Actual measurement correction method, device and system for large power grid magnetic bias model parameters and medium
By introducing measured DC current data of transformer neutral point and ground potential correction vector into large power grids, and combining them with a hybrid quasi-Newton method optimization algorithm, the bias magnetization assessment model is corrected. This solves the problem of large discrepancies between simulation results and measured data in DC bias magnetization assessment and management in large power grids, and achieves efficient and accurate risk assessment and management support.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- STATE GRID HUBEI ELECTRIC POWER RES INST
- Filing Date
- 2026-01-05
- Publication Date
- 2026-05-08
AI Technical Summary
In large power grids, the assessment and management of DC bias magnetization suffers from a large discrepancy between simulation results and measured data, leading to insufficient accuracy in risk assessment. This is especially true when the distribution of new intrusion currents cannot be measured after the power grid is in operation, and when the deviation between the simulation model and the measured data is too large, it can cause operational hazards.
By introducing measured DC current data of transformer neutral point, a measured correction model is constructed. The correction amount is solved by combining the hybrid form of quasi-Newton method. The bias evaluation model is corrected by using the substation ground potential correction vector. The hybrid quasi-Newton method (BFGS/DFP adaptive update) optimization algorithm is adopted to avoid directly calculating the second derivative and construct the substation ground potential correction column vector.
It significantly improves the accuracy and reliability of the bias magnetization assessment model, reduces the deviation between simulation results and measured data, lowers computational complexity and resource consumption, adapts to changes in power grid parameters and mitigation measures, and achieves efficient risk assessment throughout the entire life cycle.
Smart Images
Figure CN122000869A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of DC bias risk management, specifically to a method, device, system, and medium for the measured correction of bias model parameters of a large power grid. Background Technology
[0002] Due to the complexity of power grid wiring and the uneven distribution of ground resistivity in both planar and cross-sectional areas, there are discrepancies between simulation results and measured data in DC bias assessment. This is especially true in large power grids, where the complex distribution of DC grounding return currents further exacerbates the difficulty of bias risk assessment and mitigation. Before the commissioning of DC projects, bias risk mitigation relies on simulation models to predict risks, requiring high model accuracy. After commissioning, mitigation measures depend on measured data, but the distribution of new intrusion currents formed in the power grid after mitigation cannot be measured in reality and can only be assessed through simulation. If the deviation between the simulation model and measured data is too large during the commissioning phase, it will lead to insufficient accuracy in subsequent risk assessments, posing potential risks to power grid operation. Summary of the Invention
[0003] This invention addresses the problem of computational bias in large power grid DC bias assessment models caused by the complexity of the earth's resistivity structure. It provides a method, device, system, and medium for the measured correction of parameters in large power grid bias models. Based on measured DC current data at the transformer neutral point, the invention constructs a measured correction model by introducing a correction vector for the substation surface potential and solves for the correction amount using a hybrid quasi-Newton method, thereby achieving accurate correction of the bias assessment model.
[0004] A method for experimentally correcting the bias magnetization model parameters of a large power grid includes:
[0005] Construct a theoretical model of bias magnetism in a large AC power grid, and output the nodal voltage equations and the expression for the neutral point loop current of the transformer from the theoretical model.
[0006] Based on the node voltage equation, a substation ground surface potential correction column vector is introduced to generate the corrected node voltage equation. Based on the corrected node voltage equation and the transformer neutral point loop current expression, the corrected transformer neutral point loop current column vector is output.
[0007] The objective function for the substation ground potential correction vector is constructed with the goal of minimizing the sum of the absolute values of the deviations between the corrected transformer neutral point loop current column vector and the measured transformer neutral point loop current column vector.
[0008] The objective function is solved using a hybrid quasi-Newton method to obtain the corrected column vector of the substation's surface potential.
[0009] Furthermore, the construction of a large-scale AC power grid bias magnetization theoretical model, and the output of the nodal voltage equations and transformer neutral point loop current expressions of the theoretical model, includes:
[0010] The substation busbar and transformer neutral point are equivalent to nodes, and the substation DC grounding resistance, transformer winding DC resistance and line DC resistance are equivalent to branches, forming an equivalent network.
[0011] Based on the equivalent network, the nodal voltage equations of the theoretical model are derived.
[0012] Based on the aforementioned nodal voltage equations, the nodal voltage column vectors are obtained by solving.
[0013] Based on the node voltage column vector, the branch current column vector is derived, and the expression for the transformer neutral point loop current under the theoretical model is further obtained.
[0014] Furthermore, the step of using a hybrid quasi-Newton method to solve the objective function and obtain the corrected column vector of the substation surface potential includes:
[0015] Set the control error ε=1e⁻ 6 Initial point x1 = zero vector, initial matrix B1 = identity matrix, iteration number k = 1;
[0016] For the current iteration point x k Calculate the gradient g of the objective function. k ;
[0017] For B k Perform Cholesky decomposition, based on the solution increment s k =x k+1 -x k gradient increment y k =gk +1 -g k Choose either BFGS or DFP to update matrix B. k+1 ;
[0018] Repeat gradient calculation and matrix update until ||l k p k ||2≤ε, where l k For step size, p k Search direction, output correction vector ξ, the correction column vector of the substation surface potential.
[0019] Furthermore, it also includes: outputting the corrected DC current distribution and bias risk assessment results based on the corrected column vector, and adapting to scenarios of changes in grid parameters, operating modes, or adjustment of governance measures.
[0020] Furthermore, the method of outputting the corrected DC current distribution and bias risk assessment results based on the corrected column vector, and adapting to scenarios involving changes in grid parameters, operating modes, or adjustments to mitigation measures, includes:
[0021] Substituting the modified column vectors into the modified nodal voltage equations yields the updated nodal voltage equations.
[0022] The DC current distribution of the power grid is calculated based on the updated nodal voltage equations;
[0023] Based on the DC current distribution of the grid, a bias risk assessment was completed;
[0024] For scenarios involving adjustments to power grid parameters, changes in operating modes, or the implementation of mitigation measures, repeat the above steps without needing to re-measure and output updated assessment results.
[0025] A device for measuring and correcting the bias magnetization model parameters of a large power grid includes:
[0026] The theoretical model building module is used to construct a bias magnetism theoretical model of a large AC power grid and output the nodal voltage equations and transformer neutral point loop current expressions of the theoretical model.
[0027] The modified model generation module is used to generate the modified node voltage equation by introducing the modified column vector of substation ground surface potential based on the node voltage equation, and output the modified column vector of transformer neutral point loop current based on the modified node voltage equation and the expression of transformer neutral point loop current.
[0028] The objective function construction module is used to construct the objective function of the substation ground potential correction column vector with the goal of minimizing the sum of the absolute values of the deviations between the corrected transformer neutral point loop current column vector and the measured transformer neutral point loop current column vector.
[0029] The modified column vector solving module is used to solve the objective function using a hybrid quasi-Newton method to obtain the modified column vector of the substation surface potential.
[0030] Furthermore, the theoretical model construction module is specifically used for:
[0031] The substation busbar and transformer neutral point are equivalent to nodes, and the substation DC grounding resistance, transformer winding DC resistance and line DC resistance are equivalent to branches, forming an equivalent network.
[0032] Based on the equivalent network, the nodal voltage equations of the theoretical model are derived.
[0033] Based on the aforementioned nodal voltage equations, the nodal voltage column vectors are obtained by solving.
[0034] Based on the node voltage column vector, the branch current column vector is derived, and the expression for the transformer neutral point loop current under the theoretical model is further obtained.
[0035] Furthermore, the modified column vector solving module is specifically used for:
[0036] Set the control error ε=1e⁻ 6 Initial point x1 = zero vector, initial matrix B1 = identity matrix, iteration number k = 1;
[0037] For the current iteration point x k Calculate the gradient g of the objective function. k ;
[0038] For B k Perform Cholesky decomposition, based on the solution increment s k =x k+1 -x k gradient increment y k =gk +1 -g k Choose either BFGS or DFP to update matrix B. k+1 ;
[0039] Repeat gradient calculation and matrix update until ||l k p k ||2≤ε, where l k For step size, p k Search direction, output correction vector ξ, the correction column vector of the substation surface potential.
[0040] Furthermore, it also includes:
[0041] The dynamic evaluation module is used to output the corrected DC current distribution and bias risk assessment results based on the corrected column vector, and adapt to scenarios of changes in grid parameters, operating modes or adjustment of governance measures.
[0042] Furthermore, the dynamic evaluation module is specifically used for:
[0043] Substituting the modified column vectors into the modified nodal voltage equations yields the updated nodal voltage equations.
[0044] The DC current distribution of the power grid is calculated based on the updated nodal voltage equations;
[0045] Based on the DC current distribution of the grid, a bias risk assessment was completed;
[0046] For scenarios involving adjustments to power grid parameters, changes in operating modes, or the implementation of mitigation measures, repeat the above steps without needing to re-measure and output updated assessment results.
[0047] A measured correction system for bias magnetization model parameters of a large power grid includes: a computer-readable storage medium and a processor;
[0048] The computer-readable storage medium is used to store executable instructions;
[0049] The processor is used to read executable instructions stored in the computer-readable storage medium and execute the measured correction method for the bias magnetization model parameters of the large power grid.
[0050] A non-transitory computer-readable storage medium storing a computer program that, when executed by a processor, implements a method for experimentally correcting the bias magnetization model parameters of a large power grid.
[0051] The beneficial effects of this invention are as follows:
[0052] 1. Accurately compensate for model errors and improve simulation credibility: To address the calculation error of the mutual resistance matrix caused by the complexity of earth resistivity, a measured correction model is constructed by introducing the substation surface potential correction vector. This effectively reduces the deviation between the simulation results of bias magnetization assessment and the measured data, solves the problem of insufficient accuracy of traditional theoretical models due to the uncertainty of underground structures, and significantly improves the reliability of the model in predicting the DC bias magnetization distribution of the actual power grid.
[0053] 2. Efficiently solve for correction parameters and reduce computational complexity: The hybrid quasi-Newton method (BFGS / DFP adaptive update) is used as the core optimization algorithm. It does not require direct calculation of the second derivative of the objective function, avoiding the risk of non-invertible or ill-conditioned Hessian matrix. By dynamically judging the solution increment and gradient increment during the iteration process to select the update form, superlinear convergence is achieved, which greatly reduces the consumption of computational resources and the number of iterations (for example, only 14 iterations are needed to complete the correction in the implementation case), which is suitable for rapid model calibration of large-scale power grids.
[0054] 3. Dynamically adapt to changes in the power grid and simplify the risk assessment process: The revised model can be directly applied to scenarios after power grid parameter adjustments, changes in operating modes, or the implementation of bias control measures. It can quickly calculate the updated DC current distribution and complete the risk assessment without having to conduct the actual measurement of the DC current at the transformer neutral point again, which significantly reduces assessment costs and shortens the assessment cycle, providing efficient support for dynamic decision-making in power grid bias control.
[0055] 4. Enhance model adaptability and cover the entire life cycle assessment: From risk prediction before the commissioning of DC projects to the verification of mitigation measures after commissioning, the modified model can maintain high accuracy, solving the pain point of "the inability to accurately simulate the mitigation effect after commissioning" in traditional methods, and realizing a reliable assessment of the grid bias risk throughout the entire life cycle from planning to operation. Attached Figure Description
[0056] Figure 1This is a flowchart of a method for experimentally correcting the bias magnetization model parameters of a large power grid according to the present invention.
[0057] Figure 2 This is a schematic diagram of the bias magnetization theoretical model of a large AC power grid.
[0058] Figure 3 This is a schematic diagram illustrating the specific implementation steps of the measured correction method for the bias magnetization model parameters of the large power grid of the present invention.
[0059] Figure 4 This is a diagram of the theoretical assessment model for the risk of biased magnetization.
[0060] Figure 5 It is a geographic information distribution map of the site measurement values and simulation calculation results.
[0061] Figure 6 This is a diagram showing the revised results of the theoretical assessment model for the risk of magnetic bias. Detailed Implementation
[0062] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0063] Please see Figure 1 and Figure 3 The first aspect of this invention provides a method for experimentally correcting the bias magnetization model parameters of a large power grid, comprising the following steps:
[0064] The first step is to construct a theoretical model of the bias magnetization of a large AC power grid, and output the nodal voltage equations and the expression for the neutral point loop current of the transformer.
[0065] 1.1 Build according to actual power grid parameters, such as Figure 2 The diagram illustrates a biased magnetization theory model for a large AC power grid. This model is equivalent to an above-ground / underground network consisting of a series of nodes N and branches B. Substation busbars and transformer neutral points are represented as nodes in this biased magnetization model. Substation DC grounding resistance, transformer winding DC resistance, and line DC resistance are represented as branches in the biased magnetization model. Branches possess topological properties connected to nodes and two main electrical properties: resistance and current.
[0066] 1.2. Derive the nodal voltage equations of the bias magnetism theoretical model of a large AC power grid.
[0067] The nodal voltage equations of the bias magnetism theoretical model of a large AC power grid are as follows:
[0068] (1)
[0069] Among them, A G M represents the matrix relating the neutral point voltage column vector and the node voltage column vector of the transformer, A represents the matrix relating the branch voltage drop column vector and the node voltage column vector, and M represents the matrix relating the branch voltage drop column vector and the node voltage column vector. G M represents the mutual resistance matrix between the substation grounding grids. E I represents the mutual resistance torque between the substation grounding grid and the DC grounding electrode. E R is the column vector of DC grounding electrode currents. B For the branch resistor array, V N This is the column vector of node voltages. The superscript T represents the matrix transpose, and the superscript -1 represents the matrix inversion.
[0070] 1.3. Derive the expression for branch current.
[0071] Equation (1) is a linear equation. The node voltage column vector can be obtained by solving this equation, and then the branch current column vector can be obtained through Equation (2). From the definition of branch voltage, we have...
[0072] (2)
[0073] In the formula, V B Let I be the column vector of branch voltage drop. B This is the column vector of branch currents.
[0074] 1.4. Derive the expression for the neutral point circuit current of the transformer.
[0075] Finally, both the transformer winding current and the current flowing to ground through the transformer neutral point can be obtained from the correlation of the branch current column vectors. The field measurement data comes from the transformer neutral point ground current. For the bias magnetism theoretical model of a large AC power grid, the transformer neutral point loop current column vector is...
[0076] (3)
[0077] In the formula, I G B is the column vector of the neutral point loop currents of the transformer. G This is the correlation matrix between the neutral point loop current and the branch current column vectors of the transformer.
[0078] Therefore, the model results can be compared with the measured data to correct the parameters of the bias risk theoretical assessment model.
[0079] The second step is to derive the objective function for measurement correction based on the measured data of the transformer neutral point circuit current.
[0080] 2.1. Introduce the substation ground potential correction term and derive the corrected transformer neutral point loop current column vector.
[0081] In formula (1), the left-hand side M of the equation E I E This represents the substation ground potential caused by the current flowing into the DC grounding electrode. Due to the complexity of the earth resistivity model, the mutual resistance matrix M between the DC grounding electrode and the substation... E It is relatively easy to generate calculation errors. The measured correction model of the bias magnetization model parameters of the large power grid is corrected by adding the correction column vector of the substation surface potential to the formula (1):
[0082] (4)
[0083] In the formula, μ is the corrected column vector of the substation ground surface potential.
[0084] Substituting equation (4) back into equation (3), we can obtain the corrected transformer neutral point loop current vector. :
[0085] (5)
[0086] 2.2. Derive the objective function of the substation ground potential correction column vector.
[0087] The core of the measured correction model lies in solving for the substation ground potential correction column vector μ. Using the measured DC current data of the transformer neutral point, the substation ground potential correction column vector can be solved by optimizing the following objective function:
[0088] (6)
[0089] In the formula, F obj To solve for the objective function of the substation ground potential correction column vector, I G This is a column vector of DC current measurement data at the transformer neutral point. This is the column vector of the simulation results of the DC current at the neutral point of the transformer.
[0090] The third step is to perform measured corrections to the bias magnetization model parameters of the large power grid based on the hybrid quasi-Newton method.
[0091] The flowchart of the modified algorithm for the theoretical assessment model of bias risk is shown below. Figure 3 .
[0092] 3.1 Actual measurement correction initialization.
[0093] During the initialization phase, the given control error ε is set to 10. -6 The initial point x1 is set as the zero vector, B1 is preset as the identity matrix, and the number of iterations k = 1.
[0094] 3.2. For the iterative solution x1, calculate the gradient g of the objective function. k g k = .
[0095] 3.3 Regarding B k Perform Cholesky decomposition B k =L k D k L k T L k It is a lower triangular matrix, D k It is a diagonal matrix.
[0096] 3.4. Judgment: D k If the smallest diagonal element is less than 0, proceed to step 3.5; otherwise, proceed to step 3.5.
[0097] 3.5, by L k v=-g k D k L k T p k =v solves for the search direction p k .
[0098] 3.6 Calculate p k T g k .
[0099] 3.7 Judgment: p k T g k >0? Yes, end; no, go to 3.8.
[0100] 3.8. Find the condition that satisfies F. obj (x k +l k p k ) = min[F obj (x k +lp k )|λ>0] of l k Let x k+1 =x k +λ k p k .
[0101] 3.9. Determine whether the convergence condition is met. k p k If ||2≤ε? Yes, end; otherwise, go to 3.10.
[0102] 3.10 Calculate g k+1 = .
[0103] 3.11. Judgment: s k T y k ≤s k T B k s k If yes, proceed to 3.12; if no, proceed to 3.13.
[0104] s k and y k These are the solution increment and gradient increment during the iteration process, respectively.
[0105] (7)
[0106] (8)
[0107] 3.12 Implementation of B k+1 BFGS update, see version 3.14.
[0108] (9)
[0109] 3.13 Implementation of B k+1 DFP update, see 3.14.
[0110] (10)
[0111] 3.14. Perform Cholesky decomposition B k+1 =L k+1 D k L T k+1 , k = k + 1, go to 3.4.
[0112] The modified algorithm for the theoretical assessment model of biased magnetization risk is based on the quasi-Newton method, avoiding the direct calculation of the second derivative and preventing algorithm failure due to the invertibility or ill-conditioned nature of the Hessian. The modified algorithm only uses gradient information to construct an approximate matrix of the Hessian matrix through iterative updates, saving the time and computational resources of explicitly calculating the Hessian, and is especially suitable for scenarios such as equation (6) where the gradient is easy to obtain but the second derivative is complex. The modified algorithm for the theoretical assessment model of biased magnetization risk can achieve superlinear convergence, has strong adaptability, and improves the reliability of convergence.
[0113] This invention corrects the deviation of substation ground potential model parameters by comparing measured data with model simulation results, avoiding the calculation error of the mutual resistance matrix caused by the excessive complexity of the earth resistivity model. A function is constructed with the objective of minimizing the sum of the absolute values of the deviations between the simulated DC current result of the transformer neutral point (i.e., the corrected column vector of the transformer neutral point loop current) and the measured data (i.e., the measured column vector of the transformer neutral point loop current). The bias magnetization model is corrected using either BFGS or DFP update optimization methods. The corrected bias magnetization model can obtain more reliable DC current distribution calculation results for operating conditions where grid parameters, operating modes, or bias magnetization mitigation measures are implemented, assisting in the completion of a new round of bias magnetization risk assessment.
[0114] Implementation Case: The geographical distribution of the DC grounding electrode and the AC power grid within a 100km radius is shown in the figure. Figure 4 .
[0115] With a grounding current of 3750A, the DC current at the neutral point of power transformers within a 100km radius of the DC grounding electrode was measured. The geographical distribution of the measured values and simulation results is shown below. Figure 5 As shown.
[0116] Figure 5 In the diagram, the last two digits of the abbreviation of the station name represent the measured neutral point current and the simulation result, respectively. The results show that there are discrepancies between the simulation results and the measured values for some stations, thus necessitating the revision of the theoretical assessment model for magnetization risk.
[0117] The theoretical assessment model for bias risk was revised using the aforementioned hybrid quasi-Newton method. The calculations were completed after 14 iterations, and the results are shown below. Figure 6 . Figure 6 In the diagram, the dots represent the geographical location of the power station, the first number represents the error between the simulation results after model correction and the measurement data, and the second number represents the correction amount for the substation ground potential. Figure 6 The results show that substations near the four grounding electrodes, namely XN, TJ, JJ, and HNH, require significant corrections to their ground potential, while the corrections for other stations are not substantial.
[0118] After the substation's ground potential is corrected, if the grid parameters and operating mode change, formula (5) can be reused to complete the calculation of the correction model, obtain the updated grid DC current distribution, and thus complete a new round of DC bias risk assessment.
[0119] This invention has the following features and effects:
[0120] 1. Compensate for model errors and improve simulation accuracy: By introducing the substation surface potential correction vector, the problem of mutual resistance matrix calculation error caused by the complexity of earth resistivity is effectively solved, the deviation between the simulation results of the bias magnetic evaluation model and the measured data is significantly reduced, and the reliability of the model is improved.
[0121] 2. Efficient solution of correction vector, saving computational resources: The correction vector is solved by the hybrid quasi-Newton method (BFGS / DFP), which does not require direct calculation of the second derivative, avoids the problem of non-invertibility or ill-conditioned Hessian matrix, has fast convergence speed and strong adaptability, and reduces computational complexity and resource consumption.
[0122] 3. Dynamically adapt to changes in the power grid and reduce assessment costs: The revised model can be directly applied to scenarios after changes in power grid parameters, operating modes, or the implementation of bias control measures, without the need to conduct field measurements again, quickly completing a new round of bias risk assessment, and improving the safety of power grid operation and assessment efficiency.
[0123] A second aspect of the present invention provides a device for the measured correction of bias magnetism model parameters of a large power grid, comprising:
[0124] The theoretical model building module is used to construct a bias magnetism theoretical model of a large AC power grid and output the nodal voltage equations and transformer neutral point loop current expressions of the theoretical model.
[0125] The modified model generation module is used to generate the modified node voltage equation by introducing the modified column vector of substation ground surface potential based on the node voltage equation, and output the modified column vector of transformer neutral point loop current based on the modified node voltage equation and the expression of transformer neutral point loop current.
[0126] The objective function construction module is used to construct the objective function of the substation ground potential correction column vector with the goal of minimizing the sum of the absolute values of the deviations between the corrected transformer neutral point loop current column vector and the measured transformer neutral point loop current column vector.
[0127] The modified column vector solving module is used to solve the objective function using a hybrid quasi-Newton method to obtain the modified column vector of the substation surface potential.
[0128] Another aspect of the present invention provides a measured correction system for the bias magnetization model parameters of a large power grid, comprising: a computer-readable storage medium and a processor;
[0129] The computer-readable storage medium is used to store executable instructions;
[0130] The processor is used to read executable instructions stored in the computer-readable storage medium and execute the measured correction method for the bias magnetization model parameters of the large power grid described in the first aspect.
[0131] In another aspect, the present invention provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the measured correction method for the bias magnetization model parameters of a large power grid as described in the first aspect.
[0132] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0133] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0134] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0135] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0136] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.
Claims
1. A method for experimentally correcting the bias magnetism model parameters of a large power grid, characterized in that: include: Construct a theoretical model of bias magnetism in a large AC power grid, and output the nodal voltage equations and the expression for the neutral point loop current of the transformer from the theoretical model. Based on the node voltage equation, a substation ground surface potential correction column vector is introduced to generate the corrected node voltage equation. Based on the corrected node voltage equation and the transformer neutral point loop current expression, the corrected transformer neutral point loop current column vector is output. The objective function for the substation ground potential correction vector is constructed with the goal of minimizing the sum of the absolute values of the deviations between the corrected transformer neutral point loop current column vector and the measured transformer neutral point loop current column vector. The objective function is solved using a hybrid quasi-Newton method to obtain the corrected column vector of the substation's surface potential.
2. The method for experimentally correcting the bias magnetization model parameters of a large power grid as described in claim 1, characterized in that: The construction of a large-scale AC power grid bias magnetization theoretical model, outputting the nodal voltage equations and transformer neutral point loop current expressions of the theoretical model, includes: The substation busbar and transformer neutral point are equivalent to nodes, and the substation DC grounding resistance, transformer winding DC resistance and line DC resistance are equivalent to branches, forming an equivalent network. Based on the equivalent network, the nodal voltage equations of the theoretical model are derived. Based on the aforementioned nodal voltage equations, the nodal voltage column vectors are obtained by solving. Based on the node voltage column vector, the branch current column vector is derived, and the expression for the transformer neutral point loop current under the theoretical model is further obtained.
3. The method for experimentally correcting the bias magnetization model parameters of a large power grid as described in claim 2, characterized in that: The objective function is solved using a hybrid quasi-Newton method to obtain the corrected column vector of the substation surface potential, including: Set the control error ε=1e⁻ 6 Initial point x1 = zero vector, initial matrix B1 = identity matrix, iteration number k = 1; For the current iteration point x k Calculate the gradient g of the objective function. k ; For B k Perform Cholesky decomposition, based on the solution increment s k =x k+1 -x k gradient increment y k =gk +1 -g k Choose either BFGS or DFP to update matrix B. k+1 ; Repeat gradient calculation and matrix update until ||l k p k ||2≤ε, where l k For step size, p k Search direction, output correction vector ξ, the correction column vector of the substation surface potential.
4. The method for experimentally correcting the bias magnetization model parameters of a large power grid as described in claim 1, characterized in that: Also includes: Based on the corrected column vector output, the DC current distribution and bias risk assessment results are provided, and the system is adapted to scenarios involving changes in grid parameters, operating modes, or adjustments to governance measures.
5. The method for experimental correction of the bias magnetization model parameters of a large power grid as described in claim 1, characterized in that: The modified DC current distribution and bias risk assessment results based on the modified column vector output, and adapted to scenarios involving changes in grid parameters, operating modes, or adjustments to mitigation measures, include: Substituting the modified column vectors into the modified nodal voltage equations yields the updated nodal voltage equations. The DC current distribution of the power grid is calculated based on the updated nodal voltage equations; Based on the DC current distribution of the grid, a bias risk assessment was completed; For scenarios involving adjustments to power grid parameters, changes in operating modes, or the implementation of mitigation measures, repeat the above steps without needing to re-measure and output updated evaluation results.
6. A device for measuring and correcting the bias magnetism model parameters of a large power grid, characterized in that: include: The theoretical model building module is used to construct a bias magnetism theoretical model of a large AC power grid and output the nodal voltage equations and transformer neutral point loop current expressions of the theoretical model. The modified model generation module is used to generate the modified node voltage equation by introducing the modified column vector of substation ground surface potential based on the node voltage equation, and output the modified column vector of transformer neutral point loop current based on the modified node voltage equation and the expression of transformer neutral point loop current. The objective function construction module is used to construct the objective function of the substation ground potential correction column vector with the goal of minimizing the sum of the absolute values of the deviations between the corrected transformer neutral point loop current column vector and the measured transformer neutral point loop current column vector. The modified column vector solving module is used to solve the objective function using a hybrid quasi-Newton method to obtain the modified column vector of the substation surface potential.
7. The measured correction device for the bias magnetization model parameters of a large power grid as described in claim 6, characterized in that: The theoretical model construction module is specifically used for: The substation busbar and transformer neutral point are equivalent to nodes, and the substation DC grounding resistance, transformer winding DC resistance and line DC resistance are equivalent to branches, forming an equivalent network. Based on the equivalent network, the nodal voltage equations of the theoretical model are derived. Based on the aforementioned nodal voltage equations, the nodal voltage column vectors are obtained by solving. Based on the node voltage column vector, the branch current column vector is derived, and the expression for the transformer neutral point loop current under the theoretical model is further obtained.
8. The measured correction device for the bias magnetization model parameters of a large power grid as described in claim 7, characterized in that: The modified column vector solving module is specifically used for: Set the control error ε=1e⁻ 6 Initial point x1 = zero vector, initial matrix B1 = identity matrix, iteration number k = 1; For the current iteration point x k Calculate the gradient g of the objective function. k ; For B k Perform Cholesky decomposition, based on the solution increment s k =x k+1 -x k gradient increment y k =gk +1 -g k Choose either BFGS or DFP to update matrix B. k+1 ; Repeat gradient calculation and matrix update until ||l k p k ||2≤ε, where l k For step size, p k Search direction, output correction vector ξ, the correction column vector of the substation surface potential.
9. The measured correction device for the bias magnetization model parameters of a large power grid as described in claim 7, characterized in that: Also includes: The dynamic evaluation module is used to output the corrected DC current distribution and bias risk assessment results based on the corrected column vector, and adapt to scenarios of changes in grid parameters, operating modes or adjustment of governance measures.
10. The measured correction device for the bias magnetization model parameters of a large power grid as described in claim 9, characterized in that: The dynamic evaluation module is specifically used for: Substituting the modified column vectors into the modified nodal voltage equations yields the updated nodal voltage equations. The DC current distribution of the power grid is calculated based on the updated nodal voltage equations; Based on the DC current distribution of the grid, a bias risk assessment was completed; For scenarios involving adjustments to power grid parameters, changes in operating modes, or the implementation of mitigation measures, repeat the above steps without needing to re-measure and output updated evaluation results.
11. A measured correction system for bias magnetism model parameters of a large power grid, comprising: Computer-readable storage media and processors; The computer-readable storage medium is used to store executable instructions; The processor is used to read executable instructions stored in the computer-readable storage medium and execute the measured correction method for the bias magnetization model parameters of the large power grid as described in any one of claims 1-5.
12. A non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the measured correction method for the bias magnetization model parameters of a large power grid as described in any one of claims 1-5.