An adaptive optimization control system for ultra-high voltage transmission lines

Abstract

The invention relates to the technical field of power transmission line control, and in particular to an adaptive optimization control system for a UHV power transmission line, comprising: a corona effect monitoring and environment perception module: combining a multi-point distributed sensor network to monitor and obtain the corona discharge situation in the power transmission line, and collecting meteorological data in real time; an adaptive voltage and electric field control module: dynamically adjusting the voltage level of the power transmission line through an adaptive voltage regulation algorithm according to the corona discharge situation and meteorological data; a corona power loss calculation module: analyzing the electric field strength, air ion density and noise level of the power transmission line through a nonlinear model, calculating the power loss caused by the corona effect, generating a dynamic corona loss prediction curve, and fine-tuning the adjusted voltage level in the adaptive voltage and electric field control module; the invention ensures that the power loss is calculated more accurately, which not only improves the accuracy of corona effect monitoring, but also effectively avoids the risks caused by the sudden aggravation of the corona effect.

Description

Technical Field

[0001] The present invention relates to the technical field of power transmission line control, and in particular to an adaptive optimization control system for an ultra-high voltage transmission line. Background Art

[0002] During the operation of UHV transmission lines, the corona effect is a common phenomenon. The corona effect usually occurs in the high-voltage electric field around the transmission line. When the electric field strength exceeds the breakdown voltage of the air, the gas molecules in the air are ionized, resulting in corona discharge. This corona discharge can lead to a variety of adverse consequences, such as power loss, electromagnetic interference, noise pollution, etc., especially in long-distance, high-voltage transmission lines, the corona effect will be more obvious, and in severe cases it will even have a significant impact on the safety and efficiency of the transmission line.

[0003] In the existing technology, the monitoring and control of the corona effect mainly rely on the measurement of a single parameter, such as electric field strength or noise level. These methods can often only reflect some characteristics of the corona effect and cannot comprehensively and accurately evaluate the actual impact of the corona effect. In addition, the existing technology lacks the ability to dynamically predict corona power loss and can usually only take countermeasures after the corona effect occurs significantly, resulting in a lag in voltage regulation, thereby affecting the overall operating efficiency of the transmission line.

[0004] With the continuous development of power transmission technology, modern power transmission systems require higher reliability and efficiency. Therefore, there is an urgent need for an intelligent solution that can monitor the corona effect in real time and comprehensively, predict corona power loss and dynamically adjust the voltage level. By predicting the changing trend of the corona effect in advance, the transmission system can actively adjust the voltage before the power loss increases significantly, reduce the impact of the corona effect, and maintain efficient operation of the system. Summary of the invention

[0005] The invention provides an adaptive optimization control system for an ultra-high voltage power transmission line.

[0006] An adaptive optimization control system for ultra-high voltage transmission lines, comprising:

[0007] Corona effect monitoring and environmental perception module: Combined with multi-point distributed sensor network monitoring to obtain the corona discharge conditions in the transmission line, including electric field strength, air ion density and noise level, integrated meteorological environment monitoring function, and real-time collection of meteorological data (temperature, humidity, air pressure); the output data will be transmitted to the adaptive voltage and electric field control module and the corona power loss calculation module;

[0008] Adaptive voltage and electric field control module: dynamically adjusts the voltage level of the transmission line through an adaptive voltage regulation algorithm based on corona discharge conditions and meteorological data;

[0009] Corona power loss calculation module: Analyzes the electric field strength, air ion density and noise level of the transmission line through a nonlinear model, calculates the power loss caused by the corona effect, generates a dynamic corona loss prediction curve, and fine-tunes the adjusted voltage level in the adaptive voltage and electric field control module. While suppressing the corona effect, it optimizes the system energy efficiency and ensures that the overall operating efficiency of the transmission line is maximized.

[0010] Optionally, the multi-point distributed sensing network includes:

[0011] Electric field sensors: They are arranged at multiple nodes along the transmission line to monitor the electric field strength around the transmission line in real time. The electric field sensors can capture the changes in the electric field near the transmission line and detect the electric field fluctuations caused by the corona effect.

[0012] Ion density sensor: monitors the ion concentration in the air around the transmission line, and determines the intensity and range of corona discharge by detecting the density of positive and negative ions in the air;

[0013] Sound level meter (noise sensor): used to detect electromagnetic noise caused by corona effect and collect noise level data near transmission lines in real time, especially high-frequency noise.

[0014] Optionally, the meteorological environment monitoring function is implemented based on a meteorological sensor group, which includes a temperature sensor, a humidity sensor, and an air pressure sensor, and is used to monitor the meteorological conditions of the environment in which the transmission line is located by collecting ambient temperature, humidity, and air pressure data.

[0015] Optionally, the adaptive voltage regulation algorithm specifically includes:

[0016] Data acquisition: Acquire corona discharge conditions, including electric field strength, air ion density and noise level, and obtain meteorological data, including ambient temperature, humidity and air pressure;

[0017] Preliminary assessment: The real-time monitoring data is input into the voltage regulation control unit to evaluate the severity of the current corona discharge situation. If the electric field strength, air ion density or noise level exceeds the set threshold, it means that the corona effect is intensifying;

[0018] Adaptive voltage regulation algorithm execution: Based on the monitored corona discharge conditions and meteorological data, the preset adaptive voltage regulation algorithm is used to dynamically adjust the voltage level of the transmission line, and the adjusted voltage value is calculated based on the changes in corona discharge parameters (electric field strength, air ion density, noise level) and meteorological parameters (temperature, humidity, air pressure).

[0019] Optionally, the adaptive voltage regulation algorithm further includes:

[0020] Meteorological correction: The meteorological correction coefficient is used to comprehensively consider the impact of meteorological conditions on corona discharge and adjust the initially calculated voltage value in order to achieve more effective corona suppression under different meteorological conditions.

[0021] Final voltage adjustment: Based on the corona discharge situation and the voltage value after meteorological correction, the final voltage adjustment level is determined and fed back to the transmission line to perform voltage adjustment.

[0022] Optionally, the meteorological correction factor takes into account the effects of temperature, humidity and air pressure on corona discharge and is expressed as:

[0023] ,in;

[0024] , , are the weight coefficients of temperature, humidity, and air pressure respectively;

[0025] : Meteorological correction coefficient, which indicates the impact of meteorological conditions on voltage adjustment. The result will be applied to the voltage adjustment formula;

[0026] : current temperature;

[0027] : Reference temperature, indicating the temperature value under standard conditions, the reference value is 25 ℃;

[0028] : Current humidity;

[0029] : Reference humidity value: 50%;

[0030] : current air pressure;

[0031] : Reference air pressure value: 101325 Pa (standard atmospheric pressure).

[0032] Final voltage adjustment formula: Combining the meteorological correction factor with the corona discharge situation, the final formula for voltage adjustment is:

[0033] ,in;

[0034] : Final voltage level after meteorological correction;

[0035] : The voltage level calculated from the corona discharge situation;

[0036] : Meteorological correction factor, taking into account the impact of temperature, humidity and air pressure on voltage adjustment.

[0037] Optionally, the corona power loss calculation module specifically includes:

[0038] Calculate the current corona power loss: obtain numerical values ​​through electric field strength, air ion density and noise level, and use the pre-established calculation model to calculate the corona power loss at the current moment. This power loss reflects the energy loss of the transmission line caused by the corona effect under the current operating conditions;

[0039] Establish a dynamic corona loss prediction model: After calculating the corona power loss at the current moment, use the time series model to predict the development trend of the corona effect in the future. By analyzing the change rate of the electric field intensity, air ion density and noise level, predict the change of parameters at future moments. By analyzing the change trend of the parameters, predict the corona power loss at a certain moment in the future and generate a dynamic corona loss prediction curve.

[0040] After generating the dynamic corona loss prediction curve, the voltage level is adjusted in advance based on the curve results. If the prediction curve shows that the corona power loss will exceed the set safety threshold in the future, the voltage is automatically reduced to suppress the corona effect.

[0041] Optionally, the dynamic corona loss prediction curve is formed by connecting the prediction results of multiple time points into a curve, and the curve shows the trend of corona power loss changing with time.

[0042] Optionally, the corona power loss calculation takes the electric field strength, air ion density and noise level as influencing factors, and calculates the power loss caused by the corona effect through a nonlinear model:

[0043] ,in;

[0044] : Corona power loss, which indicates the energy loss caused by the corona effect;

[0045] : Corona constant, an empirical coefficient determined based on the design parameters and material properties of the transmission line;

[0046] : Electric field strength;

[0047] : Air ion density;

[0048] : noise level;

[0049] : They are the nonlinear exponents of electric field intensity, air ion density and noise level, respectively, which are obtained by fitting the experimental data and reflect the nonlinear influence on corona power loss.

[0050] Optionally, the dynamic corona loss prediction model is based on the calculated corona power loss, expressed as:

[0051] ,in;

[0052] :At time point The predicted value of corona power loss;

[0053] : Initial time The corona power loss is calculated by the above formula;

[0054] : Weight coefficients, which correspond to the contribution of electric field intensity, air ion density and noise level to the change of power loss over time, and the coefficients are determined by data fitting;

[0055] : The rate of change of electric field strength, air ion density and noise level over time, reflecting the dynamic changes of these parameters over time.

[0056] Beneficial effects of the present invention:

[0057] The present invention establishes a nonlinear model by real-time monitoring of electric field strength, air ion density and noise level, and can accurately calculate the current corona power loss. Compared with the traditional method that only focuses on the monitoring of a single parameter, the present invention integrates multiple parameters and can comprehensively and accurately reflect the actual situation of the corona effect. Through the model, the system can capture the changes of the corona effect in real time, accurately evaluate its impact on the transmission line, and ensure that the calculation of power loss is more accurate, which not only improves the accuracy of corona effect monitoring, but also effectively avoids the risks caused by the sudden intensification of the corona effect.

[0058] The present invention introduces a time series model and generates a dynamic corona loss prediction curve by analyzing the change rate of electric field strength, air ion density and noise level. The curve shows the development trend of the corona effect in the future. The system can predict the change of power loss in advance. Compared with the prior art, the voltage level can be adjusted in advance based on the prediction result before the corona effect intensifies to prevent the adverse effects of the corona effect on the transmission line, thereby effectively suppressing the corona power loss. This dynamic prediction mechanism improves the predictability of the system and makes the regulation more flexible and intelligent. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0060] Figure 1 A schematic diagram of functional modules of a control system according to an embodiment of the present invention;

[0061] Figure 2 FIG. 4 is a schematic diagram of an adaptive voltage regulation algorithm according to an embodiment of the present invention. DETAILED DESCRIPTION

[0062] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0063] It should be noted that the references to "one embodiment", "embodiment", "exemplary embodiments", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0064] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0065] like Figure 1-Figure 2 As shown, an adaptive optimization control system for ultra-high voltage transmission lines includes:

[0066] Corona effect monitoring and environmental perception module: Combined with multi-point distributed sensor network monitoring to obtain the corona discharge conditions in the transmission line, including electric field strength, air ion density and noise level, integrated meteorological environment monitoring function, and real-time collection of meteorological data (temperature, humidity, air pressure); the output data will be transmitted to the adaptive voltage and electric field control module and the corona power loss calculation module;

[0067] Adaptive voltage and electric field control module: dynamically adjusts the voltage level of the transmission line through an adaptive voltage regulation algorithm based on corona discharge conditions and meteorological data;

[0068] Corona power loss calculation module: Analyzes the electric field strength, air ion density and noise level of the transmission line through a nonlinear model, calculates the power loss caused by the corona effect, generates a dynamic corona loss prediction curve, and fine-tunes the adjusted voltage level in the adaptive voltage and electric field control module. While suppressing the corona effect, it optimizes the system energy efficiency and ensures that the overall operating efficiency of the transmission line is maximized.

[0069] The multi-point distributed sensor network includes:

[0070] Electric field sensors: They are arranged at multiple nodes along the transmission line to monitor the electric field strength around the transmission line in real time. The electric field sensors can capture the changes in the electric field near the transmission line and detect the electric field fluctuations caused by the corona effect.

[0071] Ion density sensor: monitors the ion concentration in the air around the transmission line, and determines the intensity and range of corona discharge by detecting the density of positive and negative ions in the air;

[0072] Sound level meter (noise sensor): used to detect electromagnetic noise caused by corona effect and collect noise level data near transmission lines in real time, especially high-frequency noise.

[0073] The meteorological environment monitoring function is implemented based on a meteorological sensor group, which includes a temperature sensor, a humidity sensor, and an air pressure sensor. It is used to monitor the meteorological conditions of the environment in which the transmission line is located by collecting ambient temperature, humidity, and air pressure data.

[0074] The adaptive voltage regulation algorithm specifically includes:

[0075] Data acquisition: Acquire corona discharge conditions, including electric field strength, air ion density and noise level, and obtain meteorological data, including ambient temperature, humidity and air pressure;

[0076] Preliminary assessment: The real-time monitoring data is input into the voltage regulation control unit to evaluate the severity of the current corona discharge situation. If the electric field strength, air ion density or noise level exceeds the set threshold, it means that the corona effect is intensifying;

[0077] Adaptive voltage regulation algorithm execution: Based on the monitored corona discharge conditions and meteorological data, the preset adaptive voltage regulation algorithm is used to dynamically adjust the voltage level of the transmission line, and the adjusted voltage value is calculated based on the changes in corona discharge parameters (electric field strength, air ion density, noise level) and meteorological parameters (temperature, humidity, air pressure).

[0078] Voltage regulation is as follows: The voltage level is adjusted according to the corona discharge and meteorological conditions. The core formula is:

[0079] ,in;

[0080] : The adjusted voltage level is the voltage value calculated to adapt to the current corona effect;

[0081] : The voltage level before adjustment, i.e., the current operating voltage of the transmission line;

[0082] : weight coefficients, corresponding to the influence of electric field strength, air ion density and noise level on voltage adjustment;

[0083] : The measured electric field strength is obtained through the electric field sensor, indicating the electric field strength near the current transmission line;

[0084] The electric field strength threshold indicates the maximum electric field strength allowed by the corona effect. When this threshold is exceeded, the voltage is reduced to suppress corona, with a reference value of 20 kV / m;

[0085] : Measured air ion density, indicating the concentration of ions in the air. Too high a concentration will enhance the corona effect.

[0086] : Threshold of air ion density, indicating the maximum allowed ion density value. Exceeding this value will intensify corona, with a reference value of 500 ion / m³;

[0087] : Measured noise level, used to determine the noise interference caused by the corona effect;

[0088] : Noise level threshold, indicating the maximum allowed noise level. Exceeding this value indicates that the corona effect is too strong. The reference value is 60 dB.

[0089] The adaptive voltage regulation algorithm also includes:

[0090] Meteorological correction: The meteorological correction coefficient is used to comprehensively consider the impact of meteorological conditions on corona discharge and adjust the initially calculated voltage value in order to achieve more effective corona suppression under different meteorological conditions.

[0091] Final voltage adjustment: Based on the corona discharge situation and the voltage value after meteorological correction, the final voltage adjustment level is determined and fed back to the transmission line to perform voltage adjustment.

[0092] The meteorological correction factor takes into account the effects of temperature, humidity and air pressure on corona discharge and is expressed as:

[0093] ,in;

[0094] , , are the weight coefficients of temperature, humidity, and air pressure respectively;

[0095] : Meteorological correction coefficient, which indicates the impact of meteorological conditions on voltage adjustment. The result will be applied to the voltage adjustment formula;

[0096] : current temperature;

[0097] : Reference temperature, indicating the temperature value under standard conditions, the reference value is 25 ℃;

[0098] : Current humidity;

[0099] : Reference humidity value: 50%;

[0100] : Current air pressure;

[0101] : Reference air pressure value: 101325 Pa (standard atmospheric pressure).

[0102] Final voltage adjustment formula: Combining the meteorological correction factor with the corona discharge situation, the final formula for voltage adjustment is:

[0103] ,in;

[0104] : Final voltage level after meteorological correction;

[0105] : The voltage level calculated from the corona discharge situation;

[0106] : Meteorological correction factor, taking into account the impact of temperature, humidity and air pressure on voltage adjustment.

[0107] Determine β1, β2, and β3 through experiments and data analysis:

[0108] Experimental data collection: Conduct a large number of experiments or historical data analysis to monitor the corona discharge and line operation efficiency of transmission lines under different electric field strengths, air ion densities and noise levels.

[0109] Regression analysis: Use linear or nonlinear regression analysis methods to fit the collected experimental data to find out the influence of voltage adjustment on corona discharge intensity. The values ​​of β1, β2, and β3 can be obtained through regression analysis.

[0110] Data verification: These parameters are continuously monitored and adjusted in actual applications, and continuously optimized through feedback mechanisms to ensure that they reflect the actual situation of the current system operation.

[0111] Determine γ1, γ2, and γ3 through experiments and data analysis:

[0112] Meteorological influence research: Analyze the impact of meteorological factors on the corona effect, and collect the relationship between different meteorological conditions (temperature, humidity, air pressure) and the intensity of corona discharge.

[0113] Correlation analysis: The influence of changes in meteorological factors (temperature, humidity, air pressure) on the corona effect was evaluated through correlation analysis, using the Pearson correlation coefficient.

[0114] Regression analysis: Similar to the β coefficient, linear or nonlinear regression analysis is performed to fit the relationship between meteorological factors and corona discharge intensity to obtain γ1, γ2, and γ3.

[0115] Model optimization: These coefficients are continuously adjusted through actual operating data to optimize the accuracy of the model and ensure the system's adaptability under different meteorological conditions.

[0116] Preliminary estimates of the parameters:

[0117] β coefficient: can be selected from the range of 0.1 to 1.0, and the specific value is adjusted based on experimental results and data analysis. For example:

[0118] β1 (electric field strength influence coefficient): 0.5;

[0119] β2 (influence coefficient of air ion density): 0.3;

[0120] β3 (noise level influence coefficient): 0.2;

[0121] Gamma coefficient: Select from the range of 0.1 to 1.0. The specific value also depends on the impact of meteorological factors on the corona effect:

[0122] γ1 (temperature influence coefficient): 0.4;

[0123] γ2 (humidity influence coefficient): 0.4;

[0124] γ3 (pressure influence coefficient): 0.2;

[0125] Implementation steps:

[0126] Preliminary selection: Preliminary selection of values ​​for the above coefficients.

[0127] Experimental verification: Conduct small-scale experiments to verify whether the selected parameters can effectively reflect the actual situation.

[0128] Data collection and analysis: Collect data from actual operation, analyze the impact of parameters on the system, adjust parameters to optimize performance, and continuously update and optimize parameters through real-time feedback to adapt them to the new operating environment.

[0129] Through the above method, the reasonable value of each coefficient can be effectively determined, thereby improving the accuracy and responsiveness of the adaptive voltage regulation algorithm.

[0130] The corona power loss calculation module specifically includes:

[0131] Calculate the current corona power loss: obtain numerical values ​​through electric field strength, air ion density and noise level, and use the pre-established calculation model to calculate the corona power loss at the current moment. This power loss reflects the energy loss of the transmission line caused by the corona effect under the current operating conditions;

[0132] Establish a dynamic corona loss prediction model: After calculating the corona power loss at the current moment, use the time series model to predict the development trend of the corona effect in the future. By analyzing the change rate of the electric field intensity, air ion density and noise level, predict the change of parameters at future moments. By analyzing the change trend of the parameters, predict the corona power loss at a certain moment in the future and generate a dynamic corona loss prediction curve.

[0133] After generating the dynamic corona loss prediction curve, the voltage level is adjusted in advance based on the curve results. If the prediction curve shows that the corona power loss will exceed the set safety threshold in the future, the voltage is automatically reduced to suppress the corona effect.

[0134] Based on the predicted power loss trend, the system will fine-tune the current voltage level to reduce the energy loss caused by the corona effect. Conversely, if the predicted curve shows that power loss will tend to decrease in the future, the system may restore some voltage levels to improve transmission efficiency.

[0135] The dynamic corona loss prediction curve is formed by connecting the prediction results of multiple time points into a curve. The curve shows the trend of corona power loss over time. Through this curve, it is observed whether the corona effect will strengthen, weaken or remain unchanged in the future. If the curve shows that the power loss will increase in the future time period, the system will take measures in advance to suppress this upward trend.

[0136] The corona power loss calculation takes the electric field strength, air ion density and noise level as influencing factors and calculates the power loss caused by the corona effect through a nonlinear model:

[0137] ,in;

[0138] : Corona power loss, which indicates the energy loss caused by the corona effect;

[0139] : Corona constant, an empirical coefficient determined based on the design parameters and material properties of the transmission line;

[0140] : Electric field strength;

[0141] : Air ion density;

[0142] : noise level;

[0143] : They are the nonlinear exponents of electric field strength, air ion density and noise level, respectively. They are obtained by fitting the experimental data and reflect the nonlinear influence on corona power loss. An exponent greater than 1 indicates that the influence of electric field strength, ion density and noise level on power loss is nonlinear.

[0144] The dynamic corona loss prediction model is based on the calculated corona power loss and is expressed as:

[0145] ,in;

[0146] :At time point The predicted value of corona power loss;

[0147] : Initial time The corona power loss is calculated by the above formula;

[0148] : Weight coefficients, which correspond to the contribution of electric field intensity, air ion density and noise level to the change of power loss over time, and the coefficients are determined by data fitting;

[0149] : The rate of change of electric field intensity, air ion density and noise level over time, reflecting the dynamic changes of these parameters over time;

[0150] This formula is used to predict the trend of corona power loss over time and provide a dynamic prediction of the development of corona effect in the future;

[0151] Voltage regulation formula: Based on the dynamic corona loss prediction results, the adaptive voltage regulation algorithm dynamically fine-tunes the voltage through the following formula:

[0152] ,in;

[0153] : The adjusted voltage level is adjusted according to the predicted corona power loss value;

[0154] : The voltage level before adjustment (unit: volts), that is, the current operating voltage;

[0155] : Voltage regulation coefficient, which reflects the sensitivity of corona power loss to voltage regulation. This coefficient is determined by experiments or simulations;

[0156] : At time point The predicted value of corona power loss is obtained through the prediction model;

[0157] : Corona power loss threshold, which indicates the maximum power loss that the system can accept. When this threshold is exceeded, the voltage needs to be adjusted to suppress the corona effect.

[0158] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0159] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. An adaptive optimization control system for ultra-high voltage transmission lines, characterized in that: include: Corona effect monitoring and environmental perception module: Combined with multi-point distributed sensor network monitoring to obtain the corona discharge conditions in the transmission line, including electric field strength, air ion density and noise level, integrated meteorological environment monitoring function, and real-time collection of meteorological data; the output data will be transmitted to the adaptive voltage and electric field control module and the corona power loss calculation module; Adaptive voltage and electric field control module: dynamically adjusts the voltage level of the transmission line through an adaptive voltage regulation algorithm based on corona discharge conditions and meteorological data; Corona power loss calculation module: Analyzes the electric field strength, air ion density and noise level of the transmission line through nonlinear models, calculates the power loss caused by the corona effect, generates a dynamic corona loss prediction curve, and fine-tunes the voltage level adjusted in the adaptive voltage and electric field control module to optimize the system energy efficiency while suppressing the corona effect. Specifically, it includes: Calculate the current corona power loss: obtain the numerical value through the electric field strength, air ion density and noise level, and use the pre-established calculation model to calculate the corona power loss at the current moment; Establish a dynamic corona loss prediction model: After calculating the corona power loss at the current moment, use the time series model to predict the development trend of the corona effect in the future. By analyzing the change rate of the electric field intensity, air ion density and noise level, predict the change of parameters at future moments. By analyzing the change trend of the parameters, predict the corona power loss at a certain moment in the future and generate a dynamic corona loss prediction curve. After generating the dynamic corona loss prediction curve, the voltage level is adjusted in advance based on the curve results. If the prediction curve shows that the corona power loss will exceed the set safety threshold in the future, the voltage is automatically reduced to suppress the corona effect. The corona power loss calculation takes the electric field strength, air ion density and noise level as influencing factors, and calculates the power loss caused by the corona effect through a nonlinear model: ,in; : Corona power loss, which indicates the energy loss caused by the corona effect; : corona constant; : Electric field strength; : Air ion density; : noise level; : They are the nonlinear exponents of electric field intensity, air ion density and noise level, respectively, which are obtained by fitting the experimental data and reflect the nonlinear influence on corona power loss.

2. The adaptive optimization control system for ultra-high voltage transmission lines according to claim 1, characterized in that: The multi-point distributed sensing network comprises: Electric field sensors: arranged at multiple nodes along the transmission line to monitor the electric field strength around the transmission line in real time; Ion density sensor: monitors the ion concentration in the air around the transmission line, and determines the intensity and range of corona discharge by detecting the density of positive and negative ions in the air; Sound level meter: used to detect electromagnetic noise caused by corona effect and collect noise level data near transmission lines in real time.

3. The adaptive optimization control system for ultra-high voltage transmission lines according to claim 2, characterized in that: The meteorological environment monitoring function is implemented based on a meteorological sensor group, which includes a temperature sensor, a humidity sensor, and an air pressure sensor, and is used to monitor the meteorological conditions of the environment in which the transmission line is located by collecting environmental temperature, humidity, and air pressure data.

4. The adaptive optimization control system for ultra-high voltage transmission lines according to claim 1, characterized in that: The adaptive voltage regulation algorithm specifically includes: Data acquisition: Acquire corona discharge conditions, including electric field strength, air ion density and noise level, and obtain meteorological data, including ambient temperature, humidity and air pressure; Preliminary assessment: The real-time monitoring data is input into the voltage regulation control unit to evaluate the severity of the current corona discharge situation. If the electric field strength, air ion density or noise level exceeds the set threshold, it means that the corona effect is intensifying; Adaptive voltage regulation algorithm execution: Based on the monitored corona discharge conditions and meteorological data, a preset adaptive voltage regulation algorithm is used to dynamically adjust the voltage level of the transmission line, and the adjusted voltage value is calculated based on the changes in corona discharge parameters and meteorological parameters.

5. The adaptive optimization control system for ultra-high voltage transmission lines according to claim 4, characterized in that: The adaptive voltage regulation algorithm also includes: Meteorological correction: The meteorological correction coefficient is used to comprehensively consider the impact of meteorological conditions on corona discharge and adjust the initially calculated voltage value to achieve corona suppression under different meteorological conditions; Final voltage adjustment: Based on the corona discharge situation and the voltage value after meteorological correction, the final voltage adjustment level is determined and fed back to the transmission line to perform voltage adjustment.

6. The adaptive optimization control system for ultra-high voltage transmission lines according to claim 5, characterized in that: The meteorological correction factor takes into account the effects of temperature, humidity and air pressure on corona discharge and is expressed as: in; , , are the weight coefficients of temperature, humidity, and air pressure respectively; : Meteorological correction coefficient, indicating the impact of meteorological conditions on voltage adjustment; : current temperature; : Reference temperature, indicating the temperature value under standard conditions; : Current humidity; : Reference humidity value; : Current air pressure; : Reference air pressure value.

7. The adaptive optimization control system for ultra-high voltage transmission lines according to claim 1, characterized in that: The dynamic corona loss prediction curve is formed by connecting the prediction results of multiple time points into a curve, and the curve shows the trend of corona power loss changing with time.

8. The adaptive optimization control system for ultra-high voltage transmission lines according to claim 1, characterized in that: The dynamic corona loss prediction model is based on the calculated corona power loss, expressed as: ,in; :At time point The predicted value of corona power loss; : Initial time Corona power loss; : Weight coefficients, corresponding to the contribution of electric field intensity, air ion density and noise level to the change of power loss over time; : The rate of change of electric field strength, air ion density and noise level with time.

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