DC deicing monitoring and early warning method and system

Abstract

The invention discloses a DC deicing monitoring and early warning method and system, and the method comprises the steps: collecting line parameters of DC deicing operation, reference parameters of equipment on a line, and real-time environment parameters around the line, and determining a line deicing target of the DC deicing operation, calculating an electrical reference parameter of the line and obtaining and correcting an ice melting demand reference parameter; acquiring actual electrical parameters of the line, actual ice melting thickness parameters of the line and actual equipment parameters in real time; comparing the line electrical reference parameter with the line electrical actual parameter, the ice melting demand reference parameter with the line ice melting thickness actual parameter, and the equipment reference parameter with the equipment actual parameter in real time, and judging whether various faults exist or not and the fault levels; and generating early warning information according to a judgment result, matching a corresponding preset remote control strategy, and completing remote control according to the corresponding preset remote control strategy. According to the invention, online monitoring of the ice melting process can be realized, the fault response speed is improved, and the accuracy and safety of ice melting operation are enhanced.

Description

Technical Field

[0001] The present application relates to the technical field of monitoring and early warning of DC ice melting operations in power systems, and in particular to a DC ice melting monitoring and early warning method and system. Background Art

[0002] DC de-icing technology plays a crucial role in ensuring the safe and stable operation of power grids. With the development of power systems, ice accumulation on power grid lines has become a frequent problem, particularly in severe winter conditions, severely impacting the normal operation of transmission lines. To address this challenge, the industry is continuously exploring related technologies, striving to improve the efficiency and safety of de-icing operations and reduce the impact of faults such as line disconnections and grounding caused by ice accumulation on power grid operations.

[0003] Currently, the industry typically addresses similar issues through a combination of regular inspections and simple monitoring tools. Specifically, traditional methods include manual on-site inspections, the use of portable measuring instruments to monitor line status, and periodic inspections at fixed intervals to assess ice melting effectiveness. Additionally, some scenarios utilize basic equipment such as thermometers and ammeters for simple monitoring of local parameters. However, these methods lack real-time and systematic effectiveness.

[0004] Considering the widespread inability of existing conventional methods to achieve real-time online monitoring, resulting in slow fault response and difficulty accurately grasping the dynamic changes in the ice-melting process, this lag makes it difficult to promptly detect and address potential faults, thereby affecting ice-melting efficiency and the overall security of the power grid. Therefore, it is necessary to provide a DC ice-melting monitoring and early warning method and system that supports full online monitoring of the ice-melting process, improves fault response speed, and enhances the accuracy and safety of ice-melting operations. Summary of the Invention

[0005] In order to achieve full online monitoring of the ice melting process, improve fault response speed, and enhance the accuracy and safety of ice melting operations, the present application provides a DC ice melting monitoring and early warning method and system.

[0006] In a first aspect, the present application provides a DC ice melting monitoring and early warning method, comprising: Collect line parameters for DC ice-melting operations, determine the line ice-melting targets for DC ice-melting operations, calculate line electrical benchmark parameters, and obtain ice-melting demand benchmark parameters; collect and obtain equipment benchmark parameters deployed on the lines for DC ice-melting operations; collect real-time environmental parameters around the lines for DC ice-melting operations, and modify the line electrical benchmark parameters and DC ice-melting equipment benchmark parameters based on the real-time environmental parameters; Sensors deployed at key nodes and equipment on the DC de-icing line during operation collect actual line electrical parameters, actual line de-icing thickness parameters, and actual equipment parameters in real time. They compare line electrical baseline parameters with actual line electrical parameters, de-icing demand baseline parameters with actual line de-icing thickness parameters, and equipment baseline parameters with actual equipment parameters in real time to determine whether there is an electrical fault, equipment fault, or de-icing fault, as well as the severity of each fault. Based on the acquired electrical faults, equipment faults or ice melting faults and various fault levels, early warning information is generated and matched with the corresponding preset remote control strategy, and remote control is completed according to the corresponding preset remote control strategy; the corresponding preset remote control strategy is obtained by training a remote control strategy acquisition model generated by selecting historical remote control strategies with a fault elimination rate greater than the preset fault elimination rate based on faults of corresponding types and levels.

[0007] By adopting the above solution, dynamically corrected baseline parameters are obtained from multiple angles such as line electrical parameters, ice melting thickness parameters and equipment parameters, and compared with real-time collected parameters, which can accurately determine whether various fault types exist, thereby improving the timeliness, accuracy and comprehensiveness of fault detection; based on the fault type and level, early warning information is generated and matched with the optimized remote control strategy, which effectively improves the pertinence and efficiency of fault handling, ensures the safe and reliable implementation of ice melting operations, and realizes comprehensive real-time monitoring and intelligent early warning of the line status during DC ice melting operations.

[0008] Preferably, it also includes: Combined with a deep learning algorithm, it is determined whether there is a conflict between the acquired electrical faults, equipment faults or ice melting faults, and the corresponding remote control strategies matching the various fault levels; if there is a conflict, the corresponding remote control strategies are executed step by step according to the control strategy priority for remote control; the control strategy priority setting rules include: the remote control strategy for ensuring line safety is the highest priority, the remote control strategy for ensuring equipment safety is the secondary priority, and the remote control strategy for optimizing line and equipment operating parameters is the lowest priority.

[0009] By adopting the above solution, conflicts are detected for remote control strategies matching electrical faults, equipment faults, ice melting faults, and various fault levels. The conflicting control strategies are prioritized and executed step by step, ensuring that various faults can be eliminated in an orderly and efficient manner in complex fault scenarios, thereby ensuring the safety and smooth progress of DC ice melting operations.

[0010] Preferably, it also includes: Obtain the short-circuit capacity of the grid access point where the line for DC ice melting operation is located; Determine and compare whether the de-icing current in the actual electrical parameters of the line undergoing the DC de-icing operation exceeds a preset multiple of the short-circuit capacity of the grid access point where the line undergoing the DC de-icing operation is located, collect the device status of the circuit breakers at both ends of the line undergoing the DC de-icing operation, and determine whether the line undergoing the DC de-icing operation forms a closed loop based on the device status of the circuit breakers at both ends of the line and the line parameters of the DC de-icing operation; If the judgment result is that the short-circuit capacity of the grid access point where the line for DC ice melting operation is located exceeds the preset multiple or a closed loop is not formed, a grid fault warning message is generated accordingly and a corresponding preset remote control strategy is matched.

[0011] By adopting the above solution and determining whether the ice-melting current in the DC ice-melting line exceeds the short-circuit capacity of the grid access point, the grid stability is determined and abnormal grid parameters are discovered in a timely manner. Combined with the monitoring of the grid parameters of the DC ice-melting operation line, the stability and safety of the DC ice-melting operation are more comprehensively guaranteed.

[0012] Preferably, including: Calculate the transmission rate of the line parameters of the DC ice melting operation and the equipment parameters on the line of the DC ice melting operation; Statistically analyze the parameter data and equipment specifications of the lines during historical DC ice-melting operations to obtain communication transmission benchmark data for each line parameter and the equipment parameters on it; compare the transmission rate of the line parameter data during the DC ice-melting operation with the communication transmission benchmark data for the line parameters, and the transmission rate of the equipment parameter data on the lines during the DC ice-melting operation with the communication transmission benchmark data for the equipment parameters on the lines; if any comparison result is lower than the corresponding benchmark data, it is determined that a communication failure exists; Using a hierarchical diagnostic process, device self-test, network link self-test, and multi-device status comparison self-test are sequentially controlled. The source of the communication fault is determined based on the self-test results and an early warning is generated. A corresponding communication fault recovery strategy is matched based on the generated early warning to complete communication fault recovery on the line undergoing DC ice melting operations. The communication fault recovery strategy includes: determining that the communication fault originates from the device, restarting the device or switching to a backup device; determining that the communication fault originates from the network link, switching to a backup communication link.

[0013] By adopting the above solution, the transmission rate of line parameter data and equipment parameter data during DC ice-melting operations is monitored in real time and compared with the communication transmission benchmark data, so as to accurately determine whether there is a communication failure. Once a communication failure is found, the fault source can be located through a layered diagnostic process to ensure the comprehensiveness of the fault investigation and match the corresponding communication fault recovery strategy, effectively improving the communication reliability of the system and ensuring the continuity and stability of the DC ice-melting operation.

[0014] Preferably, it also includes: Constructing a DC ice-melting digital twin model involves: building a 3D geometric and electrically equivalent physical layer model based on the DC ice-melting line parameters, line equipment parameters, and grid parameters; building an environmental layer model based on the line's surrounding real-time environmental parameters and icing characteristic parameters; and building a control layer model by integrating the line equipment control logic and mapping the response characteristics of remote control commands. The model also imports a baseline parameter set based on historical ice-melting data, including line electrical baseline parameters, ice-melting demand baseline parameters, and equipment baseline parameters. The DC ice-melting digital twin model is driven to run based on the real-time collected line electrical actual parameters, line ice-melting thickness actual parameters, and equipment actual parameters, completing data mapping and calibration of the DC ice-melting digital twin model. The DC ice-melting digital twin model is used to simulate and predict the operational data of the line during DC ice-melting operations at a preset future time, including line electrical parameters, line ice-melting thickness parameters, and equipment parameters. A fault identification model built based on a deep learning algorithm is used to obtain the predicted fault type and fault level of the line during DC ice-melting operations at a preset future time. Match the corresponding preset remote control strategy, and perform simulated control in the DC ice melting digital twin model according to the corresponding preset remote control strategy; verify the accuracy of matching the corresponding preset remote control strategy by obtaining the fault elimination status of the DC ice melting operation line in the DC ice melting digital twin model after simulated control, and execute the matching of the corresponding preset remote control strategy after the verification is passed, otherwise optimize the matching of the corresponding preset remote control strategy.

[0015] By adopting the above solution, building a DC ice-melting digital twin model and conducting simulation prediction and control verification, it is possible to predict in advance the type and level of faults that may occur in the DC ice-melting operation line at a preset time in the future, and achieve accurate early warning of potential problems.

[0016] Preferably, it also includes: For the GPS module embedded in each sensor deployed at the key nodes and equipment side of the DC ice melting operation line, whenever an electrical fault, equipment fault or ice melting fault is detected, the key nodes and equipment side of the DC ice melting operation line corresponding to the fault are locked, and drone devices are assigned to collect fault images of the key nodes and equipment side of the DC ice melting operation line. Through image analysis technology, the preset fault conditions of the key nodes and equipment side of the DC ice melting operation line are continuously obtained to verify the preset fault judgment results and determine the subsequent remote control effect.

[0017] By adopting the above solution, when it is determined that there is an electrical fault, equipment fault or ice melting fault, the GPS module embedded in the sensor is used to accurately locate the location of the fault, and the fault image is collected by drone. Combined with image analysis technology, the fault details are continuously obtained, which improves the accuracy of fault location and provides an intuitive verification method for subsequent remote control effects, thereby improving fault handling efficiency and system operation reliability.

[0018] Preferably, it also includes: Collect ice coating characteristic parameters of the DC ice melting operation line, and correct the line electrical reference parameters and DC ice melting equipment reference parameters according to the ice coating characteristic parameters; the ice coating characteristic parameters include: ice coating type.

[0019] By adopting the above solution, the characteristic parameters of ice coverage are analyzed and identified, and the line electrical reference parameters and DC ice-melting equipment reference parameters are further dynamically corrected to more accurately adapt to the ice-melting operation requirements under different environmental conditions, improve the accuracy of early warning monitoring, and enhance the safety and efficiency of the ice-melting process.

[0020] In a second aspect, the present application provides a DC ice melting monitoring and early warning system, comprising: The DC de-icing benchmark parameter acquisition module is used to collect line parameters for DC de-icing operations, determine the line de-icing targets for DC de-icing operations, calculate line electrical benchmark parameters, and obtain de-icing demand benchmark parameters; it also collects and obtains benchmark parameters for equipment deployed on the lines for DC de-icing operations; The DC de-icing reference parameter correction module is used to collect real-time environmental parameters around the DC de-icing operation line and correct the line electrical reference parameters and DC de-icing equipment reference parameters based on the real-time environmental parameters; The DC de-icing actual parameter acquisition module is used to use sensors deployed at key nodes and equipment on the line during DC de-icing operations to collect real-time line electrical parameters, line de-icing thickness parameters, and equipment parameters. The DC ice-melting fault judgment module is used to compare the line electrical reference parameters with the line electrical actual parameters, the ice-melting demand reference parameters with the line ice-melting thickness actual parameters, and the equipment reference parameters with the equipment actual parameters in real time to determine whether there is an electrical fault, equipment fault, or ice-melting fault, as well as the fault level. The DC ice melting fault control module is used to generate early warning information based on the acquired electrical faults, equipment faults or ice melting faults and various fault levels, and match the corresponding preset remote control strategy, and complete remote control according to the corresponding preset remote control strategy; the corresponding preset remote control strategy is obtained by training a remote control strategy acquisition model generated by selecting historical remote control strategies with a fault elimination rate greater than the preset fault elimination rate based on faults of corresponding types and levels.

[0021] By adopting the above solution, comprehensive monitoring and intelligent control of the line and equipment status during DC ice melting operations can be achieved.

[0022] In a third aspect, the present application provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the method as described above.

[0023] In a fourth aspect, the present application provides a computer device, which includes a memory, a processor, and a program stored and executable on the memory, and the program implements the steps of the above method when executed by the processor.

[0024] In summary, this application has the following beneficial effects: 1. By using sensors to collect line electrical parameters, ice melting thickness parameters, and equipment parameters in real time and comparing them with dynamically corrected baseline parameters in real time, the system can comprehensively and quickly determine the type and level of faults, enabling visual monitoring of the entire ice melting process and significantly improving fault response speed. Based on the fault type and level, early warning information is generated and matched with optimized remote control strategies. Through remote control, electrical faults, equipment faults, and ice melting faults are eliminated, improving the accuracy and safety of ice melting operations. 2. By obtaining the grid parameter benchmark values of the DC de-icing line where the operation is located and determining whether the line electrical benchmark parameters associated with the grid parameters exceed the grid parameter benchmark values, grid parameter anomalies can be discovered in a timely manner. This enables comprehensive monitoring of the grid parameters of the DC de-icing line where the operation is located, further improving the accuracy and safety of de-icing operations. 3. Real-time monitoring of the transmission rate of line parameter data and equipment parameter data during DC ice-melting operations, and comparison with communication transmission benchmark data, to accurately determine whether there is a communication failure; introducing a communication fault self-detection and recovery mechanism to reduce overall monitoring failures caused by communication interruptions and improve the accuracy and safety of ice-melting operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 Flowchart of the DC ice melting monitoring and early warning method described in a specific embodiment; Figure 2 It is a structural diagram of the DC ice melting monitoring and early warning system described in a specific embodiment. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0027] like Figure 1 As shown, the embodiment of the present application discloses a DC ice melting monitoring and early warning method, and the specific steps are as follows.

[0028] S1. Determination of benchmark values for DC ice melting operations.

[0029] Specifically, in order to achieve online monitoring of the entire process of DC ice melting operations, it is necessary to predetermine the baseline values of the DC ice melting operations so that abnormal conditions can be subsequently determined based on the baseline values; and for more comprehensive and accurate monitoring, the baseline values of the operation data are determined from multiple aspects, such as the key nodes of the DC ice melting operation line and the equipment on the DC ice melting operation line (such as: DC ice melting operation equipment), to assist in real-time monitoring of multiple aspects of operation data.

[0030] Considering that different lines and different ice melting requirements are both benchmark numerical influencing factors for DC ice melting operations, the line parameters of DC ice melting operations are collected, including: the material (aluminum / steel core aluminum stranded wire, etc.), cross-sectional area, length, allowable current carrying capacity, DC resistance, etc. of the DC ice melting operation line; and the line ice melting target of the DC ice melting operation is determined, such as: the target ice melting time and ice melting rate to melt the preset ice thickness.

[0031] Based on the collected DC ice melting operation line parameters, the DC ice melting operation line electrical benchmark parameters are calculated. The specific calculation formula includes: calculating the basic current value based on the heat balance formula required for conductor ice melting, the formula is: Where, P 冰 is the latent heat of ice melting, L is the length of the line conductor, δ is the ice thickness, R dc is the DC resistance of the line conductor, t is the target melting time for melting the preset ice thickness, and is set to not exceed the smaller value of 90% of the rated current of the ice melting device and the maximum allowable temperature rise current of the line; according to Ohm's law, the voltage reference value is the product of the current and the total line resistance, the formula is: V = I × R total , where R total The contact resistance of the joint is usually 5%-10% of the DC resistance of the line conductor and is set to no more than 95% of the rated current of the ice melting device; the formula for the capacity reference value is: P = I 2 ×R total , and the setting should not exceed 95% of the rated capacity of the ice melting device; the impedance reference parameter can be calculated based on the conductor material (the line impedance under DC is mainly the conductor resistance, and the inductive reactance is ignored). The formula is: Where ρ is the resistivity of the conductor material and A is the cross-sectional area of the conductor.

[0032] The ice melting requirement benchmark parameters are obtained based on the line ice melting target of the DC ice melting operation, such as the target ice melting time and ice melting rate for melting the preset ice thickness.

[0033] Collect and obtain baseline parameters of equipment deployed on the DC de-icing lines, including but not limited to the de-icing device's rated capacity, maximum output current, voltage regulation range, rectification / inversion method (such as LCC or MMC), and cooling system rated parameters (oil / water temperature thresholds). This data can be determined by directly reading the values calibrated on the de-icing equipment's nameplate.

[0034] In addition, the line conductor deformation benchmark value can be determined based on the line conductor deformation data during the normal operation of the DC ice melting operation line.

[0035] S2. Dynamic correction of the benchmark value of DC ice melting operation.

[0036] Considering that environmental factors are important factors affecting the parameters of DC ice melting operation, real-time environmental parameters around the DC ice melting operation line are collected, and the line electrical reference parameters and equipment reference parameters are corrected according to the real-time environmental parameters.

[0037] Specifically, the line electrical benchmark parameters are corrected according to real-time environmental parameters, which include meteorological data (temperature, humidity, wind speed, wind direction, and ice thickness), geographic environment data (altitude, tower structure), etc.; a deep neural network algorithm is used to obtain the correction values of the line electrical benchmark parameters and equipment benchmark parameters under different environmental parameters, such as: low temperature correction, for every 5 degrees Celsius decrease in ambient temperature, the current benchmark number increases by 3%-5%; wind speed correction, for every 2m / s increase in wind speed, the current increases by 8%-10%.

[0038] In addition, you can also select the equipment's operating life and correct the equipment's baseline value based on the equipment's operating life. You can use a deep learning algorithm to learn the equipment's operating life and the benchmark value of the corrected equipment based on expert experience to train a neural network to obtain it. For example, when the equipment's operating life exceeds 5 years, the output capacity will decay by 1%-2% each year.

[0039] Furthermore, considering different ice cover characteristic parameters as factors influencing ice melting progress, ice cover characteristic parameters of the DC ice melting line are collected and used to correct the line electrical baseline parameters and the DC ice melting equipment baseline parameters. These ice cover characteristic parameters include ice cover type. Specifically, rime ice, due to its strong conductivity, effectively increases the line surface conductivity, resulting in a decrease in impedance; dry snow ice, due to its strong insulation, increases impedance. Based on this, a mapping relationship between ice cover type and impedance correction factor is established based on experimental data, which is used to correct the impedance baseline value.

[0040] S3. Collect actual parameters of DC ice melting operation.

[0041] Specifically, sensors deployed at key nodes and equipment sides of the DC ice melting operation can be used to form a sensor assembly to collect real-time actual electrical parameters of the line, such as current, voltage, capacity, etc., actual parameters of the line ice melting thickness, such as the preset time ice melting thickness and actual equipment parameters, such as oil temperature and water temperature in the cooling system.

[0042] In addition, sensors can be used to collect actual line deformation data, including line jitter amplitude, line bending value, etc.

[0043] S4. Compare the dynamically corrected DC ice melting operation baseline value with the collected actual parameters of the DC ice melting operation, and determine whether there is a fault and the type and level of the fault based on the comparison result.

[0044] Real-time comparison is performed between the line electrical benchmark parameters and the line electrical actual parameters, the ice melting demand benchmark parameters and the line ice melting thickness actual parameters, and the equipment benchmark parameters and the equipment actual parameters to determine whether there is an electrical fault, equipment fault or ice melting fault and the level of each fault.

[0045] Specifically, when the error between the actual electrical parameters of the line and the electrical reference parameters of the line is greater than the preset first error, it is determined that an electrical fault exists; wherein, the preset first error can be multiple levels, corresponding to different fault levels; for different actual electrical parameters, when compared with the electrical reference parameters of the corresponding type of line, a fault exists, and a corresponding electrical fault of that type is generated, and a preset first error of the corresponding range is set, such as: with a DC current ±5% reference value, when the comparison result has a 5%-10% deviation, a current fault level 1 is generated, a 10%-15% deviation, a current fault level 2 is generated, and a deviation greater than 15% is generated, a current fault level 3 is generated.

[0046] Similarly, when the error between the ice melting demand benchmark parameter and the actual ice melting thickness parameter of the line is greater than a preset second error, an ice melting fault is determined to exist; wherein, the preset second error can be multiple levels, corresponding to different fault levels; for example, the preset time ice melting thickness error of ±5mm is the benchmark value; when the preset time ice melting thickness is between 5-10mm, the first level of ice melting fault is generated; when the preset time ice melting thickness is between 10-15mm, the second level of ice melting fault is generated; when the preset time ice melting thickness is greater than 15mm, the third level of ice melting fault is generated.

[0047] Similarly, when the error between the equipment reference parameter and the equipment actual parameter is greater than a preset third error, it is determined that an equipment fault exists; wherein, the preset third error can be multiple levels, corresponding to different fault levels; for different equipment actual parameters, when a fault exists when compared with the corresponding type of equipment reference parameter, a corresponding equipment fault of that type is generated, and a preset third error is set within a corresponding range. For example: taking the converter oil temperature in the DC de-icing equipment as the reference value of ±1 degree Celsius, when the comparison result has a deviation of ±3 degrees Celsius, a first-level converter fault in the DC de-icing equipment is generated; if there is a deviation of ±5 degrees Celsius, a third-level converter fault in the DC de-icing equipment is generated; if there is a deviation greater than ±5 degrees Celsius, a third-level converter fault in the DC de-icing equipment is generated.

[0048] In addition, to further ensure the accuracy of fault diagnosis, a GPS module is embedded in each sensor deployed at the key nodes and equipment side of the DC de-icing operation line. Whenever an electrical fault, equipment fault or de-icing fault is determined to exist, the key nodes and equipment side of the DC de-icing operation line corresponding to the fault are locked, and drone devices are assigned to collect fault images, including infrared images, of the key nodes and equipment side of the DC de-icing operation line. Image analysis technology is used to continuously obtain the preset fault conditions of the key nodes and equipment side of the DC de-icing operation line to verify whether the preset fault judgment results are correct, such as verifying the overtemperature fault of the DC de-icing equipment and judging the subsequent remote control effect, such as: the overtemperature fault of the DC de-icing equipment disappears after remote control.

[0049] In addition, by comparing the line conductor deformation reference value with the line conductor deformation actual value, and depending on whether the error between the two exceeds the preset deformation error, it is determined whether a line conductor jitter fault or a line conductor deformation fault exists, as well as the corresponding fault level.

[0050] S5. Match the corresponding preset remote control strategy according to the determined fault type and level, and complete remote control to eliminate the fault.

[0051] Early warning information is generated based on the acquired electrical faults, equipment faults or ice melting faults and various fault levels, that is, early warning information including fault types and fault levels.

[0052] The generated warning information is matched with a corresponding preset remote control strategy, such as a first-level warning information for a line current fault, and a remote control strategy that matches the first-level warning information for the current fault. The corresponding preset remote control strategy is obtained by training a remote control strategy acquisition model generated by selecting historical remote control strategies with a fault elimination rate greater than a preset fault elimination rate for faults of corresponding types and levels.

[0053] For example, line electrical faults such as the third level voltage fault / third level current fault, and third level impedance fault may be serious faults caused by short circuit, grounding or poor contact, and thus match the preset remote control strategy, including: immediately cutting off the ice melting power supply, remotely closing the fast grounding switches on both sides of the line, decoupling the faulty line from the power grid, and sending a power outage maintenance request to the dispatching system.

[0054] Complete remote control according to the corresponding preset remote control strategy.

[0055] In a specific embodiment, considering that a large number of different types of faults exist in a line and that different types of faults conflict with the corresponding remote control strategies for fault elimination, how to ensure the normal operation of a DC de-icing operation is discussed. The method further includes: after matching a preset remote control strategy based on the determined fault type and level, and before completing remote control to eliminate the fault, the method further includes: Incorporating a deep learning algorithm, it determines whether there is a conflict between the acquired electrical faults, equipment faults, or ice-melting faults, and the remote control strategies corresponding to each fault level. Specifically, a deep learning algorithm is used to set up a conflict judgment neural network. This training is performed by acquiring historically acquired electrical faults, equipment faults, or ice-melting faults, and the remote control strategies corresponding to each fault level, along with corresponding annotations of whether there is a conflict and the conflict strategy based on expert judgments based on actual operating results. The currently acquired fault type and level are matched to the corresponding preset remote control strategy and inputted into the conflict judgment neural network to determine whether there is a conflict and the specific conflicting remote control strategy.

[0056] If there is a conflict, the corresponding remote control strategy will be executed step by step according to the control strategy priority for remote control; wherein, the control strategy priority setting rules include: the remote control strategy to ensure line safety is the highest priority, the remote control strategy to ensure the safety of equipment and devices is the secondary priority, and the remote control strategy to optimize the line and equipment operating parameters is the lowest priority.

[0057] For example, when a line short circuit / ground fault is triggered, protection shutdown → fault isolation → status verification (remote control strategy to ensure line safety) is executed first, and other control strategies (such as power increase) are temporarily suspended. If a device fails and requires derating, the de-icing current of non-critical lines is reduced (to ensure the safety of equipment), and other control strategies (such as power increase) are temporarily suspended.

[0058] In a specific embodiment, in order to further improve the comprehensiveness, accuracy and safety of DC de-icing operation supervision and early warning, the above also includes: Obtain the short-circuit capacity of the grid access point where the line for DC ice melting operation is located; Determine and compare the actual electrical parameters of the DC de-icing line to determine whether the de-icing current exceeds a preset multiple of the short-circuit capacity of the grid access point where the DC de-icing line is located; for example, the de-icing current must be less than 30% of the short-circuit capacity of the access point. The device status of the circuit breakers at both ends of the DC de-icing line is collected, and based on the device status of the circuit breakers at both ends of the line and the line parameters (line topology) of the DC de-icing operation, determine whether the DC de-icing line forms a closed loop. For example, a 500kV line must form a de-icing loop through an in-station grounding switch. The short-circuit capacity of the grid access point where the DC de-icing line is located can be calculated and updated based on the original short-circuit capacity (the product of the grid-side voltage and the effective value of the three-phase short-circuit current) and the newly added impedance (the impedance of the newly added DC de-icing device).

[0059] If the judgment result is that the short-circuit capacity of the grid access point where the line for DC ice melting operation is located exceeds the preset multiple, or a closed loop is not formed, a grid fault warning message is generated accordingly, and the corresponding preset remote control strategy is matched. For example, in response to abnormal grid voltage / frequency faults, the input parameters of the ice melting device are dynamically adjusted; in response to grid short-circuit faults, the device is remotely triggered to shut down urgently; or in response to excessive grid harmonics or resonance faults, the AC filter matched with the ice melting device is remotely activated.

[0060] In a specific embodiment, in order to further improve the timeliness and accuracy of DC de-icing operation supervision and early warning, the method further includes: The transmission rate of the line parameters of the DC ice melting operation and the equipment parameters on the line of the DC ice melting operation is calculated, that is, the data volume of the line parameters of the DC ice melting operation and the data volume of the equipment parameters on the line of the DC ice melting operation per unit time are obtained.

[0061] Statistically analyze the parameter data and equipment specifications of the lines in historical DC ice melting operations to obtain the communication transmission benchmark data of each line parameter and the equipment parameters on it; compare the transmission rate of the line parameter data of the DC ice melting operation with the communication transmission benchmark data of the line parameters, and the transmission rate of the equipment parameter data on the line of the DC ice melting operation with the communication transmission benchmark data of the equipment parameters on the line. If any comparison result is lower than the corresponding benchmark data, it is determined that a communication failure exists.

[0062] Utilizing a hierarchical diagnostic process, the system sequentially controls device self-inspection, network link self-inspection, and multi-device status comparison self-inspection. Based on the self-inspection results, the source of the communication fault is determined and an early warning is generated. Terminal self-inspection involves remotely triggering the self-inspection procedures of the ice-melting device and monitoring terminal to determine whether a communication fault exists. Network link self-inspection involves checking VPN channels and data middleware interfaces for authentication failures, insufficient bandwidth, and other issues to verify whether a network link fault exists. Multi-device status comparison self-inspection involves comparing the communication status of multiple devices. If only a single device fails, it is considered an equipment problem. However, if multiple devices in an area are collectively disconnected, it is considered a base station / aggregation node failure.

[0063] According to the generated early warning prompt, the corresponding communication fault recovery strategy is matched to complete the communication fault recovery of the line of the DC ice melting operation; the communication fault recovery strategy includes: determining that the communication fault originates from the equipment, restarting the equipment or switching to the backup equipment; determining that the communication fault originates from the network link, switching to the backup communication link; determining that the base station / aggregation node fails, switching to the backup base station / aggregation node.

[0064] In a specific embodiment, in order to further improve the timeliness and accuracy of DC de-icing operation supervision and early warning, the method further includes: Construct a digital twin model of DC ice melting; including: constructing a three-dimensional geometric and electrically equivalent physical layer model based on the line parameters of the DC ice melting operation, the parameters of the equipment on the line, and the parameters of the power grid where the line is located; constructing an environmental layer model based on the real-time environmental parameters and icing characteristic parameters of the line's surroundings; and constructing a control layer model by integrating the line equipment control logic and mapping the response characteristics of remote control commands; importing a benchmark parameter set based on historical ice melting data, which includes line electrical benchmark parameters, ice melting demand benchmark parameters, and equipment benchmark parameters.

[0065] The DC ice melting digital twin model is driven to operate based on the real-time collected line electrical actual parameters, line ice melting thickness actual parameters and equipment actual parameters, completing the data mapping and calibration of the DC ice melting digital twin model; in addition, the twin model parameters can be dynamically corrected by adopting the Kalman filter algorithm.

[0066] The DC ice-melting digital twin model is used to simulate and predict the operating data of the line undergoing DC ice-melting operations at a preset future time, including line electrical parameters, line ice-melting thickness parameters, and equipment parameters. A fault identification model built based on a deep learning algorithm is used to obtain the predicted fault type and fault level of the line undergoing DC ice-melting operations at a preset future time. The fault identification model is generated by training using the operating data of the line undergoing historical DC ice-melting operations as well as the annotated fault types and fault levels.

[0067] Match the corresponding preset remote control strategy, and perform simulated control in the DC ice melting digital twin model according to the corresponding preset remote control strategy; verify the accuracy of matching the corresponding preset remote control strategy by obtaining the fault elimination status of the DC ice melting operation line in the DC ice melting digital twin model after simulated control (whether the fault efficiency is successful or not), and execute the matching of the corresponding preset remote control strategy after the verification is passed, otherwise optimize the matching of the corresponding preset remote control strategy, and the optimized matching of the corresponding preset remote control strategy is achieved through incremental learning.

[0068] like Figure 2 As shown, the embodiment of the present application discloses a DC ice melting monitoring and early warning system, which specifically includes: The DC de-icing benchmark parameter acquisition module 101 is used to collect line parameters for the DC de-icing operation, determine the de-icing target of the line for the DC de-icing operation, calculate the line electrical benchmark parameters and obtain the de-icing demand benchmark parameters; and collect the benchmark parameters of the equipment deployed on the line for the DC de-icing operation; The DC de-icing reference parameter correction module 102 is used to collect real-time environmental parameters around the DC de-icing operation line and correct the line electrical reference parameters and the DC de-icing equipment reference parameters according to the real-time environmental parameters; The DC de-icing actual parameter acquisition module 103 is used to use sensors deployed at key nodes and equipment sides of the DC de-icing operation line to respectively collect the actual electrical parameters of the line, the actual parameters of the line de-icing thickness, and the actual parameters of the equipment in real time; The DC ice melting fault judgment module 104 is used to compare in real time the line electrical reference parameters with the line electrical actual parameters, the ice melting demand reference parameters with the line ice melting thickness actual parameters, and the equipment reference parameters with the equipment actual parameters to determine whether there is an electrical fault, equipment fault, or ice melting fault, and the level of each fault; The DC ice melting fault control module 105 is used to generate early warning information based on the acquired electrical faults, equipment faults or ice melting faults and various fault levels, match the corresponding preset remote control strategy, and complete remote control according to the corresponding preset remote control strategy; the corresponding preset remote control strategy is obtained by selecting a remote control strategy acquisition model generated by training historical remote control strategies with a fault elimination rate greater than a preset fault elimination rate based on faults of corresponding types and levels.

[0069] In a specific embodiment, the DC ice melting fault control module 105 in the system is further used to combine a deep learning algorithm to determine whether there is a conflict between the acquired electrical faults, equipment faults or ice melting faults, and the remote control strategies corresponding to the fault levels of various types; if there is a conflict, the corresponding remote control strategies are executed step by step according to the control strategy priority to perform remote control; the control strategy priority setting rules include: the remote control strategy for ensuring line safety is the highest priority, the remote control strategy for ensuring equipment safety is the secondary priority, and the remote control strategy for optimizing line and equipment operating parameters is the lowest priority.

[0070] In a specific embodiment, the DC ice melting reference parameter acquisition module 101 in the system is further configured to acquire a reference value of a parameter of a power grid where the line in the DC ice melting operation is located; The DC ice-melting fault judgment module 104 is further configured to determine and compare whether actual electrical parameters of a line associated with a power grid in which the line undergoing the DC ice-melting operation is located meet preset multiples of a grid parameter reference value, collect the device status of the circuit breakers at both ends of the line undergoing the DC ice-melting operation, and determine whether the line undergoing the DC ice-melting operation forms a closed loop based on the device status of the circuit breakers at both ends of the line and the line parameters of the DC ice-melting operation. If the judgment result is that the grid parameter reference data is exceeded or a closed loop is not formed, a corresponding grid fault warning message is generated and a corresponding preset remote control strategy is matched.

[0071] In a specific embodiment, the DC ice melting actual parameter acquisition module 103 in the system is further configured to calculate the transmission rate of the line parameters of the DC ice melting operation and the parameters of the equipment on the line of the DC ice melting operation; The DC de-icing benchmark parameter acquisition module 101 is further used to statistically analyze the parameter data and equipment specifications of the lines in historical DC de-icing operations, and obtain communication transmission benchmark data of each line parameter and the equipment parameters thereon; The DC de-icing fault determination module 104 is further configured to compare the transmission rate of the line parameter data of the DC de-icing operation with the communication transmission reference data of the line parameters, and the transmission rate of the equipment parameter data on the line of the DC de-icing operation with the communication transmission reference data of the equipment parameters on the line. If any comparison result is lower than the corresponding reference data, a communication fault is determined to exist. The DC ice-melting fault control module 105 is further configured to utilize a hierarchical diagnostic process to sequentially control device self-tests, network link self-tests, and multi-device status comparison self-tests, determine the source of the communication fault based on the self-test results, and generate an early warning prompt. Based on the generated early warning prompt, a corresponding communication fault recovery strategy is matched to complete communication fault recovery on the line undergoing the DC ice-melting operation. The communication fault recovery strategy includes: determining that the communication fault originates from a device, restarting the device, or switching to a backup device; determining that the communication fault originates from a network link, and switching to a backup communication link.

[0072] In a specific embodiment, the system further includes: a DC ice melting fault prediction and control module 106, configured to construct a DC ice melting digital twin model; drive the DC ice melting digital twin model to operate based on real-time collected line electrical parameters, line ice melting thickness parameters, and equipment actual parameters, and complete data mapping and calibration of the DC ice melting digital twin model; utilize the DC ice melting digital twin model to simulate and predict operational data of the line undergoing DC ice melting operations at a preset future time, including line electrical parameters, line ice melting thickness parameters, and equipment parameters; utilize a fault identification model constructed based on a deep learning algorithm to obtain the predicted fault type and fault level of the line undergoing DC ice melting operations at a preset future time; match a corresponding preset remote control strategy, and perform simulated control in the DC ice melting digital twin model according to the corresponding preset remote control strategy; verify the accuracy of the preset remote control strategy by obtaining the fault elimination status of the DC ice melting operation line in the DC ice melting digital twin model after simulated control, and execute the preset remote control strategy if the verification is successful; otherwise, optimize the preset remote control strategy.

[0073] In a specific embodiment, the DC de-icing fault verification module 107 is configured to embed a GPS module in each sensor deployed at the key nodes and equipment side of the line of the DC de-icing operation. Whenever an electrical fault, equipment fault, or de-icing fault is determined to exist, the module locks the key nodes and equipment side of the line of the DC de-icing operation corresponding to the fault, and allocates drone devices to collect fault images of the key nodes and equipment side of the line of the DC de-icing operation. The module continuously obtains the preset fault conditions of the key nodes and equipment side of the line of the DC de-icing operation through image analysis technology to verify the preset fault judgment results and determine the subsequent remote control effect.

[0074] In a specific embodiment, the DC ice melting reference parameter correction module 102 is further configured to collect ice coating characteristic parameters of the DC ice melting operation line, and correct the line electrical reference parameters and the DC ice melting equipment reference parameters according to the ice coating characteristic parameters.

[0075] The embodiment of the present application also discloses a computer-readable storage medium.

[0076] Specifically, the computer-readable storage medium stores a computer program that can be loaded by a processor and executed such as the above-mentioned DC ice melting monitoring and early warning method. The computer-readable storage medium includes, for example: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0077] The embodiment of the present application also discloses a computer device.

[0078] Specifically, the computer device includes a memory and a processor, and the memory stores a computer program that can be loaded by the processor and execute the above-mentioned DC ice melting monitoring and early warning method.

[0079] The above are all preferred embodiments of the present application and are not intended to limit the scope of protection of this application. Unless otherwise stated, any feature disclosed in this specification (including the abstract and drawings) may be replaced by other equivalent or similar features. In other words, unless otherwise stated, each feature is merely an example of a series of equivalent or similar features.

Claims

1. A DC ice melting monitoring and early warning method, characterized in that: include: Collect line parameters for DC ice melting operations, determine line ice melting targets for DC ice melting operations, calculate line electrical benchmark parameters, and obtain ice melting demand benchmark parameters; Collect and obtain the baseline parameters of equipment deployed on the lines for DC ice melting operations; Collect real-time environmental parameters around the DC ice-melting operation line, and correct the line electrical reference parameters and DC ice-melting equipment reference parameters based on the real-time environmental parameters; Sensors deployed at key nodes and equipment in DC de-icing operations collect real-time line electrical parameters, line de-icing thickness parameters, and equipment parameters. Real-time comparison of line electrical benchmark parameters with actual line electrical parameters, ice melting demand benchmark parameters with actual line ice melting thickness parameters, and equipment benchmark parameters with actual equipment parameters to determine whether there is an electrical fault, equipment fault, or ice melting fault, as well as the level of each fault. Based on the acquired electrical faults, equipment faults or ice melting faults and various fault levels, early warning information is generated and matched with the corresponding preset remote control strategy, and remote control is completed according to the corresponding preset remote control strategy; the corresponding preset remote control strategy is obtained by training a remote control strategy acquisition model generated by selecting historical remote control strategies with a fault elimination rate greater than the preset fault elimination rate based on faults of corresponding types and levels.

2. The DC ice melting monitoring and early warning method according to claim 1 is characterized in that: Also includes: Combined with deep learning algorithms, it determines whether there is a conflict between the acquired electrical faults, equipment faults, or ice melting faults, and the corresponding remote control strategies for each fault level. If there is a conflict, the corresponding remote control strategy will be executed step by step according to the control strategy priority to perform remote control; The control strategy priority setting rules include: the remote control strategy for ensuring line safety is the highest priority, the remote control strategy for ensuring equipment safety is the second priority, and the remote control strategy for optimizing line and equipment operating parameters is the lowest priority.

3. The DC ice melting monitoring and early warning method according to claim 1 is characterized in that: Also includes: Obtain the short-circuit capacity of the grid access point where the line for DC ice melting operation is located; Determine and compare whether the de-icing current in the actual electrical parameters of the line undergoing the DC de-icing operation exceeds a preset multiple of the short-circuit capacity of the grid access point where the line undergoing the DC de-icing operation is located, collect the device status of the circuit breakers at both ends of the line undergoing the DC de-icing operation, and determine whether the line undergoing the DC de-icing operation forms a closed loop based on the device status of the circuit breakers at both ends of the line and the line parameters of the DC de-icing operation; If the judgment result is that the short-circuit capacity of the grid access point where the line for DC ice melting operation is located exceeds the preset multiple or a closed loop is not formed, a grid fault warning message is generated accordingly and a corresponding preset remote control strategy is matched.

4. The DC ice melting monitoring and early warning method according to claim 1 is characterized in that: Also includes: Calculate the transmission rate of the line parameters of the DC ice melting operation and the equipment parameters on the line of the DC ice melting operation; Statistically analyze the parameter data and equipment specifications of the lines during historical DC ice-melting operations to obtain communication transmission benchmark data for each line parameter and the equipment parameters on it; compare the transmission rate of the line parameter data during the DC ice-melting operation with the communication transmission benchmark data for the line parameters, and the transmission rate of the equipment parameter data on the lines during the DC ice-melting operation with the communication transmission benchmark data for the equipment parameters on the lines; if any comparison result is lower than the corresponding benchmark data, it is determined that a communication failure exists; Using a hierarchical diagnostic process, the system sequentially controls device self-test, network link self-test, and multi-device status comparison self-test. Based on the self-test results, the source of the communication fault is determined and an early warning is generated. Match the corresponding communication fault recovery strategy based on the generated early warning prompts to complete the communication fault recovery of the line for DC ice melting operation; The communication failure recovery strategy includes: determining that the communication failure originates from a device, and restarting the device or switching to a backup device; Determine that the communication failure is caused by the network link and switch to the backup communication link.

5. The DC ice melting monitoring and early warning method according to claim 1 is characterized in that: Also includes: Constructing a DC ice-melting digital twin model involves: building a 3D geometric and electrically equivalent physical layer model based on the DC ice-melting line parameters, line equipment parameters, and grid parameters; building an environmental layer model based on the line's surrounding real-time environmental parameters and icing characteristic parameters; and building a control layer model by integrating the line equipment control logic and mapping the response characteristics of remote control commands. The model also imports a baseline parameter set based on historical ice-melting data, including line electrical baseline parameters, ice-melting demand baseline parameters, and equipment baseline parameters. The DC ice-melting digital twin model is driven to run based on the real-time collected line electrical actual parameters, line ice-melting thickness actual parameters, and equipment actual parameters, completing data mapping and calibration of the DC ice-melting digital twin model. The DC ice-melting digital twin model is used to simulate and predict the operational data of the line during DC ice-melting operations at a preset future time, including line electrical parameters, line ice-melting thickness parameters, and equipment parameters. A fault identification model built based on a deep learning algorithm is used to obtain the predicted fault type and fault level of the line during DC ice-melting operations at a preset future time. Match the corresponding preset remote control strategy, and perform simulated control in the DC ice melting digital twin model according to the corresponding preset remote control strategy; verify the accuracy of matching the corresponding preset remote control strategy by obtaining the fault elimination status of the DC ice melting operation line in the DC ice melting digital twin model after simulated control, and execute the matching of the corresponding preset remote control strategy after the verification is passed, otherwise optimize the matching of the corresponding preset remote control strategy.

6. The DC ice melting monitoring and early warning method according to claim 1 is characterized in that: Also includes: For the GPS module embedded in each sensor deployed at the key nodes and equipment side of the DC ice melting operation line, whenever an electrical fault, equipment fault or ice melting fault is detected, the key nodes and equipment side of the DC ice melting operation line corresponding to the fault are locked, and drone devices are assigned to collect fault images of the key nodes and equipment side of the DC ice melting operation line. Through image analysis technology, the preset fault conditions of the key nodes and equipment side of the DC ice melting operation line are continuously obtained to verify the preset fault judgment results and assist in judging the subsequent remote control effect.

7. The DC ice melting monitoring and early warning method according to claim 1 is characterized in that: Also includes: Collect ice coverage characteristic parameters of the DC ice melting operation line, and correct the line electrical reference parameters and DC ice melting equipment reference parameters based on the ice coverage characteristic parameters; The ice coating characteristic parameters include: ice coating type.

8. A DC ice melting monitoring and early warning system, characterized in that: include: A DC ice-melting benchmark parameter acquisition module is used to collect line parameters for DC ice-melting operations, determine line ice-melting targets for DC ice-melting operations, calculate line electrical benchmark parameters, and obtain ice-melting demand benchmark parameters; Collect and obtain the baseline parameters of equipment deployed on the lines for DC ice melting operations; The DC de-icing reference parameter correction module is used to collect real-time environmental parameters around the DC de-icing operation line and correct the line electrical reference parameters and DC de-icing equipment reference parameters based on the real-time environmental parameters; The DC de-icing actual parameter acquisition module is used to collect the actual line electrical parameters, actual line de-icing thickness parameters, and actual equipment parameters in real time using sensors deployed at key line nodes and equipment during DC de-icing operations. The DC ice-melting fault judgment module is used to compare the line electrical reference parameters with the line electrical actual parameters, the ice-melting demand reference parameters with the line ice-melting thickness actual parameters, and the equipment reference parameters with the equipment actual parameters in real time to determine whether there is an electrical fault, equipment fault, or ice-melting fault, as well as the fault level. The DC ice melting fault control module is used to generate early warning information based on the acquired electrical faults, equipment faults or ice melting faults and various fault levels, and match the corresponding preset remote control strategy, and complete remote control according to the corresponding preset remote control strategy; the corresponding preset remote control strategy is obtained by training a remote control strategy acquisition model generated by selecting historical remote control strategies with a fault elimination rate greater than the preset fault elimination rate based on faults of corresponding types and levels.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the method according to any one of claims 1 to 7.

10. A computer device, characterized in that: The computer device includes a memory, a processor, and a program stored and executable on the memory, and when the program is executed by the processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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