Load distribution dynamic balance control method and device for high-voltage power grid, equipment, medium and product

Abstract

The invention discloses a load distribution dynamic balance control method and device for a high-voltage power grid, equipment, a medium and a product, and relates to the technical field of power system automation control, and the method comprises the steps: obtaining a capacity adjustment capability parameter set through an edge node load multi-frequency disturbance vector group and a center node frequency division-fault-tolerant adjustment matrix; constructing three-dimensional cross-node frequency band equilibrium tensor data, traversing the data, marking abnormal nodes, and distributing a push-back feedback weight; and obtaining a cross-band adaptive load balancing strategy table and controlling a power grid based on the back-push feedback weight of the abnormal node, the three-dimensional cross-node frequency band balancing tensor data and the edge node load multi-frequency disturbance vector group subjected to amplitude limiting smoothing processing. According to the invention, through frequency band disturbance differential response and dynamic weight distribution, unification of rapidness, accuracy and robustness of multi-frequency-band resource scheduling is realized, and the load balancing capability of a high-voltage power grid in a complex environment is remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system automatic control, in particular to a load distribution dynamic balance control method, device, equipment, medium and product for high-voltage power grid. BACKGROUND

[0002] In modern power systems, especially in high-voltage transmission networks, with the continuous increase of renewable energy access ratio and the intensification of load type diversification trend, the dynamic characteristics of power grid operation become more and more complex, and the power quality management and real-time load balance control face great challenges.

[0003] To cope with the problem of frequent disturbance response in power grid operation, the load control method in the related art relies on centralized strategy for power distribution and frequency regulation, which can guarantee system stability to a certain extent, but still has significant limitations in dealing with dynamic changes of frequency band disturbance and multi-node regulation feedback linkage. In addition, some related researches try to introduce distributed cooperative regulation mechanism to improve the regulation accuracy through local sensing ability of nodes, but most of the methods still do not fully consider the heterogeneity of frequency domain response ability between nodes, resulting in response lag and strategy imbalance in cross-frequency band load coordination.

[0004] Therefore, there is an urgent need for a load distribution dynamic balance control method for high-voltage power grid to meet the demand of high-voltage power grid for fast, accurate and robust dynamic load control. SUMMARY

[0005] The purpose of the present application is to provide a load distribution dynamic balance control method, device, equipment, medium and product for high-voltage power grid, which can effectively solve the problems of response lag, strategy imbalance and regulation rigidity of traditional methods in complex power grid environment, and significantly improve the real-time performance, robustness and economy of high-voltage power grid multi-frequency band load balance.

[0006] To achieve the above purpose, the present application provides the following solutions:

[0007] In a first aspect, the present application provides a load distribution dynamic balance control method for high-voltage power grid, comprising:

[0008] The capacity parameter set of each edge node under different frequency bands is obtained by performing matrix multiplication operation on the edge node load multi-frequency disturbance vector group and the frequency-division fault-tolerant adjustment matrix of the center node; the edge node load multi-frequency disturbance vector group is obtained by performing frequency spectrum reconstruction on the load response signal of each edge node by using the fast Fourier transform method; the frequency-division fault-tolerant adjustment matrix of the center node is composed of frequency band weight adjustment coefficients determined based on a frequency band distribution index table and a historical disturbance cumulative probability density; and the capacity parameter set is used for quantitatively representing the spectrum adjustment capacity of the edge node in the current period.

[0009] Based on the capacity parameter set of all edge nodes under different frequency bands, a three-dimensional cross-node frequency band balancing tensor data is constructed; each element in the three-dimensional cross-node frequency band balancing tensor data represents a corresponding current edge node and all edge nodes except the current edge node in a frequency band, a first adjustment record of the current edge node when adjusting cooperatively, the adjustment record including the number of times of triggering adjustment of the current edge node, the communication delay after adjustment and the load stability after adjustment.

[0010] Each element in the three-dimensional cross-node frequency band balancing tensor data is traversed, if the current element meets an abnormal condition, the edge node corresponding to the current element is marked as an abnormal node, and a back-pushing counterweight is assigned; the abnormal condition includes that the average communication delay after adjustment of all frequency bands of the current edge node corresponding to the current element is greater than a preset communication delay threshold or the number of frequency bands with the number of times of triggering adjustment being 0 is greater than a preset number threshold.

[0011] Based on the back-pushing counterweight of the abnormal node, the three-dimensional cross-node frequency band balancing tensor data and the edge node load multi-frequency disturbance vector group subjected to amplitude limiting and smoothing processing, a cross-frequency adaptive load balancing strategy table is obtained, and a high-voltage power grid is controlled; the cross-frequency adaptive load balancing strategy table includes the upper limit of the adjustment amplitude and the lower limit of the quota corresponding to each frequency band sub-block, the current adjustment bias of the abnormal node and the reverse path direction, the overall frequency band priority adjustment index and the available window state.

[0012] In a second aspect, the application provides a load distribution dynamic balancing control device for a high-voltage power grid, comprising:

[0013] The capacity adjustment capability parameter set generation module is configured to obtain a capacity adjustment capability parameter set of each edge node under different frequency bands by performing matrix multiplication operation on the edge node load multi-frequency disturbance vector group and the frequency-division fault-tolerant adjustment matrix of the central node; the edge node load multi-frequency disturbance vector group is obtained by performing frequency spectrum reconstruction on the load response signal of each edge node by using the fast Fourier transform method; the frequency-division fault-tolerant adjustment matrix of the central node is composed of frequency band weight adjustment coefficients determined based on a frequency band distribution index table and a historical disturbance cumulative probability density; and the capacity adjustment capability parameter set is used to quantitatively represent the spectrum adjustment capability of the edge node in the current period.

[0014] The three-dimensional cross-node frequency band balancing tensor construction module is configured to construct three-dimensional cross-node frequency band balancing tensor data based on the capacity adjustment capability parameter set of all edge nodes under different frequency bands; each element in the three-dimensional cross-node frequency band balancing tensor data represents a first adjustment record of a corresponding current edge node when all edge nodes except the current edge node are adjusted in a frequency band, and the adjustment record includes the number of times of triggering adjustment of the current edge node, the communication delay after adjustment, and the load stability after adjustment.

[0015] The abnormal node identification and weight distribution module is configured to traverse each element in the three-dimensional cross-node frequency band balancing tensor data, and if the current element meets an abnormal condition, mark the edge node corresponding to the current element as an abnormal node and distribute a back-pushing counterweight; the abnormal condition includes that the average communication delay after adjustment of all frequency bands of the current edge node corresponding to the current element is greater than a preset communication delay threshold or the number of frequency bands with 0 times of triggering adjustment in the number of times of triggering adjustment of all frequency bands is greater than a preset number threshold.

[0016] The cross-frequency band adaptive load balancing strategy generation module is configured to obtain a cross-frequency band adaptive load balancing strategy table based on the back-pushing counterweight of the abnormal node, the three-dimensional cross-node frequency band balancing tensor data, and the edge node load multi-frequency disturbance vector group subjected to amplitude limiting and smoothing processing, and control the high-voltage power grid; the cross-frequency band adaptive load balancing strategy table includes an adjustment amplitude upper limit and a quota lower limit corresponding to each frequency band sub-block, an abnormal node current adjustment bias and a reverse path direction, an overall frequency band priority adjustment index, and an available window state.

[0017] In a third aspect, the present application provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the load distribution dynamic balancing control method for a high-voltage power grid.

[0018] In a fourth aspect, the present application provides a computer readable storage medium, having stored thereon a computer program, which, when executed by a processor, implements the steps of the load distribution dynamic balance control method for high-voltage power grid according to any one of the preceding aspects.

[0019] In a fifth aspect, the present application provides a computer program product, comprising a computer program which, when executed by a processor, implements the steps of the load distribution dynamic balance control method for high-voltage power grid according to any one of the preceding aspects.

[0020] According to the embodiments provided in the present application, the present application has the following technical effects:

[0021] The application provides a load distribution dynamic balance control method, device, equipment, medium and product for a high-voltage power grid, which performs frequency spectrum reconstruction on the load response signal of each edge node by using a fast Fourier transform method, obtains a set of edge node load multi-frequency disturbance vectors, and performs matrix multiplication operation on a center node frequency-division fault-tolerant adjustment matrix determined based on a frequency band distribution index table and a historical disturbance cumulative probability density to obtain a set of capacity adjustment parameters of each edge node under different frequency bands, thereby solving the problem that the traditional method cannot quantitatively and accurately represent the frequency spectrum adjustment capacity of the edge node, and realizing accurate quantitative evaluation of the frequency spectrum adjustment capacity of the edge node in the current period. BRIEF DESCRIPTION OF DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative effort based on these drawings.

[0023] Figure 1 Figure 1 is an application environment diagram of a load distribution dynamic balance control method for a high-voltage power grid according to an embodiment of the present application;

[0024] Figure 2 Figure 2 is a flowchart of a load distribution dynamic balance control method for a high-voltage power grid according to an embodiment of the present application;

[0025] Figure 3 Figure 3 is a functional module diagram of a load distribution dynamic balance control device for a high-voltage power grid according to an embodiment of the present application;

[0026] Figure 4 Figure 4 is a structural diagram of a computer device according to an embodiment of the present application. DETAILED DESCRIPTION

[0027] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0028] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application will be further described in detail below with reference to the drawings and specific embodiments.

[0029] The load distribution dynamic balance control method for a high-voltage power grid provided by the embodiments of the present application can be applied to, for example, Figure 1The application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data required by the server 104 to process. The data storage system can be set up separately, or integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send the edge node load multi-frequency disturbance vector set and the center node frequency division-fault tolerance adjustment matrix to the server 104. After receiving the edge node load multi-frequency disturbance vector set and the center node frequency division-fault tolerance adjustment matrix, the server 104 obtains the capacity parameter set of each edge node under different frequency bands by performing matrix multiplication operation on the edge node load multi-frequency disturbance vector set and the center node frequency division-fault tolerance adjustment matrix; the edge node load multi-frequency disturbance vector set is obtained by performing frequency spectrum reconstruction on the load response signal of each edge node by fast Fourier transform method; the center node frequency division-fault tolerance adjustment matrix is composed of frequency band weight adjustment coefficients determined based on frequency band distribution index table and historical disturbance cumulative probability density; the capacity parameter set is used to quantitatively represent the spectrum adjustment capability of the edge node in the current period; based on the capacity parameter set of all edge nodes under different frequency bands, a three-dimensional cross-node frequency band balance tensor data is constructed; each element in the three-dimensional cross-node frequency band balance tensor data represents a corresponding current edge node and all edge nodes except the current edge node in a frequency band. When adjusting cooperatively, the current edge node corresponds to a one-time adjustment record of the current edge node, and the adjustment record includes the number of times of triggering adjustment, the communication delay after adjustment and the load stability after adjustment; traverse each element in the three-dimensional cross-node frequency band balance tensor data, if the current element satisfies the abnormal condition, mark the edge node corresponding to the current element as an abnormal node, and assign a back-pushing feedback weight; the abnormal condition includes that the average communication delay of all frequency bands of the current element corresponding to the current edge node after adjustment is greater than a preset communication delay threshold, or the number of frequency bands with the number of times of triggering adjustment being 0 is greater than a preset number threshold; based on the back-pushing feedback weight of the abnormal node, the three-dimensional cross-node frequency band balance tensor data and the edge node load multi-frequency disturbance vector set subjected to amplitude limiting and smoothing processing, a cross-frequency adaptive load balancing strategy table is obtained, and the high-voltage power grid is controlled; the cross-frequency adaptive load balancing strategy table includes the adjustment amplitude upper limit and the quota lower limit corresponding to each frequency band sub-block; the current adjustment bias of the abnormal node and the reverse path direction; the overall frequency band priority adjustment index and the available window state. The server 104 can feed back the obtained cross-frequency adaptive load balancing strategy table to the terminal 102.In addition, in some embodiments, the load distribution dynamic balancing control method for the high-voltage power grid can also be implemented by the server 104 or the terminal 102 alone, such as the dynamic balancing control of the multi-frequency disturbance vector set of the edge node load and the frequency division-fault tolerance adjustment matrix of the center node by the terminal 102 directly, or the multi-frequency disturbance vector set of the edge node load and the frequency division-fault tolerance adjustment matrix of the center node obtained from the data storage system by the server 104 and the dynamic balancing control of the multi-frequency disturbance vector set of the edge node load and the frequency division-fault tolerance adjustment matrix of the center node.

[0030] The terminal 102 can be, but is not limited to, various desktop computers, notebook computers, smart phones, tablet computers, Internet of Things devices, and portable wearable devices. The server 104 can be implemented by a single server or a server cluster composed of multiple servers, and can also be a cloud server.

[0031] In an exemplary embodiment, as shown in Figure 2 , a load distribution dynamic balancing control method for a high-voltage power grid is provided, which is executed by a computer device, specifically, can be executed by a terminal or a server alone, or can be executed by a terminal and a server together. In the embodiments of the present application, the method is applied to the server 104 in the Figure 1 , which includes the following steps 201 to 204. Wherein:

[0032] Step 201, the load adjustment capability parameter set of each edge node under different frequency bands is obtained by performing matrix multiplication operation on the multi-frequency disturbance vector set of the edge node load and the frequency division-fault tolerance adjustment matrix of the center node. The multi-frequency disturbance vector set of the edge node load is obtained by performing frequency spectrum reconstruction on the load response signal of each edge node by the fast Fourier transform method. The frequency division-fault tolerance adjustment matrix of the center node is composed of frequency band weight adjustment coefficients determined based on the frequency band distribution index table and the historical disturbance cumulative probability density. The load adjustment capability parameter set is used to quantitatively represent the frequency spectrum adjustment capability of the edge node in the current period.

[0033] Step 202, based on the load adjustment capability parameter set of all edge nodes under different frequency bands, a three-dimensional cross-node frequency band balancing tensor data is constructed. Each element in the three-dimensional cross-node frequency band balancing tensor data represents a corresponding current edge node and all edge nodes except the current edge node in a frequency band when they are adjusted together, a one-time adjustment record of the current edge node, which includes the number of times of triggering adjustment of the current edge node, the communication delay after adjustment, and the load stability after adjustment.

[0034] In step 203, each element in the three-dimensional cross-node frequency band equalization tensor data is traversed, and if the current element meets an abnormal condition, the edge node corresponding to the current element is marked as an abnormal node, and a back-pushing feedback weight is assigned; the abnormal condition includes that the adjusted communication average time delay of all frequency bands corresponding to the current edge node of the current element is greater than a preset communication time delay threshold or the number of frequency bands whose number of adjustment triggering is 0 is greater than a preset number threshold.

[0035] In step 204, a cross-frequency adaptive load balancing strategy table is obtained based on the back-pushing feedback weight of the abnormal node, the three-dimensional cross-node frequency band equalization tensor data, and the edge node load multi-frequency disturbance vector group subjected to amplitude limiting and smoothing processing, and the high-voltage power grid is controlled; the cross-frequency adaptive load balancing strategy table includes an adjustment amplitude upper limit and a quota lower limit corresponding to each frequency band sub-block, an abnormal node current adjustment bias and a reverse path direction, an overall frequency band priority adjustment index and an available window state.

[0036] By implementing the above steps 201 to 204, the present application can effectively solve the problems of high response delay, fast disturbance conduction, and strong regulation and control rigidity of traditional methods in complex power grid environment through technical means such as frequency domain feature accurate analysis, node capability dynamic matching, abnormal node fault tolerance processing, and multi-frequency resource collaborative optimization, significantly improving the real-time performance, robustness, and resource utilization rate of high-voltage power grid multi-frequency load balancing, and providing key technical support for stable operation of new power systems.

[0037] In another exemplary embodiment of the present application, step 201 specifically includes:

[0038] The frequency band weight distribution mapping vector group is obtained by performing matrix multiplication on the edge node load multi-frequency disturbance vector group and the frequency division-fault tolerance adjustment matrix of the center node.

[0039] The disturbance change rate of each frequency band in the edge node load multi-frequency disturbance vector group is extracted, and the disturbance change rate of each frequency band is multiplied by the corresponding element in the frequency band weight mapping vector group to obtain a frequency band response capability index set; each component in the response capability index set represents an adjustment response ratio of the edge node to the i th frequency band disturbance.

[0040] The response capability index set is subjected to L2 norm normalization processing to obtain a normalized response capability index set.

[0041] The normalized response capability index set and the preset inherent response lag factor of the corresponding edge node are subjected to exponential decay-based fusion operation to generate a fault tolerance adjustment capability parameter set; the preset inherent response lag factor is calculated by adjusting the average delay time between triggering and response feedback.

[0042] In another example embodiment of the present application, step 202 specifically includes:

[0043] The capacity adjustment capability parameter set of all edge nodes is longitudinally spliced by frequency band index to construct a node capability matrix in the frequency band dimension; wherein the jth element in the node capability matrix represents the capacity adjustment capability of edge node j' in frequency band k.

[0044] All elements in the node capability matrix in each frequency band are subjected to harmonic normalization based on the average power density distribution to obtain a frequency band harmonic coefficient set.

[0045] The node capability matrix and the frequency band harmonic coefficient set are jointly mapped to generate three-dimensional cross-node frequency band balance tensor data.

[0046] In another example embodiment of the present application, the node capability matrix and the frequency band harmonic coefficient set are jointly mapped to generate three-dimensional cross-node frequency band balance tensor data, specifically including:

[0047] The node capability matrix and the frequency band harmonic coefficient set are jointly mapped to generate an intermediate matrix.

[0048] Based on the intermediate matrix, the response probability and the non-response probability of each frequency band in different nodes are extracted to construct a frequency band response entropy atlas.

[0049] Based on the frequency band response entropy atlas, all frequency bands are classified by clustering analysis method to obtain frequency band response entropy clustering results.

[0050] According to the frequency band response entropy clustering results and historical load adjustment data, three-dimensional cross-node frequency band balance tensor data is generated.

[0051] In another example embodiment of the present application, step 204 specifically includes:

[0052] Based on the elements in the three-dimensional cross-node frequency band balance tensor data whose frequency band interference indexes are greater than a set disturbance threshold, a frequency band sub-block is constructed.

[0053] Based on the frequency band sub-block and the corresponding abnormal node back-propagation reconstruction, a disturbance priority index table is constructed.

[0054] The frequency band sub-blocks in the disturbance priority index table are selected in order according to a preset number threshold to obtain a set of downgraded target frequency band sub-blocks.

[0055] For each downgraded target frequency band sub-block in the set of downgraded target frequency band sub-blocks, segmented downgrading control is respectively performed, and a linearly decreasing allocation coefficient is set according to the frequency band interference index to obtain an updated adjustment quota corresponding to each downgraded target frequency band sub-block.

[0056] Replace each element in the three-dimensional cross-node frequency band equalization tensor data belonging to the target frequency band sub-block set with the corresponding updated adjustment quota to obtain the three-dimensional cross-node frequency band equalization tensor data after the reduction.

[0057] The load multi-frequency disturbance vector group is subjected to amplitude limiting and smoothing processing to obtain a smoothed load multi-frequency disturbance vector group.

[0058] Based on the smoothed load multi-frequency disturbance vector group, the maximum load disturbance amplitude in each frequency band sub-block is determined.

[0059] The dynamic amplitude limiting threshold of each frequency band sub-block is determined according to the maximum load disturbance amplitude in each frequency band sub-block.

[0060] Based on the dynamic amplitude limiting threshold of each frequency band sub-block, a multi-frequency amplitude limiting factor group is obtained.

[0061] The cross-frequency adaptive load balancing strategy table is obtained by fusing the three-dimensional cross-node frequency band equalization tensor data after the reduction, the multi-frequency amplitude limiting factor group, and the back-propagation counterweight of the abnormal node.

[0062] In another exemplary embodiment of the present application, the construction process of the frequency band distribution index table in step 201 specifically includes:

[0063] Frequency band disturbance data of a plurality of preset typical waveforms with preset sample period lengths are collected; the preset typical waveforms include early peak waveforms, late peak waveforms, and flat peak waveforms of daily load changes.

[0064] The frequency band disturbance data is filtered using a differential enhancement filtering method to extract a load change rate sequence per unit time.

[0065] Based on the load change rate sequence per unit time, the frequency band is divided into a preset fixed bandwidth to obtain a load change rate sequence in each frequency band.

[0066] The load change rate sequence in each frequency band is subjected to disturbance energy density integral calculation to obtain disturbance energy density integral results of each frequency band.

[0067] The disturbance energy density integral results of all frequency bands are subjected to normalized sorting processing using a frequency band label setter, and corresponding frequency band labels are generated for each frequency band according to the sorting results; the frequency band label is used to represent the disturbance characteristics corresponding to the frequency band.

[0068] A one-to-one mapping relationship is established between the frequency band label and the response capability determined based on the corresponding bandwidth to construct a frequency band distribution index table.

[0069] In another exemplary embodiment of the present application, a load distribution dynamic balancing control method for a high-voltage power grid is provided, specifically including the following steps:

[0070] Step 1: Collect the load response curve of the edge node at the current moment and the historical disturbance frequency spectrum of the node, construct the edge node load multi-frequency disturbance vector group, which is used to represent the response characteristics and fluctuation trend in the frequency band.

[0071] As an optional implementation, first, install a frequency band response recording module in the edge node, record the load change rate in a plurality of typical disturbance periods synchronously through a local sampling device, and construct a frequency band distribution index table by fixed bandwidth division, which is used to identify the response bandwidth interval of each frequency band; call the internal historical disturbance data of the edge node and the external periodic disturbance reference template, calculate the response deviation set under each frequency band, and aggregate it according to the frequency band distribution index table to obtain the multi-frequency response deviation vector group of the edge node; input the load change rate, the frequency band distribution index table and the multi-frequency response deviation vector group into the frequency band disturbance modeling unit of the node to construct the edge node load multi-frequency disturbance vector group as the joint representation vector of the node response and the frequency spectrum disturbance trend.

[0072] Wherein, in order to fully grasp the load response characteristics of the edge node in different frequency bands, the application preferentially deploys a dedicated frequency band response recording module at each target node end, which is composed of a high-speed sampling chip, a segmented buffer, a bandwidth identification subunit and a local cache database. In the conventional system, only the overall load metering method is used to obtain the node power change or the timing load curve. This coarse-grained method is difficult to reveal the response difference of the node to different frequency disturbance signals. The application realizes the fine correlation record of the disturbance frequency component and the real-time response of the node by introducing the module, so that the frequency band characteristics are effectively decoupled and reconstructed at the data level.

[0073] Wherein, in order to fully grasp the load response characteristics of the edge node in different frequency bands, the application preferentially deploys a dedicated frequency band response recording module at each target node end, which is composed of a high-speed sampling chip, a segmented buffer, a bandwidth identification subunit and a local cache database. In the conventional system, only the overall load metering method is used to obtain the node power change or the timing load curve. This coarse-grained method is difficult to reveal the response difference of the node to different frequency disturbance signals. The application realizes the fine correlation record of the disturbance frequency component and the real-time response of the node by introducing the module, so that the frequency band characteristics are effectively decoupled and reconstructed at the data level.

[0074] In order to enhance the system recognizability of the frequency band distribution, the application designs a frequency band distribution index table. The table is not only a storage structure, but also a mapping relationship representation framework. The core is to establish a one-to-one mapping between each frequency band disturbance and the corresponding node response capability. Through continuous sampling and spectrum mapping extraction of multiple disturbance periods, the application can clearly divide the corresponding power disturbance bandwidth of each frequency band and establish a frequency band label for subsequent modeling. The specific operation process includes: collecting multiple representative disturbance segments within 15-30 minutes of sample period to ensure that the typical waveforms such as early peak, late peak and flat peak of daily load change are covered; using differential enhancement filtering technology to extract the unit time load change rate sequence; calling the frequency band label setter to sort the disturbance energy density integral results of different frequency bands and generate a frequency band index table.

[0075] Step 2: input the load multi-frequency disturbance vector group into the frequency- fault-tolerant adjustment matrix set by the center node, extract the current node's capacity adjustment capability parameter set under different frequency bands, and use it to measure the node's available balance quota. The center node is a key computing coordination node defined according to the system architecture, which functions to uniformly receive and process the load disturbance information of the edge nodes, calculate the capacity adjustment capability parameters by means of the frequency- fault-tolerant adjustment matrix, and use them to build the cross-node balance tensor data and strategy table and other control logic, which is a logical / functional center node set for the dynamic load balancing control target of the application.

[0076] As an optional embodiment, the multi-frequency disturbance vector group is input into the preset frequency- fault-tolerant adjustment matrix in the center node, wherein the matrix is a multi-dimensional adjustment coefficient array constructed based on the frequency band distribution index table, which relies on the frequency band weight set to weight and distribute the multi-frequency disturbance vector group to obtain a frequency band weight distribution mapping vector; according to the frequency band weight distribution mapping vector, the response capability index set of the current node's frequency band distribution is extracted by combining the amplitude change rate of each frequency band disturbance in the multi-frequency disturbance vector group, wherein each component represents the adjustment response ratio of the node to the i th frequency band disturbance; the response capability index set is L2 normalized and a node inherent response lag factor is added to generate the capacity adjustment capability parameter set, which is a quantitative representation of the node's available dynamic balance quota in the current period.

[0077] Wherein, in order to implement this embodiment, it is necessary to make it clear that the adjustment matrix is not a fixed structure, but is dynamically constructed according to the frequency band distribution index table, the purpose of which is to adapt to the frequency spectrum feature distribution of different nodes under different time periods of sampling, so that the adjustment system has flexibility. Each dimension of the matrix corresponds to a frequency band, and the internal element is a weight adjustment coefficient, which represents the importance of the disturbance of the frequency band in the adjustment task. The determination of the weight set is based on the cumulative probability density of the historical disturbance, forming an adaptive frequency band weight scheduling strategy. Finally, the disturbance vector group is operated with the adjustment matrix through matrix multiplication, so as to generate a frequency band weight distribution mapping vector.

[0078] Wherein, in order to implement this embodiment, after obtaining the frequency band weight distribution mapping vector, the actual dynamic characteristics of the disturbance need to be further combined to extract the response capability index set of the node under each frequency band. Specifically, the disturbance amplitude change rate can be calculated by the derivative of the disturbance response deviation in the time sequence, that is, the degree of change of the disturbance deviation per unit time under each frequency band. This change rate reflects the agility and adjustment speed of the node response, and is the key basis for judging whether the node has the ability of fast dynamic balance. In this application, the frequency band disturbance change rate is multiplied with the frequency band weight mapping vector to obtain the weighted adjustment response index. This process can be regarded as a convolution filtering operation in the frequency dimension, effectively excluding the noise interference of low-impact disturbance frequency bands. The final result is a set of frequency band response capability indexes with physical meaning, each component clearly indicating the adjustment proportion ability of the node to the frequency band disturbance. This set not only has the function of distinguishing the spectrum sensitive area, but also can provide accurate quantitative support for subsequent quota scheduling, so that the dynamic adjustment is more targeted and real-time.

[0079] In this embodiment, in order to realize the comparable and standardized response capability evaluation of each node in the whole system, the L2 norm normalization processing is performed on the response capability index set. The purpose of this operation is to eliminate the difference in the order of magnitude of the response value between different nodes or different sampling times, so as to normalize the adjustment capability vector to a unified spatial scale. In the specific implementation, first, the L2 norm of the response index set, that is, the square root of the sum of squares of each component, is calculated, and then each component is normalized by taking the norm value as the divisor, so that the sum of the response capability of all frequency bands after normalization is 1. Thereafter, the node inherent response lag factor is introduced, which is a quantitative index reflecting the delay characteristic of the internal execution of the adjustment instruction of the node. This factor can be calculated by measuring the average delay time between the adjustment trigger and the response feedback in the system, and in this application, it is in the form of an exponential decay coefficient to participate in parameter calculation, which is used to compress the response value of the frequency band with larger response lag. Finally, the normalized response capability index vector and the lag factor work together to form a complete capacity adjustment capability parameter set. This parameter set not only represents the frequency spectrum adjustment capability of the node in the current period, but also can be used as the basis for decision-making in the dynamic balance allocation of the whole network quota. Its quantitative characteristics facilitate the comparison, integration and optimization scheduling with the capability set of other nodes, and help to realize the intelligent adaptive load balancing in the disturbance environment.

[0080] In summary, step 2 realizes the conversion from physical disturbance behavior to adjustment capability index through the standardized analysis of the disturbance vector group and the extraction of the adjustment capability of the frequency band dimension. This conversion not only enhances the overall control ability of the center node on the frequency domain characteristics in the system, but also establishes a quantifiable and comparable universal measurement basis for resource scheduling between heterogeneous nodes. The introduction of the capacity adjustment capability parameter set breaks through the limitations of the traditional scheduling model based on single frequency response, average load or static bandwidth, and truly realizes the dynamic balance control mechanism based on the frequency domain disturbance behavior. At the same time, by adding the lag factor, the system shock caused by the untimely adjustment can be effectively avoided, and the robustness of the whole system under the condition of frequent disturbance and node heterogeneity can be improved.

[0081] Step 3: Perform harmonic normalization on the capacity adjustment capability parameter set, and form a cross-node frequency band balance tensor with other access node parameter sets to form a frequency domain balance mapping relationship perceived by the center node.

[0082] As an optional implementation, the capacity adjustment capability parameter sets from all edge nodes are longitudinally spliced by frequency band index to construct a node capability matrix in the frequency band dimension, wherein the matrix elements represent the normalized capacity adjustment index of node j' in frequency band k; the harmonic normalization based on the average power density distribution is performed on each frequency band vector in the node capability matrix to generate a set of frequency band harmonic coefficients in a unified dimension to eliminate the asymmetric influence of high-load nodes on the regulation weight; the normalized node capability matrix and the harmonic coefficient set are jointly mapped to generate a cross-node frequency band balancing tensor, wherein each tensor element reflects the adjustment adaptability of node j' in phase e under frequency band k.

[0083] Wherein, to implement this embodiment, the first stage is to construct a node capability matrix in the frequency band dimension, specifically: the center node splices the capacity adjustment capability parameter sets from multiple edge nodes (including edge terminals, distributed sub-modules or regional coordination sites, etc.) by frequency band index to form a three-dimensional frequency band-node mapping representation structure, each row corresponds to a frequency band in the network, and each column represents a specific edge node; each matrix element represents the normalized capacity adjustment index of the edge node in the frequency band, which is independent of the unit and comparable in frequency bands.

[0084] Subsequently, in the second stage of step 3, the consistency and difference of frequency band response among different edge nodes are modeled and analyzed as a whole based on the encoded frequency band response sequence and the adjustment trigger result to construct a mapping relationship atlas that can reflect the degree of frequency band sensitivity. In operation, first, the frequency band response information of all edge nodes is normalized for pretreatment to ensure that the response amplitude, time characteristics and frequency band distribution dimension have comparability in a unified scale. Subsequently, from the overall response behavior of the edge node group, the probability difference of each frequency band in different edge nodes in response and non-response is extracted. To describe the response uncertainty of each frequency band, the difference degree of the frequency band in triggering feedback in each edge node is counted, which can be counted by the proportion of the number of edge nodes that respond to the frequency band, combined with the consistency of its feedback, to form a frequency band response entropy atlas. In other words, if a frequency band produces similar feedback in most edge nodes, its response entropy is low, indicating that the frequency band is more sensitive to load adjustment; on the contrary, if the feedback behavior is significantly different, the entropy value is high, representing that the interference uncertainty is large or is significantly affected by external factors.

[0085] Based on the above entropy mapping information, the application further introduces a clustering analysis method to classify all frequency bands according to their similarity in response behavior. This process can use clustering algorithms, such as hierarchical clustering or density-based clustering algorithms, to classify frequency bands with similar response entropy characteristics into the same class. Each class of frequency band will be labeled as a specific disturbance attribution type, such as "high response consistent frequency band", "low feedback frequency band" or "fluctuation sensitive frequency band", etc., to guide the subsequent strategy making. Through this process, a disturbance source identification mechanism can be formed at the frequency domain level, providing data support for the structural optimization of frequency adjustment path.

[0086] The third stage is to construct a three-dimensional data body, called frequency balance tensor, based on the frequency band response entropy clustering results and historical load adjustment data, to express the coupling relationship between edge nodes, frequency bands and adjustment behavior, and to derive an executable adjustment path priority matrix based on this tensor.

[0087] The construction of the frequency balance tensor includes: scanning each edge node to count its adjustment behavior in all frequency bands, including the number of times the edge node triggers adjustment in each frequency band, the actual feedback delay after each adjustment (which can be calculated by the average time from when the node receives the adjustment instruction to when the state change is returned), and the stability of the adjustment result (such as whether the fluctuation amplitude of the system load within a set time window after the adjustment is completed is lower than a threshold value).

[0088] By mapping the above data to a three-dimensional structure (dimensions: edge node ID, frequency band number, adjustment attribute group), a frequency balance tensor is constructed. Each element in the tensor represents a record of an adjustment by a certain edge node in a certain frequency band, with attributes including adjustment amplitude, delay time, and post-adjustment load stability.

[0089] Each edge node-frequency band combination in the frequency balance tensor is analyzed to count its adjustment performance over the past period of time.

[0090] For each edge node, the average response delay and the success adjustment ratio in all frequency bands are sorted to establish a "priority sequence" for frequency band adjustment, for example: if the average adjustment delay of edge node A in frequency band f1 is 0.5 seconds and the success rate is 98%, while the delay in frequency band f3 is 1.3 seconds and the success rate is 72%, then f1 should be placed before f3 in the path priority matrix of A. The priority matrix is represented in a two-dimensional structure: the rows represent the edge nodes and the columns represent the arrangement order of the frequency bands for adjustment priority. The content of each cell is a "priority weight label", which can be divided into high (such as priority adjustment), medium (such as backup), and low (not recommended for adjustment) according to historical performance.

[0091] In addition, considering the possible conflict of adjustment timing between multi-edge node frequency bands, a frequency band interleaving interference weight is designed as an auxiliary factor to further optimize the path priority ranking. This factor indicates whether the adjustment of a certain edge node in a certain frequency band frequently overlaps in time with the adjustment of other edge nodes in adjacent frequency bands during the historical adjustment process, resulting in a decrease in adjustment effect. The statistical method is as follows: record the frequency of overlap between the adjustment of the edge node in the frequency band and the adjustment of other edge nodes each time, if the overlap rate is high, it means that there is a risk of interleaving interference in the frequency band. According to this interleaving interference weight, the existing priority order is corrected, for example, the interleaving frequency band is adjusted backward, or it is marked as "only for non-high load period use" in the scheduling to avoid adjustment resource interference, so as to form a more robust and more compatible path priority matrix.

[0092] This matrix is finally used as a reference table for the system to execute multi-frequency band load adjustment scheduling strategy, to automatically select the optimal frequency band path when the adjustment resource is tight or the response is limited, and to improve the adjustment efficiency and frequency utilization rate.

[0093] Step 4: Identify the nodes with abnormal communication delay or incomplete frequency band response of the edge node in the frequency band balancing tensor, and assign a back-pushing feedback weight to them to start the load reverse adjustment feedback path of the edge node.

[0094] As an optional implementation, for all edge node dimension indexes m in the frequency band balancing tensor, extract its communication timing record At and the corresponding frequency band response count vector R. If the proportion of frequency bands with At greater than a set threshold θt or R(m) equal to 0 exceeds a set threshold γ, mark the edge node m as an incomplete response node. For the edge node m marked as an incomplete response node, give it a back-pushing feedback weight according to its frequency band missing situation in the frequency band balancing tensor and the deviation degree of At. Reconstruct the adjustment target vector of the node using the back-pushing feedback weight, so that the adjustment direction is inclined to the frequency response low-sensitive segment, and construct the reverse adjustment path for correcting the load adjustment direction and frequency response priority participated by the node.

[0095] Step 4 aims to identify the edge nodes that behave abnormally during the frequency band adjustment process, and assign them a "back-pushing feedback weight" to start the reverse load adjustment mechanism to ensure stable operation.

[0096] As an optional implementation, the operation process is as follows:

[0097] Traverse the communication records of each edge node in the frequency band balancing tensor, and extract the following two types of information:

[0098] Communication latency record: the average time that the node actually takes to feedback after triggering the adjustment command (can be obtained by calculating the average interval between the "adjustment trigger time" and the "feedback receiving time" in multiple adjustments).

[0099] Frequency band response count vector: the number of adjustment responses of the node in all frequency bands, for example, a node responds 3 times in f1, 0 times in f2, and 7 times in f3, etc.

[0100] If a node has one of the following conditions, it can be considered "response abnormal": the average communication latency exceeds the set threshold (such as 2 seconds); it does not produce any valid adjustment feedback in at least half of the frequency bands (for example, 6 of the 10 frequency bands have a response count of 0).

[0101] For the node judged to be "response abnormal", the feedback confidence is calculated according to the severity of the missing frequency band response and the deviation of the communication delay. It can be executed as follows: the more the frequency band is missing, the more serious the communication delay is, and the higher the feedback weight is given; the weight value can be set as a level identifier, such as low (level 1), medium (level 2), and high (level 3), which is used for reference in subsequent adjustment strategies. Based on the feedback weight, the adjustment target vector of the node is redefined, and the adjustment direction is shifted from the high-sensitive response frequency band to the low-sensitive response frequency band, so as to avoid unstable load changes in the key frequency band.

[0102] For the node that has been given a high feedback weight, it is excluded from the original adjustment main path, and a reverse adjustment path is planned for it separately. The path is only activated when the load is extremely unbalanced or the frequency band resource is seriously conflicted. The path is mainly used to perform frequency adjustment rollback, load cancellation operation, or transfer the adjustment responsibility to a more stable adjacent node (such as a node that is physically close or connected in network topology).

[0103] Among them, in order to prevent the reverse adjustment storm caused by a large number of nodes entering the reverse adjustment state at the same time, the application further designs a dynamic backoff threshold drift mechanism. An initial trigger threshold is set (for example, based on the average communication latency and the response integrity ratio), but the threshold is not static. In actual operation, the number of abnormal nodes identified in a unit of time is counted, and the distribution density is evaluated. If a large number of abnormal nodes appear in a certain time window, the trigger threshold is appropriately increased (i.e. the start of the reverse adjustment is delayed) to buffer the concentrated burst of feedback paths. On the contrary, if the abnormal nodes are distributed sparsely, the system can appropriately reduce the threshold to start the reverse path in advance.

[0104] This dynamic adjustment threshold mechanism models the feedback quantity-density response curve of the overall stability, so as to realize more intelligent control of the start of the reverse adjustment path and improve the dynamic fault tolerance capability of the entire network to load abnormalities.

[0105] In the above manner, after identifying the response abnormal node, the responsibility of the node in the frequency band adjustment task can be dynamically allocated, and the participation degree of the node in the adjustment task can be adjusted through the weight mechanism, thereby improving the stability and response reliability of the entire load adjustment network.

[0106] Step 5: According to the back-pushing feedback weight and the frequency band interference degree, a segmented downgrading strategy is applied to the high disturbance sub-block in the frequency band equalization tensor, and an amplitude limiting smoothing process is performed on the load multi-frequency disturbance vector group, and finally a cross-frequency adaptive load balancing strategy table is output.

[0107] In the frequency band equalization tensor, identify the frequency band sub-blocks with disturbance gradient greater than the set disturbance threshold, and construct a disturbance priority index table in combination with the corresponding node feedback weight. Perform segmented downgrading control on the frequency band sub-blocks ranked in the top u in the disturbance priority index table, set a linearly decreasing allocation coefficient according to the disturbance amplitude, update the original adjustment quota, and output the downgraded tensor. Perform amplitude limiting smoothing operation on the original load multi-frequency disturbance vector group, set the amplitude limiting threshold according to the linearly decreasing allocation coefficient and the disturbance amplitude of each frequency band, construct a multi-frequency amplitude limiting factor group, and output an adaptive load balancing strategy table containing the cross-frequency adjustment amplitude of each node.

[0108] Step 5 aims to use the back-pushing feedback weight and the frequency band interference index generated in the previous steps to differentially control the high disturbance area, and to realize the construction of adaptive load balancing strategy among multiple frequency bands through segmented downgrading and amplitude limiting smoothing mechanism.

[0109] As an optional implementation, first, the disturbance change rate corresponding to each frequency band sub-block in the frequency band equalization tensor is extracted, which can be inferred by combining the change amplitude of the adjustment result and the adjustment request frequency within a continuous time window. For example, if a sub-block shows a continuously expanding adjustment amplitude or frequent negative feedback within the last three adjustment periods, it can be determined that its disturbance gradient is large.

[0110] After identifying these sub-blocks with significant disturbance gradient, a disturbance priority index table is further constructed in combination with the back-pushing feedback weight of the node associated with the sub-block. When constructing, the sub-blocks that meet the following two conditions are given priority consideration: (1) the disturbance amplitude is significantly higher than that of other regions in the frequency band, and (2) the corresponding node feedback weight is at a high level overall. The system generates a disturbance priority ranking number for each sub-block based on this, which is used for subsequent selection of controlled areas.

[0111] In the aforementioned priority index table, select a number of sub-blocks with high ranking, which can be set to a certain percentage of the current frequency band capacity (e.g. the top 10% or 20%), and these sub-blocks are the downgrading target areas in the current period.

[0112] For each selected sub-block, a progressively reduced resource allocation ratio is set according to its disturbance amplitude in the previous cycles. For example, if the adjustment amplitude of a sub-block in the last three cycles is more than twice the system average, its adjustment quota for the next cycle can be reduced to 30% to 50% of the original quota.

[0113] These proportionally reduced quota values are re-woven into a reduced allocation tensor as the core scheduling parameter of subsequent adjustment instructions. The reduced allocation tensor not only embodies the current adjustment intensity limiting strategy, but also retains the node frequency band matching logic in the original balancing tensor, ensuring the integrity of the adjustment path structure.

[0114] This stage makes the adjustment more restrained and dynamically flexible by directional compression of the adjustment freedom of high disturbance areas, effectively avoiding the risk of frequency band congestion or resource consumption.

[0115] After the construction of the reduced allocation tensor, further amplitude limiting and smoothing operations are performed on the load multi-frequency disturbance vector group to prevent high-frequency switching or extreme load fluctuations from affecting system stability.

[0116] As an optional implementation, the maximum load disturbance amplitude in the past several rounds of adjustment in each frequency sub-block is identified. Then, combined with the reduced allocation ratio allocated to the sub-block in the second stage, a dynamic amplitude limiting threshold is set for it. For example, if the reduced allocation ratio of a sub-block is high, the amplitude limiting threshold is set relatively low to suppress overshoot.

[0117] According to the amplitude limiting threshold of all sub-blocks, a multi-frequency amplitude limiting factor group is constructed, which represents the maximum disturbance increment that each frequency band can withstand in future adjustment cycles. During the adjustment process, if a certain adjustment operation is expected to cause an amplitude change exceeding the allowed value of the amplitude limiting factor, the operation will be automatically compressed or postponed.

[0118] The reduced allocation tensor, multi-frequency amplitude limiting factor group, and node back-propagation inverse weight are integrated to form a cross-frequency adaptive load balancing strategy table. The strategy table contains the following contents:

[0119] The upper limit of the adjustment amplitude and the lower limit of the quota for each frequency sub-block; the current adjustment bias and the reverse path direction of each node; the overall frequency band priority adjustment index and the available window state.

[0120] This strategy table can be called in subsequent running cycles to achieve dynamic and adaptive adjustment path reconstruction and load guidance, thereby improving the stability and emergency response capability of the entire frequency band network.

[0121] The application also provides an application scenario of the load distribution dynamic balance control method for high-voltage power grids. Specifically, the load distribution dynamic balance control method for high-voltage power grids can be applied in a high-voltage power grid multi-frequency band dynamic load regulation and stability optimization scenario. The scenario includes a data acquisition and preprocessing link, a core calculation and strategy generation link, and a power grid regulation execution and feedback link. Data from the data acquisition and preprocessing link enters the core calculation and strategy generation link. In the data acquisition and preprocessing link, the load response signals of each edge node are collected in real time through various sensors, historical disturbance data is collected to construct a frequency band distribution index table, and the historical disturbance cumulative probability density is calculated. After the collected edge node load response signals enter the core calculation and strategy generation link, a cross-frequency band adaptive load balance strategy table is obtained, and enters the power grid regulation execution and feedback link. In the power grid regulation execution and feedback link, the cross-frequency band adaptive load balance strategy table is applied to control the high-voltage power grid, and the running state of the power grid is monitored in real time, and the running data is fed back to the data acquisition and preprocessing link for the next round of data acquisition and strategy adjustment, forming a closed-loop regulation system. The load distribution dynamic balance control method for high-voltage power grids provided in the embodiment belongs to the core calculation and strategy generation link in the automatic operation control process of the high-voltage power grid. Specifically, the method focuses on solving the problems of high response delay, fast disturbance conduction, and the like in multi-frequency band resource scheduling of high-voltage power grids under complex working conditions such as high proportion of new energy access and intensified load fluctuation, and provides real-time and accurate load balance regulation strategies for the power grid control center through the construction of a complete technology chain of frequency domain feature perception-node capability evaluation-dynamic strategy generation, which is a key technical support unit for ensuring the safe and stable operation of the power grid.

[0122] The application significantly improves the load balance capability of the multi-frequency band resource scheduling system in a high-interference environment by introducing a disturbance perception mechanism and a dynamic adjustment strategy driven by feedback weight. By differentiating the response control of the disturbance characteristics between frequency bands, the system instability risk caused by local frequency band resource overload or fluctuation diffusion can be effectively inhibited, and the robustness and cooperativeness of the overall adjustment process are enhanced. Through the joint action of amplitude limiting smoothing processing and priority guiding mechanism, the system can adaptively adjust the frequency band resource allocation rhythm, realize flexible absorption and rapid convergence of sudden disturbances, and ensure the frequency band coordination continuity and load regulation fairness between multiple nodes. The application further takes into account the node importance and frequency band stability, realizes hierarchical and dynamic redistribution of adjustment targets in resource scheduling, significantly reduces scheduling conflicts and excessive intervention phenomena, and improves the overall operation efficiency and resource utilization of the system. In summary, the application effectively solves the problems of high response delay, fast disturbance conduction, and strong regulation rigidity in the existing technology in multi-frequency band load balancing.

[0123] Based on the same inventive concept, the application further provides a high-voltage power grid-oriented load distribution dynamic balance control device for implementing the above-mentioned high-voltage power grid-oriented load distribution dynamic balance control method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above-mentioned method, and therefore the specific limitations in one or more high-voltage power grid-oriented load distribution dynamic balance control device embodiments provided below can refer to the limitations of the high-voltage power grid-oriented load distribution dynamic balance control method described above, which will not be described here again.

[0124] In one exemplary embodiment, as shown in Figure 3 A high-voltage power grid-oriented load distribution dynamic balance control device is provided, comprising:

[0125] A capacity adjustment capability parameter set generation module 301 is configured to obtain a capacity adjustment capability parameter set of each edge node under different frequency bands by performing matrix multiplication operation on the edge node load multi-frequency disturbance vector group and the frequency division-fault tolerance adjustment matrix of the center node; the edge node load multi-frequency disturbance vector group is obtained by performing frequency spectrum reconstruction on the load response signal of each edge node by using the fast Fourier transform method; the frequency division-fault tolerance adjustment matrix of the center node is composed of frequency band weight adjustment coefficients determined based on the frequency band distribution index table and the historical disturbance cumulative probability density; and the capacity adjustment capability parameter set is used to quantitatively represent the frequency spectrum adjustment capability of the edge node in the current period.

[0126] A three-dimensional cross-node frequency band balance tensor construction module 302 is configured to construct three-dimensional cross-node frequency band balance tensor data based on the capacity adjustment capability parameter set of all edge nodes under different frequency bands; each element in the three-dimensional cross-node frequency band balance tensor data represents a corresponding one-time adjustment record of the current edge node when the current edge node and all edge nodes except the current edge node cooperatively adjust under a frequency band, and the adjustment record includes the number of times of triggering adjustment of the current edge node, the communication time delay after adjustment, and the load stability after adjustment.

[0127] An abnormal node identification and weight distribution module 303 is configured to traverse each element in the three-dimensional cross-node frequency band balance tensor data, and if the current element satisfies an abnormal condition, mark the edge node corresponding to the current element as an abnormal node and distribute a back-propagation counterweight; the abnormal condition includes that the average communication time delay after adjustment of all frequency bands of the current edge node corresponding to the current element is greater than a preset communication time delay threshold or the number of frequency bands with the number of times of triggering adjustment being 0 is greater than a preset number threshold.

[0128] The cross-band adaptive load balancing strategy generation module 304 is configured to obtain a cross-band adaptive load balancing strategy table based on the back-propagation counterweight of the abnormal node, the three-dimensional cross-node band balancing tensor data, and the edge node load multi-frequency disturbance vector group after the amplitude limiting and smoothing processing, and control the high-voltage power grid; the cross-band adaptive load balancing strategy table includes the upper limit of the adjustment amplitude and the lower limit of the quota corresponding to each frequency band sub-block, the current adjustment bias of the abnormal node and the reverse path direction, the overall frequency band priority adjustment index, and the available window state.

[0129] In an exemplary embodiment, a computer device, which can be a server or a terminal, is provided, and an internal structure diagram of the computer device can be as shown in Figure 4 The computer device includes a processor, a memory, an input / output interface (I / O), and a communication interface. The processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The database of the computer device is configured to store high-voltage power grid load processing data. The input / output interface of the computer device is configured to exchange information between the processor and external devices. The communication interface of the computer device is configured to communicate with external terminals through a network connection. The computer program is executed by the processor to implement a load distribution dynamic balancing control method for a high-voltage power grid.

[0130] Those skilled in the art can understand that Figure 4 The structure shown in the above is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. Specifically, the computer device can include more or fewer components than those shown in the diagram, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0131] In an exemplary embodiment, a computer readable storage medium is provided, which stores a computer program. The computer program is executed by a processor to implement the steps in the above method embodiments.

[0132] In an exemplary embodiment, a computer program product is provided, which includes a computer program. The computer program is executed by a processor to implement the steps in the above method embodiments.

[0133] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant regulations.

[0134] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing related hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments of each method. Any reference to memory, database or other medium used in each embodiment provided by the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ReadOnly Memory, ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive random access memory (Resistive Random Access Memory, ReRAM), magnetoresistive random access memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric memory (Ferroelectric Random Access Memory, FRAM), phase change memory (Phase Change Memory, PCM), graphene memory, etc. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), etc.

[0135] The database involved in each embodiment provided by the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in each embodiment provided by the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0136] Any technical features in the above embodiments can be combined, and for the sake of brevity, not all possible combinations are described above, however, it should be understood that the application encompasses all possible combinations of the technical features unless such a combination is not technically possible.

[0137] The principles and implementation manners of the present application are described herein by using specific examples, and the above embodiments are only used to help understand the method of the present application and its core idea; meanwhile, according to the idea of the present application, the specific implementation manners and application scopes will be changed by those skilled in the art. In conclusion, the content of the present specification should not be understood as a limitation of the present application.

Claims

1. A load distribution dynamic balancing control method for a high-voltage power grid, characterized in that, The high-voltage power grid-oriented load distribution dynamic balance control method comprises the following steps: By performing matrix multiplication operation on the edge node load multi-frequency disturbance vector group and the frequency division-fault tolerance adjustment matrix of the center node, a set of capacity adjustment capability parameters of each edge node under different frequency bands is obtained; the edge node load multi-frequency disturbance vector group is obtained by performing frequency spectrum reconstruction on the load response signal of each edge node through the fast Fourier transform method; the frequency division-fault tolerance adjustment matrix of the center node is composed of frequency band weight adjustment coefficients determined based on a frequency band distribution index table and a historical disturbance cumulative probability density; the set of capacity adjustment capability parameters is used to quantitatively represent the frequency spectrum adjustment capability of the edge node in the current period; Based on the set of capacity adjustment capability parameters of all edge nodes under different frequency bands, a three-dimensional cross-node frequency band balance tensor data is constructed; each element in the three-dimensional cross-node frequency band balance tensor data represents a corresponding current edge node and all edge nodes except the current edge node in a frequency band, and a first adjustment record of the current edge node when the current edge node is adjusted, the adjustment record including the number of times of triggering adjustment, the communication time delay after adjustment and the load stability after adjustment; Each element in the three-dimensional cross-node frequency band balance tensor data is traversed, and if the current element meets an abnormal condition, the edge node corresponding to the current element is marked as an abnormal node, and a back-pushing counterweight is allocated; the abnormal condition includes that the average communication time delay after adjustment of all frequency bands of the current edge node corresponding to the current element is greater than a preset communication time delay threshold or the number of frequency bands with 0 times of triggering adjustment in the number of times of triggering adjustment of all frequency bands is greater than a preset number threshold; Based on the back-pushing counterweight of the abnormal node, the three-dimensional cross-node frequency band balance tensor data and the edge node load multi-frequency disturbance vector group subjected to amplitude limiting and smoothing processing, a cross-frequency adaptive load balance strategy table is obtained, and the high-voltage power grid is controlled; the cross-frequency adaptive load balance strategy table includes the upper limit of the adjustment amplitude and the lower limit of the quota corresponding to each frequency band sub-block; the current adjustment bias and the reverse path direction of the abnormal node; the overall frequency band priority adjustment index and the available window state.

2. The load distribution dynamic balancing control method for high voltage power grid according to claim 1, characterized in that, By performing matrix multiplication operation on the edge node load multi-frequency disturbance vector group and the frequency division-fault tolerance adjustment matrix of the center node, a set of capacity adjustment capability parameters of each edge node under different frequency bands is obtained, specifically including: By performing matrix multiplication operation on the edge node load multi-frequency disturbance vector group and the frequency division-fault tolerance adjustment matrix of the center node, a set of frequency band weight distribution mapping vectors is obtained; The disturbance change rate of each frequency band in the edge node load multi-frequency disturbance vector group is extracted, and the disturbance change rate of each frequency band is multiplied by the corresponding element in the frequency band weight mapping vector group, respectively, to obtain a set of frequency band response capability indexes; each component in the response capability index set represents the adjustment response ratio of the edge node to the i th frequency band disturbance; The response capability index set is subjected to L2 norm normalization processing to obtain a normalized response capability index set; The normalized response capability index set is subjected to exponential decay-based fusion operation with preset inherent response lag factors of the corresponding edge nodes to generate a capacity adjustment capability parameter set; the preset inherent response lag factors are calculated by adjusting the average delay time between triggering and response feedback.

3. The load distribution dynamic balancing control method for high voltage power grid according to claim 1, characterized in that, Based on the capacity adjustment capability parameter sets of all edge nodes under different frequency bands, a three-dimensional cross-node frequency band equalization tensor data is constructed, specifically including: The capacity adjustment capability parameter sets of all edge nodes are longitudinally spliced according to frequency band indexes to construct a node capability matrix in the frequency band dimension; wherein the jth element in the node capability matrix represents the capacity adjustment capability of the edge node j' under the frequency band k; All elements under each frequency band in the node capability matrix are subjected to harmonic normalization processing based on the average power density distribution to obtain a frequency band harmonic coefficient set; The node capability matrix and the frequency band harmonic coefficient set are jointly mapped to generate the three-dimensional cross-node frequency band equalization tensor data.

4. The load distribution dynamic balancing control method for high voltage power grid according to claim 3, characterized in that, The node capability matrix and the frequency band harmonic coefficient set are jointly mapped to generate the three-dimensional cross-node frequency band equalization tensor data, specifically including: The node capability matrix and the frequency band harmonic coefficient set are jointly mapped to generate an intermediate matrix; Based on the intermediate matrix, the response probability and the non-response probability of each frequency band in different nodes are extracted to construct a frequency band response entropy atlas; Based on the frequency band response entropy atlas, all frequency bands are classified by a clustering analysis method to obtain a frequency band response entropy clustering result; Based on the frequency band response entropy clustering result and historical load adjustment data, the three-dimensional cross-node frequency band equalization tensor data is generated.

5. The load distribution dynamic balancing control method for high voltage power grid according to claim 1, characterized in that, Based on the back-propagation response weight of the abnormal node, the three-dimensional cross-node frequency band equalization tensor data, and the edge node load multi-frequency disturbance vector group subjected to amplitude limiting and smoothing processing, a cross-frequency adaptive load balancing strategy table is obtained, specifically including: Based on the elements with frequency band interference indexes greater than a set disturbance threshold in the three-dimensional cross-node frequency band equalization tensor data, a frequency band sub-block is constructed; Based on the frequency band sub-block and the back-propagation response weight of the corresponding abnormal node, a disturbance priority index table is constructed; The frequency band sub-blocks in the disturbance priority index table are sequentially selected according to a preset number threshold to obtain a set of downgraded target frequency band sub-blocks; For each downgraded target frequency band sub-block in the set of downgraded target frequency band sub-blocks, segmented downgrading control is respectively performed, and a linearly decreasing allocation coefficient is set according to the frequency band interference index to obtain an updated adjustment quota corresponding to each downgraded target frequency band sub-block; The original adjustment quota of each element belonging to the target frequency band sub-block set in the three-dimensional cross-node frequency band equalization tensor data is replaced by the corresponding updated adjustment quota to obtain the three-dimensional cross-node frequency band equalization tensor data after downgrading; The load multi-frequency disturbance vector group is subjected to amplitude limiting and smoothing processing to obtain a smoothed load multi-frequency disturbance vector group; Based on the smoothed load multi-frequency disturbance vector group, the maximum load disturbance amplitude in each frequency band sub-block is determined; According to the maximum load disturbance amplitude in each frequency band sub-block, a dynamic amplitude limiting threshold of each frequency band sub-block is determined; Based on the dynamic amplitude limiting threshold of each frequency band sub-block, a multi-frequency amplitude limiting factor group is obtained; The three-dimensional cross-node frequency band equalization tensor data after downgrading, the multi-frequency limiting factor group and the back-propagation reverse weight of the abnormal node are fused to obtain a cross-frequency band adaptive load balancing strategy table.

6. The load distribution dynamic balancing control method for high voltage power grid according to claim 1, characterized in that, The construction process of the frequency band distribution index table specifically includes: Collect frequency band disturbance data of a plurality of preset typical waveforms with preset sample period lengths; the preset typical waveforms include early peak waveforms, late peak waveforms and flat peak waveforms of daily load changes; Perform filtering processing on the frequency band disturbance data by using a differential enhancement filtering method to extract a load change rate sequence per unit time; Based on the load change rate sequence per unit time, divide the frequency bands with a preset fixed bandwidth to obtain a load change rate sequence in each frequency band; Perform disturbance energy density integral calculation on the load change rate sequence in each frequency band to obtain disturbance energy density integral results of each frequency band; Call a frequency band label setter to perform normalized sorting processing on the disturbance energy density integral results of all frequency bands, and generate corresponding frequency band labels for each frequency band according to the sorting results; the frequency band label is used to represent the disturbance characteristics corresponding to the frequency band; Establish a one-to-one mapping relationship between the frequency band labels and the response energy determined based on the corresponding bandwidth to construct a frequency band distribution index table.

7. A load distribution dynamic balancing control device for a high-voltage power grid, characterized by The high-voltage power grid-oriented load distribution dynamic balancing control device applies the high-voltage power grid-oriented load distribution dynamic balancing control method according to any one of claims 1-6, and the high-voltage power grid-oriented load distribution dynamic balancing control device comprises: A capacity adjustment capability parameter set generation module is configured to obtain a capacity adjustment capability parameter set of each edge node under different frequency bands by performing matrix multiplication operation on the edge node load multi-frequency disturbance vector group and the frequency division-fault tolerance adjustment matrix of the center node; the edge node load multi-frequency disturbance vector group is obtained by performing frequency spectrum reconstruction on the load response signal of each edge node by using the fast Fourier transform method; the frequency division-fault tolerance adjustment matrix of the center node is composed of frequency band weight adjustment coefficients determined based on the frequency band distribution index table and the historical disturbance cumulative probability density; the capacity adjustment capability parameter set is used to quantitatively represent the frequency spectrum adjustment capability of the edge node in the current period; A three-dimensional cross-node frequency band equalization tensor construction module is configured to construct three-dimensional cross-node frequency band equalization tensor data based on the capacity adjustment capability parameter sets of all edge nodes under different frequency bands; each element in the three-dimensional cross-node frequency band equalization tensor data represents a one-time adjustment record of the corresponding current edge node when the current edge node and all edge nodes except the current edge node cooperatively adjust under a frequency band, and the adjustment record includes the number of times of triggering adjustment of the current edge node, the communication time delay after adjustment and the load stability after adjustment; The abnormal node identification and weight distribution module is configured to traverse each element in the three-dimensional cross-node frequency band balancing tensor data, and if a current element satisfies an abnormal condition, mark an edge node corresponding to the current element as an abnormal node and assign a back-pushing feedback weight to the abnormal node; the abnormal condition includes that an adjusted communication average time delay of all frequency bands of a current edge node corresponding to the current element is greater than a preset communication time delay threshold or a number of frequency bands with a number of times of triggering adjustment being 0 is greater than a preset number threshold. The cross-frequency band adaptive load balancing strategy generation module is configured to obtain a cross-frequency band adaptive load balancing strategy table based on the back-pushing feedback weight of the abnormal node, the three-dimensional cross-node frequency band balancing tensor data and the edge node load multi-frequency disturbance vector group subjected to amplitude limiting and smoothing processing, and control the high-voltage power grid; the cross-frequency band adaptive load balancing strategy table includes an adjustment amplitude upper limit and a quota lower limit corresponding to each frequency band sub-block, an abnormal node current adjustment bias and a reverse path direction, an overall frequency band priority adjustment index and an available window state.

8. A computer device comprising: A memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the high-voltage power grid-oriented load distribution dynamic balancing control method of any one of claims 1-6.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the high-voltage power grid-oriented load distribution dynamic balancing control method of any one of claims 1-6.

10. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the high-voltage power grid-oriented load distribution dynamic balancing control method of any one of claims 1-6.

Images