Coordinated control method and system for double-set ice melting device based on closed-loop dynamic balancing

Abstract

This invention discloses a collaborative control method and system for dual-set de-icing devices based on closed-loop dynamic equilibrium. The method includes: detecting mechanical stress data, torsional angle data, line temperature data, and first impedance time-series data of a transmission line under uneven ice shedding conditions; predicting the stress state using a first neural network model based on the mechanical stress data, torsional angle data, and line temperature data; determining the fluctuation range of electrical parameters using the predicted stress change time-series data and first impedance time-series data; predicting the phase difference of the predicted output current of the two de-icing systems in the dual-set de-icing devices using a second neural network model based on the fluctuation range of electrical parameters; and performing dynamic phase compensation on the real-time output current of the two de-icing systems based on the predicted output current phase difference and closed-loop dynamic equilibrium compensation parameters. This invention can effectively suppress electrical parameter fluctuations caused by mechanical imbalance, reduce the risk of line oscillation, and improve de-icing efficiency.

Description

Technical Field

[0001] This invention relates to the field of power transmission line de-icing technology, and in particular to a collaborative control method and system for dual de-icing devices based on closed-loop dynamic equilibrium. Background Technology

[0002] The dual-set de-icing device consists of two independent but collaborative de-icing devices. By installing the two de-icing devices on different power transmission lines or on the same power transmission line, the dual-set de-icing device can perform live de-icing on one or two power transmission lines, thereby improving de-icing efficiency.

[0003] Currently, the core technology of dual-set de-icing devices lies in using Joule heating generated by electric current to melt the ice layer attached to the conductor. When current flows through the conductor, its resistance generates heat, which is conducted to the ice layer, causing it to gradually melt. However, in extremely uneven icing scenarios (such as ice flares), the stress state of the conductor changes drastically, potentially causing instantaneous fluctuations in the conductor's torsional recovery rate, which can easily lead to problems such as line oscillation or electrical parameter instability. Especially when the ice layer on an ice flare asymmetrically breaks off, the stress state of the conductor changes dramatically, causing abrupt changes in the location of local stress release. This sudden mechanical imbalance then triggers a sudden and drastic change in the conductor's torsional recovery rate. This high-speed dynamic mechanical behavior directly causes fluctuations in the line's electrical parameters, manifested as significant jumps in characteristic impedance, thus affecting the de-icing effect. Therefore, how to suppress electrical parameter fluctuations caused by mechanical imbalance and reduce the risk of line oscillation has become a key problem that urgently 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 collaborative control method and system for dual-set de-icing devices based on closed-loop dynamic balancing, which can effectively suppress electrical parameter fluctuations caused by mechanical imbalance, reduce the risk of line oscillation, and improve de-icing efficiency.

[0005] In a first aspect, embodiments of the present invention provide a collaborative control method for two sets of ice-melting devices based on closed-loop dynamic equilibrium, comprising:

[0006] The mechanical stress data, torsion angle data, line temperature data, and ice thickness data of the transmission line under uneven ice shedding conditions are detected.

[0007] Based on the mechanical stress data, the torsion angle data, the line temperature data, and the icing thickness data, the stress state is predicted by a pre-trained first neural network model to obtain the time series data of the stress change of the transmission line.

[0008] Detect the first impedance timing data of the transmission line under uneven icing shedding conditions;

[0009] Based on the stress change time series data and the first impedance time series data, the fluctuation range of the electrical parameters of the transmission line is determined;

[0010] Based on the fluctuation range of the electrical parameters, the predicted value of the output current phase difference between the two ice-melting systems in the dual-set ice-melting device is determined; wherein, the two ice-melting systems are connected in parallel to the transmission line;

[0011] Based on the predicted output current phase difference and the closed-loop dynamic equalization compensation parameters, dynamic phase compensation is performed on the real-time output current of the two ice-melting systems in the dual-set ice-melting device.

[0012] As an improvement to the above solution, multiple monitoring points are deployed on the transmission line at predetermined intervals; each monitoring point is equipped with a capacitance sensor, a temperature sensor, a stress sensor, an angle sensor, and a voltage and current sensor.

[0013] The capacitive sensor is used to monitor the ice thickness at the corresponding monitoring point of the transmission line.

[0014] The temperature sensor is used to monitor the line temperature at the corresponding monitoring point of the transmission line.

[0015] The stress sensor is used to monitor the mechanical stress at the corresponding monitoring point of the transmission line;

[0016] The angle sensor is used to monitor the torsion angle at the corresponding monitoring point of the transmission line;

[0017] The voltage and current sensors are used to monitor the voltage and current at the corresponding monitoring points of the transmission line.

[0018] As an improvement to the above solution, the detection of mechanical stress data, torsion angle data, line temperature data, and ice thickness data of transmission lines under uneven icing conditions includes:

[0019] Based on the ice thickness detected by the corresponding capacitive sensors and the mechanical stress detected by the corresponding stress sensors at each monitoring point, the location of the stress abrupt change in uneven ice shedding of the transmission line is determined.

[0020] Acquire mechanical stress data and torsional angle data at the monitoring point corresponding to the stress abrupt change location; wherein, the mechanical stress data includes mechanical stress within a set time period, and the torsional angle data includes torsional angle within the set time period;

[0021] The line temperature at the monitoring point corresponding to the stress change location within the set time period is obtained as the line temperature data at the monitoring point corresponding to the stress change location.

[0022] The ice thickness at the monitoring point corresponding to the stress change location within the set time period is obtained as the ice thickness data at the monitoring point corresponding to the stress change location.

[0023] As an improvement to the above scheme, determining the location of the stress abrupt change in uneven icing shedding of the transmission line based on the ice thickness monitored by the corresponding capacitive sensors and the mechanical stress monitored by the corresponding stress sensors at each monitoring point includes:

[0024] The ice thickness at each monitoring point is compared with the preset thickness threshold.

[0025] The mechanical stress at each monitoring point is compared with the preset stress threshold.

[0026] When the ice thickness at any of the monitoring points is greater than the thickness threshold, and the change in mechanical stress at the corresponding monitoring point at two adjacent sampling times is greater than the stress threshold, it is determined that uneven ice shedding has occurred at the corresponding monitoring point, and the corresponding monitoring point is designated as the location of stress abrupt change.

[0027] As an improvement to the above solution, the step of predicting the stress state of the transmission line using a pre-trained first neural network model based on the mechanical stress data, the torsion angle data, the line temperature data, and the icing thickness data, to obtain the time-series data of stress changes in the transmission line, includes:

[0028] Based on the line temperature data and the icing thickness data, the natural frequency within the set time period is determined through a preset first mapping relationship related to the natural frequency, and used as the natural frequency data at the monitoring point corresponding to the stress change location.

[0029] The mechanical stress data, torsion angle data, line temperature data, and natural frequency data are time-aligned and interpolated to obtain the corresponding mechanical stress time series data, torsion angle time series data, line temperature time series data, and natural frequency time series data.

[0030] A multidimensional parameter vector matrix is ​​constructed based on the mechanical stress time series data, the torsion angle time series data, the line temperature time series data, and the natural frequency time series data.

[0031] The multidimensional parameter vector matrix is ​​input into the first neural network model to predict the stress state, thereby obtaining the time series data of stress change at the stress abrupt change location of the transmission line.

[0032] As an improvement to the above scheme, the detection of the first impedance timing data of the transmission line under uneven icing shedding conditions includes:

[0033] The voltage and current sensors at the monitoring points corresponding to the stress abrupt change locations are obtained within the set time period, and the voltage and current are monitored by the sensors.

[0034] Based on the voltage and current monitored at each sampling time within the set time period at the stress mutation location, the first impedance at each sampling time within the set time period at the stress mutation location is calculated to obtain the first impedance time series data.

[0035] As an improvement to the above scheme, determining the fluctuation range of the electrical parameters of the transmission line based on the stress change time series data and the first impedance time series data includes:

[0036] Obtain the output current of the two ice-melting systems in the dual-set ice-melting device within a set time period;

[0037] Based on the output current of the two ice-melting systems within a set time period, calculate the first output current phase difference of the two ice-melting systems within the set time period, and construct the first current phase difference timing data based on the first output current phase difference of the two ice-melting systems within the set time period.

[0038] A sliding window is used to detect abrupt phase difference points in the timing data of the first current phase difference; wherein, the absolute value of the difference between the phase difference of the first output current corresponding to the abrupt phase difference point and the phase difference of the first output current corresponding to the previous moment is greater than a preset difference threshold.

[0039] Calculate the first transient power at each phase difference abrupt change point based on the output current and output voltage at each phase difference abrupt change point.

[0040] Based on the phase difference of the first output current and the first transient power corresponding to each phase difference abrupt point, the risk of subsynchronous oscillation is predicted by a pre-trained support vector machine model.

[0041] When the risk of subsynchronous oscillation is predicted, second current phase difference time series data is constructed based on the first output current phase difference at each phase difference abrupt change point; first transient power time series data is constructed based on the first output transient power at each phase difference abrupt change point.

[0042] Based on the time-series data of force changes, the first impedance time-series data, the second current phase difference time-series data, and the first transient power time-series data, electrical parameter fluctuations are predicted using a pre-trained long short-term memory network model to determine the range of electrical parameter fluctuations for the transmission line; wherein, the range of electrical parameter fluctuations includes the range of current phase difference fluctuations.

[0043] As an improvement to the above scheme, the step of dynamically compensating the real-time output current of the two ice-melting systems in the dual-set ice-melting device based on the predicted output current phase difference and the closed-loop dynamic equalization compensation parameters includes:

[0044] Using the predicted output current phase difference as the target current phase difference, the phase difference of the real-time output current of the two ice-melting systems in the dual-set ice-melting device is adjusted.

[0045] The voltage and current at the monitoring point corresponding to the stress change location in the transmission line are re-detected at the current moment;

[0046] Re-detect the output current of the two ice-melting systems at the current moment;

[0047] Based on the voltage and current corresponding to the re-detected stress change location and the output current corresponding to the two ice-melting systems, dynamic phase compensation is dynamically performed on the real-time output current of the two ice-melting systems in the dual ice-melting device.

[0048] As an improvement to the above solution, the step of dynamically compensating the real-time output current of the two ice-melting systems in the dual-set ice-melting device based on the voltage and current corresponding to the re-detected stress change location and the output current corresponding to the two ice-melting systems includes:

[0049] Calculate the second impedance at the current moment based on the detected voltage and current;

[0050] Based on the re-detected output current, calculate the second current phase difference and the second transient power of the two ice-melting systems at the current moment;

[0051] Calculate the closed-loop dynamic balance compensation parameters based on the second impedance, the second current phase difference, and the second transient power.

[0052] The closed-loop dynamic equalization compensation parameters are used to perform dynamic phase compensation on the real-time output current of the two ice-melting systems in the dual-set ice-melting device.

[0053] Secondly, embodiments of the present invention provide a collaborative control system for two sets of ice-melting devices based on closed-loop dynamic equilibrium, comprising:

[0054] The first data detection module is used to detect the mechanical stress data, torsion angle data, line temperature data, and ice thickness data of the transmission line under uneven ice shedding conditions.

[0055] The stress prediction module is used to predict the stress state of the transmission line based on the mechanical stress data, the torsion angle data, the line temperature data, and the icing thickness data, using a pre-trained first neural network model, and to obtain the time series data of the stress change of the transmission line.

[0056] The second data detection module is used to detect the first impedance timing data of the transmission line under the condition of uneven ice shedding.

[0057] An electrical parameter fluctuation determination module is used to determine the fluctuation range of electrical parameters of the transmission line based on the stress change time series data and the first impedance time series data.

[0058] The current phase difference determination module is used to determine the predicted value of the output current phase difference between the two ice-melting systems in the dual-set ice-melting device based on the fluctuation range of the electrical parameters; wherein the two ice-melting systems are connected in parallel to the transmission line;

[0059] The current adjustment module is used to perform dynamic phase compensation on the real-time output current of the two ice-melting systems in the dual-set ice-melting device based on the predicted output current phase difference and the closed-loop dynamic balance compensation parameters.

[0060] Compared to existing technologies, this invention provides a collaborative control method and system for dual-set de-icing devices based on closed-loop dynamic equilibrium. This method detects mechanical stress data, torsional angle data, line temperature data, and ice thickness data of a transmission line under uneven ice shedding conditions. Based on the mechanical stress data, torsional angle data, line temperature data, and ice thickness data, a pre-trained first neural network model is used to predict the stress state, obtaining the time-series data of the stress changes of the transmission line. The method also detects the first impedance time-series data of the transmission line under uneven ice shedding conditions. The time-series data of the force change and the first impedance time-series data are used to determine the fluctuation range of the electrical parameters of the transmission line; based on the fluctuation range of the electrical parameters, the predicted value of the output current phase difference of the two de-icing systems in the dual-set de-icing device is determined; wherein, the two de-icing systems are connected in parallel to the transmission line; based on the predicted value of the output current phase difference and the closed-loop dynamic balance compensation parameters, dynamic phase compensation is performed on the real-time output current of the two de-icing systems in the dual-set de-icing device; the embodiment of the present invention can effectively suppress the fluctuation of electrical parameters caused by mechanical imbalance, reduce the risk of line oscillation, and improve de-icing efficiency. 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 1 This is a flowchart of a collaborative control method for dual-set ice-melting devices based on closed-loop dynamic equilibrium provided by an embodiment of the present invention;

[0063] Figure 2 This is a structural block diagram of a collaborative control system for dual ice-melting devices based on closed-loop dynamic equilibrium, 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] In embodiments of the invention, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, without necessarily requiring or implying any such actual relationship or order between these entities or operations. The terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0067] See Figure 1 , Figure 1 This is a flowchart illustrating a collaborative control method for dual-set ice-melting devices based on closed-loop dynamic equilibrium, provided by an embodiment of the present invention. The collaborative control method for dual-set ice-melting devices based on closed-loop dynamic equilibrium specifically includes:

[0068] S11: Detect mechanical stress data, torsion angle data, line temperature data, and ice thickness data of transmission lines under uneven ice shedding conditions;

[0069] S12: Based on the mechanical stress data, the torsion angle data, the line temperature data, and the icing thickness data, the stress state is predicted using a pre-trained first neural network model to obtain the time-series data of the stress change of the transmission line;

[0070] S13: Detect the first impedance timing data of the transmission line under uneven icing and shedding conditions;

[0071] S14: Determine the fluctuation range of the electrical parameters of the transmission line based on the stress change time series data and the first impedance time series data;

[0072] S15: Determine the predicted value of the output current phase difference between the two ice-melting systems in the dual-set ice-melting device based on the fluctuation range of the electrical parameters; wherein the two ice-melting systems are connected in parallel to the transmission line;

[0073] S16: Based on the predicted output current phase difference and the closed-loop dynamic equalization compensation parameters, perform dynamic phase compensation on the real-time output current of the two ice-melting systems in the dual-set ice-melting device.

[0074] It should be noted that the collaborative control method of the dual-set de-icing device based on closed-loop dynamic equilibrium described in this embodiment of the invention can be executed by the central controller of the dual-set de-icing device. This central controller is used to collaboratively control the current output of the two de-icing systems in the dual-set de-icing device. In this embodiment of the invention, by installing dual-set de-icing devices on the same transmission line, such as distributing one de-icing system of the dual-set de-icing device in different sections of the transmission line to de-ic the different sections, de-icing can be performed on different sections of the transmission line simultaneously. At this time, the current flows in from one end of the line section, returns to the neutral point through the line resistance, and heat is generated along a single path throughout the entire process. The heat is distributed along the line in a "high at the beginning and low at the end" pattern. Alternatively, the two de-icing systems can be distributed at both ends of the transmission line. Current is injected into the beginning and end of the transmission line simultaneously through the two de-icing systems. At this time, the current path becomes "beginning end → front section of the line → midpoint < rear section of the line < end". The current at the midpoint of the line is superimposed to the maximum value, and the heat distribution exhibits a symmetrical characteristic of "high in the middle and low at both ends". Although using dual-set de-icing devices can improve de-icing efficiency to some extent, the output current of the two de-icing systems in the dual-set de-icing devices must be synchronized in phase during the de-icing process to ensure that the output current is kept as balanced as possible.

[0075] In this embodiment of the invention, two de-icing systems of a dual-set de-icing device are deployed at the beginning and end of a transmission line, respectively. At this time, the heat distribution exhibits the characteristic of "high in the middle and low at both ends". In addition, under extremely uneven icing scenarios (such as ice wing), uneven ice shedding is likely to occur during the de-icing process. The stress state of the transmission line will change drastically, and sudden mechanical imbalance is likely to occur, which will then cause a sudden and drastic change in the conductor torsion recovery rate, thereby causing line oscillation and instability of electrical parameters, thus affecting the de-icing effect. This invention embodiment is based on closed-loop dynamic equalization control technology, considering mechanical stress data, torsional angle data, line temperature data, and first impedance timing data under uneven ice shedding conditions, to control the output current phase difference of the two ice-melting systems of the dual-set ice-melting device; specifically, it includes: first, based on the mechanical stress data, torsional angle data, and line temperature data of the transmission line within a set time period under uneven ice shedding conditions, predicting the stress state through a pre-trained first neural network model to obtain the stress change timing data of the transmission line under uneven ice shedding conditions; then, combining the first impedance timing data and stress change timing data under uneven ice shedding conditions, determining the set... The electrical parameters fluctuate within a certain time period. Then, based on this fluctuation range, a pre-trained second neural network model predicts the phase difference of the predicted output current of the two de-icing systems in the dual-set de-icing device. This predicted output current phase difference is used as the target output current phase difference of the two de-icing systems. Combined with dynamically adjustable closed-loop dynamic balance compensation parameters, dynamic phase compensation is performed on the real-time output current of the two de-icing systems in the dual-set de-icing device. This ensures that the phase difference of the output current of the two de-icing systems is within a certain range. On the one hand, this ensures uniform heat distribution on the line, avoiding local overheating or incomplete de-icing. On the other hand, it effectively suppresses electrical parameter fluctuations caused by mechanical imbalance, reduces the risk of line oscillation, and improves de-icing efficiency.

[0076] Specifically, multiple monitoring points are deployed on the transmission line at predetermined intervals; each monitoring point is equipped with a capacitance sensor, a temperature sensor, a stress sensor, an angle sensor, and a voltage and current sensor.

[0077] The capacitive sensor is used to monitor the ice thickness at the corresponding monitoring point of the transmission line.

[0078] The temperature sensor is used to monitor the line temperature at the corresponding monitoring point of the transmission line.

[0079] The stress sensor is used to monitor the mechanical stress at the corresponding monitoring point of the transmission line;

[0080] The angle sensor is used to monitor the torsion angle at the corresponding monitoring point of the transmission line;

[0081] The voltage and current sensors are used to monitor the voltage and current at the corresponding monitoring points of the transmission line.

[0082] For example, by setting up monitoring points at set intervals (such as 10 meters, 50 meters, or even longer) on the power transmission line, multi-point sensor data can be collected and processed to form a multi-point monitoring network, thereby realizing the status monitoring of the power transmission line during the icing and de-icing process.

[0083] For example, ice thickness can be measured by detecting changes in capacitance between a transmission line (considered as one half-pole) and another plate (such as the ground) using a capacitance sensor. When a transmission line is covered in ice, the ice layer acts as a dielectric between the plates, causing a change in capacitance. As the ice thickness increases, the effective dielectric constant between the plates increases, and the capacitance also increases. For instance, the initial capacitance can be measured in an icy state, and then measured again after icing. By artificially simulating icing (e.g., covering the line with ice layers of different thicknesses), a mapping relationship between the capacitance after icing and the ice thickness can be established. Then, based on the currently detected capacitance, the current ice thickness of the transmission line can be determined using this mapping relationship.

[0084] In an optional embodiment, S11: Detecting mechanical stress data, torsion angle data, line temperature data, and ice thickness data of the transmission line under uneven icing shedding conditions includes:

[0085] Based on the ice thickness detected by the corresponding capacitive sensors and the mechanical stress detected by the corresponding stress sensors at each monitoring point, the location of the stress abrupt change in uneven ice shedding of the transmission line is determined.

[0086] Acquire mechanical stress data and torsional angle data at the monitoring point corresponding to the stress abrupt change location; wherein, the mechanical stress data includes mechanical stress within a set time period, and the torsional angle data includes torsional angle within the set time period;

[0087] The line temperature at the monitoring point corresponding to the stress change location within the set time period is obtained as the line temperature data at the monitoring point corresponding to the stress change location.

[0088] The ice thickness at the monitoring point corresponding to the stress change location within the set time period is obtained as the ice thickness data at the monitoring point corresponding to the stress change location.

[0089] Specifically, determining the location of the stress abrupt change in uneven icing shedding of the transmission line based on the ice thickness monitored by the corresponding capacitive sensors and the mechanical stress monitored by the corresponding stress sensors at each monitoring point includes:

[0090] The ice thickness at each monitoring point is compared with the preset thickness threshold.

[0091] The mechanical stress at each monitoring point is compared with the preset stress threshold.

[0092] When the ice thickness at any of the monitoring points is greater than the thickness threshold, and the change in mechanical stress at the corresponding monitoring point at two adjacent sampling times is greater than the stress threshold, it is determined that uneven ice shedding has occurred at the corresponding monitoring point, and the corresponding monitoring point is designated as the location of stress abrupt change.

[0093] In this embodiment of the invention, the first step is to identify the location of stress abrupt changes in the transmission line where uneven ice shedding occurs, based on the ice thickness monitored by the capacitive sensors and the mechanical stress monitored by the stress sensors at each monitoring point. For example, a thickness threshold (e.g., 20 mm) can be empirically set to indicate that uneven ice shedding is likely to occur when the ice thickness reaches or exceeds this threshold. Simultaneously, a stress threshold can be empirically set to indicate that uneven ice shedding is likely to occur when the axial mechanical stress change of the transmission line reaches or exceeds this stress threshold. Based on the above principles, by combining the ice thickness and mechanical stress monitored at each monitoring point, uneven ice shedding can be detected promptly through threshold comparison. For instance, if the ice thickness is greater than the thickness threshold and the change in mechanical stress between two adjacent sampling times is greater than the stress threshold, the location of the monitoring point corresponding to this stress threshold is the location of the stress abrupt change in uneven ice shedding. It is understood that the stress abrupt change location is generally in the middle of the conductor span.

[0094] After identifying the stress abrupt change location in the transmission line, the mechanical stress and torsional angle at that location are obtained within a set time period (e.g., the time between the start of ice melting and the sampling time when uneven ice shedding occurs). These are then sorted chronologically to obtain the mechanical stress time series as mechanical stress data and the torsional angle time series as torsional angle data. The mechanical stress data represents the stress change at the stress abrupt change location within the set time period, and the torsional angle data represents the torsional angle change at the stress abrupt change location within the set time period.

[0095] Simultaneously, the line temperature at the stress abrupt change location within the set time period is obtained and sorted according to time order to obtain the line temperature time series as line temperature data; the icing thickness at the stress abrupt change location within the set time period is obtained and sorted according to time order to obtain the icing thickness time series as icing thickness data.

[0096] In an optional embodiment, S12: Based on the mechanical stress data, the torsion angle data, the line temperature data, and the icing thickness data, a pre-trained first neural network model is used to predict the stress state and obtain the time-series data of the stress changes of the transmission line, including:

[0097] Based on the line temperature data and the icing thickness data, the natural frequency within the set time period is determined through a preset first mapping relationship related to the natural frequency, and used as the natural frequency data at the monitoring point corresponding to the stress change location.

[0098] The mechanical stress data, torsion angle data, line temperature data, and natural frequency data are time-aligned and interpolated to obtain the corresponding mechanical stress time series data, torsion angle time series data, line temperature time series data, and natural frequency time series data.

[0099] A multidimensional parameter vector matrix is ​​constructed based on the mechanical stress time series data, the torsion angle time series data, the line temperature time series data, and the natural frequency time series data.

[0100] The multidimensional parameter vector matrix is ​​input into the first neural network model to predict the stress state, thereby obtaining the time series data of stress change at the stress abrupt change location of the transmission line.

[0101] For example, the natural frequency of a transmission line is related to the line temperature and the thickness of the ice accretion. It can be understood that the thicker the ice accretion, the lower the natural frequency; conversely, the higher the line temperature, the lower the conductor tension, and thus the lower the natural frequency. After obtaining the line temperature data and ice thickness data at the stress abrupt change location within a set time period, the natural frequency at each sampling moment within the set time period can be calculated using a pre-constructed first mapping relationship, and then sorted chronologically to obtain the natural frequency data. The functional expression of the first mapping relationship is as follows:

[0102] (1);

[0103] (2);

[0104] (3);

[0105] Where f represents the natural frequency, L represents the span length (i.e., the straight-line distance between the center points of two adjacent towers in the transmission line on the horizontal plane), T represents the conductor tension after the transmission line is covered with ice, and m represents the mass per unit length of the transmission line after it is covered with ice. This indicates the tension of the conductors in the transmission line that are not covered with ice. This represents the elastic modulus of a power transmission line. This indicates the conductor cross-sectional area of ​​the transmission line. This represents the conductor expansion coefficient of a power transmission line. Indicates the initial temperature. Indicates the line temperature. This represents the mass per unit length of a transmission line that is not covered with ice. This indicates the density of ice. The value represents the icing thickness, and d represents the diameter of the transmission line.

[0106] The mechanical stress data, torsion angle data, line temperature data, and natural frequency data obtained above are then time-aligned and interpolated to obtain corresponding mechanical stress time series data, torsion angle time series data, line temperature time series data, and natural frequency time series data. This ensures that the time of each data is aligned and missing data is filled in to ensure data integrity.

[0107] A multidimensional parameter vector matrix is ​​constructed using time-series data of mechanical stress, torsion angle, line temperature, and natural frequency. Each row in the multidimensional parameter vector matrix represents a time-series data point, and each column represents the mechanical stress, torsion angle, line temperature, and natural frequency corresponding to a sampling time. This multidimensional parameter vector matrix is ​​input into the first neural network model for stress state prediction, which yields time-series data of stress changes at stress abrupt changes in the transmission line. This stress change time-series data indicates the stress state (i.e., stress level) at each sampling time point at the stress abrupt change location.

[0108] It should be noted that the embodiments of the present invention do not specifically limit the network structure and training process of the first neural network model. For example, a Long Short-Term Memory (LTSM) network can be used. Specifically, the historical mechanical stress, torsion angle, line temperature and natural frequency of the transmission line (i.e., sample data) and historical stress state (i.e., label data) can be used to train the LTSM network. The specific training process belongs to the prior art and will not be described in detail here.

[0109] In an optional embodiment, S13: Detecting the first impedance timing data of the transmission line under uneven icing shedding conditions includes:

[0110] The voltage and current sensors at the monitoring points corresponding to the stress abrupt change locations are obtained within the set time period, and the voltage and current are monitored by the sensors.

[0111] Based on the voltage and current monitored at each sampling time within the set time period at the stress mutation location, the first impedance at each sampling time within the set time period at the stress mutation location is calculated to obtain the first impedance time series data.

[0112] For example, when voltage and current are monitored at locations of sudden stress changes, combined with the natural frequency f, it can be... Calculate the first impedance at the corresponding time, where U represents the voltage at the stress abrupt change location at a certain sampling time, and I represents the current at the stress abrupt change location at a certain sampling time. Let represent the first impedance at the location of the stress abrupt change at a certain sampling time, j represent the imaginary unit, and X represent the inductive reactance at the location of the stress abrupt change at a certain sampling time. , This represents the self-inductance coefficient of a power transmission line.

[0113] By sorting the calculated first impedance in chronological order, the time series data of the first impedance can be obtained.

[0114] In an optional embodiment, S14: determining the electrical parameter fluctuation range of the transmission line based on the stress change time series data and the first impedance time series data, including:

[0115] Obtain the output current of the two ice-melting systems in the dual-set ice-melting device within a set time period;

[0116] Based on the output current of the two ice-melting systems within a set time period, calculate the first output current phase difference of the two ice-melting systems within the set time period, and construct the first current phase difference timing data based on the first output current phase difference of the two ice-melting systems within the set time period.

[0117] A sliding window is used to detect abrupt phase difference points in the timing data of the first current phase difference; wherein, the absolute value of the difference between the phase difference of the first output current corresponding to the abrupt phase difference point and the phase difference of the first output current corresponding to the previous moment is greater than a preset difference threshold.

[0118] Calculate the first transient power at each phase difference abrupt change point based on the output current and output voltage at each phase difference abrupt change point.

[0119] Based on the phase difference of the first output current and the first transient power corresponding to each phase difference abrupt point, the risk of subsynchronous oscillation is predicted by a pre-trained support vector machine model.

[0120] When the risk of subsynchronous oscillation is predicted, second current phase difference time series data is constructed based on the first output current phase difference at each phase difference abrupt change point; first transient power time series data is constructed based on the first output transient power at each phase difference abrupt change point.

[0121] Based on the time-series data of force changes, the first impedance time-series data, the second current phase difference time-series data, and the first transient power time-series data, electrical parameter fluctuations are predicted using a pre-trained long short-term memory network model to determine the range of electrical parameter fluctuations for the transmission line; wherein, the range of electrical parameter fluctuations includes the range of current phase difference fluctuations.

[0122] In this embodiment of the invention, when it is determined that uneven icing has occurred, a subsynchronous oscillation risk assessment is further performed on the transmission line. The specific process is as follows:

[0123] First, based on the output current of the two ice-melting systems in the dual-set ice-melting device within a set time period, the phase difference of the first output current of the two ice-melting systems within the set time period is calculated. Then, the phase differences of the first output current corresponding to each sampling moment are sorted in chronological order to construct the time-series data of the first current phase difference. It should be noted that the calculation of the current phase difference is existing technology and will not be described in detail here.

[0124] Then, a sliding window with a set step size is used to detect abrupt phase difference in the timing data of the first current phase difference. The system detects whether there is a moment in each window where the absolute value of the difference between the phase difference of the first output current and the phase difference corresponding to the previous moment is greater than a preset difference threshold. If such a moment exists, it is taken as a point of abrupt phase difference.

[0125] By using a sliding window, at least one phase difference abrupt change point can be detected, and the output current and output voltage corresponding to each phase difference abrupt change point (including the output current and output voltage of the two ice-melting systems) can be obtained. Based on the output current and output voltage of each phase difference abrupt change point, the first transient power of each ice-melting system at the corresponding phase difference abrupt change point can be calculated; for example, the first transient power... ;in, , These represent the output voltage and output current of a certain ice-melting system at a certain point of abrupt change in phase difference.

[0126] Then, the phase difference of the first output current and the first transient power corresponding to each phase difference abrupt change point are used to predict the risk of subsynchronous oscillation through a pre-trained support vector machine model, so as to obtain the prediction result of whether the transmission line has the risk of subsynchronous oscillation.

[0127] It should be noted that, in the embodiments of the present invention, the support vector machine model can adopt the radial basis kernel function, and the model can be trained by the output current phase difference and transient power (i.e. sample data) of the two ice melting systems when the transmission line experiences subsynchronous oscillation risk (i.e., tag data). The specific training process belongs to the prior art and will not be described in detail here.

[0128] Given the predicted risk of subsynchronous oscillations in the transmission line, the fluctuation range of the transmission line's electrical parameters over a past period is further determined, such as the fluctuation range of the current phase difference. Specifically, the first output current phase difference and the first transient power at each phase difference abrupt change point are sorted in chronological order to construct the second current phase difference time series data and the first transient power time series data, respectively. Then, based on the second current phase difference time series data or the first transient power time series data, the obtained force change time series data and first impedance time series data are time-aligned and preprocessed with the second current phase difference time series data or the first transient power time series data (e.g., filling missing values ​​and deleting redundant values). The processed force change time series data, first impedance time series data, second current phase difference time series data, and first transient power time series data are input into a pre-trained long short-term memory network model to predict the fluctuation of electrical parameters, thereby obtaining the fluctuation range of the current phase difference of the transmission line over a future period (e.g., a time series of current phase difference fluctuations over a certain future period), which is used as the fluctuation range of the electrical parameters of the transmission line. Current phase difference fluctuation indicates the difference between the current phase difference at the current moment and the desired / ideal output current phase difference.

[0129] Based on the fluctuation range of electrical parameters, the current phase difference over a certain period of time can be obtained, which can be used as a predicted value for the output current phase difference between the two ice-melting systems in a dual-set ice-melting device. For example, the fluctuation of the current phase difference at a certain time t is... Then the predicted value of the output current phase difference at time t = , This represents the desired / ideal output current phase difference. It can be understood that if the current phase difference at a certain moment is less than the desired / ideal output current phase difference, the current phase difference fluctuates. If the value is positive, the current phase difference at the corresponding moment needs to be increased. If the current phase difference at a certain moment is greater than the desired / ideal output current phase difference, the current phase difference fluctuation is... If the value is negative, the current phase difference at the corresponding moment needs to be reduced.

[0130] It should be noted that the long short-term memory network model here can be trained using the historical stress changes, impedance, current phase difference, and transient power of the transmission line before uneven icing and shedding (i.e., sample data), and the expected / ideal output current phase difference after uneven icing (i.e., label data, under which the heat distribution on the line is relatively uniform and there is no risk of line oscillation). The specific training process is existing technology and will not be described in detail here.

[0131] In an optional embodiment, S16: Based on the predicted output current phase difference and the closed-loop dynamic equalization compensation parameters, dynamic phase compensation is performed on the real-time output current of the two ice-melting systems in the dual-set ice-melting device, including:

[0132] Using the predicted output current phase difference as the target current phase difference, the phase difference of the real-time output current of the two ice-melting systems in the dual-set ice-melting device is adjusted.

[0133] The voltage and current at the monitoring point corresponding to the stress change location in the transmission line are re-detected at the current moment;

[0134] Re-detect the output current of the two ice-melting systems at the current moment;

[0135] Based on the voltage and current corresponding to the re-detected stress change location and the output current corresponding to the two ice-melting systems, dynamic phase compensation is dynamically performed on the real-time output current of the two ice-melting systems in the dual ice-melting device.

[0136] Specifically, the step of dynamically compensating the real-time output current of the two ice-melting systems in the dual-set ice-melting device based on the voltage and current corresponding to the re-detected stress change location and the re-detected output current of the two ice-melting systems includes:

[0137] Calculate the second impedance at the current moment based on the detected voltage and current;

[0138] Based on the re-detected output current, calculate the second current phase difference and the second transient power of the two ice-melting systems at the current moment;

[0139] Calculate the closed-loop dynamic balance compensation parameters based on the second impedance, the second current phase difference, and the second transient power.

[0140] The closed-loop dynamic equalization compensation parameters are used to perform dynamic phase compensation on the real-time output current of the two ice-melting systems in the dual-set ice-melting device.

[0141] In this embodiment of the invention, the predicted output current phase difference value obtained above at the current time t (i.e., the first time) = The phase difference of the real-time output current of the two de-icing systems in the dual-set de-icing device is adjusted to the target current phase difference to ensure that the phase difference of the real-time output current of the two de-icing systems is close to the expected / ideal output current phase difference under the condition of uneven ice shedding. This ensures that the phase difference of the real-time output current of the two de-icing systems is within a certain range under the condition of uneven ice shedding. On the one hand, this can ensure that the heat distribution on the line is uniform, avoiding local overheating or incomplete de-icing. On the other hand, it can effectively suppress the fluctuation of electrical parameters caused by mechanical imbalance, reduce the risk of line oscillation, and improve de-icing efficiency.

[0142] After adjusting the phase difference of the real-time output current of the two de-icing systems in the dual-set de-icing device, the voltage and current at the stress change location in the transmission line at the current moment, as well as the output current of the two de-icing systems at the current moment, are re-detected. Then, based on the voltage and current corresponding to the re-detected stress change location, the second impedance at the current moment is calculated. Based on the re-detected output current, the second current phase difference and the second transient power of the two de-icing systems at the current moment are calculated. The specific calculation process is described above and will not be repeated here.

[0143] Then, based on the recalculated second impedance, second current phase difference, and second transient power at the current moment, the closed-loop dynamic balance compensation coefficient is calculated, such as the closed-loop dynamic balance compensation coefficient. The calculation process is as follows:

[0144] (4);

[0145] in, These represent the normalized second transient power, second impedance, and second current phase difference, respectively. b, c, and d are the importance coefficients of the transient power, impedance, and current phase difference in the transmission line on de-icing, respectively. b, c, and d can be pre-configured and set.

[0146] Adopting closed-loop dynamic equilibrium compensation coefficient Adjust the phase difference of the target current at the next time t+1, for example... = The phase difference of the real-time output current of the two ice-melting systems in the dual-set ice-melting device is adjusted to the phase difference of the target current after adjustment at time t+1. = This technology enables closed-loop dynamic equalization phase compensation of the real-time output current of the two ice-melting systems in the dual-set ice-melting device. It further ensures that the phase difference of the real-time output current of the two ice-melting systems is close to the expected / ideal phase difference under the condition of uneven ice shedding, so as to suppress the fluctuation of electrical parameters caused by mechanical imbalance, reduce the risk of line oscillation, and improve the ice-melting efficiency.

[0147] See Figure 2 , Figure 2 This invention provides a structural block diagram of a collaborative control system for two sets of ice-melting devices based on closed-loop dynamic equilibrium. The collaborative control system for two sets of ice-melting devices based on closed-loop dynamic equilibrium includes:

[0148] The first data detection module 11 is used to detect the mechanical stress data, torsion angle data, line temperature data and ice thickness data of the transmission line under the condition of uneven ice shedding.

[0149] The stress prediction module 12 is used to predict the stress state of the transmission line by using a pre-trained first neural network model based on the mechanical stress data, the torsion angle data, the line temperature data, and the icing thickness data, so as to obtain the time series data of the stress change of the transmission line.

[0150] The second data detection module 13 is used to detect the first impedance timing data of the transmission line under the condition of uneven icing and shedding.

[0151] The electrical parameter fluctuation determination module 14 is used to determine the fluctuation range of the electrical parameters of the transmission line based on the stress change time series data and the first impedance time series data.

[0152] The current phase difference determination module 15 is used to determine the predicted value of the output current phase difference between the two ice-melting systems in the dual-set ice-melting device based on the fluctuation range of the electrical parameters; wherein the two ice-melting systems are connected in parallel to the transmission line;

[0153] The current adjustment module 16 is used to perform dynamic phase compensation on the real-time output current of the two ice-melting systems in the dual-set ice-melting device based on the predicted output current phase difference and the closed-loop dynamic balance compensation parameters.

[0154] In one optional embodiment, multiple monitoring points are deployed on the transmission line at predetermined intervals; each monitoring point is equipped with a capacitance sensor, a temperature sensor, a stress sensor, an angle sensor, and a voltage and current sensor.

[0155] The capacitive sensor is used to monitor the ice thickness at the corresponding monitoring point of the transmission line.

[0156] The temperature sensor is used to monitor the line temperature at the corresponding monitoring point of the transmission line.

[0157] The stress sensor is used to monitor the mechanical stress at the corresponding monitoring point of the transmission line;

[0158] The angle sensor is used to monitor the torsion angle at the corresponding monitoring point of the transmission line;

[0159] The voltage and current sensors are used to monitor the voltage and current at the corresponding monitoring points of the transmission line.

[0160] In an optional embodiment, the first data detection module 11 includes:

[0161] The stress change location determination unit is used to determine the stress change location of uneven ice shedding in the transmission line based on the ice thickness monitored by the corresponding capacitive sensor and the mechanical stress monitored by the corresponding stress sensor at each monitoring point.

[0162] The first data acquisition unit is used to acquire mechanical stress data and torsion angle data at the monitoring point corresponding to the stress change location; wherein, the mechanical stress data includes mechanical stress within a set time period, and the torsion angle data includes torsion angle within the set time period;

[0163] The second data acquisition unit is used to acquire the line temperature of the monitoring point corresponding to the stress change location within the set time period, as the line temperature data of the monitoring point corresponding to the stress change location.

[0164] The third data acquisition unit is used to acquire the ice thickness at the monitoring point corresponding to the stress change location within the set time period, as the ice thickness data at the monitoring point corresponding to the stress change location.

[0165] In an optional embodiment, the stress change location determination unit includes:

[0166] The first threshold comparison subunit is used to compare the ice thickness at each monitoring point with a preset thickness threshold.

[0167] The second threshold comparison subunit is used to compare the mechanical stress at each monitoring point with the preset stress threshold.

[0168] The determination subunit is used to determine that uneven ice shedding has occurred at the corresponding monitoring point when the ice thickness at any of the monitoring points is greater than the thickness threshold and the change in mechanical stress at the corresponding monitoring point at two adjacent sampling times is greater than the stress threshold, and to designate the corresponding monitoring point as the location of stress abrupt change.

[0169] In an optional embodiment, the force prediction module 12 includes:

[0170] The fourth data acquisition unit is used to determine the natural frequency within the set time period based on the line temperature data and the icing thickness data, through a preset first mapping relationship related to the natural frequency, and use it as the natural frequency data at the monitoring point corresponding to the stress change location.

[0171] The fifth data acquisition unit is used to perform time alignment and interpolation processing on the mechanical stress data, the torsion angle data, the line temperature data and the natural frequency data to obtain the corresponding mechanical stress time series data, torsion angle time series data, line temperature time series data and natural frequency time series data.

[0172] The vector matrix construction unit is used to construct a multidimensional parameter vector matrix based on the mechanical stress time series data, the torsion angle time series data, the line temperature time series data, and the natural frequency time series data.

[0173] The stress state prediction unit is used to input the multidimensional parameter vector matrix into the first neural network model to predict the stress state and obtain the time series data of stress change at the stress change location of the transmission line.

[0174] In an optional embodiment, the second data detection module 13 includes:

[0175] The voltage and current acquisition unit is used to acquire the voltage and current monitored by the voltage and current sensor at the monitoring point corresponding to the stress change location within the set time period.

[0176] The impedance calculation unit is used to calculate the first impedance of the stress change location at each sampling time within the set time period based on the voltage and current monitored at each sampling time within the set time period, and obtain the first impedance time series data.

[0177] In an optional embodiment, the electrical parameter fluctuation determination module 14 includes:

[0178] An output current acquisition unit is used to acquire the output current of the two ice-melting systems in the dual ice-melting devices within a set time period.

[0179] The first output current phase difference calculation unit is used to calculate the first output current phase difference of the two ice melting systems within a set time period based on the output current of the two ice melting systems within a set time period, and to construct the first current phase difference timing data based on the first output current phase difference of the two ice melting systems within a set time period.

[0180] A phase difference abrupt change point detection unit is used to detect phase difference abrupt change points on the first current phase difference time series data using a sliding window; wherein, the absolute value of the difference between the first output current phase difference corresponding to the phase difference abrupt change point and the first output current phase difference corresponding to the previous moment is greater than a preset difference threshold.

[0181] The first transient power calculation unit is used to calculate the first transient power of each phase difference abrupt change point based on the output current and output voltage of each phase difference abrupt change point.

[0182] The oscillation risk prediction unit is used to predict the subsynchronous oscillation risk based on the first output current phase difference and the first transient power corresponding to each phase difference abrupt point, using a pre-trained support vector machine model.

[0183] The phase difference timing data construction unit is used to construct second current phase difference timing data based on the first output current phase difference at each phase difference abrupt change point when the risk of subsynchronous oscillation is predicted; and to construct first transient power timing data based on the first output transient power at each phase difference abrupt change point.

[0184] An electrical parameter fluctuation prediction unit is used to predict electrical parameter fluctuations based on the stress change time series data, the first impedance time series data, the second current phase difference time series data, and the first transient power time series data, using a pre-trained long short-term memory network model, and to determine the electrical parameter fluctuation range of the transmission line; wherein, the electrical parameter fluctuation range includes the current phase difference fluctuation range.

[0185] In an optional embodiment, the current adjustment module 16 includes:

[0186] A phase difference adjustment unit is used to adjust the phase difference of the real-time output current of the two ice-melting systems in the dual-set ice-melting device, with the predicted output current phase difference value as the target current phase difference.

[0187] The first detection unit is used to re-detect the voltage and current at the monitoring point corresponding to the stress change location in the transmission line at the current moment;

[0188] The second detection unit is used to re-detect the output current of the two ice-melting systems at the current moment;

[0189] The phase compensation unit is used to dynamically compensate the real-time output current of the two ice-melting systems in the dual ice-melting device based on the voltage and current corresponding to the re-detected stress change position and the output current corresponding to the two ice-melting systems.

[0190] In one optional embodiment, the phase compensation unit includes:

[0191] The first calculation subunit is used to calculate the second impedance at the current moment based on the detected voltage and current;

[0192] The second calculation subunit is used to calculate the second current phase difference and the second transient power of the two ice melting systems at the current moment based on the re-detected output current.

[0193] The third calculation subunit is used to calculate the closed-loop dynamic balance compensation parameters based on the second impedance, the second current phase difference, and the second transient power.

[0194] The output current phase compensation subunit is used to perform dynamic phase compensation on the real-time output current of the two ice-melting systems in the dual-set ice-melting device using the closed-loop dynamic equalization compensation parameters.

[0195] It should be noted that the working process of each module in the collaborative control system of the dual-set ice-melting device based on closed-loop dynamic equilibrium described in the embodiments of the present invention can refer to the working process of the collaborative control method of the dual-set ice-melting device based on closed-loop dynamic equilibrium described in the above embodiments. The technical effect achieved is also the same as that of the collaborative control method of the dual-set ice-melting device based on closed-loop dynamic equilibrium described in the above embodiments, and will not be repeated here.

[0196] 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.

[0197] 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 collaborative control method for two sets of ice-melting devices based on closed-loop dynamic equilibrium, characterized in that, include: The mechanical stress data, torsion angle data, line temperature data, and ice thickness data of the transmission line under uneven ice shedding conditions are detected. Based on the mechanical stress data, the torsion angle data, the line temperature data, and the icing thickness data, the stress state is predicted by a pre-trained first neural network model to obtain the time series data of the stress change of the transmission line. Detect the first impedance timing data of the transmission line under uneven icing shedding conditions; Based on the stress change time series data and the first impedance time series data, the fluctuation range of the electrical parameters of the transmission line is determined; Based on the fluctuation range of the electrical parameters, the predicted value of the output current phase difference between the two ice-melting systems in the dual-set ice-melting device is determined; wherein, the two ice-melting systems are connected in parallel to the transmission line; Based on the predicted output current phase difference and the closed-loop dynamic equalization compensation parameters, dynamic phase compensation is performed on the real-time output current of the two ice-melting systems in the dual-set ice-melting device.

2. The collaborative control method for dual-set ice-melting devices based on closed-loop dynamic equilibrium as described in claim 1, characterized in that, Multiple monitoring points are deployed at predetermined intervals along the transmission line; each monitoring point is equipped with a capacitance sensor, a temperature sensor, a stress sensor, an angle sensor, and a voltage and current sensor. The capacitive sensor is used to monitor the ice thickness at the corresponding monitoring point of the transmission line. The temperature sensor is used to monitor the line temperature at the corresponding monitoring point of the transmission line. The stress sensor is used to monitor the mechanical stress at the corresponding monitoring point of the transmission line; The angle sensor is used to monitor the torsion angle at the corresponding monitoring point of the transmission line; The voltage and current sensors are used to monitor the voltage and current at the corresponding monitoring points of the transmission line.

3. The collaborative control method for dual-set ice-melting devices based on closed-loop dynamic equilibrium as described in claim 2, characterized in that, The data collected on the mechanical stress, torsion angle, line temperature, and ice thickness of the transmission line under uneven icing conditions include: Based on the ice thickness detected by the corresponding capacitive sensors and the mechanical stress detected by the corresponding stress sensors at each monitoring point, the location of the stress abrupt change in uneven ice shedding of the transmission line is determined. Acquire mechanical stress data and torsional angle data at the monitoring point corresponding to the stress abrupt change location; wherein, the mechanical stress data includes mechanical stress within a set time period, and the torsional angle data includes torsional angle within the set time period; The line temperature at the monitoring point corresponding to the stress change location within the set time period is obtained as the line temperature data at the monitoring point corresponding to the stress change location. The ice thickness at the monitoring point corresponding to the stress change location within the set time period is obtained as the ice thickness data at the monitoring point corresponding to the stress change location.

4. The collaborative control method for dual-set ice-melting devices based on closed-loop dynamic equilibrium as described in claim 3, characterized in that, The determination of the stress abrupt change location of uneven icing shedding on the transmission line based on the ice thickness monitored by the corresponding capacitive sensors and the mechanical stress monitored by the corresponding stress sensors at each monitoring point includes: The ice thickness at each monitoring point is compared with the preset thickness threshold. The mechanical stress at each monitoring point is compared with the preset stress threshold. When the ice thickness at any of the monitoring points is greater than the thickness threshold, and the change in mechanical stress at the corresponding monitoring point at two adjacent sampling times is greater than the stress threshold, it is determined that uneven ice shedding has occurred at the corresponding monitoring point, and the corresponding monitoring point is designated as the location of stress abrupt change.

5. The collaborative control method for dual-set ice-melting devices based on closed-loop dynamic equilibrium as described in claim 3, characterized in that, The step of predicting the stress state of the transmission line using a pre-trained first neural network model based on the mechanical stress data, the torsion angle data, the line temperature data, and the icing thickness data, to obtain the time-series data of stress changes in the transmission line, includes: Based on the line temperature data and the icing thickness data, the natural frequency within the set time period is determined through a preset first mapping relationship related to the natural frequency, and used as the natural frequency data at the monitoring point corresponding to the stress change location. The mechanical stress data, torsion angle data, line temperature data, and natural frequency data are time-aligned and interpolated to obtain the corresponding mechanical stress time series data, torsion angle time series data, line temperature time series data, and natural frequency time series data. A multidimensional parameter vector matrix is ​​constructed based on the mechanical stress time series data, the torsion angle time series data, the line temperature time series data, and the natural frequency time series data. The multidimensional parameter vector matrix is ​​input into the first neural network model to predict the stress state, thereby obtaining the time series data of stress change at the stress abrupt change location of the transmission line.

6. The collaborative control method for dual-set ice-melting devices based on closed-loop dynamic equilibrium as described in claim 3, characterized in that, The detection of the first impedance timing data of the transmission line under uneven icing and shedding conditions includes: The voltage and current sensors at the monitoring points corresponding to the stress abrupt change locations are obtained within the set time period, and the voltage and current are monitored by the sensors. Based on the voltage and current monitored at each sampling time within the set time period at the stress mutation location, the first impedance at each sampling time within the set time period at the stress mutation location is calculated to obtain the first impedance time series data.

7. The collaborative control method for dual-set ice-melting devices based on closed-loop dynamic equilibrium as described in claim 6, characterized in that, The step of determining the fluctuation range of the electrical parameters of the transmission line based on the stress change time series data and the first impedance time series data includes: Obtain the output current of the two ice-melting systems in the dual-set ice-melting device within a set time period; Based on the output current of the two ice-melting systems within a set time period, calculate the first output current phase difference of the two ice-melting systems within the set time period, and construct the first current phase difference timing data based on the first output current phase difference of the two ice-melting systems within the set time period. A sliding window is used to detect abrupt phase difference points in the timing data of the first current phase difference; wherein, the absolute value of the difference between the phase difference of the first output current corresponding to the abrupt phase difference point and the phase difference of the first output current corresponding to the previous moment is greater than a preset difference threshold. Calculate the first transient power at each phase difference abrupt change point based on the output current and output voltage at each phase difference abrupt change point. Based on the phase difference of the first output current and the first transient power corresponding to each phase difference abrupt point, the risk of subsynchronous oscillation is predicted by a pre-trained support vector machine model. When the risk of subsynchronous oscillation is predicted, second current phase difference time series data is constructed based on the first output current phase difference at each phase difference abrupt change point; first transient power time series data is constructed based on the first output transient power at each phase difference abrupt change point. Based on the time-series data of force changes, the first impedance time-series data, the second current phase difference time-series data, and the first transient power time-series data, electrical parameter fluctuations are predicted using a pre-trained long short-term memory network model to determine the range of electrical parameter fluctuations for the transmission line; wherein, the range of electrical parameter fluctuations includes the range of current phase difference fluctuations.

8. The collaborative control method for dual-set ice-melting devices based on closed-loop dynamic equilibrium as described in claim 7, characterized in that, The step of dynamically compensating the real-time output current of the two ice-melting systems in the dual-set ice-melting device based on the predicted output current phase difference and the closed-loop dynamic equalization compensation parameters includes: Using the predicted output current phase difference as the target current phase difference, the phase difference of the real-time output current of the two ice-melting systems in the dual-set ice-melting device is adjusted. The voltage and current at the monitoring point corresponding to the stress change location in the transmission line are re-detected at the current moment; Re-detect the output current of the two ice-melting systems at the current moment; Based on the voltage and current corresponding to the re-detected stress change location and the output current corresponding to the two ice-melting systems, dynamic phase compensation is dynamically performed on the real-time output current of the two ice-melting systems in the dual ice-melting device.

9. The collaborative control method for dual-set ice-melting devices based on closed-loop dynamic equilibrium as described in claim 8, characterized in that, The step of dynamically compensating the real-time output current of the two ice-melting systems in the dual-set ice-melting device based on the voltage and current corresponding to the re-detected stress change location and the re-detected output current of the two ice-melting systems includes: Calculate the second impedance at the current moment based on the detected voltage and current; Based on the re-detected output current, calculate the second current phase difference and the second transient power of the two ice-melting systems at the current moment; Calculate the closed-loop dynamic balance compensation parameters based on the second impedance, the second current phase difference, and the second transient power. The closed-loop dynamic equalization compensation parameters are used to perform dynamic phase compensation on the real-time output current of the two ice-melting systems in the dual-set ice-melting device.

10. A collaborative control system for two sets of ice-melting devices based on closed-loop dynamic equilibrium, characterized in that, include: The first data detection module is used to detect the mechanical stress data, torsion angle data, line temperature data, and ice thickness data of the transmission line under uneven ice shedding conditions. The stress prediction module is used to predict the stress state of the transmission line based on the mechanical stress data, the torsion angle data, the line temperature data, and the icing thickness data, using a pre-trained first neural network model, and to obtain the time series data of the stress change of the transmission line. The second data detection module is used to detect the first impedance timing data of the transmission line under the condition of uneven icing and shedding. An electrical parameter fluctuation determination module is used to determine the fluctuation range of electrical parameters of the transmission line based on the stress change time series data and the first impedance time series data. The current phase difference determination module is used to determine the predicted value of the output current phase difference between the two ice-melting systems in the dual-set ice-melting device based on the fluctuation range of the electrical parameters; wherein the two ice-melting systems are connected in parallel to the transmission line; The current adjustment module is used to perform dynamic phase compensation on the real-time output current of the two ice-melting systems in the dual-set ice-melting device based on the predicted output current phase difference and the closed-loop dynamic balance compensation parameters.

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