the application probability of each measurement node according to the accuracy mean; wherein the application probability is proportional to the accuracy mean;

[0042] The power distribution management module is used to randomly select a preset number of measurement nodes based on the application probability at the corresponding moment when a power distribution management request is received, and perform power distribution management based on the data at the selected measurement nodes.

[0043] Furthermore, the calculation node selection module includes:

[0044] A line model creation unit, used to obtain the line position of the power grid line and construct a line model according to a preset scale; the line model is based on a circuit model of the actual line;

[0045] An electricity consumption information acquisition unit is used to query a power supply point in a power grid line and acquire electricity consumption information at the power supply point; the electricity consumption information is instantaneous electricity consumption with time as an independent variable;

[0046] The power consumption information analysis unit is used to analyze the power consumption information and determine the measurement nodes in the line model.

[0047] Furthermore, the power consumption information analysis unit includes:

[0048] A data acquisition subunit, used to acquire the instantaneous power consumption of each power consumption unit according to a preset time interval;

[0049] A fitting subunit is used to count the instantaneous power consumption of each power consumption unit based on the same time axis and fit the power consumption curve;

[0050] A first calculation subunit is used to obtain the integral of each power consumption curve within a time period, and calculate the integral sum as a first value; the first value is used to characterize the total power supply of the power supply point;

[0051] The second calculation subunit is used to calculate the derivative curve of each power consumption curve, convert the derivative curve into absolute value and accumulate it, and the obtained curve is called the fluctuation curve; the integral of the fluctuation curve in the time period is calculated as the second value; the second value is used to characterize the power supply stability of the power supply point;

[0052] An execution subunit is selected to determine an evaluation value according to the first numerical value and the second numerical value, and a power supply point is selected as a measurement node based on the evaluation value.

[0053] Furthermore, the step of determining an evaluation value according to the first value and the second value, and selecting a power supply point based on the evaluation value as a measurement node includes:

[0054] Adding the first value and the second value according to a preset weight;

[0055] For any power supply point, calculate the distance between the selected measurement node and the power supply point;

[0056] Determine an evaluation value according to the summed distance and the distance, and when the evaluation value reaches a preset evaluation value threshold, select a power supply point as a measurement node;

[0057] The calculation process of the evaluation value is:

[0058] In the formula, x0 represents the transformer to be analyzed, x i is the i-th transformer; Value(x0) is the evaluation value of the transformer to be analyzed, α and β are both preset weights; A is the first value, B is the second value; dis(x0,x i ) is the distance between the transformer to be analyzed and the i-th transformer.

[0059] Compared with the prior art, the beneficial effects of the present invention are: the present invention selects a limited number of instruments to obtain data, and reversely infers all data in the entire line model through the limited data, from point to surface, greatly reducing the pressure of obtaining data while reducing part of the accuracy, and providing support for improving the distribution frequency. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention.

[0061] Figure 1 A flowchart of a power distribution management method based on an AVC system provided in an embodiment of the present invention;

[0062] Figure 2This is a structural block diagram of the composition of the power distribution management system based on the AVC system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0063] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0064] Figure 1 The implementation process of the power distribution management method based on the AVC system provided by the embodiment of the present invention is shown, and is described in detail below:

[0065] Step S100: Modeling the power grid line to obtain a line model, obtaining power consumption information of the power supply points of the power grid line, identifying the power consumption information, selecting a measurement node in the line model; installing a sensor unit at the measurement node;

[0066] The line model is a circuit model created based on the power network. When building a power network, creating a line model is a task that most entities will complete. In the technical solution of the present invention, the line model can be directly read; the power supply point refers to the power consumption port, such as each household or production workshop, etc. The transformer is selected in the line model according to the power consumption information and location distribution of the power supply point, and a monitoring unit is installed at the transformer to obtain the power grid data during the power supply process.

[0067] Among them, the monitoring unit at least includes a sensor unit, specifically a SCADA system (Supervisory Control and Data Acquisition System); the SCADA system is a system used to monitor and control industrial processes in real time, including energy, water, transportation and other fields. In the power system, the SCADA system is connected to power equipment and controllers through sensors and actuators to monitor and control the operation status of the power grid in real time. The SCADA system can collect various data, including power load, switch status, voltage and other information, and provide it to the operation and maintenance personnel for analysis and operation through the human-machine interface.

[0068] Step S200: randomly selecting two measurement nodes at a preset frequency, obtaining the series-parallel relationship of the measurement nodes in the line model, and determining the theoretical data relationship at the measurement nodes according to the series-parallel relationship;

[0069] Two transformers are randomly selected, and the series-parallel relationship of the transformers can be determined based on the line model itself. Circuit analysis is performed in the line model to calculate the theoretical relationship between the data of the two transformers, which is called the theoretical data relationship. The simplest theoretical data relationship is a size relationship. At this time, the accuracy requirement is relatively low. On this basis, a numerical relationship can also be introduced. This relationship has high accuracy requirements and the analysis results are more accurate.

[0070] Step S300: determining the accuracy of the theoretical data relationship based on the actual data of the two measurement nodes;

[0071] Any measurement node can upload measurement data, called actual data, and compare the actual data of the two selected measurement nodes to verify the accuracy of the theoretical data relationship. Since this application is applied to actual scenarios, the theoretical data relationship is determined based on the circuit analysis process of an ideal state, and it may differ greatly from the actual situation. In actual scenarios, there will be many kinds of interference, and these interferences cannot be known in advance. Therefore, there is an accuracy problem.

[0072] Step S400: for any measurement node, calculate the accuracy mean of it and other measurement nodes, and adjust the application probability of each measurement node according to the accuracy mean; wherein the application probability is proportional to the accuracy mean;

[0073] For any measurement node, the mean of its accuracy with other measurement nodes is calculated, which is called the mean accuracy. The higher the mean accuracy, the more accurate the prediction result obtained by predicting the data of other measurement nodes with its data. At this time, the application probability is increased.

[0074] Step S500: upon receiving a power distribution management request, randomly selecting a preset number of calculation nodes based on the application probability at the corresponding moment, and performing power distribution management based on the data at the selected calculation nodes;

[0075] When a distribution management request is received, the application probability of each measurement node at the current moment is obtained, a limited number of measurement nodes are randomly selected based on the application probability, and the data of all other measurement nodes are predicted based on the data of the selected measurement nodes; the data of all measurement nodes are used as input to obtain a distribution management plan, wherein the data of all measurement nodes can be represented by a matrix, and the distribution management plan can be represented by a table (a certain serial number corresponds to a certain plan), multiple samples are obtained in advance, and a matrix-to-table model is trained (when the input and output formats are known, this is actually shallow learning and does not reach the level of deep learning, and the model generation process is not complicated). In actual use, as long as the data of all measurement nodes are predicted, the corresponding distribution management plan can be obtained.

[0076] The core of the above content is to reverse the data of all the measurement nodes in the entire line model through limited measurement nodes, from point to surface, without the need to obtain the data of all the measurement nodes in real time, and the working pressure of the measurement nodes is very small.

[0077] The above step S100 is described in detail as follows:

[0078] The steps of modeling the power grid line to obtain a line model, obtaining power consumption information of power supply points of the power grid line, identifying the power consumption information, and selecting a measurement node in the line model include:

[0079] Obtaining the line position of the power grid line, and constructing a line model according to a preset scale; the line model is based on a circuit model of the actual line;

[0080] Querying the power supply point in the power grid line to obtain power consumption information at the power supply point; the power consumption information is instantaneous power consumption with time as an independent variable;

[0081] The power consumption information is analyzed and a measurement node is determined in a line model.

[0082] The above content limits the transformer selection process, obtains the line position of the power grid line, and builds a line model according to a preset scale. During the power supply process of the power grid, each power user will be recorded, and the part of the power grid model that is connected to the power user is generally the transformer; in actual applications, one transformer corresponds to at least one power user (generally a one-to-many relationship).

[0083] Among them, the transformer is the power supply point mentioned above, the port connected to the power supply point is the power user, and the power consumption of the power user is the power consumption information. The power consumption information is generally represented by the instantaneous power consumption at each moment. By analyzing these power consumption information, certain transformers can be selected as measurement nodes.

[0084] Furthermore, the step of analyzing the power consumption information and determining the measurement node in the line model includes:

[0085] Obtaining the instantaneous power consumption of each power consumption unit according to a preset time interval;

[0086] Based on the same time axis, the instantaneous power consumption of each power consumption unit is counted and the power consumption curve is fitted;

[0087] Obtaining the integral of each power consumption curve within a time period, calculating the integral sum as a first value; the first value is used to characterize the total power supply of the power supply point;

[0088] Calculate the derivative curve of each power consumption curve, convert the derivative curve into absolute value and accumulate it, and the obtained curve is called the fluctuation curve; calculate the integral of the fluctuation curve in the time period as the second value; the second value is used to characterize the power supply stability of the power supply point;

[0089] An evaluation value is determined according to the first value and the second value, and a power supply point is selected as a measurement node based on the evaluation value.

[0090] In an example of the technical solution of the present invention, a specific electricity consumption information analysis solution is provided. First, for a certain transformer, the instantaneous power consumption of all power consuming units is uniformly obtained at regular intervals. Coordinates are created with time as the independent variable and the instantaneous power consumption as the dependent variable. A power consumption curve can be fitted for each power consuming unit. For the convenience of analysis, the power consumption curves of all power consuming units are counted on the same time axis to obtain a curve set.

[0091] Then, calculate the time integral of each power consumption curve (definite integral within a time period) to obtain the total power of each power consumption unit. Calculate the sum of the total power to obtain how much power the transformer supplies within a time period, which is called the first value. Calculate the derivative curve of each power consumption curve, take the absolute value of each derivative curve, and the curve after taking the absolute value reflects the severity of each power consumption unit. Accumulate all the curves after taking the absolute value to reflect the severity of the transformer's power supply changes. Calculate its integral within a time period and convert the severity of the transformer's power supply changes into a numerical value, called the second value.

[0092] Finally, the first value and the second value are used as independent variables to calculate the evaluation value, and the transformer can be selected as the transformer based on the evaluation value.

[0093] Specifically, the step of determining an evaluation value according to the first value and the second value, and selecting a power supply point as a measurement node based on the evaluation value includes:

[0094] Adding the first value and the second value according to a preset weight;

[0095] For any power supply point, calculate the distance between the selected measurement node and the power supply point;

[0096] An evaluation value is determined according to the summed distance and the distance, and when the evaluation value reaches a preset evaluation value threshold, a power supply point is selected as a measurement node.

[0097] The above content describes in detail the calculation process of the evaluation value. The management party pre-sets two weights, and the two weights are multiplied by the first value and the second value respectively, and then the sum is obtained; on this basis, the distance between other transformers serving as transformers and the transformer is obtained, and an evaluation value is determined by the sum and the distance. When the evaluation value reaches the preset evaluation value threshold, the transformer is selected as the target transformer for installing the monitoring unit.

[0098] The calculation process of the evaluation value is:

[0099] In the formula, x0 represents the transformer to be analyzed, x iis the i-th transformer; Value(x0) is the evaluation value of the transformer to be analyzed, α and β are both preset weights; A is the first value, B is the second value; dis(x0,x i ) is the distance between the transformer to be analyzed and the i-th transformer.

[0100] The calculation process of the evaluation value is described as follows:

[0101] αA+βB represents the influence of the first and second values ​​on the evaluation value. The larger the first value, the higher the total power generation, the easier it is to be selected as a transformer, and the larger the evaluation value. The larger the second value, the more drastic the change in the power generation process, the easier it is to be selected as a transformer, and the larger the evaluation value.

[0102] On this basis, the influence of other transformers on the current transformer is introduced, dis(x0,x i ) is the distance between other transformers that have been used as transformers and the current transformer, It indicates the degree of influence. The farther the distance, the smaller the influence. The sum of all influences is calculated as the denominator in the evaluation value calculation process. Its function is that the farther a transformer is from other transformers, the higher its evaluation value is and the easier it is to be selected as a transformer.

[0103] Regarding step S400, the step of calculating the accuracy mean of any measurement node and other measurement nodes, and adjusting the application probability of each measurement node according to the accuracy mean includes:

[0104] For any measurement node, query its accuracy with other measurement nodes during the historical calculation process;

[0105] Calculate the average of all accuracies found as the average accuracy;

[0106] The application probability of each measurement node is determined based on the accuracy mean.

[0107] After processing of step S200 and step S300, the application probability between each measurement node has been calculated. For any measurement node, in the historical calculation process, the calculated accuracy of it and other measurement nodes is queried, the average of the accuracy is calculated, and the average accuracy is obtained. The larger the average accuracy is, the higher the corresponding application probability is. The simplest solution is to calculate the sum of all accuracy means, and then divide each accuracy mean by the calculated sum, and convert the obtained ratio into a percentage to obtain the application probability. On this basis, in the process of calculating the sum of all accuracy means, a function can be compounded on the accuracy mean to amplify the difference, for example, a piecewise function can be compounded. When it is less than a certain value, the function value is zero. At this time, when the average accuracy is less than a threshold, the calculated application probability is zero.

[0108] Regarding step S400, when receiving the power distribution management request, randomly selecting a preset number of measurement nodes based on the application probability at the corresponding time, and performing power distribution management based on the data at the selected measurement nodes includes:

[0109] When receiving a power distribution management request, query the application probability of each measurement node at the current moment;

[0110] Randomly calculating nodes based on the application probability until the number of selected calculation nodes reaches a preset number;

[0111] Predicting the data at all measurement nodes based on the data at the selected calculation nodes and the theoretical data relationship between the measurement nodes;

[0112] The data at all measurement nodes are input into the trained power distribution management model, and the power distribution management plan is output.

[0113] The distribution management request is sent by the distribution staff. When the distribution management request is received, the receiving time is recorded, and the application probability of each measurement node at the receiving time is queried. Then, the nodes are randomly measured based on the application probability, and the number of measurement nodes is recorded. When the number is large enough (reaching the preset number), the data at all measurement nodes is predicted based on the data at the selected measurement node and the theoretical data relationship between the measurement nodes. The predicted data is input into the trained distribution management model, and the distribution management plan is output.

[0114] Figure 2 The structure block diagram of the power distribution management system based on the AVC system provided by an embodiment of the present invention is shown. The power distribution management system 10 based on the AVC system includes:

[0115] The calculation node selection module 11 is used to model the power grid line, obtain the line model, obtain the power consumption information of the power supply point of the power grid line, identify the power consumption information, and select the calculation node in the line model; the sensor unit is installed at the calculation node;

[0116] The circuit relationship determination module 12 is used to randomly select two measurement nodes at a preset frequency, obtain the series-parallel relationship of the measurement nodes in the circuit model, and determine the theoretical data relationship at the measurement nodes according to the series-parallel relationship;

[0117] The accuracy determination module 13 is used to determine the accuracy of the theoretical data relationship based on the actual data of the two measurement nodes;

[0118] The application probability calculation module 14 is used to calculate the accuracy mean of any measurement node and other measurement nodes, and adjust the application probability of each measurement node according to the accuracy mean; wherein the application probability is proportional to the accuracy mean;

[0119] The power distribution management module 15 is used to randomly select a preset number of measurement nodes based on the application probability at the corresponding moment when receiving a power distribution management request, and perform power distribution management based on the data at the selected measurement nodes.

[0120] Furthermore, the measurement node selection module 11 includes:

[0121] A line model creation unit, used to obtain the line position of the power grid line and construct a line model according to a preset scale; the line model is based on a circuit model of the actual line;

[0122] An electricity consumption information acquisition unit is used to query a power supply point in a power grid line and acquire electricity consumption information at the power supply point; the electricity consumption information is instantaneous electricity consumption with time as an independent variable;

[0123] The power consumption information analysis unit is used to analyze the power consumption information and determine the measurement nodes in the line model.

[0124] Specifically, the power consumption information analysis unit includes:

[0125] A data acquisition subunit, used to acquire the instantaneous power consumption of each power consumption unit according to a preset time interval;

[0126] A fitting subunit is used to count the instantaneous power consumption of each power consumption unit based on the same time axis and fit the power consumption curve;

[0127] A first calculation subunit is used to obtain the integral of each power consumption curve within a time period, and calculate the integral sum as a first value; the first value is used to characterize the total power supply of the power supply point;

[0128] The second calculation subunit is used to calculate the derivative curve of each power consumption curve, convert the derivative curve into absolute value and accumulate it, and the obtained curve is called the fluctuation curve; the integral of the fluctuation curve in the time period is calculated as the second value; the second value is used to characterize the power supply stability of the power supply point;

[0129] An execution subunit is selected to determine an evaluation value according to the first numerical value and the second numerical value, and a power supply point is selected as a measurement node based on the evaluation value.

[0130] Furthermore, the determining of the evaluation value according to the first value and the second value, and selecting the power supply point based on the evaluation value as the content of the measurement node include:

[0131] Adding the first value and the second value according to a preset weight;

[0132] For any power supply point, calculate the distance between the selected measurement node and the power supply point;

[0133] Determine an evaluation value according to the summed distance and the distance, and when the evaluation value reaches a preset evaluation value threshold, select a power supply point as a measurement node;

[0134] The calculation process of the evaluation value is:

[0135] In the formula, x0 represents the transformer to be analyzed, x i is the i-th transformer; Value(x0) is the evaluation value of the transformer to be analyzed, α and β are both preset weights; A is the first value, B is the second value; dis(x0,x i ) is the distance between the transformer to be analyzed and the 9th transformer.

[0136] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A power distribution management method based on AVC system, characterized in that: The method comprises: Modeling the power grid line to obtain a line model, obtaining power consumption information of the power supply point of the power grid line, identifying the power consumption information, and selecting a measurement node in the line model; installing a sensor unit at the measurement node; At a preset frequency, two measurement nodes are randomly selected to obtain the series-parallel relationship of the measurement nodes in the line model, and the theoretical data relationship at the measurement nodes is determined according to the series-parallel relationship; Determine the accuracy of the theoretical data relationship based on the actual data of the two measurement nodes; For any measurement node, calculate the accuracy mean of it and other measurement nodes, and adjust the application probability of each measurement node according to the accuracy mean; wherein the application probability is proportional to the accuracy mean; When receiving a power distribution management request, a preset number of measurement nodes are randomly selected based on the application probability at the corresponding time, and power distribution management is performed based on the data at the selected measurement nodes; The steps of modeling the power grid line to obtain a line model, obtaining power consumption information of power supply points of the power grid line, identifying the power consumption information, and selecting a measurement node in the line model include: Obtaining the line position of the power grid line, and constructing a line model according to a preset scale; the line model is based on a circuit model of the actual line; Querying the power supply point in the power grid line to obtain power consumption information at the power supply point; the power consumption information is instantaneous power consumption with time as an independent variable; Analyzing the power consumption information and determining the measurement nodes in the line model; The step of analyzing the power consumption information and determining the measurement node in the line model includes: Obtaining the instantaneous power consumption of each power consumption unit according to a preset time interval; Based on the same time axis, the instantaneous power consumption of each power consumption unit is counted and the power consumption curve is fitted; Obtaining the integral of each power consumption curve within a time period, and calculating the integral sum as a first value; the first value is used to characterize the total power supply of the power supply point; Calculate the derivative curve of each power consumption curve, convert the derivative curve into absolute value and accumulate it, and the obtained curve is called the fluctuation curve; calculate the integral of the fluctuation curve in the time period as the second value; the second value is used to characterize the power supply stability of the power supply point; Determine an evaluation value according to the first value and the second value, and select a power supply point as a measurement node based on the evaluation value; The step of determining an evaluation value according to the first value and the second value, and selecting a power supply point as a measurement node based on the evaluation value comprises: Adding the first value and the second value according to a preset weight; For any power supply point, calculate the distance between the selected measurement node and the power supply point; Determine an evaluation value according to the summed distance and the distance, and when the evaluation value reaches a preset evaluation value threshold, select a power supply point as a measurement node; The calculation process of the evaluation value is: ; In the formula, represents the transformer to be analyzed, is the i-th transformer; is the evaluation value of the transformer to be analyzed, and All are preset weights; is the first value, is the second value; is the distance between the transformer to be analyzed and the i-th transformer.

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