Ice melting method and system based on flexible conductor quick connection and all-fiber isolation interface

Abstract

This invention discloses a de-icing method and system based on a flexible conductor quick-connect and all-fiber isolation interface. The method includes: obtaining the preload relaxation timing characteristics based on the strain data of the connector interface of the transmission line through a preload prediction model; obtaining the contact resistance timing characteristics by curve fitting of the contact resistance data at the connector interface; performing a finite element analysis-based thermal conduction simulation of the connector based on the contact resistance timing characteristics to obtain abnormal temperature rise timing characteristics and micro-motion high-frequency timing characteristics; performing spectral analysis on the micro-discharge spectral data at the contact gap of the connector to obtain micro-discharge spectral timing characteristics; and optimizing the reference de-icing current curve of the transmission line based on the above timing characteristics to obtain de-icing current timing data, so as to adjust the current in the transmission line through de-icing equipment for de-icing of the transmission line. This invention fully considers the influence of the connector interface of the transmission line on the de-icing of the transmission line, thereby improving the de-icing efficiency of the transmission line.

Description

Technical Field

[0001] This invention relates to the field of de-icing technology for power transmission lines, and in particular to a de-icing method and system based on a flexible conductor quick-connect and a fully optical fiber isolation interface. Background Technology

[0002] Ice accumulation on transmission lines can cause minor issues like insulator flashover and line galloping, or more serious problems such as conductor breakage, pole collapse, and tower toppling, leading to widespread power grid outages and significant economic losses. This poses a major challenge to power grid operation. Therefore, ice-melting technology for transmission lines is crucial for ensuring the stable operation of the power grid under extreme weather conditions.

[0003] Currently, existing de-icing technologies primarily focus on current control and thermal effect optimization. Based on Joule's law, they consider the inherent resistance of transmission lines and generate sufficient heat by controlling the current in the lines to melt the ice covering them. However, in real-world scenarios, due to factors such as terrain, construction and maintenance requirements, and line length, high-voltage transmission lines are typically composed of multiple conductor segments connected by connectors of different lengths and specifications, forming a continuous conductive path for long-distance power transmission. Existing de-icing technologies only consider the conductors themselves during the de-icing process, neglecting the connectors as crucial nodes for energy transmission. The degradation mechanism of the connector interface (the connection between different parts of the transmission line, such as conductors to conductors or conductors to equipment) under repeated thermal expansion and contraction significantly impacts the de-icing effect. Therefore, accurately quantifying the degradation process of the connector interface to improve de-icing efficiency is a pressing technical problem that needs to be solved in this field. Summary of the Invention

[0004] To address the problems existing in the prior art, this invention provides a de-icing method and system based on a flexible conductor quick-connect and a fully optical fiber isolation interface. This method fully considers the impact of the connector interface of the transmission line on the de-icing of the transmission line, thereby improving the de-icing efficiency of the transmission line.

[0005] In a first aspect, embodiments of the present invention provide a de-icing method based on a flexible conductor quick-connect and an all-fiber isolation interface, comprising:

[0006] The strain data of the connector interface of the transmission line is detected, and the preload relaxation timing characteristics are obtained based on the strain data through a pre-built preload prediction model; wherein the connector includes a flexible conductor quick contact and / or a fully optical fiber isolated interface.

[0007] The contact resistance change at the connector interface is detected to obtain contact resistance data, and the contact resistance data is curve fitted to obtain the contact resistance timing characteristics.

[0008] Based on the contact resistance timing characteristics, a heat conduction simulation based on finite element analysis is performed on the corresponding connector to obtain the abnormal temperature rise timing characteristics and micro-motion high-frequency timing characteristics of the connector.

[0009] The micro-discharge spectral data at the contact point gap of the connector is detected, and the micro-discharge spectral data is subjected to spectral analysis to obtain the micro-discharge spectral temporal characteristics.

[0010] Based on the preload relaxation timing characteristics, the contact resistance timing characteristics, the abnormal temperature rise timing characteristics, the micro-motion high-frequency timing characteristics, and the micro-discharge spectrum timing characteristics, the reference de-icing current curve of the transmission line is optimized to obtain de-icing current timing data.

[0011] Based on the ice-melting current timing data, the current value input to the transmission line is adjusted by the ice-melting equipment to melt the ice in the transmission line.

[0012] As an improvement to the above solution, the method of detecting strain data at the connector interface of the transmission line and obtaining preload relaxation timing characteristics based on the strain data using a pre-built preload prediction model includes:

[0013] The strain data is obtained by collecting the strain value of the connector interface within a set time period using a strain gauge installed at the connector interface.

[0014] Principal component analysis is performed on the strain data to obtain the first eigenvector of the strain data;

[0015] The first feature vector of the strain data is input into the preload prediction model based on the support vector regression algorithm to predict the preload time series data for the set time period.

[0016] The preload timing data is processed by sliding window, and the percentage of preload change in the corresponding window is calculated based on the preload timing data in each window to obtain the preload relaxation degree corresponding to the window.

[0017] Based on the preload relaxation process corresponding to all windows, the preload relaxation timing characteristics are obtained.

[0018] As an improvement to the above solution, the step of curve fitting the contact resistance data to obtain the contact resistance timing characteristics includes:

[0019] Calculate the preload relaxation rate based on the preload relaxation timing characteristics.

[0020] When the preload relaxation rate is greater than the set relaxation rate threshold, the contact resistance data is curve fitted to obtain the contact resistance timing characteristics.

[0021] As an improvement to the above solution, the step of curve fitting the contact resistance data to obtain the contact resistance timing characteristics includes:

[0022] The contact resistance values ​​in the contact resistance data are sampled to generate contact resistance time series data; wherein, the contact resistance data includes the contact resistance values ​​at the connector interface within a set time period;

[0023] The least-multiply-two method is used to perform polynomial curve fitting on the contact resistance time series data to obtain the first fitted curve of the contact resistance value changing with time.

[0024] The first fitted curve and the preset standard contact resistance variation curve are matched for similarity.

[0025] When the first fitted curve matches the standard variation curve of the contact resistance, the contact resistance timing characteristics are generated based on the first fitted curve.

[0026] When the first fitted curve does not match the standard variation curve of the contact resistance, the sampling frequency is adjusted and the contact resistance value in the contact resistance data is resampled until the first fitted curve generated based on the resampled contact resistance time series data matches the standard variation curve of the contact resistance.

[0027] As an improvement to the above solution, the step of performing a finite element analysis-based thermal conduction simulation on the corresponding connector based on the contact resistance timing characteristics to obtain the abnormal temperature rise timing characteristics and micro-motion high-frequency timing characteristics of the connector includes:

[0028] The heat conduction of the connector interface is simulated using the finite element analysis algorithm, and a heat distribution mesh model of the connector interface is constructed.

[0029] Based on the heat distribution grid model and each contact resistance value in the contact resistance time series characteristics, calculate the Joule thermal power of each first grid cell in the heat distribution grid model;

[0030] The heat distribution matrix of the connector interface is obtained based on the Joule thermal power of each first grid cell in the calculated heat distribution grid model.

[0031] Based on the heat distribution matrix, determine the time-series characteristics of the abnormal temperature rise rate;

[0032] The connector was simulated for thermal conduction using the finite element analysis algorithm, and a thermal stress mesh model of the connector was constructed.

[0033] Based on the heat distribution matrix, calculate the time-series data of the temperature rise rate of the connector interface;

[0034] Based on the thermal stress grid model and the time series data of the temperature rise rate, the high-frequency time series characteristics of micro-motion are determined.

[0035] As an improvement to the above scheme, determining the time-series characteristics of the abnormal temperature rise rate based on the heat distribution matrix includes:

[0036] Extract the heat time series data of the first grid cell corresponding to the central region of the connector interface from the heat distribution matrix, and calculate the local temperature rise rate time series data of the connector interface based on the extracted heat time series data.

[0037] The temperature rise rate exceeding a preset temperature rise rate threshold is extracted from the local temperature rise rate time series data to generate abnormal temperature rise rate time series features.

[0038] As an improvement to the above scheme, the step of determining the high-frequency timing characteristics of micro-motion based on the thermal stress grid model and the temperature rise rate time series data includes:

[0039] Based on the thermal stress grid model and each temperature rise rate in the temperature rise rate time series data, calculate the thermal stress of each second grid cell in the thermal stress grid model at the corresponding time.

[0040] Based on the calculated thermal stress of each second grid cell in the heat distribution grid model at each time, the thermal stress distribution matrix of the connector interface is obtained.

[0041] Electromagnetic field simulation was performed on the thermal stress mesh model to simulate the electromagnetic force distribution matrix when current passes through the connector interface;

[0042] The thermal stress distribution matrix and the electromagnetic force distribution matrix are coupled to obtain the coupling force distribution matrix of the connector interface;

[0043] Based on the force amplitude of each coupled force in the coupled force distribution matrix, generate the force amplitude time series data;

[0044] Perform a Fourier transform on the force amplitude time series data to extract the frequency data of the force amplitude time series data;

[0045] Based on the frequency components in the frequency data, the micro-motion strength data of the connector interface is obtained;

[0046] High-frequency features are extracted from the micro-motion intensity data to generate high-frequency time-series features of micro-motion.

[0047] As an improvement to the above solution, the step of detecting the micro-discharge spectral data at the contact gap of the connector and performing spectral analysis on the micro-discharge spectral data to obtain the micro-discharge spectral temporal characteristics includes:

[0048] Micro-discharge spectral signals generated by the gap change at the contact points of the connector are detected by a photoelectric sensor to obtain micro-discharge spectral data;

[0049] The micro-discharge spectral data are subjected to Fast Fourier Transform to extract the micro-discharge spectral temporal features.

[0050] As an improvement to the above scheme, the optimization of the reference de-icing current curve of the transmission line based on the preload relaxation timing characteristics, the contact resistance timing characteristics, the abnormal temperature rise timing characteristics, the micro-motion high-frequency timing characteristics, and the micro-discharge spectrum timing characteristics to obtain de-icing current timing data includes:

[0051] Based on the preload relaxation timing characteristics, contact resistance timing characteristics, abnormal temperature rise timing characteristics, micro-discharge spectrum timing characteristics, and micro-motion high-frequency timing characteristics, the interface degradation degree of the connector interface is predicted by a long short-term memory network model, and the interface degradation process timing curve is obtained.

[0052] Based on the interface degradation process timing curve, the reference de-icing current curve of the transmission line is corrected to obtain de-icing current timing data.

[0053] Secondly, embodiments of the present invention provide an ice-melting system based on a flexible conductor quick-connect and a fully optical fiber isolation interface, comprising:

[0054] The preload relaxation feature acquisition module is used to detect strain data of the connector interface of the transmission line, and obtain the preload relaxation timing features based on the strain data and a pre-built preload prediction model; wherein the connector includes a flexible conductor quick contact and / or a fully optical fiber isolated interface.

[0055] The contact resistance data acquisition module is used to detect the change in contact resistance at the connector interface, obtain contact resistance data, and perform curve fitting on the contact resistance data to obtain the contact resistance timing characteristics.

[0056] The heat conduction simulation module is used to perform heat conduction simulation based on finite element analysis on the corresponding connector according to the contact resistance timing characteristics, so as to obtain the abnormal temperature rise timing characteristics and micro-motion high-frequency timing characteristics of the connector.

[0057] The micro-discharge spectral feature acquisition module is used to detect the micro-discharge spectral data at the contact point gap of the connector, and to perform spectral analysis on the micro-discharge spectral data to obtain the micro-discharge spectral temporal features.

[0058] The de-icing current data acquisition module is used to optimize the reference de-icing current curve of the transmission line based on the preload relaxation timing characteristics, the contact resistance timing characteristics, the abnormal temperature rise timing characteristics, the micro-motion high-frequency timing characteristics, and the micro-discharge spectrum timing characteristics to obtain de-icing current timing data.

[0059] The de-icing control module is used to adjust the current value input to the transmission line through the de-icing equipment to de-ic the transmission line according to the de-icing current timing data.

[0060] Compared to existing technologies, this invention provides a de-icing method and system based on a flexible conductor quick-connect and all-fiber isolation interface. This method detects strain data at the connector interface of a transmission line and, based on this strain data, obtains preload relaxation timing characteristics using a pre-built preload prediction model. It also detects changes in contact resistance at the connector interface, obtains contact resistance data, and performs curve fitting on this data to obtain contact resistance timing characteristics. Based on these contact resistance timing characteristics, it performs finite element analysis-based heat conduction simulation on the corresponding connector to obtain abnormal temperature rise timing characteristics and micro-motion high-frequency timing characteristics. Finally, it detects micro-discharge light at the contact point gap of the connector. The micro-discharge spectral data is analyzed to obtain the micro-discharge spectral timing characteristics. Then, based on the preload relaxation timing characteristics, contact resistance timing characteristics, abnormal temperature rise timing characteristics, micro-motion high-frequency timing characteristics, and micro-discharge spectral timing characteristics, the reference de-icing current curve of the transmission line is optimized to obtain de-icing current timing data. Subsequently, based on the de-icing current timing data, the current value input to the transmission line is adjusted by the de-icing equipment to perform de-icing of the transmission line. This embodiment of the invention fully considers the impact of changes in preload relaxation, contact resistance, and temperature rise at the connector interface of the transmission line on de-icing of the transmission line, thereby improving the de-icing efficiency of the transmission line. Attached Figure Description

[0061] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0062] Figure 1This is a flowchart of an ice-melting method based on a flexible conductor quick-connect and all-fiber isolation interface provided by an embodiment of the present invention;

[0063] Figure 2 This is a structural block diagram of an ice-melting system based on a flexible conductor quick-connect and a fully optical fiber isolation interface, provided by an embodiment of the present invention. Detailed Implementation

[0064] 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.

[0065] It is understood that the various numerical designations used in the embodiments of this invention are merely for descriptive convenience and are not intended to limit the scope of this application. The order of the process numbers does not imply the order of execution; the execution order of each process should be determined by its function and internal logic.

[0066] See Figure 1 , Figure 1 This is a flowchart illustrating a de-icing method based on a flexible conductor quick-connect and an all-fiber isolation interface, provided by an embodiment of the present invention. The de-icing method based on the flexible conductor quick-connect and all-fiber isolation interface specifically includes:

[0067] S11: Detect the strain data of the connector interface of the transmission line, and obtain the preload relaxation timing characteristics based on the strain data and a pre-built preload prediction model.

[0068] S12: Detect the change in contact resistance at the connector interface, obtain contact resistance data, and perform curve fitting on the contact resistance data to obtain contact resistance timing characteristics;

[0069] S13: Based on the contact resistance timing characteristics, perform a heat conduction simulation based on finite element analysis on the corresponding connector to obtain the abnormal temperature rise timing characteristics and micro-motion high-frequency timing characteristics of the connector.

[0070] S14: Detect the micro-discharge spectral data at the contact point gap of the connector, and perform spectral analysis on the micro-discharge spectral data to obtain the micro-discharge spectral temporal characteristics;

[0071] S15: Based on the preload relaxation timing characteristics, the contact resistance timing characteristics, the abnormal temperature rise timing characteristics, the micro-motion high-frequency timing characteristics, and the micro-discharge spectrum timing characteristics, optimize the reference de-icing current curve of the transmission line to obtain de-icing current timing data.

[0072] S16: Based on the ice-melting current timing data, the current value input to the transmission line is adjusted by the ice-melting equipment to melt the ice in the transmission line.

[0073] It should be noted that the de-icing method based on flexible conductor quick-connect and all-fiber isolation interface described in the embodiments of the present invention can be executed by a de-icing system based on flexible conductor quick-connect and all-fiber isolation interface. This de-icing system based on flexible conductor quick-connect and all-fiber isolation interface can be deployed on a server or a de-icing device, and is not limited in the present invention.

[0074] It is understood that connectors for power transmission lines are devices used to connect different parts of a power transmission line (such as connecting conductors to conductors, conductors to equipment, etc.). For example, connectors can be flexible conductor connectors (i.e., flexible cable connectors, also known as flexible braided conductor quick-connects, flexible conductor quick-connects) and / or all-fiber isolation interfaces, etc., and this invention does not limit this. A power transmission line can be equipped with one or more connectors. The connector interface can be understood as the connection interface / contact surface between different conductors in a power transmission line (such as conductors to conductors, conductors to equipment, etc.), which is the area where the two conductors connected by the connector come into contact and interact. The design and performance of the connector interface are crucial to the overall reliability and power transmission efficiency of the power transmission line. During the de-icing process, the interface performance of the connector can directly affect the de-icing efficiency and system reliability.

[0075] For power transmission lines, connector interface strain, preload, contact resistance, and micro-discharge caused by gap changes are crucial parameters affecting connector interface performance and, consequently, the de-icing effect. During de-icing, temperature changes cause thermal expansion and contraction of the conductors, resulting in strain at the connector interface. Preload is the force required to maintain stable contact between the conductors at the connector interface; however, the cyclical expansion and contraction of the conductor material due to temperature changes can alter the connector preload, potentially leading to relaxation. Contact resistance is primarily caused by microscopic irregularities, oxide films, impurities, and contact pressure on the conductor contact surfaces at the connector interface. Excessive contact resistance generates significant heat during de-icing, increasing line losses, reducing de-icing efficiency, and accelerating the aging of connectors and conductors due to prolonged high temperatures, thus shortening their lifespan. During the de-icing process, the connector interface is affected by factors such as mechanical vibration, thermal expansion and contraction, and electrodynamic forces. The gap between conductors at the connector interface may change. When the gap decreases to a certain extent, the gas in the gap may ionize under the action of an electric field, forming a micro-discharge phenomenon. The micro-discharge will raise the temperature of the gas in the gap, generating a thermal effect, which will further aggravate the aging and damage of the connector interface. At the same time, the long-term micro-discharge effect may also erode the contact surface, increase the contact resistance, deteriorate the contact performance, and ultimately affect the reliability of the transmission line and the de-icing efficiency.

[0076] Based on the above principles, this embodiment of the invention considers the performance parameters of the connector interface to control the de-icing process of transmission lines. The specific process includes: detecting strain data at the connector interface of the transmission line within a set time period, and obtaining the preload relaxation time sequence characteristics based on the strain data using a pre-built preload prediction model; detecting the change in contact resistance at the connector interface within the set time period to obtain contact resistance data, and performing curve fitting on the contact resistance data to obtain contact resistance time sequence characteristics; detecting the micro-discharge spectral data at the contact point gap of the connector within the set time period, and performing... Spectral analysis is performed to obtain the micro-discharge spectral timing characteristics. Then, based on these contact resistance timing characteristics, a finite element method (FEM)-based thermal conduction simulation is conducted on the corresponding connector to obtain the abnormal temperature rise timing characteristics and the micro-motion high-frequency timing characteristics. Subsequently, based on the preload relaxation timing characteristics, contact resistance timing characteristics, abnormal temperature rise timing characteristics, micro-motion high-frequency timing characteristics, and micro-discharge spectral timing characteristics, the reference de-icing current curve of the transmission line is optimized to obtain de-icing current timing data. This allows the de-icing equipment to adjust the current value input to the transmission line according to the de-icing current timing data for de-icing. This embodiment of the invention fully considers the influence of parameters such as preload relaxation changes, contact resistance changes, and temperature rise changes at the connector interface of the transmission line on de-icing, thereby improving the de-icing efficiency of the transmission line.

[0077] In an optional embodiment, S11: Detecting strain data at the connector interface of the transmission line, and obtaining preload relaxation timing characteristics based on the strain data using a pre-built preload prediction model, including:

[0078] The strain data is obtained by collecting the strain value of the connector interface within a set time period using a strain gauge installed at the connector interface.

[0079] Principal component analysis is performed on the strain data to obtain the first eigenvector of the strain data;

[0080] The first feature vector of the strain data is input into the preload prediction model based on the support vector regression algorithm to predict the preload time series data for the set time period.

[0081] The preload timing data is processed by sliding window, and the percentage of preload change in the corresponding window is calculated based on the preload timing data in each window to obtain the preload relaxation degree corresponding to the window.

[0082] Based on the preload relaxation process corresponding to all windows, the preload relaxation timing characteristics are obtained.

[0083] For example, strain gauges can be installed at the connector interface to sense stress changes at the connector interface. For instance, when the de-icing device is activated to begin de-icing the transmission line (e.g., based on a reference de-icing current curve indicating the time-current correspondence, the current input to the transmission line is controlled), the strain values ​​of the connector interface within a set time period are periodically collected to obtain strain data. This strain data includes strain values ​​with timestamps within the set time period corresponding to at least one connector interface. That is, the strain data can include a time series of strain values ​​of at least one connector interface. The time series of strain values ​​of at least one connector interface can form a strain value matrix, with one row in the strain value matrix corresponding to one strain value time series.

[0084] Then, time alignment is performed on the time series of each strain value in the strain value matrix to improve data accuracy. Principal component analysis is then performed on the time-aligned strain value matrix to reduce the dimensionality of the strain data and decrease computational complexity. Specifically, this includes: standardizing the time series of each strain value in the strain value matrix to obtain a standardized strain value matrix; calculating the covariance matrix of the standardized strain value matrix and performing eigenvalue decomposition on the covariance matrix to obtain eigenvalues ​​and corresponding eigenvectors; selecting principal components according to the magnitude of the eigenvalues ​​based on certain criteria. For example, selecting the eigenvectors corresponding to the first k eigenvalues ​​in descending order to ensure that the cumulative variance contribution rate meets the requirements (e.g., 90%), or selecting an appropriate number of eigenvalues ​​and corresponding eigenvectors based on actual needs. The standardized strain value matrix is ​​then projected onto the selected principal components to obtain the first eigenvector of the dimensionality-reduced strain data.

[0085] The first feature vector of the strain data is then input into a preload prediction model based on support vector regression to predict the preload time series data for a set time period. The preload time series data includes the preload at multiple time points of at least one connector interface.

[0086] It should be noted that the preload prediction model based on the support vector regression algorithm can be obtained by sequentially processing historical strain data and historical preload data (i.e., tag data) of the connector interface of the transmission line. The specific model training process is existing technology, and the processing of historical strain data before it is input into the preload prediction model is the same as that of strain data, which will not be described in detail here.

[0087] The preload time series data output by the preload prediction model is processed using a sliding window. The percentage of preload change within each window is calculated to obtain the preload relaxation degree corresponding to that window. For example, if the sliding window length is set to 10 seconds, and the preload of a connector interface within a window (i.e., 10 seconds) is 100N, 98N, ..., 95N, then the preload change within that window is 100N - 95N = 5N. The percentage of preload change is 5N / 100N = 0.05. This percentage of preload change is taken as the preload relaxation degree of the corresponding connector interface within that window. Similarly, the preload relaxation degree of another connector interface within the window can be calculated. By summing the preload relaxation degrees of all windows, a preload relaxation time series feature can be constructed, which includes a sequence of preload relaxation degrees for at least one connector interface.

[0088] In one optional embodiment, the step of curve fitting the contact resistance data to obtain the contact resistance timing characteristics includes:

[0089] Calculate the preload relaxation rate based on the preload relaxation timing characteristics.

[0090] When the preload relaxation rate is greater than the set relaxation rate threshold, the contact resistance data is curve fitted to obtain the contact resistance timing characteristics.

[0091] Specifically, the step of performing curve fitting on the contact resistance data to obtain the contact resistance time-series characteristics includes:

[0092] The contact resistance values ​​in the contact resistance data are sampled to generate contact resistance time series data; wherein, the contact resistance data includes the contact resistance values ​​at the connector interface within a set time period;

[0093] The least-multiply-two method is used to perform polynomial curve fitting on the contact resistance time series data to obtain the first fitted curve of the contact resistance value changing with time.

[0094] The first fitted curve and the preset standard contact resistance variation curve are matched for similarity.

[0095] When the first fitted curve matches the standard variation curve of the contact resistance, the contact resistance timing characteristics are generated based on the first fitted curve.

[0096] When the first fitted curve does not match the standard variation curve of the contact resistance, the sampling frequency is adjusted and the contact resistance value in the contact resistance data is resampled until the first fitted curve generated based on the resampled contact resistance time series data matches the standard variation curve of the contact resistance.

[0097] For example, for each relaxation degree sequence in the preload relaxation time sequence characteristics obtained above, a linear regression algorithm is applied to fit its changing trend in the time dimension. The function is expressed as y = ax + b, where a represents the leakage of the function, b represents the intercept of the function, x represents time, and y represents the relaxation degree. The slope 'a' of the fitted function is the relaxation rate of the corresponding relaxation degree sequence, i.e., the relaxation rate of the preload at the corresponding connector interface.

[0098] It is understandable that the preload at the connector interface decreases, while the preload relaxation rate in the corresponding preload relaxation sequence increases. This relaxation rate can be used to assess the connector's service life and guide maintenance cycle optimization; a higher relaxation rate results in a shorter service life. Through the above process, the relationship between strain and preload at the transmission line connector interface can be uncovered, the preload variation pattern at the connector interface can be identified, and thus the variation pattern of preload relaxation at the connector interface can be derived.

[0099] Since the contact resistance of connector interfaces is typically affected by factors such as interface preload and material aging, its data reflects the stability of the connection point. Based on this, this embodiment of the invention sets a relaxation rate threshold. For each connector, when its corresponding preload relaxation rate exceeds this threshold, it is determined that the contact resistance at the connector interface has increased, indicating that the interface may be loosening or worn, potentially affecting heat generation during the de-icing process. At this point, the following curve fitting process is triggered on the detected contact resistance data, including:

[0100] In this embodiment of the invention, a resistance measuring device can be used to detect changes in contact resistance at the connector interface; similarly, when the de-icing device is activated to begin de-icing the transmission line, the contact resistance values ​​of the connector interface within a set time period can be periodically collected to obtain contact resistance data. This contact resistance data includes contact resistance values ​​with timestamps within a set time period corresponding to at least one connector interface. That is, the contact resistance data can include a time series of contact resistance values ​​of at least one connector interface. The time series of contact resistance values ​​of at least one connector interface can form a contact resistance value matrix, and one row in the contact resistance value matrix corresponds to one contact resistance value time series.

[0101] The time series of each contact resistance value in the contact resistance value matrix is ​​time-aligned with the time series of strain values ​​in the strain value matrix. The contact resistance values ​​in the time-aligned contact resistance value matrix are sampled to generate contact resistance time series data, thereby reducing the data dimensionality of the contact resistance data and decreasing the computational load. It is understood that the contact resistance time series data includes the sampled time series of contact resistance values ​​for at least one connector interface.

[0102] The least-multiply-two method is used to fit polynomial curves to the contact resistance time series data. For example, an nth-degree polynomial model R(t) = c0 + c1t + c2t is established. 2 +...+c n t n Where y represents time, R(t) represents the contact resistance value at time t, and c0, c1, c2...c n These are the polynomial coefficients to be determined. Then, given the contact resistance time-series data (i.e., the contact resistance value and its corresponding time point are known), the least-multiply-two method is used to fit the above polynomial model to solve for the optimal polynomial coefficients, minimizing the sum of squared errors between the fitted curve and the actual data. Substituting the solved polynomial coefficients into the polynomial model yields the corresponding fitted curve. Performing the above polynomial curve fitting operation on each sequence in the contact resistance time-series data yields the first fitted curve of the corresponding connector interface regarding the change of contact resistance value over time.

[0103] Each first fitted curve is matched with the corresponding connector's standard contact resistance variation curve for similarity. Similarity algorithms such as cosine similarity and Euclidean distance are used to calculate the similarity between the two curves. If the similarity exceeds a set similarity threshold, the two curves are considered to match; otherwise, they are not. It should be noted that the standard contact resistance variation curve can be pre-configured at the connector factory.

[0104] If the two curves do not match, the sampling frequency is adjusted and the contact resistance value is resampled (e.g., the sampling frequency is increased) until the first fitted curve generated based on the resampled contact resistance time series data matches the standard contact resistance variation curve. If the two curves match, a contact resistance value time series is generated based on the first fitted curve; wherein, this contact resistance value time series is time-aligned with the contact resistance value time series. The contact resistance value time series of at least one connector constitutes the contact resistance timing feature.

[0105] In this embodiment of the invention, considering that the connector interface may relax due to thermal expansion under high temperature conditions, and the contact resistance growth rate may exceed expectations, the data acquisition frequency needs to be adjusted to capture more subtle contact resistance change characteristics and improve data accuracy.

[0106] In an optional embodiment, S13: Based on the contact resistance timing characteristics, perform a finite element analysis-based thermal conduction simulation on the corresponding connector to obtain the abnormal temperature rise timing characteristics and micro-motion high-frequency timing characteristics of the connector, including:

[0107] The heat conduction of the connector interface is simulated using the finite element analysis algorithm, and a heat distribution mesh model of the connector interface is constructed.

[0108] Based on the heat distribution grid model and each contact resistance value in the contact resistance time series characteristics, calculate the Joule thermal power of each first grid cell in the heat distribution grid model;

[0109] The heat distribution matrix of the connector interface is obtained based on the Joule thermal power of each first grid cell in the calculated heat distribution grid model.

[0110] Based on the heat distribution matrix, determine the time-series characteristics of the abnormal temperature rise rate;

[0111] The connector was simulated for thermal conduction using the finite element analysis algorithm, and a thermal stress mesh model of the connector was constructed.

[0112] Based on the heat distribution matrix, calculate the time-series data of the temperature rise rate of the connector interface;

[0113] Based on the thermal stress grid model and the time series data of the temperature rise rate, the high-frequency time series characteristics of micro-motion are determined.

[0114] Specifically, determining the time-series characteristics of the abnormal temperature rise rate based on the heat distribution matrix includes:

[0115] Extract the heat time series data of the first grid cell corresponding to the central region of the connector interface from the heat distribution matrix, and calculate the local temperature rise rate time series data of the connector interface based on the extracted heat time series data.

[0116] The temperature rise rate exceeding a preset temperature rise rate threshold is extracted from the local temperature rise rate time series data to generate abnormal temperature rise rate time series features.

[0117] Specifically, determining the high-frequency time-series characteristics of micro-motion based on the thermal stress grid model and the temperature rise rate time-series data includes:

[0118] Based on the thermal stress grid model and each temperature rise rate in the temperature rise rate time series data, calculate the thermal stress of each second grid cell in the thermal stress grid model at the corresponding time.

[0119] Based on the calculated thermal stress of each second grid cell in the heat distribution grid model at each time, the thermal stress distribution matrix of the connector interface is obtained.

[0120] Electromagnetic field simulation was performed on the thermal stress mesh model to simulate the electromagnetic force distribution matrix when current passes through the connector interface;

[0121] The thermal stress distribution matrix and the electromagnetic force distribution matrix are coupled to obtain the coupling force distribution matrix of the connector interface;

[0122] Based on the force amplitude of each coupled force in the coupled force distribution matrix, generate the force amplitude time series data;

[0123] Perform a Fourier transform on the force amplitude time series data to extract the frequency data of the force amplitude time series data;

[0124] Based on the frequency components in the frequency data, the micro-motion strength data of the connector interface is obtained;

[0125] High-frequency features are extracted from the micro-motion intensity data to generate high-frequency time-series features of micro-motion.

[0126] For example, first, set the heat-related parameters of each connector interface, including: geometry (e.g., circle), size, material properties (e.g., material, thermal conductivity, specific heat capacity, density), ambient temperature, convective heat transfer coefficient, and heat load (i.e., heat source power). Then, perform geometric modeling on the connector interface to obtain the connector interface geometric model. Use the finite element analysis algorithm to mesh the connector interface geometric model. Perform heat conduction simulation based on the heat-related parameters of the connector interface to obtain the temperature distribution within the corresponding connector interface, i.e., the temperature distribution within the connector interface geometric model with the first mesh unit as the basic unit. Based on the heat-temperature conversion formula, the temperature distribution within the connector interface can be converted into a heat distribution to obtain a heat distribution mesh model, i.e., the heat distribution within the connector interface geometric model with the first mesh unit as the basic unit.

[0127] Based on the heat distribution of each first grid cell in the heat distribution grid model as the basic unit, the heat proportion of each first grid cell is calculated.

[0128] Based on each contact resistance value R in the time series of contact resistance values ​​corresponding to the connector interface in the contact resistance time sequence characteristics, calculate the Joule heat power P=I. 2 R, where I represents the current flowing through the connector interface. The calculated Joule thermal power P is multiplied by the heat percentage of each first grid cell in the heat distribution grid model to obtain the Joule thermal power of each first grid cell in the heat distribution grid model. Then, for the calculated Joule thermal power of each first grid cell in the heat distribution grid model at each time step, the corresponding heat Q=I of each first grid cell at that time step is calculated. 2 Rt, where t represents the duration, i.e., the time from the start of contact resistor detection to the current time. Based on the heat distribution of each first grid cell at each moment, the heat distribution matrix of the connector interface can be obtained, including the heat time-series data of each first grid cell.

[0129] Then, the heat time series data of the first grid cell corresponding to the central region of the connector interface is extracted from the heat distribution matrix obtained above. Based on the extracted heat time series data, the temperature rise rate of the first grid cell corresponding to the central region at each time moment is calculated to obtain the local temperature rise rate time series data of the connector interface; for example, the temperature rise rate. Where c represents the specific heat capacity of the connector interface and m represents the mass of the connector interface portion. This represents the heat at time t corresponding to the first grid cell in the central region. Temperature rise rates exceeding a preset threshold are extracted from the local temperature rise rate time series data to generate abnormal temperature rise rate time series features.

[0130] For example, similar to the heat distribution mesh model mentioned above, the thermal stress-related parameters of each connector are first determined, including: geometry (e.g., circular), size, material properties (e.g., material, thermal conductivity, specific heat capacity, density, etc.), ambient temperature, coefficient of thermal expansion, elastic modulus, Poisson's ratio, convective heat transfer coefficient, and thermal load (i.e., heat source power). Then, the connector is geometrically modeled to obtain the connector geometric model. The finite element analysis algorithm is used to mesh the connector interface geometric model. Based on the thermal stress-related parameters of the connector, heat conduction simulation is performed to obtain the thermal stress distribution of the corresponding connector, that is, the thermal stress distribution within the connector geometric model with the second mesh unit as the basic unit, thus obtaining the thermal stress mesh model.

[0131] Based on the heat distribution matrix of the corresponding connector interface obtained above, the temperature rise rate of the connector interface at each time moment is calculated. Based on the temperature rise rate at each time moment, time series data of temperature rise rate can be generated; the specific calculation of temperature rise rate is described above and will not be repeated here.

[0132] Based on the thermal stress distribution of each second grid cell in the thermal stress grid model as the basic cell, the thermal stress ratio of each second grid cell is calculated.

[0133] Based on each temperature rise rate in the time-series data corresponding to the connector interface, calculate the thermal stress of each second mesh element at the corresponding time. Where E represents the elastic modulus of the connector interface, which can be obtained by calculating the average elastic modulus of the two conductors at the connector interface. This represents the coefficient of thermal expansion at the connector interface. Indicates the amount of temperature change. =v , The Poisson's ratio of the connector interface can be obtained by calculating the average of the Poisson's ratios of the two conductors at the connector interface.

[0134] The calculated thermal stress of each second grid cell at the corresponding time is multiplied by the proportion of thermal stress in each second grid cell in the thermal stress distribution grid model to obtain the thermal stress of each second grid cell in the thermal stress distribution grid model at each time. Then, the thermal stress distribution matrix of the connector is obtained from the calculated thermal stress of each second grid cell in the thermal stress distribution grid model at each time, including the time series data of thermal stress of each second grid cell.

[0135] Meanwhile, electromagnetic field simulation is performed on the thermal stress mesh model obtained above to simulate the electromagnetic force distribution matrix when current (such as the actual current value input into the transmission line within a set time period) passes through the connector interface. This is the electromagnetic force distribution matrix of each second mesh unit in the thermal stress mesh model, including the electromagnetic force of each second mesh unit at each time.

[0136] By weighting and summing the thermal stress in the thermal stress distribution matrix and the electromagnetic force in the electromagnetic force distribution matrix according to the grid and timestamp, the coupled force of each second grid cell in the electromagnetic force distribution matrix at each time step can be obtained. Based on the coupled force, i.e., the force amplitude, of each second grid cell in the electromagnetic force distribution matrix at each time step, the coupled force distribution matrix of the corresponding connector interface can be obtained, including the time-series data of the force amplitude of each second grid cell.

[0137] For each force amplitude time-series data in the obtained coupling force distribution matrix, a Fast Fourier Transform (FFT) is performed to extract the frequency data of that force amplitude time-series data, and then extract the frequency components from that frequency data to identify the fretting intensity of the connector interface. For example, if there are frequency components in the frequency data whose force amplitude exceeds a set amplitude threshold, it indicates the presence of fretting. For instance, after the force amplitude time-series data is transformed, if the force amplitude corresponding to a dominant frequency of 120Hz exceeds the preset amplitude threshold of 0.1, it indicates the presence of significant fretting. This embodiment of the invention uses the Fast Fourier Transform method to quickly decompose the signal, facilitating the determination of fretting intensity.

[0138] After identifying micro-motion at the connector interface, the second grid cell that experiences the most instances of force amplitude exceeding a set amplitude threshold is identified as the region best characterizing the connector interface vibration. Micro-motion intensity is then determined, and the specific process is as follows:

[0139] Based on the time-series data of the force amplitude of the second mesh element, the fretting strength of the connector interface at various times is calculated, thus obtaining the fretting strength data of the connector interface, i.e., the time-series data of the fretting strength of the connector interface. The specific calculation formula is as follows:

[0140] ;

[0141] in, The force amplitude of the second mesh element at time t represents the interface of the connector, and r represents the equivalent radius of curvature of the connector interface. , These represent the elastic moduli of the two conductors at the connector interface. , These represent the Poisson's ratios of the two conductors at the connector interface. This represents the magnitude of the micro-displacement of the second mesh cell at time t at the connector interface; This represents the fretting wear rate of the second mesh cell at time t on the connector interface. This represents the contact pressure at the connector interface at time t, calculated by the preload at time t and the contact area of ​​the connector interface. The quotient is obtained, where k represents the wear coefficient of the connector interface, which is obtained by calculating the average wear coefficient of the two conductors at the connector interface. This represents the fretting strength of the connector interface at time t.

[0142] By integrating the micro-motion intensity of the connector interface at various times, the micro-motion intensity data of the connector interface can be obtained. Then, wavelet transform is performed on the micro-motion intensity data to decompose it into micro-motion intensity data within a specific frequency range (e.g., 10-20kHz). For the micro-motion intensity data within the specific frequency range (e.g., 10-20kHz), a peak detection algorithm is used to identify the peak points (i.e., micro-motion intensities) in the micro-motion intensity data exceeding a set first peak threshold. Simultaneously, the number of each peak point is counted to obtain the corresponding number of micro-motion events. Finally, the peak points (micro-motion intensities) and the number of micro-motion events are combined in chronological order to obtain the high-frequency temporal characteristics of the micro-motion intensity data.

[0143] In one optional embodiment, detecting the micro-discharge spectral data at the contact gap of the connector and performing spectral analysis on the micro-discharge spectral data to obtain micro-discharge spectral temporal characteristics includes:

[0144] Micro-discharge spectral signals generated by the gap change at the contact points of the connector are detected by a photoelectric sensor to obtain micro-discharge spectral data;

[0145] The micro-discharge spectral data are subjected to Fast Fourier Transform to extract the micro-discharge spectral temporal features.

[0146] For example, a photoelectric sensor can be used to collect the instantaneous optical signal generated by micro-discharge caused by changes in the contact gap of the connector within a set time period. By processing the instantaneous optical signals collected at each moment using a spectrometer, a micro-discharge spectral signal can be formed. By combining the micro-discharge spectral signal data from each moment, the micro-discharge spectral signal can be obtained.

[0147] Then, a Fast Fourier Transform (FFT) is performed on the micro-discharge spectral data to extract the dominant frequency component, whose proportion exceeds a set frequency proportion threshold. For example, the dominant frequency component is in the range of 10kHz to 50kHz. For the micro-discharge spectral data within the frequency range of the dominant frequency component, a peak detection algorithm is used to find the peak points (i.e., the amplitude of the micro-discharge spectral signal) that exceed a set second peak threshold in the micro-discharge spectral data within the frequency range of the dominant frequency component. At the same time, the number of each peak point is counted to obtain the corresponding number of micro-discharge events. Then, the peak points (amplitude of the micro-discharge spectral signal) and the number of micro-discharge events are combined in chronological order to obtain the micro-discharge spectral temporal characteristics.

[0148] In an optional embodiment, optimizing the reference de-icing current curve of the transmission line based on the preload relaxation timing characteristics, the contact resistance timing characteristics, the abnormal temperature rise timing characteristics, the micro-motion high-frequency timing characteristics, and the micro-discharge spectral timing characteristics to obtain de-icing current timing data includes:

[0149] Based on the preload relaxation timing characteristics, contact resistance timing characteristics, abnormal temperature rise timing characteristics, micro-discharge spectrum timing characteristics, and micro-motion high-frequency timing characteristics, the interface degradation degree of the connector interface is predicted by a long short-term memory network model, and the interface degradation process timing curve is obtained.

[0150] Based on the interface degradation process timing curve, the reference de-icing current curve of the transmission line is corrected to obtain de-icing current timing data.

[0151] For example, the preload relaxation timing features, contact resistance timing features, abnormal temperature rise timing features, micro-discharge spectrum timing features, and micro-motion high-frequency timing features obtained above are time-aligned and then normalized. Then, based on the normalized preload relaxation timing features, contact resistance timing features, abnormal temperature rise timing features, micro-discharge spectrum timing features, and micro-motion high-frequency timing features, a multi-dimensional feature vector is constructed. This vector is then input into a pre-trained long short-term memory network model to predict the interface degradation degree of the connector interface at different times. Curve fitting is then performed on the interface degradation degree at different times to obtain the interface degradation process timing curve.

[0152] In this embodiment of the invention, an objective function for calculating the overall interface degradation score can be set, such as the overall interface degradation score f = w1G + w2R + w3. +w4(H+N)+w5(D+M), where w1, w2, w3, w4, and w5 are the set weighting coefficients, and G, R, ... H, N, D, and M represent the normalized preload relaxation degree, contact resistance, temperature rise, and the amplitude, number of micro-discharges, fretting intensity, and number of fretting in the micro-discharge spectral signal caused by gap changes, respectively. This allows for the generation of multiple interface degradation comprehensive scores under different preload relaxation degrees, contact resistance, temperature rise, and gap changes. Then, the score interval to which each interface degradation comprehensive score belongs is determined, yielding the interface degradation degree corresponding to that score interval. The above calculations can obtain multiple sets of training samples (including the amplitude, number of micro-discharges, fretting intensity, and number of micro-discharges in the micro-discharge spectral signal caused by preload relaxation degree, contact resistance, temperature rise, and gap changes) and corresponding multiple sets of labeled data (including the interface degradation degree corresponding to different training samples). Then, the Long Short-Term Memory (LSTM) network model is trained using these multiple sets of training samples and labeled data to obtain a trained LTM network model. It should be noted that the training process of the LTM network model is prior art and will not be described in detail in this embodiment of the invention.

[0153] Based on the degree of interface degradation at each moment in the interface degradation process time-series curve, the de-icing current value at the corresponding moment in the reference de-icing current curve of the transmission line is adjusted. Based on this, the de-icing current value at each moment in the reference de-icing current curve of the transmission line can be adjusted to obtain an optimized reference de-icing current curve.

[0154] Finally, the melting current value was sampled using the optimized baseline melting current curve to obtain the melting current time series data.

[0155] It should be noted that the benchmark de-icing current curve can be based on the de-icing related data of the historical de-icing process of the transmission line, including the time series data of the current value and ice thickness of each de-icing process. Then, the time series data of the current value corresponding to the de-icing process with the highest de-icing efficiency are selected to construct the benchmark de-icing current curve based on the current value-time, which reflects the de-icing working condition.

[0156] Finally, the ice-melting equipment adjusts the current value input to the transmission line based on the ice-melting current timing data to melt the ice in the transmission line.

[0157] This invention fully considers the influence parameters of interface performance on the connector interface of transmission lines, such as the degree of preload relaxation, contact resistance, temperature rise, and micro-discharge and fretting intensity caused by gap changes. By adjusting the de-icing current value of the transmission line, the invention can effectively reduce the impact of these interface performance parameters on the de-icing effect of the transmission line, improve the de-icing efficiency, and reduce the loosening or wear of the transmission line connectors, thereby improving the reliability and service life of the transmission line connectors.

[0158] See Figure 2 , Figure 2 This invention provides a structural block diagram of an ice-melting system based on a flexible conductor quick-connect and a fully fiber optic isolated interface. The ice-melting system includes:

[0159] The preload relaxation feature acquisition module 11 is used to detect strain data of the connector interface of the transmission line, and obtain the preload relaxation timing features based on the strain data and a pre-built preload prediction model; wherein, the connector includes a flexible conductor quick contact and / or a fully optical fiber isolated interface.

[0160] The contact resistance data acquisition module 12 is used to detect the change in contact resistance at the connector interface, obtain contact resistance data, and perform curve fitting on the contact resistance data to obtain the contact resistance timing characteristics.

[0161] The heat conduction simulation module 13 is used to perform heat conduction simulation based on finite element analysis on the corresponding connector according to the contact resistance timing characteristics, so as to obtain the abnormal temperature rise timing characteristics and micro-motion high frequency timing characteristics of the connector.

[0162] The micro-discharge spectral feature acquisition module 14 is used to detect the micro-discharge spectral data at the contact point gap of the connector, and perform spectral analysis on the micro-discharge spectral data to obtain the micro-discharge spectral temporal features.

[0163] The de-icing current data acquisition module 15 is used to optimize the reference de-icing current curve of the transmission line based on the preload relaxation timing characteristics, the contact resistance timing characteristics, the abnormal temperature rise timing characteristics, the micro-motion high-frequency timing characteristics, and the micro-discharge spectrum timing characteristics to obtain de-icing current timing data.

[0164] The de-icing control module 16 is used to adjust the current value input to the transmission line through the de-icing equipment to de-ic the transmission line according to the de-icing current timing data.

[0165] In an optional embodiment, the preload relaxation feature acquisition module 11 includes:

[0166] The interface strain acquisition unit is used to acquire the strain value of the connector interface within a set time period by using a strain gauge set at the connector interface to obtain the strain data.

[0167] The first principal component analysis unit is used to perform principal component analysis on the strain data to obtain the first feature vector of the strain data.

[0168] The preload prediction unit is used to input the first feature vector of the strain data into the preload prediction model established based on the support vector regression algorithm, and predict the preload time series data for the set time period.

[0169] The sliding window processing unit is used to perform sliding window processing on the preload timing data, and calculate the percentage of preload change in the corresponding window based on the preload timing data in each window, so as to obtain the preload relaxation degree corresponding to the corresponding window.

[0170] The preload relaxation timing feature generation unit is used to obtain the preload relaxation timing features based on the preload relaxation process corresponding to all windows.

[0171] In an optional embodiment, the contact resistance data acquisition module 12 includes:

[0172] The preload relaxation rate calculation unit is used to calculate the preload relaxation rate based on the preload relaxation timing characteristics.

[0173] The curve fitting unit is used to perform curve fitting on the contact resistance data when the preload relaxation rate is greater than a set relaxation rate threshold, so as to obtain the contact resistance timing characteristics.

[0174] In one optional embodiment, the curve fitting unit includes:

[0175] A resistance value sampling subunit is used to sample the contact resistance values ​​in the contact resistance data to generate contact resistance time series data; wherein, the contact resistance data includes the contact resistance values ​​at the connector interface within a set time period.

[0176] The polynomial curve fitting subunit is used to perform polynomial curve fitting on the contact resistance time series data using the least multiplier-two method to obtain the first fitting curve of the contact resistance value changing with time.

[0177] The curve matching subunit is used to perform similarity matching between the first fitted curve and the preset standard change curve of contact resistance;

[0178] A contact resistance timing feature generation subunit is used to generate contact resistance timing features based on the first fitted curve when the first fitted curve matches the standard change curve of the contact resistance.

[0179] The sampling frequency update subunit is used to adjust the sampling frequency and resample the contact resistance values ​​in the contact resistance data when the first fitted curve does not match the standard change curve of the contact resistance, until the first fitted curve generated based on the resampled contact resistance time series data matches the standard change curve of the contact resistance.

[0180] In an optional embodiment, the heat conduction simulation module 13 includes:

[0181] The first heat conduction simulation unit is used to simulate the heat conduction of the connector interface using the finite element analysis algorithm and to construct a heat distribution grid model of the connector interface.

[0182] The Joule thermal power calculation unit is used to calculate the Joule thermal power of each first grid cell in the heat distribution grid model based on each contact resistance value in the heat distribution grid model and the contact resistance time sequence characteristics.

[0183] The heat distribution matrix acquisition unit is used to obtain the heat distribution matrix of the connector interface based on the Joule thermal power of each first grid cell in the calculated heat distribution grid model.

[0184] An abnormal temperature rise rate time-series characteristic determination unit is used to determine the abnormal temperature rise rate time-series characteristics based on the heat distribution matrix.

[0185] The second heat conduction simulation unit is used to simulate the heat conduction of the connector using the finite element analysis algorithm and to construct the thermal stress mesh model of the connector.

[0186] The temperature rise rate time series data calculation unit is used to calculate the temperature rise rate time series data of the connector interface based on the heat distribution matrix.

[0187] The micro-motion high-frequency timing characteristic determination unit is used to determine the micro-motion high-frequency timing characteristics based on the thermal stress grid model and the temperature rise rate timing data.

[0188] In an optional embodiment, the abnormal temperature rise rate time-series characteristic determination unit includes:

[0189] The local temperature rise rate time series data calculation subunit is used to extract the heat time series data of the first grid cell corresponding to the central region of the connector interface from the heat distribution matrix, and calculate the local temperature rise rate time series data of the connector interface based on the extracted heat time series data.

[0190] An abnormal temperature rise rate time series feature generation subunit is used to extract temperature rise rates greater than a preset temperature rise rate threshold from the local temperature rise rate time series data and generate abnormal temperature rise rate time series features.

[0191] In one optional embodiment, the micro-motion high-frequency timing feature determination unit includes:

[0192] A thermal stress calculation subunit is used to calculate the thermal stress of each second grid cell in the thermal stress grid model at the corresponding time based on the thermal stress grid model and each temperature rise rate in the temperature rise rate time series data.

[0193] The thermal stress distribution matrix is ​​obtained by sub-units, which are used to obtain the thermal stress distribution matrix of the connector interface based on the thermal stress of each second grid unit in the calculated thermal distribution grid model at each time.

[0194] The electromagnetic field simulation subunit is used to perform electromagnetic field simulation on the thermal stress mesh model and simulate the electromagnetic force distribution matrix when current passes through the connector interface.

[0195] A thermal stress electromagnetic force coupling subunit is used to couple the thermal stress distribution matrix and the electromagnetic force distribution matrix to obtain the coupling force distribution matrix of the connector interface;

[0196] The force amplitude time series data generation subunit is used to generate force amplitude time series data based on the force amplitude of each coupled force in the coupled force distribution matrix;

[0197] The Fourier transform subunit is used to perform Fourier transform on the force amplitude time series data and extract the frequency data of the force amplitude time series data;

[0198] The micro-motion strength data acquisition subunit is used to obtain the micro-motion strength data of the connector interface based on the frequency component in the frequency data.

[0199] The micro-motion high-frequency time-series feature generation subunit is used to extract high-frequency features from the micro-motion intensity data and generate micro-motion high-frequency time-series features.

[0200] In an optional embodiment, the micro-discharge spectral feature acquisition module 14 includes:

[0201] The micro-discharge spectral signal detection unit is used to detect the micro-discharge spectral signal generated by the gap change of the contact point of the connector through a photoelectric sensor, and obtain micro-discharge spectral data;

[0202] The micro-discharge spectral time-series feature extraction unit is used to perform fast Fourier transform on the micro-discharge spectral data to extract the micro-discharge spectral time-series features.

[0203] In an optional embodiment, the ice-melting current data acquisition module 15 includes:

[0204] The interface degradation process prediction unit is used to predict the degree of interface degradation of the connector interface through a long short-term memory network model based on the preload relaxation timing characteristics, the contact resistance timing characteristics, the abnormal temperature rise timing characteristics, the micro-discharge spectrum timing characteristics, and the micro-motion high-frequency timing characteristics, and obtain the interface degradation process timing curve.

[0205] The de-icing current curve correction unit is used to correct the reference de-icing current curve of the transmission line according to the interface degradation process time-series curve to obtain de-icing current time-series data.

[0206] It should be noted that the working process of each module in the ice-melting system based on flexible conductor quick-connect and all-fiber isolation interface described in the embodiments of the present invention can refer to the working process of the ice-melting method based on flexible conductor quick-connect and all-fiber isolation interface described in the above embodiments. The technical effect achieved is also the same as that of the ice-melting method based on flexible conductor quick-connect and all-fiber isolation interface described in the above embodiments, and will not be repeated here.

[0207] It should be noted that the system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the system embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0208] The above description represents the preferred embodiments of the present invention. It should be noted that, for those skilled in the art, various improvements and modifications can be made without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A de-icing method based on a flexible conductor quick-connect and an all-fiber isolation interface, characterized in that, include: The strain data of the connector interface of the transmission line is detected, and the preload relaxation timing characteristics are obtained based on the strain data through a pre-built preload prediction model; wherein the connector includes a flexible conductor quick contact and / or a fully optical fiber isolated interface. The contact resistance change at the connector interface is detected to obtain contact resistance data, and the contact resistance data is curve fitted to obtain the contact resistance timing characteristics. Based on the contact resistance timing characteristics, a heat conduction simulation based on finite element analysis is performed on the corresponding connector to obtain the abnormal temperature rise timing characteristics and micro-motion high-frequency timing characteristics of the connector. The micro-discharge spectral data at the contact point gap of the connector is detected, and the micro-discharge spectral data is subjected to spectral analysis to obtain the micro-discharge spectral temporal characteristics. Based on the preload relaxation timing characteristics, the contact resistance timing characteristics, the abnormal temperature rise timing characteristics, the micro-motion high-frequency timing characteristics, and the micro-discharge spectrum timing characteristics, the reference de-icing current curve of the transmission line is optimized to obtain de-icing current timing data. Based on the ice-melting current timing data, the current value input to the transmission line is adjusted by the ice-melting equipment to melt the ice in the transmission line.

2. The de-icing method based on flexible conductor quick-connect and all-fiber isolation interface as described in claim 1, characterized in that, The strain data of the connector interface of the transmission line is detected, and based on the strain data, the preload relaxation timing characteristics are obtained through a pre-built preload prediction model, including: The strain data is obtained by collecting the strain value of the connector interface within a set time period using a strain gauge installed at the connector interface. Principal component analysis is performed on the strain data to obtain the first eigenvector of the strain data; The first feature vector of the strain data is input into the preload prediction model based on the support vector regression algorithm to predict the preload time series data for the set time period. The preload timing data is processed by sliding window, and the percentage of preload change in the corresponding window is calculated based on the preload timing data in each window to obtain the preload relaxation degree corresponding to the window. Based on the preload relaxation process corresponding to all windows, the preload relaxation timing characteristics are obtained.

3. The de-icing method based on flexible conductor quick-connect and all-fiber isolation interface as described in claim 1 or 2, characterized in that, The step of curve fitting the contact resistance data to obtain the contact resistance time-series characteristics includes: Calculate the preload relaxation rate based on the preload relaxation timing characteristics. When the preload relaxation rate is greater than the set relaxation rate threshold, the contact resistance data is curve fitted to obtain the contact resistance timing characteristics.

4. The de-icing method based on flexible conductor quick-connect and all-fiber isolation interface as described in claim 3, characterized in that, The step of curve fitting the contact resistance data to obtain the contact resistance time-series characteristics includes: The contact resistance values ​​in the contact resistance data are sampled to generate contact resistance time series data; wherein, the contact resistance data includes the contact resistance values ​​at the connector interface within a set time period; The least-multiply-two method is used to perform polynomial curve fitting on the contact resistance time series data to obtain the first fitted curve of the contact resistance value changing with time. The first fitted curve and the preset standard contact resistance variation curve are matched for similarity. When the first fitted curve matches the standard variation curve of the contact resistance, the contact resistance timing characteristics are generated based on the first fitted curve. When the first fitted curve does not match the standard variation curve of the contact resistance, the sampling frequency is adjusted and the contact resistance value in the contact resistance data is resampled until the first fitted curve generated based on the resampled contact resistance time series data matches the standard variation curve of the contact resistance.

5. The de-icing method based on flexible conductor quick-connect and all-fiber isolation interface as described in claim 1, characterized in that, The step of performing a finite element analysis-based thermal conduction simulation on the corresponding connector based on the contact resistance timing characteristics to obtain the abnormal temperature rise timing characteristics and micro-motion high-frequency timing characteristics of the connector includes: The heat conduction of the connector interface is simulated using the finite element analysis algorithm, and a heat distribution mesh model of the connector interface is constructed. Based on the heat distribution grid model and each contact resistance value in the contact resistance time series characteristics, calculate the Joule thermal power of each first grid cell in the heat distribution grid model; The heat distribution matrix of the connector interface is obtained based on the Joule thermal power of each first grid cell in the calculated heat distribution grid model. Based on the heat distribution matrix, determine the time-series characteristics of the abnormal temperature rise rate; The connector was simulated for thermal conduction using the finite element analysis algorithm, and a thermal stress mesh model of the connector was constructed. Based on the heat distribution matrix, calculate the time-series data of the temperature rise rate of the connector interface; Based on the thermal stress grid model and the time series data of the temperature rise rate, the high-frequency time series characteristics of micro-motion are determined.

6. The de-icing method based on flexible conductor quick-connect and all-fiber isolation interface as described in claim 5, characterized in that, The step of determining the time-series characteristics of the abnormal temperature rise rate based on the heat distribution matrix includes: Extract the heat time series data of the first grid cell corresponding to the central region of the connector interface from the heat distribution matrix, and calculate the local temperature rise rate time series data of the connector interface based on the extracted heat time series data. The temperature rise rate exceeding a preset temperature rise rate threshold is extracted from the local temperature rise rate time series data to generate abnormal temperature rise rate time series features.

7. The de-icing method based on flexible conductor quick-connect and all-fiber isolation interface as described in claim 5, characterized in that, The step of determining the high-frequency time-series characteristics of micro-motion based on the thermal stress grid model and the temperature rise rate time-series data includes: Based on the thermal stress grid model and each temperature rise rate in the temperature rise rate time series data, calculate the thermal stress of each second grid cell in the thermal stress grid model at the corresponding time. Based on the calculated thermal stress of each second grid cell in the heat distribution grid model at each time, the thermal stress distribution matrix of the connector interface is obtained. Electromagnetic field simulation was performed on the thermal stress mesh model to simulate the electromagnetic force distribution matrix when current passes through the connector interface; The thermal stress distribution matrix and the electromagnetic force distribution matrix are coupled to obtain the coupling force distribution matrix of the connector interface; Based on the force amplitude of each coupled force in the coupled force distribution matrix, generate the force amplitude time series data; Perform a Fourier transform on the force amplitude time series data to extract the frequency data of the force amplitude time series data; Based on the frequency components in the frequency data, the micro-motion strength data of the connector interface is obtained; High-frequency features are extracted from the micro-motion intensity data to generate high-frequency time-series features of micro-motion.

8. The de-icing method based on flexible conductor quick-connect and all-fiber isolation interface as described in claim 6, characterized in that, The process of detecting micro-discharge spectral data at the contact point gap of the connector and performing spectral analysis on the micro-discharge spectral data to obtain micro-discharge spectral temporal characteristics includes: Micro-discharge spectral signals generated by the gap change at the contact points of the connector are detected by a photoelectric sensor to obtain micro-discharge spectral data; The micro-discharge spectral data are subjected to Fast Fourier Transform to extract the micro-discharge spectral temporal features.

9. The de-icing method based on flexible conductor quick-connect and all-fiber isolation interface as described in claim 1, characterized in that, The optimization of the reference de-icing current curve of the transmission line based on the preload relaxation timing characteristics, contact resistance timing characteristics, abnormal temperature rise timing characteristics, micro-motion high-frequency timing characteristics, and micro-discharge spectral timing characteristics to obtain de-icing current timing data includes: Based on the preload relaxation timing characteristics, contact resistance timing characteristics, abnormal temperature rise timing characteristics, micro-discharge spectrum timing characteristics, and micro-motion high-frequency timing characteristics, the interface degradation degree of the connector interface is predicted by a long short-term memory network model, and the interface degradation process timing curve is obtained. Based on the interface degradation process timing curve, the reference de-icing current curve of the transmission line is corrected to obtain de-icing current timing data.

10. An ice-melting system based on a flexible conductor quick-connect and a fully fiber optic isolated interface, characterized in that, include: The preload relaxation feature acquisition module is used to detect strain data of the connector interface of the transmission line, and obtain the preload relaxation timing features based on the strain data and a pre-built preload prediction model. The contact resistance data acquisition module is used to detect the change in contact resistance at the connector interface, obtain contact resistance data, and perform curve fitting on the contact resistance data to obtain the contact resistance timing characteristics. The heat conduction simulation module is used to perform heat conduction simulation based on finite element analysis on the corresponding connector according to the contact resistance timing characteristics, so as to obtain the abnormal temperature rise timing characteristics and micro-motion high-frequency timing characteristics of the connector. The micro-discharge spectral feature acquisition module is used to detect the micro-discharge spectral data at the contact point gap of the connector, and to perform spectral analysis on the micro-discharge spectral data to obtain the micro-discharge spectral temporal features. The de-icing current data acquisition module is used to optimize the reference de-icing current curve of the transmission line based on the preload relaxation timing characteristics, the contact resistance timing characteristics, the abnormal temperature rise timing characteristics, the micro-motion high-frequency timing characteristics, and the micro-discharge spectrum timing characteristics to obtain de-icing current timing data. The de-icing control module is used to adjust the current value input to the transmission line through the de-icing equipment to de-ic the transmission line according to the de-icing current timing data.

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