A model prediction-based power transmission line icing thickness data processing system

Abstract

The application belongs to the technical field of power transmission, and particularly discloses a power transmission line icing thickness data processing system based on model prediction, which comprises a central processing part and a terminal processing part, the central processing part comprises a central computer, and a central processing unit is arranged in the central computer. The power transmission line icing thickness data processing system based on model prediction is provided with a layered architecture mode processing unit, a central computer is used to connect multiple terminal computers, the terminal computers are connected with a certain section of the power transmission line, and therefore a large amount of monitoring data generated in the long-distance power transmission line is dispersed to the terminal processing units of the terminal computers for processing, the predicted icing thickness of the certain section of the power transmission line is obtained, the central processing unit of the central computer performs secondary processing on the predicted icing thickness, the statistical visualization display of the icing thickness is realized, and the real-time monitoring of the surface icing thickness of the power transmission line is facilitated.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power transmission, in particular to a power transmission line icing thickness data processing system based on model prediction. BACKGROUND

[0002] Power transmission refers to the process of transmitting the power generated by power plants to power consumption areas through certain equipment and lines, and the power transmission line is usually exposed to the external environment. Due to the cold weather in the northern region, the exposed power transmission line will appear icing, which refers to the phenomenon that rain, fog or wet snow freezes on the power transmission line. It has a significant impact on public transportation safety, power, communication industry and other public infrastructure, and is one of the key factors causing heavy power grid accidents.

[0003] In order to ensure the normal operation of the power transmission line, it is necessary to monitor the power transmission line in real time to observe the change of the icing condition on the surface of the power transmission line and to conduct timely warning of the power transmission line icing. At present, the icing condition warning of the power transmission line of the power grid is based on the model prediction according to the meteorological data. However, under the normal circumstances, the power transmission line is long distance transmission. With the continuous expansion of the scale of the power transmission line, the amount of data from different positions and different types of sensors is increasingly large. The traditional data processing system is difficult to effectively integrate and analyze the data, and with the increasing amount of data, the risk of processor overload increases, thereby limiting the comprehensive evaluation and accurate judgment of the icing condition of the power transmission line. SUMMARY

[0004] The purpose of the present application is to provide a power transmission line icing thickness data processing system based on model prediction to solve the problem of long power transmission line and large amount of monitoring data processed by the same processor, which is prone to overload.

[0005] To achieve the above purpose, the present application provides the following technical scheme: a power transmission line icing thickness data processing system based on model prediction, comprising a central processing part and an end processing part, the central processing part comprising a central computer, the central computer being provided with a central processing unit, the end processing part comprising an end computer, the end computer being provided with an end processing unit, the central computer and the end computer being connected by wireless signal bidirectional intercommunication, one central computer being connected with multiple end computers, one central computer and the connected end computers forming a hierarchical architecture mode, the hierarchical architecture mode being connected with the power transmission line for line icing thickness prediction.

[0006] Preferably, the end-processing unit includes a data acquisition module, a data processing module, and a model prediction module. The data acquisition module uses sensors to collect data on the surface of the transmission line. The data processing module receives and processes the data from the data acquisition module. The model prediction module receives the transmission line data from the data processing module and inputs it into the model to predict the icing thickness.

[0007] Using the above technical solution, the icing thickness of a certain section of the transmission line can be calculated and predicted by the end-processing unit.

[0008] Preferably, the data acquisition module is connected to sensors installed along the transmission line. These sensors include a temperature sensor, a humidity sensor, a wind speed sensor, and an image sensor. The sensors collect real-time data on the temperature, humidity, wind speed, and icing images of the transmission line surface. The data processing module includes data preprocessing, which involves data cleaning and noise reduction to remove abnormal data and noise. The preprocessed data is then processed to extract feature parameters strongly correlated with icing thickness, which are input into the model prediction module. This module has a built-in model for predicting the icing thickness of the transmission line. The prediction model uses multi-source meteorological data as independent variables and the icing thickness on the transmission line surface as the dependent variable to construct a prediction model for icing thickness and outputs the predicted icing thickness of the transmission line surface based on the collected data.

[0009] Using the above technical solution, the icing thickness of the transmission line can be predicted and calculated by utilizing the prediction model set in the terminal processing unit.

[0010] Preferably, the central processing unit includes a database module, a model verification module, and a statistical analysis module. The database module is connected to the meteorological station system via a network and stores historical meteorological data from the meteorological station system and historical icing thickness of the transmission line. The model verification module constructs a verification model based on the historical meteorological data and historical icing thickness in the database module. The verification model outputs the verified icing thickness on the surface of the transmission line based on the data collected by the sensor.

[0011] Using the above technical solution, the predicted icing thickness can be verified by utilizing the central processing unit within the central processing unit.

[0012] Preferably, the deviation between the verified icing thickness and the predicted icing thickness data of the same section of the transmission line is calculated, and a maximum value is set for the deviation value. If the calculated deviation value exceeds If the predicted icing thickness data is not found, it will be discarded.

[0013] By adopting the above technical solution, the accuracy of the calculation results can be guaranteed by utilizing the deviation between the verified icing thickness and the predicted icing thickness.

[0014] Preferably, the statistical analysis module receives the predicted icing thickness data after deviation value calculation and filtering, and performs statistical analysis. The statistical analysis module includes a data analysis part and a visualization display part. The data analysis part performs basic statistical calculations on the predicted icing thickness data, including extreme value calculation, average value calculation, median and standard deviation. The basic statistical measures describe the overall characteristics and distribution of the icing thickness of the transmission line. The visualization display part displays the results of the basic statistical calculations in the form of visual tables, visual graphs, and a visual map of the predicted icing thickness.

[0015] Using the above technical solution, the statistical analysis module can be used to visualize the predicted ice thickness data, making it easier for operators to observe.

[0016] Preferably, the hierarchical architecture consisting of the central computer and terminal computers is distributed across the surface of a transmission line. Multiple sets of sensors are equidistantly arranged on the transmission line surface, each set of sensors being connected to a terminal processing unit. An overlapping section, marked as a cross-validation section, is provided between adjacent terminal processing units. This cross-validation section allows for data overlap between adjacent terminal processing units. The cross-validation section utilizes two terminal processing units to calculate the predicted icing thickness, calculates a deviation value for the predicted icing thickness calculated by the cross-validation section, and sets a maximum value for this deviation value. If the calculated deviation value exceeds If the predicted icing thickness data is not found, the data should be discarded, and the end-processing unit equipment on both sides of the cross-validation section should be checked promptly.

[0017] By adopting the above technical solution, the cross-validation section can be used to cross-validate the data of the end processing units on both sides, and abnormal equipment can be identified in a timely manner.

[0018] Compared with the prior art, the beneficial effects of the present invention are: the model-predicted transmission line icing thickness data processing system:

[0019] 1. The processing unit in this invention adopts a hierarchical architecture, using a central computer connected to multiple terminal computers. Each terminal computer is connected to a section of the transmission line. Therefore, the large amount of monitoring data generated in the long-distance transmission line is distributed to the terminal processing units at multiple terminal computers for processing, resulting in the predicted icing thickness of a certain section of the transmission line. The central processing unit at the central computer performs secondary processing on the predicted icing thickness to achieve statistical visualization of the icing thickness, which facilitates real-time monitoring of the icing thickness on the surface of the transmission line. Each terminal processing unit independently collects, processes, and predicts local line data, reducing the direct data processing load of the central computer and avoiding overload of the central computer due to excessive data volume. This improves data processing efficiency and system stability, ensuring the timeliness and accuracy of the prediction of icing thickness of the transmission line.

[0020] 2. In this invention, a prediction model for icing thickness is set in the end-of-line processing unit, which calculates the predicted icing thickness of the transmission line using data collected by sensors. A verification model for icing thickness is set in the central processing unit to verify the results calculated by the end-of-line processing unit. By analyzing the deviation between the predicted and verified values, data with excessive deviations are discarded to ensure the reliability of the prediction results. At the same time, end-of-line processing units are distributed across the surface of the transmission line, and cross-verification sections are set between adjacent end-of-line processing units. Data from the cross-verification sections is used to verify the data of the end-of-line processing units on both sides, which facilitates the timely detection of abnormal equipment in the end-of-line processing units, ensures the accuracy of the predictions of the end-of-line processing units, and thus promptly detects potential icing hazards on the transmission line. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the system structure of the present invention;

[0022] Figure 2 This is a schematic diagram of the layered architecture pattern of the present invention;

[0023] Figure 3 This is a schematic diagram of the layered architecture mode and transmission line installation structure of the present invention. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] Please see Figures 1-3 The present invention provides a technical solution: a data processing system for icing thickness of transmission lines based on model prediction.

[0026] The central processing unit includes a central computer, which houses a central processing unit (CPU). The terminal processing unit includes terminal computers, each housing a terminal processing unit (RPU). The central and terminal computers are interconnected via wireless signals. One central computer can simultaneously connect to multiple terminal computers. This hierarchical architecture, where the central computer and connected terminal computers form a layered structure, is connected to the transmission line to predict icing thickness. The terminal processing unit includes a data acquisition module, a data processing module, and a model prediction module. The data acquisition module uses sensors to collect surface data from the transmission line. The data processing module receives and processes the data from the data acquisition module. The model prediction module receives the transmission line data from the data processing module and inputs it into the model to predict icing thickness. The data acquisition module is connected to sensors installed along the transmission line. These sensors include temperature sensors, humidity sensors, wind speed sensors, and image sensors. The sensors collect real-time data on the temperature, humidity, wind speed, and icing images of the transmission line surface. The data processing module includes data preprocessing, which involves data cleaning and noise reduction to remove abnormal data and noise. After preprocessing, the data is processed to extract feature parameters that are strongly correlated with icing thickness and input into the model prediction module. The model prediction module has a built-in prediction model for the icing thickness of the transmission line. The prediction model uses the detected multi-source meteorological data as the independent variable and the icing thickness on the transmission line surface as the dependent variable to construct a prediction model for icing thickness and output the predicted icing thickness on the transmission line surface based on the collected data.

[0027] like Figure 1 As shown, a hierarchical monitoring system consisting of a central computer and terminal computers is set up along a transmission line. The central computer is connected to multiple terminal computers, and each terminal computer is connected to a terminal processing unit set on the surface of the transmission line. The data acquisition module set in the terminal processing unit collects data on the surface of the transmission line, including temperature, humidity, wind speed and icing image data of the transmission line surface. The data is preprocessed in the terminal computer to remove abnormal data and data noise, and then input into the model prediction module. The model prediction module generates the icing thickness of the line surface at each terminal processing unit, and the calculation results are input into the central processing unit for verification and visualization output.

[0028] The central processing unit includes a database module, a model validation module, and a statistical analysis module. The database module is connected to the meteorological station system via a network and stores historical meteorological data and historical icing thickness of transmission lines from the meteorological station system. The model validation module constructs a validation model based on the historical meteorological data and historical icing thickness in the database module. The validation model outputs the validated icing thickness of the transmission line surface based on the data collected by sensors. It calculates the deviation between the validated icing thickness and the predicted icing thickness data for the same section of the transmission line and sets a maximum value for the deviation. If the calculated deviation value exceeds If the predicted icing thickness data is not found, it will be discarded. The statistical analysis module receives the predicted icing thickness data after the deviation value calculation and performs statistical analysis. The statistical analysis module includes a data analysis part and a visualization display part. The data analysis part performs basic statistical calculations on the predicted icing thickness data, including extreme value calculation, average value calculation, median and standard deviation. The basic statistics describe the overall characteristics and distribution of the icing thickness of the transmission line. The visualization display part presents the results of the basic statistical calculations in visual tables, visual graphs and generates a visual map of the predicted icing thickness.

[0029] like Figure 1 and Figure 2 As shown, the central processing unit includes a database module, a model validation module, and a statistical analysis module. The database module is connected to the meteorological station system, receiving historical meteorological data and historical icing thickness of the transmission lines. This data is then input into the model validation module to build a validation model. For the predicted icing thickness of the transmission line surface in the terminal processing unit, the same parameters are input into the validation model to generate the verified icing thickness of the transmission line surface. The deviation between the verified and predicted icing thickness data for the same section of the transmission line is calculated, and the calculated deviation data is compared with the set maximum value. In comparison, it exceeds the maximum value. This indicates a large discrepancy between the predicted and verified values ​​of icing thickness, suggesting the data is unreliable, especially for values ​​exceeding the maximum value. The predicted icing thickness data was discarded, and data smaller than [a certain value] was also discarded. The predicted icing thickness data is input into the statistical analysis module. The module performs statistical analysis on the predicted icing thickness data, calculates the maximum and minimum values, average values, median, and standard deviation of the icing thickness on the transmission line surface, thereby describing the overall characteristics and distribution of the icing thickness on the transmission line. The visualization display section outputs visualization tables, visualization graphs, and generates a visualization map of the predicted icing thickness based on the statistical data, which makes it easy for staff to observe the surface icing thickness of the transmission line in real time and intuitively, and facilitates timely early warning of the transmission line.

[0030] A hierarchical architecture consisting of a central computer and terminal computers is distributed across the surface of a power transmission line. Multiple sets of sensors are equidistantly positioned on the line surface, each set connected to a terminal processing unit. An overlapping section, designated as a cross-validation section, is established between adjacent terminal processing units. This cross-validation section allows for data overlap between adjacent terminal processing units. Each cross-validation section utilizes two terminal processing units to calculate the predicted icing thickness. A deviation value is calculated from the predicted icing thickness obtained by the cross-validation section, and a maximum value is set for this deviation. If the calculated deviation value exceeds If the predicted icing thickness data is discarded, the end processing unit equipment on both sides of the cross-validation section should be checked in a timely manner.

[0031] like Figure 3 As shown, multiple sets of sensors are evenly distributed on the surface of a transmission line. Each set of sensors is connected to an end-processing unit. An overlapping section is provided between adjacent end-processing units, resulting in data overlap between them. Data from this overlapping section can be used to cross-validate the data from the end-processing units on both sides. If the deviation between the data from the end-processing units on both sides within a cross-validation section is greater than... Therefore, it is necessary to promptly inspect the equipment in the end-processing units on both sides to avoid errors in the predicted data caused by equipment malfunctions, which in turn affect the prediction of the ice thickness on the transmission line surface.

[0032] Working Principle: In the process of predicting the icing thickness of a transmission line section, a central computer is connected to multiple terminal computers. Each terminal computer is equipped with a terminal processing unit connected to a set of sensors on the transmission line surface. The terminal processing unit predicts the icing thickness of a section of the transmission line, and the results are input into the central processing unit at the central computer for secondary processing. This verifies and processes the predicted values, making the statistical visualization of the icing thickness visible and facilitating real-time monitoring by staff. The distributed data processing between the terminal and central processing units reduces the data processing load on the central computer, thus avoiding overload during long-distance transmission line monitoring and ensuring system stability. Cross-validation sections are set between adjacent terminal processing units. Data from these cross-validation sections can verify the data of the terminal processing units, facilitating the timely detection of abnormal data and identifying any malfunctioning equipment within the terminal processing units, ensuring the accuracy of the icing thickness prediction.

[0033] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention.

Claims

1. A data processing system for icing thickness of transmission lines based on model prediction, comprising a central processing unit and a terminal processing unit, wherein the central processing unit includes a central computer and a central processing unit is installed within the central computer, and the terminal processing unit includes a terminal computer and a terminal processing unit is installed within the terminal computer, wherein the central computer and the terminal computer are bidirectionally interconnected via wireless signals, characterized in that: A central computer is simultaneously connected to multiple terminal computers. The central computer and the connected terminal computers form a hierarchical architecture. The hierarchical architecture is connected to the power transmission line to predict the icing thickness of the line. The terminal processing unit includes a data acquisition module, a data processing module, and a model prediction module. The data acquisition module is equipped with sensors to collect data on the surface of the transmission line. The data processing module receives the data from the data acquisition module and processes it. The model prediction module receives the transmission line data from the data processing module and outputs a model prediction of the icing thickness. The central processing unit includes a database module, a model verification module, and a statistical analysis module. The database module is connected to the meteorological station system via a network and stores historical meteorological data from the meteorological station system and historical icing thickness of the transmission lines. The model verification module constructs a verification model based on the historical meteorological data and historical icing thickness in the database module. The verification model outputs the verified icing thickness on the surface of the transmission lines based on the data collected by the sensors. The deviation between the verified icing thickness and the predicted icing thickness data for the same section of the transmission line is calculated, and a maximum value is set for the deviation value. If the calculated deviation value exceeds If the predicted icing thickness is not found, the data is discarded. The hierarchical architecture consisting of the central computer and terminal computers is distributed across the surface of a transmission line. Multiple sets of sensors are equidistantly arranged on the transmission line surface. Each set of sensors is connected to a terminal processing unit. An overlapping section, marked as a cross-validation section, is provided between adjacent terminal processing units. The cross-validation section allows for data overlap between adjacent terminal processing units. Each cross-validation section uses two terminal processing units to calculate the predicted icing thickness. A deviation value is calculated for the predicted icing thickness calculated by the cross-validation section, and a maximum value is set for this deviation value. If the calculated deviation value exceeds If the predicted icing thickness data is not found, the data should be discarded, and the end-processing unit equipment on both sides of the cross-validation section should be checked promptly.

2. The data processing system for icing thickness of transmission lines based on model prediction according to claim 1, characterized in that: The data acquisition module is connected to sensors installed along the transmission line. These sensors include a temperature sensor, a humidity sensor, a wind speed sensor, and an image sensor. The sensors collect real-time data on the temperature, humidity, wind speed, and icing images of the transmission line surface. The data processing module includes data preprocessing, which involves data cleaning and noise reduction to remove abnormal data and noise. The preprocessed data is then processed to extract feature parameters strongly correlated with icing thickness, which are then input into the model prediction module. The model prediction module has a built-in prediction model for transmission line icing thickness. This prediction model uses multi-source meteorological data as independent variables and the icing thickness on the transmission line surface as the dependent variable to construct a prediction model for icing thickness and outputs the predicted icing thickness of the transmission line surface based on the collected data.

3. The data processing system for icing thickness of transmission lines based on model prediction according to claim 1, characterized in that: The statistical analysis module receives the predicted icing thickness data after deviation value calculation and filtering, and performs statistical analysis. The statistical analysis module includes a data analysis part and a visualization display part. The data analysis part performs basic statistical calculations on the predicted icing thickness data, including extreme value calculation, average value calculation, median and standard deviation. The basic statistical measures describe the overall characteristics and distribution of the icing thickness of the transmission line. The visualization display part presents the results of the basic statistical calculations in visual tables, visual graphs, and generates a visual map of the predicted icing thickness.

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