Power transmission and transformation project evaluation method and system based on intelligent management and control platform

Abstract

The invention discloses a power transmission and transformation project evaluation method and system based on an intelligent management and control platform, and relates to the technical field of power transmission and transformation projects, and the method comprises the following steps: obtaining meteorological data of a to-be-detected region, and predicting an icing rate; obtaining line current data to carry out thermal correction on the icing rate, and generating an icing thickness dynamic distribution heat map; according to the icing thickness distribution heat map, structural safety domain analysis is carried out through a finite element analysis method; according to the method, the problems of line safety and stability under complex meteorological conditions are solved through accurate prediction of the icing rate and thickness of the power transmission and transformation line, structural safety evaluation and load optimization regulation and control.

Description

Technical Field

[0001] The present invention relates to the technical field of power transmission and transformation engineering, and more specifically, to a power transmission and transformation engineering evaluation method and system based on a smart management and control platform. Background Art

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

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

[0004] For example, the invention patent with announcement number: CN112232625A discloses a method for cost assessment of power transmission and transformation projects based on spatiotemporal big data, including: a data collection module, the data collection module is connected to a data classification storage module, the data classification storage module is connected to a data verification module, the data verification module is connected to an audit and evaluation module, the audit and evaluation module simulates representative attributes of the project through cluster analysis of samples and combines the sorting of cost influencing factors to determine the classification principle of samples, that is, to determine the influencing factors according to voltage levels and single and double circuit lines; according to the determined sample classification principle, a multivariate regression model is constructed to distinguish the influence of a single factor, that is, the independent variable, from the total cost of the project, that is, the dependent variable; the present invention takes into account the independence of various factors, that is, the various factors do not affect each other, and can be applied to investment estimates of planning schemes, pre-feasibility study investment estimates and technical scheme comparisons.

[0005] For example, the invention patent with announcement number: CN117252453A discloses a method for evaluating the progress of a power transmission and transformation project. First, a power transmission and transformation project progress evaluation index system including multiple input indicators and multiple output indicators is constructed. Then, the project with the best progress and the project with the worst progress are introduced into the power transmission and transformation project set. The efficiency evaluation index of the project with the worst progress is minimized as the objective function. A relative efficiency optimization model is constructed. The relative efficiency optimization model must meet the constraints a, b, and c. Then, the relative efficiency optimization model is solved to obtain the weights of each indicator. Finally, the efficiency evaluation index of each power transmission and transformation project in the power transmission and transformation project set is calculated according to the weights of each indicator. The power transmission and transformation projects in the power transmission and transformation project set are arranged in descending order 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 best progress and the project with the worst progress.

[0006] The above disclosed technical solutions have at least the following technical problems: In the traditional power transmission and transformation project assessment method, the risk assessment of the line adopts the catenary statics model, ignores the dynamic effect of ice coating, and only checks whether the maximum tension exceeds the standard. The safety domain of multiple failure mode interactions is not constructed, resulting in inaccurate risk assessment. In view of the above problems, the present invention proposes a solution. Summary of the invention

[0007] In order to overcome the above-mentioned defects of the prior art, the 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 solves the problem of line safety and stability under complex meteorological conditions by accurately predicting the icing rate and thickness of the transmission and transformation lines, structural safety evaluation and load optimization and control.

[0008] To achieve the above object, the present invention provides the following technical solutions: The power transmission and transformation project evaluation method based on the intelligent management and control platform includes the following steps: obtaining meteorological data of the area to be tested to predict the icing rate; obtaining line current data to perform thermal correction on the icing rate and generate a dynamic distribution heat map of icing thickness; performing structural safety domain analysis through finite element analysis method according to the icing thickness distribution heat map; combining the results of the structural safety domain analysis with the grid topology and load data to coordinate and perform load transfer.

[0009] In a preferred embodiment, the meteorological data of the area to be measured is obtained to predict the ice coverage rate, specifically as follows: the meteorological data of the area to be measured is obtained, and features related to the ice coverage rate are extracted from the meteorological data, the features including temperature, humidity, wind speed, wind direction, and air pressure; the extracted features are screened and optimized through a feature selection algorithm, and the feature subset that contributes most to the prediction model is selected; the prediction model is trained based on a 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 area to be measured is predicted by the trained prediction model.

[0010] In a preferred embodiment, the acquisition of line current data performs thermal correction on the ice coating rate to generate a dynamic distribution heat map of ice coating thickness, specifically as follows: high-frequency current harmonic data of the line to be tested is acquired, and based on wavelet packet decomposition, fundamental and harmonic components are separated to extract the key frequency band of conductor heating; Through the long short-term memory network, the current change trend is predicted based on the historical current data, and the predicted current data is decomposed by wavelet packets and divided into several frequency bands; the divided frequency bands are matched with the key frequency bands of conductor heating, and the conductor heating value is output according to the fundamental and harmonic components of the matched frequency bands combined with the Joule heat formula; according to the ambient temperature and wind speed data of the area to be measured, a heat dissipation model is established, and the temperature change data of the line surface is obtained in combination with the conductor heating value; according to the temperature change data of the line, the ice melting rate 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, and the discrete point data of the ice thickness prediction result is mapped to the continuous space through Kriging interpolation, and the interpolation weight is optimized in combination with the line topology structure to generate a dynamic distribution heat map of ice thickness.

[0011] In a preferred embodiment, the extraction of the key frequency band for wire heating is specifically as follows: performing fast Fourier transform on the frequency band after wavelet packet decomposition to obtain the frequency band range corresponding to each node; obtaining the effective harmonic power data of each frequency band according to the frequency band range, and obtaining the energy contribution of each frequency band by an energy concentration analysis method; if the energy contribution exceeds a threshold, it is the key frequency band for wire heating.

[0012] In a preferred embodiment, the structural safety domain analysis is performed by a finite element analysis method based on the ice thickness distribution heat map, specifically as follows: data is extracted from the ice thickness dynamic heat map, and the data extraction includes timestamps, spatial distribution and meteorological data; a three-dimensional structural model of the transmission line is established based on the extracted data; ice loads and wind loads are applied to the conductors and towers according to the three-dimensional structural model to obtain boundary constraints, and the boundary constraints include applying tension to the ends of the conductors and fixing the tower base; the ice weight per unit line length is calculated based on the boundary constraints and the ice thickness distribution heat map, and the wind load applied to the line is output based on the ice thickness; a static analysis is performed on the line based on the ice weight per unit line length and the wind load, the stress distribution of the tower and the line is output, and the maximum stress is screened; the yield strength of the conductor material is obtained, and the safety value of the line is output in combination with the maximum stress calculation; the lines in the area to be tested are divided according to the safety value and the ice thickness distribution heat map.

[0013] In a preferred embodiment, the structural safety domain analysis results are combined with the grid topology and load data to coordinate and perform load transfer, as follows: a threshold is set according to the safety value, and line sections with insufficient safety margins are screened out, and in combination with the ice thickness distribution heat map, areas where the ice thickness exceeds the design standard are identified to screen high-risk lines; high-risk lines are divided into several sub-areas according to geographical location and grid topology; according to the grid topology division, based on the graph theory algorithm, starting from the starting point and end point of the high-risk line, load transfer paths are screened; based on the power flow analysis method, line load change data under different transfer paths are obtained; based on the line load change data, the load transfer path is determined, and load transfer is performed.

[0014] In a preferred embodiment, the specific steps of determining the load transfer path based on the line load change data and performing load transfer are as follows: obtaining the maximum load of the current line, combining the line load change data, and outputting the unloaded load of the line to be loaded and transferred; determining the line load transfer path based on the safety value and the unloaded load, and calculating the available capacity of the standby line; adjusting the power flow of the substation according to the available capacity of the standby line, reducing the load on the high-risk line, and performing load transfer.

[0015] 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 the meteorological data of the area to be tested to predict the icing rate; the thermal correction module is used to obtain the line current data to perform thermal correction on the icing rate and generate a dynamic distribution heat map of icing thickness; the structural safety domain analysis module is used to perform structural safety domain analysis through the finite element analysis method according to the icing thickness distribution heat map; the load transfer module is used to coordinate the grid topology and load data according to the structural safety domain analysis results and perform load transfer.

[0016] 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 are as follows: 1. The present invention extracts timestamps, spatial distribution and meteorological data based on the dynamic distribution heat map of ice thickness, and establishes a three-dimensional structural model of the transmission line accordingly. In this model, ice loads and wind loads are applied to the conductors and towers, and corresponding boundary constraints are set, such as the tension of the conductor ends and the fixation of the tower base. By calculating the ice weight and wind load per unit line length, the static analysis method is used to output the stress distribution of the tower and the line, 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 ice thickness distribution heat map, the lines in the test area are divided into safety domains.

[0017] 2. The present invention obtains high-frequency current harmonic data of the transmission line, separates the fundamental and harmonic components based on wavelet packet decomposition technology, and extracts the key frequency bands of conductor heating. Subsequently, the long short-term memory network is used to predict the short-term current change trend, and the heat generation of the conductor is calculated in combination with the Joule heat formula. Further combined with ambient temperature, wind speed and other data, a heat dissipation model is constructed to calculate the surface temperature change of the line, and the influence of conductor heating on the ice thickness is calculated based on the phase change heat of ice, so as to dynamically adjust the ice thickness prediction result. In addition, the graph attention network is combined with the Kriging interpolation method to map the discrete ice thickness data to a continuous space, and the interpolation weights are optimized in combination with the line topology to finally generate a dynamic distribution heat map of ice thickness. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flow chart of the power transmission and transformation project evaluation method based on the intelligent management and control platform of the present invention.

[0019] Figure 2 It is a schematic diagram of the structure of the power transmission and transformation project evaluation system based on the intelligent management and control platform of the present invention.

[0020] Figure 3 It is an ice coverage rate prediction curve diagram of the power transmission and transformation project evaluation method based on the intelligent management and control platform of the present invention. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] Embodiment 1, Figure 1 The present invention provides a power transmission and transformation project evaluation method based on a smart management and control platform, which includes the following steps: S1, obtaining meteorological data of the area to be tested to predict the ice coverage rate.

[0023] In this embodiment, the meteorological data of the area to be measured is obtained to predict the ice coverage rate, as follows: Use meteorological monitoring stations, satellite remote sensing, radar detection and other technical means to obtain meteorological data of the area to be measured in real time; Remove duplicate, erroneous or abnormal data to ensure data quality and consistency, and integrate data from different sources to form a complete meteorological data set. For missing data, interpolation methods are used to fill in the gaps to ensure data integrity; Extracting features related to ice accumulation rate from a meteorological data set, the features including 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; Based on feature subsets and data characteristics, the prediction model is trained based on the support vector machine. By continuously adjusting the parameters and structure of the model, the model can learn the laws and patterns in the data. Use cross-validation technology to verify the prediction model to ensure the accuracy and generalization ability of the model; The ice coverage rate of the test area is predicted using the trained prediction model.

[0024] The prediction formula of the ice coverage rate is as follows:

[0025] Where: is the ice accumulation rate, is the collision coefficient, is the wind speed, is the liquid water content, is the freezing efficiency function.

[0026] S2, obtain line current data to perform thermal correction on the ice coverage rate and generate a dynamic distribution heat map of ice thickness.

[0027] In this embodiment, the line current data is obtained to perform thermal correction on the ice coating rate, and a dynamic distribution heat map of ice coating thickness is generated, 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, extract the key frequency band related to conductor heating, suppress noise interference through adaptive threshold noise reduction algorithm, and improve the signal-to-noise ratio of current data; Through the long short-term memory network, the future short-term current change trend is predicted based on the historical current data, the conductor temperature rise trend is predicted in advance, 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; According to the ambient temperature and wind speed data of the area to be tested, a heat dissipation model is established, and the temperature change data of the line surface is obtained by combining 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. The interpolation weights are optimized in combination with the line topology to generate a heat map of the dynamic distribution of ice thickness.

[0028] The formula for thermal correction of ice thickness by line current data is as follows:

[0029]

[0030] Where: is the corrected ice thickness, is the initial predicted ice thickness, is the ice melting rate, is the melting coefficient, is the conductor temperature, is the ambient temperature.

[0031] The Joule heat formula is as follows:

[0032] 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.

[0033] Extract the key frequency band of wire heating, 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 the effective harmonic power data of each frequency band according to the frequency band range, set the heating threshold, and obtain the energy contribution 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 the frequency band has a greater impact on the overall temperature rise of the line and may be the main source of heating; If the energy contribution exceeds the threshold, it is the critical frequency band for conductor heating.

[0034] The energy contribution calculation formula is as follows:

[0035] Where: is the proportion of frequency band n in all harmonic energies, is the harmonic power loss in frequency band n, is the total power loss in all harmonic frequency bands.

[0036] It should be noted that in order to identify the harmonic frequency band that contributes most to conductor heating, a reasonable heating threshold needs to be set. When the effective harmonic power of a frequency band exceeds the threshold, it is considered that the frequency band may have a significant impact on the line temperature rise. In addition, energy concentration analysis is used to further determine which harmonic frequency bands are the main sources of heating.

[0037] S3, according to the ice thickness distribution heat map, the structural safety zone analysis is carried out by finite element analysis method.

[0038] In this embodiment, according to the ice thickness distribution heat map, the structural safety zone analysis is performed by the finite element analysis method, as follows: Data extraction is performed on the dynamic heat map of ice thickness, including timestamps (changes in ice thickness in different time periods), spatial distribution (ice thickness on tower bases, conductors and insulators) and meteorological data (temperature, wind speed, wind direction, humidity); 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 based on the boundary constraints and the ice thickness distribution heat map. If the ice is unevenly distributed, an asymmetric load is applied according to the heat map partitions, and the wind load applied to the line is obtained 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.

[0039] The calculation formula of ice weight per unit line length is as follows:

[0040] The calculation formula of the wind load is as follows:

[0041] Where: is the ice weight per unit line length, is the ice density, is the acceleration due to gravity, is the wire diameter, is the corrected ice thickness, is the wind load, It's wind resistance. is the air density, It's the wind speed.

[0042] The calculation formula of the safety value is as follows:

[0043] Where: is a safe value. is the yield strength of the line material, It is the maximum stress in the current line.

[0044] 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 occurring.

[0045] S4, based on the structural safety domain analysis results, combined with the grid topology and load data, and load transfer is performed.

[0046] In this embodiment, the structural safety domain analysis results are combined with the grid topology and load data to coordinate and perform load transfer, 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, starting from the starting and ending points of high-risk lines, excluding lines that are currently overloaded or have other risks, and screening 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 based on line load change data and perform load transfer.

[0047] 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: 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 safety values ​​and load shedding, and calculate the available capacity of the backup line to ensure that the load transfer will not cause overload of the new line; Adjust the power flow of substations through dispatching control, reduce the load on high-risk lines, and perform load transfer.

[0048] The calculation formula of the power flow analysis method is as follows:

[0049] 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.

[0050] Embodiment 2, Figure 2 A system of a power transmission and transformation project evaluation method based on a smart management and control platform is provided in the present invention, which is characterized by comprising 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 ice-covering rate prediction module is used to obtain the 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.

[0051] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0052] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0053] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0054] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0055] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0056] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in 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; Obtain line current data to perform thermal correction on ice accumulation rate and generate a dynamic distribution heat map of ice accumulation 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, the grid topology and load data are coordinated and load transfer is carried out.

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 tested, and extract features related to ice coverage rate from the meteorological data, wherein the features 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 line current data is obtained to perform thermal correction on the ice coating rate and generate a dynamic distribution heat map of ice coating 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; According to the ambient temperature and wind speed data of the area to be tested, a heat dissipation model is established, and the temperature change data of the line surface is obtained by combining 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. 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.

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: 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.

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: 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.

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 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.

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 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.

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 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.

9. The power transmission and transformation project evaluation method based on the intelligent management and control platform according to claim 8 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.

10. 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 9, 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.

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