A power stress test method and system for load transfer path of power distribution network

Abstract

This invention relates to the field of stress testing technology, and discloses a method and system for power stress testing of a distribution network load transfer path. The method includes: acquiring the static impedance parameters and real-time operating parameters of the initial path in the distribution network load transfer path; performing linear growth programming on the static impedance parameters and real-time operating parameters to obtain a power loading strategy; applying pressure to the transmission nodes of the target path step by step based on the power loading strategy to obtain a dynamic stress distribution; simultaneously monitoring the electrical stress parameters and thermal stress parameters of the target path according to the dynamic stress distribution; performing dual threshold evaluation on the electrical safety margin and thermal stability margin of the target path based on the electrical stress parameters and thermal stress parameters to obtain a margin exceedance judgment conclusion; and performing multi-dimensional monitoring on the margin exceedance judgment conclusion to determine the power carrying capacity limit of the target path. This invention can improve the accuracy of power stress testing of a distribution network load transfer path.

Description

Technical Field

[0001] This invention relates to the field of stress testing technology, and in particular to a power stress testing method and system for load transfer paths in a power distribution network. Background Technology

[0002] In the field of power stress testing for load transfer paths in distribution networks, existing technologies lack scientific rigor in integrating test parameters and formulating power loading strategies. Traditional methods for acquiring path parameters rely solely on collecting static impedance data or real-time operational data without coordinating their analysis. This results in a one-sided view of the parameters, failing to fully reflect the actual operational characteristics of the path. Furthermore, when formulating power loading strategies, the lack of weighting and multi-dimensional integration of parameters based on distribution network operational safety criteria, coupled with the application of a fixed power growth pattern, easily leads to unreasonable loading rates. This not only affects test accuracy but may also cause additional damage to path equipment.

[0003] Existing technologies have significant shortcomings in stress monitoring and power carrying capacity limit determination. In the stress monitoring stage, electrical or thermal stress parameters are often monitored separately without a synchronous monitoring mechanism, failing to capture the dynamic correlation between parameters and resulting in monitoring data that cannot comprehensively reflect the stress state of the power carrying capacity path. When determining the power carrying capacity limit, assessments are based solely on a single margin threshold without multi-dimensional verification and time-dimensional analysis of the margin exceeding the limit. This makes it difficult to accurately identify the critical carrying capacity state of the power carrying capacity path, leading to a significant deviation between the determined power carrying capacity limit and the actual situation, and failing to provide a reliable basis for the safe operation of power transfer paths in the distribution network. Summary of the Invention

[0004] This invention provides a power stress testing method and system for load transfer paths in a distribution network to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides a power stress testing method for load transfer paths in a distribution network, comprising:

[0006] S1. Obtain the static impedance parameters and real-time operating parameters of the initial path in the load transfer path of the distribution network;

[0007] S2. Perform linear growth planning on the static impedance parameter and the real-time operating parameter to obtain the power loading strategy of the initial path;

[0008] S3. Based on the power loading strategy, apply pressure to the transmission nodes of the target path step by step to obtain the dynamic stress distribution of the target path;

[0009] S4. Based on the dynamic stress distribution, simultaneously monitor the electrical stress parameters and thermal stress parameters of the target path;

[0010] S5. Based on the electrical stress parameters and thermal stress parameters, perform a dual threshold evaluation on the electrical safety margin and thermal stability margin of the target path to obtain a margin exceeding the limit judgment conclusion of the target path;

[0011] S6. Perform multi-dimensional monitoring on the margin exceedance judgment conclusion to determine the power carrying capacity limit of the target path.

[0012] In a preferred embodiment, obtaining the static impedance parameters and real-time operating parameters of the initial path in the distribution network load transfer path includes:

[0013] The inherent characteristic records of the load transfer path in the distribution network are analyzed to obtain the static impedance parameters of the initial path.

[0014] Feature extraction is performed on the real-time monitoring data of the distribution network load transfer path to obtain the real-time operating parameters of the initial path.

[0015] In a preferred embodiment, the step of performing linear growth programming on the static impedance parameter and the real-time operating parameters to obtain the power loading strategy for the initial path includes:

[0016] The static impedance parameters and the real-time operating parameters are combined to convert the impedance change rate parameter and operating trend parameter of the initial path;

[0017] The impedance change rate parameter and the running trend parameter are subjected to trend quantization processing to obtain the impedance change gradient and running trend intensity of the initial path;

[0018] According to the preset distribution network operation safety criteria, different weighting coefficients are assigned to the impedance change gradient and the operation trend intensity to obtain the first weighting coefficient of the impedance change gradient and the second weighting coefficient of the operation trend intensity.

[0019] Based on the first weighting coefficient and the second weighting coefficient, the impedance change gradient and the running trend intensity are fused in multiple dimensions to obtain the comprehensive planning parameters of the initial path;

[0020] The power growth sequence of the initial path is obtained by linearly expanding the integrated planning parameters.

[0021] A progressive loading scheme is formulated based on the power growth sequence to obtain the power loading strategy for the initial path.

[0022] In a preferred embodiment, the step of multi-dimensionally fusing the impedance change gradient and the running trend intensity based on the first weighting coefficient and the second weighting coefficient to obtain the comprehensive planning parameters of the initial path includes:

[0023] The impedance change gradient is scaled with the first weighting coefficient to obtain the impedance influence factor of the impedance change gradient.

[0024] The trend intensity is weighted by the second weighting coefficient to obtain the trend influence factor of the trend intensity.

[0025] The impedance influence factor and the trend influence factor are coupled in a multi-dimensional manner to obtain the preliminary fusion parameters of the initial path. The calculation formula for the preliminary fusion parameters is as follows:

[0026] ;

[0027] In the formula, The initial fusion parameters are... The impedance influence factor is... The trend influencing factor, These are the coupling weight coefficients. This is the difference adjustment coefficient. The stabilization coefficient;

[0028] The preliminary fusion parameters are normalized to obtain the comprehensive planning parameters of the initial path.

[0029] In a preferred embodiment, the step of applying pressure to the transmission nodes of the target path step by step based on the power loading strategy to obtain the dynamic stress distribution of the target path includes:

[0030] The power loading strategy is parsed step by step to obtain the staged loading instruction sequence of the power loading strategy;

[0031] According to the phased loading instruction sequence, the transmission nodes of the target path are loaded in stages to obtain the node response data of the target path;

[0032] Dynamic feature extraction is performed on the node response data to obtain the node dynamic response features of the target path;

[0033] The dynamic response characteristics of the nodes are subjected to multimodal evolution to obtain the stress evolution spectrum of the target path;

[0034] The stress evolution spectrum is analyzed to generate the dynamic stress distribution of the target path.

[0035] In a preferred embodiment, the step of synchronously monitoring the electrical stress parameters and thermal stress parameters of the target path based on the dynamic stress distribution includes:

[0036] Electrical and thermal characteristics are extracted from the dynamic stress distribution to obtain the initial monitoring data of the dynamic stress distribution;

[0037] Real-time feature identification is performed on the initial monitoring data to obtain the electrical feature vector and thermal feature vector of the initial monitoring data;

[0038] Based on the dynamic correlation between the electrical feature vector and the thermal feature vector, a synchronous monitoring matrix for the dynamic stress distribution is constructed.

[0039] Based on the synchronous monitoring matrix, the target path is subjected to parallel acquisition of two parameters to obtain the electrical stress parameters and thermal stress parameters of the target path.

[0040] In a preferred embodiment, the step of performing a dual threshold assessment of the electrical safety margin and thermal stability margin of the target path based on the electrical stress parameters and thermal stress parameters to obtain a margin exceedance determination conclusion for the target path includes:

[0041] The electrical stress parameters are normalized to obtain the electrical safety index of the electrical stress parameters;

[0042] The thermal stress parameters are standardized and reconstructed to obtain the thermal stability index of the thermal stress parameters.

[0043] Based on preset electrical safety thresholds and thermal stability thresholds, a dual comparison analysis is performed on the electrical safety index and thermal stability index to obtain the margin state evaluation matrix of the target path.

[0044] Cross-validation is performed on the margin state evaluation matrix to obtain the margin exceedance determination conclusion of the target path.

[0045] In a preferred embodiment, the step of performing a dual comparative analysis on the electrical safety index and the thermal stability index based on preset electrical safety thresholds and thermal stability thresholds to obtain the margin state assessment matrix of the target path includes:

[0046] The electrical safety index is dynamically compared with a preset electrical safety threshold to obtain an electrical safety margin assessment vector for the electrical safety index.

[0047] The thermal stability index is compared with a preset thermal stability threshold to obtain the thermal stability margin evaluation vector of the thermal stability index.

[0048] The electrical safety margin assessment vector and the thermal stability margin assessment vector are matrix-combined to obtain the initial assessment matrix of the target path, wherein the calculation formula of the initial assessment matrix is ​​as follows:

[0049] ;

[0050] In the formula, The initial evaluation matrix is... The coupling coefficient is... Let be the electrical safety margin assessment vector. This is the transpose operation for a vector. This is the difference adjustment coefficient. Let be the thermal stability margin evaluation vector. For absolute difference vectors, The stabilization coefficient is... For element-wise multiplication;

[0051] The initial evaluation matrix is ​​normalized to obtain the margin state evaluation matrix of the target path.

[0052] In a preferred embodiment, the step of performing multi-dimensional monitoring on the margin exceedance determination conclusion to determine the power carrying capacity limit of the target path includes:

[0053] The time dimension is analyzed to obtain the time series state data of the target path from the margin exceedance judgment conclusion;

[0054] The time-series state data is processed by pattern recognition to obtain the load-bearing characteristic pattern of the target path;

[0055] Limit state deduction is performed on the load-bearing characteristic mode to obtain the critical load-bearing parameters of the target path;

[0056] The critical load-bearing parameters are subjected to safety calibration to determine the power load-bearing limit of the target path.

[0057] To address the aforementioned problems, the present invention also provides a power stress testing system for load transfer paths in a distribution network, the system comprising:

[0058] The parameter acquisition module is used to acquire the static impedance parameters and real-time operating parameters of the initial path in the load transfer path of the distribution network.

[0059] The strategy loading module is used to perform linear growth planning on the static impedance parameters and the real-time operating parameters to obtain the power loading strategy of the initial path;

[0060] The stress distribution module is used to apply pressure to the transmission nodes of the target path step by step based on the power loading strategy to obtain the dynamic stress distribution of the target path.

[0061] The synchronous monitoring module is used to synchronously monitor the electrical stress parameters and thermal stress parameters of the target path according to the dynamic stress distribution.

[0062] The threshold evaluation module is used to perform dual threshold evaluation on the electrical safety margin and thermal stability margin of the target path based on the electrical stress parameters and thermal stress parameters, and obtain the margin exceeding the limit judgment conclusion of the target path.

[0063] The limit load module is used to perform multi-dimensional monitoring of the margin exceedance judgment conclusion and determine the power load limit of the target path.

[0064] Compared with the prior art, the present invention has the following beneficial effects:

[0065] 1. This invention obtains the static impedance parameters and real-time operating parameters of the load transfer path of the distribution network in a collaborative manner, achieves multi-dimensional parameter fusion through linear growth programming, formulates a scientific progressive power loading strategy, and then applies pressure to the transmission nodes of the target path step by step to obtain the dynamic stress distribution, while simultaneously monitoring electrical stress parameters and thermal stress parameters. This allows the test data to cover the inherent characteristics of the path and the real-time operating status, with more comprehensive parameter dimensions, more controllable loading process, and more complete monitoring results, significantly improving the accuracy of power stress testing and providing solid and comprehensive data support for subsequent margin assessment.

[0066] 2. This invention assesses electrical safety margin and thermal stability margin based on dual thresholds, and deeply analyzes the margin over-limit judgment conclusions through multi-dimensional monitoring. By analyzing the time dimension and extrapolating the extreme state, it accurately determines the power carrying capacity limit of the target path, so that the judgment result can fully reflect the actual carrying capacity of the path. The determined power carrying capacity limit is more in line with the actual situation, providing a reliable basis for the safe operation of the load transfer path of the distribution network, effectively ensuring the safety and stability of the distribution network operation, and enhancing the practical value of the testing technology. Attached Figure Description

[0067] Figure 1 This is a flowchart illustrating a power stress test method for a power distribution network load transfer path according to an embodiment of the present invention.

[0068] Figure 2 This is a functional block diagram of a power stress testing system for a power transfer path in a distribution network, provided in an embodiment of the present invention.

[0069] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0070] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0071] This application provides a power stress test method for a distribution network load transfer path. The execution entity of this power stress test method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the power stress test method for a distribution network load transfer path can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

[0072] Reference Figure 1 The diagram shown is a flowchart illustrating a power stress testing method for a distribution network load transfer path according to an embodiment of the present invention. In this embodiment, the power stress testing method for a distribution network load transfer path includes:

[0073] S1. Obtain the static impedance parameters and real-time operating parameters of the initial path in the load transfer path of the distribution network;

[0074] In this embodiment of the invention, obtaining the static impedance parameters and real-time operating parameters of the initial path in the distribution network load transfer path includes:

[0075] The inherent characteristic records of the load transfer path in the distribution network are analyzed to obtain the static impedance parameters of the initial path.

[0076] Feature extraction is performed on the real-time monitoring data of the distribution network load transfer path to obtain the real-time operating parameters of the initial path.

[0077] Specifically, the inherent characteristic records of the load transfer path of the distribution network are retrieved from the distribution network management system. These records contain fixed attribute information such as line material, conductor cross-sectional area, line length, tower type and layout during the construction of the path. Through a combination of manual review and system data verification, duplicate or erroneous information is eliminated to ensure the completeness and accuracy of the records. Then, based on the static impedance parameter calculation logic, combined with the resistivity, conductor cross-sectional area, line length and other information corresponding to the line material, the line resistance component and inductance component are determined. After integration, the static impedance parameters of the initial path are obtained.

[0078] Furthermore, voltage sensors, current sensors, and power sensors are installed at key nodes along the load transfer path of the distribution network. The sensors acquire voltage, current, and power data of the path operation at a preset acquisition frequency, and transmit them to the distribution network data monitoring platform via a wireless communication module. The platform filters and removes abnormal data caused by sensor failures, classifies and organizes voltage, current, and power sequences, calculates derived data such as voltage deviation, current fluctuation amplitude, and power factor, and integrates them to obtain the real-time operating parameters of the initial path.

[0079] In summary, analyzing inherent characteristic records to obtain static impedance parameters can accurately extract fixed attribute information such as line material and length. Data verification ensures the accuracy of parameters, providing a reliable static basis for testing and preventing subsequent strategies from deviating from the inherent characteristics of the path.

[0080] In summary, by extracting real-time monitoring data to obtain real-time operating parameters, collecting dynamic data through sensors and filtering and organizing it, the path conditions can be reflected in real time, making up for the shortcomings of traditional methods that rely solely on static data, and providing dynamic basis for strategy formulation.

[0081] In summary, the simultaneous acquisition of two types of parameters enables data fusion, breaking the limitations of single data and allowing subsequent planning to take into account both the inherent characteristics of the path and the real-time status, thus laying a data foundation for improving the accuracy of power stress testing.

[0082] S2. Perform linear growth planning on the static impedance parameter and the real-time operating parameter to obtain the power loading strategy of the initial path;

[0083] In this embodiment of the invention, the step of performing linear growth planning on the static impedance parameter and the real-time operating parameters to obtain the power loading strategy for the initial path includes:

[0084] The static impedance parameters and the real-time operating parameters are combined to convert the impedance change rate parameter and operating trend parameter of the initial path;

[0085] The impedance change rate parameter and the running trend parameter are subjected to trend quantization processing to obtain the impedance change gradient and running trend intensity of the initial path;

[0086] According to the preset distribution network operation safety criteria, different weighting coefficients are assigned to the impedance change gradient and the operation trend intensity to obtain the first weighting coefficient of the impedance change gradient and the second weighting coefficient of the operation trend intensity.

[0087] Based on the first weighting coefficient and the second weighting coefficient, the impedance change gradient and the running trend intensity are fused in multiple dimensions to obtain the comprehensive planning parameters of the initial path;

[0088] The power growth sequence of the initial path is obtained by linearly expanding the integrated planning parameters.

[0089] A progressive loading scheme is formulated based on the power growth sequence to obtain the power loading strategy for the initial path.

[0090] The step of fusing the impedance change gradient and the running trend intensity from multiple dimensions based on the first weighting coefficient and the second weighting coefficient to obtain the comprehensive planning parameters of the initial path includes:

[0091] The impedance change gradient is scaled with the first weighting coefficient to obtain the impedance influence factor of the impedance change gradient.

[0092] The trend intensity is weighted by the second weighting coefficient to obtain the trend influence factor of the trend intensity.

[0093] The impedance influence factor and the trend influence factor are coupled in a multi-dimensional manner to obtain the preliminary fusion parameters of the initial path. The calculation formula for the preliminary fusion parameters is as follows:

[0094] ;

[0095] In the formula, The initial fusion parameters are... The impedance influence factor is... The trend influencing factor, These are the coupling weight coefficients. This is the difference adjustment coefficient. The stabilization coefficient;

[0096] The preliminary fusion parameters are normalized to obtain the comprehensive planning parameters of the initial path.

[0097] Specifically, the static impedance parameters and real-time operating parameters of the initial path are retrieved from the distribution network data storage module. The resistance and inductance components of the static impedance parameters are correlated with the voltage and current sequences of the real-time operating parameters. By analyzing the correspondence between the static impedance parameters and the real-time operating parameters under different operating periods, the change value of the static impedance parameters per unit time is calculated, and this value is defined as the impedance change rate parameter of the initial path. At the same time, the trend analysis of the power sequence, voltage deviation, and current fluctuation amplitude in the real-time operating parameters is performed over a continuous period to determine whether these parameters are rising, falling, or stable. This state characteristic is defined as the operating trend parameter of the initial path.

[0098] Furthermore, path data with the same type and similar operating environment as the current initial path in the historical operation database of the distribution network are selected as reference samples. The actual change amplitude corresponding to the impedance change rate parameter and the actual state intensity corresponding to the operating trend parameter in the reference samples are extracted. The correspondence between the reference sample data and the quantization standard is established. For example, the maximum change amplitude of the impedance change rate parameter in the reference samples corresponds to the quantization value 10 and the minimum change amplitude corresponds to the quantization value 1. The impedance change rate parameter of the current initial path is numerically converted according to this correspondence to obtain the impedance change gradient of the initial path. Similarly, the strongest state intensity of the operating trend parameter in the reference samples corresponds to the quantization value 10 and the weakest state intensity corresponds to the quantization value 1. The operating trend parameter of the current initial path is numerically converted according to this standard to obtain the operating trend intensity of the initial path.

[0099] Furthermore, the pre-set distribution network operation safety criteria document is retrieved. This document clearly stipulates the degree of impact of impedance changes and operating trends on path safety under different operating scenarios. For example, when the distribution network is in a peak load operation scenario, the impact of impedance changes on safety has a higher weight than that of operating trends. When it is in a stable load operation scenario, the impact of operating trends on safety has a higher weight than that of impedance changes. Based on the actual operating scenario of the current distribution network, the weight allocation standard of impedance changes and operating trends under the corresponding scenario is extracted from the safety criteria document. According to this standard, specific values ​​are assigned to the impedance change gradient of the initial path as the first weight coefficient, and specific values ​​are assigned to the intensity of the operating trend as the second weight coefficient.

[0100] Furthermore, the impedance change gradient of the initial path is multiplied by the first weighting coefficient to obtain a weighted result of the impedance change gradient; simultaneously, the operating trend strength is multiplied by the second weighting coefficient to obtain a weighted result of the operating trend strength; then, these two weighted results are summed, and the summed value is used as the core planning basis. Combined with basic attribute information such as the line length and rated load of the initial path, the summed result is supplemented and adjusted. For example, when the line length is long, the summed result is appropriately increased; when the rated load is low, the summed result is appropriately decreased, and finally, the comprehensive planning parameters of the initial path are obtained.

[0101] Furthermore, a preset time period for the distribution network load transfer is determined, and this time period is evenly divided into multiple equal time intervals. Using the comprehensive planning parameters of the initial path as the starting base value, the power growth value corresponding to each time interval is calculated according to the principle of uniform power growth within each time interval. For example, if the preset time period is 10 hours, it is divided into 10 time intervals of 1 hour each. If the comprehensive planning parameter is 100kW, then the power growth value of each time interval is 10kW. The power values ​​corresponding to each time interval are arranged in chronological order to form the power growth sequence of the initial path.

[0102] Furthermore, based on the power growth sequence of the initial path, the power value to be loaded in each time interval is determined. Combined with the operating requirements of the load control equipment of the distribution network, specific operation steps for loading power at each step are formulated. For example, in the first time interval, the power of the path is increased from the current initial value to the power value corresponding to the first time interval through the voltage regulator, while the reactive power compensation device is turned on to maintain voltage stability. In each subsequent time interval, similar operations are repeated to gradually increase the power to the final value of the power growth sequence. This scheme, which includes time intervals, power values, and equipment operation steps, is documented to obtain the power loading strategy for the initial path.

[0103] Specifically, the impedance change gradient and the first weighting coefficient of the initial path are obtained. The value of the impedance change gradient is multiplied by the value of the first weighting coefficient to obtain the product. The product is then compared with the preset impedance scaling reference value of the distribution network. According to the preset scaling conversion rules, the product is converted into a value that conforms to the scaling range. The converted value is the impedance influence factor of the impedance change gradient.

[0104] Furthermore, the running trend strength and the second weight coefficient of the initial path are obtained. The value of the running trend strength is multiplied by the value of the second weight coefficient to directly obtain the product result. This product result does not need to be converted and can be directly used as the trend influence factor of the running trend strength.

[0105] Furthermore, the impedance influence factor and trend influence factor are incorporated into the multi-dimensional coupled analysis model of the distribution network. This model contains two input ports, corresponding to the impedance influence factor and the trend influence factor, respectively. The model has a coupling logic set up inside, that is, when the impedance influence factor increases, it will proportionally enhance the effective effect of the trend influence factor, and conversely, when the impedance influence factor decreases, the effective effect of the trend influence factor will proportionally weaken. Through the internal calculation of the model, the effects of the two factors are superimposed and fused to output a comprehensive value, which is the preliminary fusion parameter of the initial path.

[0106] Furthermore, a preset normalization range for the distribution network planning parameters is determined, and the values ​​of the preliminary fusion parameters are matched with this range. The relative position of the preliminary fusion parameters within the preset range is calculated, and the value of this relative position is used as the result of the normalization process, which is the comprehensive planning parameter for the initial path.

[0107] Specifically, in the calculation formula of the preliminary fusion parameters, the impedance influence factor comes from the result of scaling the impedance change gradient of the initial path and the first weighting coefficient. Specifically, the value of the impedance change gradient is multiplied by the value of the first weighting coefficient, and then combined with the impedance scaling reference value and scaling conversion rules preset by the distribution network, the product result is converted into a value that conforms to the scaling range. This value is the impedance influence factor.

[0108] Furthermore, the trend influence factor is derived from the weighted processing of the trend strength of the initial path and the second weight coefficient. Specifically, the value of the trend strength is multiplied by the value of the second weight coefficient, and the product is directly used as the trend influence factor without any additional conversion steps.

[0109] Furthermore, the coupling weight coefficient is pre-set based on the operational safety requirements of the distribution network and the multi-dimensional integration objectives. During the distribution network planning and design phase, technicians will combine the actual effects of impedance influence factors and trend influence factors on path planning in historical operational data to determine a fixed coupling weight coefficient, which is used to adjust the overall influence strength of the two influence factors in the integration process.

[0110] Furthermore, the difference adjustment coefficient is set based on the common difference range between impedance influence factor and trend influence factor in distribution network operation. Technicians will statistically analyze the distribution of the difference between the two influence factors in a large amount of historical data, and set a fixed difference adjustment coefficient according to the degree of interference of the difference on the fusion result, in order to balance the impact of the difference between the two influence factors on the initial fusion parameters.

[0111] Furthermore, the stabilization coefficient is set to prevent the denominator of the formula from approaching zero due to the small difference between the impedance influence factor and the trend influence factor, which would cause abnormal fluctuations in the initial fusion parameters. During the commissioning phase of the distribution network system, technicians will conduct multiple simulation experiments to determine a fixed stabilization coefficient to ensure that the denominator of the formula always remains within a reasonable range, thus guaranteeing the stability of the initial fusion parameters.

[0112] Furthermore, the significance of this formula lies in achieving a scientific fusion of the impedance influence factor and the trend influence factor to obtain preliminary fusion parameters for the initial path. By using a coupling weighting coefficient to strengthen the synergistic effect of the two influence factors, a difference adjustment coefficient to balance the interference of the differences between the two influence factors on the fusion result, and a stabilization coefficient to avoid abnormal values ​​during the fusion process, the formula ultimately integrates the information of the two influence factors into a comprehensive preliminary fusion parameter. This provides a foundation for obtaining comprehensive planning parameters, ensuring that the comprehensive planning parameters fully reflect the impact of impedance changes and operating trends of the initial path on the power loading strategy.

[0113] Furthermore, from the perspective of formula trends, when the impedance influence factor or trend influence factor increases, if the other influence factor remains unchanged, the value of the numerator of the formula will increase accordingly, and if the value of the denominator remains unchanged, the value of the preliminary fusion parameter will increase accordingly. When the difference between the impedance influence factor and the trend influence factor increases, the value of the denominator of the formula will increase accordingly, and if the value of the numerator remains unchanged, the value of the preliminary fusion parameter will decrease accordingly. When the difference between the impedance influence factor and the trend influence factor decreases, the value of the denominator of the formula will decrease accordingly, and if the value of the numerator remains unchanged, the value of the preliminary fusion parameter will increase accordingly. However, the coupling weight coefficient, difference adjustment coefficient, and stabilization coefficient are fixed values ​​and will not change with the changes in the impedance influence factor and trend influence factor. They only play a fixed adjustment role on the trend of the preliminary fusion parameter and do not affect the overall direction of parameter change.

[0114] In summary, by coordinating static and real-time parameters into impedance change rate and operational trend parameters, the two types of parameters are correlated and integrated, avoiding the one-sidedness of single-parameter analysis. This approach can more comprehensively reflect the dynamic changes in path impedance and the overall operational trend, providing accurate basic data for subsequent quantitative processing.

[0115] In summary, trend quantification of the two types of parameters yields the impedance change gradient and the intensity of the operational trend, transforming abstract parameter changes into quantifiable feature indicators. This makes the analysis of path operation status more intuitive and provides a clear quantitative basis for weight allocation and multi-dimensional integration.

[0116] In summary, weighting coefficients are allocated according to the safety criteria for distribution network operation, so that the weighting settings meet the actual safety operation requirements, ensure that the impact of impedance changes and operating trends on strategy formulation is reasonable, and avoid the strategy deviating from the safety standard due to weight imbalance.

[0117] In summary, multi-dimensional integration yields comprehensive planning parameters, integrates the key influences of impedance and operating trends, and forms a core planning basis that can take into account the various characteristics of the path. This provides scientific guidance for the linear expansion generation of power growth sequences and ensures that the sequences conform to the actual carrying capacity of the path.

[0118] In summary, linear expansion yields a power growth sequence and allows for a progressive loading scheme, enabling power loading to proceed in an orderly and controllable manner. This avoids the unreasonable loading rate issues that may arise from traditional fixed power growth modes, reduces additional losses to path devices, and improves the accuracy of power stress testing.

[0119] In summary, scaling the impedance change gradient and the first weighting coefficient to obtain the impedance influence factor can standardize the impedance change gradient according to the weights, transforming the impedance-related influence into a factor that conforms to a unified analysis standard. This avoids analytical biases caused by differences in parameter dimensions or numerical ranges, and ensures that it has a basis for synergistic analysis with subsequent trend influence factors.

[0120] In summary, the trend influence factor is obtained by weighting the trend strength and the second weight coefficient. This can strengthen the role of the trend strength in the fusion process. Furthermore, the weight is adapted to the second weight coefficient set in the early stage based on the safety criteria, ensuring that the influence of the trend on the comprehensive planning is reasonable and forms a corresponding and balanced analysis dimension with the impedance influence factor.

[0121] In summary, preliminary fusion parameters are obtained by coupling the two types of influencing factors in multiple dimensions using a formula that includes coupling weight coefficient, difference adjustment coefficient, and stabilization coefficient. The coupling weight coefficient can enhance the synergistic effect of the two factors, the difference adjustment coefficient can balance the interference of differences between factors on the fusion results, and the stabilization coefficient can avoid parameter anomalies caused by an excessively small denominator. This achieves the scientific integration of the two types of factors and ensures that the preliminary fusion parameters can accurately reflect the comprehensive influence of path impedance and operating trend.

[0122] In summary, normalizing the initial fusion parameters to obtain comprehensive planning parameters can transform them into values ​​that conform to the standard range of distribution network planning, eliminating the impact of differences in absolute parameter values. This makes the comprehensive planning parameters practically applicable in directly guiding the generation of subsequent power growth sequences, providing a precise core basis for formulating power loading strategies that fit the actual characteristics of the path.

[0123] S3. Based on the power loading strategy, apply pressure to the transmission nodes of the target path step by step to obtain the dynamic stress distribution of the target path;

[0124] In this embodiment of the invention, the step of applying pressure to the transmission nodes of the target path step by step based on the power loading strategy to obtain the dynamic stress distribution of the target path includes:

[0125] The power loading strategy is parsed step by step to obtain the staged loading instruction sequence of the power loading strategy;

[0126] According to the phased loading instruction sequence, the transmission nodes of the target path are loaded in stages to obtain the node response data of the target path;

[0127] Dynamic feature extraction is performed on the node response data to obtain the node dynamic response features of the target path;

[0128] The dynamic response characteristics of the nodes are subjected to multimodal evolution to obtain the stress evolution spectrum of the target path;

[0129] The stress evolution spectrum is analyzed to generate the dynamic stress distribution of the target path.

[0130] Specifically, the generated power loading strategy is retrieved from the storage module. This strategy contains power values ​​and equipment operation steps corresponding to multiple time intervals. These contents are decomposed in chronological order, and the power value of each time interval and the corresponding equipment operation steps are combined into an independent loading instruction. For example, the power value in a certain time interval is combined with the specific operation requirements of the voltage regulator and reactive power compensation device into an instruction. All loading instructions arranged in chronological order are organized into an ordered set to obtain the phased loading instruction sequence of the power loading strategy.

[0131] Furthermore, based on the phased loading instruction sequence, starting from the first loading instruction, power loading operations are performed on the transmission nodes of the target path according to the power values ​​and equipment operation steps specified in the instruction. After the first instruction is completed, the current operating status of the transmission node is recorded, and the above operation is repeated according to the second loading instruction until all loading instructions are executed. Throughout the process, sensors installed on the transmission nodes collect data such as voltage fluctuations, current changes, and temperature rises in real time at different loading stages. These data are classified and organized according to the loading stage to obtain the node response data of the target path.

[0132] Furthermore, the node response data is classified into different types, such as voltage fluctuation data, current change data, and temperature rise data. For each type of data, features that change with the loading stage are extracted, such as the change in fluctuation amplitude in voltage fluctuation data, the time point of peak occurrence in current change data, and the rate of temperature rise in temperature rise data. These features extracted from different types of data are summarized to form a comprehensive feature set that can reflect the dynamic changes of transmission nodes during power loading, thus obtaining the node dynamic response features of the target path.

[0133] Furthermore, the voltage fluctuation characteristics, current change characteristics, and temperature rise characteristics in the node dynamic response characteristics are input into different input channels of the preset multimodal evolution model. The model has feature association rules set up inside, which can identify the intrinsic relationship between different features, such as the corresponding change law of the current change peak when the voltage fluctuation amplitude increases. The model performs synchronous evolution analysis on these features and presents the change process of the features with the loading stage in a graphical form. The horizontal axis of the graph represents the loading stage, and the vertical axis corresponds to the degree of change of different features, forming a stress evolution map of the target path.

[0134] Furthermore, the stress evolution map is imported into the map analysis system. According to the preset stress identification rules, the system converts the graphical changes corresponding to the characteristics of voltage fluctuations, current changes, and temperature rises in different loading stages in the map into specific stress values. For example, the graphical height corresponding to the voltage fluctuation amplitude in a certain loading stage is converted into the voltage stress value of the transmission node in that stage. Then, the stress values ​​of different transmission nodes in each loading stage are arranged according to the node position and loading stage to form a three-dimensional distribution table containing the node position, loading stage, and stress value. This distribution table is the dynamic stress distribution of the target path.

[0135] In summary, by parsing the power loading strategy step by step to obtain a staged loading instruction sequence, the abstract strategy can be decomposed into independent instructions arranged in chronological order, containing specific power values ​​and equipment operation steps. This makes the subsequent hierarchical loading operations on transmission nodes more executable, avoids confusion in the loading process due to ambiguous instructions, and ensures the orderly progress of the pressure application process.

[0136] In summary, by loading transmission nodes in stages according to a phased loading instruction sequence and obtaining node response data, dynamic data such as voltage fluctuations, current changes, and temperature rises at different loading stages can be captured in real time. Moreover, the data corresponds precisely to the loading stage, providing real and complete raw data for subsequent extraction of dynamic features, and avoiding the impact of missing or misaligned data on the accuracy of stress analysis.

[0137] In summary, extracting dynamic features from node response data to obtain node dynamic response features can filter out key features reflecting node stress changes from complex response data, eliminate invalid data interference, make the node stress change pattern clearer, and provide a focused feature basis for multimodal evolution.

[0138] In summary, multimodal evolution of the dynamic response characteristics of nodes yields stress evolution maps. By correlating multidimensional features such as voltage, current, and temperature and presenting their changes with loading stages in graphical form, the evolution trend of path stress is intuitively displayed, allowing staff to quickly grasp the stress change patterns and laying a visual foundation for analyzing dynamic stress distribution.

[0139] In summary, analyzing stress evolution maps to generate dynamic stress distribution can transform the graphical changes in the map into three-dimensional distribution data that includes node locations, loading stages, and stress values. This accurately presents the stress state of different nodes at different loading stages, breaking the limitations of traditional single-node or fixed-stage stress monitoring. It provides a comprehensive stress distribution basis for subsequent synchronous monitoring of electrical and thermal stress parameters, further ensuring the accuracy of power stress testing.

[0140] S4. Based on the dynamic stress distribution, simultaneously monitor the electrical stress parameters and thermal stress parameters of the target path;

[0141] In this embodiment of the invention, the step of simultaneously monitoring the electrical stress parameters and thermal stress parameters of the target path based on the dynamic stress distribution includes:

[0142] Electrical and thermal characteristics are extracted from the dynamic stress distribution to obtain the initial monitoring data of the dynamic stress distribution;

[0143] Real-time feature identification is performed on the initial monitoring data to obtain the electrical feature vector and thermal feature vector of the initial monitoring data;

[0144] Based on the dynamic correlation between the electrical feature vector and the thermal feature vector, a synchronous monitoring matrix for the dynamic stress distribution is constructed.

[0145] Based on the synchronous monitoring matrix, the target path is subjected to parallel acquisition of two parameters to obtain the electrical stress parameters and thermal stress parameters of the target path.

[0146] Specifically, data related to electrical characteristics are selected from the dynamic stress distribution, including voltage stress values ​​and current stress values ​​of each transmission node at different loading stages, and these data are classified as electrical characteristic quantities. At the same time, data related to thermal characteristics are selected, including temperature stress values ​​and heat conduction rates of each transmission node at different loading stages, and these data are classified as thermal characteristic quantities. The electrical characteristic quantities and thermal characteristic quantities are integrated to obtain the initial monitoring data of the dynamic stress distribution.

[0147] Furthermore, the initial monitoring data is input into the feature recognition system. The system has preset electrical feature templates and thermal feature templates. The electrical feature templates include typical voltage stress change patterns, current stress change patterns, etc., while the thermal feature templates include typical temperature stress change patterns, heat conduction rate change patterns, etc. The system matches the electrical feature quantities in the initial monitoring data with the electrical feature templates and extracts the electrical data sequences that conform to the template features as electrical feature vectors. At the same time, the system matches the thermal feature quantities in the initial monitoring data with the thermal feature templates and extracts the thermal data sequences that conform to the template features as thermal feature vectors.

[0148] Furthermore, the correspondence between electrical and thermal eigenvectors at different loading stages is analyzed. For example, when the voltage stress value in the electrical eigenvector increases at a certain loading stage, the temperature stress value in the thermal eigenvector changes. This correlation pattern that changes with the loading stage is recorded. The correlation pattern between the electrical and thermal eigenvectors is presented in matrix form with the loading stage as the horizontal axis and the eigenvector values ​​as the vertical axis. Each element in the matrix corresponds to the correlation strength between the two eigenvectors at a specific loading stage, thus obtaining the synchronous monitoring matrix of dynamic stress distribution.

[0149] Furthermore, based on the synchronous monitoring matrix, the electrical parameter acquisition devices and thermal parameter acquisition devices installed on the target path are activated. The electrical parameter acquisition devices collect data such as voltage and current of the transmission nodes at the corresponding loading stage according to the changing pattern of the electrical feature vector in the matrix. The thermal parameter acquisition devices collect data such as temperature and heat conduction of the transmission nodes synchronously at the same loading stage according to the changing pattern of the thermal feature vector in the matrix. The collected electrical data is organized into electrical stress parameters, and the collected thermal data is organized into thermal stress parameters.

[0150] In summary, extracting electrical and thermal characteristics from dynamic stress distribution to obtain initial monitoring data can accurately screen out key data reflecting the electrical and thermal properties of the path, avoid interference from irrelevant data, provide focused and comprehensive basic data for subsequent feature identification, and ensure that the monitoring data can specifically reflect the electrical and thermal stress state of the path.

[0151] In summary, real-time feature identification of initial monitoring data yields electrical and thermal feature vectors. By matching preset feature templates, the scattered monitoring data is transformed into an ordered vector form, clearly presenting the changing patterns of electrical and thermal features with the loading stage. This makes the analysis of the two types of features more systematic and provides a structured feature basis for constructing a synchronous monitoring matrix.

[0152] In summary, constructing a synchronous monitoring matrix based on the dynamic correlation between two types of feature vectors can capture the intrinsic relationship between electrical and thermal features at different loading stages, integrate the two isolated features into a correlated matrix form, break the limitations of traditional separate monitoring of electrical or thermal stress, and provide a correlated guidance framework for parallel acquisition of two parameters.

[0153] In summary, the parallel acquisition of electrical and thermal stress parameters based on the synchronous monitoring matrix enables the synchronous acquisition of electrical and thermal parameters during the same loading stage, ensuring the consistency of the time dimension of the two types of parameters, avoiding parameter correlation deviations caused by asynchronous acquisition, and enabling the acquired stress parameters to more accurately reflect the actual stress state of the path, providing reliable parameter support for subsequent dual threshold assessment.

[0154] S5. Based on the electrical stress parameters and thermal stress parameters, perform a dual threshold evaluation on the electrical safety margin and thermal stability margin of the target path to obtain a margin exceeding the limit judgment conclusion of the target path;

[0155] In this embodiment of the invention, the step of performing a dual threshold assessment of the electrical safety margin and thermal stability margin of the target path based on the electrical stress parameters and thermal stress parameters to obtain a margin exceedance determination conclusion for the target path includes:

[0156] The electrical stress parameters are normalized to obtain the electrical safety index of the electrical stress parameters;

[0157] The thermal stress parameters are standardized and reconstructed to obtain the thermal stability index of the thermal stress parameters.

[0158] Based on preset electrical safety thresholds and thermal stability thresholds, a dual comparison analysis is performed on the electrical safety index and thermal stability index to obtain the margin state evaluation matrix of the target path.

[0159] Cross-validation is performed on the margin state evaluation matrix to obtain the margin exceedance determination conclusion of the target path.

[0160] Based on preset electrical safety thresholds and thermal stability thresholds, a dual comparative analysis is performed on the electrical safety index and thermal stability index to obtain the margin state assessment matrix of the target path, including:

[0161] The electrical safety index is dynamically compared with a preset electrical safety threshold to obtain an electrical safety margin assessment vector for the electrical safety index.

[0162] The thermal stability index is compared with a preset thermal stability threshold to obtain the thermal stability margin evaluation vector of the thermal stability index.

[0163] The electrical safety margin assessment vector and the thermal stability margin assessment vector are matrix-combined to obtain the initial assessment matrix of the target path, wherein the calculation formula of the initial assessment matrix is ​​as follows:

[0164] ;

[0165] In the formula, The initial evaluation matrix is... The coupling coefficient is... Let be the electrical safety margin assessment vector. This is the transpose operation for a vector. This is the difference adjustment coefficient. Let be the thermal stability margin evaluation vector. For absolute difference vectors, The stabilization coefficient is... For element-wise multiplication;

[0166] The initial evaluation matrix is ​​normalized to obtain the margin state evaluation matrix of the target path.

[0167] Specifically, the electrical stress parameters of the target path are obtained. These parameters include data such as voltage and current of the transmission nodes. These data are compared with the preset electrical parameter reference range of the distribution network. According to the preset normalization rules, the value of each electrical stress parameter is converted into the corresponding value within the reference range. For example, electrical stress parameters exceeding the upper limit of the reference range are converted into the maximum value of the reference range, those below the lower limit of the reference range are converted into the minimum value of the reference range, and those within the range are converted proportionally. The converted value is the electrical safety index of the electrical stress parameter.

[0168] Furthermore, the thermal stress parameters of the target path are obtained. These parameters include data such as the temperature and heat conduction of the transmission nodes. The preset thermal parameter standard range of the distribution network is retrieved, and the value of each thermal stress parameter is matched with the standard range. Values ​​that exceed the standard range are adjusted to fall within the standard range, while values ​​that are within the standard range remain unchanged. All the adjusted thermal stress parameters are then recombined in a preset order to form a new thermal parameter sequence. This sequence is the thermal stability index of the thermal stress parameters.

[0169] Furthermore, the pre-set electrical safety threshold and thermal stability threshold of the distribution network are retrieved. The electrical safety threshold is the critical value for determining whether the electrical safety index is in a safe state, and the thermal stability threshold is the critical value for determining whether the thermal stability index is in a stable state. The electrical safety index is compared with the electrical safety threshold to determine the electrical safety state of each transmission node at different loading stages. At the same time, the thermal stability index is compared with the thermal stability threshold to determine the thermal stability state of each transmission node at different loading stages. These electrical safety states and thermal stability states are organized into a matrix form according to transmission nodes and loading stages. Each element in the matrix represents the dual state of the corresponding node at the corresponding stage, thus obtaining the margin state evaluation matrix of the target path.

[0170] Furthermore, the margin status assessment matrix is ​​input into the cross-validation system. The system performs cross-checks on the electrical safety status and thermal stability status of each transmission node in the matrix during the same loading phase, according to preset validation rules. For example, if a node is in an unsafe electrical safety status and an unstable thermal stability status during a certain phase, it is marked as a double violation; if only one of the states is a violation, it is marked as a single violation; if both states are normal, it is marked as no violation. The system summarizes the marking results of all nodes at all phases to form a comprehensive judgment document containing the location of the violation node, the violation phase, and the violation type. This document is the margin violation judgment conclusion of the target path.

[0171] Specifically, a preset electrical safety threshold is retrieved, which includes electrical safety critical values ​​corresponding to different loading stages. The value of each transmission node in the electrical safety index at each loading stage is compared one by one with the electrical safety threshold of the corresponding stage. When the value of the electrical safety index is less than or equal to the electrical safety threshold of the corresponding stage, the electrical safety margin status of the node at that stage is determined to be normal; when the value of the electrical safety index is greater than the electrical safety threshold of the corresponding stage, it is determined to be abnormal. The electrical safety margin status of all nodes at all stages is arranged into an ordered vector according to the node order and loading stage to obtain the electrical safety margin evaluation vector of the electrical safety index.

[0172] Furthermore, a preset thermal stability threshold is retrieved, which includes thermal stability critical values ​​corresponding to different loading stages. The value of each transmission node in the thermal stability index at each loading stage is synchronously compared with the corresponding thermal stability threshold. During the comparison, the electrical safety margin status of the node at the same time is used for collaborative judgment. When the value of the thermal stability index is less than or equal to the thermal stability threshold of the corresponding stage, and the electrical safety margin status at the same time is normal, the thermal stability margin status of the node at that stage is judged to be normal. When the value of the thermal stability index is greater than the thermal stability threshold of the corresponding stage, or the electrical safety margin status at the same time is abnormal, it is judged to be abnormal. The thermal stability margin status of all nodes at all stages is arranged into an ordered vector according to the node order and loading stage to obtain the thermal stability margin evaluation vector of the thermal stability index.

[0173] Furthermore, a matrix framework is constructed with transmission nodes as rows and loading stages as columns. Each state value in the electrical safety margin assessment vector is filled into the first set of elements in the matrix according to the corresponding node and stage. Each state value in the thermal stability margin assessment vector is filled into the second set of elements in the matrix according to the corresponding node and stage. This ensures that each node in the matrix contains two elements in each stage, corresponding to the electrical safety margin state and the thermal stability margin state, respectively, forming the initial assessment matrix of the target path.

[0174] Furthermore, in accordance with the matrix normalization standard preset by the distribution network, the state values ​​in the initial evaluation matrix are uniformly transformed. The state values ​​representing normal conditions are converted into preset standard normal indicators, and the state values ​​representing abnormal conditions are converted into preset standard abnormal indicators, ensuring that the indicator format of all elements in the matrix is ​​consistent. The transformed matrix is ​​the margin state evaluation matrix of the target path.

[0175] Specifically, the electrical safety margin assessment vector is derived from the result of dynamically comparing the electrical safety index with preset electrical safety thresholds. Specifically, it retrieves preset electrical safety thresholds containing electrical safety critical values ​​for different loading stages, compares the value of each transmission node in the electrical safety index at each loading stage with the corresponding stage threshold one by one, determines the electrical safety margin status of each node at each stage, and then arranges these statuses according to the node order and loading stage to form an ordered vector, which is the electrical safety margin assessment vector.

[0176] Furthermore, the thermal stability margin assessment vector is derived from the result of a collaborative comparison between the thermal stability index and a preset thermal stability threshold. Specifically, the preset thermal stability threshold, which includes thermal stability critical values ​​for different loading stages, is retrieved. The value of each transmission node in the thermal stability index at each loading stage is synchronously compared with the corresponding stage threshold. At the same time, the thermal stability margin status of each node at each stage is collaboratively judged in conjunction with the electrical safety margin status of the node during the same period. These statuses are then arranged in order of node sequence and loading stage to form an ordered vector, which is the thermal stability margin assessment vector.

[0177] Furthermore, the coupling coefficient is a fixed value set by technicians during the initialization phase of the distribution network assessment system, based on historical initial assessment matrix construction data. When setting it, the coupling effect of a large number of past electrical safety margin assessment vectors and thermal stability margin assessment vectors is referenced to ensure that the coefficient can effectively adjust the synergistic effect of the two vectors in matrix construction, so that the initial assessment matrix can accurately reflect the correlation between the two.

[0178] Furthermore, the difference adjustment coefficient is a fixed value set based on the difference distribution between the electrical safety margin assessment vector and the thermal stability margin assessment vector in historical data. Technicians statistically analyze the frequency and magnitude of the differences between the two vectors at different loading stages and nodes in the past. Based on the degree of influence of the difference on the accuracy of the initial assessment matrix, the specific value of this coefficient is determined to balance the interference of the difference between the two vectors on the matrix result.

[0179] Furthermore, the stabilization coefficient is a fixed value set to avoid abnormal values ​​during the calculation process due to the small difference between the electrical safety margin assessment vector and the thermal stability margin assessment vector. During the system debugging phase, technicians determined a fixed value that ensures the initial assessment matrix value is always within a reasonable range by simulating the calculation process under different vector difference scenarios multiple times, thus ensuring the stability of the matrix results.

[0180] Furthermore, the transpose operation of the vector is an operation that transforms the row and column orientation of the thermal stability margin assessment vector. Specifically, it transforms the thermal stability margin assessment vector, which was originally arranged horizontally according to the node order and loading stage, into a vertically arranged vector. This makes the transformed vector match the electrical safety margin assessment vector in the row and column dimensions, satisfying the vector dimension requirements of subsequent element-by-element multiplication and other calculations, and ensuring that the calculation process proceeds smoothly.

[0181] Furthermore, element-wise multiplication is an operation that multiplies the elements at corresponding positions in two dimension-matched vectors or matrices. In this formula, specifically, the electrical safety margin assessment vector after coupling coefficient adjustment is multiplied at corresponding positions by the transposed thermal stability margin assessment vector. That is, the state value of a certain node at a certain stage in the electrical safety margin assessment vector is multiplied by the state value at the corresponding position in the transposed thermal stability margin assessment vector to obtain the product result at that position. The product results at all positions together constitute an intermediate matrix.

[0182] Furthermore, the significance of this formula lies in constructing an initial evaluation matrix for the target path. It adjusts the synergistic effect of the electrical safety margin evaluation vector and the thermal stability margin evaluation vector through a coupling coefficient. Vector transpose is used to match the dimensions of the two vectors to meet the element-wise multiplication requirement. A difference adjustment coefficient balances the interference of the differences between the two vectors on the calculation results. An absolute difference vector reflects the degree of difference between the two vectors. A stabilization coefficient is then used to avoid abnormal values ​​during the calculation process. Finally, a series of operations integrate the information from the electrical safety margin evaluation vector and the thermal stability margin evaluation vector into an initial evaluation matrix, providing a foundation for obtaining the subsequent margin state evaluation matrix. This ensures that the initial evaluation matrix comprehensively and accurately reflects the correlation between the electrical safety margin and the thermal stability margin of the target path.

[0183] Furthermore, looking at the trends in the formulas, when the state values ​​in the electrical safety margin assessment vector or the thermal stability margin assessment vector increase, if the state value of the other vector remains unchanged, the value of the intermediate matrix obtained by element-wise multiplication will increase accordingly. With other coefficients fixed, the value of the initial assessment matrix will increase accordingly. When the difference between the electrical safety margin assessment vector and the thermal stability margin assessment vector increases, the value of the absolute difference vector will increase accordingly. With other conditions remaining unchanged, this will increase the value of the part in the formula "1 + difference adjustment coefficient × absolute difference vector," thus leading to a corresponding increase in the value of the initial assessment matrix. When the difference between the electrical safety margin assessment vector and the thermal stability margin assessment vector decreases, the value of the absolute difference vector will decrease accordingly. With other conditions remaining unchanged, the value of "1 + difference adjustment coefficient × absolute difference vector" will decrease, and the value of the initial assessment matrix will also decrease accordingly. The coupling coefficient, difference adjustment coefficient, and stabilization coefficient are all fixed values ​​and will not change with the changes in the two vectors. They only play a fixed adjustment role on the value of the initial assessment matrix and do not affect the overall direction of the matrix value change.

[0184] In summary, normalizing electrical stress parameters to obtain an electrical safety index can convert electrical stress data of different dimensions and numerical ranges into an index within a unified benchmark range, eliminating analytical biases caused by differences in absolute parameter values, making the assessment of electrical safety status more standardized, and avoiding misjudgments of safety margins due to confusion in parameter dimensions.

[0185] In summary, the thermal stability index is obtained by standardizing and reconstructing thermal stress parameters. This allows thermal stress data such as temperature and heat conduction to be adjusted and recombined according to a preset standard range, ensuring that the thermal stress data meets the unified evaluation standard. At the same time, it retains the key characteristics of thermal stress changes, providing a suitable thermal stability evaluation index for subsequent collaborative analysis with the electrical safety index, thus overcoming the limitations of traditional single-dimensional evaluation.

[0186] In summary, a margin status assessment matrix is ​​obtained by performing a dual comparison analysis of the two types of indices based on preset thresholds. This matrix can be combined with electrical and thermal safety critical standards to organize the assessment results of the two types of indices into a matrix form according to transmission nodes and loading stages. This clearly presents the dual margin status of different nodes at different stages, enabling simultaneous assessment of electrical and thermal safety margins and avoiding risk omissions caused by relying solely on a single margin assessment.

[0187] In summary, cross-validation of the margin status assessment matrix yields margin exceedance judgments. By examining the correlation between electrical and thermal margin status at the same node and stage, the actual margin exceedance situation of the path can be accurately identified, eliminating the one-sidedness of single-dimensional assessment and ensuring that the judgments are highly consistent with the actual safety status of the path. This provides a reliable safety assessment basis for subsequently determining the power carrying capacity limit.

[0188] In summary, by dynamically comparing the electrical safety index with the preset electrical safety threshold to obtain the electrical safety margin assessment vector, the electrical safety status of each transmission node can be verified one by one according to the loading stage. This ensures that the assessment results are accurately matched with the actual electrical conditions of the nodes at different stages, avoiding the problem that fixed threshold assessments cannot adapt to the dynamic loading process, and providing accurate electrical margin data for subsequent matrix combination.

[0189] In summary, the thermal stability index is compared with the preset thermal stability threshold to obtain the thermal stability margin assessment vector. During the comparison, the electrical safety margin status of the node is taken into account to ensure that the thermal stability assessment not only refers to the thermal threshold but also relates to the electrical safety status. This avoids the deviation caused by isolated assessment of thermal stability, and makes the thermal stability margin result more consistent with the overall operational safety of the path, providing reliable thermal stability margin data for matrix combination.

[0190] In summary, an initial evaluation matrix is ​​obtained by matrix-combining two types of vectors using formulas that include coupling coefficients and difference adjustment coefficients. The coupling coefficients can enhance the synergistic effect of the two types of vectors, the difference adjustment coefficients balance the interference of vector differences, the stabilization coefficients avoid computational anomalies, and vector transpose and element-wise multiplication ensure dimension matching and element-corresponding operations. This achieves the scientific integration of the two types of margin data and lays a comprehensive initial matrix foundation for normalization processing.

[0191] In summary, the margin state evaluation matrix is ​​obtained by normalizing the initial evaluation matrix. The state values ​​in the matrix are uniformly converted into standard labels, eliminating the differences in different state representations. This makes the dual margin states of each node and stage in the matrix clear and unified, which facilitates the rapid identification of over-limit situations during subsequent cross-validation. It provides a structured and standardized matrix basis for accurately generating margin over-limit judgment conclusions.

[0192] S6. Perform multi-dimensional monitoring on the margin exceedance judgment conclusion to determine the power carrying capacity limit of the target path.

[0193] In this embodiment of the invention, the step of performing multi-dimensional monitoring on the margin exceedance determination conclusion to determine the power carrying capacity limit of the target path includes:

[0194] The time dimension is analyzed to obtain the time series state data of the target path from the margin exceedance judgment conclusion;

[0195] The time-series state data is processed by pattern recognition to obtain the load-bearing characteristic pattern of the target path;

[0196] Limit state deduction is performed on the load-bearing characteristic mode to obtain the critical load-bearing parameters of the target path;

[0197] The critical load-bearing parameters are subjected to safety calibration to determine the power load-bearing limit of the target path.

[0198] Specifically, the margin exceedance determination conclusion is retrieved. This conclusion includes information such as the location of the exceedance node, the exceedance stage, and the exceedance type. This information is sorted out according to the time sequence of power loading, and the exceedance status corresponding to each time node is associated with the power loading situation at that time node. For example, if a certain time node is in the low power loading stage and there is no exceedance, and another time node is in the high power loading stage and there is a double exceedance, the "time-power loading-exceedance status" information of all time nodes is arranged in chronological order to form an ordered data set, thus obtaining the time series status data of the target path.

[0199] Furthermore, the time-series state data is input into the pattern recognition system. The system has several preset typical load-bearing characteristic templates, including normal load-bearing template, critical load-bearing template, and overload load-bearing template. The normal load-bearing template corresponds to the characteristic of continuous occurrence of no overload state, the critical load-bearing template corresponds to the characteristic of occasional occurrence of single overload state, and the overload load-bearing template corresponds to the characteristic of frequent occurrence of double overload state. The system matches the state of each time node in the time-series state data with the template one by one, and counts the proportion of the data that conforms to a certain template feature. When the proportion of the data that conforms to a certain template feature reaches a preset standard, the template is determined to be the feature pattern corresponding to the time-series state data. This pattern is the load-bearing characteristic pattern of the target path.

[0200] Furthermore, based on the load-bearing characteristic mode, the limit state simulation system is activated. The system will simulate the process of gradually increasing the power loading level on the basis of the current characteristic mode. For example, if the current mode is normal, the system will gradually increase the power by a fixed amount. At the same time, it will simulate the electrical safety margin and thermal stability margin corresponding to each power increase stage in real time. When the dual limit-breaking state occurs for the first time in the simulation process and the state remains stable, the corresponding power loading value, electrical stress parameter value, and thermal stress parameter value are recorded. These values ​​are integrated into a set of parameters to obtain the critical load-bearing parameters of the target path.

[0201] Furthermore, the pre-set safety calibration standard of the distribution network is retrieved. This standard includes safety factors and calibration rules for different types of target paths. The power loading value in the critical load-bearing parameters is combined with the safety factor in the safety calibration standard. The power loading value is adjusted according to the calibration rules. For example, if the safety factor is a reduction ratio and the power loading value in the critical load-bearing parameters is at a certain level, then the value is reduced by that ratio. At the same time, it is verified whether the electrical stress parameters and thermal stress parameters corresponding to the adjusted value meet the safety standards to ensure that the adjusted parameters will not cause an over-limit state. The finally determined adjusted power loading value is the power load limit of the target path.

[0202] In summary, time-series state data is obtained by analyzing the margin over-limit judgment conclusions in the time dimension. It can associate the over-limit state and power loading status of each time node in the power loading time sequence, forming an ordered data set of "time-power loading-over-limit state". It clearly presents the law of over-limit state change with time and power, avoids the fragmentation of load state analysis caused by deviating from the time dimension, and provides a coherent time-series data foundation for subsequent pattern recognition.

[0203] In summary, by performing pattern recognition processing on time series state data to obtain load bearing characteristic patterns, and by matching with preset typical load bearing templates such as normal, critical, and overload, the current load bearing characteristic type of the path can be accurately located, avoiding the subjectivity and error of manual judgment of load bearing patterns, making the definition of load bearing status more standardized, and providing a clear direction for extreme state deduction.

[0204] In summary, by performing limit state extrapolation on load-bearing characteristic modes to obtain critical load-bearing parameters, and by simulating the gradual increase of power loading levels and monitoring the occurrence of dual over-limit states, the power and related parameters of the first stable occurrence of dual over-limit states along the path can be accurately captured. This avoids the deviation of traditional methods that rely solely on experience to determine critical values, and provides objective critical data support for determining power-bearing limits.

[0205] In summary, by performing safety calibration on critical load-bearing parameters to determine the power carrying capacity limit, and by adjusting the critical power value in conjunction with the pre-set safety standards of the distribution network and verifying its safety, the potential problem of insufficient risk margin in the critical parameters can be eliminated. This ensures that the final determined power carrying capacity limit not only conforms to the actual carrying capacity of the path but also meets the requirements for safe operation, providing a reliable limit basis for the safe operation of the load transfer path of the distribution network.

[0206] like Figure 2 The diagram shown is a functional block diagram of a power stress testing system for a power distribution network load transfer path provided in an embodiment of the present invention.

[0207] The power stress testing system 100 for a distribution network load transfer path described in this invention can be installed in an electronic device. Depending on the functions implemented, the power stress testing system 100 may include a parameter acquisition module 101, a strategy loading module 102, a stress distribution module 103, a synchronous monitoring module 104, a threshold evaluation module 105, and an ultimate load-bearing module 106. The module described in this invention can also be called a unit, referring to a series of computer program segments that can be executed by the processor of an electronic device and perform a fixed function, stored in the memory of the electronic device.

[0208] In this embodiment, the functions of each module / unit are as follows:

[0209] The parameter acquisition module 101 is used to acquire the static impedance parameters and real-time operating parameters of the initial path in the load transfer path of the distribution network.

[0210] The strategy loading module 102 is used to perform linear growth planning on the static impedance parameter and the real-time operating parameter to obtain the power loading strategy of the initial path.

[0211] The stress distribution module 103 is used to apply pressure to the transmission nodes of the target path step by step based on the power loading strategy to obtain the dynamic stress distribution of the target path.

[0212] The synchronous monitoring module 104 is used to synchronously monitor the electrical stress parameters and thermal stress parameters of the target path according to the dynamic stress distribution.

[0213] The threshold evaluation module 105 is used to perform dual threshold evaluation on the electrical safety margin and thermal stability margin of the target path based on the electrical stress parameters and thermal stress parameters, and obtain the margin exceeding the limit judgment conclusion of the target path.

[0214] The limit load module 106 is used to perform multi-dimensional monitoring of the margin exceedance judgment conclusion and determine the power load limit of the target path.

[0215] In the several embodiments provided by this invention, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0216] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0217] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0218] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0219] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0220] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A power stress test method for load transfer paths in a distribution network, characterized in that, The method includes: S1. Obtain the static impedance parameters and real-time operating parameters of the initial path in the load transfer path of the distribution network; S2. Perform linear growth planning on the static impedance parameter and the real-time operating parameter to obtain the power loading strategy of the initial path; S3. Based on the power loading strategy, apply pressure to the transmission nodes of the target path step by step to obtain the dynamic stress distribution of the target path; S4. Based on the dynamic stress distribution, simultaneously monitor the electrical stress parameters and thermal stress parameters of the target path; S5. Based on the electrical stress parameters and thermal stress parameters, perform a dual threshold assessment on the electrical safety margin and thermal stability margin of the target path to obtain a margin exceedance determination conclusion for the target path, including: The electrical stress parameters are normalized to obtain the electrical safety index of the electrical stress parameters; The thermal stress parameters are standardized and reconstructed to obtain the thermal stability index of the thermal stress parameters. Based on preset electrical safety thresholds and thermal stability thresholds, a dual comparative analysis is performed on the electrical safety index and thermal stability index to obtain the margin state evaluation matrix of the target path, including: The electrical safety index is dynamically compared with a preset electrical safety threshold to obtain an electrical safety margin assessment vector for the electrical safety index. The thermal stability index is compared with a preset thermal stability threshold to obtain the thermal stability margin evaluation vector of the thermal stability index. The electrical safety margin assessment vector and the thermal stability margin assessment vector are matrix-combined to obtain the initial assessment matrix of the target path, wherein the calculation formula of the initial assessment matrix is ​​as follows: ； In the formula, The initial evaluation matrix is... The coupling coefficient is... Let be the electrical safety margin assessment vector. This is the transpose operation for a vector. This is the difference adjustment coefficient. Let be the thermal stability margin evaluation vector. For absolute difference vectors, The stabilization coefficient is... For element-wise multiplication; The initial evaluation matrix is ​​normalized to obtain the margin state evaluation matrix of the target path; Cross-validation is performed on the margin state evaluation matrix to obtain the margin exceedance determination conclusion of the target path; S6. Perform multi-dimensional monitoring on the margin exceedance judgment conclusion to determine the power carrying capacity limit of the target path.

2. The power stress test method for a distribution network load transfer path as described in claim 1, characterized in that, The acquisition of the static impedance parameters and real-time operating parameters of the initial path in the load transfer path of the distribution network includes: The inherent characteristic records of the load transfer path in the distribution network are analyzed to obtain the static impedance parameters of the initial path. Feature extraction is performed on the real-time monitoring data of the distribution network load transfer path to obtain the real-time operating parameters of the initial path.

3. The power stress test method for a distribution network load transfer path as described in claim 1, characterized in that, The process of performing linear growth programming on the static impedance parameter and the real-time operating parameters to obtain the power loading strategy for the initial path includes: The static impedance parameters and the real-time operating parameters are combined to convert the impedance change rate parameter and operating trend parameter of the initial path; The impedance change rate parameter and the running trend parameter are subjected to trend quantization processing to obtain the impedance change gradient and running trend intensity of the initial path; According to the preset distribution network operation safety criteria, different weighting coefficients are assigned to the impedance change gradient and the operation trend intensity to obtain the first weighting coefficient of the impedance change gradient and the second weighting coefficient of the operation trend intensity. Based on the first weighting coefficient and the second weighting coefficient, the impedance change gradient and the running trend intensity are fused in multiple dimensions to obtain the comprehensive planning parameters of the initial path; The power growth sequence of the initial path is obtained by linearly expanding the integrated planning parameters. A progressive loading scheme is formulated based on the power growth sequence to obtain the power loading strategy for the initial path.

4. The power stress test method for a distribution network load transfer path as described in claim 3, characterized in that, The step of fusing the impedance change gradient and the running trend intensity from multiple dimensions based on the first weighting coefficient and the second weighting coefficient to obtain the comprehensive planning parameters of the initial path includes: The impedance change gradient is scaled with the first weighting coefficient to obtain the impedance influence factor of the impedance change gradient. The trend intensity is weighted by the second weighting coefficient to obtain the trend influence factor of the trend intensity. The impedance influence factor and the trend influence factor are coupled in a multi-dimensional manner to obtain the preliminary fusion parameters of the initial path. The calculation formula for the preliminary fusion parameters is as follows: ； In the formula, The initial fusion parameters are as follows: The impedance influence factor is... The trend influencing factor, These are the coupling weight coefficients. This is the difference adjustment coefficient. The stabilization coefficient; The preliminary fusion parameters are normalized to obtain the comprehensive planning parameters of the initial path.

5. The power stress test method for a distribution network load transfer path as described in claim 1, characterized in that, The step of applying pressure to the transmission nodes of the target path step by step based on the power loading strategy to obtain the dynamic stress distribution of the target path includes: The power loading strategy is parsed step by step to obtain the staged loading instruction sequence of the power loading strategy; According to the phased loading instruction sequence, the transmission nodes of the target path are loaded in stages to obtain the node response data of the target path; Dynamic feature extraction is performed on the node response data to obtain the node dynamic response features of the target path; The dynamic response characteristics of the nodes are subjected to multimodal evolution to obtain the stress evolution spectrum of the target path; The stress evolution spectrum is analyzed to generate the dynamic stress distribution of the target path.

6. The power stress test method for a distribution network load transfer path as described in claim 1, characterized in that, The step of synchronously monitoring the electrical stress parameters and thermal stress parameters of the target path based on the dynamic stress distribution includes: Electrical and thermal characteristics are extracted from the dynamic stress distribution to obtain the initial monitoring data of the dynamic stress distribution; Real-time feature identification is performed on the initial monitoring data to obtain the electrical feature vector and thermal feature vector of the initial monitoring data; Based on the dynamic correlation between the electrical feature vector and the thermal feature vector, a synchronous monitoring matrix for the dynamic stress distribution is constructed. Based on the synchronous monitoring matrix, the target path is subjected to parallel acquisition of two parameters to obtain the electrical stress parameters and thermal stress parameters of the target path.

7. The power stress test method for a distribution network load transfer path as described in claim 1, characterized in that, The process of multi-dimensionally monitoring the margin exceedance determination to determine the power carrying capacity limit of the target path includes: The time dimension is analyzed to obtain the time series state data of the target path from the margin exceedance judgment conclusion; The time-series state data is processed by pattern recognition to obtain the load-bearing characteristic pattern of the target path; Limit state deduction is performed on the load-bearing characteristic mode to obtain the critical load-bearing parameters of the target path; The critical load-bearing parameters are subjected to safety calibration to determine the power load-bearing limit of the target path.

8. A power stress testing system for load transfer paths in a distribution network, characterized in that, The system includes: The parameter acquisition module is used to acquire the static impedance parameters and real-time operating parameters of the initial path in the load transfer path of the distribution network. The strategy loading module is used to perform linear growth planning on the static impedance parameters and the real-time operating parameters to obtain the power loading strategy of the initial path; The stress distribution module is used to apply pressure to the transmission nodes of the target path step by step based on the power loading strategy to obtain the dynamic stress distribution of the target path. The synchronous monitoring module is used to synchronously monitor the electrical stress parameters and thermal stress parameters of the target path according to the dynamic stress distribution. The threshold evaluation module is used to perform a dual threshold evaluation of the electrical safety margin and thermal stability margin of the target path based on the electrical stress parameters and thermal stress parameters, and to obtain a margin exceedance determination conclusion for the target path, including: The electrical stress parameters are normalized to obtain the electrical safety index of the electrical stress parameters; The thermal stress parameters are standardized and reconstructed to obtain the thermal stability index of the thermal stress parameters. Based on preset electrical safety thresholds and thermal stability thresholds, a dual comparative analysis is performed on the electrical safety index and thermal stability index to obtain the margin state evaluation matrix of the target path, including: The electrical safety index is dynamically compared with a preset electrical safety threshold to obtain an electrical safety margin assessment vector for the electrical safety index. The thermal stability index is compared with a preset thermal stability threshold to obtain the thermal stability margin evaluation vector of the thermal stability index. The electrical safety margin assessment vector and the thermal stability margin assessment vector are matrix-combined to obtain the initial assessment matrix of the target path, wherein the calculation formula of the initial assessment matrix is ​​as follows: ； In the formula, The initial evaluation matrix is... The coupling coefficient is... Let be the electrical safety margin assessment vector. This is the transpose operation for a vector. This is the difference adjustment coefficient. Let be the thermal stability margin evaluation vector. For absolute difference vectors, The stabilization coefficient is... For element-wise multiplication; The initial evaluation matrix is ​​normalized to obtain the margin state evaluation matrix of the target path; Cross-validation is performed on the margin state evaluation matrix to obtain the margin exceedance determination conclusion of the target path; The limit load module is used to perform multi-dimensional monitoring of the margin exceedance judgment conclusion and determine the power load limit of the target path.

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