Method and device for sensing ice-covered state and assessing operation risk of satellite-ground integrated transmission lines

Abstract

The present invention discloses a method and device for sensing the icing state and assessing the operation risk of a satellite-ground integrated power transmission line. The method comprises: acquiring line icing monitoring data and meteorological monitoring data of an icing-affected area acquired by a data acquisition system, inputting the meteorological monitoring data into an icing prediction model to perform icing prediction, obtaining an initial prediction result, constructing a prediction correction model according to the line icing monitoring data, inputting the initial prediction result into the prediction correction model for correction, acquiring a target icing prediction result, constructing a line failure probability model for the icing-affected area based on the target icing prediction result, and performing icing risk assessment of the power transmission line in the icing-affected area according to the line failure probability model, thereby greatly improving the monitoring scope, ensuring the timeliness and accuracy of the monitoring data, effectively improving the efficiency and accuracy of the line risk assessment, and avoiding the problem of deviation in the prediction result caused by single data prediction.

Description

Technical Field

[0001] The present invention relates to the field of electric power technology, and in particular to a method and device for sensing ice coverage and assessing operation risks of satellite-ground integrated power transmission lines. Background Art

[0002] As an important infrastructure for the transmission of electric energy, it is vital to ensure the safe and stable operation of overhead transmission lines. The wide coverage and complex terrain of the transmission network determine that it is vulnerable to natural disasters. Transmission line icing disasters caused by extreme ice and snow weather can cause line flashover and tripping accidents at the least, or even cause line disconnection and tower collapse, or even cause large-scale power outages in the entire region.

[0003] The scenarios in which power transmission lines are located are complex and changeable, with large spans involved, and icing often occurs in mountainous areas where few people go. At present, ground networks are used to carry out icing monitoring, which has problems such as limited data transmission capacity, slow update frequency and small coverage. In addition, the laying of ground devices must also take into account the harsh natural conditions along the transmission lines, which makes investment and maintenance difficult. Existing icing prediction methods are mostly traditional models that rely on historical experience, and at the same time have high requirements for the accuracy of meteorological data. However, most of the current meteorological data is obtained and transmitted through ground networks, which has the problems of low timeliness and poor accuracy of data transmission, and cannot provide accurate and comprehensive data support for technical personnel to assess the risk of icing disasters. Summary of the invention

[0004] The main purpose of the present invention is to provide a method and device for sensing the icing status and assessing the operation risk of a satellite-ground integrated transmission line, aiming to solve the problems of limited monitoring range in the prior art, poor timeliness and accuracy of monitoring data, and inability to provide data support for line icing status perception, resulting in low efficiency and accuracy in line risk assessment.

[0005] To achieve the above object, the present invention provides a method for sensing ice coverage and assessing operation risk of a satellite-ground integrated power transmission line, the method comprising the following steps:

[0006] Acquire line icing monitoring data and meteorological monitoring data in the icing disaster area collected by the data acquisition system, wherein the line icing monitoring data includes monitoring data of icing monitoring probes and monitoring data of satellite equipment, and the meteorological monitoring data includes ground meteorological data and satellite remote sensing meteorological data;

[0007] Inputting the meteorological monitoring data into an icing prediction model to perform icing prediction and obtain an initial prediction result;

[0008] Constructing a prediction correction model according to the line icing monitoring data, and inputting the initial prediction result into the prediction correction model for correction to obtain a target icing prediction result;

[0009] Constructing a line failure probability model for the icing-affected area based on the target icing prediction result;

[0010] An icing risk assessment of the power transmission lines in the icing affected area is performed based on the line failure probability model.

[0011] Optionally, before acquiring the line icing monitoring data and meteorological monitoring data of the icing disaster area collected by the data collection system, the method further includes:

[0012] Obtaining a line network diagram of the power transmission lines in the ice-affected area;

[0013] Based on the line network structure diagram and the important quantitative model, the line distribution area of ​​the ice-affected area and the important quantitative parameters of each line distribution area are determined, and the important quantitative model is:

[0014]

[0015]

[0016] in, Indicates the importance of the line in the line network. represents the identity matrix, Representation Node right The central contribution of represents the network feature matrix, and represents the network structure before and after the line is removed, represents the centrality in the equivalent network diagram of the power grid before and after the line is removed, and the centrality represents the importance of the line, and are model parameters, is the adjacency matrix of the power grid equivalent network graph;

[0017] Determine the probe arrangement position according to the line distribution area and the important quantitative parameters, and arrange the ice monitoring probe based on the probe arrangement position;

[0018] Among them, the ice monitoring probe is communicatively connected with the satellite equipment, and the ice monitoring probe transmits the collected line ice monitoring data to the satellite equipment. The satellite equipment adjusts the monitoring range based on the line ice monitoring data, and collects the line ice monitoring data of the ice-affected area based on the monitoring range.

[0019] Optionally, inputting the meteorological monitoring data into an icing prediction model to perform icing prediction to obtain an initial prediction result includes:

[0020] The ground meteorological data is integrated with the satellite remote sensing meteorological data to obtain meteorological analysis field data:

[0021]

[0022] in, represents the meteorological analysis field data obtained after fusion, represents the meteorological background field data, which is the forecast data of the meteorological model at the previous moment. represents the background error covariance matrix, which represents the uncertainty of the background field data, represents observation data, which includes ground meteorological data and satellite remote sensing meteorological data, represents an observation operator, which is used to map the analysis field to the observation space, represents an observation error covariance matrix, which is used to characterize the uncertainty of observation data;

[0023] The meteorological analysis field data is input into an icing prediction model to perform icing prediction and obtain an initial prediction result. The icing prediction model includes:

[0024]

[0025] in, represents the initial prediction result of ice thickness, and are the densities of ice and water, respectively. Indicates Hourly precipitation rate, Indicates the liquid water content in the air. Indicates the wind speed at the corresponding position of the line, Indicates the accumulated ice cover time.

[0026] Optionally, constructing a prediction correction model according to the line icing monitoring data, and inputting the initial prediction result into the prediction correction model for correction to obtain a target icing prediction result, includes:

[0027] Obtaining predicted icing data of the monitoring line corresponding to the icing monitoring probe;

[0028] Generate time series data based on the time information of the line icing monitoring data and the predicted icing data, and construct a data set based on the time series data;

[0029] Constructing an original deep learning model, wherein the original deep learning model includes an LSTM model;

[0030] The original deep learning model is trained based on the data set to obtain a prediction correction model:

[0031]

[0032] in, is the loss function, is the number of samples in the dataset, is the actual value, is the corrected prediction model value;

[0033] The initial prediction result is input into the prediction correction model for correction to obtain a target icing prediction result.

[0034] Optionally, the step of performing transmission line icing risk assessment on the icing-affected area according to the line failure probability model includes:

[0035] Obtain a set of fault scenarios for the ice-affected area;

[0036] Determine the information entropy value of each fault scenario in the fault scenario set:

[0037]

[0038] in, is the information entropy value, represents the set of transmission lines in the ice-affected area, For line exist The probability of failure at time Indicates line exist The fault situation at the moment, Indicates the duration of ice coverage;

[0039] Filtering a target scenario from the set of fault scenarios based on the information entropy value;

[0040] An icing risk assessment of the power transmission lines in the icing disaster area is performed based on the target scenario and the line failure probability information output by the line failure probability model.

[0041] Optionally, the step of performing transmission line icing risk assessment on the icing disaster area according to the target scenario and the line failure probability information output by the line failure probability model includes:

[0042] Calculate the risk index of the icing disaster area based on the target scenario, the risk index including a load loss index, a load loss rate index and a voltage over-limit risk index;

[0043] Conducting an icing risk assessment on the power transmission lines in the icing disaster area according to the risk index and the line failure probability information output by the line failure probability model;

[0044] The calculation formula of the load loss index is as follows:

[0045]

[0046] in, is the scene number of the target scene, For the Sampling time node The load loss indicator represents the total amount of electricity lost due to the power supply interruption caused by the distribution network failure;

[0047] The calculation formula of the load loss rate index is as follows:

[0048]

[0049] in, is the load loss indicator, is the total load, It is the load loss rate indicator;

[0050] The voltage over-limit risk index calculation formula is as follows:

[0051]

[0052]

[0053] in, Indicates the voltage over-limit risk indicator. For the Sampling time node The voltage exceeds the limit, For Node The weight of is the rated voltage, and are the upper and lower thresholds of the rated voltage.

[0054] In addition, to achieve the above-mentioned purpose, the present invention also proposes a satellite-ground fusion transmission line icing state perception and operation risk assessment device, the satellite-ground fusion transmission line icing state perception and operation risk assessment device comprising:

[0055] A data acquisition module, used to obtain line icing monitoring data and meteorological monitoring data in icing-affected areas collected by a data acquisition system, wherein the line icing monitoring data includes monitoring data of icing monitoring probes and monitoring data of satellite equipment, and the meteorological monitoring data includes ground meteorological data and satellite remote sensing meteorological data;

[0056] An icing prediction module is used to input the meteorological monitoring data into an icing prediction model to perform icing prediction and obtain an initial prediction result;

[0057] A prediction correction module, used to construct a prediction correction model according to the line icing monitoring data, and input the initial prediction result into the prediction correction model for correction to obtain a target icing prediction result;

[0058] A model building module, used to build a line failure probability model for the icing-affected area based on the target icing prediction result;

[0059] The risk assessment module is used to conduct transmission line icing risk assessment on the icing-affected area according to the line failure probability model.

[0060] In addition, to achieve the above-mentioned purpose, the present application also proposes a satellite-ground integrated transmission line icing status perception and operation risk assessment device, the device comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, the computer program being configured to implement the steps of the satellite-ground integrated transmission line icing status perception and operation risk assessment method as described above.

[0061] In addition, to achieve the above-mentioned purpose, the present application also proposes a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method for sensing the icing status and assessing the operation risk of the satellite-ground integrated transmission line are implemented as described above.

[0062] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the method for sensing the icing status and assessing the operation risk of the satellite-ground integrated transmission line as described above.

[0063] The present invention obtains line icing monitoring data and meteorological monitoring data collected by a data acquisition system in an icing disaster area, wherein the line icing monitoring data includes monitoring data of an icing monitoring probe and monitoring data of a satellite device, and the meteorological monitoring data includes ground meteorological data and satellite remote sensing meteorological data, inputs the meteorological monitoring data into an icing prediction model to perform icing prediction, obtains an initial prediction result, constructs a prediction correction model according to the line icing monitoring data, and inputs the initial prediction result into the prediction correction model for correction, obtains a target icing prediction result, and constructs the icing disaster area based on the target icing prediction result. A line failure probability model is used to evaluate the icing risk of power transmission lines in the icing-affected areas according to the line failure probability model; since the present invention combines line icing monitoring data to predict and correct the prediction results based on meteorological monitoring data, the problem of deviation in prediction results caused by single data prediction is effectively avoided, a line failure probability model is constructed based on the target icing prediction results, and an icing risk assessment of power transmission lines is performed based on the line failure probability model, thereby achieving wide coverage and high-precision perception of the icing situation of power transmission lines, thereby greatly improving the monitoring scope, ensuring the timeliness and accuracy of the monitoring data, and effectively improving the efficiency and accuracy of line risk assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0065] Figure 1 It is a structural diagram of a satellite-ground integrated power transmission line icing state perception and operation risk assessment device in a hardware operating environment involved in an embodiment of the present invention;

[0066] Figure 2 It is a flow chart of the first embodiment of the satellite-ground integrated power transmission line icing state perception and operation risk assessment method of the present invention;

[0067] Figure 3 It is a flow chart of the second embodiment of the method for sensing ice-covered state and assessing operation risk of satellite-ground integrated power transmission lines according to the present invention;

[0068] Figure 4 It is a schematic diagram of a risk assessment process of an embodiment of a method for sensing ice coverage and assessing operation risk of a satellite-ground integrated power transmission line according to the present invention;

[0069] Figure 5 This is a structural block diagram of the first embodiment of the satellite-ground integrated transmission line icing state perception and operation risk assessment device of the present invention.

[0070] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0071] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0072] Reference Figure 1 , Figure 1 This is a schematic diagram of the structure of a satellite-ground integrated power transmission line icing state perception and operation risk assessment device in the hardware operating environment involved in an embodiment of the present invention.

[0073] like Figure 1 As shown, the satellite-ground integrated transmission line ice state perception and operation risk assessment device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a wireless fidelity (Wireless-Fidelity, WI-FI) interface). The memory 1005 may be a high-speed random access memory (Random Access Memory, RAM) or a stable non-volatile memory (Non-Volatile Memory, NVM), such as a disk storage. The memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0074] Those skilled in the art will understand that Figure 1 The structure shown in the figure does not constitute a limitation on the satellite-ground integrated transmission line icing status perception and operation risk assessment device, and may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0075] like Figure 1 As shown, the memory 1005 as a computer-readable storage medium may include an operating system, a network communication module, a user interface module, and a satellite-ground integrated power transmission line icing state perception and operation risk assessment program.

[0076] exist Figure 1In the satellite-ground integrated transmission line icing state perception and operation risk assessment device shown, the network interface 1004 is mainly used for data communication with the network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and the memory 1005 in the satellite-ground integrated transmission line icing state perception and operation risk assessment device of the present invention can be set in the satellite-ground integrated transmission line icing state perception and operation risk assessment device, and the satellite-ground integrated transmission line icing state perception and operation risk assessment device calls the satellite-ground integrated transmission line icing state perception and operation risk assessment program stored in the memory 1005 through the processor 1001, and executes the satellite-ground integrated transmission line icing state perception and operation risk assessment method provided in the embodiment of the present invention.

[0077] The embodiment of the present invention provides a method for sensing ice coverage and assessing operation risk of a satellite-ground integrated power transmission line, referring to Figure 2 , Figure 2 It is a flow chart of the first embodiment of the satellite-ground integrated transmission line icing state perception and operation risk assessment method of the present invention.

[0078] In this embodiment, the satellite-ground integrated transmission line icing state perception and operation risk assessment method includes the following steps:

[0079] Step S10: Acquire line icing monitoring data and meteorological monitoring data of the icing disaster area collected by the data collection system.

[0080] It should be understood that the execution subject of this embodiment may be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or a terminal electronic device capable of realizing the above functions, etc. The following takes the satellite-ground integrated transmission line ice state perception and operation risk assessment device (hereinafter referred to as the assessment device) as an example to illustrate this embodiment and the following embodiments.

[0081] It should be noted that the line icing monitoring data includes monitoring data from icing monitoring probes and monitoring data from satellite equipment, and the meteorological monitoring data includes ground meteorological data and satellite remote sensing meteorological data.

[0082] It should be noted that the line icing monitoring data may include the severity of the damage to the transmission lines and distribution lines in the icing-affected areas, such as the thickness of the icing, the duration of the icing, etc. The above-mentioned meteorological monitoring data may include the temperature, humidity, wind speed, etc. in the icing-affected areas.

[0083] In some embodiments, the evaluation equipment can use convolutional neural networks to perform deep feature extraction on multi-source monitoring data provided by satellite remote sensing, monitoring probes, and ground monitoring systems, mainly including data information such as temperature, humidity, wind speed, and severity of the disaster in ice-affected areas. The icing situation of the entire power transmission network is judged through satellite remote sensing, and satellite navigation is used to achieve high-precision positioning and tracking of the ice-covered areas of the lines. The monitoring range of satellite remote sensing is readjusted according to satellite navigation positioning to complete efficient full coverage monitoring of ice-covered lines. When there is a line break or communication interruption caused by unfavorable terrain, satellite communication can still provide a stable and efficient data transmission channel to ensure that the data information collected by satellite remote sensing, ground monitoring systems, and satellite navigation can be transmitted to the icing monitoring center, and the monitoring center generates an icing situation map of the transmission line based on the data information.

[0084] Furthermore, in order to increase the monitoring scope and implement focused monitoring of important areas, before the above step S10, the following steps may be included:

[0085] Step S11: obtaining a line network structure diagram of the power transmission lines in the ice-affected area;

[0086] Step S12: determining the line distribution area of ​​the ice-affected area and the important quantitative parameters of each line distribution area based on the line network structure diagram and the important quantitative model;

[0087] Step S13: determining the probe arrangement position according to the line distribution area and the important quantitative parameters, and arranging the ice monitoring probe based on the probe arrangement position;

[0088] It should be noted that the importance of a transmission line is related to its location in the transmission network. Using the equivalent network diagram of the transmission network, the importance of the line is represented based on the centrality of the quantified edge. By simulating the disconnection of a specific line and observing the changes in the network centrality caused by this, the importance of the line can be effectively evaluated. Generally speaking, if the change in centrality caused by the disconnection of a line is more significant, it means that the line is more important in the network. According to the order of line importance, the layout location of the ice monitoring probe is selected. The importance quantification model refers to the following formula:

[0089]

[0090]

[0091] in, Indicates the importance of the line in the line network. represents the identity matrix, Representation Node right The central contribution of represents the network feature matrix, and represents the network structure before and after the line is removed, represents the centrality in the equivalent network diagram of the power grid before and after the line is removed, and the centrality represents the importance of the line, and are model parameters, is the adjacency matrix of the power grid equivalent network graph.

[0092] It should be noted that the ice monitoring probe is communicatively connected to the satellite device, and the ice monitoring probe transmits the collected line ice monitoring data to the satellite device. The satellite device adjusts the monitoring range based on the line ice monitoring data, and collects the line ice monitoring data of the ice-affected area based on the monitoring range.

[0093] In some embodiments, the evaluation device captures ice images of the transmission line based on the installed ice monitoring camera, and combines low-orbit satellite Internet to achieve high-frequency acquisition and rapid transmission of ice images. Image processing and machine learning algorithms are used to accurately identify the actual ice thickness of the transmission line, providing massive data support for the ice prediction method based on model-data joint drive.

[0094] Step S20: inputting the meteorological monitoring data into an icing prediction model to perform icing prediction and obtain an initial prediction result.

[0095] It should be noted that this embodiment can combine ground meteorological data and satellite remote sensing meteorological data for comprehensive analysis, fuse multi-dimensional meteorological data, so as to obtain more accurate meteorological data information, perform ice cover prediction based on the fused meteorological monitoring data, and obtain initial prediction results.

[0096] In some embodiments, the evaluation device may use the data weather forecast model of WRF v4.3 to integrate ground meteorological station data with remote sensing satellite meteorological data, and improve the prediction effect of meteorological elements (precipitation, wind speed and temperature) through parameter tuning.

[0097] Furthermore, in order to reduce the error between data and improve the accuracy of fused data, thereby improving analysis efficiency, the above step S20 may include:

[0098] Step S201: fusing the ground meteorological data with the satellite remote sensing meteorological data to obtain meteorological analysis field data;

[0099] Step S202: inputting the meteorological analysis field data into an icing prediction model to perform icing prediction and obtain an initial prediction result.

[0100] It should be noted that the satellite-ground meteorological data fusion uses the GSI system. By analyzing the information and error characteristics of the background field and the observation field, the fusion and assimilation process is transformed into an optimization problem to solve the minimum value of the cost function (J). The cost function represents the error between the analysis field and the background field and the observation data. The cost function of the fused meteorological data refers to the following formula:

[0101]

[0102] in, represents the meteorological analysis field data obtained after fusion, represents the meteorological background field data, which is the forecast data of the meteorological model at the previous moment. represents the background error covariance matrix, which represents the uncertainty of the background field data, represents observation data, which includes ground meteorological data and satellite remote sensing meteorological data, represents an observation operator, which is used to map the analysis field to the observation space, represents the observation error covariance matrix, which is used to characterize the uncertainty of the observation data.

[0103] It should be noted that the above cost function may include a background error term and the observation error term , refer to the following formula:

[0104]

[0105]

[0106] Among them, the background error term represents the difference between the analysis field and the background field, and its purpose is to keep the analysis field and the background field as close as possible to avoid excessive deviation from the physical constraints of the model and forecast errors; the observation error term is the difference between the analysis field and the actual observation data, and its function is to ensure that the analysis field can fully reflect the information in the observation data and take into account the error characteristics of the observation data. By finding the minimum value of the cost function to correspond to the optimal analysis field, the optimized core meteorological elements of temperature, precipitation and wind speed for ice cover prediction are obtained.

[0107] It should be noted that the initial prediction result may include the prediction of the overall ice thickness of the transmission line. In this embodiment, the Jones model may be used as a physical model for ice prediction. The ice prediction model includes:

[0108]

[0109] in, represents the initial prediction result of ice thickness, and are the densities of ice and water, respectively. Indicates Hourly precipitation rate, Indicates the liquid water content in the air. Indicates the wind speed at the corresponding position of the line, Indicates the accumulated ice cover time.

[0110] Step S30: constructing a prediction correction model according to the line icing monitoring data, and inputting the initial prediction result into the prediction correction model for correction to obtain a target icing prediction result.

[0111] It should be noted that the icing prediction model in some embodiments is an empirical model constructed based on historical experience data, so there are deviations in the initial prediction results. This embodiment obtains the actual ice thickness value of one or more lines in the transmission line through an ice monitoring camera, and constructs a prediction correction model from the time dimension according to the actual ice thickness value. The initial prediction result is corrected based on the prediction correction model, thereby realizing the prediction of the line icing situation from the time dimension and the space dimension.

[0112] Furthermore, in order to accurately correct the initial prediction result and improve the analysis accuracy, the above step S30 may include:

[0113] Step S301: obtaining predicted icing data of the monitoring line corresponding to the icing monitoring probe;

[0114] Step S302: generating time series data based on the time information of the line icing monitoring data and the predicted icing data, and constructing a data set based on the time series data;

[0115] Step S303: constructing an original deep learning model;

[0116] Step S304: training the original deep learning model based on the data set to obtain a prediction correction model;

[0117] Step S305: inputting the initial prediction result into the prediction correction model for correction to obtain a target icing prediction result.

[0118] It should be noted that in extreme snowy weather, the ice thickness of the transmission lines increases over time. The predicted values ​​of the multiple ice thicknesses of the overall m transmission lines at T moments in a certain period of time are calculated according to the Jones model. In this embodiment, the actual values ​​of the ice thickness of the corresponding n lines at T moments are obtained through ice monitoring probes. In this embodiment, LSTM (Long Short-Term Memory Network) can be used to carry out prediction and correction of ice thickness. The LSTM model is a deep learning model that is very suitable for processing time series data and tasks with temporal dependencies.

[0119] In some embodiments, the evaluation device can prepare data based on the data collected by the ice monitoring probe: the predicted values ​​and actual values ​​of n lines installed with ice monitoring cameras are organized into time series data, and the specific data format is (n, T, 2) representing n lines, T moments for each line, and 2 feature predicted values ​​and actual values ​​for each moment).

[0120] In some embodiments, the prediction correction model may include an input layer, an LSTM layer, a Dropout layer, and an output layer. The input layer is used to input the sorted data in a format; the prediction correction model may use two LSTM layers, each with 64 units, to improve the expressiveness of the model; in order to prevent overfitting, a Dropout layer may be added after the LSTM layer to discard some neurons and enhance the generalization ability of the model; the above-mentioned output layer may be a fully connected layer, which will output the corrected prediction value.

[0121] In some embodiments, the evaluation device trains the LSTM model so that it can correct the predicted values ​​of the remaining routes by using the predicted values ​​and actual values ​​of the existing routes. The commonly used loss function is the mean square error (MSE) according to the following formula, that is, the model is trained by minimizing the error between the predicted value and the actual value:

[0122]

[0123] in, is the loss function, is the number of samples in the dataset, is the actual value, is the corrected prediction model value.

[0124] In some embodiments, the weights of the LSTM model are randomly initialized, and the input data is processed by the LSTM model to obtain an output result. The output value is compared with the true value, and the weight is adjusted by the optimization algorithm to minimize the loss function. The predicted values ​​of the remaining mn lines are input into the trained LSTM model to obtain the corrected ice thickness prediction value.

[0125] Step S40: constructing a line failure probability model for the icing-affected area based on the target icing prediction result.

[0126] It should be noted that in ice disaster weather, the strain force causing line failure mainly comes from excessive ice load caused by excessive ice coverage. This embodiment constructs a line failure probability model for ice-affected areas and analyzes the failure probability of power transmission lines in ice-affected areas through the line failure probability model.

[0127] In some embodiments, first, a line failure probability model after a power grid icing disaster is established based on the icing prediction results corrected by the icing disaster factors; then, the Monte Carlo simulation method is used to generate fault scenarios, and a typical fault scenario screening method based on system information entropy is proposed; finally, refined risk assessment indicators are constructed, including device-level assessment indicators and system-level assessment indicators.

[0128] In some embodiments, when the actual ice thickness of the line does not exceed the designed maximum ice resistance thickness, the line will not be affected and will operate normally; when the actual ice thickness of the line exceeds five times the designed maximum ice resistance thickness, the line will definitely stop operating; when the actual ice thickness is between the two, the probability of outage increases exponentially with the increase of ice thickness. The failure probability model of the transmission line is expressed by referring to the following formula:

[0129] The failure probability model includes:

[0130]

[0131] in, represents the failure probability of the line due to icing, Indicates the actual ice thickness of the line. Indicates the maximum ice resistance thickness of the line.

[0132] Step S50: conducting an icing risk assessment on the power transmission lines in the icing disaster area according to the line failure probability model.

[0133] In a specific implementation, the assessment device can obtain the failure probability of the transmission line based on the line failure probability model, and calculate the risk indicator parameters of the transmission line from multiple dimensions based on the line failure probability, so as to conduct a multi-dimensional transmission line icing risk assessment in the icing-affected areas.

[0134] In this embodiment, line icing monitoring data and meteorological monitoring data of the icing-affected area collected by a data acquisition system are obtained, wherein the line icing monitoring data includes monitoring data of icing monitoring probes and monitoring data of satellite equipment, and the meteorological monitoring data includes ground meteorological data and satellite remote sensing meteorological data, and the meteorological monitoring data is input into an icing prediction model to perform icing prediction and obtain an initial prediction result, a prediction correction model is constructed according to the line icing monitoring data, and the initial prediction result is input into the prediction correction model for correction to obtain a target icing prediction result, and the icing-affected area is constructed based on the target icing prediction result. A line failure probability model is used to evaluate the icing risk of power transmission lines in the icing-affected areas according to the line failure probability model. Since the present embodiment combines the line icing monitoring data to perform prediction corrections on the prediction results based on the meteorological monitoring data, the problem of deviation in the prediction results caused by single data prediction is effectively avoided. A line failure probability model is constructed based on the target icing prediction results. Based on the line failure probability model, an icing risk assessment of power transmission lines is performed to achieve wide coverage and high-precision perception of the icing situation of power transmission lines, thereby greatly improving the monitoring scope, ensuring the timeliness and accuracy of the monitoring data, and effectively improving the efficiency and accuracy of line risk assessment.

[0135] refer to Figure 3 , Figure 3 It is a flow chart of the second embodiment of the satellite-ground integrated transmission line icing state perception and operation risk assessment method of the present invention.

[0136] Based on the above first embodiment, in this embodiment, the step S50 further includes:

[0137] Step S501: Obtain a set of fault scenarios in the ice-affected area.

[0138] It should be noted that the fault scenario set may include multiple fault scenarios.

[0139] In some embodiments, the evaluation device may utilize the Monte Carlo method to simulate a large number of fault scenarios. For example, 1,000 fault scenarios may be generated through Monte Carlo simulation, and a fault scenario set may be formed by the 1,000 fault scenarios. Then, typical fault scenarios may be screened out from the fault scenario set.

[0140] Step S502: Determine the information entropy value of each fault scenario in the fault scenario set.

[0141] It should be noted that there are many components in the transmission line, and the line faults caused by different meteorological conditions are not the same. The risk assessment process is also more complicated, so it is necessary to select typical fault scenarios. The Monte Carlo method is used to simulate a large number of typical fault scenarios, calculate the information entropy value of each scenario to fit the probability distribution, and select some scenarios corresponding to the information entropy value with the highest probability as typical scenarios. The information entropy value calculation refers to the following formula:

[0142]

[0143] in, is the information entropy value, represents the set of transmission lines in the ice-affected area, For line exist The probability of failure at time Indicates line exist Fault condition at the time (1 when the line is faulty, 0 when the line is not faulty), Indicates the duration of ice coverage.

[0144] Step S503: Filter out a target scenario from the set of fault scenarios based on the information entropy value.

[0145] It should be noted that the target scenario may be a typical failure scenario in the failure scenario set used to assess risks.

[0146] In some embodiments, the evaluation device may screen out fault scenarios whose information entropy values ​​exceed a preset threshold as target scenarios based on the information entropy values ​​of each fault scenario, or may screen out 5% of the fault scenarios as target scenarios, such as screening out 50 target scenarios from 1,000 fault scenarios.

[0147] Step S504: conducting an icing risk assessment on the power transmission lines in the icing disaster area according to the target scenario and the line failure probability information output by the line failure probability model.

[0148] In a specific implementation, the evaluation device can traverse all target scenarios to perform power flow calculations, calculate risk indicator parameters for each target scenario, and conduct icing risk assessment of the transmission lines in the icing-affected areas in combination with the failure probability.

[0149] Reference Figure 4 , Figure 4The present invention is a schematic diagram of the risk assessment process in an embodiment. The assessment device predicts the ice thickness of the transmission line, calculates the failure probability of the transmission line based on the ice thickness prediction result, generates multiple fault scenarios through Monte Carlo simulation, screens out typical target scenarios based on the information entropy value of each fault scenario, traverses each target scenario to perform power flow calculation, calculates the load loss risk index, voltage over-limit risk index and equipment loss risk index based on the power flow calculation results, and performs risk assessment on the transmission line from risk indicators of multiple dimensions.

[0150] Furthermore, in order to accurately assess the icing risk of the transmission line, the above step S504 may include:

[0151] Step S5041: Calculating the risk index of the ice-affected area based on the target scenario;

[0152] Step S5042: Conducting an icing risk assessment on the power transmission lines in the icing disaster area according to the risk index and the line failure probability information output by the line failure probability model.

[0153] It should be noted that this embodiment can propose a series of key indicators from both the device level and the system level to comprehensively measure the possible impact on the power grid. The device level includes line failure risk (refer to the above-mentioned failure probability model). The system level includes multiple risk indicators, including load loss indicators, load loss rate indicators and voltage over-limit risk indicators. The load loss indicator is used to quantify the total amount of electricity lost due to power supply interruptions caused by distribution network failures; the load loss rate indicator is an important parameter for assessing the impact of disasters on the continuity of power supply; the voltage over-limit risk indicator is used to assess the possibility of voltage fluctuations in the distribution network exceeding the normal operating range under disaster conditions, posing a threat to power grid stability and equipment safety.

[0154] It should be noted that the calculation formula of the load loss index is as follows:

[0155]

[0156] in, is the scene number of the target scene, For the Sampling time node The load loss indicator represents the total amount of electricity lost due to the power supply interruption caused by the distribution network failure;

[0157] The calculation formula of the load loss rate index is as follows:

[0158]

[0159] in, is the load loss indicator, is the total load, It is the load loss rate indicator;

[0160] The voltage over-limit risk index calculation formula is as follows:

[0161]

[0162]

[0163] in, Indicates the voltage over-limit risk indicator. For the Sampling time node The voltage exceeds the limit, For Node The weight of is the rated voltage, and are the upper and lower thresholds of the rated voltage, e.g. and Usually 93% and 107% of the rated voltage are taken.

[0164] This embodiment obtains a set of fault scenarios in the ice-affected area, determines the information entropy value of each fault scenario in the set of fault scenarios, screens out a target scenario from the set of fault scenarios based on the information entropy value, and conducts an icing risk assessment on the transmission lines in the ice-affected area according to the target scenario and the line failure probability information output by the line failure probability model, thereby conducting a comprehensive analysis in combination with typical fault scenarios under different meteorological conditions, thereby avoiding the problem of inaccurate assessment results caused by single data and single scenario analysis, and effectively improving the assessment accuracy.

[0165] In addition, an embodiment of the present invention further proposes a computer-readable storage medium, on which is stored a satellite-ground integrated transmission line icing state perception and operation risk assessment program. When the satellite-ground integrated transmission line icing state perception and operation risk assessment program is executed by a processor, the steps of the satellite-ground integrated transmission line icing state perception and operation risk assessment method as described above are implemented.

[0166] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0167] The above-mentioned computer-readable storage medium may be included in the satellite-ground integrated transmission line icing state perception and operation risk assessment device; or it may exist independently without being assembled into the satellite-ground integrated transmission line icing state perception and operation risk assessment device.

[0168] In addition, an embodiment of the present invention also proposes a computer program product, including a satellite-ground integrated transmission line icing state perception and operation risk assessment program, and when the satellite-ground integrated transmission line icing state perception and operation risk assessment program is executed by a processor, the steps of the satellite-ground integrated transmission line icing state perception and operation risk assessment method as described above are implemented.

[0169] The specific implementation manner of the computer program product of the present invention is basically the same as the embodiments of the above-mentioned satellite-ground integrated transmission line icing state perception and operation risk assessment method, and will not be repeated here.

[0170] Reference Figure 5 , Figure 5 This is a structural block diagram of the first embodiment of the satellite-ground integrated transmission line icing state perception and operation risk assessment device of the present invention.

[0171] like Figure 5 As shown, the satellite-ground integrated power transmission line icing state perception and operation risk assessment device proposed in the embodiment of the present invention includes:

[0172] The data acquisition module 10 is used to obtain line icing monitoring data and meteorological monitoring data in the icing disaster area collected by the data acquisition system, wherein the line icing monitoring data includes monitoring data of icing monitoring probes and monitoring data of satellite equipment, and the meteorological monitoring data includes ground meteorological data and satellite remote sensing meteorological data;

[0173] An icing prediction module 20 is used to input the meteorological monitoring data into an icing prediction model to perform icing prediction and obtain an initial prediction result;

[0174] A prediction correction module 30 is used to construct a prediction correction model according to the line icing monitoring data, and input the initial prediction result into the prediction correction model for correction to obtain a target icing prediction result;

[0175] A model building module 40, configured to build a line failure probability model for the icing-affected area based on the target icing prediction result;

[0176] The risk assessment module 50 is used to perform transmission line icing risk assessment on the icing disaster area according to the line failure probability model.

[0177] Furthermore, the satellite-ground integrated transmission line icing state perception and operation risk assessment device further includes:

[0178] The monitoring probe arrangement module 60 is used to obtain a line network structure diagram of the transmission line in the ice-affected area; based on the line network structure diagram and an important quantification model, the line distribution area of ​​the ice-affected area and the important quantification parameters of each line distribution area are determined, and the important quantification model is:

[0179]

[0180]

[0181] in, Indicates the importance of the line in the line network. represents the identity matrix, Representation Node right The central contribution of represents the network feature matrix, and represents the network structure before and after the line is removed, represents the centrality in the equivalent network diagram of the power grid before and after the line is removed, and the centrality represents the importance of the line, and are model parameters, is the adjacency matrix of the power grid equivalent network graph;

[0182] Determine the probe arrangement position according to the line distribution area and the important quantitative parameters, and arrange the ice monitoring probe based on the probe arrangement position;

[0183] Among them, the ice monitoring probe is communicatively connected with the satellite equipment, and the ice monitoring probe transmits the collected line ice monitoring data to the satellite equipment. The satellite equipment adjusts the monitoring range based on the line ice monitoring data, and collects the line ice monitoring data of the ice-affected area based on the monitoring range.

[0184] Furthermore, the ice cover prediction module 20 is also used to fuse the ground meteorological data with the satellite remote sensing meteorological data to obtain meteorological analysis field data:

[0185]

[0186] in, represents the meteorological analysis field data obtained after fusion, represents the meteorological background field data, which is the forecast data of the meteorological model at the previous moment. represents the background error covariance matrix, which represents the uncertainty of the background field data, represents observation data, which includes ground meteorological data and satellite remote sensing meteorological data, represents an observation operator, which is used to map the analysis field to the observation space, represents an observation error covariance matrix, which is used to characterize the uncertainty of observation data;

[0187] The meteorological analysis field data is input into an icing prediction model to perform icing prediction and obtain an initial prediction result. The icing prediction model includes:

[0188]

[0189] in, represents the initial prediction result of ice thickness, and are the densities of ice and water, respectively. Indicates Hourly precipitation rate, Indicates the liquid water content in the air. Indicates the wind speed at the corresponding position of the line, Indicates the accumulated ice cover time.

[0190] Furthermore, the prediction correction module 30 is also used to obtain predicted icing data of the monitoring line corresponding to the icing monitoring probe; generate time series data based on the time information of the line icing monitoring data and the predicted icing data, and construct a data set based on the time series data; construct an original deep learning model, the original deep learning model includes an LSTM model; train the original deep learning model based on the data set to obtain a prediction correction model:

[0191]

[0192] in, is the loss function, is the number of samples in the dataset, is the actual value, is the corrected prediction model value;

[0193] The initial prediction result is input into the prediction correction model for correction to obtain a target icing prediction result.

[0194] Furthermore, the risk assessment module 50 is also used to obtain a set of fault scenarios in the ice-affected area;

[0195] Determine the information entropy value of each fault scenario in the fault scenario set:

[0196]

[0197] in, is the information entropy value, represents the set of transmission lines in the ice-affected area, For line exist The probability of failure at time Indicates line exist The fault situation at the moment, Indicates the duration of ice coverage;

[0198] A target scenario is screened out from the fault scenario set based on the information entropy value; and an icing risk assessment of the power transmission lines in the icing-affected area is performed according to the target scenario and the line failure probability information output by the line failure probability model.

[0199] Furthermore, the risk assessment module 50 is further configured to calculate the risk index of the icing disaster area based on the target scenario, wherein the risk index includes a load loss index, a load loss rate index, and a voltage over-limit risk index; and to perform icing risk assessment on the transmission line in the icing disaster area according to the risk index and the line failure probability information output by the line failure probability model;

[0200] The calculation formula of the load loss index is as follows:

[0201]

[0202] in, is the scene number of the target scene, For the Sampling time node The load loss indicator represents the total amount of electricity lost due to the power supply interruption caused by the distribution network failure;

[0203] The calculation formula of the load loss rate index is as follows:

[0204]

[0205] in, is the load loss indicator, is the total load, It is the load loss rate indicator;

[0206] The voltage over-limit risk index calculation formula is as follows:

[0207]

[0208]

[0209] in, Indicates the voltage over-limit risk indicator. For the Sampling time node The voltage exceeds the limit, For Node The weight of is the rated voltage, and are the upper and lower thresholds of the rated voltage.

[0210] In this embodiment, line icing monitoring data and meteorological monitoring data of the icing-affected area collected by a data acquisition system are obtained, wherein the line icing monitoring data includes monitoring data of icing monitoring probes and monitoring data of satellite equipment, and the meteorological monitoring data includes ground meteorological data and satellite remote sensing meteorological data, and the meteorological monitoring data is input into an icing prediction model to perform icing prediction and obtain an initial prediction result, a prediction correction model is constructed according to the line icing monitoring data, and the initial prediction result is input into the prediction correction model for correction to obtain a target icing prediction result, and the icing-affected area is constructed based on the target icing prediction result. A line failure probability model is used to evaluate the icing risk of power transmission lines in the icing-affected areas according to the line failure probability model. Since the present embodiment combines the line icing monitoring data to perform prediction corrections on the prediction results based on the meteorological monitoring data, the problem of deviation in the prediction results caused by single data prediction is effectively avoided. A line failure probability model is constructed based on the target icing prediction results. Based on the line failure probability model, an icing risk assessment of power transmission lines is performed to achieve wide coverage and high-precision perception of the icing situation of power transmission lines, thereby greatly improving the monitoring scope, ensuring the timeliness and accuracy of the monitoring data, and effectively improving the efficiency and accuracy of line risk assessment.

[0211] The device for sensing the icing state and operating risk assessment of the satellite-ground fusion transmission line provided in the present application adopts the method for sensing the icing state and operating risk assessment of the satellite-ground fusion transmission line in the above-mentioned embodiment, which can solve the technical problems of sensing the icing state and operating risk assessment of the satellite-ground fusion transmission line. Compared with the prior art, the beneficial effects of the device for sensing the icing state and operating risk assessment of the satellite-ground fusion transmission line provided in the present application are the same as the beneficial effects of the method for sensing the icing state and operating risk assessment of the satellite-ground fusion transmission line provided in the above-mentioned embodiment, and the other technical features of the device for sensing the icing state and operating risk assessment of the satellite-ground fusion transmission line are the same as the features disclosed in the above-mentioned embodiment method, which will not be repeated here.

[0212] It should be understood that the above is only an example and does not constitute any limitation on the technical solution of the present invention. In specific applications, technicians in this field can make settings as needed, and the present invention does not limit this.

[0213] It should be noted that the workflow described above is merely illustrative and does not limit the scope of protection of the present invention. In practical applications, technicians in this field can select part or all of them according to actual needs to achieve the purpose of the present embodiment, and no limitation is made here.

[0214] In addition, for technical details not described in detail in this embodiment, reference can be made to the method for sensing ice coverage and assessing operation risks of satellite-ground integrated transmission lines provided in any embodiment of the present invention, which will not be described in detail here.

[0215] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0216] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0217] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as a read-only memory / random access memory, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0218] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A satellite-ground integrated transmission line icing state perception and operation risk assessment method, characterized in that: The method comprises: Acquire line icing monitoring data and meteorological monitoring data in the icing disaster area collected by the data acquisition system, wherein the line icing monitoring data includes monitoring data of icing monitoring probes and monitoring data of satellite equipment, and the meteorological monitoring data includes ground meteorological data and satellite remote sensing meteorological data; Inputting the meteorological monitoring data into an icing prediction model to perform icing prediction and obtain an initial prediction result; Constructing a prediction correction model according to the line icing monitoring data, and inputting the initial prediction result into the prediction correction model for correction to obtain a target icing prediction result; Constructing a line failure probability model for the icing-affected area based on the target icing prediction result; Conducting an icing risk assessment on the power transmission lines in the icing disaster area according to the line failure probability model; Before acquiring the line icing monitoring data and meteorological monitoring data of the icing disaster area collected by the data collection system, the method further includes: Obtaining a line network diagram of the power transmission lines in the ice-affected area; Based on the line network structure diagram and the important quantitative model, the line distribution area of ​​the ice-affected area and the important quantitative parameters of each line distribution area are determined, and the important quantitative model is: in, Indicates the importance of the line in the line network. represents the identity matrix, Representation Node right The central contribution of represents the network feature matrix, and represents the network structure before and after the line is removed, and represents the centrality in the equivalent network diagram of the power grid before and after the line is removed, and the centrality represents the importance of the line, and are model parameters, is the adjacency matrix of the power grid equivalent network graph; Determine the probe arrangement position according to the line distribution area and the important quantitative parameters, and arrange the ice monitoring probe based on the probe arrangement position; Among them, the ice monitoring probe is communicatively connected with the satellite equipment, and the ice monitoring probe transmits the collected line ice monitoring data to the satellite equipment. The satellite equipment adjusts the monitoring range based on the line ice monitoring data, and collects the line ice monitoring data of the ice-affected area based on the monitoring range.

2. The method for sensing ice coverage and assessing operation risk of a satellite-ground integrated power transmission line according to claim 1, characterized in that: The step of inputting the meteorological monitoring data into an ice cover prediction model to perform ice cover prediction and obtain an initial prediction result includes: The ground meteorological data is integrated with the satellite remote sensing meteorological data to obtain meteorological analysis field data: in, represents the meteorological analysis field data obtained after fusion, represents the meteorological background field data, which is the forecast data of the meteorological model at the previous moment. represents the background error covariance matrix, which represents the uncertainty of the background field data, represents observation data, which includes ground meteorological data and satellite remote sensing meteorological data, represents an observation operator, which is used to map the analysis field to the observation space, represents an observation error covariance matrix, which is used to characterize the uncertainty of observation data; The meteorological analysis field data is input into an icing prediction model to perform icing prediction and obtain an initial prediction result. The icing prediction model includes: in, represents the initial prediction result of ice thickness, and denote the densities of ice and water, respectively. Indicates Hourly precipitation rate, Indicates the liquid water content in the air. Indicates the wind speed at the corresponding position of the line, Indicates the accumulated ice cover time.

3. The method for sensing ice coverage and assessing operation risk of a satellite-ground integrated power transmission line according to claim 2, characterized in that: The step of constructing a prediction correction model according to the line icing monitoring data, and inputting the initial prediction result into the prediction correction model for correction to obtain a target icing prediction result includes: Obtaining predicted icing data of the monitoring line corresponding to the icing monitoring probe; Generate time series data based on the time information of the line icing monitoring data and the predicted icing data, and construct a data set based on the time series data; Constructing an original deep learning model, wherein the original deep learning model includes an LSTM model; The original deep learning model is trained based on the data set to obtain a prediction correction model: in, is the loss function, is the number of samples in the dataset, is the actual value, is the corrected prediction model value; The initial prediction result is input into the prediction correction model for correction to obtain a target icing prediction result.

4. The method for sensing ice coverage and assessing operation risk of a satellite-ground integrated power transmission line according to claim 3, characterized in that: The step of conducting an icing risk assessment on the transmission line in the icing disaster area according to the line failure probability model includes: Obtain a set of fault scenarios for the ice-affected area; Determine the information entropy value of each fault scenario in the fault scenario set: in, is the information entropy value, represents the set of transmission lines in the ice-affected area, For Line exist The probability of failure at time Indicates line exist The fault situation at the moment, Indicates the duration of ice coverage; Filtering a target scenario from the set of fault scenarios based on the information entropy value; An icing risk assessment of the power transmission lines in the icing disaster area is performed based on the target scenario and the line failure probability information output by the line failure probability model.

5. The method for sensing ice coverage and assessing operation risk of a satellite-ground integrated power transmission line according to claim 4, characterized in that: The step of performing transmission line icing risk assessment on the icing disaster area according to the target scenario and the line failure probability information output by the line failure probability model includes: Calculate the risk index of the icing disaster area based on the target scenario, the risk index including a load loss index, a load loss rate index and a voltage over-limit risk index; Conducting an icing risk assessment on the power transmission lines in the icing disaster area according to the risk index and the line failure probability information output by the line failure probability model; The calculation formula of the load loss index is as follows: in, is the scene number of the target scene, For the Sampling time node The load loss indicator represents the total amount of electricity lost due to the power supply interruption caused by the distribution network failure; The calculation formula of the load loss rate index is as follows: in, is the load loss indicator, is the total load, It is the load loss rate indicator; The voltage over-limit risk index calculation formula is as follows: in, Indicates the voltage over-limit risk indicator. For the Sampling time node The voltage exceeds the limit, For Node The weight of is the rated voltage, and are the upper and lower thresholds of the rated voltage.

6. A satellite-ground integrated transmission line icing state perception and operation risk assessment device, characterized in that: The device comprises: A data acquisition module, used to obtain line icing monitoring data and meteorological monitoring data in icing-affected areas collected by a data acquisition system, wherein the line icing monitoring data includes monitoring data of icing monitoring probes and monitoring data of satellite equipment, and the meteorological monitoring data includes ground meteorological data and satellite remote sensing meteorological data; An icing prediction module is used to input the meteorological monitoring data into an icing prediction model to perform icing prediction and obtain an initial prediction result; A prediction correction module, used to construct a prediction correction model according to the line icing monitoring data, and input the initial prediction result into the prediction correction model for correction to obtain a target icing prediction result; A model building module, used to build a line failure probability model for the icing-affected area based on the target icing prediction result; A risk assessment module, used for conducting an icing risk assessment on the power transmission lines in the icing disaster area according to the line failure probability model; A monitoring probe arrangement module is used to obtain a line network structure diagram of the power transmission lines in the ice-affected area; Based on the line network structure diagram and the important quantitative model, the line distribution area of ​​the ice-affected area and the important quantitative parameters of each line distribution area are determined, and the important quantitative model is: in, Indicates the importance of the line in the line network. represents the identity matrix, Representation Node right The central contribution of represents the network feature matrix, and represents the network structure before and after the line is removed, and represents the centrality in the equivalent network diagram of the power grid before and after the line is removed, and the centrality represents the importance of the line, and are model parameters, is the adjacency matrix of the power grid equivalent network graph; Determine the probe arrangement position according to the line distribution area and the important quantitative parameters, and arrange the ice monitoring probe based on the probe arrangement position; Among them, the ice monitoring probe is communicatively connected with the satellite equipment, and the ice monitoring probe transmits the collected line ice monitoring data to the satellite equipment. The satellite equipment adjusts the monitoring range based on the line ice monitoring data, and collects the line ice monitoring data of the ice-affected area based on the monitoring range.

7. A satellite-ground integrated transmission line icing state perception and operation risk assessment device, characterized in that: The satellite-ground fusion transmission line icing state perception and operation risk assessment device includes: a memory, a processor, and a satellite-ground fusion transmission line icing state perception and operation risk assessment program stored in the memory and executable on the processor. The satellite-ground fusion transmission line icing state perception and operation risk assessment program is configured to implement the satellite-ground fusion transmission line icing state perception and operation risk assessment method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a satellite-ground integrated transmission line icing state perception and operation risk assessment program, and when the satellite-ground integrated transmission line icing state perception and operation risk assessment program is executed by the processor, the satellite-ground integrated transmission line icing state perception and operation risk assessment method as described in any one of claims 1 to 5 is implemented.

9. A computer program product, characterized in that The computer program product includes a satellite-ground integrated transmission line icing state perception and operation risk assessment program, and when the satellite-ground integrated transmission line icing state perception and operation risk assessment program is executed by a processor, the steps of the satellite-ground integrated transmission line icing state perception and operation risk assessment method according to any one of claims 1 to 5 are implemented.

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