Method and system for optimizing intelligent ice melting device of ultra-high voltage power transmission line

Abstract

The invention discloses a method and a system for optimizing an intelligent ice melting device of an ultra-high voltage transmission line, which are applied to the field of transmission line maintenance, and comprise the following steps of: analyzing and processing state data of an iced line to obtain time-domain dynamic data reflecting ice layer thickness and wire temperature change, extracting harmonic components from the time-domain dynamic data, and calculating the ice layer thickness and the wire temperature change according to the harmonic components. Obtaining ice layer state frequency domain distribution data according to the harmonic component, and determining an ice layer state change trend according to the ice layer state frequency domain distribution data; constructing a power transmission line harmonic state space model, and reconstructing the optimized power transmission line harmonic state space model to obtain an ice layer impedance dynamic prediction model; in the actual ice melting process, the operation parameters of the ice melting device are dynamically optimized and adjusted according to the ice layer impedance dynamic prediction model. According to the method, the dynamic characteristics of the ice layer state can be accurately extracted, the ice melting efficiency and accuracy are improved, excessive ice melting or insufficient ice melting is avoided, and the operation safety of the power transmission line is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of transmission line maintenance, and in particular to an optimization method and system for an intelligent ice melting device of an ultra-high voltage and extra-high voltage transmission line. Background Art

[0002] With the development of the power system, ultra-high voltage and extra-high voltage transmission lines have gradually become an important part of the modern power grid due to their advantages of high power transmission capacity and long-distance power transmission. However, the icing phenomenon seriously affects the safe operation of transmission lines, which may lead to accidents such as line breakage and tower collapse, and even cause large-scale power outages, bringing huge losses to the social economy. Therefore, researching effective ice melting technologies and methods to ensure the safe and stable operation of transmission lines under adverse weather conditions has important practical significance.

[0003] In the prior art, although there are already some methods for controlling ice melting devices by monitoring the ice layer thickness and conductor temperature, these methods have many deficiencies. First, most of the existing methods can only provide simple data collection and preliminary analysis, and cannot accurately model the impedance characteristics of the ice layer, nor can they predict the dynamic changes during the ice melting process in real time. Second, when evaluating the stability of transmission lines during the ice melting process, the prior art lacks effective quantitative analysis means and is difficult to accurately judge the impact of ice melting strategies on line safety. In addition, when facing complex working conditions, the prior art often fails to achieve precise regulation, easily leading to problems such as energy waste or incomplete ice melting, and cannot meet the high reliability requirements of modern ultra-high voltage and extra-high voltage transmission lines.

[0004] Therefore, how to dynamically optimize and adjust the operating parameters of the ice melting device according to the line state during ice melting to precisely control the ice melting process has become a technical problem that needs to be solved urgently by those skilled in the art. Summary of the Invention

[0005] The present invention provides an optimization method and system for an intelligent ice melting device of an ultra-high voltage and extra-high voltage transmission line to optimize the design of the ice melting device.

[0006] To solve the above technical problems, an embodiment of the present invention provides an optimization method for an intelligent ice melting device of an ultra-high voltage and extra-high voltage transmission line, including: Obtaining the icing line state data of the target transmission line when the ice melting device is operating.

[0007] Analyzing and processing the icing line state data by using time domain analysis method to obtain time domain dynamic data reflecting the changes of ice layer thickness and conductor temperature, extracting harmonic components from the time domain dynamic data, and obtaining ice layer state frequency domain distribution data according to the harmonic components.

[0008] Extract the frequency-domain harmonic distribution characteristics from the frequency-domain distribution data of the ice layer state, and determine the changing trend of the ice layer state according to the frequency-domain harmonic distribution characteristics.

[0009] Construct a transmission line harmonic state space model based on the frequency-domain distribution data of the ice layer state and the changing trend of the ice layer state.

[0010] Optimize the model parameters of the transmission line harmonic state space model based on the ice-covered line state data, and reconstruct the transmission line harmonic state space model based on the optimized model parameters to obtain a dynamic prediction model of ice layer impedance.

[0011] During the actual ice melting process, dynamically optimize and adjust the operating parameters of the ice melting device according to the dynamic prediction model of ice layer impedance.

[0012] Furthermore, the ice-covered line state data includes ice layer thickness data and conductor temperature data.

[0013] The obtaining of the ice-covered line state data of the target transmission line during the operation of the ice melting device includes: Obtain the ice layer thickness value collected by the sensor of the target transmission line during the operation of the ice melting device, and perform low-pass filtering on the ice layer thickness value to obtain ice layer thickness data.

[0014] Obtain the conductor temperature value collected by the sensor of the target transmission line during the operation of the ice melting device, and perform linear calibration on the conductor temperature value to obtain conductor temperature data.

[0015] Furthermore, the use of the time-domain analysis method to analyze and process the ice-covered line state data to obtain time-domain dynamic data reflecting the changes in ice layer thickness and conductor temperature, and extract harmonic components from the time-domain dynamic data, and obtain the frequency-domain distribution data of the ice layer state according to the harmonic components, includes: Use the time-domain analysis method to perform a sliding window analysis on the ice layer thickness data and the conductor temperature data, and calculate the time-domain dynamic data reflecting the changes in ice layer thickness and conductor temperature.

[0016] Perform Fourier transform on the time-domain dynamic data to extract the harmonic components of the time-domain dynamic data.

[0017] Screen the amplitude and frequency of the harmonic components to obtain significant harmonic components.

[0018] Generate the frequency-domain distribution data of the ice layer state based on the significant harmonic components.

[0019] Furthermore, the extraction of the frequency-domain harmonic distribution characteristics from the frequency-domain distribution data of the ice layer state, and the determination of the changing trend of the ice layer state according to the frequency-domain harmonic distribution characteristics, includes: Analyze the frequency-domain distribution data of the ice layer state to extract the characteristic difference information of each significant harmonic component, and form the frequency-domain harmonic distribution characteristics.

[0020] Calculate the amplitude change rate and frequency change rate of each significant harmonic component according to the frequency-domain harmonic distribution characteristics.

[0021] Obtain the ice layer state change trend according to the amplitude change rate and the frequency change rate, and the ice layer state change trend includes a stable state, a growth state, and a melting state.

[0022] Further, constructing a transmission line harmonic state space model according to the frequency-domain distribution data of the ice layer state and the ice layer state change trend includes: Determine the initial state parameters of the transmission line according to the frequency-domain distribution data of the ice layer state.

[0023] Adjust the initial state parameters according to the ice layer state change trend to obtain dynamic state parameters.

[0024] Construct a transmission line harmonic state space model of the target transmission line based on the dynamic state parameters.

[0025] Further, the ice-covered line state data further includes an ice layer impedance value.

[0026] Optimizing the model parameters of the transmission line harmonic state space model based on the ice-covered line state data, and reconstructing the transmission line harmonic state space model based on the optimized model parameters to obtain an ice layer impedance dynamic prediction model, including: Use the transmission line harmonic state space model to predict the harmonic state of the target transmission line to obtain an ice layer impedance prediction value.

[0027] Calculate the prediction error according to the difference between the ice layer impedance prediction value and the ice layer impedance value.

[0028] Optimize the model parameters of the transmission line harmonic state space model based on the prediction error using the gradient descent method until the prediction error is less than a preset error threshold.

[0029] Reconstruct the transmission line harmonic state space model based on the optimized model parameters to obtain an ice layer impedance dynamic prediction model.

[0030] Further, reconstructing the transmission line harmonic state space model based on the optimized model parameters to obtain an ice layer impedance dynamic prediction model includes: Update the state matrix, input matrix, output matrix, and feedforward matrix of the transmission line harmonic state space model according to the optimized parameters.

[0031] Based on the updated matrices, reconstruct the state equation and output equation of the harmonic state space model of the transmission line to obtain the dynamic prediction model of ice layer impedance.

[0032] Another embodiment of the present invention provides an optimization system for an intelligent ice melting device of an ultra - extra high voltage transmission line, including: A data acquisition module, configured to acquire the icing line state data of the target transmission line during the operation of the ice melting device.

[0033] A harmonic extraction module, configured to analyze and process the icing line state data by using the time - domain analysis method to obtain the time - domain dynamic data reflecting the changes in ice layer thickness and conductor temperature, extract the harmonic components from the time - domain dynamic data, and obtain the frequency - domain distribution data of the ice layer state according to the harmonic components.

[0034] A trend acquisition module, configured to extract the frequency - domain harmonic distribution characteristics from the frequency - domain distribution data of the ice layer state, and determine the change trend of the ice layer state according to the frequency - domain harmonic distribution characteristics.

[0035] A model construction module, configured to construct a harmonic state space model of the transmission line according to the frequency - domain distribution data of the ice layer state and the change trend of the ice layer state.

[0036] A model reconstruction module, configured to optimize the model parameters of the harmonic state space model of the transmission line based on the icing line state data, and reconstruct the harmonic state space model of the transmission line based on the optimized model parameters to obtain the dynamic prediction model of ice layer impedance.

[0037] A device adjustment module, configured to dynamically optimize and adjust the operating parameters of the ice melting device according to the dynamic prediction model of ice layer impedance during the actual ice melting process.

[0038] The model construction module is specifically configured to: Determine the initial state parameters of the transmission line according to the frequency - domain distribution data of the ice layer state.

[0039] Adjust the initial state parameters according to the change trend of the ice layer state to obtain the dynamic state parameters.

[0040] Construct the harmonic state space model of the target transmission line based on the dynamic state parameters.

[0041] The model reconstruction module is specifically configured to: Predict the harmonic state of the target transmission line by using the harmonic state space model of the transmission line to obtain the predicted value of ice layer impedance.

[0042] Calculate the prediction error according to the difference between the predicted ice layer impedance value and the ice layer impedance value.

[0043] Based on the gradient descent method, optimize the model parameters of the transmission line harmonic state space model according to the prediction error until the prediction error is less than a preset error threshold.

[0044] Reconstruct the transmission line harmonic state space model based on the optimized model parameters to obtain a dynamic prediction model of ice layer impedance.

[0045] Compared with the prior art, the beneficial effects of the embodiments of the present invention are at least one of the following: By collecting operation data such as the ice layer thickness and conductor temperature of the ice-covered line, and combining time-domain analysis and frequency-domain analysis methods, the dynamic characteristics of the ice layer state can be accurately extracted, the ice melting efficiency and accuracy can be improved, over-ice melting or insufficient ice melting can be avoided, the operation safety of the transmission line can be enhanced, the risk of line faults caused by icing can be reduced, the ice resistance and toughness of the power grid under extreme climate conditions can be enhanced, the impact of power outages caused by icing on social economy and residents' lives can be reduced, and the stable operation of the transmission line can be ensured. Brief Description of the Drawings

[0046] Figure 1 is a flowchart of the steps of an optimization method for an intelligent ice melting device of an ultra-high voltage transmission line in one embodiment of the present invention; Figure 2 is a structural block diagram of an optimization system for an intelligent ice melting device of an ultra-high voltage transmission line in one embodiment of the present invention. Detailed Embodiments

[0047] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. The purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0048] In the description of the present application, the terms "first", "second", "third", etc. are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first", "second", "third", etc. may explicitly or implicitly include one or more of such features. In the description of the present application, unless otherwise specified, the meaning of "a plurality" is two or more.

[0049] In the description of the present application, it should be noted that unless otherwise clearly defined and limited, the terms "installation", "connection", and "coupling" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two components. The terms "vertical", "horizontal", "left", "right", "upper", "lower" and similar expressions used herein are only for the purpose of illustration, rather than indicating or implying that the device or component referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus cannot be construed as a limitation on the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.

[0050] In the description of the present application, it should be noted that unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.

[0051] In the power system, icing on transmission lines is a problem that seriously affects the safe operation of the power grid. Icing will increase the mechanical load of the transmission line, and may even cause accidents such as line breakage and tower collapse. At the same time, it will also have an adverse impact on the electrical performance of the line. Therefore, de-icing the iced line is an important link to ensure the stable operation of the power grid. However, existing de-icing devices often de-ice the iced line according to fixed preset operating parameters and cannot adjust the working state of the de-icing device (such as current intensity, power, acoustic frequency, etc.) according to the real-time state of the iced line, resulting in unsatisfactory de-icing effects. For example, when the icing is light, it may consume excessive energy, and when the icing is severe, it cannot remove the ice layer in time and effectively, and may even damage the insulation performance of the transmission line. Therefore, real-time monitoring and accurate assessment of the state of the iced line are also important links to ensure the stable operation of the power grid.

[0052] An embodiment of the present invention provides an optimization method for an intelligent de-icing device for ultra-high voltage transmission lines. Specifically, please refer to Figure 1 , Figure 1 which shows a flowchart of the steps of the optimization method for the intelligent de-icing device for ultra-high voltage transmission lines in one embodiment of the present invention, including: S11. Obtain the icing line state data of the target transmission line when the de-icing device is operating.

[0053] The icing line state data is the key information reflecting the operating conditions of the transmission line under icing conditions, mainly including ice layer thickness data, conductor temperature data, etc. The ice layer thickness data can directly reflect the severity of icing, while the conductor temperature data can reflect the actual temperature of the conductor during the operation of the de-icing device. The two together constitute an important basis for evaluating the state of the icing line.

[0054] The process of obtaining the icing line state data of the target transmission line during the operation of the de-icing device is a key step in achieving accurate monitoring and effective de-icing control. Specifically, the process of obtaining the icing line state data in this embodiment includes: The ultrasonic sensor can monitor the change of the ice layer thickness on the surface of the transmission line in real time and obtain the ice layer thickness value collected by the ultrasonic sensor of the target transmission line during the operation of the de-icing device. However, the ice layer thickness value collected by the sensor is often affected by various factors, such as environmental noise, the accuracy error of the sensor itself, etc. In order to improve the accuracy and reliability of the data, it is necessary to perform low-pass filtering on the collected ice layer thickness value to remove the high-frequency noise components, so as to obtain smoother and more accurate ice layer thickness data.

[0055] The conductor temperature is also a key factor affecting the de-icing effect. Obtain the conductor temperature value collected by the temperature sensor of the target transmission line during the operation of the de-icing device. The conductor temperature value collected by the sensor may also be affected by various factors, such as the installation position of the sensor, environmental temperature changes, etc. These factors may cause a certain deviation in the collected temperature value. In order to ensure the accuracy of the conductor temperature data, it is necessary to perform linear calibration on the collected conductor temperature value. By performing a linear transformation on the collected temperature value, it is made closer to the actual conductor temperature, so as to obtain accurate conductor temperature data.

[0056] S12. Analyze and process the icing line state data by using the time-domain analysis method to obtain the time-domain dynamic data reflecting the changes in ice layer thickness and conductor temperature, extract the harmonic components from the time-domain dynamic data, and obtain the frequency-domain distribution data of the ice layer state according to the harmonic components.

[0057] After obtaining the ice layer thickness data and the conductor temperature data, it is necessary to analyze the data. Specifically, this embodiment uses the time-domain analysis method to analyze the two and calculates the time-domain dynamic data reflecting the changes in ice layer thickness and conductor temperature. When performing time-domain analysis, the data is processed based on a sliding window. The length of the sliding window is set according to the sampling frequency of the data and the actual monitoring requirements. For high-frequency sampled data, the length of the sliding window is set to a shorter time, while for low-frequency sampled data, the length of the sliding window is appropriately increased.

[0058] In order to further analyze the periodic variation law of the data, in this embodiment, Fourier transform is performed on the time-domain dynamic data to extract the harmonic components in the signal that reflect the ice layer state.

[0059] After performing Fourier transform on the ice layer thickness data and the conductor temperature data, a large number of harmonic components will be obtained. These harmonic components contain signal components of different frequencies. Some of these components may have an important impact on the variation law of the data, while some other components may be caused by noise and other irrelevant factors. Therefore, in this embodiment, the amplitudes and frequencies of the harmonic components are screened to obtain the significant harmonic components, and then the frequency-domain distribution data of the ice layer state is generated based on the significant harmonic components.

[0060] S13. Extract the frequency-domain harmonic distribution characteristics in the frequency-domain distribution data of the ice layer state, and determine the change trend of the ice layer state according to the frequency-domain harmonic distribution characteristics.

[0061] There are certain differences among the significant harmonic components in the frequency-domain distribution data of the ice layer state, which can reflect the variation law of the ice layer thickness and the conductor temperature in the frequency domain. The amplitudes of some frequency components may be relatively large, while the amplitudes of some other frequency components are relatively small, which can reflect the change characteristics of the ice layer state at different frequencies. By analyzing the characteristic differences of these significant harmonic components, the frequency-domain harmonic distribution characteristics can be formed.

[0062] After forming the frequency-domain harmonic distribution characteristics, calculate the amplitude change rate and frequency change rate of each significant harmonic component according to the frequency-domain harmonic distribution characteristics. The amplitude change rate reflects the change of the harmonic component amplitude over time, while the frequency change rate reflects the change of the harmonic component frequency over time.

[0063] According to the calculated amplitude change rate and frequency change rate, the change trend of the ice layer state can be obtained. The change trend can be represented by a numerical value. According to the range of the numerical value, the change trend can be divided into a stable state, a growth state, and a melting state. The growth state means that both the amplitude change rate and the frequency change rate of the ice layer thickness are positive values, indicating that the ice layer thickness is increasing continuously, which may be due to a decrease in temperature or other factors leading to increased icing. The melting state means that the amplitude change rate of the ice layer thickness is negative, and the frequency change rate may be negative or close to zero, indicating that the ice layer thickness is gradually decreasing, which may be due to an increase in temperature or the operation of the ice melting device causing the ice layer to melt.

[0064] S14. Construct a transmission line harmonic state space model according to the frequency-domain distribution data of the ice layer state and the change trend of the ice layer state.

[0065] When monitoring and analyzing the ice layer state of a transmission line, the frequency-domain distribution data of the ice layer state provides the frequency characteristics of the ice layer thickness and the change of conductor temperature, which is the basis for building a model. Specifically, first, determine the initial state parameters of the transmission line according to the frequency-domain distribution data of the ice layer state. The initial state parameters reflect the basic state of the transmission line at the current monitoring moment, including information such as the ice layer thickness, conductor temperature, and the distribution of their frequency components.

[0066] The change trend of the ice layer state, such as stable, growing, or melting, directly affects the actual operating state of the transmission line. If the ice layer is in a growing state, the ice layer thickness in the initial state parameters may increase, and the conductor temperature may decrease. Conversely, if the ice layer is in a melting state, the ice layer thickness may decrease, and the conductor temperature may increase. Therefore, after determining the initial state parameters, it is necessary to adjust the initial state parameters according to the change trend of the ice layer state to obtain dynamic state parameters that are more in line with the actual operating conditions.

[0067] Build the harmonic state space model of the target transmission line based on the dynamic state parameters. Through the harmonic state space model of the transmission line, the dynamic relationship between the ice layer thickness, conductor temperature, and ice layer impedance can be reflected.

[0068] Under the ice-covered state, several physical characteristics (such as ice layer thickness, conductor temperature, ice layer impedance, etc.) that interact with each other in the harmonic state space model of the transmission line are dynamically changing. As a general dynamic system description framework, the state space model can more conveniently introduce various inputs (such as ice layer thickness, conductor temperature, etc.) and outputs (such as ice layer impedance), and more intuitively and clearly describe the dynamic relationship between different characteristics.

[0069] S15. Optimize the model parameters of the harmonic state space model of the transmission line based on the ice-covered line state data, and reconstruct the harmonic state space model of the transmission line based on the optimized model parameters to obtain a dynamic prediction model of the ice layer impedance.

[0070] Preferably, the ice-covered line state data in this embodiment not only includes ice layer thickness data and conductor temperature data, but also includes ice layer impedance values. The ice layer impedance values are used to reflect the electrical characteristics of the transmission line under ice-covered conditions and directly affect the operating state and safety of the line.

[0071] Use the harmonic state space model of the transmission line to predict the harmonic state of the target transmission line to obtain the predicted value of the ice layer impedance. This prediction process is based on the current model parameters and dynamic state parameters, and calculates the ice layer impedance of the transmission line under the current ice-covered conditions through the model.

[0072] There is often an error between the predicted value and the actual value. Therefore, the prediction error is calculated based on the difference between the predicted value of the ice layer impedance and the value of the ice layer impedance to quantify the prediction accuracy of the model.

[0073] When constructing the harmonic state space model of the transmission line, due to the random fluctuations, noise interference, and possible measurement errors in the state data of the ice-covered line, the initial parameter settings of the model may not accurately reflect the actual line state. In this embodiment, based on the gradient descent method, the model parameters of the harmonic state space model of the transmission line are optimized according to the prediction error. Specifically, the model parameters are gradually adjusted by calculating the gradient of the prediction error with respect to the model parameters to reduce the prediction error, and this process is iterated repeatedly until the prediction error is less than a preset error threshold.

[0074] The initially constructed harmonic state space model of the transmission line may have certain errors due to factors such as data noise and model assumptions. By optimizing the model parameters, these errors can be reduced, enabling the model to more accurately reflect the actual physical process.

[0075] The harmonic state space model of the transmission line is a space model that dynamically reflects multiple states. Optimizing the parameters based on the harmonic state space model of the transmission line is more efficient. After the optimization is completed, the harmonic state space model of the transmission line is reconstructed to obtain the dynamic prediction model of the ice layer impedance. The dynamic prediction model of the ice layer impedance can be used more efficiently and accurately for actual de-icing operations. The specific reconstruction process is as follows: Update each matrix of the harmonic state space model of the transmission line according to the optimized parameters. The state matrix, input matrix, output matrix, and feedforward matrix are the core components of the harmonic state space model, which respectively describe the evolution of the internal state of the system, the influence of external inputs on the system, the relationship between the system output and the internal state, and the direct effect of the input on the output.

[0076] After updating each matrix of the harmonic state space model of the transmission line, it is necessary to reconstruct the state equation and output equation of the model. The state equation describes the evolution process of the internal state of the system over time, while the output equation describes the relationship between the system output and the internal state. Based on the updated matrix, the reconstructed state equation and output equation can more accurately reflect the dynamic behavior of the transmission line under ice-covered conditions. The reconstructed dynamic prediction model of the ice layer impedance predicts the ice layer impedance of the transmission line at different time points according to real-time dynamic state parameters, which can provide a scientific basis for the operation and maintenance of the power system and ice-covered warning.

[0077] S16. During the actual de-icing process, dynamically optimize and adjust the operating parameters of the de-icing device according to the dynamic prediction model of the ice layer impedance.

[0078] The ice layer impedance dynamic prediction model can predict the impedance change trend of the ice layer under different de-icing conditions based on the current ice layer state, environmental conditions, and the electrical characteristics of the transmission line. According to the ice layer impedance dynamic prediction model, the operating parameters such as the de-icing current, voltage, de-icing time, and de-icing power of the de-icing device are dynamically optimized and adjusted.

[0079] The optimization method and system of the intelligent de-icing device for extra-high voltage and ultra-high voltage transmission lines of the present invention can accurately extract the dynamic characteristics of the ice layer state by collecting operation data such as the ice layer thickness and conductor temperature of the ice-covered line, and combining time-domain analysis and frequency-domain analysis methods, improve the de-icing efficiency and accuracy, avoid over-de-icing or insufficient de-icing, enhance the safety of the transmission line operation, reduce the risk of line faults caused by icing, enhance the anti-icing ability and resilience of the power grid under extreme climate conditions, reduce the impact of power outages caused by icing on social economy and residents' lives, and ensure the stable operation of the transmission line.

[0080] Another embodiment of the present invention provides an optimization system for an intelligent de-icing device for extra-high voltage and ultra-high voltage transmission lines, including: A data acquisition module 21 for acquiring the ice-covered line state data of the target transmission line during the operation of the de-icing device; A harmonic extraction module 22 for analyzing and processing the ice-covered line state data by using time-domain analysis method to obtain time-domain dynamic data reflecting the changes of ice layer thickness and conductor temperature, extracting harmonic components from the time-domain dynamic data, and obtaining frequency-domain distribution data of the ice layer state according to the harmonic components; A trend acquisition module 23 for extracting the frequency-domain harmonic distribution characteristics in the frequency-domain distribution data of the ice layer state and determining the ice layer state change trend according to the frequency-domain harmonic distribution characteristics; A model construction module 24 for constructing a transmission line harmonic state space model according to the frequency-domain distribution data of the ice layer state and the ice layer state change trend; A model reconstruction module 25 for optimizing the model parameters of the transmission line harmonic state space model based on the ice-covered line state data, and reconstructing the transmission line harmonic state space model based on the optimized model parameters to obtain an ice layer impedance dynamic prediction model; A device adjustment module 26 for dynamically optimizing and adjusting the operating parameters of the de-icing device according to the ice layer impedance dynamic prediction model during the actual de-icing process.

[0081] Exemplarily, the model construction module is specifically used for: Determining the initial state parameters of the transmission line according to the frequency-domain distribution data of the ice layer state; Adjusting the initial state parameters according to the ice layer state change trend to obtain dynamic state parameters; Construct a transmission line harmonic state space model of the target transmission line based on the dynamic state parameters.

[0082] Exemplarily, the model reconstruction module is specifically configured to: Use the transmission line harmonic state space model to predict the harmonic state of the target transmission line, and obtain an ice impedance prediction value; Calculate a prediction error according to the difference between the ice impedance prediction value and the ice impedance value; Based on the gradient descent method, optimize the model parameters of the transmission line harmonic state space model according to the prediction error until the prediction error is less than a preset error threshold; Reconstruct the transmission line harmonic state space model based on the optimized model parameters to obtain an ice impedance dynamic prediction model.

[0083] The technical features and technical effects of the system proposed in the embodiments of the present invention are the same as those of the method proposed in the embodiments of the present invention, and will not be elaborated here. Each module in the above system can be implemented in whole or in part by software, hardware, and their combination. The above-mentioned modules can be embedded in the processor of the computer device in hardware form or independent thereof, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the above modules.

[0084] The above embodiments only represent several implementation manners of the present invention. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the patent of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the appended claims.

Claims

1. An optimization method for an intelligent ice melting device of an ultra - extra high voltage transmission line, characterized in that Including: Obtaining icing line state data of a target transmission line during the operation of an ice melting device; Analyzing and processing the icing line state data by using time domain analysis method to obtain time domain dynamic data reflecting the changes of ice layer thickness and conductor temperature, extracting harmonic components from the time domain dynamic data, and obtaining frequency domain distribution data of ice layer state according to the harmonic components; Extracting frequency domain harmonic distribution characteristics in the frequency domain distribution data of ice layer state, and determining the change trend of ice layer state according to the frequency domain harmonic distribution characteristics; Constructing a harmonic state space model of the transmission line according to the frequency domain distribution data of ice layer state and the change trend of ice layer state; Optimizing the model parameters of the harmonic state space model of the transmission line based on the icing line state data, and reconstructing the harmonic state space model of the transmission line based on the optimized model parameters to obtain a dynamic prediction model of ice layer impedance; During the actual ice melting process, dynamically optimizing and adjusting the operation parameters of the ice melting device according to the dynamic prediction model of ice layer impedance.

2. The optimization method for the intelligent ice melting device of the ultra - extra high voltage transmission line according to claim 1, wherein, The icing line state data includes ice layer thickness data and conductor temperature data; The obtaining of the icing line state data of the target transmission line during the operation of the ice melting device includes: Obtaining the ice layer thickness value collected by the sensor of the target transmission line during the operation of the ice melting device, and performing low-pass filtering on the ice layer thickness value to obtain ice layer thickness data; Obtaining the conductor temperature value collected by the sensor of the target transmission line during the operation of the ice melting device, and performing linear calibration on the conductor temperature value to obtain conductor temperature data.

3. The intelligent ice melting device optimization method for ultra - extra high voltage transmission lines according to claim 2, characterized in that, The analyzing and processing of the icing line state data by using time domain analysis method to obtain time domain dynamic data reflecting the changes of ice layer thickness and conductor temperature, extracting harmonic components from the time domain dynamic data, and obtaining frequency domain distribution data of ice layer state according to the harmonic components includes: Performing sliding window analysis on the ice layer thickness data and the conductor temperature data by using time domain analysis method, and calculating to obtain time domain dynamic data reflecting the changes of ice layer thickness and conductor temperature; Performing Fourier transform on the time domain dynamic data to extract harmonic components of the time domain dynamic data; Screening the amplitude and frequency of the harmonic components to obtain significant harmonic components; Generating frequency domain distribution data of ice layer state based on the significant harmonic components.

4. The intelligent ice melting device optimization method for ultra - extra high voltage transmission lines according to claim 3, characterized in that, The extracting of the frequency domain harmonic distribution characteristics in the frequency domain distribution data of ice layer state, and determining the change trend of ice layer state according to the frequency domain harmonic distribution characteristics includes: Analyzing the frequency domain distribution data of ice layer state to extract characteristic difference information of each significant harmonic component, and forming frequency domain harmonic distribution characteristics; Calculating the amplitude change rate and frequency change rate of each significant harmonic component according to the frequency domain harmonic distribution characteristics; Obtaining the change trend of ice layer state according to the amplitude change rate and the frequency change rate, and the change trend of ice layer state includes stable state, growth state and melting state.

5. The optimization method for the intelligent ice melting device of the ultra - extra high voltage transmission line according to claim 1, characterized in that, The constructing of a harmonic state space model of the transmission line according to the frequency domain distribution data of ice layer state and the change trend of ice layer state includes: Determining the initial state parameters of the transmission line according to the frequency domain distribution data of ice layer state; Adjust the initial state parameters according to the changing trend of the ice layer state to obtain dynamic state parameters; Construct a transmission line harmonic state space model of the target transmission line based on the dynamic state parameters.

6. The intelligent ice melting device optimization method for ultra - extra high voltage transmission lines according to claim 1, characterized in that, The ice-covered line state data further includes an ice layer impedance value; Optimizing the model parameters of the transmission line harmonic state space model based on the ice-covered line state data, and reconstructing the transmission line harmonic state space model based on the optimized model parameters to obtain an ice layer impedance dynamic prediction model, including: Use the transmission line harmonic state space model to predict the harmonic state of the target transmission line to obtain an ice layer impedance prediction value; Calculate the prediction error according to the difference between the ice layer impedance prediction value and the ice layer impedance value; Based on the gradient descent method, optimize the model parameters of the transmission line harmonic state space model according to the prediction error until the prediction error is less than a preset error threshold; Reconstruct the transmission line harmonic state space model based on the optimized model parameters to obtain an ice layer impedance dynamic prediction model.

7. The intelligent ice melting device optimization method for ultra - extra high voltage transmission lines according to claim 1, characterized in that The reconstructing the transmission line harmonic state space model based on the optimized model parameters to obtain an ice layer impedance dynamic prediction model includes: Update the state matrix, input matrix, output matrix, and feedforward matrix of the transmission line harmonic state space model according to the optimized parameters; Reconstruct the state equation and output equation of the transmission line harmonic state space model based on the updated matrices to obtain an ice layer impedance dynamic prediction model.

8. An intelligent ice melting device optimization system for ultra-high voltage transmission lines, characterized in that, Including: A data acquisition module for acquiring ice-covered line state data of the target transmission line during the operation of the ice melting device; A harmonic extraction module for analyzing and processing the ice-covered line state data by using a time-domain analysis method to obtain time-domain dynamic data reflecting the changes in ice layer thickness and conductor temperature, extracting harmonic components from the time-domain dynamic data, and obtaining ice layer state frequency-domain distribution data according to the harmonic components; A trend acquisition module for extracting the frequency-domain harmonic distribution characteristics in the ice layer state frequency-domain distribution data and determining the changing trend of the ice layer state according to the frequency-domain harmonic distribution characteristics; A model construction module for constructing a transmission line harmonic state space model according to the ice layer state frequency-domain distribution data and the changing trend of the ice layer state; A model reconstruction module for optimizing the model parameters of the transmission line harmonic state space model based on the ice-covered line state data, and reconstructing the transmission line harmonic state space model based on the optimized model parameters to obtain an ice layer impedance dynamic prediction model; A device adjustment module for dynamically optimizing and adjusting the operating parameters of the ice melting device according to the ice layer impedance dynamic prediction model during the actual ice melting process.

9. The intelligent ice melting device optimization system for ultra - extra high voltage transmission lines according to claim 8, wherein, The model construction module is specifically used for: Determine the initial state parameters of the transmission line according to the ice layer state frequency-domain distribution data; Adjust the initial state parameters according to the changing trend of the ice layer state to obtain dynamic state parameters; Construct a transmission line harmonic state space model of the target transmission line based on the dynamic state parameters.

10. The intelligent ice melting device optimization system for ultra - extra high voltage transmission lines according to claim 8, characterized in that, The model reconstruction module is specifically used for: Predict the harmonic state of the target transmission line using the harmonic state space model of the transmission line to obtain a predicted value of the ice layer impedance; Calculate the prediction error according to the difference between the predicted value of the ice layer impedance and the ice layer impedance value; Optimize the model parameters of the harmonic state space model of the transmission line based on the gradient descent method according to the prediction error until the prediction error is less than a preset error threshold; Reconstruct the harmonic state space model of the transmission line based on the optimized model parameters to obtain a dynamic prediction model of the ice layer impedance.

Images