Power grid analysis method and system based on dynamic grid division

Abstract

The invention discloses a power grid analysis method and system based on dynamic grid division, and relates to the field of power grid analysis, and the method comprises the steps: building a power grid analysis model based on power grid data, and initializing a non-uniform grid based on the power grid analysis model; dynamically coarsening or refining the non-uniform grid, dynamically adjusting nodes of the non-uniform grid according to the change of the power grid, and reconstructing the non-uniform grid if the power grid is short-circuited or short-circuited; and carrying out iterative error calculation on the non-uniform network, judging the precision of the current non-uniform grid, determining the optimal non-uniform grid configuration, and carrying out power grid analysis based on the optimal non-uniform grid configuration. According to the method, the key area and the common area are identified through the adaptive algorithm, and dynamic adjustment of the grid density is realized. The non-uniform grid division method ensures that high-precision analysis is kept in a key area, and meanwhile, relatively thick grids are adopted in a common area, so that the calculation precision and efficiency are effectively balanced.

Description

Technical Field

[0001] The invention relates to the field of power grid analysis, and in particular to a power grid analysis method and system based on dynamic grid division. Background Art

[0002] According to the "Guiding Principles for Grid Planning of State Grid Corporation's Distribution Network", the grid is the smallest unit for distribution network planning, project management and user access, and is also the smallest unit for distribution network operation and maintenance management. Gridding refers to dividing a complex distribution network into multiple relatively independent power supply grids according to the actual situation of the distribution network development status, load distribution, load characteristics and geographical boundaries. However, most of the existing grid division technologies are static and cannot be adjusted in real time. With the development of smart grids, accurate simulation and analysis of power grids have become particularly important.

[0003] The dynamic characteristics of power grid systems and emergencies (such as short circuit faults) have put forward higher requirements on the accuracy of analysis results. Traditional static grid division methods are difficult to adapt to the dynamic changes of power grid systems and cannot adjust the analysis strategy in time to cope with the rapid changes in system status. This not only affects the accuracy of the analysis results, but also limits the application of power grid analysis technology in real-time monitoring and fault warning. Therefore, developing an analysis method that can adaptively adjust the grid structure according to the characteristics and dynamic changes of the power grid system has become the key to improving the efficiency and accuracy of power grid analysis. Summary of the invention

[0004] In view of the problems existing in the existing power grid analysis method, the present invention is proposed.

[0005] Therefore, the problem to be solved by the present invention is that it is difficult to adapt to the dynamic changes of the power grid system and it is impossible to adjust the analysis strategy in time to cope with the rapid changes in the system status.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a power grid analysis method based on dynamic grid partitioning, which includes:

[0008] Establishing a power grid analysis model based on the power grid data, and initializing a non-uniform grid based on the power grid analysis model;

[0009] Dynamically coarsening or refining the non-uniform grid and dynamically adjusting the nodes of the non-uniform grid according to changes in the power grid, and reconstructing the non-uniform grid if a short circuit or short-circuit occurs in the power grid;

[0010] Iterative error calculation is performed on the non-uniform network to determine the accuracy of the current non-uniform grid and determine the optimal non-uniform grid configuration, and power grid analysis is performed based on the optimal non-uniform grid configuration.

[0011] As a preferred solution of the power grid analysis method based on dynamic grid division described in the present invention, the initialization of the non-uniform grid includes the following steps:

[0012] Establishing an initial weight model based on topological structure data in the power grid data; the topological structure data includes substation location, transmission line direction and distribution network structure data;

[0013] Calculating the reliability weight according to the operation history data in the power grid data; the operation history data includes a load curve and a voltage distribution;

[0014] Calculating load distribution weight according to the distribution network structure data;

[0015] The initial weight model, reliability weight and load distribution weight are comprehensively calculated to obtain a comprehensive weight score;

[0016] A non-uniform network is generated according to the comprehensive weight scores.

[0017] As a preferred solution of the power grid analysis method based on dynamic grid division described in the present invention, the initial weight model is established according to the topological structure data in the power grid data, including the following steps:

[0018] Based on the location point x, the distance to the nearest critical infrastructure I is determined, and the relative importance is adjusted by parameters to obtain the relative importance of the distance;

[0019] The relative capacity importance of location point x is calculated based on the line capacity of location point x and the maximum line capacity of the entire power grid;

[0020] Adding the distance relative importance and the capacity importance to obtain an initial weight, i.e., an initial weight model;

[0021] Calculating the reliability weight according to the operation history data in the power grid data comprises the following steps:

[0022] Calculate standard values ​​of voltage fluctuation frequency, load variation amplitude and fault occurrence rate based on the operation history data;

[0023] linearly combining the normalized values ​​and subtracting an adjustment threshold;

[0024] Apply the logistic function to normalize the standardized values ​​of the linear combination to the interval [0,1] to obtain the reliability weight;

[0025] Calculating the load distribution weight according to the distribution network structure data comprises the following steps:

[0026] The power consumption U(x) at the location x handles the large-scale power consumption differences and is normalized using the maximum value in the grid area;

[0027] Calculate the error function value of the power consumption fluctuation frequency V(x) at the location point x, and use the maximum error function value in the power grid area for normalization;

[0028] Multiply the above two normalized results to obtain the load distribution weight;

[0029] The initial weight model, reliability weight and load distribution weight are calculated comprehensively to obtain a comprehensive weight score, including the following steps:

[0030] The initial weight, reliability weight and load distribution weight of the location point x are multiplied, and the normalization factor is calculated for the entire grid area R;

[0031] Dividing the product of the initial weight, the reliability weight and the load distribution weight by the normalization factor to obtain a comprehensive weight;

[0032] The Sigmoid function is introduced to fine-tune the comprehensive weight to obtain the final comprehensive weight score.

[0033] As a preferred solution of the power grid analysis method based on dynamic grid division described in the present invention, the dynamic adjustment of the nodes of the non-uniform grid includes the following steps:

[0034] Determining a change trend of the flow field according to the electric field intensity gradient and the current density around each node of the non-uniform grid;

[0035] Treating the edges of the non-uniform grid as springs, calculating the spring forces and forming a set of equilibrium equations;

[0036] Solving the equilibrium equations to obtain the displacement vector of each node and limiting the maximum distance of node movement;

[0037] If the movement of a node causes the quality of the mesh it is in to degrade, a relaxation factor is applied to restrict the movement;

[0038] The above process is iterated until the preset convergence criterion or the maximum number of iterations is reached.

[0039] As a preferred solution of the power grid analysis method based on dynamic grid division described in the present invention, the calculation of the electric field intensity gradient includes the following steps:

[0040] Determine the electric field strength difference and distance between a certain neighboring node j and node i in the neighboring node set;

[0041] Determining the intensity change rate according to the electric field intensity difference and the distance;

[0042] Determine the direction from a certain neighboring node j in the neighboring node set to the node i, obtain a direction vector, and multiply the intensity change rate and the direction vector to obtain the component of the electric field intensity gradient in this direction;

[0043] Sum the components of all neighboring nodes to obtain the electric field intensity gradient at node i;

[0044] The calculation of the current density comprises the following steps:

[0045] For node n and a neighboring node m in the neighboring node set, calculate the current and the corresponding cross-sectional area to obtain the current intensity per unit area;

[0046] Determine the direction vector from node n to a neighboring node m in the neighboring node set;

[0047] The current intensity per unit area is multiplied by the direction vector, and the current density components of all adjacent nodes are summed to obtain the total current density at node n.

[0048] As a preferred solution of the power grid analysis method based on dynamic grid division described in the present invention, the iterative error calculation includes:

[0049] Compute approximate solutions using current non-uniform grids and numerical methods, compare the actual solution with the approximate solution, and calculate the error using mean square error and relative error;

[0050] The calculated error is compared with the predetermined error tolerance. If the error is less than the tolerance, it is considered that the accuracy of the current non-uniform grid meets the requirements; otherwise, the grid needs to be adjusted;

[0051] If the accuracy of the current non-uniform grid does not meet the requirements, the approximation is changed by refining the non-uniform grid or changing the value or adjusting the parameter, and the error is recalculated to determine whether the accuracy meets the requirements;

[0052] The non-uniform grid parameters, approximate solutions and errors of each iteration are recorded to determine the optimal grid configuration.

[0053] As a preferred solution of the power grid analysis method based on dynamic grid division described in the present invention, the calculation of the comprehensive weight score is shown in the following formula:

[0054]

[0055] Among them, S is the final comprehensive weight score function; k is the weight adjustment sensitivity coefficient; S 0 is the benchmark weight score, W i (x) is the initial weight function, H(x) is the reliability weight function, and L(x) is the load distribution weight function.

[0056] In a second aspect, an embodiment of the present invention provides a power grid analysis system based on dynamic grid division, which includes:

[0057] An initialization module, used to establish a power grid analysis model based on power grid data, and initialize a non-uniform grid based on the power grid analysis model;

[0058] a dynamic adjustment module for dynamically coarsening or refining the non-uniform grid and dynamically adjusting the nodes of the non-uniform grid according to changes in the power grid, and reconstructing the non-uniform grid if a short circuit or short-circuit occurs in the power grid;

[0059] The error calculation module is used to perform iterative error calculation on the non-uniform network, determine the current non-uniform grid accuracy, and determine the optimal non-uniform grid configuration, and perform power grid analysis based on the optimal non-uniform grid configuration.

[0060] In a third aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the processor executes the computer program, any step of the above-mentioned power grid analysis method based on dynamic grid division is implemented.

[0061] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the above-mentioned power grid analysis method based on dynamic grid partitioning is implemented.

[0062] The beneficial effect of the present invention is that the key areas and ordinary areas are identified by an adaptive algorithm, and the dynamic adjustment of the grid density is realized. This non-uniform grid division method ensures that high-precision analysis is maintained in key areas (such as substations and trunk lines), while a coarser grid is used in ordinary areas, effectively balancing the calculation accuracy and efficiency. A comprehensive weight model based on the topology of the power grid, historical operation data and the importance of electrical equipment is introduced. This enables the grid division to dynamically adapt to the actual operation status and importance distribution of the power grid, and improves the accuracy and pertinence of the analysis. The introduction of AMR technology enables the grid to be automatically refined or coarsened according to the changes in the power grid flow field. This greatly improves the flexibility and accuracy of the analysis, especially when the power grid state changes rapidly, by moving the grid node position, the grid can better adapt to the changes in the power grid flow field without changing the grid topology. This improves the stability and calculation efficiency of the model, and can trigger the local reconstruction grid function when major changes occur in the power grid (such as short circuit, disconnection). This ensures that high-quality analysis results can be maintained under abnormal conditions. By regularly performing error estimation, it is ensured that the grid accuracy always meets the requirements. This self-correction mechanism improves the reliability of the analysis results. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0064] Figure 1 The flowchart of the power grid analysis method based on dynamic grid division. DETAILED DESCRIPTION

[0065] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0066] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0067] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0068] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0069] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0070] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0071] Example 1

[0072] Reference Figure 1 , which is the first embodiment of the present invention, and provides a power grid analysis method based on dynamic grid division, comprising:

[0073] Collect the actual structure and operation data of the power grid, including the power grid topology, equipment parameters, and operation history data, and clean and organize the data to ensure the accuracy and completeness of the data. The power grid topology data includes the location of the substation, the direction of the transmission line, the distribution network structure, etc. The operation history data includes the load curve, voltage distribution, etc.

[0074] Based on the collected data, a power grid analysis model is established using power system simulation software or a custom model. The model should be able to accurately reflect the actual operation and characteristics of the power grid. On this basis, an adaptive algorithm is designed that can identify key areas and ordinary areas in the power grid. Specifically, for key areas such as substations, trunk lines, and large power-consuming equipment, the algorithm will automatically increase the grid density and generate a more detailed grid structure to ensure the calculation accuracy of these areas. On the contrary, in ordinary areas with small loads and sparse electrical equipment, the algorithm will generate relatively sparse grids to reduce the amount of calculation. During the generation of this non-uniform grid, the algorithm will continuously evaluate and adjust the grid density to ensure that the calculation efficiency is maximized while ensuring the overall calculation accuracy. For example, hundreds of dense grid cells will be generated around the substation, while there may be only dozens of sparse grid cells in areas far away from key equipment. In this way, accurate analysis of important parts of the power grid can be achieved with limited computing resources, while not over-consuming computing resources in less important areas.

[0075] Preferably, the algorithm will establish an initial weight model based on the grid topology data, in which key infrastructure such as substations and trunk transmission lines will receive higher initial weights. Then, the algorithm will analyze historical operating data, including voltage fluctuation frequency, load change amplitude, fault occurrence rate, etc., and dynamically adjust the initial weights. For example, if a line has frequently experienced voltage fluctuations in history, its weight will be increased.

[0076] Then, the importance and density of electrical equipment are taken into account. For example, areas with large industrial users will receive higher weights. In addition, the algorithm will analyze the load distribution characteristics and give higher weights to areas with high electricity consumption and frequent fluctuations. Based on these comprehensive factors, a comprehensive weight score is calculated for each area in the power grid. Combined with the preset threshold, the grid density of each area is determined. Areas above a certain threshold are classified as high-density grids, while areas below another threshold are classified as low-density grids.

[0077] More specifically, in power grid analysis, infrastructure such as substations and trunk transmission lines are crucial, so the initial weights are constructed to ensure the density of their grids. Considering the characteristics of voltage and power decay with distance in the power system, an exponential decay model is used to reflect the impact of distance on importance, and combined with line capacity, the initial weights can accurately reflect the initial importance of different locations in the power grid topology, as shown in the following formula:

[0078]

[0079] Among them, Wi is the initial weight function; x is the location point in the power grid; α is the importance coefficient of the substation; β is the distance attenuation coefficient; d(x,I) is the distance from location x to the nearest critical infrastructure I; γ is the line capacity coefficient; C(x) is the line capacity at location x; Cmax is the maximum line capacity in the network.

[0080] The stability and reliability of the power grid are directly related to the quality of power supply. The Sigmoid function is selected to integrate the three key indicators of voltage fluctuation frequency, load change amplitude and fault occurrence rate. This allows areas with historically unstable performance (such as lines with frequent voltage fluctuations) to receive higher weights and thus receive more attention in subsequent grid division, as shown in the following formula:

[0081]

[0082] Among them, H is the reliability weight; λ is the adjustment sensitivity coefficient; V(x) is the standardized value of the voltage fluctuation frequency at position x; L(x) is the standardized value of the load change amplitude at position x; F(x) is the standardized value of the fault occurrence rate at position x; μ is the adjustment threshold.

[0083] In the power grid, the distribution of important electrical equipment such as large industrial users has an important impact on grid division. The inverse square distance weighting is used to simulate the impact of equipment on the surrounding area. At the same time, the equipment importance coefficient P_i is introduced so that different types of equipment (such as large industrial users vs small residential users) can have different impact weights. As shown in the following formula:

[0084]

[0085] Among them, L is the load distribution weight; U(x) is the power consumption at location x; V(x) is the power consumption fluctuation frequency at location x; R is the entire power grid area; erf is the error function.

[0086] Finally, all the above factors are combined by multiplication. To ensure the comparability of weights between different regions, the regional integral is used to normalize ∫_R[...]dy, and the Sigmoid function is introduced for fine-tuning, so that the weight score can more sensitively reflect regional differences, which helps to make more accurate grid density division decisions in areas near the threshold. Finally, a comprehensive weight score is calculated for each region in the power grid, as shown in the following formula:

[0087]

[0088] Among them, S is the final comprehensive weight score function; k is the weight adjustment sensitivity coefficient; S_0 is the baseline weight score.

[0089] Furthermore, the calculated comprehensive weight score generates an adaptive initial grid. Specifically,

[0090] The entire power grid area is divided into uniform small grid units as the basis for initial calculation. For the center point of each grid unit, the given comprehensive weight score function S(x) is used to calculate its weight value and set the grid density threshold, including three parts, namely low-density, medium-density and high-density grids.

[0091] Based on the mesh density mapping, the corresponding mesh generation algorithm is applied to each region, and transition units are used at the junction of meshes with different densities to ensure smooth changes in the mesh. The adaptive initial mesh is obtained.

[0092] Adding AMR to the adaptive initial grid specifically includes: setting AMR refinement parameters (refinement conditions, maximum / minimum grid size, refinement ratio, transition zone thickness) according to the needs of power grid analysis. AMR will automatically refine or coarsen the grid dynamically according to changes in the power grid flow field.

[0093] The mesh is dynamically adjusted according to the adaptive mesh refinement parameters, including coarsening the adaptive mesh when it exceeds the refinement condition; refining the adaptive mesh when the refinement condition is met; the refined or coarsened mesh must not exceed the maximum / minimum mesh size, and the refinement ratio and transition zone thickness are followed when refining or coarsening.

[0094] Mobile grid technology enables the grid to better adapt to changes in the power grid flow field by moving the positions of grid nodes while keeping the grid topology unchanged.

[0095] According to the changes in the power grid flow field, the moving direction and distance of the grid nodes are calculated, and the positions of the grid nodes are updated. Through continuous iteration, the grid can closely fit the actual form of the power grid flow field;

[0096] Specifically, the decision on whether to refine is based on a comprehensive indicator. Specifically, the gradient of voltage, current, power and the time rate of change of power are comprehensively considered to fully reflect the change of the power grid state as follows:

[0097]

[0098] Where V, I, P, S represent voltage, current, active power and apparent power respectively. α, β, γ, δ are weight coefficients. threshold When , the mesh refinement is triggered, Φ threshold is the refinement threshold, which is set according to the actual situation. If it is higher than the refinement threshold, the grid will be coarsened.

[0099] The refined mesh size can be calculated by the following formula:

[0100]

[0101] Among them, h old is the original mesh size, r is the refinement ratio, h min is the minimum allowed mesh size.

[0102] Furthermore, the electric field intensity gradient and current density around each node are calculated to determine the changing trend of the flow field. Then, the grid edge is regarded as a spring and the spring force is calculated. The spring stiffness is dynamically adjusted according to the local flow field characteristics and the degree of grid distortion. Next, the equilibrium equations composed of the spring force of each grid edge are solved to obtain the displacement vector of each node, and the maximum distance of node movement is limited. The grid quality index is continuously checked during the movement. If a certain movement will cause a significant decrease in grid quality, a relaxation factor is applied to limit its movement. Finally, this process is iterated to gradually adjust the node position until the preset convergence standard or the maximum number of iterations is reached.

[0103] More specifically, the electric field intensity gradient and current density are calculated as follows:

[0104]

[0105]

[0106] in, is the electric field intensity gradient at node i, E i ,E j is the electric field strength at nodes i and j, x i , x jis the position vector of nodes i and j, J i is the current density at node i, I ij is the current between nodes i and j, A ij is the corresponding cross-sectional area. It should be noted that j is a critical node in the set of adjacent nodes of node i.

[0107] Considering each mesh edge as a spring, the spring force Fij is given by Hooke's law:

[0108]

[0109] Among them, F ij is the spring force of node j on node i, k ij is the spring stiffness, l ij is the natural length of the spring.

[0110] When the power grid flow field undergoes a significant change, the local grid reconstruction function is started: the local grid is re-divided according to the reconstruction parameters, and the simulation and analysis of the power grid flow field is continued through the re-divided grid;

[0111] In the case of real-time monitoring of the changes in the power grid flow field, when the power grid flow field changes significantly, such as short circuit, disconnection and other faults, the local grid reconstruction function is triggered. When some areas of the grid have poor quality, including the grid size is too small, the grid distortion is too large, or the aspect ratio of the grid and the maximum internal angle of the unit do not meet the requirements;

[0112] In the case of large movement, in the dynamic mesh calculation, when the movement of the boundary is large, the original mesh will lose its calculation value due to excessive deformation, and even cause the calculation to not converge. Specifically, it includes setting the reconstruction parameters: including the minimum mesh size, maximum mesh size, maximum mesh distortion, and mesh reconstruction interval; setting the minimum reconstruction mesh size to 0.4 times the global minimum mesh size, the maximum mesh size to 1.4 times the global maximum mesh size, and the maximum mesh distortion to 0.7 or 0.8;

[0113] Regularly perform error estimation to determine whether the accuracy of the current grid meets the requirements;

[0114] In numerical computing, simulation or engineering applications, it is very important to perform error estimation regularly, because it can help us judge whether the currently used mesh (or discretization method) is accurate enough for our needs. Error estimation usually involves calculating the difference between the actual solution and the approximate solution, and based on these differences, evaluating the accuracy of the mesh.

[0115] The following is a simplified step to illustrate how to make an error estimate and determine whether the mesh accuracy meets the requirements:

[0116] Compute an approximate solution using the current mesh and numerical method, compare the actual solution to the approximate solution, and calculate the error using mean square error and relative error.

[0117] The calculated error is compared with the predetermined error tolerance. If the error is less than the tolerance, it is considered that the accuracy of the current grid meets the requirements; otherwise, the grid needs to be adjusted.

[0118] If the accuracy of the current mesh does not meet the requirements, you can try to improve the approximate solution by refining the mesh, changing the numerical method, or adjusting other parameters. Then, recalculate the error and determine whether the accuracy meets the requirements.

[0119] Throughout the process, the mesh parameters, approximate solution, and error for each iteration are recorded. This helps you analyze the impact of mesh accuracy on the solution and determine the optimal mesh configuration.

[0120] Furthermore, this embodiment also provides a power grid analysis system based on dynamic grid division, including:

[0121] An initialization module, used to establish a power grid analysis model based on power grid data, and initialize a non-uniform grid based on the power grid analysis model;

[0122] a dynamic adjustment module for dynamically coarsening or refining the non-uniform grid and dynamically adjusting the nodes of the non-uniform grid according to changes in the power grid, and reconstructing the non-uniform grid if a short circuit or short-circuit occurs in the power grid;

[0123] The error calculation module is used to perform iterative error calculation on the non-uniform network, determine the current non-uniform grid accuracy, and determine the optimal non-uniform grid configuration, and perform power grid analysis based on the optimal non-uniform grid configuration.

[0124] This embodiment also provides a computer device, which is suitable for the case of a power grid analysis method based on dynamic grid division, including a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the power grid analysis method based on dynamic grid division as proposed in the above embodiment.

[0125] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used 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 and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0126] This embodiment further provides a storage medium on which a computer program is stored. When the program is executed by a processor, the power grid analysis method based on dynamic grid division as proposed in the above embodiment is implemented.

[0127] The storage medium proposed in this embodiment and the data storage method proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be found in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0128] Example 2

[0129] This is an embodiment of the present invention, which provides a specific implementation method for calculating a comprehensive weight score for each area in the power grid.

[0130] For example, in a feasible embodiment, the comprehensive weight of a certain area is calculated as follows:

[0131] For a specific location x, calculate W_i(x). Through the previous data analysis, we get α=1.5, β=0.1, γ=0.5, d(x,I)=2km (the distance from the location to the nearest critical infrastructure), C(x)=800MW, C_max=1000MW (the line capacity and maximum line capacity of the location) as follows:

[0132] W_i(x)=1.5*e^(-0.1*2)+0.5*(800 / 1000)=1.22+0.4=1.62

[0133] Then calculate H(x). Through the previous data analysis, we get λ=2,μ=1.5,V(x)=0.6,L(x)=0.5,F(x)=0.4 (these are standardized values), and we get:

[0134] H(x)=1 / (1+e^(-2*(0.6+0.5+0.4-1.5)))=0.5

[0135] Calculate E(x): There are two important devices in this specific location x. Through the previous data analysis, we get P_1=1, d(x,E_1)=1km, P_2=0.8, d(x,E_2)=1.5km, σ=0.5, ε=0.1. We get:

[0136] E(x)=(1*e^(-0.5*1) / (1^2+0.1))+(0.8*e^(-0.5*1.5) / (1.5^2+0.1))=0.55+0.16=0.71.

[0137] Calculate L(x):

[0138] U(x)=50MW,max(U(y))=100MW

[0139] V(x)=0.6,max(V(y))=1

[0140] L(x)=(log(1+50) / log(1+100))*(erf(0.6) / erf(1))=0.78*0.68=0.53.

[0141] The final comprehensive weight score is integrated and calculated as follows:

[0142] Calculation of normalization factor:

[0143] The normalization factor is actually the weighted integral of all points in the entire power grid area R. However, in practical applications, since the power grid is discrete, this integral is usually converted into the summation of discrete points:

[0144]

[0145] Where ΔA is the area represented by each discrete point.

[0146] Then the entire power grid area R is discretized into m points, and W_i(y), H(y), E(y), and L(y) are calculated for each point y. The calculation results of all points are added together. In this embodiment, only 5 discrete points are used, and each point represents an area ΔA=1km 2 The calculation results of 5 discrete points are as follows:

[0147] Point 1 (previous x): 1.62*1.5*0.71*0.53=0.91.

[0148] Point 2: 1.45*1.3*0.65*0.48=0.59.

[0149] Point 3: 1.78*1.6*0.82*0.61=1.43.

[0150] Point 4: 1.33*1.2*0.58*0.45=0.41.

[0151] Point 5: 1.56*1.4*0.75*0.57=0.93.

[0152] Then the normalization factor N=(0.91+0.59+1.43+0.41+0.93)*1=4.27.

[0153] Then S(x) is as follows:

[0154]

[0155] This result shows that for this particular location x, its comprehensive weight score of 0.0161 belongs to the lower weight range. It is determined whether it is a low-density grid or a high-density grid according to the set threshold. In addition, experiments show that by adjusting different parameters, different scenarios can be simulated, including,

[0156] Increasing d(x,I) will reduce the initial weight, reflecting the impact of distance on importance. Increasing V(x), L(x), and F(x) will increase the adjustment factor of historical operation data, reflecting the importance of unstable areas. Increasing the number or importance of nearby important equipment will increase E(x), reflecting the importance of equipment-intensive areas. Increasing power consumption or fluctuation frequency will increase L(x), reflecting the importance of high-load areas. This makes adaptive grid differentiation not only adaptable to various complex power grid scenarios, but also can be used for power grid division, providing a reasonable grid density distribution for the entire power grid, significantly improving the accuracy of power grid analysis and simulation, and helping power grid planners and operators to better allocate resources.

[0157] Example 3

[0158] This is an embodiment of the present invention, which provides a power grid analysis method based on dynamic grid division. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0159] In order to verify the effectiveness of the power grid analysis method based on dynamic grid division, a provincial power grid was selected as the research object. The power grid contains 15 330kV substations, 42 110kV substations, about 3000km of transmission lines and multiple large industrial users. First, the operation data of the power grid for nearly 5 years was collected, including the power grid topology, equipment parameters and historical operation data. The data cleaning script written in Python was used to preprocess the raw data, remove outliers and supplement missing data to ensure the integrity and accuracy of the data.

[0160] Based on the processed data, a power grid analysis model was constructed using MATLAB. The model contains detailed power grid topology information, equipment parameters, and load characteristics. Subsequently, an adaptive algorithm was implemented to identify key areas and common areas in the power grid. The algorithm first established an initial weight model based on the power grid topology structure, and the initial weight was calculated using the formula W_i(x)=α·e^(-β·d(x,I))+γ·C(x) / C_max. Among them, α is set to 1.5, β is 0.1km^-1, and γ is 0.5.

[0161] Next, the algorithm analyzed the historical operating data and used the Sigmoid function H(x)=1 / (1+e^(-λ(V(x)+L(x)+F(x)-μ))) to integrate the voltage fluctuation frequency, load change amplitude and fault occurrence rate, where λ is set to 2 and μ is set to 1.5. Considering the importance of electrical equipment, the inverse square distance weighted model L(x)=∑(P_i / d_i^2(x)) is used to calculate the load distribution weight. Finally, considering these factors, the final comprehensive weight score is calculated using the formula S(x)=1 / (1+e^(-k(W_i(x)·H(x)·L(x) / ∫_R[...]dy-S_0))) where k is set to 5 and S_0 is set to 0.5.

[0162] Based on the calculated comprehensive weight score, the power grid area is divided into three types of grid densities: high, medium, and low. High-density grids are used for areas with weight scores greater than 0.8, medium-density grids are used for areas with weight scores between 0.4 and 0.8, and low-density grids are used for areas with weight scores less than 0.4. At the junction of grids of different densities, transition units are used to ensure smooth changes in the grid.

[0163] Based on the adaptive initial grid, the AMR technology is introduced. The AMR refinement parameters are set as follows: refinement condition threshold ε = 0.05, maximum grid size 100m, minimum grid size 5m, refinement ratio r = 0.5, and transition zone thickness 3 grid units. Using the formula Determine whether the grid needs to be refined, where the weight coefficients w_1 = 0.3, w_2 = 0.3, w_3 = 0.2, and w_4 = 0.2.

[0164] The moving mesh technology is implemented to determine the changing trend of the flow field by calculating the electric field intensity gradient and current density around each node. The mesh edge is regarded as a spring, and the spring stiffness kij is dynamically adjusted according to the local flow field characteristics. The displacement vector of the node is obtained by solving the equilibrium equations composed of spring forces. During the movement process, the mesh quality indicators are continuously checked. If a certain movement causes the mesh quality to drop by more than 20%, a relaxation factor of 0.8 is applied to limit its movement.

[0165] In addition, a local grid reconstruction function is also implemented. This function is triggered when a large change in the grid flow field is detected (such as a short circuit fault). The reconstruction parameters are set as follows: the minimum grid size is 0.4 times the global minimum grid size, the maximum grid size is 1.4 times the global maximum grid size, the maximum grid distortion is 0.75, and the grid reconstruction interval is 100 iterations.

[0166] Finally, an error estimation is performed every 1000 iterations to calculate the mean square error and relative error. If the relative error is less than 1%, the current grid accuracy is considered to meet the requirements; otherwise, the approximate solution is improved by refining the grid or adjusting other parameters.

[0167] In order to evaluate the performance of this method, the traditional uniform grid method and the dynamic grid division method of the present invention are used to analyze the power grid, and the calculation time, memory usage, calculation accuracy and other indicators are compared. The test scenarios include normal operation, load mutation, short circuit fault and other different situations. The results are shown in the following table:

[0168] Table 1 Experimental data table

[0169]

[0170] By comparing and analyzing the data in the table, we can draw the following conclusions:

[0171] Significant improvement in computational efficiency: In all test scenarios, the computational time of the dynamic meshing method was reduced by about 50% compared to the traditional uniform meshing method. For example, in the normal operation scenario, the computational time was reduced from 3600 seconds to 1800 seconds. This efficiency improvement is mainly attributed to the intelligent mesh allocation of the adaptive algorithm, which reduces unnecessary waste of computational resources.

[0172] The memory usage of the dynamic meshing method is reduced by about 40% compared with the traditional method. In the short circuit fault scenario, the memory usage is reduced from 18.7GB to 11.2GB. This reflects the advantages of the method in resource utilization, especially for large-scale power grid analysis, this memory saving can significantly improve the scalability of the system.

[0173] In summary, the power grid analysis method based on dynamic grid division shows obvious advantages in terms of computational efficiency, memory usage, computational accuracy, and abnormal situation handling. This method can not only significantly improve the analysis efficiency and accuracy of large-scale complex power grid systems, but also better cope with various dynamic changes and abnormal situations in the power grid. This has important practical application value for improving the real-time monitoring capability of the power grid, optimizing resource allocation, and enhancing the overall reliability and stability of the system.

[0174] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A power grid analysis method based on dynamic grid division, characterized in that: include, Establishing a power grid analysis model based on the power grid data, and initializing a non-uniform grid based on the power grid analysis model; Dynamically coarsening or refining the non-uniform grid and dynamically adjusting the nodes of the non-uniform grid according to changes in the power grid, and reconstructing part of the non-uniform grid if the power grid is short-circuited or short-circuited; Iterative error calculation is performed on the non-uniform network to determine the accuracy of the current non-uniform grid and determine the optimal non-uniform grid configuration, and power grid analysis is performed based on the optimal non-uniform grid configuration.

2. The power grid analysis method based on dynamic grid division according to claim 1, characterized in that: The initialization of the non-uniform grid comprises the following steps: Establishing an initial weight model based on topological structure data in the power grid data; the topological structure data includes substation location, transmission line direction and distribution network structure data; Calculating the reliability weight according to the operation history data in the power grid data; the operation history data includes a load curve and a voltage distribution; Calculating load distribution weight according to the distribution network structure data; The initial weight model, reliability weight and load distribution weight are comprehensively calculated to obtain a comprehensive weight score; A non-uniform network is generated according to the comprehensive weight scores.

3. The power grid analysis method based on dynamic grid division according to claim 2, characterized in that: Establishing an initial weight model according to the topological structure data in the power grid data comprises the following steps: Based on the location point x, the distance to the nearest critical infrastructure I is determined, and the relative importance is adjusted by parameters to obtain the relative importance of the distance; The relative capacity importance of location point x is calculated based on the line capacity of location point x and the maximum line capacity of the entire power grid; Adding the distance relative importance and the capacity importance to obtain an initial weight, i.e., an initial weight model; Calculating the reliability weight according to the operation history data in the power grid data comprises the following steps: Calculate standard values ​​of voltage fluctuation frequency, load variation amplitude and fault occurrence rate based on the operation history data; linearly combining the normalized values ​​and subtracting an adjustment threshold; Apply the logistic function to normalize the standardized values ​​of the linear combination to the interval [0,1] to obtain the reliability weight; Calculating the load distribution weight according to the distribution network structure data comprises the following steps: The power consumption U(x) at the location x handles the large-scale power consumption differences and is normalized using the maximum value in the grid area; Calculate the error function value of the power consumption fluctuation frequency V(x) at the location point x, and use the maximum error function value in the power grid area for normalization; Multiply the above two normalized results to obtain the load distribution weight; The initial weight model, reliability weight and load distribution weight are calculated comprehensively to obtain a comprehensive weight score, including the following steps: The initial weight, reliability weight and load distribution weight of the location point x are multiplied, and the normalization factor is calculated for the entire grid area R; Dividing the product of the initial weight, the reliability weight and the load distribution weight by the normalization factor to obtain a comprehensive weight; The Sigmoid function is introduced to fine-tune the comprehensive weight to obtain the final comprehensive weight score.

4. The power grid analysis method based on dynamic grid division according to claim 3, characterized in that: The dynamically adjusting the nodes of the non-uniform grid comprises the following steps: Determining a change trend of the flow field according to the electric field intensity gradient and the current density around each node of the non-uniform grid; Treating the edges of the non-uniform grid as springs, calculating the spring forces and forming a set of equilibrium equations; Solving the equilibrium equations to obtain the displacement vector of each node and limiting the maximum distance of node movement; If the movement of a node causes the quality of the mesh it is in to degrade, a relaxation factor is applied to restrict the movement; The above process is iterated until the preset convergence criterion or the maximum number of iterations is reached.

5. The power grid analysis method based on dynamic grid division according to claim 4, characterized in that: The calculation of the electric field intensity gradient comprises the following steps: Determine the electric field strength difference and distance between a certain neighboring node j and node i in the neighboring node set; Determining the intensity change rate according to the electric field intensity difference and the distance; Determine the direction from a certain neighboring node j in the neighboring node set to the node i, obtain a direction vector, and multiply the intensity change rate and the direction vector to obtain the component of the electric field intensity gradient in this direction; Sum the components of all neighboring nodes to obtain the electric field intensity gradient at node i; The calculation of the current density comprises the following steps: For node n and a neighboring node m in the neighboring node set, calculate the current and the corresponding cross-sectional area to obtain the current intensity per unit area; Determine the direction vector from node n to a neighboring node m in the neighboring node set; The current intensity per unit area is multiplied by the direction vector, and the current density components of all adjacent nodes are summed to obtain the total current density at node n.

6. The power grid analysis method based on dynamic grid division according to claim 5, characterized in that: The iterative error calculation includes: Compute approximate solutions using current non-uniform grids and numerical methods, compare the actual solution with the approximate solution, and calculate the error using mean square error and relative error; The calculated error is compared with the predetermined error tolerance. If the error is less than the tolerance, it is considered that the accuracy of the current non-uniform grid meets the requirements; otherwise, the grid needs to be adjusted; If the accuracy of the current non-uniform grid does not meet the requirements, the approximation is changed by refining the non-uniform grid or changing the value or adjusting the parameter, and the error is recalculated to determine whether the accuracy meets the requirements; The non-uniform grid parameters, approximate solutions and errors of each iteration are recorded to determine the optimal grid configuration.

7. The power grid analysis method based on dynamic grid division according to any one of claims 2 or 3, characterized in that: The calculation of the comprehensive weight score is shown in the following formula: Among them, S is the final comprehensive weight score function; k is the weight adjustment sensitivity coefficient; S0 is the benchmark weight score, W i (x) is the initial weight function, H(x) is the reliability weight function, and L(x) is the load distribution weight function.

8. A power grid analysis system based on dynamic grid division, based on the power grid analysis method based on dynamic grid division according to any one of claims 1 to 7, characterized in that: include, An initialization module, used to establish a power grid analysis model based on power grid data, and initialize a non-uniform grid based on the power grid analysis model; a dynamic adjustment module for dynamically coarsening or refining the non-uniform grid and dynamically adjusting the nodes of the non-uniform grid according to changes in the power grid, and reconstructing the non-uniform grid if a short circuit or short-circuit occurs in the power grid; The error calculation module is used to perform iterative error calculation on the non-uniform network, determine the current non-uniform grid accuracy, and determine the optimal non-uniform grid configuration, and perform power grid analysis based on the optimal non-uniform grid configuration.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the power grid analysis method based on dynamic grid division according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the power grid analysis method based on dynamic grid division according to any one of claims 1 to 7 are implemented.

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