A method for evaluating transient voltage stability boundary of a power transmission section and related equipment

Abstract

The application discloses a power transmission section transient voltage stability boundary evaluation method and related equipment, and relates to the field of power system operation control. The method comprises the following steps: acquiring a section power limit and a transient voltage stability index limit of a power transmission section, obtaining section power based on key operation parameters through power flow calculation, and constructing a two-dimensional sample data set with the transient voltage stability index. The mapping relationship between the section power and the stability index is obtained by jointly modeling the key operation parameters affecting the transient voltage stability, combining polynomial approximation with Monte Carlo sampling to generate multiple operation scene samples. A confusion matrix is constructed based on the two-dimensional sample data set, the operation scene is divided into true positive examples, false negative examples, false positive examples and true negative examples, and adaptive evaluation indexes are calculated on this basis. Comprehensive quantitative analysis is performed on the evaluation indexes to output the adaptability evaluation result of the power transmission section operation boundary.

Description

Technical Field

[0001] This invention relates to the field of power system operation control, specifically to a method and related equipment for evaluating the transient voltage stability boundary of a transmission section. Background Technology

[0002] A transmission section refers to a set of transmission channels connecting different regions in a power system, and is a crucial part of the power grid's energy transmission. In traditional power systems dominated by synchronous generators, the power source side mainly consists of conventional units such as thermal power and hydropower. These units are directly coupled to the grid through rotating machinery, possessing large physical rotational inertia and strong voltage support capabilities, and their electromechanical transient characteristics are relatively clear and predictable. The transient stability of the system has a significant and strong coupling relationship with the transmission power of the transmission section. Based on this deterministic relationship, dispatchers can determine a fixed section power limit under typical fault scenarios through offline simulation calculations. As long as the actual operating power is controlled not to exceed this limit, the system can be physically guaranteed to meet the requirements of transient voltage stability.

[0003] Unlike traditional synchronous generators, renewable energy units primarily connect to the grid via power electronic converters, lacking physical rotational inertia. Furthermore, their disturbance rejection capabilities and voltage support characteristics differ significantly from traditional units. The output of renewable energy is highly random, causing the grid's operation to be constantly dynamic and lacking the deterministic nature of traditional grid operation.

[0004] In this new power system environment with a high proportion of renewable energy integration, the corresponding mapping relationship between transmission section power and system transient stability is broken. Even if the total transmission power of the transmission section is the same, the transient voltage response characteristics of the system when facing faults may vary greatly due to differences in the combination of renewable energy generation, output distribution, and reactive power support levels. Specifically, for a given transmission section, the corresponding stability index is no longer a fixed value, but rather has a certain range.

[0005] This presents challenges to the existing static cross-section quota management model. On the one hand, in some scenarios where the output of new energy sources is high but the voltage support is weak, even if the cross-section power does not exceed the prescribed static limit, the system may still face the risk of transient voltage instability. On the other hand, in some scenarios with good operating conditions, the traditional static limit may be too conservative, limiting the actual transmission capacity of the cross-section and causing economic waste. Summary of the Invention

[0006] The technical problem to be solved by this invention is that in the new power system environment with a high proportion of new energy access, it is difficult to accurately characterize the power grid safety boundary under complex and variable operating conditions by relying on a single, fixed power value. The purpose is to provide a method and related equipment for evaluating the transient voltage stability boundary of transmission sections, which solves the problem of how to adapt to the random fluctuation characteristics of new energy through a new comprehensive evaluation method, quantitatively evaluate the adaptability of existing operating boundaries, and effectively balance safety risks and transmission efficiency.

[0007] This invention is achieved through the following technical solution:

[0008] A method for evaluating the transient voltage stability boundary of a power transmission section includes:

[0009] Obtain the cross-sectional power limit and transient voltage stability index limit of the transmission section to be evaluated, wherein transient voltage instability is determined to have occurred when the transient voltage stability index is lower than the transient voltage stability index limit;

[0010] A two-dimensional sample dataset of transmission section power and transient voltage stability index is constructed, specifically including: selecting key operating parameters affecting transient voltage stability, the key operating parameters including at least the output of clean energy units and the system load level, and obtaining the corresponding transmission section power through power flow calculation based on the key operating parameters; fitting the joint probability distribution of the key operating parameters based on historical operating data; selecting distribution points in the parameter space of the key operating parameters and obtaining the true value of the transient voltage stability index corresponding to the distribution points through power system time-domain simulation, thereby establishing a polynomial approximation function of the transient voltage stability index with respect to the key operating parameters; performing Monte Carlo sampling based on the joint probability distribution to generate multiple sets of operating scenario samples, and using the polynomial approximation function to calculate the transient voltage stability index corresponding to each operating scenario sample, resulting in a two-dimensional sample dataset composed of multiple data points;

[0011] Constructing a confusion matrix based on the two-dimensional sample dataset specifically includes: mapping the cross-sectional power and transient voltage stability index of each data point to a two-dimensional plane with cross-sectional power as one dimension and transient voltage stability index as the other dimension, and classifying each data point into four categories: true positives TP, false negatives FN, false positives FP, and true negatives TN according to the cross-sectional power limit and transient voltage stability index limit.

[0012] The adaptability evaluation index is calculated based on the confusion matrix, and the adaptability evaluation index is comprehensively quantified to output the adaptability results of the transmission section operation boundary.

[0013] Furthermore, the adaptive evaluation metrics include at least basic statistical metrics and advanced statistical metrics, wherein: basic statistical metrics include at least accuracy, recall, false positive rate, precision, and false negative rate; advanced statistical metrics include at least implicit risk safety, implicit risk stability, and area under the ROC curve (AUC).

[0014] Furthermore, the implicit risk safety factor is obtained by normalizing the mean of the transient voltage stability index within the false negative example FN in combination with the physical critical value. The implicit risk stability factor is obtained by normalizing the standard deviation of the transient voltage stability index within the false negative example FN in combination with the physical critical value and the exponential function. The AUC is obtained by scanning the cross-sectional power limit within a preset range and calculating the false alarm rate and recall rate corresponding to different scanning points to form an ROC curve and calculating the area under the curve.

[0015] Furthermore, the classification of true negative examples (TP), false negative examples (FN), false positive examples (FP), and true negative examples (TN) includes: when the cross-sectional power is greater than the cross-sectional power limit and the transient voltage stability index is lower than the transient voltage stability index limit, it is classified as a true negative example (TP); when the cross-sectional power is less than or equal to the cross-sectional power limit and the transient voltage stability index is lower than the transient voltage stability index limit, it is classified as a false negative example (FN); when the cross-sectional power is greater than the cross-sectional power limit and the transient voltage stability index is greater than or equal to the transient voltage stability index limit, it is classified as a false positive example (FP); when the cross-sectional power is less than or equal to the cross-sectional power limit and the transient voltage stability index is greater than or equal to the transient voltage stability index limit, it is classified as a true negative example (TN).

[0016] Furthermore, the establishment of a polynomial approximation function of the transient voltage stability index with respect to the key operating parameters includes: determining the coefficients of the polynomial approximation function using the least squares method, and evaluating the approximation error based on cross-validation or leave-one-out method; when the approximation error is greater than a preset error threshold, increasing the polynomial order and / or increasing the number of collocation points before refitting.

[0017] Furthermore, a comprehensive quantitative evaluation of the adaptability assessment indicators is performed to output the adaptability results of the transmission section operating boundary. Specifically, this includes: dividing the adaptability assessment indicators into positive and negative indicators and converting the negative indicators into a positive form where larger values ​​are better; setting weight coefficients for each positive indicator and constructing a linear weighted evaluation function to calculate the comprehensive adaptability score S; comparing the comprehensive adaptability score S with a preset grading threshold to output the adaptability level determination result of the transmission section operating boundary.

[0018] Furthermore, the calculation of the comprehensive adaptability score S includes: performing dimensionless processing on each positive indicator and then performing a weighted summation; and the grading threshold includes at least a good threshold and a qualified threshold, wherein when S is not less than the good threshold, it is judged as good; when S is less than the good threshold but not less than the qualified threshold, it is judged as qualified; and when S is less than the qualified threshold, it is judged as unqualified.

[0019] This invention also provides a system for evaluating the transient voltage stability boundary of a transmission section, used to implement the aforementioned method for evaluating the transient voltage stability boundary of a transmission section, comprising:

[0020] The acquisition module is used to acquire the cross-sectional power limit and transient voltage stability index limit of the transmission section to be evaluated. When the transient voltage stability index is lower than the transient voltage stability index limit, it is determined that transient voltage instability has occurred.

[0021] The sample construction module is used to construct a two-dimensional sample dataset of transmission section power and transient voltage stability index. Specifically, it includes: selecting key operating parameters affecting transient voltage stability, wherein the key operating parameters include at least the output of clean energy units and the system load level, and obtaining the corresponding transmission section power through power flow calculation based on the key operating parameters; fitting the joint probability distribution of the key operating parameters based on historical operating data; selecting matching points in the parameter space of the key operating parameters and obtaining the true value of the transient voltage stability index corresponding to the matching points through power system time-domain simulation, thereby establishing a polynomial approximation function of the transient voltage stability index with respect to the key operating parameters; performing Monte Carlo sampling based on the joint probability distribution to generate multiple sets of operating scenario samples, and using the polynomial approximation function to calculate the transient voltage stability index corresponding to each operating scenario sample, thereby obtaining a two-dimensional sample dataset composed of multiple data points.

[0022] The matrix construction module is used to construct a confusion matrix based on the two-dimensional sample dataset. Specifically, it includes mapping the cross-sectional power and transient voltage stability index of each data point to a two-dimensional plane with cross-sectional power as one dimension and transient voltage stability index as the other dimension, and classifying each data point into four categories: true positives TP, false negatives FN, false positives FP, and true negatives TN according to the cross-sectional power limit and transient voltage stability index limit.

[0023] The indicator calculation module is used to calculate the adaptive evaluation indicators based on the confusion matrix. The adaptive evaluation indicators include at least basic statistical indicators and advanced statistical indicators. The basic statistical indicators include at least accuracy, recall, false positive rate, precision, and false negative rate. The advanced statistical indicators include at least implicit risk safety, implicit risk stability, and area under the ROC curve (AUC).

[0024] The comprehensive evaluation module is used to perform a comprehensive quantitative evaluation of the adaptability evaluation indicators to output the adaptability results of the transmission section operation boundary.

[0025] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the transient voltage stability boundary assessment method for transmission sections as described above.

[0026] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the aforementioned method for evaluating the transient voltage stability boundary of a power transmission section.

[0027] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0028] This invention utilizes an evaluation system based on confusion matrices and statistical learning to systematically and quantitatively assess the safety and effectiveness of transmission line operation boundaries under complex and uncertain operating conditions. This significantly improves the scientific rigor and reliability of boundary setting and maintenance. By mapping the power limit and transient voltage stability index limit of the transmission line to a two-dimensional discrimination plane, and introducing a four-class sample classification mechanism (true positive, false negative, false positive, and true negative) within this plane, the boundary judgment of the transmission line operation is upgraded from traditional single-point or single-line judgment to a classification criterion in the full probability space. This avoids the problem of relying solely on a single margin index while ignoring the boundary misjudgment structure.

[0029] This invention combines polynomial approximation with Monte Carlo sampling to efficiently construct a two-dimensional sample dataset covering a large number of operating scenarios while maintaining the physical consistency of transient simulations. Compared with methods that only analyze a limited number of typical operating conditions, this invention can more comprehensively reflect the impact of new energy output fluctuations, load changes, and their correlations on the adaptability of cross-sectional stability boundaries. This invention also introduces basic statistical indicators and advanced risk indicators, which not only provide the overall accuracy, recall, and false positive rate of the cross-sectional boundary, but also quantify the stability margin and dispersion within the false negative region through implicit risk safety and implicit risk stability, thus providing a more targeted basis for optimizing cross-sectional operation strategies.

[0030] In summary, this invention, while maintaining the existing transient simulation and stability criterion system, introduces a probabilistic, statistical, and comparable evaluation mechanism oriented towards the cross-sectional boundary, upgrading the safety and adaptability of the transmission cross-sectional operation boundary from qualitative judgment to a quantifiable, comparable, and decision-making technical system. Attached Figure Description

[0031] To more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be considered as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort. In the drawings:

[0032] Figure 1 This is a schematic diagram of the overall process of Embodiment 1 of the present invention.

[0033] Figure 2 This is a schematic comparison diagram of traditional power grids and power grids after large-scale integration of clean energy.

[0034] Figure 3 The ROC curve for the operational boundary adaptability of the power transmission section in Example 1 is shown. Detailed Implementation

[0035] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of this invention are only for explaining this invention and are not intended to limit this invention.

[0036] Example 1

[0037] A method for evaluating the transient voltage stability boundary of a power transmission section, such as Figure 1 As shown, it includes:

[0038] Obtain the cross-sectional power limit and transient voltage stability index limit of the transmission section to be evaluated. When the transient voltage stability index falls below the transient voltage stability index limit, transient voltage instability is determined to have occurred. The power limit of the transmission section is defined as follows: ) and transient voltage stability index limits ( The cross-sectional power and stability index samples based on a large number of operating scenarios are mapped onto a two-dimensional plane. The operating scenarios are divided into four quadrants according to power and index limits, and a confusion matrix is ​​constructed.

[0039] A two-dimensional sample dataset of transmission section power and transient voltage stability index is constructed, specifically including: selecting key operating parameters affecting transient voltage stability, the key operating parameters including at least the output of clean energy units and the system load level, and obtaining the corresponding transmission section power based on the key operating parameters through power flow calculation (specifically, the transmission section power under each operating scenario is obtained through the BPA power flow calculation program); fitting the joint probability distribution of the key operating parameters based on historical operating data; selecting distribution points in the parameter space of the key operating parameters and obtaining the true value of the transient voltage stability index corresponding to the distribution points through power system time-domain simulation, thereby establishing a polynomial approximation function of the transient voltage stability index with respect to the key operating parameters; performing Monte Carlo sampling based on the joint probability distribution to generate multiple sets of operating scenario samples, and using the polynomial approximation function to calculate the transient voltage stability index corresponding to each operating scenario sample, resulting in a two-dimensional sample dataset composed of multiple data points;

[0040] Constructing a confusion matrix based on the two-dimensional sample dataset specifically includes: mapping the cross-sectional power and transient voltage stability index of each data point to a two-dimensional plane with cross-sectional power as one dimension and transient voltage stability index as the other dimension; and classifying each data point into four categories—true positives (TP), false negatives (FN), false positives (FP), and true negatives (TN)—according to the cross-sectional power limit and transient voltage stability index limit. The classification of true positives (TP), false negatives (FN), false positives (FP), and true negatives (TN) includes: when the cross-sectional power is greater than the cross-sectional power limit and the transient voltage stability index is stable... When the cross-sectional power is lower than the transient voltage stability index limit, it is classified as a true negative example (TP); when the cross-sectional power is less than or equal to the cross-sectional power limit and the transient voltage stability index is lower than the transient voltage stability index limit, it is classified as a false negative example (FN); when the cross-sectional power is greater than the cross-sectional power limit and the transient voltage stability index is greater than or equal to the transient voltage stability index limit, it is classified as a false positive example (FP); when the cross-sectional power is less than or equal to the cross-sectional power limit and the transient voltage stability index is greater than or equal to the transient voltage stability index limit, it is classified as a true negative example (TN). Specifically:

[0041] True example TP satisfies and This indicates that the scheduling decision exceeded the limit and that instability actually occurred.

[0042] False negatives FN satisfy but This indicates that the scheduling decision was safe, but instability actually occurred. This is a hidden risk area in the system's operation.

[0043] False positive examples satisfy FP but This indicates that the scheduling decision exceeded the limit, but the system remained stable in practice. This represents a conservative, wasteful area in system operation.

[0044] True negative example TN satisfies and This indicates that the scheduling decision is safe and that the system is indeed stable.

[0045] Let the number of samples in the above four categories be . , , , Total number of samples .

[0046] The adaptability evaluation index is calculated based on the confusion matrix, and the adaptability evaluation index is comprehensively quantified to output the adaptability results of the transmission section operation boundary.

[0047] In this embodiment, the adaptive evaluation metrics include at least basic statistical metrics and advanced statistical metrics, wherein: basic statistical metrics include at least accuracy, recall, false positive rate, precision, and false negative rate; advanced statistical metrics include at least implicit risk safety, implicit risk stability, and area under the ROC curve (AUC).

[0048] The basic statistical indicators include:

[0049] ①Accuracy (ACC): It represents the overall accuracy of the judgment.

[0050] ②TPR (Total Recall Rate): This characterizes the ability to intercept unstable samples.

[0051] ③ False Alarm Rate (FPR): This represents the proportion of misclassifications for stable samples.

[0052] ④ Precision Rate (PPV): This characterizes the reliability of the over-limit alarm.

[0053] ⑤ False negative rate (FOR): It represents the probability of instability actually occurring in a scenario that the scheduler considers safe, reflecting hidden security risks.

[0054] For advanced statistical indicators, the implicit risk safety factor is obtained by normalizing the mean of the transient voltage stability index within the false negative example FN in conjunction with the physical critical value. The implicit risk stability factor is obtained by normalizing the standard deviation of the transient voltage stability index within the false negative example FN in conjunction with the physical critical value and an exponential function. The AUC is obtained by scanning the cross-sectional power limit within a preset range and calculating the false alarm rate and recall rate corresponding to different scan points to form an ROC curve and calculating the area under the curve. Specifically, it is expressed as follows:

[0055] ① Hidden risk safety

[0056] To assess the specific distribution of samples within each region, the transient stability index sample set for each of the four regions (TP, FN, FP, TN) of the confusion matrix is ​​calculated. The mean of the corresponding indicator with standard deviation :

[0057]

[0058] To quantify the average severity of instability when latent risks occur, we focus on the mean of the false negative region (FN). And utilize physical critical values Normalization is performed to obtain the implicit risk safety level. :

[0059]

[0060] Characterizes the average severity of instability when latent instability occurs. The range of values ​​is , The closer the value is to 1, the more likely it is that even if a false positive occurs, the system will only be in a state of slight instability, and the risk and consequences will be relatively controllable.

[0061] ②Hidden risk stability

[0062] To quantify the volatility and uncertainty of the consequences of implicit risks, we focus on the standard deviation of the false negative region (FN). Similarly, using physical critical values Normalization with the exponential function yields the implicit risk stability. .

[0063]

[0064] It characterizes the degree of uncertainty in instability when latent instability occurs. The range of values ​​is , The closer it is to 1, the smaller the standard deviation, the more concentrated the distribution of the system state after missed detection, and the more predictable the instability result.

[0065] ③Area under the ROC curve

[0066] To further evaluate the adaptability of transmission section operation to the boundary, the upper limit of transmission section power is used. As a variable, scan changes within a preset range; calculate different... The corresponding false alarm rate for the given value ( ) and recall rate ( ).by The x-axis is... Plot the ROC (Receiver Operating Characteristic) curve on the ordinate, such as... Figure 3 ; , The range of values ​​is ,exist Evenly Divide into n parts and set n scan points, satisfying:

[0067]

[0068] according to Calculate the corresponding , For the first The coordinates of each scan point are used to calculate the area under the ROC curve using the trapezoidal rule. (Area Under Curve) is used to assess the overall adaptability under different cross-sectional limit settings. The calculation formula is as follows:

[0069]

[0070] The range of values ​​is . The closer the value is to 1, the higher the controllability and the better the boundary adaptability of the cross section under the current new energy access scenario.

[0071] The obtained dataset is categorized and organized to obtain the aforementioned indicators, and a comprehensive adaptive scoring method with full indicator weighting is used for quantitative evaluation, specifically including the following steps:

[0072] 1) Indicator Classification Processing

[0073] The adaptability assessment indicators are comprehensively and quantitatively evaluated to output the adaptability results of the transmission section operating boundary. Specifically, this includes: dividing the adaptability assessment indicators into positive and negative indicators, and converting the negative indicators into a positive form where larger values ​​are better; all of the above indicators satisfy the following conditions: Within the range.

[0074] Positive metrics include accuracy. Recall rate Accuracy Hidden risks and safety Hidden risk stability Area under the ROC curve These indicators remain at their original values.

[0075] Negative indicators include false alarm rate False negative rate These indicators are obtained through Convert to a positive indicator.

[0076] 2) Calculation of comprehensive score

[0077] Weight coefficients are set for each positive indicator, and a linear weighted evaluation function is constructed to calculate the comprehensive adaptability score S. The calculation of the comprehensive adaptability score S includes: performing dimensionless processing on each positive indicator and then performing a weighted summation; specifically,

[0078] Based on the different emphases of power grid operation on safety, economy, and the severity of risk, weight coefficients are set for each normalized indicator. ,satisfy A linear weighted evaluation function is constructed to calculate the comprehensive adaptability score of the transmission section's operating boundary.

[0079]

[0080] Adaptability Level Determination

[0081] The comprehensive adaptability score S is compared with a preset grading threshold to output the adaptability level determination result of the transmission section operation boundary. The grading threshold includes at least a good threshold and a qualified threshold. A score of good is determined when S is not less than the good threshold; a score of qualified is determined when S is less than the good threshold but not less than the qualified threshold; and a score of unqualified is determined when S is less than the qualified threshold. This is expressed as:

[0082] Preset adaptive rating threshold and According to the rating Determine the fitness level of the current boundary:

[0083] like The transmission section is deemed to have good adaptability to the operating boundary, and the current boundary is maintained.

[0084] like The transmission section is deemed to have qualified operational boundary adaptability, and the boundary can be finely adjusted according to the actual needs of the power grid.

[0085] like The transmission section was deemed to have unqualified operational boundary adaptability, and the boundary needs to be reconstructed.

[0086] The establishment of a polynomial approximation function for the transient voltage stability index with respect to the key operating parameters includes: determining the coefficients of the polynomial approximation function using the least squares method, and evaluating the approximation error based on cross-validation or leave-one-out method; when the approximation error is greater than a preset error threshold, increasing the polynomial order and / or increasing the number of collocation points before refitting.

[0087] In some implementations, the power limit for transmission sections is not a fixed constant, but rather adaptively adjusted based on the output of the adaptive evaluation index to form an iteratively updatable operating boundary. Specifically, the power limit for the transmission section is denoted as... Within the preset search range The system scans or optimizes candidate quotas; for each candidate quota... Confusion matrix is ​​constructed based on the two-dimensional sample dataset, and the corresponding comprehensive fitness score is calculated. The updated cross-sectional power limit is determined under the premise of meeting safety constraints, wherein the safety constraints include at least: the missed detection rate is not greater than a preset missed detection rate threshold. The security level for both contingent and hidden risks shall not be less than a preset security threshold. From the candidate set that satisfies the aforementioned safety constraints, the candidate limit that maximizes the overall adaptability score is selected as the updated cross-sectional power limit.

[0088] In some implementations, to reduce the impact of systematic biases of the polynomial approximation function on the transient voltage stability index on the evaluation results, a surrogate model error calibration mechanism is introduced. Specifically, the simulated true value of the transient voltage stability index is calculated on the collocation set or an additional validation set. With polynomial predictions residual And calculate the statistics of the residuals, including the residual mean. With residual standard deviation When calculating the metrics for a two-dimensional sample dataset, a conservative correction is applied to the polynomial output to obtain the calibrated metrics:

[0089]

[0090] in A non-negative coefficient related to risk preference. Based on the calibrated index. To avoid underestimating the instability risk under certain operating modes, confusion matrix partitioning and subsequent evaluation are performed, thereby improving the credibility and engineering usability of the boundary adaptability evaluation results.

[0091] In some implementations, to accurately characterize the correlation between clean energy output and load levels, the joint probability distribution of the key operating parameters is constructed using a correlation-constrained modeling approach. This includes: calculating the correlation matrix of the key operating parameters based on historical samples, and maintaining consistency between the correlation matrix and sample statistics during the joint distribution fitting process; or using a Copula function to couple the marginal distributions to obtain the joint distribution. When Monte Carlo sampling is performed based on the joint distribution, the generated operating scenario samples can simultaneously maintain the marginal fluctuation characteristics of each variable and the correlation structure between variables, thereby improving the effectiveness of the two-dimensional sample dataset in covering the actual operating mode.

[0092] The following embodiments use a power transmission section in a certain regional power grid as the research object, constructing multi-scenario operation samples and experimentally evaluating the transient voltage stability adaptability of the transmission section's operating boundary. These embodiments combine simulation calculations and statistical analysis to model and verify the relationship between section power and transient voltage stability indicators under different operating scenarios, illustrating the quantitative evaluation capability of the method of the present invention for the adaptability of the transmission section's operating boundary under uncertain operating conditions. This embodiment is an experimental verification example, intended to verify the feasibility and effectiveness of the method of the present invention, and does not constitute a limitation on the scope of protection of the present invention.

[0093] The specific values ​​involved in the following embodiments, such as cross-sectional power limits, transient voltage stability index thresholds, sample size, number of matching points, polynomial order, Monte Carlo sampling times, index weighting coefficients, and various threshold ranges, are only used as illustrative examples of the technical solutions of this invention to facilitate understanding of the implementation and technical effects of this invention, and do not constitute a limitation on the scope of protection of this invention. Those skilled in the art can adjust, replace, or equivalently set the above parameters according to the specific power grid scale, operating characteristics, and risk preferences without departing from the technical concept of this invention, and all such adjustments should fall within the scope of protection of this invention. The specific implementation steps are as follows:

[0094] like Figure 1 As shown, the evaluation index system for the operational boundary adaptability of transmission sections based on the confusion matrix includes:

[0095] The upper limit of the dispatch power of the transmission section is set as follows: The lower limit of the transient voltage stability index is (That is, when the stability index is below 1.5, the system is judged to have experienced transient voltage instability).

[0096] Using the two-dimensional sample dataset generated in step (2), a confusion matrix is ​​constructed, which is then used to analyze the running data. Scattered points are mapped onto a two-dimensional plane, and according to... and Divided into four areas:

[0097] True Case (TP): Satisfies and Let the sample size be . Sample size .

[0098] False negative (FN): satisfies and Let the sample size be . Sample size .

[0099] False positive (FP): satisfies and Let the sample size be . Sample size .

[0100] True negative example (TN): satisfies and Let the sample size be . Sample size .

[0101] Total number of samples .

[0102] like Figure 1 As shown, the specific implementation steps for constructing the power and index distribution of transmission sections based on polynomial approximation and Monte Carlo sampling include:

[0103] Selecting photovoltaic power output Wind power output and system load level As a key operating parameter, its joint probability distribution is fitted based on historical operating data. A transient voltage stability index is then constructed. Polynomial approximation function for input variables By selecting a small number of collocation points in the variable space, calculating the actual index values ​​using power system time-domain simulation software, and solving the polynomial coefficients using the least squares method, an explicit analytic function relationship is established.

[0104] Based on the fitted joint probability distribution, perform Sub-Monte Carlo random sampling. Utilizing the constructed polynomial approximation function. It quickly calculates the predicted value of the transient voltage stability index for each group of samples, generating a value containing... A two-dimensional sample dataset of data points. This dataset serves as the input data source for the evaluation.

[0105] like Figure 1 As shown, a comprehensive adaptability score is calculated to assess the adaptability of the transmission section's operational boundary, including:

[0106] Based on the confusion matrix statistics, the following indicators are calculated:

[0107] (1) Basic statistical indicators

[0108] accuracy

[0109] Recall rate

[0110] False alarm rate

[0111] Accuracy

[0112] False negative rate

[0113] (2) Advanced statistical indicators

[0114] Hidden risk safety level ( ): Calculate the mean of the transient stability index for all samples within the false negative region (FN). and normalize:

[0115]

[0116] Hidden risk stability ( ): Calculate the standard deviation of samples within the false negative region (FN). and normalize:

[0117]

[0118] Area under the ROC curve ( ): Based on the upper limit of power transmission section Scan the variables and calculate the limits under different conditions. and Plot the ROC curve and calculate the area under the curve using the formula. :

[0119]

[0120] The comprehensive adaptability scoring method with all indicators weighted was used for evaluation:

[0121] (1) Indicator Classification Processing: All indicators are converted into a form where larger values ​​are considered better. Among them... and It is a negative indicator and needs to be converted. and The rest are all positive indicators.

[0122] (2) Calculation of comprehensive score: setting weight vector ,satisfy Substitute into the formula to calculate the score. :

[0123]

[0124] (3) Adaptability level determination: preset grading threshold and .

[0125] like The transmission section is deemed to have good adaptability to the operating boundary, and the current boundary is maintained.

[0126] like The transmission section is deemed to have qualified operational boundary adaptability, and the boundary can be finely adjusted according to the actual needs of the power grid.

[0127] like The transmission section was deemed to have unqualified operational boundary adaptability, and the boundary needs to be reconstructed.

[0128] because The current operational boundary adaptability of the transmission section is determined to be qualified.

[0129] As can be seen from the above embodiments, the method of the present invention can systematically and quantitatively evaluate the transient voltage stability adaptability of the transmission section operating boundary in a large number of operating scenarios, considering the uncertainty of new energy output and load. Experimental results show that the comprehensive evaluation system constructed by using confusion matrix, implicit risk indicators, and statistics such as ROC and AUC can effectively reveal the degree of matching between the section power limit and the actual transient stability risk, providing a calculable basis for the rationality analysis of the section operating boundary.

[0130] This invention addresses the difficulty in accurately characterizing the transient voltage stability boundary of transmission sections under conditions of high renewable energy integration. From the perspective of "operational boundary adaptability," it proposes a statistical and quantifiable evaluation method for the operational boundary of transmission sections. Unlike traditional methods that rely solely on a single transient stability margin or static power limit for safety assessment, this invention constructs a two-dimensional discrimination space using section power limits and transient voltage stability indicators. Through a large number of operational scenario samples, it systematically analyzes the boundary effectiveness within this discrimination space, thereby achieving an objective measurement of the quality of the transmission section's operational boundary.

[0131] This invention introduces a multinomial surrogate model and a Monte Carlo sampling mechanism to efficiently construct a two-dimensional sample dataset covering uncertain operating conditions while maintaining the physical consistency of transient simulations. This allows for the full reflection of new energy output fluctuations, load changes, and their correlations during boundary assessment. Furthermore, this invention utilizes a confusion matrix to structurally classify the determination results of cross-sectional boundaries under various operating scenarios, thereby transforming the boundary assessment problem into a computable statistical classification problem.

[0132] This invention not only uses basic statistical indicators such as accuracy and recall to evaluate the overall performance of boundary discrimination, but also addresses the critical issue of "hidden risks" in transmission line operation by introducing risk safety and stability indicators based on the false negative region. This allows for the quantitative characterization of operating states that appear to meet the section limits but actually possess transient instability risks. Furthermore, by scanning the section power limits and constructing ROC curves and AUC indicators, an objective comparison between different boundary schemes is achieved within a unified evaluation framework.

[0133] Ultimately, this invention upgrades the adaptability of transmission section operation boundaries from traditional empirical, single-indicator judgments to a quantifiable, comparable, and decision-making evaluation system through multi-indicator fusion and comprehensive scoring mechanisms. This enables dispatchers and planners to make more refined and verifiable trade-offs between safety and channel utilization. This method is independent of specific grid structures or control strategies, possesses good versatility and engineering applicability, and can provide a new technical approach for the safe operation and boundary management of transmission sections under the background of high-proportion renewable energy integration.

[0134] Example 2

[0135] A system for evaluating the transient voltage stability boundary of a power transmission section is provided to implement the transient voltage stability boundary evaluation method for power transmission sections as described in Example 1, comprising:

[0136] The acquisition module is used to acquire the cross-sectional power limit and transient voltage stability index limit of the transmission section to be evaluated. When the transient voltage stability index is lower than the transient voltage stability index limit, it is determined that transient voltage instability has occurred.

[0137] The sample construction module is used to construct a two-dimensional sample dataset of transmission section power and transient voltage stability index. Specifically, it includes: selecting key operating parameters affecting transient voltage stability, wherein the key operating parameters include at least the output of clean energy units and the system load level, and obtaining the corresponding transmission section power through power flow calculation based on the key operating parameters; fitting the joint probability distribution of the key operating parameters based on historical operating data; selecting matching points in the parameter space of the key operating parameters and obtaining the true value of the transient voltage stability index corresponding to the matching points through power system time-domain simulation, thereby establishing a polynomial approximation function of the transient voltage stability index with respect to the key operating parameters; performing Monte Carlo sampling based on the joint probability distribution to generate multiple sets of operating scenario samples, and using the polynomial approximation function to calculate the transient voltage stability index corresponding to each operating scenario sample, thereby obtaining a two-dimensional sample dataset composed of multiple data points.

[0138] The matrix construction module is used to construct a confusion matrix based on the two-dimensional sample dataset. Specifically, it includes mapping the cross-sectional power and transient voltage stability index of each data point to a two-dimensional plane with cross-sectional power as one dimension and transient voltage stability index as the other dimension, and classifying each data point into four categories: true positives TP, false negatives FN, false positives FP, and true negatives TN according to the cross-sectional power limit and transient voltage stability index limit.

[0139] The indicator calculation module is used to calculate the adaptive evaluation indicators based on the confusion matrix. The adaptive evaluation indicators include at least basic statistical indicators and advanced statistical indicators. The basic statistical indicators include at least accuracy, recall, false positive rate, precision, and false negative rate. The advanced statistical indicators include at least implicit risk safety, implicit risk stability, and area under the ROC curve (AUC).

[0140] The comprehensive evaluation module is used to perform a comprehensive quantitative evaluation of the adaptability evaluation indicators to output the adaptability results of the transmission section operation boundary.

[0141] Example 3

[0142] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the transient voltage stability boundary assessment method for transmission sections as described in Embodiment 1.

[0143] Example 4

[0144] A computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the transient voltage stability boundary assessment method for transmission sections as described in Example 1.

[0145] 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.

[0146] 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, as well as 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. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0147] 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.

[0148] 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.

[0149] Those skilled in the art will understand that all or part of the steps in the above facts and methods can be implemented by a program instructing related hardware. The program or the program described therein can be stored in a computer-readable storage medium. When the program is executed, it includes the following steps: at this time, the corresponding method steps are introduced. The storage medium can be ROM / RAM, magnetic disk, optical disk, etc.

[0150] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for evaluating the transient voltage stability boundary of a power transmission section, characterized in that, include: Obtain the cross-sectional power limit and transient voltage stability index limit of the transmission section to be evaluated, wherein transient voltage instability is determined to have occurred when the transient voltage stability index is lower than the transient voltage stability index limit; A two-dimensional sample dataset of transmission section power and transient voltage stability index is constructed, specifically including: selecting key operating parameters affecting transient voltage stability, the key operating parameters including at least the output of clean energy units and the system load level, and obtaining the corresponding transmission section power through power flow calculation based on the key operating parameters; fitting the joint probability distribution of the key operating parameters based on historical operating data; selecting distribution points in the parameter space of the key operating parameters and obtaining the true value of the transient voltage stability index corresponding to the distribution points through power system time-domain simulation, thereby establishing a polynomial approximation function of the transient voltage stability index with respect to the key operating parameters; performing Monte Carlo sampling based on the joint probability distribution to generate multiple sets of operating scenario samples, and using the polynomial approximation function to calculate the transient voltage stability index corresponding to each operating scenario sample, resulting in a two-dimensional sample dataset composed of multiple data points; Constructing a confusion matrix based on the two-dimensional sample dataset specifically includes: mapping the cross-sectional power and transient voltage stability index of each data point to a two-dimensional plane with cross-sectional power as one dimension and transient voltage stability index as the other dimension, and classifying each data point into four categories: true positives TP, false negatives FN, false positives FP, and true negatives TN according to the cross-sectional power limit and transient voltage stability index limit. The adaptability evaluation index is calculated based on the confusion matrix, and the adaptability evaluation index is comprehensively quantified to output the adaptability results of the transmission section operation boundary.

2. The method for evaluating the transient voltage stability boundary of a transmission section according to claim 1, characterized in that, The adaptive evaluation metrics include at least basic statistical metrics and advanced statistical metrics, wherein: basic statistical metrics include at least accuracy, recall, false positive rate, precision, and false negative rate; advanced statistical metrics include at least implicit risk safety, implicit risk stability, and area under the ROC curve (AUC).

3. The method for evaluating the transient voltage stability boundary of a transmission section according to claim 2, characterized in that, The implicit risk safety factor is obtained by normalizing the mean of the transient voltage stability index within the false negative example FN and combining it with the physical critical value. The implicit risk stability factor is obtained by normalizing the standard deviation of the transient voltage stability index within the false negative example FN and combining it with the physical critical value and the exponential function. The AUC is obtained by scanning the cross-sectional power limit within a preset range and calculating the false alarm rate and recall rate corresponding to different scanning points to form an ROC curve and calculating the area under the curve.

4. The method for evaluating the transient voltage stability boundary of a transmission section according to claim 1, characterized in that, The classification of true negative cases (TP), false negative cases (FN), false positive cases (FP), and true negative cases (TN) includes: when the cross-sectional power is greater than the cross-sectional power limit and the transient voltage stability index is lower than the transient voltage stability index limit, it is classified as a true negative case (TP); when the cross-sectional power is less than or equal to the cross-sectional power limit and the transient voltage stability index is lower than the transient voltage stability index limit, it is classified as a false negative case (FN); when the cross-sectional power is greater than the cross-sectional power limit and the transient voltage stability index is greater than or equal to the transient voltage stability index limit, it is classified as a false positive case (FP); when the cross-sectional power is less than or equal to the cross-sectional power limit and the transient voltage stability index is greater than or equal to the transient voltage stability index limit, it is classified as a true negative case (TN).

5. The method for evaluating the transient voltage stability boundary of a transmission section according to claim 1, characterized in that, The establishment of a polynomial approximation function for the transient voltage stability index with respect to the key operating parameters includes: determining the coefficients of the polynomial approximation function using the least squares method, and evaluating the approximation error based on cross-validation or leave-one-out method; when the approximation error is greater than a preset error threshold, increasing the polynomial order and / or increasing the number of collocation points before refitting.

6. The method for evaluating the transient voltage stability boundary of a transmission section according to claim 1, characterized in that, The adaptive evaluation index is comprehensively and quantitatively evaluated to output the adaptive result of the transmission section operation boundary. Specifically, this includes: dividing the adaptive evaluation index into positive and negative indicators and converting the negative indicators into a positive form where larger values ​​are better; setting the weight coefficients of each positive indicator and constructing a linear weighted evaluation function to calculate the comprehensive adaptive score S; comparing the comprehensive adaptive score S with a preset grading threshold to output the adaptive level determination result of the transmission section operation boundary.

7. The method for evaluating the transient voltage stability boundary of a transmission section according to claim 6, characterized in that, The calculation of the comprehensive adaptability score S includes: performing dimensionless processing on each positive indicator and then performing a weighted summation; and the grading threshold includes at least a good threshold and a qualified threshold, wherein when S is not less than the good threshold, it is judged as good; when S is less than the good threshold but not less than the qualified threshold, it is judged as qualified; and when S is less than the qualified threshold, it is judged as unqualified.

8. A transient voltage stability boundary assessment system for power transmission sections, characterized in that, The method for evaluating the transient voltage stability boundary of a transmission section as described in any one of claims 1 to 7 includes: The acquisition module is used to acquire the cross-sectional power limit and transient voltage stability index limit of the transmission section to be evaluated. When the transient voltage stability index is lower than the transient voltage stability index limit, it is determined that transient voltage instability has occurred. The sample construction module is used to construct a two-dimensional sample dataset of transmission section power and transient voltage stability index. Specifically, it includes: selecting key operating parameters affecting transient voltage stability, wherein the key operating parameters include at least the output of clean energy units and the system load level, and obtaining the corresponding transmission section power through power flow calculation based on the key operating parameters; fitting the joint probability distribution of the key operating parameters based on historical operating data; selecting matching points in the parameter space of the key operating parameters and obtaining the true value of the transient voltage stability index corresponding to the matching points through power system time-domain simulation, thereby establishing a polynomial approximation function of the transient voltage stability index with respect to the key operating parameters; performing Monte Carlo sampling based on the joint probability distribution to generate multiple sets of operating scenario samples, and using the polynomial approximation function to calculate the transient voltage stability index corresponding to each operating scenario sample, thereby obtaining a two-dimensional sample dataset composed of multiple data points. The matrix construction module is used to construct a confusion matrix based on the two-dimensional sample dataset. Specifically, it includes mapping the cross-sectional power and transient voltage stability index of each data point to a two-dimensional plane with cross-sectional power as one dimension and transient voltage stability index as the other dimension, and classifying each data point into four categories: true positives TP, false negatives FN, false positives FP, and true negatives TN according to the cross-sectional power limit and transient voltage stability index limit. The indicator calculation module is used to calculate the adaptive evaluation indicators based on the confusion matrix. The adaptive evaluation indicators include at least basic statistical indicators and advanced statistical indicators. The basic statistical indicators include at least accuracy, recall, false positive rate, precision, and false negative rate. The advanced statistical indicators include at least implicit risk safety, implicit risk stability, and area under the ROC curve (AUC). The comprehensive evaluation module is used to perform a comprehensive quantitative evaluation of the adaptability evaluation indicators to output the adaptability results of the transmission section operation boundary.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the transient voltage stability boundary assessment method for transmission sections as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the transient voltage stability boundary assessment method for transmission sections as described in any one of claims 1 to 7.

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