Power Transmission and Transformation Project Evaluation Method and System Based on Intelligent Management and Control Platform

Abstract

The present invention discloses a method and system for evaluating a power transmission and transformation project based on an intelligent control platform, relating to the technical field of power transmission and transformation projects, including the following steps: obtaining meteorological data of a to-be-tested area to predict the icing rate; obtaining line current data to perform thermal correction on the icing rate and generating a dynamic heat map of icing thickness distribution; performing structural safety domain analysis through a finite element analysis method according to the icing thickness distribution heat map; and coordinating with grid topology and load data based on the results of the structural safety domain analysis and performing load transfer. The present application solves the problem of line safety and stability under complex meteorological conditions through accurate prediction of the icing rate and thickness of power transmission lines, structural safety assessment, and load optimization control.

Description

Technical Field

[0001] The present invention relates to the technical field of power transmission and transformation engineering. More specifically, the present invention relates to an evaluation method and system for power transmission and transformation engineering based on an intelligent control platform. Background Art

[0002] With the rapid development of information technology, especially the wide application of cutting-edge technologies such as cloud computing, Internet of Things, big data, and artificial intelligence, the intelligent control platform has become an important support tool in the power industry. In the field of evaluation of power transmission and transformation projects, the application of the intelligent control platform has promoted the innovation and upgrading of evaluation methods, providing a more scientific, comprehensive, and accurate basis for project decision-making.

[0003] Traditional evaluation methods for power transmission and transformation projects often rely on manual data collection and analysis, which are not only time-consuming and laborious, but also easily affected by human factors, resulting in doubts about the accuracy and objectivity of evaluation results. The emergence of the intelligent control platform effectively solves these problems. It integrates various on-line monitoring devices and sensors to collect real-time operation data of power transmission and transformation projects, including key parameters such as current, voltage, temperature, and humidity, ensuring the accuracy and timeliness of the data. Based on data collection, the intelligent control platform also has powerful data processing and analysis capabilities. It uses advanced data processing algorithms and artificial intelligence technologies to deeply mine and analyze the collected data, identify potential risk points and hidden faults. At the same time, the platform also predicts and evaluates the operation status of equipment based on historical data and real-time data, providing a scientific basis for the maintenance and management of engineering projects.

[0004] For example, a method for evaluating the cost of power transmission and transformation projects based on spatio-temporal big data disclosed in the invention patent with the publication number of CN112232625A includes: a data collection module, the data collection module is connected to a data classification and storage module, the data classification and storage module is connected to a data verification module, the data verification module is connected to a review and evaluation module, the review and evaluation module determines the classification principle of samples through cluster analysis of samples and combines the sorting of cost influencing factors to simulate the representative attributes of the project, that is, determines the influencing factors according to the voltage level and single / double-circuit lines; according to the determined sample classification principle, constructs a multiple regression model to distinguish the influence magnitude of individual factors (i.e., independent variables) from the total cost of the project (i.e., the dependent variable); the present invention takes into account the independence between various factors, that is, each factor does not affect each other, and can be applied to investment estimation of planning schemes, pre-feasibility research investment estimation, and technical scheme comparison and selection.

[0005] For example, a power transmission and transformation project progress evaluation method disclosed in a patent for invention with the publication number CN117252453A first constructs a power transmission and transformation project progress evaluation index system including multiple input indicators and multiple output indicators, then introduces the project with the optimal progress and the project with the worst progress into the power transmission and transformation project set, takes the minimum efficiency evaluation index of the project with the worst progress as the objective function, constructs a relative efficiency optimization model, and this relative efficiency optimization model needs to meet constraint conditions a, b, and c. Then solve the relative efficiency optimization model to obtain the weights of each indicator, and finally calculate the efficiency evaluation index of each power transmission and transformation project in the power transmission and transformation project set according to the weights of each indicator, and make a descending order of each power transmission and transformation project in the power transmission and transformation project set according to the size of the efficiency evaluation index to obtain the progress evaluation result. This design not only realizes the progress evaluation of the power transmission and transformation project, but also avoids the problem of insufficient universality of the weight vector by introducing the project with the optimal progress and the project with the worst progress.

[0006] In the above disclosed technical solution, there are at least the following technical problems:

[0007] In traditional power transmission and transformation project evaluation methods, the catenary static model is used for the risk assessment of the line, ignoring the ice accretion dynamics effect, and only checking whether the maximum tension exceeds the standard, without constructing a safety domain for the interaction of multiple failure modes, resulting in inaccurate risk assessment. In view of the above problems, the present invention proposes a solution. Summary of the Invention

[0008] In order to overcome the above defects of the prior art, embodiments of the present invention provide a power transmission and transformation project evaluation method and system based on an intelligent management and control platform, which solve the problem of line safety and stability under complex meteorological conditions through accurate prediction of the ice accretion rate and thickness of the power transmission line, structural safety assessment, and load optimization control.

[0009] To achieve the above object, the present invention provides the following technical solutions:

[0010] A power transmission and transformation project evaluation method based on an intelligent management and control platform includes the following steps: obtaining meteorological data of the area to be measured to predict the ice accretion rate; obtaining line current data to perform thermal correction on the ice accretion rate to generate a dynamic distribution heat map of the ice accretion thickness; performing structural safety domain analysis through the finite element analysis method according to the ice accretion thickness distribution heat map; and coordinating according to the structural safety domain analysis result in combination with the power grid topology and load data, and performing load transfer.

[0011] In a preferred embodiment, obtaining the meteorological data of the area to be measured to predict the icing rate is specifically as follows: Obtain the meteorological data of the area to be measured, and extract the features related to the icing rate from the meteorological data. The features include temperature, humidity, wind speed, wind direction, and air pressure; Screen and optimize the extracted features through a feature selection algorithm, and select the feature subset with the greatest contribution to the prediction model; Train a prediction model based on the support vector machine according to the feature subset and data characteristics, and use the cross-validation technique to verify the prediction model; Predict the icing rate of the area to be measured through the trained prediction model.

[0012] In a preferred embodiment, obtaining the line current data to perform thermal correction on the icing rate and generating a dynamic distribution heat map of the ice thickness is specifically as follows: Obtain the high-frequency current harmonic data of the line to be measured, and based on wavelet packet decomposition, separate the fundamental wave and harmonic components, and extract the key frequency band of wire heating;

[0013] Through a long short-term memory network, predict the current change trend based on historical current data, and perform wavelet packet decomposition on the predicted current data and divide it into several frequency bands; Match the divided frequency bands with the key frequency band of wire heating, and output the wire heat generation according to the fundamental wave and harmonic components of the frequency bands after matching in combination with the Joule heat formula; Establish a heat dissipation model according to the environmental temperature and wind speed data of the area to be measured, and combine the wire heat generation to obtain the temperature change data on the surface of the line; According to the temperature change data of the line, calculate the ice melting rate caused by current heating based on the phase change heat of ice, correct the icing rate, and dynamically adjust the ice thickness prediction result according to the corrected icing rate; Construct a graph attention network, map the discrete point data of the ice thickness prediction result to a continuous space through Kriging interpolation, and optimize the interpolation weight in combination with the line topology structure to generate a dynamic distribution heat map of the ice thickness.

[0014] In a preferred embodiment, extracting the key frequency band of wire heating is specifically as follows: Perform a fast Fourier transform on the frequency bands after wavelet packet decomposition to obtain the corresponding frequency band ranges of each node; Obtain the effective harmonic power data of each frequency band according to the frequency band range, and obtain the energy contribution degree of each frequency band through the energy concentration analysis method; If the energy contribution degree exceeds the threshold, it is the key frequency band of wire heating.

[0015] In a preferred embodiment, the structural safety domain analysis is carried out by means of finite element analysis according to the icing thickness distribution heat map, specifically as follows: Extract data from the dynamic heat map of icing thickness, and the data extraction includes time stamps, spatial distribution and meteorological data; Establish a three-dimensional structural model of the transmission line according to the extracted data; Apply icing loads and wind loads to the conductors and towers according to the three-dimensional structural model to obtain boundary constraint conditions, and the boundary constraint conditions include applying a tension force to the conductor ends and fixing the tower bases; Calculate the icing weight per unit line length according to the boundary constraint conditions and the icing thickness distribution heat map, and output the wind load applied to the line according to the icing thickness; Carry out a static analysis of the line according to the icing weight per unit line length and the wind load, output the stress distribution of the tower and the line, and screen out the maximum stress; Obtain the yield strength of the conductor material, and calculate in combination with the maximum stress to output the safety value of the line; Divide the lines in the area to be measured according to the safety value and the icing thickness distribution heat map.

[0016] In a preferred embodiment, the load transfer is carried out in coordination with the grid topology and load data according to the results of the structural safety domain analysis, specifically as follows: Set a threshold according to the safety value, screen out the line segments with insufficient safety margins, and combine with the icing thickness distribution heat map to identify the areas where the icing thickness exceeds the design standard, and screen out the high-risk lines; Divide the high-risk lines into several sub-regions according to the geographical location and grid topology; According to the grid topology division, based on the graph theory algorithm, start from the starting point and the ending point of the high-risk line to screen the load transfer paths; Based on the power flow analysis method, obtain the line load change data under different transfer paths; Determine the load transfer path according to the line load change data and carry out load transfer.

[0017] In a preferred embodiment, the specific steps of determining the load transfer path according to the line load change data and carrying out load transfer are as follows: Obtain the maximum load of the current line, combine with the line load change data, and output the unloading load of the line to be load transferred; Determine the line load transfer path according to the safety value and the unloading load, and calculate the available capacity of the standby line; Adjust the power flow of the substation according to the available capacity of the standby line to reduce the load of the high-risk line and carry out load transfer.

[0018] The power transmission and transformation project evaluation system based on the intelligent management and control platform includes an icing rate prediction module, a thermal correction module, a structural safety domain analysis module, and a load transfer module, and there are connections between the modules; the icing rate prediction module is used to obtain meteorological data of the area to be measured and predict the icing rate; the thermal correction module is used to obtain line current data to perform thermal correction on the icing rate and generate a thermal map of the dynamic distribution of icing thickness; the structural safety domain analysis module is used to perform structural safety domain analysis by means of finite element analysis according to the thermal map of icing thickness distribution; the load transfer module is used to cooperate according to the results of structural safety domain analysis in combination with power grid topology and load data, and perform load transfer.

[0019] The technical effects and advantages of the power transmission and transformation project evaluation method and system based on the intelligent management and control platform of the present invention:

[0020] 1. The present invention extracts the timestamp, spatial distribution, and meteorological data based on the thermal map of the dynamic distribution of icing thickness, and accordingly establishes a three-dimensional structure model of the transmission line. In this model, icing loads and wind loads are applied to the conductors and towers, and corresponding boundary constraint conditions are set, such as the tension at the ends of the conductors and the fixation of the tower bases. By calculating the icing weight and wind load per unit line length, the stress distribution of the towers and lines is output by means of static analysis, the maximum stress value is screened, and compared with the yield strength of the conductor material to calculate the safety value of the line. Finally, according to the safety value and the thermal map of icing thickness distribution, the safety domain of the line in the area to be measured is divided.

[0021] 2. The present invention obtains the high-frequency current harmonic data of the transmission line, separates the fundamental wave and harmonic components based on the wavelet packet decomposition technology, and extracts the key frequency band of conductor heating. Subsequently, the long short-term memory network is used to predict the short-term current change trend, and the conductor heat generation is calculated in combination with the Joule heat formula. Further, in combination with data such as ambient temperature and wind speed, a heat dissipation model is constructed to calculate the change in the surface temperature of the line, and the influence of conductor heating on the icing thickness is calculated based on the latent heat of phase change of ice, so as to dynamically adjust the prediction result of the icing thickness. In addition, the graph attention network is combined with the Kriging interpolation method to map the discrete icing thickness data to the continuous space, and the interpolation weight is optimized in combination with the line topology, and finally a thermal map of the dynamic distribution of icing thickness is generated. Description of the Drawings

[0022] Figure 1 It is a schematic flow chart of the power transmission and transformation project evaluation method based on the intelligent management and control platform of the present invention.

[0023] Figure 2 It is a schematic structural diagram of the power transmission and transformation project evaluation system based on the intelligent management and control platform of the present invention.

[0024] Figure 3 It is a curve graph of icing rate prediction of the power transmission and transformation project evaluation method based on the intelligent management and control platform of the present invention. Detailed implementation manners

[0025] Next, in combination with the accompanying drawings in the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0026] Embodiment 1 Figure 1 A power transmission and transformation project evaluation method based on an intelligent management and control platform of the present invention is given, including the following steps:

[0027] S1. Obtain meteorological data of the area to be measured to predict the ice accretion rate.

[0028] In this embodiment, obtaining meteorological data of the area to be measured to predict the ice accretion rate is specifically as follows:

[0029] Utilize technical means such as meteorological monitoring stations, satellite remote sensing, and radar detection to obtain meteorological data of the area to be measured in real time;

[0030] Remove duplicate, incorrect, or abnormal data to ensure the quality and consistency of the data, and integrate data from different sources to form a complete meteorological data set. For missing data, interpolation methods are used to fill it to ensure the integrity of the data;

[0031] Extract features related to the ice accretion rate from the meteorological data set. The features include temperature, humidity, wind speed, wind direction, and air pressure;

[0032] Screen and optimize the extracted features through a feature selection algorithm, and select the feature subset with the greatest contribution to the prediction model;

[0033] Based on the feature subset and data characteristics, train a prediction model based on a support vector machine. By continuously adjusting the parameters and structure of the model, the model can learn the rules and patterns in the data;

[0034] Adopt cross-validation technology to verify the prediction model to ensure the accuracy and generalization ability of the model;

[0035] Predict the ice accretion rate of the area to be measured through the trained prediction model.

[0036] The prediction formula for the ice accretion rate is specifically as follows:

[0037]

[0038] In the formula: is the ice accretion rate, is the collision coefficient is the wind speed, is the liquid water content, is the freezing efficiency function.

[0039] S2. Obtain the line current data to perform thermal correction on the ice accretion rate and generate a thermal map of the dynamic distribution of ice accretion thickness.

[0040] In this embodiment, obtaining the line current data to perform thermal correction on the ice accretion rate and generate a thermal map of the dynamic distribution of ice accretion thickness is specifically as follows:

[0041] Obtain the high-frequency current harmonic data of the line to be measured. Based on wavelet packet decomposition, separate the fundamental wave and harmonic components, extract the key frequency bands related to wire heating, and suppress noise interference through the adaptive threshold denoising algorithm to improve the signal-to-noise ratio of the current data;

[0042] Through the long short-term memory network, predict the future short-term current change trend based on historical current data, anticipate the wire temperature rise trend in advance, and perform wavelet packet decomposition on the predicted current data and divide it into several frequency bands;

[0043] Match the divided frequency bands with the key frequency bands of wire heating, and output the wire heat generation based on the fundamental wave and harmonic components of the matched frequency bands in combination with the Joule heat formula;

[0044] Establish a heat dissipation model according to the environmental temperature and wind speed data of the area to be measured, and combine the wire heat generation to obtain the temperature change data on the surface of the line;

[0045] According to the temperature change data of the line, calculate the ice accretion melting rate caused by current heating based on the latent heat of phase change of ice, correct the ice accretion rate, and dynamically adjust the ice accretion thickness prediction result according to the corrected ice accretion rate;

[0046] Construct a graph attention network, map the discrete point data of the ice accretion thickness prediction result to a continuous space through Kriging interpolation, and optimize the interpolation weights in combination with the line topology structure to generate a thermal map of the dynamic distribution of ice accretion thickness.

[0047] The formula for performing thermal correction on the ice accretion thickness by the line current data is specifically as follows:

[0048]

[0049]

[0050] In the formula: is the corrected ice accretion thickness, is the initially predicted ice accretion thickness, is the ice accretion melting rate, is the melting coefficient, is the wire temperature, is the ambient temperature.

[0051] The Joule heat formula is specifically as follows:

[0052]

[0053] In the formula: is the heat generation of the wire, is the wire resistance, is the fundamental wave, is the harmonic component, is the number of frequency bands.

[0054] Extract the key frequency bands of wire heating, specifically as follows:

[0055] Perform a fast Fourier transform on the frequency bands after wavelet packet decomposition to obtain the frequency band ranges corresponding to each node;

[0056] Obtain the effective harmonic power data of each frequency band according to the frequency band range, set a heating threshold, and obtain the energy contribution degree of each frequency band through the energy concentration analysis method. If the effective harmonic power of a certain frequency band increases significantly, it is judged that this frequency band has a greater impact on the overall temperature rise of the line and may be the main heat source;

[0057] If the energy contribution degree exceeds the threshold, it is the key frequency band of wire heating.

[0058] The calculation formula of the energy contribution degree is specifically as follows:

[0059]

[0060] In the formula: is the proportion of frequency band n in all harmonic energies, is the harmonic power loss within frequency band n, is the total power loss of all harmonic frequency bands.

[0061] It should be noted that in order to identify the harmonic frequency band that contributes the most to wire heating, a reasonable heating threshold needs to be set. When the effective harmonic power of a certain frequency band exceeds this threshold, it is considered that this frequency band may have a significant impact on the line temperature rise. In addition, through the energy concentration analysis, further judge which harmonic frequency bands are the main heat sources.

[0062] S3. According to the icing thickness distribution heat map, perform structural safety domain analysis through the finite element analysis method.

[0063] In this embodiment, according to the icing thickness distribution heat map, perform structural safety domain analysis through the finite element analysis method, specifically as follows:

[0064] Extract data from the dynamic thermal map of ice coating thickness, where the data extraction includes time stamps (the change of ice coating thickness in different time periods), spatial distribution (the ice coating thickness on tower bases, conductors and insulators), and meteorological data (temperature, wind speed, wind direction, humidity);

[0065] Establish a three-dimensional structural model of the transmission line according to the extracted data;

[0066] Apply ice coating load and wind load to the conductor and tower according to the three-dimensional structural model to obtain boundary constraint conditions, where the boundary constraint conditions include applying a tension force at the end of the conductor and fixing the tower base;

[0067] Calculate the ice coating weight per unit line length according to the boundary constraint conditions and the thermal map of ice coating thickness distribution. When the ice coating distribution is uneven, apply asymmetric loads according to the thermal map partition, and obtain the wind load applied to the line according to the ice coating thickness;

[0068] Conduct a static analysis of the line according to the ice coating weight per unit line length and the wind load, output the stress distribution of the tower and the line, and screen the maximum stress;

[0069] Obtain the yield strength of the conductor material, and calculate and output the safety value of the line in combination with the maximum stress;

[0070] Divide the line in the area to be measured according to the safety value and the thermal map of ice coating thickness distribution.

[0071] The calculation formula for the ice coating weight per unit line length is as follows:

[0072]

[0073] The calculation formula for the wind load is as follows:

[0074]

[0075] In the formula: is the ice coating weight per unit line length, is the ice coating density, is the acceleration of gravity, is the conductor diameter, is the corrected ice coating thickness, is the wind load, is the wind resistance, is the air density, is the wind speed.

[0076] The calculation formula for the safety value is specifically as follows:

[0077]

[0078] In the formula: is the safety value, is the yield strength of the line material, is the maximum stress in the current line.

[0079] It should be noted that the higher the safety value, the higher the risk level of the line and the greater the probability of problems.

[0080] S4. According to the structural safety domain analysis results, collaborate with the power grid topology and load data, and perform load transfer.

[0081] In this embodiment, according to the structural safety domain analysis results, collaborate with the power grid topology and load data, and perform load transfer, specifically as follows:

[0082] Set a threshold according to the safety value, screen out the line segments with insufficient safety margins, and combine with the icing thickness distribution heat map to identify the areas where the icing thickness exceeds the design standard, and screen out the high-risk lines;

[0083] Divide the high-risk lines into several sub-regions according to the geographical location and power grid topology;

[0084] According to the power grid topology division, based on the graph theory algorithm, starting from the starting point and ending point of the high-risk line, exclude the lines that are currently overloaded or have other risks, and screen out the load transfer paths;

[0085] Based on the power flow analysis method, obtain the line load change data under different transfer paths;

[0086] Determine the load transfer path according to the line load change data, and perform load transfer.

[0087] In this embodiment, the specific steps of determining the load transfer path according to the line load change data and performing load transfer are as follows:

[0088] Obtain the maximum load of the current line, combine with the line load change data, and output the unloading load of the line to be load transferred;

[0089] Determine the line load transfer path according to the safety value and the unloading load, and calculate the available capacity of the standby line to ensure that the load transfer will not cause the new line to be overloaded;

[0090] Adjust the power flow of the substation through dispatching control, reduce the load of the high-risk line, and perform load transfer.

[0091] The calculation formula of the power flow analysis method is as follows:

[0092]

[0093] In the formula: is the line power transmission, and is the bus voltage, is the line admittance, and is the voltage phase angle, is the line conductance angle.

[0094] Embodiment 2 Figure 2 provides a system for an evaluation method of a power transmission and transformation project based on an intelligent management and control platform, which is characterized by including an ice accretion rate prediction module, a thermal correction module, a structural safety domain analysis module, and a load transfer module, and there are connections between the modules;

[0095] The ice accretion rate prediction module is used to obtain meteorological data of the area to be measured and predict the ice accretion rate;

[0096] The thermal correction module is used to obtain line current data to perform thermal correction on the ice accretion rate and generate a thermal map of the dynamic distribution of ice thickness;

[0097] The structural safety domain analysis module is used to perform structural safety domain analysis by means of finite element analysis according to the thermal map of ice thickness distribution;

[0098] The load transfer module is used to cooperate according to the structural safety domain analysis result in combination with the power grid topology and load data, and perform load transfer.

[0099] The above formulas are all dimensionless and take their numerical values for calculation. The formula is a formula obtained by collecting a large amount of data for software simulation to approximate the real situation. The preset parameters in the formula are set by those skilled in the art according to the actual situation.

[0100] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product.

[0101] Those of ordinary skill in the art can realize that the modules and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0102] In addition, in each embodiment of the present application, the functional modules can be integrated in one processing module, or each module can exist physically alone, or two or more modules can be integrated in one module.

[0103] As described above, it is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the said claims.

[0104] Finally: The above is only the preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A power transmission and transformation project evaluation method based on a smart management and control platform, characterized in that: The steps include: Obtain meteorological data of the area to be tested to predict the ice coverage rate; The line current data is obtained to perform thermal correction on the ice coverage rate and generate a dynamic distribution heat map of ice thickness, as follows: Obtain high-frequency current harmonic data of the line to be tested, separate the fundamental and harmonic components based on wavelet packet decomposition, and extract the key frequency band of conductor heating; The current change trend is predicted based on the historical current data through the long short-term memory network, and the predicted current data is decomposed by wavelet packets and divided into several frequency bands; Match the divided frequency bands with the key frequency bands for conductor heating, and output the conductor heating value based on the fundamental and harmonic components of the matched frequency bands combined with the Joule heat formula; Obtain the ambient temperature and wind speed data of the area to be tested, establish a heat dissipation model, and obtain the temperature change data of the line surface in combination with the heat generated by the conductor; According to the temperature change data of the line, the melting rate of ice caused by current heating is calculated based on the phase change heat of ice, the ice rate is corrected, and the ice thickness prediction result is dynamically adjusted according to the corrected ice rate; A graph attention network is constructed to map the discrete point data of ice thickness prediction results to continuous space through Kriging interpolation, and the interpolation weights are optimized in combination with the line topology to generate a dynamic distribution heat map of ice thickness. According to the ice thickness distribution heat map, the structural safety zone analysis is carried out by finite element analysis method; According to the structural safety domain analysis results, combined with the grid topology and load data, load transfer is carried out; The Joule heat formula is as follows: Where: is the heat generated by the wire, is the wire resistance, is the fundamental wave, is the harmonic component, is the number of frequency bands.

2. The power transmission and transformation project evaluation method based on the intelligent management and control platform according to claim 1 is characterized in that: The meteorological data of the area to be measured is obtained to predict the ice coverage rate, specifically as follows: Acquire meteorological data of the area to be measured, and extract features related to the ice-covering rate from the meteorological data, wherein the features related to the ice-covering rate include temperature, humidity, wind speed, wind direction, and air pressure; The extracted features are screened and optimized through feature selection algorithms to select the feature subset that contributes most to the prediction model; The prediction model is trained based on the support vector machine according to the feature subset and data characteristics, and the prediction model is verified by cross-validation technology; The ice coverage rate of the test area is predicted using the trained prediction model.

3. The power transmission and transformation project evaluation method based on the intelligent management and control platform according to claim 2 is characterized in that: The key frequency band for extracting the heating of the conductor is as follows: Perform fast Fourier transform on the frequency band after wavelet packet decomposition to obtain the frequency band range corresponding to each node; Obtain effective harmonic power data of each frequency band according to the frequency band range, and obtain the energy contribution of each frequency band through energy concentration analysis method; If the energy contribution exceeds the threshold, it is the critical frequency band for conductor heating.

4. The power transmission and transformation project evaluation method based on the intelligent management and control platform according to claim 3 is characterized in that: According to the ice thickness distribution heat map, the structural safety domain analysis is performed by the finite element analysis method, as follows: Extracting data from the dynamic heat map of ice thickness, including timestamp, spatial distribution and meteorological data; Establish a three-dimensional structural model of the transmission line based on the extracted data; Apply ice load and wind load to the conductor and the tower according to the three-dimensional structural model to obtain boundary constraints, wherein the boundary constraints include applying tension force to the conductor end and fixing the tower base; The ice weight per unit line length is calculated according to the boundary constraints and the ice thickness distribution heat map, and the wind load imposed on the line is output according to the ice thickness; According to the ice weight and wind load per unit line length, static analysis is performed on the line, the stress distribution of the tower and line is output, and the maximum stress is screened; Obtain the yield strength of the conductor material and combine it with the maximum stress calculation to output the safety value of the line; The lines in the area to be tested are divided according to safety values ​​and ice thickness distribution heat map.

5. The power transmission and transformation project evaluation method based on the intelligent management and control platform according to claim 4 is characterized in that: The structural safety domain analysis results are combined with the grid topology and load data to coordinate and transfer loads, as follows: Thresholds are set based on safety values ​​to screen out line sections with insufficient safety margins. In combination with the ice thickness distribution heat map, areas where ice thickness exceeds the design standard are identified to screen out high-risk lines. Divide high-risk lines into several sub-areas based on geographical location and grid topology; According to the grid topology, based on graph theory algorithms, we screen the load transfer paths from the starting and ending points of high-risk lines; Based on the power flow analysis method, obtain the line load change data under different transfer paths; Determine the load transfer path based on line load change data and perform load transfer.

6. The power transmission and transformation project evaluation method based on the intelligent management and control platform according to claim 5 is characterized in that: The specific steps of determining the load transfer path according to the line load change data and performing load transfer are as follows: Obtain the maximum load of the current line, combine it with the line load change data, and output the unloaded load of the line to be transferred; Determine the line load transfer path based on the safety value and load shedding amount, and calculate the available capacity of the backup line; According to the available capacity of the backup line, the dispatching control adjusts the power flow of the substation, reduces the load on the high-risk lines, and performs load transfer.

7. The power transmission and transformation project evaluation method based on the intelligent management and control platform according to claim 6 is characterized in that: The formula for thermal correction of ice thickness by line current data is as follows: Where: is the corrected ice thickness, is the initial predicted ice thickness, is the ice melting rate, is the ice accumulation rate, is the melting coefficient, is the conductor temperature, is the ambient temperature, is the ice thickness, is the temperature change of the wire, It is the melting temperature of the ice.

8. The power transmission and transformation project evaluation method based on the intelligent management and control platform according to claim 7 is characterized in that: The calculation formula of the safety value is as follows: The calculation formula of the power flow analysis method is as follows: Where: is the line power transfer, and is the bus voltage, is the line admittance, and is the voltage phase angle, is the line admittance phase angle, is a safe value. is the yield strength of the line material, It is the maximum stress in the current line.

9. A system using the power transmission and transformation project evaluation method based on the intelligent management and control platform as claimed in any one of claims 1 to 8, characterized in that: It includes an ice rate prediction module, a thermal correction module, a structural safety domain analysis module and a load transfer module, and there are connections between the modules; An ice-covering rate prediction module is used to obtain meteorological data of the area to be tested to predict the ice-covering rate; A thermal correction module is used to obtain line current data to perform thermal correction on the ice coverage rate and generate a dynamic distribution thermal map of ice thickness; Structural safety zone analysis module, used to perform structural safety zone analysis using finite element analysis method based on ice thickness distribution heat map; The load transfer module is used to coordinate the power grid topology and load data according to the structural safety domain analysis results and perform load transfer.

Images