Method for establishing main grid and distribution network integrated calculation data model based on real-time measurement data

Abstract

This invention discloses a method for establishing an integrated calculation data model for power distribution networks based on real-time measurement data, relating to the field of power distribution networks. It addresses the problem of existing methods lacking regional specificity in establishing integrated calculation data models for power distribution networks. The method includes steps S1: periodically monitoring load changes at power nodes in a sample power network, obtaining the load calculation complexity corresponding to each power node based on the monitoring results, and obtaining time-period load analysis data; step S2: periodically monitoring frequency changes at power nodes in the sample power network based on the time-period load analysis data, obtaining the frequency control complexity corresponding to each power node based on the monitoring results, and obtaining time-period frequency analysis data; and step S3: matching the calculation data model of the sample power network based on the time-period frequency analysis data and the time-period load analysis data. This invention improves the regional specificity of the integrated calculation data model establishment method for power distribution networks.

Description

Technical Field

[0001] This invention belongs to the field of power main and distribution networks and relates to measurement data analysis technology, specifically a method for establishing an integrated calculation data model of the main and distribution networks based on real-time measurement data. Background Technology

[0002] The existing methods for establishing integrated computing data models for main and distribution networks have the following drawbacks:

[0003] 1. When monitoring the load of each power node in the sample power network, the existing data model matching is difficult to determine the load change period based on the actual power load change of each power node. It is impossible to create a targeted main and distribution network integrated calculation data model by analyzing the load calculation complexity of the power node during the load change period, resulting in the main and distribution network integrated calculation data model lacking load targeting.

[0004] 2. When monitoring the frequency of each power node in the sample power network, the existing data model matching makes it difficult to determine the frequency change period based on the actual power frequency change of each power node. It is also impossible to create a targeted integrated calculation data model for the main and distribution networks by analyzing the load change complexity of power nodes during the frequency change period, resulting in a lack of frequency specificity in the integrated calculation data model for the main and distribution networks.

[0005] To address this, we propose a method for establishing an integrated computing data model for the main and distribution networks based on real-time measurement data. Summary of the Invention

[0006] To address the shortcomings of existing technologies, the purpose of this invention is to provide a method for establishing an integrated computing data model of the main distribution network based on real-time measurement data. This invention aims to improve the regional specificity of the matching and establishment process of the integrated computing data model of the main distribution network.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: a method for establishing an integrated calculation data model of the main distribution network based on real-time measurement data, comprising the following steps:

[0008] Step S1: Perform periodic load change monitoring on the power nodes in the sample power network, obtain the load calculation complexity corresponding to the power nodes based on the monitoring results, and obtain the time period load analysis data;

[0009] Step S2: Based on the time-period load analysis data, periodic frequency change monitoring is performed on the power nodes in the sample power network. Based on the monitoring results, the frequency control complexity corresponding to the power nodes is obtained, and time-period frequency analysis data is obtained.

[0010] Step S3: Perform data model matching on the sample power network based on the time period frequency analysis data and the time period load analysis data.

[0011] Furthermore, step S1 also includes the following steps:

[0012] Step S11: Obtain the power network for which the integrated calculation data model of the main and distribution networks needs to be established, obtain multiple power networks, and arbitrarily select one sample power network from the multiple obtained power networks;

[0013] Step S12: During the historical period when the sample power network is in operation, arbitrarily select several historical load analysis periods of equal duration, and arbitrarily select one sample load analysis period from the multiple historical load analysis periods obtained;

[0014] Step S13: Perform node load analysis on the sample power network during the sample load analysis period, and obtain the load calculation complexity corresponding to the sample power network based on the analysis results;

[0015] Step S14: Obtain the load calculation complexity corresponding to each power node to obtain the time period load analysis data.

[0016] Furthermore, step S13 also includes the following steps:

[0017] Step S131: Obtain the power nodes contained in the sample power network, and arbitrarily select one sample power node from the obtained power nodes;

[0018] Step S132: During the sample load analysis period, the time points when the power load corresponding to the sample power node changes are obtained, resulting in multiple load change time points. The time interval between any two adjacent load change time points is set as the load change period, and the obtained load change periods are named sequentially from F1 load change period to Fa load change period in chronological order.

[0019] Step S133: Perform load change rate analysis on the F1 load change period, and obtain the F1 load change rate during the period based on the analysis results;

[0020] Step S134: Perform load change rate analysis on the periods from F2 load change to Fa load change, and obtain the load change rate from F2 load period to Fa load period based on the analysis results;

[0021] Step S135: Reorder the load change rate from F1 to Fa in descending order of numerical value, and rename the load change rate from F1 to Fa to S1 to Sa according to the sorting order.

[0022] Furthermore, step S13 also includes the following steps:

[0023] Step S136: Obtain the median of the load change rate from S1 to Sa to get the first load index value corresponding to the sample power node;

[0024] Step S137: Sum the load change rate from S1 to Sa to obtain the cumulative load change rate. Calculate the ratio of the load change rate from S1 to Sa to obtain the cumulative load change rate from S1 to Sa. Sum the load change rate from S1 to Sa to obtain the sum of the S2 change rates. Calculate the ratio of the sum of the S2 change rates to the cumulative load change rate from S1 to Sa to obtain the cumulative load change rate from S1 to Sa.

[0025] Step S138: Obtain the median value of the cumulative ratio, calculate the absolute value of the difference between the cumulative ratio of load descending order S1 to the cumulative ratio of load descending order Sa and the median value of the cumulative ratio, and obtain the median deviation of load S1 to the median deviation of load Sa;

[0026] Step S139: Compare the values ​​of the load median deviation from S1 to Sa. Set the load median deviation with the smallest value as the second load index value. Calculate the average of the first and second load index values ​​to obtain the average load index value corresponding to the sample power node. Obtain the median value of the voltage level range corresponding to the sample power node to obtain the voltage level median value. Calculate the load calculation complexity corresponding to the sample power node by combining the voltage level median value and the load index median value.

[0027] Furthermore, step S133 also includes the following steps:

[0028] Set the start time of the period corresponding to the F1 load change period as the first period characteristic time point, and set the end time of the period corresponding to the F1 load change period as the second period characteristic time point.

[0029] The interval between the characteristic time points of the first time period and the characteristic time points of the second time period is obtained to obtain the characteristic interval duration;

[0030] The actual loads corresponding to the characteristic time points of the sample power nodes in the first and second time periods are obtained respectively, thus obtaining the characteristic loads of the first and second time periods.

[0031] The F1 load period change rate is obtained by calculating the characteristic load of the first period, the characteristic load of the second period, and the characteristic interval duration.

[0032] Furthermore, step S2 also includes the following steps:

[0033] Step S21: Obtain time period load analysis data. Based on the time period load analysis data, obtain the sample power network. Within the historical time period when the sample power network is in operation, arbitrarily select several historical frequency analysis periods of equal duration, and arbitrarily select one sample frequency analysis period from the multiple historical frequency analysis periods obtained.

[0034] Step S22: Perform node frequency analysis on the sample power network during the sample frequency analysis period, and obtain the time period frequency complexity of the sample power network based on the analysis results;

[0035] Step S23: Obtain the power nodes contained in the sample power network, and arbitrarily select a sample power node from the obtained power nodes. Perform frequency change analysis on the sample power node to obtain the frequency calculation complexity corresponding to the sample power node.

[0036] Step S24: Obtain the frequency computation complexity corresponding to each power node to obtain the time period frequency analysis data corresponding to the sample power network.

[0037] Furthermore, step S23 also includes the following steps:

[0038] Step S231: During the sample frequency analysis period, the time points when the power frequency corresponding to the sample power node changes are obtained, resulting in multiple frequency change time points. The time interval between any two adjacent frequency change time points is set as the frequency change period, and the obtained frequency change periods are named sequentially from P1 frequency change period to Pc frequency change period in chronological order.

[0039] Step S232: Analyze the frequency change rate during the P1 frequency change period, and obtain the frequency change rate during the P1 period based on the analysis results;

[0040] Step S233: Perform frequency change rate analysis on the time periods from P2 frequency change to Pc frequency change, and obtain the frequency change rate from P2 frequency change period to Pc frequency change period based on the analysis results;

[0041] Step S234: Sort the frequency change rates from P1 to Pc in descending order according to their numerical values, and rename them according to the sorting order as frequency change rates from P1 to Pc to Q1 to Qc.

[0042] Step S235: Obtain the median of the frequency change rate from Q1 to Qc to get the first frequency index value corresponding to the sample power node.

[0043] Furthermore, step S23 also includes the following steps:

[0044] Step S236: Sum the frequency change rates from Q1 to Qc to obtain the cumulative frequency change rate. Calculate the ratio of the frequency change rate from Q1 to Qc to obtain the cumulative frequency change rate in descending order. Sum the frequency change rates from Q1 to Q2 to obtain the sum of the frequency change rates in Q2. Calculate the ratio of the sum of the frequency change rates in Q2 to the cumulative frequency change rate in descending order to obtain the cumulative frequency change rate in descending order to obtain the cumulative frequency change rate in descending order to obtain the cumulative frequency change rate in descending order to obtain the cumulative frequency change rate in descending order to obtain the cumulative frequency change rate in descending order to obtain the cumulative frequency change rate in descending order to obtain the sum of the frequency change rates from Q1 to Qc. Calculate the ratio of the sum of the frequency change rates in Qc to the cumulative frequency change rate in descending order to obtain ...

[0045] Step S237: Obtain the median value of the cumulative ratio, calculate the absolute value of the difference between the cumulative ratio in descending order of Q1 frequency and the cumulative ratio in descending order of Qc frequency and the median value of the cumulative ratio, and obtain the median deviation of Q1 frequency to the median deviation of Qc frequency;

[0046] Step S238: Compare the numerical values ​​of the frequency median deviation from Q1 to Qc, set the frequency median deviation with the smallest value as the second frequency index value, and calculate the average of the first and second frequency index values ​​to obtain the average frequency index value corresponding to the sample power nodes.

[0047] Step S239: Obtain the intermediate value of the voltage level range corresponding to the sample power node, and calculate the frequency calculation complexity corresponding to the sample power node by combining the intermediate value of the voltage level and the intermediate value of the frequency index.

[0048] Furthermore, step S232 also includes the following steps:

[0049] Set the start time of the period corresponding to the frequency change of P1 as the first frequency characteristic time point, and set the end time of the period corresponding to the frequency change of P1 as the second frequency characteristic time point.

[0050] The interval between the first frequency characteristic time point and the second frequency characteristic time point is obtained to obtain the characteristic interval duration;

[0051] The actual frequencies corresponding to the first frequency characteristic time point and the second frequency characteristic time point of the sample power nodes are obtained respectively to obtain the characteristic frequency of the first time period and the characteristic frequency of the second time period.

[0052] The frequency variation rate of P1 during the time period is obtained by calculating the characteristic frequency of the first time period, the characteristic frequency of the second time period, and the characteristic interval duration.

[0053] The rate of change of frequency P1 over time is calculated using the following formula:

[0054] ;

[0055] Wherein, BlP1 is the frequency change rate of P1 during the time period, TPb1 is the characteristic frequency of the first time period, TPb2 is the characteristic frequency of the second time period, and Tsc is the duration of the characteristic interval.

[0056] Furthermore, step S3 also includes the following steps:

[0057] Obtain time-period load analysis data, calculate the load complexity corresponding to each power node based on the time-period load analysis data, and calculate the average of the obtained multiple load calculation complexities to obtain the model matching load coefficient;

[0058] Obtain time period frequency analysis data, calculate the frequency calculation complexity corresponding to each power node based on the time period frequency analysis data, and calculate the average of the obtained multiple frequency calculation complexities to obtain the model matching load coefficient;

[0059] Obtain the load matching interval and frequency matching interval of the distributed computing model;

[0060] If the model matching load factor is within the load matching range of the distributed computing model and the model matching frequency factor is within the frequency matching range of the distributed computing model, then a distributed main and distribution network integrated computing data model is established for the sample power network.

[0061] If the model matching load factor is not within the load matching range of the distributed computing model or the model matching frequency factor is not within the frequency matching range of the distributed computing model, then a centralized integrated main and distribution network computing data model is established for the sample power network.

[0062] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0063] 1. When monitoring the load of each power node in the sample power network, this invention determines the load change period based on the actual power load change of each power node. By analyzing the load calculation complexity of the power node during the load change period, the integrated calculation data model of the main and distribution network is created in a targeted manner, thereby improving the load targeting of the integrated calculation data model of the main and distribution network.

[0064] 2. When monitoring the frequency of each power node in the sample power network, this invention determines the frequency change period based on the actual power frequency change of each power node. By analyzing the load change complexity of the power node during the frequency change period, a targeted creation of the integrated calculation data model of the main and distribution networks is made, thereby improving the frequency targeting of the integrated calculation data model of the main and distribution networks. Attached Figure Description

[0065] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0066] Figure 1 This is an overall system block diagram of the present invention;

[0067] Figure 2 This is a schematic diagram of the cumulative load ratio in descending order according to the present invention. Detailed Implementation

[0068] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0069] Example 1

[0070] Please see Figure 1 This invention provides a technical solution: a method for establishing an integrated calculation data model of the main distribution network based on real-time measurement data, comprising the following steps:

[0071] Step S1: Perform periodic load change monitoring on the power nodes in the sample power network, obtain the load calculation complexity corresponding to the power nodes based on the monitoring results, and obtain the time period load analysis data;

[0072] Step S1 further includes the following steps:

[0073] The power network that needs to be used to establish the integrated calculation data model of the main and distribution networks is acquired, resulting in multiple power networks. Then, a sample power network is randomly selected from the acquired power networks.

[0074] Within the historical period when the sample power network is in operation, several historical load analysis periods of equal duration are arbitrarily selected, and one sample load analysis period is arbitrarily selected from the multiple historical load analysis periods obtained.

[0075] Perform node load analysis on the sample power network during the sample load analysis period, and obtain the time period load complexity of the sample power network based on the analysis results;

[0076] Specifically as follows:

[0077] The power nodes contained in the sample power network are acquired, and one sample power node is randomly selected from the acquired power nodes.

[0078] During the sample load analysis period, the time points when the power load corresponding to the sample power node changes are obtained, resulting in multiple load change time points. The time interval between any two adjacent load change time points is set as the load change period, and the obtained load change periods are named sequentially from F1 load change period to Fa load change period in chronological order.

[0079] It should be noted here that:

[0080] In this application, 1, 2, 3...a in the load change period from F1 to Fa are the numbers corresponding to the load change period, and a is an integer greater than 0.

[0081] Perform load change rate analysis on the F1 load change period, and obtain the F1 load change rate during the period based on the analysis results;

[0082] Specifically as follows:

[0083] Set the start time of the period corresponding to the F1 load change period as the first period characteristic time point, and set the end time of the period corresponding to the F1 load change period as the second period characteristic time point.

[0084] The interval between the characteristic time points of the first time period and the characteristic time points of the second time period is obtained to obtain the characteristic interval duration;

[0085] The actual loads corresponding to the characteristic time points of the sample power nodes in the first and second time periods are obtained respectively, thus obtaining the characteristic loads of the first and second time periods.

[0086] The F1 load period change rate is obtained by calculating the characteristic load of the first period, the characteristic load of the second period, and the characteristic interval duration.

[0087] The formula for calculating the rate of change of load F1 during the time period is as follows:

[0088] ;

[0089] Where Blf1 is the F1 load change rate during the period, Tfb1 is the characteristic load of the first period, Tfb2 is the characteristic load of the second period, and Tsc is the characteristic interval duration;

[0090] Repeat the process of obtaining the load change rate of F1, and perform load change rate analysis for the periods from F2 to Fa respectively. Based on the analysis results, obtain the load change rate from F2 to Fa.

[0091] Reorder the load change rate from F1 to Fa in descending order of numerical value, and rename the load change rate from F1 to Fa to S1 to Sa according to the sorting order.

[0092] The median of the load change rate from S1 to Sa is used to obtain the first load index value corresponding to the sample power node;

[0093] Please see Figure 2 The load change rate from S1 to Sa is summed to obtain the cumulative load change rate. The ratio of the load change rate from S1 to Sa is calculated to obtain the cumulative load change rate from S1 to Sa. The load change rate from S1 to Sa is summed to obtain the cumulative load change rate from S1 to Sa. The load change rate from S1 to Sa is summed to obtain the cumulative load change rate from S2 to Sa. The load change rate from S2 to Sa is summed to obtain the cumulative load change rate from S1 to Sa. The load change rate from S1 to Sa is summed to obtain the cumulative load change rate from S1 to Sa. The load change rate from Sa to Sa is summed to obtain the cumulative load change rate from S1 to Sa.

[0094] Obtain the median value of the cumulative ratio, calculate the absolute values ​​of the differences between the cumulative ratio of S1 load descending order to the cumulative ratio of Sa load descending order and the median value of the cumulative ratio, and obtain the median deviation of S1 load to Sa load median deviation;

[0095] Compare the values ​​of the load median deviation from S1 to Sa, and set the load median deviation with the smallest value as the second load index value.

[0096] The average of the first load index value and the second load index value is calculated to obtain the average load index value corresponding to the sample power node;

[0097] The intermediate value of the voltage level range corresponding to the corresponding sample power node is obtained to obtain the intermediate value of the voltage level;

[0098] The calculation complexity of the load corresponding to the sample power node is obtained by calculating the intermediate values ​​of voltage levels and load indices.

[0099] The computational complexity of the load corresponding to the sample power node is calculated using the following formula:

[0100] ;

[0101] Where Fjd is the load calculation complexity corresponding to the sample power node, Vfz is the intermediate value of the voltage level, and Fzj is the intermediate value of the load index;

[0102] Repeat the process of obtaining the load calculation complexity corresponding to the sample power nodes, and obtain the load calculation complexity corresponding to each power node to obtain the time period load analysis data;

[0103] It should be noted here that:

[0104] When the load monitoring of each power node in the sample power network is carried out in step S1 above, the load change period is determined according to the actual power load change of each power node. The subsequent step S3 performs targeted model matching on the integrated calculation data model of the main and distribution network by analyzing the load calculation complexity of the power node during the load change period. By tracking the load change of each node in real time, the key period is accurately defined, so that the modeling focuses on the stage of significant load fluctuation, avoiding redundant analysis of the stable operation period, and forming an adaptive modeling strategy.

[0105] Secondly, based on the time period characteristics, centralized or distributed models are matched differently: distributed models are enabled during periods of sudden load changes, and the complexity analysis capability is improved through multi-node parallel computing to ensure the high-precision analysis requirements. At the same time, the modular model architecture supports independent optimization of steady-state and transient models, which maintains overall consistency and facilitates local improvements.

[0106] Furthermore, accurate identification of critical periods enhances the ability to detect abnormal events, achieving a balance between rapid early warning and low false alarm rates by setting dynamic sensitivity thresholds. This design also provides data support for operation and maintenance decisions, identifying network weaknesses through complexity distribution characteristics and driving a shift in maintenance mode from passive response to proactive prevention. Ultimately, this closed-loop optimization method significantly shortens the time interval between load changes and system response, improving the dynamic adjustment capability of the power network.

[0107] Step S2: Based on the time-period load analysis data, periodic frequency change monitoring is performed on the power nodes in the sample power network. Based on the monitoring results, the frequency control complexity corresponding to the power nodes is obtained, and time-period frequency analysis data is obtained.

[0108] Step S2 further includes the following steps:

[0109] Obtain time period load analysis data, and acquire sample power networks based on time period load analysis data. Within the historical time period when the sample power network is in operation, arbitrarily select several historical frequency analysis periods of equal duration, and arbitrarily select one sample frequency analysis period from the acquired historical frequency analysis periods.

[0110] Perform node frequency analysis on the sample power network during the sample frequency analysis period, and obtain the time period frequency complexity of the sample power network based on the analysis results;

[0111] Specifically as follows:

[0112] The power nodes contained in the sample power network are acquired, and a sample power node is randomly selected from the acquired power nodes. The frequency change of the sample power node is analyzed to obtain the frequency calculation complexity corresponding to the sample power node.

[0113] Specifically as follows:

[0114] During the sample frequency analysis period, the time points when the power frequency corresponding to the sample power node changes are obtained, resulting in multiple frequency change time points. The time interval between any two adjacent frequency change time points is set as the frequency change period, and the obtained frequency change periods are named sequentially from P1 frequency change period to Pc frequency change period in chronological order.

[0115] In this application, 1, 2, 3...c in the frequency change period from P1 to Pc are the numbers corresponding to the frequency change period, and c is an integer greater than 0.

[0116] The frequency change rate analysis was performed during the P1 frequency change period, and the frequency change rate of P1 during the period was obtained based on the analysis results.

[0117] Specifically as follows:

[0118] Set the start time of the period corresponding to the frequency change of P1 as the first frequency characteristic time point, and set the end time of the period corresponding to the frequency change of P1 as the second frequency characteristic time point.

[0119] The interval between the first frequency characteristic time point and the second frequency characteristic time point is obtained to obtain the characteristic interval duration;

[0120] The actual frequencies corresponding to the first frequency characteristic time point and the second frequency characteristic time point of the sample power nodes are obtained respectively to obtain the characteristic frequency of the first time period and the characteristic frequency of the second time period.

[0121] It should be noted here that:

[0122] In this application, the frequency referred to herein is specifically the rate of periodic change of alternating current.

[0123] The frequency variation rate of P1 during the time period is obtained by calculating the characteristic frequency of the first time period, the characteristic frequency of the second time period, and the characteristic interval duration.

[0124] The rate of change of frequency P1 over time is calculated using the following formula:

[0125] ;

[0126] Wherein, BlP1 is the frequency change rate of P1 during the time period, TPb1 is the characteristic frequency of the first time period, TPb2 is the characteristic frequency of the second time period, and Tsc is the duration of the characteristic interval.

[0127] Repeat the process of obtaining the frequency change rate during the P1 time period, and perform frequency change rate analysis for the P2 frequency change period to the Pc frequency change period respectively. Based on the analysis results, obtain the frequency change rate from the P2 time period to the Pc frequency change period.

[0128] Rearrange the frequency change rate from P1 to Pc in descending order according to the numerical value, and rename the frequency change rate from P1 to Pc to Q1 to Qc according to the sorting order.

[0129] The median of the rate of change from frequency period Q1 to frequency period Qc is obtained to get the first frequency index value corresponding to the sample power node;

[0130] Summing the frequency change rates from Q1 to Qc, we obtain the cumulative frequency change rate for each time period. Calculating the ratio of the frequency change rate of Q1 to the cumulative frequency change rate for each time period yields the descending cumulative ratio of Q1. Similarly, summing the frequency change rates from Q1 to Q2, we obtain the sum of the frequency change rates of Q2. Calculating the ratio of the sum of the frequency change rates of Q2 to the cumulative frequency change rate for each time period yields the descending cumulative ratio of Q2. This process is repeated for each time period, from Q1 to Qc, to obtain the sum of the frequency change rates of Qc. Finally, we calculate the ratio of the sum of the frequency change rates of Qc to the cumulative frequency change rate for each time period yields the descending cumulative ratio of Qc.

[0131] Obtain the median value of the cumulative ratio, calculate the absolute values ​​of the differences between the cumulative ratio in descending order of Q1 frequency and the cumulative ratio in descending order of Qc frequency and the median value of the cumulative ratio, and obtain the median deviation of Q1 frequency to the median deviation of Qc frequency.

[0132] Compare the numerical values ​​of the median deviation of frequency Q1 to the median deviation of frequency Qc, and set the median deviation of the frequency with the smallest value as the second frequency index value.

[0133] The average value of the first frequency index and the second frequency index is calculated to obtain the average value of the frequency index corresponding to the sample power node.

[0134] The intermediate value of the voltage level range corresponding to the corresponding sample power node is obtained to obtain the intermediate value of the voltage level;

[0135] The calculation complexity of the frequency corresponding to the sample power node is obtained by calculating the intermediate value of the voltage level and the intermediate value of the frequency index.

[0136] The computational complexity of the frequency corresponding to the sample power node is calculated using the following formula:

[0137] ;

[0138] Where Pjd is the frequency calculation complexity corresponding to the sample power node, Vpz is the intermediate value of the voltage level, and Pzj is the intermediate value of the frequency index.

[0139] Repeat the process of obtaining the frequency computation complexity corresponding to the sample power nodes, and obtain the frequency computation complexity corresponding to each power node to obtain the time period frequency analysis data corresponding to the sample power network.

[0140] In step S2 above, when monitoring the frequency of each power node in the sample power network, the frequency change period is determined based on the actual power frequency change of each power node. By analyzing the load change complexity of the power node during the frequency change period, a targeted model for the integrated calculation of the main and distribution networks is created. Based on real-time frequency monitoring, the frequency change period is accurately defined, allowing modeling to focus on stages with significant frequency fluctuations, avoiding redundant analysis of stable operation periods, and forming a dynamically adaptive modeling strategy. This design can quickly capture abnormal frequency events, such as frequency deviations or oscillations, providing a critical time window for system stability assessment.

[0141] Secondly, by analyzing the complexity of load changes during frequency fluctuation periods, different model types can be matched: a high-precision distributed model is used during periods of drastic frequency fluctuations, leveraging the parallel computing capabilities of multiple nodes to analyze complex interaction relationships; a centralized simplified model is used during stable periods to improve computational efficiency and achieve on-demand allocation of computing resources. Simultaneously, the modular model architecture supports independent optimization of steady-state and transient models, maintaining overall consistency while facilitating local improvements.

[0142] Furthermore, accurate identification of critical frequency periods enhances the ability to detect abnormal events, achieving a balance between rapid early warning and low false alarm rates by setting dynamic thresholds. This design also provides data support for operation and maintenance decisions, identifying system weaknesses through complexity distribution characteristics and driving a shift in maintenance mode from passive response to proactive prevention. Ultimately, this closed-loop optimization method significantly shortens the time interval between frequency changes and system response, improving the dynamic adjustment capability of the power network.

[0143] Step S3: Perform data model matching on the sample power network based on the time period frequency analysis data and the time period load analysis data.

[0144] Specifically as follows:

[0145] Obtain time-period load analysis data, calculate the load complexity corresponding to each power node based on the time-period load analysis data, and calculate the average of the obtained multiple load calculation complexities to obtain the model matching load coefficient;

[0146] Obtain time period frequency analysis data, calculate the frequency calculation complexity corresponding to each power node based on the time period frequency analysis data, and calculate the average of the obtained multiple frequency calculation complexities to obtain the model matching load coefficient;

[0147] Obtain the load matching interval and frequency matching interval of the distributed computing model;

[0148] It should be noted here that:

[0149] Obtain several historical power distribution networks that have already created distributed main and distribution network integrated computing data models, obtain the historical model matching load coefficient corresponding to each historical power distribution network, obtain multiple historical model matching load coefficients, and set the numerical range composed of multiple historical model matching load coefficients as the load matching range of the distributed computing model.

[0150] Obtain several historical power distribution networks that have already created distributed main and distribution network integrated computing data models, obtain the historical model matching frequency coefficients corresponding to each historical power distribution network, obtain multiple historical model matching frequency coefficients, and set the numerical range composed of multiple historical model matching frequency coefficients as the frequency matching range of the distributed computing model.

[0151] If the model matching load factor is within the load matching range of the distributed computing model and the model matching frequency factor is within the frequency matching range of the distributed computing model, then a distributed main and distribution network integrated computing data model is established for the sample power network.

[0152] If the model matching load factor is not within the load matching range of the distributed computing model or the model matching frequency factor is not within the frequency matching range of the distributed computing model, then a centralized integrated main and distribution network calculation data model is established for the sample power network.

[0153] It should be noted here that:

[0154] In this application, a distributed main and distribution network integrated calculation data model is established for the sample power network, including the cases where the model matching load factor is at the boundary of the distributed calculation model load matching interval and the case where the model matching frequency factor is at the boundary of the distributed calculation model frequency matching interval.

[0155] The distributed integrated main and distribution network computing data model involved here disperses the data storage and computing tasks of the power monitoring system to multiple network nodes and achieves collaborative processing through message passing. The centralized integrated main and distribution network computing data model involved here concentrates all data storage and computing tasks in a single central node and is uniformly scheduled by the center.

[0156] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for establishing an integrated calculation data model for the main and distribution networks based on real-time measurement data, characterized in that, include: Step S1: Perform periodic load change monitoring on the power nodes in the sample power network, obtain the load calculation complexity corresponding to the power nodes based on the monitoring results, and obtain the time period load analysis data; Step S2: Based on the time-period load analysis data, periodic frequency change monitoring is performed on the power nodes in the sample power network. Based on the monitoring results, the frequency control complexity corresponding to the power nodes is obtained, and time-period frequency analysis data is obtained. Step S3: Perform data model matching on the sample power network based on the time period frequency analysis data and the time period load analysis data.

2. The method for establishing an integrated main and distribution network calculation data model based on real-time measurement data according to claim 1, characterized in that, Step S1 further includes the following steps: Step S11: Obtain the power network for which the integrated calculation data model of the main and distribution networks needs to be established, obtain multiple power networks, and arbitrarily select one sample power network from the multiple obtained power networks; Step S12: During the historical period when the sample power network is in operation, arbitrarily select several historical load analysis periods of equal duration, and arbitrarily select one sample load analysis period from the multiple historical load analysis periods obtained; Step S13: Perform node load analysis on the sample power network during the sample load analysis period, and obtain the load calculation complexity corresponding to the sample power network based on the analysis results; Step S14: Obtain the load calculation complexity corresponding to each power node to obtain the time period load analysis data.

3. The method for establishing an integrated main and distribution network calculation data model based on real-time measurement data according to claim 2, characterized in that, Step S13 further includes the following steps: Step S131: Obtain the power nodes contained in the sample power network, and arbitrarily select one sample power node from the obtained power nodes; Step S132: During the sample load analysis period, the time points when the power load corresponding to the sample power node changes are obtained, resulting in multiple load change time points. The time interval between any two adjacent load change time points is set as the load change period, and the obtained load change periods are named sequentially from F1 load change period to Fa load change period in chronological order. Step S133: Perform load change rate analysis on the F1 load change period, and obtain the F1 load change rate during the period based on the analysis results; Step S134: Perform load change rate analysis on the periods from F2 load change to Fa load change, and obtain the load change rate from F2 load period to Fa load period based on the analysis results; Step S135: Rearrange the load change rates from F1 to Fa in descending order of their numerical values ​​to obtain the load change rates from S1 to Sa.

4. The method for establishing an integrated main and distribution network calculation data model based on real-time measurement data according to claim 3, characterized in that, Step S13 further includes the following steps: Step S136: Obtain the median of the load change rate from S1 to Sa to get the first load index value; Step S137: Analyze the load change rate from S1 to Sa, and obtain the cumulative ratio of S1 load in descending order to Sa load in descending order based on the analysis results; Step S138: Obtain the median value of the cumulative ratio, calculate the absolute value of the difference between the cumulative ratio of load descending order S1 to the cumulative ratio of load descending order Sa and the median value of the cumulative ratio, and obtain the median deviation of load S1 to the median deviation of load Sa; Step S139: Set the load median deviation with the smallest value among the load median deviations from S1 to Sa as the second load index value. Calculate the average of the first and second load index values ​​to obtain the average load index value corresponding to the sample power node. Obtain the median value of the voltage level range corresponding to the sample power node to obtain the voltage level median value. Calculate the load calculation complexity corresponding to the sample power node by combining the voltage level median value and the load index median value.

5. The method for establishing an integrated main and distribution network calculation data model based on real-time measurement data according to claim 3, characterized in that, Step S133 further includes the following steps: Set the start time of the period corresponding to the F1 load change period as the first period characteristic time point, and set the end time of the period corresponding to the F1 load change period as the second period characteristic time point. The interval between the characteristic time points of the first time period and the characteristic time points of the second time period is obtained to obtain the characteristic interval duration; The actual load corresponding to the characteristic time points of the first and second time periods of the sample power nodes is obtained to obtain the characteristic load of the first and second time periods. The F1 load period change rate is obtained by calculating the characteristic load of the first period, the characteristic load of the second period, and the characteristic interval duration.

6. The method for establishing an integrated main and distribution network calculation data model based on real-time measurement data according to claim 1, characterized in that, Step S2 further includes the following steps: Step S21: Obtain time period load analysis data. Based on the time period load analysis data, obtain the sample power network. Within the historical time period when the sample power network is in operation, arbitrarily select several historical frequency analysis periods of equal duration, and arbitrarily select one sample frequency analysis period from the multiple historical frequency analysis periods obtained. Step S22: Perform node frequency analysis on the sample power network during the sample frequency analysis period, and obtain the time period frequency complexity of the sample power network based on the analysis results; Step S23: Obtain the power nodes contained in the sample power network, and arbitrarily select a sample power node from the obtained power nodes. Perform frequency change analysis on the sample power node to obtain the frequency calculation complexity corresponding to the sample power node. Step S24: Obtain the frequency computation complexity corresponding to each power node to obtain the time period frequency analysis data corresponding to the sample power network.

7. The method for establishing an integrated main and distribution network calculation data model based on real-time measurement data according to claim 6, characterized in that, Step S23 further includes the following steps: Step S231: During the sample frequency analysis period, the time points when the power frequency corresponding to the sample power node changes are obtained, resulting in multiple frequency change time points. The time interval between any two adjacent frequency change time points is set as the frequency change period, and the obtained frequency change periods are named sequentially from P1 frequency change period to Pc frequency change period in chronological order. Step S232: Analyze the frequency change rate during the P1 frequency change period, and obtain the frequency change rate during the P1 period based on the analysis results; Step S233: Perform frequency change rate analysis on the time periods from P2 frequency change to Pc frequency change, and obtain the frequency change rate from P2 frequency change period to Pc frequency change period based on the analysis results; Step S234: Sort the frequency change rates from P1 to Pc in descending order according to their numerical values, and rename them according to the sorting order as frequency change rates from P1 to Pc to Q1 to Qc. Step S235: Obtain the median of the frequency change rate from Q1 to Qc to get the first frequency index value corresponding to the sample power node.

8. The method for establishing an integrated main and distribution network calculation data model based on real-time measurement data according to claim 7, characterized in that, Step S23 further includes the following steps: Step S236: Accumulate the rate of change of frequency from Q1 to Qc to obtain the cumulative ratio of frequency in descending order from Q1 to Qc. Step S237: Obtain the median value of the cumulative ratio, calculate the absolute value of the difference between the cumulative ratio in descending order of Q1 frequency and the cumulative ratio in descending order of Qc frequency and the median value of the cumulative ratio, and obtain the median deviation of Q1 frequency to the median deviation of Qc frequency; Step S238: Set the frequency median deviation with the smallest value as the second frequency index value, and calculate the average of the first frequency index value and the second frequency index value to obtain the average frequency index value corresponding to the sample power node. Step S239: Obtain the intermediate value of the voltage level range corresponding to the sample power node, and calculate the frequency calculation complexity corresponding to the sample power node by combining the intermediate value of the voltage level and the intermediate value of the frequency index.

9. The method for establishing an integrated main and distribution network calculation data model based on real-time measurement data according to claim 7, characterized in that, Step S232 further includes the following steps: Set the start time of the period corresponding to the frequency change of P1 as the first frequency characteristic time point, and set the end time of the period corresponding to the frequency change of P1 as the second frequency characteristic time point. The interval between the first frequency characteristic time point and the second frequency characteristic time point is obtained to obtain the characteristic interval duration; The actual frequencies corresponding to the first and second frequency characteristic time points of the sample power nodes are obtained to obtain the characteristic frequencies of the first and second time periods. The frequency variation rate of P1 during the time period is obtained by calculating the characteristic frequency of the first time period, the characteristic frequency of the second time period, and the characteristic interval duration.

10. The method for establishing an integrated main and distribution network calculation data model based on real-time measurement data according to claim 1, characterized in that, Step S3 further includes the following steps: Obtain time-period load analysis data, calculate the load complexity corresponding to each power node based on the time-period load analysis data, and calculate the average of the obtained multiple load calculation complexities to obtain the model matching load coefficient; Obtain time period frequency analysis data, calculate the frequency calculation complexity corresponding to each power node based on the time period frequency analysis data, and calculate the average of the obtained multiple frequency calculation complexities to obtain the model matching load coefficient; Obtain the load matching interval and frequency matching interval of the distributed computing model; If the model matching load factor is within the load matching range of the distributed computing model and the model matching frequency factor is within the frequency matching range of the distributed computing model, then a distributed main and distribution network integrated computing data model is established for the sample power network. If the model matching load factor is not within the load matching range of the distributed computing model or the model matching frequency factor is not within the frequency matching range of the distributed computing model, then a centralized integrated main and distribution network computing data model is established for the sample power network.

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