Edge power distribution optimization method and system for power grid equipment

Abstract

The present application discloses an edge power distribution optimization method and system for power grid equipment, relating to the technical field of power distribution. The method includes: forming a set of target power grid substation equipment; constructing a first substation equipment model; rendering first real-time operation data to the first substation equipment model to obtain a first visualization model; determining whether the first substation equipment meets a predetermined constraint; if it meets the constraint, performing a simulation using first simulation operation parameters generated based on the first real-time operation data to obtain a first simulation record; analyzing and determining whether the first simulation record meets the predetermined constraint, and if it meets the constraint, obtaining a first real-time fitness and a first simulation fitness; comparing the first real-time fitness and the first simulation fitness to determine a first edge power distribution decision and form a target power distribution decision. Thereby achieving the technical effects of improving the power distribution response speed, reducing energy waste, and further improving the system stability and reliability.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution, and particularly to an edge power distribution optimization method and system for power grid equipment. Background Art

[0002] Power grids and power grid equipment are important infrastructure in social life. Power grids have the characteristics of wide coverage, diverse equipment types, and complex and changeable service requirements. Traditional power distribution systems may have fixed configurations and static optimization strategies, and fail to fully adapt to the dynamic changes and uncertainties of the power system. There are technical problems such as slow response, resulting in energy waste, and affecting the stability and reliability of the power system. Summary of the Invention

[0003] The purpose of this application is to provide an edge power distribution optimization method and system for power grid equipment, so as to solve the technical problems of slow response, energy waste, and affecting the stability and reliability of the power system in the prior art.

[0004] In view of the above technical problems, this application provides an edge power distribution optimization method and system for power grid equipment.

[0005] In the first aspect, this application provides an edge power distribution optimization method for power grid equipment, wherein the method includes:

[0006] Form a target power grid substation equipment set, where the target power grid substation equipment set refers to a set of multiple substation equipment in the target power grid;

[0007] Construct a first substation equipment model, where the first substation equipment model is a physical model of the first substation equipment, and the first substation equipment is any one of the multiple substation equipment;

[0008] Render the first real-time operation data of the first substation equipment monitored based on a predetermined characteristic index to the first substation equipment model to obtain a first visualization model;

[0009] Judge whether the first substation equipment meets a predetermined constraint according to the first visualization model;

[0010] If it meets the requirement, simulate the first simulated operation parameters obtained based on an initial optimization item through the first substation equipment model to obtain a first simulation record, where the initial optimization item refers to the first real-time operation data;

[0011] Analyze and judge whether the first simulation record meets the predetermined constraint. If it meets the requirement, call a predetermined fitness function to obtain the first real-time fitness of the first real-time operation data and the first simulated fitness of the first simulated operation parameters in sequence;

[0012] Determine the first edge power distribution decision by comparing the first real-time fitness and the first simulated fitness, and form a target power distribution decision, which is used to optimize the edge power distribution of the target power grid.

[0013] In a second aspect, the present application also provides an edge power distribution optimization system for power grid equipment. Among them, the system includes:

[0014] A target device acquisition module, which is used to form a set of target power grid substation equipment. The set of target power grid substation equipment refers to a set of multiple substation equipment in the target power grid;

[0015] A blank model construction module, which is used to construct a first substation equipment model. The first substation equipment model is a physical model of the first substation equipment, and the first substation equipment is any one of the multiple substation equipment;

[0016] An initialization module, which is used to render the first real-time operation data of the first substation equipment monitored based on predetermined characteristic indicators to the first substation equipment model to obtain a first visualization model;

[0017] A discrimination module, which is used to judge whether the first substation equipment meets the predetermined constraints according to the first visualization model;

[0018] A simulation operation module, which is used to, if it meets the requirements, perform a simulation of the first simulated operation parameters obtained based on the initial optimization items through the first substation equipment model to obtain a first simulation record, where the initial optimization items refer to the first real-time operation data;

[0019] An adaptation calculation module, which is used to analyze and judge whether the first simulation record meets the predetermined constraints. If it meets the requirements, call a predetermined fitness function to sequentially obtain the first real-time fitness of the first real-time operation data and the first simulated fitness of the first simulated operation parameters;

[0020] A decision optimization module, which is used to determine the first edge power distribution decision by comparing the first real-time fitness and the first simulated fitness, and form a target power distribution decision, which is used to optimize the edge power distribution of the target power grid.

[0021] One or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0022] By forming a set of target power grid substation equipment, where the set of target power grid substation equipment refers to the collection of multiple substation equipment in the target power grid; constructing a first substation equipment model, the first substation equipment model being the physical model of the first substation equipment, and the first substation equipment being any one of the multiple substation equipment; rendering the first real-time operation data of the first substation equipment monitored based on predetermined characteristic indicators to the first substation equipment model to obtain a first visualization model; judging whether the first substation equipment meets the predetermined constraints according to the first visualization model; if it meets, performing a simulation on the first simulated operation parameters obtained based on the initial optimization items through the first substation equipment model to obtain a first simulation record, where the initial optimization items refer to the first real-time operation data; analyzing and judging whether the first simulation record meets the predetermined constraints, and if it meets, calling a predetermined fitness function to sequentially obtain the first real-time fitness of the first real-time operation data and the first simulated fitness of the first simulated operation parameters; determining a first edge power distribution decision by comparing the first real-time fitness and the first simulated fitness, and forming a target power distribution decision, where the target power distribution decision is used to optimize the edge power distribution of the target power grid. Thus, the technical effects of improving the power distribution response speed, reducing energy waste, and further improving the system stability and reliability are achieved.

[0023] The above description is only an overview of the technical solution of the present application. In order to be able to more clearly clarify the technical means of the present application, it can be implemented in accordance with the content of the specification. And in order to make the above and other purposes, features and advantages of the present application more obvious and understandable, the following specifically illustrates the specific embodiments of the present application. Brief Description of the Drawings

[0024] The embodiments of the present invention and the following brief description are illustrated in conjunction with the drawings. The brief description of the drawings is as follows:

[0025] Figure 1 It is a schematic flowchart of the method for optimizing edge power distribution of power grid equipment in the present application;

[0026] Figure 2 It is a schematic flowchart of judging whether the first substation equipment meets the predetermined constraints according to the first visualization model in the method for optimizing edge power distribution of power grid equipment in the present application;

[0027] Figure 3 It is a schematic structural diagram of the system for optimizing edge power distribution of power grid equipment in the present application.

[0028] Description of the reference numerals: Target equipment acquisition module 11, blank model construction module 12, initialization module 13, discrimination module 14, simulation operation module 15, adaptation calculation module 16, decision optimization module 17. Detailed Description of the Embodiments

[0029] By providing an edge power distribution optimization method and system for power grid equipment, this application solves the technical problems faced by the prior art, such as slow response, resulting in energy waste, and affecting the stability and reliability of the power system.

[0030] The overall idea adopted in the solution of this technical embodiment to solve the above problems is as follows:

[0031] First, form a target power grid substation equipment set, where the target power grid substation equipment set refers to a set of multiple substation equipment in the target power grid; construct a first substation equipment model, and the first substation equipment model is a physical model of any substation equipment in the target power grid; map the first real-time operation data of the first substation equipment monitored based on a predetermined characteristic index to the first substation equipment model to form a first visualization model; determine whether the first substation equipment meets the predetermined constraints based on the first visualization model; if it meets, use the first substation equipment model to simulate the first simulated operation parameters obtained based on the initial optimization item to obtain a first simulation record, where the initial optimization item refers to the first real-time operation data; analyze and judge whether the first simulation record meets the predetermined constraints, if it meets, sequentially call a predetermined fitness function to obtain the first real-time fitness of the first real-time operation data and the first simulated fitness of the first simulated operation parameters; determine the first edge power distribution decision by comparing the first real-time fitness and the first simulated fitness, form a target power distribution decision, and perform edge power distribution optimization of the target power grid. Furthermore, the technical effects of improving the power distribution response speed, reducing energy waste, and then improving the system stability and reliability are achieved.

[0032] To better understand the above technical solution, the following will combine the description of the accompanying drawings and specific implementation manners to elaborate on the above technical solution in detail. It should be noted that the described embodiments are only a part of the embodiments of this application, rather than all the embodiments of this application. It should be understood that this application is not limited by the example embodiments described here. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention. Additionally, it should be noted that for the sake of convenience of description, only parts related to the present invention are shown in the drawings rather than all of them.

[0033] Embodiment 1

[0034] As Figure 1 shown, this application provides an edge power distribution optimization method for power grid equipment, and the method includes:

[0035] S100: Form a target power grid substation equipment set, where the target power grid substation equipment set refers to a set of multiple substation equipment in the target power grid;

[0036] The target grid substation equipment set refers to a collection of multiple substation equipment in the target grid. This collection includes key equipment in the power system, such as substations, transformers, and switchgear. By forming such a target grid substation equipment set, the system can more comprehensively consider the status and performance of each key part of the grid.

[0037] Optionally, the target power grid substation equipment set includes multiple edge substation groups corresponding to multiple edge substation nodes of the target power grid, and includes key control equipment in the edge substation nodes.

[0038] S200: constructing a first substation model, where the first substation model is a physical model of the first substation, and the first substation is any one of the multiple substations.

[0039] Optionally, the first substation equipment model is a twin digital model constructed based on the first substation equipment. The model is a physical model of the first substation equipment, describing various physical properties, structures and operating principles of the equipment. The construction of the first substation equipment model is based on the principle of the digital twin method and is created through engineering modeling software such as CAD, PSS / E, MATLAB or other simulation tools.

[0040] S300: Rendering first real-time operation data of the first power substation monitored based on a predetermined characteristic indicator to the first power substation model to obtain a first visualization model;

[0041] Optionally, the first real-time operation data of the first substation equipment is collected through data acquisition paths such as sensors and power grid equipment monitoring platforms. Exemplary information includes current, voltage, temperature, frequency, power, insulation resistance, phase difference, etc. Then, the real-time collected data is integrated with the first substation equipment model, and the digital model is updated using the real-time data to ensure that the model is synchronized with the actual operation status.

[0042] Optionally, before rendering the first real-time operation data to the first substation equipment model, the acquired first real-time operation data is processed and converted to ensure the accuracy and consistency of the data, which may include data cleaning, calibration, unit conversion, etc.

[0043] Optionally, ensure real-time update of data to ensure the timeliness of the first real-time operation data obtained, thereby accurately reflecting the current state of the first substation. Real-time update of data is based on a preset data update frequency or based on a data update instruction, wherein the data update instruction is a manual update instruction issued by the control terminal or the control personnel.

[0044] Optionally, combine the real-time rendered data with the physical model to generate a visualization model of the first power transformation equipment. This model can be displayed in places such as the monitoring center and the operation room to help operators intuitively understand the operating status of the equipment.

[0045] Further, the steps further include:

[0046] Obtain an edge power distribution database, and extract the first historical power distribution record in the edge power distribution database. The first historical power distribution record includes a first historical power transformation equipment operation data record and a first historical edge power distribution fitness record;

[0047] Conduct a correlation analysis on the first historical power transformation equipment operation data record and the first historical edge power distribution fitness record to obtain a first correlation analysis result;

[0048] Determine the predetermined characteristic index based on the first correlation analysis result.

[0049] Optionally, the edge power distribution database is a relational database containing historical power distribution data of multiple power transformation equipment in the target power grid substation. Through database query languages, API calls, etc., obtain access rights to the edge power distribution database, and extract the first historical power distribution record. The first historical power distribution record includes a first historical power transformation equipment operation data record and a first historical edge power distribution fitness record, and the first historical power transformation equipment operation data record and the first historical edge power distribution fitness record have a corresponding relationship.

[0050] Optionally, conduct a correlation analysis on the first historical power transformation equipment operation data record and the first historical edge power distribution fitness record. Through statistical methods, machine learning algorithms, or other correlation analysis tools, determine the degree of association between them. Furthermore, identify the power transformation equipment operation characteristics related to the power distribution fitness. Exemplarily, use the CORR function in SQL to calculate the correlation coefficient between multiple characteristic index data columns in the first historical power transformation equipment operation data record and the data column of the first historical edge power distribution fitness record.

[0051] Optionally, determine the predetermined characteristic index based on the result of the correlation analysis. The predetermined characteristic index refers to the power transformation equipment operation parameters that have a greater impact on the edge power distribution fitness, such as current overload, voltage fluctuation, etc. Through the above steps, ensure that the selected indicators are closely related to the operating conditions and performance of the system.

[0052] S400: Determine whether the first power transformation equipment meets the predetermined constraints according to the first visualization model;

[0053] Further, as Figure 2As shown, the predetermined constraints include a predetermined single-threshold constraint and a predetermined state constraint. To determine whether the first power transformation device conforms to the predetermined constraints according to the first visualization model, the steps are as follows:

[0054] Extract the first index in the predetermined characteristic indicators, and match the first single-threshold constraint of the first index in the predetermined single-threshold constraint;

[0055] Judge whether the first real-time data of the first index in the first visualization model satisfies the first single-threshold constraint;

[0056] If it is satisfied, perform a weighted calculation on the first real-time data to obtain a first real-time state index;

[0057] Judge whether the first real-time state index satisfies the predetermined state constraint;

[0058] If it is satisfied, the first power transformation device conforms to the predetermined constraints.

[0059] Optionally, the predetermined constraints include a predetermined single-threshold constraint and a predetermined state constraint. Among them, the predetermined single-threshold constraint is used to perform single-item constraints on multiple characteristic indicators in the first real-time data, and the predetermined state constraint is used to perform overall constraints on the first real-time data. Through the local constraint based on the predetermined single-threshold constraint and the constraint discrimination on the first real-time operation data in the first visualization model based on the predetermined state constraint, a comprehensive judgment is achieved.

[0060] Optionally, the predetermined constraints are used to determine whether the first power transformation device needs to perform power distribution optimization. The predetermined single-threshold constraint and the predetermined state constraint in the predetermined constraints are expressed as a predetermined single-threshold constraint interval and a predetermined state constraint interval. Exemplarily, if the first real-time operation data of the first power transformation device is within the above constraint interval, power distribution optimization needs to be performed on the first power transformation device; if the first real-time operation data of the first power transformation device is greater than the upper limit of the above predetermined single-threshold constraint interval and the predetermined state constraint interval, it indicates that the operation state of the first power transformation device is good; if the first real-time operation data of the first power transformation device is less than the lower limit of the above predetermined single-threshold constraint interval and the predetermined state constraint interval, it indicates that there is an abnormal situation in the operation of the first power transformation device, and emergency response or handling is required.

[0061] Optionally, a weighted calculation is performed on the first real-time data to obtain a first real-time state index. Among them, the weight value of the first real-time data is determined based on the first correlation analysis result, and the characteristic index item with a large correlation coefficient in the correlation analysis result corresponds to a high weighted weight value. Exemplarily, it includes normalizing the correlation coefficient in the correlation analysis result to ensure that the sum of multiple weighted weight values is equal to 1.

[0062] S500: If it meets the requirements, perform a simulation on the first simulated operating parameters obtained based on the initial optimization item through the first power transformation equipment model to obtain a first simulation record, where the initial optimization item refers to the first real-time operating data;

[0063] Further, the steps of performing a simulation on the first simulated operating parameters obtained based on the initial optimization item through the first power transformation equipment model to obtain a first simulation record include:

[0064] Obtain a first neighborhood of the first real-time data, where the first neighborhood includes multiple data;

[0065] Randomly obtain first neighborhood data from the multiple data in combination with the predetermined single-threshold constraint;

[0066] Form the first simulated operating parameters based on the first neighborhood data.

[0067] Optionally, the first simulated operating parameters are operating parameters that randomly vary based on the first real-time operating data and are a set of operating parameters for which the optimization effect is to be determined. Exemplarily, first, obtain first neighborhood data related to the target power transformation equipment from the first real-time operating data. The first neighborhood includes multiple data points adjacent to or related to the equipment, such as the operating parameters of adjacent equipment, environmental conditions, etc. Exemplarily, the first neighborhood is determined based on the historical operating records of the first power transformation equipment or the equipment technical documentation of the first power transformation equipment, and the first neighborhood reflects the operating parameter adjustment range of the first power transformation equipment. Then, randomly select from the first neighborhood data to obtain the first neighborhood data, where the first neighborhood data is any data point in the first neighborhood and satisfies the predetermined single-threshold constraint. Finally, combine the selected first neighborhood data into the first simulated operating parameters. The first simulated operating parameters will be used to simulate the operating conditions of the first power transformation equipment.

[0068] Optionally, perform a simulation through the first simulated operating parameters, apply the first simulated operating parameters to the first power transformation equipment model, and set the initial state. This includes setting initial values such as current, voltage, power, etc. Then, run the simulation to simulate the operating behavior of the first power transformation equipment under the given parameters. And record the numerical values and changes of various parameters and performance indicators of the simulated operation to form the first simulation record.

[0069] S600: Analyze and determine whether the first simulation record meets the predetermined constraint. If it meets the requirements, call the predetermined fitness function to sequentially obtain the first real-time fitness of the first real-time operating data and the first simulated fitness of the first simulated operating parameters;

[0070] Optionally, analyze the simulation records to evaluate the performance, stability, and other key indicators of the device. Further, understand the behavior of the first power transformation device under the first simulated operating parameters. If the first simulation record meets the predetermined state constraint in the predetermined constraints, it indicates that the first power transformation device under the first simulated operating parameters is in a normal operating state.

[0071] Further, call the predetermined fitness function to obtain the first real-time fitness of the first real-time operating data and the first simulated fitness of the first simulated operating parameters in sequence. The steps include:

[0072] Obtain the first edge distribution device, where the first edge distribution device refers to the distribution device corresponding to the first power transformation device in the distributed edge distribution device group;

[0073] Monitor and obtain the first real-time distribution information of the first edge distribution device under the first real-time operating data;

[0074] Extract the first simulated distribution information of the first edge distribution device in the first simulation record;

[0075] Call the predetermined fitness function to analyze the first real-time distribution information in sequence to obtain the first real-time fitness and the first simulated fitness of the first simulated distribution information.

[0076] Optionally, interact with the distributed edge distribution management platform to obtain the first edge distribution device corresponding to the first power transformation device. Monitor the first edge distribution device in real time to obtain the real-time distribution information under the current real-time operating data. Then, extract the simulated distribution information related to the first edge distribution device from the first simulation record. Exemplarily, it includes parameters such as current, voltage, and power during the simulation, as well as the simulated state of the distribution device. Then, use the predetermined fitness function to analyze the first real-time distribution information and the first simulated distribution information respectively to obtain the first real-time fitness and the first simulated fitness. The fitness function is configured based on the optimization objectives of the target power grid, including power balance, efficiency, harmonic conditions, etc.

[0077] Further, the above steps further include:

[0078] The first real-time distribution information includes the first real-time inter-harmonics of the public power grid, the first real-time three-phase imbalance, the first real-time voltage transient event information, the first real-time voltage instantaneous event information, and the first real-time frequency quality index;

[0079] Perform encoding processing on the first real-time voltage transient event information and the first real-time voltage instantaneous event information in sequence to obtain the first encoded data and the second encoded data respectively;

[0080] Call the predetermined fitness function to analyze the first real-time interharmonics of the public power grid, the first real-time three-phase imbalance, the first coded data, the second coded data, and the first real-time frequency quality index, and obtain the first real-time fitness. The expression of the predetermined fitness function is as follows: , where represents the first real-time fitness of the first real-time operation data, and and and and are the normalization results of the first real-time interharmonics of the public power grid, the first real-time three-phase imbalance, the first coded data, the second coded data, and the first real-time frequency quality index respectively, and and and and are the first weight coefficient, the second weight coefficient, the third weight coefficient, the fourth weight coefficient, and the fifth weight coefficient respectively, and .

[0081] Among them, interharmonics are an important factor affecting the power distribution quality of the power grid. Poor performance of interharmonics will reduce the efficiency of power generation, transmission, and consumption, cause overheating of electrical equipment, lead to vibration and noise, age the equipment, shorten its service life, and even cause failures or burns. Monitor the current and voltage waveforms, identify and extract the interharmonic components of the public power grid. This is achieved through harmonic analysis techniques, including using digital power quality analyzers or other harmonic analysis equipment.

[0082] Optionally, by monitoring the current and voltage of each phase, calculate the unbalance degree of the current and voltage to obtain the first real-time three-phase imbalance. This includes calculating the unbalance degrees of the positive sequence, negative sequence, and zero sequence components. Detect and record the instantaneous changes in voltage, such as sudden voltage jumps, through a high-speed sampling voltage recording device, and perform event detection and classification on the recorded waveforms to generate the first real-time voltage transient event information.

[0083] Optionally, perform one-hot encoding processing on the first real-time voltage transient event information and the first real-time voltage transient event information to obtain the first coded data and the second coded data. Exemplarily, the severity levels of the first real-time voltage transient event information and the first real-time voltage transient event information are converted into binary coded data based on one-hot encoding from the original event information, facilitating subsequent calculations based on the predetermined fitness function.

[0084] Furthermore, the above steps further include:

[0085] Obtain the first frequency curve of the first frequency time series in the first real-time power distribution information;

[0086] Analyze the first frequency curve to obtain the first average frequency deviation and the first frequency flicker count;

[0087] Use the coefficient of variation principle to weight the first average frequency deviation and the first frequency flicker count to obtain the first real-time frequency quality index.

[0088] Optionally, extract the data of the first frequency time series from the first real-time power distribution information. And according to the extracted data, draw a curve graph of the first frequency time series to generate the first frequency curve. This first frequency curve reflects the change of the power frequency of the power distribution equipment over time. Then, analyze the first frequency curve, calculate the first average frequency deviation and count the first frequency flicker count. Among them, the first average frequency deviation refers to the standard deviation of the power frequencies at each time series point in the first frequency curve. Then, use the coefficient of variation principle to weight the first average frequency deviation and the first frequency flicker count, calculate the coefficient of variation of the first average frequency deviation and the coefficient of variation of the first frequency flicker count respectively, and obtain the weight of the first average frequency deviation and the weight of the first frequency flicker count. The weight of the first average frequency deviation and the weight of the first frequency flicker count reflect the degree of dispersion and risk level of the first average frequency deviation and the first frequency flicker count. Finally, according to the weights, perform a weighted calculation on the first average frequency deviation and the first frequency flicker count to obtain the first real-time frequency quality index.

[0089] Through the above steps, comprehensively considering various fluctuation factors affecting frequency quality, and adopting the coefficient of variation principle, combined with the dispersion and risk characteristics of various fluctuation factors, the frequency quality evaluation of the first real-time power distribution information is realized.

[0090] S700: Determine the first marginal power distribution decision by comparing the first real-time fitness and the first simulated fitness, and form the target power distribution decision, which is used to optimize the marginal power distribution of the target power grid.

[0091] Optionally, compare the first real-time fitness and the first simulation. According to the comparison result of the fitness, determine the one with more superior fitness as the first marginal power distribution decision. Among them, combine the first marginal power distribution decision into the target power distribution decision. It involves traversing the target power grid and integrating the decisions of multiple substation equipment to form an overall power distribution optimization strategy.

[0092] Optionally, apply the target power distribution decision to the target power grid for edge power distribution optimization. This includes operations such as transmitting the target power distribution decision, integrity verification, parsing, updating the configuration of grid equipment, and adjusting current distribution to meet specific performance goals and optimization criteria. This ensures better meeting the requirements of performance and benefits in actual grid operation.

[0093] In summary, the edge power distribution optimization method for grid equipment provided by the present invention has the following technical effects:

[0094] By forming a set of target grid substation equipment, where the set of target grid substation equipment refers to the collection of multiple substation equipment in the target grid; constructing a first substation equipment model, where the first substation equipment model is the physical model of the first substation equipment, and the first substation equipment is any one of the multiple substation equipment; rendering the first real-time operation data of the first substation equipment monitored based on predetermined characteristic indicators to the first substation equipment model to obtain a first visualization model; determining whether the first substation equipment meets the predetermined constraints according to the first visualization model; if it meets, simulating the first simulated operation parameters obtained based on the initial optimization items through the first substation equipment model to obtain a first simulation record, where the initial optimization items refer to the first real-time operation data; analyzing and determining whether the first simulation record meets the predetermined constraints, if it meets, calling a predetermined fitness function to sequentially obtain the first real-time fitness of the first real-time operation data and the first simulated fitness of the first simulated operation parameters; determining the first edge power distribution decision by comparing the first real-time fitness and the first simulated fitness, and forming a target power distribution decision, where the target power distribution decision is used for edge power distribution optimization of the target power grid. Thus, the technical effects of improving the power distribution response speed, reducing energy waste, and further improving the system stability and reliability are achieved.

[0095] Embodiment 2

[0096] Based on the same concept as the edge power distribution optimization method for grid equipment in the above embodiment, as Figure 3 shown, the present application also provides an edge power distribution optimization system for grid equipment, and the system includes:

[0097] A target device acquisition module 11, configured to form a set of target grid substation equipment, where the set of target grid substation equipment refers to the collection of multiple substation equipment in the target grid;

[0098] A blank model construction module 12, configured to construct a first substation equipment model, where the first substation equipment model is the physical model of the first substation equipment, and the first substation equipment is any one of the multiple substation equipment;

[0099] An initialization module 13 for rendering the first real-time operation data of the first power transformation device detected based on a predetermined feature index to the first power transformation device model to obtain a first visualization model;

[0100] A discrimination module 14 for determining whether the first power transformation device meets a predetermined constraint according to the first visualization model;

[0101] A simulation operation module 15 for, if it meets the requirement, performing a simulation on the first simulation operation parameters obtained based on an initial optimization item through the first power transformation device model to obtain a first simulation record, where the initial optimization item refers to the first real-time operation data;

[0102] An adaptation calculation module 16 for analyzing and determining whether the first simulation record meets the predetermined constraint, and if it meets the requirement, calling a predetermined fitness function to sequentially obtain a first real-time fitness of the first real-time operation data and a first simulation fitness of the first simulation operation parameters;

[0103] A decision optimization module 17 for determining a first edge power distribution decision by comparing the first real-time fitness and the first simulation fitness, and forming a target power distribution decision, where the target power distribution decision is used for optimizing the edge power distribution of the target power grid.

[0104] Furthermore, the initialization module 13 further includes:

[0105] A power distribution record unit for obtaining an edge power distribution database and extracting a first historical power distribution record in the edge power distribution database, where the first historical power distribution record includes a first historical power transformation device operation data record and a first historical edge power distribution fitness record;

[0106] A correlation analysis unit for performing a correlation analysis on the first historical power transformation device operation data record and the first historical edge power distribution fitness record to obtain a first correlation analysis result;

[0107] A feature index selection unit for determining the predetermined feature index based on the first correlation analysis result.

[0108] Furthermore, the discrimination module 14 further includes:

[0109] A constraint matching unit for extracting a first index in the predetermined feature index and matching a first single-threshold constraint of the first index in the predetermined single-threshold constraint;

[0110] A single-threshold constraint unit for determining whether the first real-time data of the first index in the first visualization model meets the first single-threshold constraint;

[0111] A status index calculation unit, which is configured to perform weighted calculation on the first real-time data to obtain a first real-time status index if the condition is met;

[0112] A status constraint unit, which is configured to determine whether the first real-time status index meets the predetermined status constraint. If it meets, the first power transformation device conforms to the predetermined constraint.

[0113] Further, the simulation operation module 15 further includes:

[0114] A first neighborhood acquisition unit, which is configured to acquire a first neighborhood of the first real-time data, and the first neighborhood includes a plurality of data;

[0115] A neighborhood data selection unit, which is configured to randomly acquire first neighborhood data from the plurality of data in combination with the predetermined single-threshold constraint;

[0116] A simulation operation parameter generation unit, which is configured to form the first simulation operation parameter based on the first neighborhood data.

[0117] Further, the adaptation calculation module 16 further includes:

[0118] A power distribution equipment acquisition unit, which is configured to acquire a first edge power distribution equipment, and the first edge power distribution equipment refers to the power distribution equipment corresponding to the first power transformation device in the distributed edge power distribution equipment group;

[0119] A power distribution monitoring unit, which is configured to monitor and obtain first real-time power distribution information of the first edge power distribution equipment under the first real-time operation data;

[0120] An information extraction unit, which is configured to extract first simulated power distribution information of the first edge power distribution equipment in the first simulated record;

[0121] A function call unit, which is configured to call the predetermined fitness function to sequentially analyze the first real-time power distribution information to obtain the first real-time fitness and the first simulated fitness of the first simulated power distribution information.

[0122] Further, the function call unit further includes:

[0123] The first real-time power distribution information includes first real-time interharmonics of the public power grid, first real-time three-phase imbalance, first real-time voltage transient event information, first real-time voltage instantaneous event information, and first real-time frequency quality index;

[0124] An encoding unit, which is configured to perform encoding processing on the first real-time voltage transient event information and the first real-time voltage instantaneous event information in sequence to obtain first encoded data and second encoded data respectively;

[0125] A calculation unit for analyzing the first real-time interharmonics of the public power grid, the first real-time three-phase imbalance, the first coded data, the second coded data, and the first real-time frequency quality index by invoking the predetermined fitness function to obtain the first real-time fitness, where the expression of the predetermined fitness function is as follows: , where represents the first real-time fitness of the first real-time operation data, and and and and are the normalization results of the first real-time interharmonics of the public power grid, the first real-time three-phase imbalance, the first coded data, the second coded data, and the first real-time frequency quality index respectively, and and and and are the first weight coefficient, the second weight coefficient, the third weight coefficient, the fourth weight coefficient, and the fifth weight coefficient respectively, and .

[0126] Furthermore, the system further includes a frequency quality index unit for:

[0127] Obtaining the first frequency curve of the first frequency time series in the first real-time distribution information;

[0128] Analyzing the first frequency curve to obtain the first average frequency deviation and the first frequency flicker count;

[0129] Using the coefficient of variation principle to weight the first average frequency deviation and the first frequency flicker count to obtain the first real-time frequency quality index.

[0130] It should be understood that the embodiments mentioned in this specification focus on their differences from other embodiments. The specific embodiments in the foregoing Embodiment 1 are equally applicable to the edge distribution optimization system for power grid equipment described in Embodiment 2. For the sake of brevity of the specification, no further elaboration is made here.

[0131] It should be understood that the embodiments and the above descriptions disclosed in this application can enable those skilled in the art to implement this application using this application. At the same time, this application is not limited to this part of the embodiments mentioned above. Obvious modifications, combinations, and substitutions to the embodiments mentioned in this application also fall within the protection scope of this application.

Claims

1. A method for optimizing edge power distribution for power grid equipment, characterized in that: include: Establishing a target power grid substation equipment set, wherein the target power grid substation equipment set refers to a collection of multiple substation equipment in the target power grid; Constructing a first substation equipment model, wherein the first substation equipment model is a physical model of a first substation equipment, and the first substation equipment is any one of the multiple substation equipments; Rendering first real-time operation data of the first power substation monitored based on a predetermined characteristic indicator to the first power substation model to obtain a first visualization model; determining whether the first power transformation equipment meets a predetermined constraint according to the first visualization model; If it is in compliance, simulating the first simulated operation parameter obtained based on the initial optimization item through the first substation equipment model to obtain a first simulation record, wherein the initial optimization item refers to the first real-time operation data; Analyze and determine whether the first simulation record meets the predetermined constraint, and if so, call a predetermined fitness function to sequentially obtain a first real-time fitness of the first real-time operation data and a first simulation fitness of the first simulation operation parameter; Determine a first edge power distribution decision by comparing the first real-time fitness and the first simulation fitness, and form a target power distribution decision, wherein the target power distribution decision is used to optimize edge power distribution of the target power grid; Calling a predetermined fitness function to sequentially obtain a first real-time fitness of the first real-time operation data and a first simulation fitness of the first simulation operation parameter includes: Acquire a first edge power distribution device, where the first edge power distribution device refers to a power distribution device in a distributed edge power distribution device group corresponding to the first power transformer; Monitoring and obtaining first real-time power distribution information of the first edge power distribution device under the first real-time operation data; Extracting first simulation power distribution information of the first edge power distribution device in the first simulation record; Calling the predetermined fitness function to sequentially analyze the first real-time power distribution information to obtain the first real-time fitness and the first simulation fitness of the first simulation power distribution information; The first real-time power distribution information includes first real-time public grid interharmonics, first real-time three-phase imbalance, first real-time voltage transient event information, first real-time voltage transient event information, and first real-time frequency quality index; Sequentially encoding the first real-time voltage transient event information and the first real-time voltage transient event information to obtain first encoded data and second encoded data, respectively; The predetermined fitness function is called to analyze the first real-time public grid interharmonic, the first real-time three-phase imbalance, the first coded data, the second coded data, and the first real-time frequency quality index to obtain the first real-time fitness, wherein the expression of the predetermined fitness function is as follows: ， in, characterizing the first real-time fitness of the first real-time operation data, and and and and are respectively normalized results of the first real-time public grid interharmonics, the first real-time three-phase unbalance, the first coded data, the second coded data, and the first real-time frequency quality index, and and and and are the first weight coefficient, the second weight coefficient, the third weight coefficient, the fourth weight coefficient, and the fifth weight coefficient respectively, and ; Acquire a first frequency curve of a first frequency time series in the first real-time power distribution information; Analyze the first frequency curve to obtain a first average frequency deviation and a first frequency flicker number; The first average frequency deviation and the first frequency flicker times are weighted by using the variation coefficient principle to obtain the first real-time frequency quality index.

2. The method according to claim 1, characterized in that: include: Acquire an edge power distribution database, and extract a first historical power distribution record from the edge power distribution database, wherein the first historical power distribution record includes a first historical substation equipment operation data record and a first historical edge power distribution adaptability record; Performing a correlation analysis on the first historical substation equipment operation data record and the first historical edge distribution adaptability record to obtain a first correlation analysis result; The predetermined characteristic index is determined based on the first correlation analysis result.

3. The method according to claim 2, characterized in that: The predetermined constraint includes a predetermined single threshold constraint and a predetermined state constraint, and judging whether the first power transformation device meets the predetermined constraint according to the first visualization model includes: Extracting a first indicator from the predetermined characteristic indicators, and matching a first single threshold constraint of the first indicator in the predetermined single threshold constraint; Determining whether first real-time data of the first indicator in the first visualization model satisfies the first single threshold constraint; If the conditions are met, weighted calculation is performed on the first real-time data to obtain a first real-time status index; Determining whether the first real-time status index satisfies the predetermined status constraint; If so, the first power transformation equipment complies with the predetermined constraint.

4. The method according to claim 3, characterized in that: The first simulation operation parameter obtained based on the initial optimization item is simulated by the first substation equipment model to obtain a first simulation record, including: Acquire a first neighborhood of the first real-time data, where the first neighborhood includes a plurality of data; randomly acquiring first neighborhood data from the plurality of data in combination with the predetermined single threshold constraint; The first simulation operation parameters are composed based on the first neighborhood data.

5. Edge power distribution optimization system for power grid equipment, characterized in that: include: A target device acquisition module, the target device acquisition module is used to form a target power grid substation device set, the target power grid substation device set refers to a collection of multiple substation devices in the target power grid; A blank model building module, wherein the blank model building module is used to build a first substation model, wherein the first substation model is a physical model of the first substation, and the first substation is any one of the multiple substations; An initialization module, the initialization module is used to render first real-time operation data of the first power substation monitored based on a predetermined characteristic indicator to the first power substation model to obtain a first visualization model; A determination module, the determination module is used to determine whether the first power transformation equipment meets a predetermined constraint according to the first visualization model; A simulation operation module, wherein if the conditions are met, the simulation operation module is used to simulate the first simulation operation parameter obtained based on the initial optimization item through the first substation equipment model to obtain a first simulation record, wherein the initial optimization item refers to the first real-time operation data; an adaptability calculation module, the adaptability calculation module being used to analyze and determine whether the first simulation record meets the predetermined constraint, and if so, calling a predetermined fitness function to sequentially obtain a first real-time fitness of the first real-time operation data and a first simulation fitness of the first simulation operation parameter; A decision optimization module, the decision optimization module is used to determine a first edge power distribution decision by comparing the first real-time fitness and the first simulation fitness, and to form a target power distribution decision, wherein the target power distribution decision is used to optimize edge power distribution of the target power grid; The adaptive computing module also includes: A power distribution equipment acquisition unit, configured to acquire a first edge power distribution equipment, where the first edge power distribution equipment refers to a power distribution equipment in a distributed edge power distribution equipment group corresponding to the first power transformer; A power distribution monitoring unit, configured to monitor and obtain first real-time power distribution information of the first edge power distribution device under the first real-time operation data; an information extraction unit, configured to extract first simulated power distribution information of the first edge power distribution device in the first simulated record; A function calling unit, configured to call the predetermined fitness function to sequentially analyze the first real-time power distribution information to obtain the first real-time fitness and the first simulated fitness of the first simulated power distribution information; The function call unit also includes: The first real-time power distribution information includes first real-time public grid interharmonics, first real-time three-phase imbalance, first real-time voltage transient event information, first real-time voltage transient event information, and first real-time frequency quality index; an encoding unit, configured to sequentially encode the first real-time voltage transient event information and the second real-time voltage transient event information to obtain first encoded data and second encoded data, respectively; A calculation unit is used to call the predetermined fitness function to analyze the first real-time public grid interharmonic, the first real-time three-phase imbalance, the first coded data, the second coded data, and the first real-time frequency quality index to obtain the first real-time fitness, wherein the expression of the predetermined fitness function is as follows: ， in, the first real-time fitness characterizing the first real-time operation data, and and and and are respectively normalized results of the first real-time public grid interharmonics, the first real-time three-phase unbalance, the first coded data, the second coded data, and the first real-time frequency quality index, and and and and are the first weight coefficient, the second weight coefficient, the third weight coefficient, the fourth weight coefficient, and the fifth weight coefficient respectively, and The system further comprises a frequency quality index unit for: Acquire a first frequency curve of a first frequency time series in the first real-time power distribution information; Analyze the first frequency curve to obtain a first average frequency deviation and a first frequency flicker number; The first average frequency deviation and the first frequency flicker times are weighted by using the variation coefficient principle to obtain the first real-time frequency quality index.

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