Line correlation analysis method, device, computer equipment and storage medium

Abstract

The present application relates to a line correlation analysis method, apparatus, computer equipment, storage medium, and computer program product. The method comprises: dividing a line simulation model into multiple analysis areas; each analysis area includes at least one generator and at least two cables; adjusting the voltage parameters of the generator in each analysis area to obtain power variation curves of multiple target cables in the analysis area; performing data processing on the power variation curve of each target cable in the same analysis area to obtain intermediate data; and determining the correlation coefficient between each two target cables in the same analysis area based on the intermediate data. This method is based on the correlation of cables, and facilitates timely monitoring of related cables when abnormal conditions occur in the cables, thereby improving the efficiency of monitoring the operating status of the cables.

Description

Technical Field

[0001] The present application relates to the field of electrical technology, and in particular to a circuit correlation analysis method, apparatus, computer equipment, storage medium, and computer program product. Background Art

[0002] Medium-voltage power cables are a crucial component of power transmission systems and are widely used. When an anomaly or even a fault occurs during cable operation, the load needs to be transferred to an adjacent line.

[0003] In traditional technologies, when a cable is abnormal or even fails, load transfer mostly relies on the dispatcher's manual experience. The status detection of cables that are closely related to the faulty cable is not timely enough, and the dispatch reliability is low. Summary of the Invention

[0004] Based on this, it is necessary to provide a line correlation analysis method, apparatus, computer equipment, computer-readable storage medium and computer program product to address the above technical issues.

[0005] In a first aspect, the present application provides a line correlation analysis method. The method comprises:

[0006] Dividing the line simulation model into a plurality of analysis areas; each analysis area includes at least one generator and at least two cables;

[0007] Adjust the voltage parameters of the generator in each analysis area to obtain the power variation curves of multiple target cables in the analysis area;

[0008] Perform data processing on the power variation curve of each target cable in the same analysis area to obtain intermediate data;

[0009] Based on the intermediate data, the correlation coefficient between every two target cables in the same analysis area is determined.

[0010] In one embodiment, adjusting the voltage parameters in each analysis area to obtain power variation curves of multiple target cables in the analysis area includes:

[0011] For a generator in each analysis area, multiple cables with the shortest distance to the generator node are determined as target cables;

[0012] The voltage parameters of the generator are adjusted to obtain the power variation curves corresponding to the multiple target cables.

[0013] In one embodiment, adjusting the voltage parameters of the generator in each analysis area includes:

[0014] Each time, the voltage parameter is increased or decreased according to a preset percentage or a preset value.

[0015] In one embodiment, data processing is performed on the power variation curve of each target cable in the same analysis area to obtain intermediate data, including:

[0016] Based on the power change curve corresponding to each target cable, determine the power change array corresponding to each cable;

[0017] Calculate the mean and variance of each power variation array;

[0018] The power change array, mean, and variance corresponding to each cable are normalized to obtain a normalized array corresponding to each target cable as intermediate data.

[0019] In one embodiment, the intermediate data is in array form, and determining the correlation coefficient between every two target cables in the same analysis area based on the intermediate data includes:

[0020] Based on the intermediate data corresponding to each target cable, obtain the array change value corresponding to each cable;

[0021] Based on the array change values ​​corresponding to each two target cables, a ratio of the array change values ​​corresponding to each two target cables is obtained;

[0022] Based on the ratio of the array change values ​​corresponding to each two target cables, a correlation coefficient between each two target cables is determined.

[0023] In one embodiment, the method further includes:

[0024] Based on the power variation curves of multiple target cables in the analysis area, the power-time characteristics of the multiple target cables in the same time interval are determined; wherein the power-time characteristics refer to the trend of power variation over time;

[0025] Determining a first ranking of correlations between the plurality of target cables based on power-time characteristics of the plurality of target cables within the same time interval;

[0026] Determining a second ranking of the magnitude of the correlations among the plurality of target cables based on the correlation coefficients between every two target cables within the same analysis area;

[0027] A second ordering among the plurality of target cables is verified based on the first ordering among the plurality of target cables.

[0028] In a second aspect, the present application further provides a line correlation analysis device. The device comprises:

[0029] A region division module is used to divide the line simulation model into multiple analysis regions; each analysis region includes at least one generator and at least two cables;

[0030] A power variation curve acquisition module is used to adjust the voltage parameters of the generator in each analysis area and obtain the power variation curves of multiple target cables in the analysis area;

[0031] A data processing module is used to process the power variation curve of each target cable in the same analysis area to obtain intermediate data;

[0032] The correlation coefficient determination module is used to determine the correlation coefficient between every two target cables in the same analysis area based on the intermediate data.

[0033] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method described in any one of the above embodiments when executing the computer program.

[0034] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any of the above embodiments.

[0035] In a fifth aspect, the present application further provides a computer program product, which includes a computer program that implements the steps of the method described in any of the above embodiments when executed by a processor.

[0036] The aforementioned line correlation analysis method, apparatus, computer device, storage medium, and computer program product first divide the line simulation model into multiple analysis regions; each analysis region includes at least one generator and at least two cables. The voltage parameters of the generators in each analysis region are then adjusted to obtain power variation curves for multiple target cables within the analysis region. Furthermore, the power variation curves for each target cable within the same analysis region are processed to obtain intermediate data. Finally, based on the intermediate data, the correlation coefficient between each two target cables within the same analysis region is determined. This cable correlation facilitates timely monitoring of related cables when abnormal conditions occur, thereby improving the efficiency of monitoring cable operating conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 1 is a flow chart of a line correlation analysis method according to an embodiment;

[0038] Figure 2 A simplified single-phase circuit diagram in one embodiment;

[0039] Figure 3 is a single-phase equivalent circuit diagram corresponding to a unidirectional simplified circuit diagram in one embodiment;

[0040] Figure 4 is a circuit block diagram of a system to be analyzed in one embodiment;

[0041] Figure 5 FIG. 1 is a circuit diagram of a generator G9 and its corresponding five target cables in one embodiment;

[0042] FIG6( a ) is a graph showing the active power variation of five cables after adding the voltage parameters of the generator in one embodiment;

[0043] FIG6( b ) is a graph showing reactive power variation of five cables after adding the voltage parameters of the generator in one embodiment;

[0044] FIG7( a ) is a graph showing the active power variation of five cables after adding the voltage parameters of the generator in another embodiment;

[0045] FIG7( b ) is a graph showing reactive power variation of five cables after reducing the voltage parameters of the generator in another embodiment;

[0046] Figure 8 is a structural block diagram of a circuit correlation analysis device in one embodiment;

[0047] Figure 9 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0049] The line correlation analysis method provided in the embodiment of the present application can be applied to a single-side implementation of a server or a terminal. First, the server 104 can divide the line simulation model 102 into multiple analysis areas; each analysis area includes at least one generator and at least two cables. Then, the server 104 can adjust the voltage parameters of the generator in each analysis area to obtain the power change curves of multiple target cables in the analysis area. Further, the server 104 can perform data processing on the power change curve of each target cable in the same analysis area to obtain intermediate data. Finally, the server 104 can determine the correlation coefficient between each two target cables in the same analysis area based on the intermediate data. Among them, the server 104 can be implemented as an independent server or a server cluster composed of multiple servers. The line simulation model 102 can be a MATLAB model established based on the system to be analyzed. Specifically, the server 104 can first obtain the specific parameters of each electrical component in the system to be analyzed, and based on the above parameters, build the line simulation model through MATLAB.

[0050] In one embodiment, Figure 1 As shown, a line correlation analysis method is provided, which is described by taking the application of the method on the server side as an example, including the following steps 102 to 108.

[0051] Step 102: Divide the line simulation model into multiple analysis areas; each analysis area includes at least one generator and at least two cables.

[0052] In this embodiment, the server may divide the analysis area based on the multiple generators on the line simulation model, so that each analysis area including the generators includes at least two cables.

[0053] In this embodiment, the multiple analysis regions divided by the server for the line simulation model can overlap with each other, that is, each generator or each cable can exist in multiple analysis regions simultaneously. For example, assuming that the line simulation model includes generator G1, generator G2, generator G3, cable 1, cable 2, cable 3, cable 4, and cable 5, then the line simulation model can be divided into a first analysis region (including: generator G1, cable 1, and cable 2), a second analysis region (including: generator G1, generator G2, cable 1, cable 3, and cable 4), and a third analysis region (including: generator G2, generator G3, cable 2, cable 3, cable 4, and cable 5).

[0054] In this embodiment, when the node distance between two cables within the same analysis area is less than or equal to the preset node distance, the two cables are considered a pair of target cables. It will be appreciated that when the node distance between multiple cables within the same analysis area is less than or equal to the preset node distance, the cables are considered a group of target cables.

[0055] Step 104 : adjusting the voltage parameters of the generator in each analysis area to obtain power variation curves of multiple target cables in the analysis area.

[0056] In this embodiment, the server adjusts the voltage parameters of the generators in each analysis area and obtains power variation curves for multiple target cables within the same target cable group within the analysis area. For example, if there are two target cable groups within an analysis area, when the server adjusts the voltage parameters of the generators in that analysis area, the server can obtain power variation curves for each cable in both target cable groups, or it can obtain power variation curves for each cable in only one target cable group.

[0057] In this embodiment, when the server adjusts the voltage parameters of the generator in each analysis area, it can obtain the power variation curve of the target cable within the preset sampling time based on the preset sampling time.

[0058] Step 106: Process the power variation curve of each target cable in the same analysis area to obtain intermediate data.

[0059] In this embodiment, the intermediate data can be represented in the form of an array. In this embodiment, the server can obtain a target cable power variation array based on the target cable's power variation curve, and perform data processing on the target cable power variation array to obtain the intermediate data. Step 108 determines the correlation coefficient between each pair of target cables within the same analysis area based on the intermediate data.

[0060] In this embodiment, the correlation coefficient θ between the two target cables represents the ratio of the power change values ​​between the two target cables, and the correlation coefficient θ between the two target cables ranges from 0 to 1. The closer θ is to 1, the weaker the correlation between the two target cables, and the closer θ is to 0, the stronger the correlation between the two target cables.

[0061] In the above-mentioned line correlation analysis method, the line simulation model is first divided into multiple analysis regions; each analysis region includes at least one generator and at least two cables. Then, the voltage parameters of the generators in each analysis region are adjusted to obtain power variation curves for multiple target cables within the analysis region. Furthermore, the power variation curves for each target cable within the same analysis region are processed to obtain intermediate data. Finally, based on the intermediate data, the correlation coefficient between each pair of target cables within the same analysis region is determined. This cable correlation facilitates timely monitoring of related cables when abnormal conditions occur, thereby improving the efficiency of monitoring cable operating conditions.

[0062] In some embodiments, adjusting the voltage parameters in each analysis area to obtain power change curves of multiple target cables in the analysis area can include: for a generator in each analysis area, determining multiple cables with the shortest distance to the generator node as target cables; adjusting the voltage parameters of the generator to obtain the power change curves corresponding to each of the multiple target cables.

[0063] In this embodiment, for a generator in each analysis area, the server can select multiple cables with the shortest distances to the generator node as target cables. For example, within the first analysis area, generator G1, generator G3, cable 2, cable 3, cable 4, cable 5, and cable 6 are all 3 node distances away from generator G1, cable 4 and cable 6 are 2 node distances away from generator G1, and cable 5 is 4 node distances away from generator G1. Because 2 < 3 < 4, within the first analysis area, for generator G1, the server can select cable 4 and cable 6 as target cables.

[0064] In another embodiment, for a generator in each analysis area, the server may also select multiple cables whose node distances from the generator are less than or equal to a preset node distance as target cables. For example, if the first analysis area includes generator G1, generator G3, cable 2, cable 3, cable 4, cable 5, and cable 6, and for generator G1, the node distances between cables 2 and 3 and generator G1 are both 3 node distances, the node distances between cables 4 and 6 and generator G1 are 2 node distances, and the node distance between cable 5 and generator G1 is 4 node distances, and if the preset node distance is 3 node distances, then the server may select cables 2, 3, 4, and 6 as target cables.

[0065] In some embodiments, adjusting the voltage parameter of the generator in each analysis area may include increasing or decreasing the voltage parameter by a preset percentage or a preset value each time.

[0066] In this embodiment, the server may adjust the voltage parameter of the generator by increasing the voltage parameter each time, or may decrease the voltage parameter each time, or may include both increasing and decreasing the voltage parameter.

[0067] In this embodiment, the server can increase or decrease the voltage parameter of the generator through MATLAB to obtain the power change curves corresponding to the multiple target cables. Figure 2 As shown, the application in a single-phase simplified circuit is taken as an example for explanation.

[0068] Specifically, it will be Figure 2 The unidirectional simplified circuit shown is equivalent, and the excitation branch of the transformer and the ground branch of the transmission line are ignored. The single-phase equivalent circuit is as follows Figure 3 As shown. Among them, the circuit equivalent impedance R ∑ As shown in formula (1), the circuit equivalent reactance X X As shown in formula (2):

[0069]

[0070]

[0071] Among them, R r is the equivalent resistance of the transformer, X r is the equivalent reactance of the transformer, R L is the equivalent resistance of the transmission line, X L is the equivalent reactance of the transmission line.

[0072] Then for this single-phase equivalent circuit, there exists a relationship as shown in formula (3):

[0073]

[0074] in, is the total voltage of the transmission line, is the node voltage, is the current of the transmission line.

[0075] Furthermore, by replacing current with power, the node voltage can be derived as shown in formula (4):

[0076]

[0077] Among them, P1 is the active power emitted by the generator endpoint, Q1 is the reactive power emitted by the generator endpoint (that is, the power at the endpoint where U1 is located), corresponding to Figure 2 The generator node G shown in FIG2 is as follows; P2 is the active power absorbed by the load, and Q2 is the reactive power absorbed by the load, corresponding to Figure 2The load node LD shown; j represents a complex number (in the complex domain, active power represents the real part, and reactive power represents the complex domain part).

[0078] Similarly, the node power can be derived as shown in formula (5):

[0079]

[0080] In summary, for the above single-phase equivalent circuit, an increase in the generator terminal voltage will increase the voltage and power of the nodes connected to it. Similarly, a decrease in the generator terminal voltage will decrease the voltage and power of the nodes connected to it.

[0081] In some embodiments, data processing is performed on the power change curve of each target cable in the same analysis area to obtain intermediate data, which may include: determining the power change array corresponding to each cable based on the power change curve corresponding to each target cable; calculating the mean and variance of each power change array; and normalizing the power change array, mean, and variance corresponding to each cable to obtain a normalized array corresponding to each target cable as intermediate data.

[0082] In this embodiment, after adjusting the voltage parameters of the generator, the server may obtain a power variation curve of each target cable within a preset time, and determine a power variation array of each target cable within the preset time.

[0083] In this embodiment, the server calculates the mean and variance of the power change array based on the power change array of the target cable within a preset time, and then normalizes the mean and variance to obtain the normalized array corresponding to each target cable as intermediate data. For example, the target cables are cable A and cable B, where the power change array of cable A is S A =[A1, A2, A3, ... A n ], the server can A Perform mean μ A The calculation of is shown in formula (6):

[0084]

[0085] Furthermore, the server can A Variance σ A The calculation of is shown in formula (7):

[0086]

[0087] The server can A Normalize and get μ A The corresponding normalized array a i, as shown in formula (8):

[0088]

[0089] Wherein, i=1, 2, 3, ..., n, and n is a positive integer.

[0090] Similarly, the server can also change the power of cable B to array S B , calculate S B The corresponding mean μ B , variance σ B With the normalized array b i .

[0091] In some embodiments, the intermediate data is in array form. Determining the correlation coefficient between every two target cables in the same analysis area based on the intermediate data may include: obtaining the array change value corresponding to each target cable based on the intermediate data corresponding to each target cable; obtaining the ratio of the array change values ​​corresponding to every two target cables based on the array change values ​​corresponding to every two target cables; and determining the correlation coefficient between every two target cables based on the ratio of the array change values ​​corresponding to every two target cables.

[0092] In this embodiment, the server can calculate the ratio d of the normalized array change values ​​between the two cables based on the normalized arrays corresponding to the two cables. i For example, when the target cables are cable A and cable B, the normalized array of cable A is a i , the normalized array of cable B is b i , then the ratio of the normalized array change value between cable A and cable B is d i As shown in formula (9):

[0093]

[0094] Wherein, i=1, 2, 3, ..., n-1, and n is a positive integer greater than or equal to 2.

[0095] Furthermore, the server can calculate the ratio d of the normalized array change values ​​between the two cables based on the ratio d i , calculate the correlation coefficient θ of the two cables, as shown in formula (10):

[0096] θ=|μ d -1| (10)

[0097] Among them, the ratio of multiple change values ​​of the normalized array between the two cables d i The mean μ d As shown in formula (11):

[0098]

[0099] In this embodiment, the value range of θ is [0, 1], where the closer θ is to 1, the weaker the correlation between the two target cables, and the closer θ is to 0, the stronger the correlation between the two target cables.

[0100] In some embodiments, the above method may also include: determining the power-time characteristics of multiple target cables in the same time interval based on the power change curves of multiple target cables in the analysis area; wherein the power-time characteristics refer to the trend of power changes over time; determining a first ranking of the correlation sizes between multiple target cables based on the power-time characteristics of multiple target cables in the same time interval; determining a second ranking of the correlation sizes between multiple target cables based on the correlation coefficient between every two target cables in the same analysis area; and verifying the second ranking between multiple target cables based on the first ranking between multiple target cables.

[0101] In this embodiment, the server can be based on Figure 4 The specific parameters of each electrical component in the system to be analyzed are shown in Figure 5 As shown, for generator G9, the server can select five cables, Line29to26, Line29to28, Line28to26, Line26to27, and Line26to25, as target cables, where Line29to26 represents the power cable between nodes 29 and 26, Line29to28 represents the power cable between nodes 29 and 28, Line28to26 represents the power cable between nodes 28 and 26, Line26to27 represents the power cable between nodes 26 and 27, and Line26to25 represents the power cable between nodes 26 and 25.

[0102] In this embodiment, the server can increase the voltage parameter of the generator according to a preset percentage. For example, the voltage of the excitation system of the generator G9 is marked as U G9 =0.83, the server can use 10% of the U G9 Increase the value of , so that they are:

[0103] 0.83×(1+0.1)=0.913

[0104] 0.83×(1+0.2)=0.996

[0105] 0.83×(1+0.3)=1.079

[0106] That is, the power was increased by 10% each time, resulting in the power variation curves for the five target cables shown in Figures 6(a) and 6(b). As shown in Figure 6(a), for cables "Line 29 to 28," "Line 29 to 26," and "Line 28 to 26," while the active power transmitted by these three target cables is not exactly identical in value, their temporal characteristics are very similar. That is, their transmitted power shows similar trends over time. Furthermore, as the voltage of generator G9's excitation system increases, the active power transmitted by these three cables increases, while maintaining highly consistent temporal characteristics. Therefore, based on the power variation curves, it can be seen that the three target cables are highly correlated. As shown in Figure 6(a), for cable "Line 26 to 27," its temporal characteristics also show some similarity to those of the three cables mentioned above, and the transmitted active power also increases as the voltage of generator G9's excitation system increases, but the similarity is lower than that of the three target cables mentioned above. Finally, for cable "Line26to25", after stabilization, the transmitted active power also meets the characteristic of increasing with the excitation system voltage of generator G9. However, its time characteristic is significantly different from that of the other four cables. Therefore, the correlation between cable "Line26to25" and the other four target cables is not high.

[0107] In this embodiment, based on the active power transmitted by the five target cables, it can be seen that the active power of the five target cables has obvious oscillation characteristics in the first 2 seconds and gradually stabilizes after 2 seconds.

[0108] Furthermore, as shown in Figure 6(b), based on the reactive power transmitted by the five target cables, it can be seen that the cables "Line 29 to 26" and "Line 28 to 26" have a strong correlation in terms of numerical value and time characteristics. Therefore, it can be considered that the cables with the highest degree of correlation among the five cables are "Line 29 to 26" and "Line 28 to 26".

[0109] In another embodiment, the server can reduce the voltage parameter of the generator according to a preset percentage. For example, the voltage of the excitation system of the generator G9 is marked as U G9 =0.83, the server can use 10% of the U G9 Increase the value of , so that they are:

[0110] 0.83×(1-0.1)=0.747

[0111] 0.83×(1-0.2)=0.664

[0112] 0.83×(1-0.3)=0.581

[0113] That is, by reducing the power by 10% each time, the simulation results show the power variation curves for the five target cables, as shown in Figures 7(a) and 7(b). Similarly, as shown in Figure 7(a), based on the active power transmitted by the five target cables, the time characteristics of cables "Line 29 to 28," "Line 29 to 26," and "Line 28 to 26" are highly similar, with the trends in the time variation of the transmitted active power being highly similar. However, for cables "Line 26 to 27" and "Line 26 to 25," their time characteristics are not very similar to those of the other cables.

[0114] In this embodiment, the five target cables basically all meet the requirement that the transmitted active power decreases as the voltage of the excitation system of the generator G9 decreases.

[0115] In this embodiment, as shown in FIG7( b ), based on the reactive power transmitted by the five target cables, it can be seen that the cables “Line 29 to 26” and “Line 28 to 26” have a strong correlation in terms of numerical value and time characteristics. Therefore, it can be considered that these two cables have the highest similarity.

[0116] In summary, by adjusting the voltage value of the excitation system of generator G9 for simulation and observing the curves of the active power and reactive power transmitted by each cable over time, it can be seen that the cables with the highest correlation are "Line 29 to 26" and "Line 28 to 26", while the cables with the lowest correlation are "Line 26 to 27" and "Line 26 to 25".

[0117] Furthermore, based on the analysis of the power variation curves, a first ranking of the correlations between the multiple target cables can be determined. Then, based on the correlation coefficients between every two target cables within the same analysis area, the server can determine a second ranking of the correlations between the multiple target cables. Finally, the server can verify the second ranking of the multiple target cables based on the first ranking.

[0118] In this embodiment, if the first and second rankings are consistent, the second ranking is considered correct. When a cable fault occurs in the line, the cables with a high correlation with the faulty cable can be monitored and maintained promptly based on the correlation. If the first and second rankings are inconsistent, a fault may have occurred in the target cable, and the server can promptly troubleshoot the line.

[0119] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0120] Based on the same inventive concept, embodiments of the present application also provide a circuit correlation analysis device for implementing the aforementioned circuit correlation analysis method. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more circuit correlation analysis device embodiments provided below can be found in the limitations of the circuit correlation analysis method described above and will not be further elaborated here.

[0121] In one embodiment, Figure 8 As shown, a line correlation analysis device is provided, comprising: a region division module 802, a power change curve acquisition module 804, a data processing module 806 and a correlation coefficient determination module 808, wherein:

[0122] The region division module 802 is used to divide the line simulation model into multiple analysis regions; each analysis region includes at least one generator and at least two cables.

[0123] The power variation curve acquisition module 804 is used to adjust the voltage parameters of the generator in each analysis area and obtain the power variation curves of multiple target cables in the analysis area.

[0124] The data processing module 806 is used to process the power variation curve of each target cable in the same analysis area to obtain intermediate data.

[0125] The correlation coefficient determination module 808 is used to determine the correlation coefficient between every two target cables in the same analysis area based on the intermediate data.

[0126] In one embodiment, the power variation curve acquisition module 804 may include:

[0127] The target cable determination submodule is used to determine, for a generator in each analysis area, multiple cables with the shortest distance to the generator node as target cables.

[0128] The power variation curve determination submodule is used to adjust the voltage parameters of the generator to obtain the power variation curves corresponding to the multiple target cables.

[0129] In one embodiment, the power variation curve acquisition module 804 may further include:

[0130] The voltage parameter adjustment submodule is used to increase or decrease the voltage parameter according to a preset percentage or preset value each time.

[0131] In one embodiment, the data processing module 806 may include:

[0132] The power change array determination submodule is used to determine the power change array corresponding to each cable based on the power change curve corresponding to each target cable.

[0133] The numerical calculation submodule is used to calculate the mean and variance of each power change array.

[0134] The normalization submodule is used to normalize the power change array, mean and variance corresponding to each cable to obtain the normalized array corresponding to each target cable as intermediate data.

[0135] In one embodiment, the intermediate data is in array form, and the correlation coefficient determination module 808 may include:

[0136] The array change value acquisition submodule is used to obtain the array change value corresponding to each cable based on the intermediate data corresponding to each target cable.

[0137] The ratio determination submodule is used to obtain the ratio of the array change values ​​corresponding to each two target cables based on the array change values ​​corresponding to each two target cables.

[0138] The coefficient determination submodule is used to determine the correlation coefficient of each two target cables based on the ratio of the array change values ​​corresponding to each two target cables.

[0139] In one embodiment, the above device may further include:

[0140] The power time characteristic determination module is used to determine the power time characteristics of multiple target cables in the same time interval based on the power change curves of multiple target cables in the analysis area; wherein the power time characteristic refers to the trend of power change over time.

[0141] The first ranking determination module is configured to determine a first ranking of correlations among the multiple target cables based on power-time characteristics of the multiple target cables within the same time interval.

[0142] The second ranking determination module is configured to determine a second ranking of the correlation between the plurality of target cables based on the correlation coefficient between every two target cables in the same analysis area.

[0143] The sorting verification module is configured to verify a second sorting among the plurality of target cables based on the first sorting among the plurality of target cables.

[0144] Each module in the aforementioned circuit correlation analysis device can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in the computer device in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0145] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 9 As shown. The computer device includes a processor, a memory, and a network interface connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store data such as power change arrays and intermediate data. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a line correlation analysis method is implemented.

[0146] Those skilled in the art will understand that Figure 9 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0147] In one embodiment, a computer device is provided, comprising a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented: dividing a line simulation model into multiple analysis areas; each analysis area includes at least one generator and at least two cables; adjusting the voltage parameters of the generator in each analysis area to obtain power change curves of multiple target cables in the analysis area; performing data processing on the power change curve of each target cable in the same analysis area to obtain intermediate data; and determining the correlation coefficient between each two target cables in the same analysis area based on the intermediate data.

[0148] In one embodiment, when the processor executes the computer program, it also adjusts the voltage parameters in each analysis area to obtain the power change curves of multiple target cables in the analysis area, which can include: for a generator in each analysis area, determining multiple cables with the shortest distance to the generator node as target cables; adjusting the voltage parameters of the generator to obtain the power change curves corresponding to each of the multiple target cables.

[0149] In one embodiment, when the processor executes the computer program, it further adjusts the voltage parameter of the generator in each analysis area, which may include increasing or decreasing the voltage parameter by a preset percentage or a preset value each time.

[0150] In one embodiment, when the processor executes the computer program, it also implements data processing on the power change curve of each target cable in the same analysis area to obtain intermediate data, which may include: determining the power change array corresponding to each cable based on the power change curve corresponding to each target cable; calculating the mean and variance of each power change array; normalizing the power change array, mean and variance corresponding to each cable to obtain a normalized array corresponding to each target cable as intermediate data.

[0151] In one embodiment, the intermediate data is in array form, and when the processor executes the computer program, it also determines the correlation coefficient between every two target cables in the same analysis area based on the intermediate data, which may include: obtaining the array change value corresponding to each target cable based on the intermediate data corresponding to each target cable; obtaining the ratio of the array change values ​​corresponding to every two target cables based on the array change values ​​corresponding to every two target cables; and determining the correlation coefficient between every two target cables based on the ratio of the array change values ​​corresponding to every two target cables.

[0152] In one embodiment, when the processor executes the computer program, it also implements the following steps: based on the power change curves of multiple target cables in the analysis area, determining the power-time characteristics of the multiple target cables in the same time interval; wherein the power-time characteristics refer to the trend of power change over time; based on the power-time characteristics of the multiple target cables in the same time interval, determining a first ranking of the correlation sizes between the multiple target cables; based on the correlation coefficient between every two target cables in the same analysis area, determining a second ranking of the correlation sizes between the multiple target cables; based on the first ranking between the multiple target cables, verifying the second ranking between the multiple target cables.

[0153] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented: dividing a line simulation model into multiple analysis areas; each analysis area includes at least one generator and at least two cables; adjusting the voltage parameters of the generator in each analysis area to obtain power change curves of multiple target cables in the analysis area; performing data processing on the power change curve of each target cable in the same analysis area to obtain intermediate data; and determining the correlation coefficient between each two target cables in the same analysis area based on the intermediate data.

[0154] In one embodiment, when the computer program is executed by the processor, it also adjusts the voltage parameters in each analysis area and obtains the power change curves of multiple target cables in the analysis area, which may include: for a generator in each analysis area, determining multiple cables with the shortest distance to the generator node as target cables; adjusting the voltage parameters of the generator to obtain the power change curves corresponding to each of the multiple target cables.

[0155] In one embodiment, when the computer program is executed by the processor, it further adjusts the voltage parameter of the generator in each analysis area, which may include increasing or decreasing the voltage parameter by a preset percentage or a preset value each time.

[0156] In one embodiment, when the computer program is executed by the processor, it also implements data processing of the power change curve of each target cable in the same analysis area to obtain intermediate data, which may include: determining the power change array corresponding to each cable based on the power change curve corresponding to each target cable; calculating the mean and variance of each power change array; normalizing the power change array, mean and variance corresponding to each cable to obtain a normalized array corresponding to each target cable as intermediate data.

[0157] In one embodiment, the intermediate data is in array form, and when the computer program is executed by the processor, it is also implemented to determine the correlation coefficient between every two target cables in the same analysis area based on the intermediate data, which may include: based on the intermediate data corresponding to each target cable, obtaining the array change value corresponding to each cable; based on the array change value corresponding to each two target cables, obtaining the ratio of the array change values ​​corresponding to each two target cables; based on the ratio of the array change values ​​corresponding to each two target cables, determining the correlation coefficient between each two target cables.

[0158] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: based on the power change curves of multiple target cables in the analysis area, the power-time characteristics of the multiple target cables in the same time interval are determined; wherein the power-time characteristics refer to the trend of power change over time; based on the power-time characteristics of the multiple target cables in the same time interval, a first ranking of the correlation sizes between the multiple target cables is determined; based on the correlation coefficient between every two target cables in the same analysis area, a second ranking of the correlation sizes between the multiple target cables is determined; based on the first ranking between the multiple target cables, the second ranking between the multiple target cables is verified.

[0159] In one embodiment, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the following steps: dividing a line simulation model into multiple analysis areas; each analysis area includes at least one generator and at least two cables; adjusting the voltage parameters of the generator in each analysis area to obtain power variation curves of multiple target cables in the analysis area; performing data processing on the power variation curve of each target cable in the same analysis area to obtain intermediate data; and determining, based on the intermediate data, a correlation coefficient between each two target cables in the same analysis area.

[0160] In one embodiment, when the computer program is executed by the processor, it also adjusts the voltage parameters in each analysis area and obtains the power change curves of multiple target cables in the analysis area, which may include: for a generator in each analysis area, determining multiple cables with the shortest distance to the generator node as target cables; adjusting the voltage parameters of the generator to obtain the power change curves corresponding to each of the multiple target cables.

[0161] In one embodiment, when the computer program is executed by the processor, it further adjusts the voltage parameter of the generator in each analysis area, which may include increasing or decreasing the voltage parameter by a preset percentage or a preset value each time.

[0162] In one embodiment, when the computer program is executed by the processor, it also implements data processing of the power change curve of each target cable in the same analysis area to obtain intermediate data, which may include: determining the power change array corresponding to each cable based on the power change curve corresponding to each target cable; calculating the mean and variance of each power change array; normalizing the power change array, mean and variance corresponding to each cable to obtain a normalized array corresponding to each target cable as intermediate data.

[0163] In one embodiment, the intermediate data is in array form, and when the computer program is executed by the processor, it is also implemented to determine the correlation coefficient between every two target cables in the same analysis area based on the intermediate data, which may include: based on the intermediate data corresponding to each target cable, obtaining the array change value corresponding to each cable; based on the array change value corresponding to each two target cables, obtaining the ratio of the array change values ​​corresponding to each two target cables; based on the ratio of the array change values ​​corresponding to each two target cables, determining the correlation coefficient between each two target cables.

[0164] In one embodiment, when the computer program is executed by the processor, the following steps can also be implemented: based on the power change curves of multiple target cables in the analysis area, the power-time characteristics of the multiple target cables in the same time interval are determined; wherein the power-time characteristics refer to the trend of power change over time; based on the power-time characteristics of the multiple target cables in the same time interval, a first ranking of the correlation sizes between the multiple target cables is determined; based on the correlation coefficient between every two target cables in the same analysis area, a second ranking of the correlation sizes between the multiple target cables is determined; based on the first ranking between the multiple target cables, the second ranking between the multiple target cables is verified.

[0165] It should be noted that the data involved in this application (including but not limited to data used for analysis, stored data, displayed data, etc.) are all information and data authorized by the user or fully authorized by all parties.

[0166] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processor involved in the various embodiments provided herein may be, but are not limited to, a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, and the like.

[0167] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0168] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A line correlation analysis method, characterized in that: The method comprises: Dividing the line simulation model into a plurality of analysis areas; each of the analysis areas includes at least one generator and at least two cables; Adjusting the voltage parameters of the generator in each of the analysis areas to obtain power variation curves of multiple target cables in the analysis area; Performing data processing on the power variation curve of each target cable in the same analysis area to obtain intermediate data; determining, based on the intermediate data, a correlation coefficient between every two target cables in the same analysis area; The step of processing the power variation curve of each target cable in the same analysis area to obtain intermediate data includes: Determining a power change array corresponding to each of the target cables based on a power change curve corresponding to each of the target cables; Calculating the mean and variance of each of the power change arrays; The power change array, mean value and variance corresponding to each of the cables are normalized to obtain a normalized array corresponding to each of the target cables as intermediate data.

2. The method according to claim 1, characterized in that The step of adjusting the voltage parameters in each of the analysis areas to obtain power change curves of multiple target cables in the analysis area includes: For each generator in the analysis area, determining a plurality of cables with the shortest distances to the generator node as target cables; The voltage parameters of the generator are adjusted to obtain power variation curves corresponding to the plurality of target cables.

3. The method according to claim 1, characterized in that The adjusting the voltage parameters of the generator in each of the analysis areas includes: The voltage parameter is increased or decreased each time according to a preset percentage or a preset value.

4. The method according to claim 1, wherein The intermediate data is in array form, and determining the correlation coefficient between every two target cables in the same analysis area based on the intermediate data includes: Based on the intermediate data corresponding to each target cable, obtaining an array change value corresponding to each target cable; Based on the array change values ​​corresponding to each two target cables, obtaining a ratio of the array change values ​​corresponding to each two target cables; Based on the ratio of the array change values ​​corresponding to each two target cables, a correlation coefficient between each two target cables is determined.

5. The method according to claim 1, wherein The method further comprises: Determining power-time characteristics of the plurality of target cables within the same time interval based on power change curves of the plurality of target cables within the analysis area; wherein the power-time characteristics refer to a trend of power change over time; Determining a first ranking of correlations between the plurality of target cables based on power-time characteristics of the plurality of target cables within the same time interval; Determining a second ranking of the magnitude of the correlations between the plurality of target cables based on the correlation coefficient between every two target cables in the same analysis area; The second ordering among the plurality of target cables is verified based on the first ordering among the plurality of target cables.

6. A line correlation analysis device, characterized in that: The device comprises: A region division module, configured to divide the line simulation model into a plurality of analysis regions; each of the analysis regions includes at least one generator and at least two cables; A power variation curve acquisition module, configured to adjust the voltage parameters of the generator in each of the analysis areas and acquire power variation curves of multiple target cables in the analysis area; A data processing module, configured to process the power variation curve of each target cable in the same analysis area to obtain intermediate data; a correlation coefficient determination module, configured to determine the correlation coefficient between every two target cables in the same analysis area based on the intermediate data; The data processing module includes: A power change array determination submodule is used to determine the power change array corresponding to each cable based on the power change curve corresponding to each target cable; Numerical calculation submodule, used to calculate the mean and variance of each power change array; The normalization submodule is used to normalize the power change array, mean and variance corresponding to each cable to obtain the normalized array corresponding to each target cable as intermediate data.

7. The device according to claim 6, characterized in that The power change curve acquisition module includes: a target cable determination submodule, for determining, for a generator in each analysis area, a plurality of cables with the shortest distance to the generator node as target cables; The power variation curve determination submodule is used to adjust the voltage parameters of the generator to obtain the power variation curves corresponding to the multiple target cables.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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