A power transmission line icing risk assessment system and an assessment method

Abstract

The present application relates to the technical field of circuit operation and maintenance, and more particularly to a power transmission line icing risk assessment system and method, comprising a data acquisition module, a data collection module, a spectrum analysis module, a data analysis module and a risk assessment module; wherein the data analysis module determines the icing trend of each characteristic time period according to historical icing data and determines the icing characterization tendency in combination with real-time temperature to determine whether to perform icing risk assessment; the risk assessment module controls the data collection module to start data collection, determines whether the preset distribution condition is met according to the frequency distribution, and determines the risk assessment state according to whether the preset distribution condition is met and the power transmission efficiency to determine the subsequent deicing method; the present application effectively monitors and assesses the icing risk, and provides the corresponding deicing method to ensure the safe and stable operation of the power transmission line under the icing condition, improves the stability of the power system, and reduces the adverse effects of icing disasters.

Description

Technical Field

[0001] This invention relates to the field of circuit operation and maintenance technology, and in particular to a transmission line icing risk assessment system and assessment method. Background Technology

[0002] With the continuous development of power systems and the widespread laying of transmission lines, the safe and stable operation of transmission lines is of paramount importance. However, in many regions, especially under specific climatic conditions such as high altitude, cold and humid conditions, transmission lines face a serious threat of icing. Icing not only significantly increases the weight of conductors, causing towers to bear huge vertical and unbalanced loads, leading to structural damage such as tower tilting and collapse, but also changes the electrical properties of conductors, such as reducing the insulation performance of insulator strings and exacerbating corona discharge, thereby generating electromagnetic interference, affecting transmission efficiency and the normal operation of surrounding communication and other equipment.

[0003] Traditional transmission line operation and maintenance methods often lack effective early warning and accurate risk assessment tools when facing icing problems. This makes it difficult to scientifically select appropriate de-icing methods based on the actual icing situation. This can lead to untimely or incomplete de-icing, increasing the probability of power outages and operational risks to the power system, and may also result in unnecessary waste of manpower, resources, and time. Therefore, there is an urgent need for a technical system that can comprehensively and accurately assess the icing risk of transmission lines and rationally guide de-icing operations.

[0004] A prior art method for assessing the icing risk of transmission lines (Chinese patent application number 202410411473.4) is disclosed. This method includes: Step 1: Setting a time period and collecting temperature data of the area where the target transmission line is located at each time point within that time period to form a temperature data set; Step 2: Calculating the probability of ice crystal formation at each time point based on the temperature data set; Step 3: Calculating the ice cover thickness at each time point based on the precipitation data set; Step 4: Calculating the wind speed influence factor at each time point based on the wind speed influence factor; Step 5: Calculating the ice cover risk index at each time point based on the wind speed influence factor; Step 6: Calculating the overall risk assessment for the entire time period based on the ice cover risk index at each time point. This invention improves the accuracy of ice cover risk assessment and enhances decision support. However, this invention has the following problems:

[0005] The invention fails to effectively assess the risk of icing by combining historical icing data with the current actual temperature, nor does it consider the issue of determining an effective de-icing method based on the risk of icing. Summary of the Invention

[0006] To address this, the present invention provides a transmission line icing risk assessment system and method to overcome the problems in existing technologies that fail to effectively assess icing risk by combining historical icing data with current actual temperature, and also fail to consider determining effective de-icing methods based on icing risk. This effectively ensures the safe and stable operation of transmission lines under icing conditions, improves the reliability and stability of the power system, and reduces the adverse effects of icing disasters.

[0007] To achieve the above objectives, in one aspect, the present invention provides a transmission line icing risk assessment system, comprising:

[0008] The data acquisition module includes a data search unit for acquiring historical icing data of transmission lines and a data networking unit for acquiring real-time temperature of the location of the transmission line. The historical icing data includes historical icing time periods and corresponding historical icing thicknesses.

[0009] The data acquisition module includes a spectrum acquisition unit for receiving electromagnetic signals generated by corona discharge and a meter monitoring unit for acquiring power transmission efficiency.

[0010] A spectrum analysis module, which is connected to the spectrum acquisition unit, is used to perform spectrum analysis on the electromagnetic signal to determine the frequency distribution of electromagnetic interference generated by corona discharge.

[0011] The frequency distribution refers to the intensity distribution of electromagnetic waves at different frequencies;

[0012] The data analysis module, which is connected to the data acquisition module, is used to determine the icing trend of each characteristic time period based on historical icing data, and to determine the icing characterization tendency in combination with real-time temperature to determine whether to conduct an icing risk assessment.

[0013] The icing trend includes a severe icing trend and a mild icing trend, and the icing characterization tendency includes an overt icing tendency and a covert icing tendency.

[0014] The risk assessment module is connected to the data acquisition module, the spectrum analysis module, and the data analysis module respectively. It is used to control the data acquisition module to start data acquisition based on whether to conduct an icing risk assessment, determine whether the preset distribution conditions are met based on the frequency distribution, and determine the risk assessment status based on the result of whether the preset distribution conditions are met and the power transmission efficiency to determine the subsequent de-icing method.

[0015] The de-icing methods include natural de-icing, thermal de-icing, and mechanical de-icing.

[0016] As a preferred technical solution for the transmission line icing risk assessment system, the spectrum analysis module performs spectrum analysis on the electromagnetic signal. The spectrum analysis includes windowing and filtering, power spectrum and unit conversion, peak detection, and frequency estimation.

[0017] As a preferred technical solution for the transmission line icing risk assessment system, the data analysis module determines the historical icing time periods as characteristic time periods, and determines whether to merge the corresponding two characteristic time periods based on the time interval between two adjacent characteristic time periods, including...

[0018] If the time interval between two adjacent feature time periods is less than or equal to the preset time interval, then it is determined that the two corresponding feature time periods will be merged into one feature time period.

[0019] If the time interval between two adjacent feature time periods is greater than the preset time interval, then the two corresponding feature time periods will not be merged.

[0020] As a preferred technical solution for the transmission line icing risk assessment system, the data analysis module determines the icing trend for each characteristic time period based on the average icing thickness, including...

[0021] If the average ice thickness during a characteristic time period is greater than the preset ice thickness, then the ice trend during the corresponding characteristic time period is determined to be a severe ice trend.

[0022] If the average icing thickness during a characteristic time period is less than or equal to the preset icing thickness, then the icing trend of the corresponding characteristic time period is determined to be a light icing trend.

[0023] As a preferred technical solution for a transmission line icing risk assessment system, the data analysis module determines the icing tendency based on the current icing trend assessment result and the real-time temperature, including:

[0024] If the time period corresponding to the current time is not the characteristic time period, then the icing characterization tendency is no icing tendency;

[0025] If the current time corresponds to a characteristic time period, then the icing characteristic tendency is determined based on the corresponding icing trend, where,

[0026] If the icing trend is a severe icing trend, then the icing characteristic tendency is determined to be a dominant icing tendency.

[0027] If the icing trend is a mild icing trend, then the icing characterization tendency is determined in conjunction with the real-time temperature, wherein,

[0028] If the real-time temperature is lower than the icing temperature, then the icing tendency is determined to be a dominant icing tendency.

[0029] If the real-time temperature is higher than the icing temperature, then the icing tendency is determined to be a latent icing tendency.

[0030] The icing temperature is determined based on the freezing point of water and the pressure at the location of the power transmission line.

[0031] As a preferred technical solution for the transmission line icing risk assessment system, the data analysis module determines to conduct an icing risk assessment based on the determination result that the icing characteristic tendency is an obvious icing tendency, so that the risk assessment module controls the data acquisition module to start data acquisition.

[0032] As a preferred technical solution for the transmission line icing risk assessment system, the risk assessment module determines whether the preset distribution conditions are met by determining the frequency distribution based on a machine learning model.

[0033] The preset distribution conditions include the electromagnetic interference frequency center value being within a preset range and the intensity on both sides of the frequency center value gradually decreasing.

[0034] As a preferred technical solution for the transmission line icing risk assessment system, the risk assessment module determines the frequency where the maximum frequency intensity is located as the frequency center value.

[0035] As a preferred technical solution for a transmission line icing risk assessment system, the risk assessment module determines the risk assessment status based on whether the preset distribution conditions are met and the transmission efficiency, and then determines the subsequent de-icing method, including:

[0036] If the preset distribution conditions are not met and the power transmission efficiency is greater than or equal to the preset power transmission efficiency, then the risk assessment status is determined to be a low-risk status, and the subsequent de-icing method is determined to be the natural de-icing method.

[0037] If the preset distribution conditions are met or the power transmission efficiency is less than the preset power transmission efficiency, the risk assessment status is determined to be a medium-risk status, and the subsequent de-icing method is determined to be mechanical de-icing.

[0038] If the preset distribution conditions are met and the power transmission efficiency is less than the preset power transmission efficiency, then the risk assessment state is determined to be a high-risk state, and the subsequent de-icing method is determined to be thermal de-icing and mechanical de-icing.

[0039] On the other hand, the present invention also provides a method for assessing the icing risk of transmission lines, comprising:

[0040] Historical icing data of transmission lines are obtained, and characteristic time periods and icing trends of each characteristic time period are determined based on the historical icing data.

[0041] Determine the current icing trend and combine it with real-time temperature to determine the icing characterization tendency to determine whether to conduct an icing risk assessment;

[0042] The data acquisition module is controlled to start data acquisition based on the results of the icing risk assessment.

[0043] Spectrum analysis is performed on the collected electromagnetic signals to determine the frequency distribution of electromagnetic interference generated by corona discharge and to determine whether the preset distribution conditions are met.

[0044] The risk assessment status is determined based on whether the preset distribution conditions are met and the power transmission efficiency.

[0045] The subsequent de-icing method will be determined based on the risk assessment status.

[0046] Compared with existing technologies, the beneficial effects of this invention are as follows: The transmission line icing risk assessment system provided by this invention collects historical icing data and real-time temperature through a comprehensive and accurate data acquisition module, providing a solid foundation for subsequent analysis; it effectively monitors corona discharge electromagnetic interference using a spectrum acquisition and analysis module, ensuring the stability of the transmission line's electromagnetic environment; it accurately judges icing trends and characteristics using a data analysis module, scientifically deciding whether to conduct a risk assessment; and it comprehensively determines the risk assessment status and subsequent de-icing methods by integrating information from various aspects using a risk assessment module. Therefore, it plays an important role in improving the accuracy of icing risk assessment, enhancing the operational stability of transmission lines, reducing the probability of faults, lowering de-icing costs and risks, and ensuring the continuity of power supply, providing strong support for the safe and reliable operation of transmission lines in icing-prone environments.

[0047] In particular, the data analysis module identifies historical icing periods as characteristic time periods and compares the time intervals between adjacent characteristic time periods with preset time intervals to determine whether to merge characteristic time periods. On the one hand, this allows for precise and effective integration of icing periods, avoiding resource waste and ineffective use of computing resources caused by frequent icing cycles within a short period, thus improving system operating efficiency. On the other hand, setting reasonable preset time intervals allows the system to standby at appropriate times to save resources and reduces the number of power on / off cycles, protecting the assessment system. As a result, the entire transmission line icing risk assessment system is significantly optimized in terms of resource utilization, operating cost control, and system stability maintenance. This provides strong support for more efficient and accurate assessment of transmission line icing risks, ensuring the safe and stable operation of transmission lines, reducing the risk of power outages caused by icing, improving the reliability and continuity of power supply, and providing a more scientific and reasonable basis for subsequent de-icing decisions.

[0048] In particular, the data analysis module accurately determines the icing trend based on the comparison between the average icing thickness and the preset icing thickness for each characteristic time period. At the same time, it combines real-time temperature and various environmental factors to clarify the icing characteristics, providing a key and accurate basis for the risk assessment of icing on transmission lines.

[0049] In particular, the risk assessment module scientifically and accurately determines the risk assessment status by comprehensively analyzing the results of the preset distribution conditions and the transmission efficiency, and rationally selects subsequent de-icing methods accordingly. This provides an efficient and targeted solution to the problem of icing on transmission lines, effectively ensuring the safe and stable operation of transmission lines and the electromagnetic compatibility of the surrounding environment. Attached Figure Description

[0050] Figure 1 This is a connection diagram of the transmission line icing risk assessment system according to an embodiment of the present invention;

[0051] Figure 2 This is a flowchart illustrating the workflow of the data analysis module in an embodiment of the present invention.

[0052] Figure 3 This is a flowchart illustrating the workflow of the risk assessment module in an embodiment of the present invention.

[0053] Figure 4 This is a flowchart illustrating the steps of the transmission line icing risk assessment method according to an embodiment of the present invention. Detailed Implementation

[0054] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0055] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0056] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0057] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0058] Please see Figure 1 The diagram shown is a connection diagram of the transmission line icing risk assessment system according to an embodiment of the present invention. An embodiment of the present invention provides a transmission line icing risk assessment system, comprising:

[0059] The data acquisition module includes a data search unit for acquiring historical icing data of transmission lines and a data networking unit for acquiring real-time temperature of the location of the transmission line. The historical icing data includes historical icing time periods and corresponding historical icing thicknesses.

[0060] The data acquisition module includes a spectrum acquisition unit for receiving electromagnetic signals generated by corona discharge and a meter monitoring unit for acquiring power transmission efficiency.

[0061] A spectrum analysis module, which is connected to the spectrum acquisition unit, is used to perform spectrum analysis on the electromagnetic signal to determine the frequency distribution of electromagnetic interference generated by corona discharge.

[0062] In detail, based on the above embodiments, the spectrum analysis module and the spectrum acquisition unit can be a spectrum analyzer in the prior art: the spectrum acquisition unit is the receiving antenna of the spectrum analyzer, and the spectrum analysis module is other parts of the spectrum analyzer besides the receiving antenna; it can be understood that the spectrum analyzer is used to analyze the spectrum of electromagnetic waves generated by corona discharge, and the receiving antenna of the spectrum analyzer is placed in a suitable position around the transmission line to receive the electromagnetic signals generated by corona discharge. The spectrum analyzer can display the frequency components and amplitude of the signal, thereby understanding the frequency distribution of electromagnetic interference generated by corona discharge;

[0063] In practice, (1) the spectrum acquisition unit (receiving antenna) is placed at a certain horizontal distance from the edge of the line. Generally, it is placed at a certain distance from the outside of the projection of the conductor on the side of the transmission line, and this distance is determined according to the transmission line of different voltage levels: ① For 110kV transmission lines, it may be placed 10m to 20m from the outside of the projection of the conductor on the side; for 500kV and above ultra-high voltage transmission lines, this distance may reach about 30m to 50m; this is because the intensity of electromagnetic interference generated by corona discharge decreases with the increase of distance, and safety factors and the influence of other interference sources must be considered; (2) The height of the spectrum acquisition unit (receiving antenna) is usually matched with the height of the transmission line conductor. If the antenna is placed too low, ground reflection and other factors will interfere with the reception of electromagnetic signals; if If placed too high, it may be interfered with by other high-altitude electromagnetic sources (such as communication base station antennas, aviation navigation signals, etc.); generally, the antenna is placed at a height of about 2m to 5m above the ground (in the case of flat ground) so that it is in a relatively pure electromagnetic environment to receive the signal generated by the corona discharge of the transmission line; (3) Since the wind direction will affect the distribution of corona discharge products (such as ions), the wind direction should be considered when placing the antenna; in one implementation, placing the antenna on the downwind side of the prevailing wind may receive a stronger electromagnetic signal. At the same time, for areas with complex terrain, such as mountains or places near large buildings, the antenna should be avoided in places that may cause signal reflection or blockage; therefore, choose an open and flat location so that the antenna can receive the electromagnetic signal generated by the corona discharge in all directions and reduce the distortion effect of the terrain on the signal;

[0064] The frequency distribution refers to the intensity distribution of electromagnetic waves at different frequencies;

[0065] The data analysis module, which is connected to the data acquisition module, is used to determine the icing trend of each characteristic time period based on historical icing data, and to determine the icing characterization tendency in combination with real-time temperature to determine whether to conduct an icing risk assessment.

[0066] The icing trend includes a severe icing trend and a mild icing trend, and the icing characterization tendency includes an overt icing tendency and a covert icing tendency.

[0067] The risk assessment module is connected to the data acquisition module, the spectrum analysis module, and the data analysis module respectively. It is used to control the data acquisition module to start data acquisition based on whether to conduct an icing risk assessment, determine whether the preset distribution conditions are met based on the frequency distribution, and determine the risk assessment status based on the result of whether the preset distribution conditions are met and the power transmission efficiency to determine the subsequent de-icing method.

[0068] The de-icing methods include natural de-icing, thermal de-icing, and mechanical de-icing.

[0069] Understandably, the data acquisition module, through its data search and data networking units, can comprehensively collect historical icing data of transmission lines and real-time temperatures at the location, providing detailed information for subsequent analysis. Real-time temperature monitoring helps determine the current icing tendency by combining historical icing data. This precise and comprehensive data acquisition method lays a solid foundation for the system to accurately assess icing risk, allowing for early prediction of potential icing situations and reducing the probability of transmission failures caused by icing. The data analysis module determines the icing trend (severe icing trend and mild icing trend) for each characteristic time period based on historical icing data, and clarifies the icing manifestation tendency (obvious icing tendency and latent icing tendency) by combining real-time temperature. This precise judgment makes icing risk assessment more scientific and targeted, enabling assessments to be conducted in the early stages or potential stages of icing. Accurate assessment during the icing risk stage allows time for timely and effective de-icing measures, reducing the likelihood of severe damage to transmission lines and ensuring their normal operation during winter and other icing-prone periods. The risk assessment module integrates information from the data acquisition, spectrum analysis, and data analysis modules, determining the risk assessment status based on whether preset distribution conditions and transmission efficiency are met. This allows for the rational selection of subsequent de-icing methods (natural de-icing, thermal de-icing, and mechanical de-icing). This decision-making process selects the most suitable de-icing method for different icing conditions and transmission line statuses, improving de-icing efficiency, reducing unnecessary manpower, material, and time costs, and minimizing potential damage to transmission lines during de-icing, thus ensuring the safe and stable operation of transmission lines and the continuity of power supply.

[0070] Specifically, the spectrum analysis module performs spectrum analysis on the electromagnetic signal, which includes windowing and filtering, power spectrum and unit conversion, peak detection, and frequency estimation.

[0071] In detail, spectrum analysis includes: (1) Receiving the electromagnetic signal generated by corona discharge is an analog signal, which may be relatively weak and will be amplified by a preamplifier; (2) The amplified analog signal is converted into a digital signal by an analog-to-digital converter. In this process, the analog signal is discretized according to a certain sampling frequency to obtain a series of digital samples; (3) The digital signal enters the digital signal processor (DSP) and the fast Fourier transform (FFT) algorithm is used to convert the time domain signal into a frequency domain signal. FFT is an efficient algorithm that can quickly calculate the spectrum of the signal, that is, the distribution of the signal at different frequencies; (4) In order to reduce problems such as spectrum leakage, the signal can be windowed. The actual sampled signal window may not be an integer multiple of the signal period. Windowing functions (such as Hanning window, Hanning window, etc.) can be used to adjust the windowing function. (5) Filter the signal to remove some known background noise or other interference signals, leaving only the electromagnetic interference signal generated by corona discharge; low-pass, high-pass, band-pass or band-stop filters can be used to achieve this purpose; (6) Convert the signal spectrum data into power spectrum form and perform unit conversion according to specific requirements, such as converting to RMS value, peak-to-peak value and other power spectrum units, so as to understand the distribution of signal power at different frequencies more intuitively; (7) Determine the peak frequency in the spectrum and its corresponding power estimate through peak detection and frequency estimation operations, which helps to quickly locate the main frequency components and intensity of electromagnetic interference generated by corona discharge, thereby understanding its frequency distribution.

[0072] Please see Figure 2 The diagram shown illustrates the workflow of the data analysis module in this embodiment of the invention. Specifically, the data analysis module determines the historical icing period as a characteristic time period and determines whether to merge the two corresponding characteristic time periods based on the time interval between two adjacent characteristic time periods, including...

[0073] If the time interval between two adjacent feature time periods is less than or equal to the preset time interval, then it is determined that the two corresponding feature time periods will be merged into one feature time period.

[0074] If the time interval between two adjacent feature time periods is greater than the preset time interval, then the two corresponding feature time periods will not be merged.

[0075] It is understandable that the icing period is a certain time. Therefore, the historical icing period of the past two to three years can be determined by the data search unit, and the corresponding period is determined as the characteristic period. In one implementation, the historical icing period is from 21:21 on December 25 to 9:00 on December 26 two years ago. Then, the corresponding period from 21:21 on December 25 to 9:00 on December 26 this year is recorded as the characteristic period.

[0076] It is understandable that in some areas during winter, transmission lines may cycle through icing at night, thawing during the day, and then icing again at night. If the interval between two characteristic time periods is too close, a new judgment and adjustment must be started, which will lead to high resource consumption and wasted computing resources. Therefore, in implementation, a preset time interval is set, and based on this, it is determined whether to merge two adjacent characteristic time periods. If the time interval is less than the preset time interval, the two characteristic time periods are merged, and the system is left in standby mode or its icing trend is re-determined based on the real-time temperature before subsequent judgments are made. If the time interval is greater than or equal to the preset time interval, the two characteristic time periods are not merged, and all subsequent judgments are ended directly after the previous characteristic time period ends, until the transmission line icing risk assessment starts from the beginning at the start of the next characteristic time period. Generally, the preset time interval is 3h to 6h. If the preset time is too long, the original intention of saving resources and reducing the number of power on / off cycles to protect the assessment system is lost. It is preferable to set it to 5h.

[0077] Specifically, the data analysis module determines the icing trend for each characteristic time period based on the average icing thickness, including:

[0078] If the average ice thickness during a characteristic time period is greater than the preset ice thickness, then the ice trend during the corresponding characteristic time period is determined to be a severe ice trend.

[0079] If the average icing thickness during a characteristic time period is less than or equal to the preset icing thickness, then the icing trend of the corresponding characteristic time period is determined to be a light icing trend.

[0080] In practice, the preset icing thickness is usually set to 10mm; it is understandable that (1) when the average icing thickness is ≤10mm, the impact of icing on the transmission line is relatively small. For general transmission lines, 2mm to 5mm of icing may only slightly increase the weight of the conductor and increase the vertical load on the tower to a certain extent, but generally will not cause obvious deformation or tilting of the tower, and the tension change of the line is within the safe range; moreover, under this icing thickness, the insulation performance of the insulator string decreases less, the reduction of the corona initiation voltage is also limited, and the line can usually still maintain (1) Maintain normal power transmission function; (2) When the ice thickness exceeds 10mm, the degree of harm of ice to the transmission line will increase significantly. When the ice thickness reaches 15mm to 20mm, the weight of the conductor will increase significantly, which will cause the conductor tension to rise sharply, which may cause the conductor to stretch or even break. At the same time, the vertical and unbalanced loads borne by the tower may exceed the design limit, which may easily lead to the sinking of the tower foundation and the twisting of the crossarm. For insulator strings, it will also significantly reduce the insulation performance and increase the leakage current, which can easily cause flashover accidents. In addition, the corona phenomenon will intensify, resulting in more energy loss and electromagnetic interference.

[0081] In practice, for transmission lines located in harsh environments such as windy areas and high altitudes, serious consequences may occur due to the superposition of other adverse factors. Therefore, the preset icing thickness is less than 10mm, and is usually set to 5mm to 8mm.

[0082] Understandably, the data analysis module uses a preset icing thickness as a boundary to accurately classify light and heavy icing trends, enabling a clear assessment of the severity of icing on transmission lines. For example, when the average icing thickness exceeds 10mm (the preset value is adjusted according to the actual situation in special harsh environments), it is judged as a heavy icing trend. This precise definition helps to detect icing conditions that may cause serious damage to the line in a timely manner, such as conductor breakage or tower deformation, so that effective countermeasures can be taken in advance to ensure the physical integrity and mechanical stability of the transmission line.

[0083] Specifically, the data analysis module determines the icing trend based on the current icing trend assessment result and the real-time temperature, including:

[0084] If the time period corresponding to the current time is not the characteristic time period, then the icing characterization tendency is no icing tendency;

[0085] If the current time corresponds to a characteristic time period, then the icing characteristic tendency is determined based on the corresponding icing trend, where,

[0086] If the icing trend is a severe icing trend, then the icing characteristic tendency is determined to be a dominant icing tendency.

[0087] If the icing trend is a mild icing trend, then the icing characterization tendency is determined in conjunction with the real-time temperature, wherein,

[0088] If the real-time temperature is lower than the icing temperature, then the icing tendency is determined to be a dominant icing tendency.

[0089] If the real-time temperature is higher than the icing temperature, then the icing tendency is determined to be a latent icing tendency.

[0090] The icing temperature is determined based on the freezing point of water and the pressure at the location of the power transmission line.

[0091] Understandably, water's freezing point is 0°C under standard atmospheric pressure, which is the critical temperature at which water transitions from a liquid to a solid (ice). When the ambient temperature drops to 0°C or below, water releases heat, its molecular motion gradually slows down, and the distance between molecules becomes relatively fixed, thus forming a regular crystal structure—ice. Furthermore, pressure affects water's freezing point; as pressure increases, the freezing point decreases. In practice, the air pressure at high altitudes is lower than standard atmospheric pressure, so the freezing point of water will be slightly higher than 0°C, although this change is relatively small. However, under certain high-pressure environments, the freezing point of water may decrease significantly.

[0092] In implementation, the freezing point of water at each pressure is known in the prior art; therefore, the icing temperature is determined based on the freezing point of water at each pressure. Simultaneously, considering the influence of dissolved substances in the water and / or strong winds on the freezing point, a temperature correction value needs to be added, typically ∈ [2℃, 5℃], preferably set to 3℃. Therefore, the icing temperature in this invention = freezing point of water at each pressure + temperature correction value. It is understood that in determining the icing temperature, not only is the freezing point of water under standard atmospheric pressure considered, but also the pressure at the location of the transmission line, as well as the influence of dissolved substances in the water and strong winds on the freezing point, are taken into account. Adding a temperature correction value makes the determination of the icing temperature more closely reflect the actual situation, more accurately reflects the real icing conditions of the environment where the transmission line is located, further optimizes the judgment of icing trends and characteristics, and provides more scientific and practical data support for the entire transmission line icing risk assessment system, effectively ensuring the safe and stable operation of transmission lines in complex environments.

[0093] Understandably, the data analysis module combines real-time temperature and icing trends to comprehensively judge the tendency of icing, avoiding the limitations of judging by a single factor. For cases with a slight icing trend, real-time temperature is further referenced. When the temperature is lower than the icing temperature (the temperature after correction for factors such as pressure, dissolved substances, and strong winds), it is judged as an overt icing tendency. When the temperature is higher than the icing temperature, it is judged as a covert icing tendency. This scientific judgment method can more accurately reflect the potential risks and actual development of icing, providing a reasonable basis for whether to initiate an icing risk assessment, effectively reducing the possibility of misjudgment and omission, and improving the accuracy and reliability of risk assessment.

[0094] Specifically, the data analysis module determines to conduct an icing risk assessment based on the determination result that the icing characteristic tendency is a dominant icing tendency, so that the risk assessment module controls the data acquisition module to start data acquisition.

[0095] Understandably, if the icing tendency is latent or nonexistent, it is determined that there is no possibility of icing at present, and therefore no icing risk assessment is conducted; this time period may be the time interval between two adjacent time periods that are merged, or it may be due to the difference between this year's temperature and historical temperatures.

[0096] Please see Figure 3 The diagram shown illustrates the workflow of the risk assessment module in an embodiment of the present invention. Specifically, the risk assessment module determines whether the preset distribution conditions are met based on the frequency distribution determined by the machine learning model.

[0097] The preset distribution conditions include the electromagnetic interference frequency center value being within a preset range and the intensity on both sides of the frequency center value gradually decreasing.

[0098] It is understandable that the frequency distribution of electromagnetic interference generated by corona discharge refers to the intensity distribution of electromagnetic waves generated by corona discharge at different frequencies: (1) Corona discharge generates broadband noise, the frequency range of which is generally from kHz to hundreds of MHz. Within this range, the intensity of electromagnetic interference at different frequencies is different, and the overall performance shows a certain continuity. As the frequency increases, the intensity usually gradually decreases. (2) Corona discharge also generates pulse noise, the frequency range of which is concentrated in the range of tens of kHz to several MHz. The intensity of electromagnetic interference in this frequency band is relatively high, while the interference intensity decreases rapidly at frequencies outside this range. (3) Corona discharge also generates flicker noise, the frequency of which is relatively fixed, usually between 200Hz and 400Hz. The electromagnetic interference at this frequency has a certain regularity and may interfere with radio navigation and radar signals. In summary, the high-frequency radio waves generated by corona discharge often take 0.5MHz as the center value. The intensity of electromagnetic interference in the frequency range near it is relatively large and gradually decreases towards both sides. The interference in this frequency band will affect communication systems such as wired telephone, radio reception and television.

[0099] Therefore, a pre-trained machine learning model can be used to determine whether the frequency distribution meets the preset distribution conditions, and then combined with the bookstore's efficiency to determine the subsequent de-icing method.

[0100] Specifically, the risk assessment module determines the frequency at which the maximum frequency intensity is located as the frequency center value.

[0101] Specifically, the risk assessment module determines the risk assessment status based on whether the preset distribution conditions are met and the transmission efficiency, and then determines the subsequent de-icing method, including:

[0102] If the preset distribution conditions are not met and the power transmission efficiency is greater than or equal to the preset power transmission efficiency, the risk assessment state is determined to be a low-risk state, and the subsequent de-icing method is determined to be natural de-icing. It can be understood that if the preset distribution conditions are not met and the power transmission efficiency is greater than or equal to the preset power transmission efficiency, it means that the corona phenomenon is not obvious and the power transmission process is normal. Therefore, it does not affect the normal operation of the power transmission line or the surrounding communication system. In this case, there is no need to use external force to remove the ice on the power transmission line, and it can be allowed to de-iced naturally.

[0103] If the preset distribution conditions are met or the transmission efficiency is less than the preset transmission efficiency, the risk assessment status is determined to be a medium-risk status, and the subsequent de-icing method is determined to be mechanical de-icing. It can be understood that if the preset distribution conditions are met or the transmission efficiency is less than the preset transmission efficiency, it means that there is an abnormality in the corona phenomenon or the transmission process, which affects the normal operation of the transmission line or the surrounding communication system. This indicates that the icing effect has already existed and external force (mechanical de-icing) is needed to remove the icing on the transmission line.

[0104] If the preset distribution conditions are met and the transmission efficiency is less than the preset transmission efficiency, the risk assessment state is determined to be a high-risk state, and the subsequent de-icing method is determined to be thermal de-icing and mechanical de-icing. It can be understood that if the preset distribution conditions are met and the transmission efficiency is less than the preset transmission efficiency, it means that the corona phenomenon and the transmission process are abnormal, and at the same time, it affects the normal operation of the transmission line and the surrounding communication system, indicating that the impact of icing is very serious. At this time, mechanical de-icing alone cannot achieve a good de-icing effect. Therefore, it is necessary to combine thermal de-icing and mechanical de-icing methods to remove the icing on the transmission line.

[0105] It is understandable that (1) a high-risk status in the risk assessment means that the ice layer is thick and the coverage area is wide. At this time, mechanical de-icing will face huge challenges, while the short-circuit current de-icing and DC de-icing in thermal de-icing can quickly generate a large amount of heat, so that the thick ice layer melts in a short time. Short-circuit current de-icing is achieved by short-circuiting the three-phase conductors at the end of the transmission line and applying a certain voltage at the beginning to generate a strong short-circuit current in the line, and using the heat generated by the resistance of the conductor itself to melt the ice. DC de-icing can also provide enough heat. Its advantage is that it can more accurately control the de-icing current and heat. For long-distance, large-area, severely iced transmission lines, this efficient de-icing method can quickly reduce the burden on the line. (2) Secondly, severe icing may This leads to the formation of various irregular ice ridges, icicles, and even complex structures such as insulators on the line. Thermal de-icing can gradually melt these complex-shaped ices through heat transfer, avoiding the problems of line damage or incomplete de-icing that may occur when mechanical de-icing deals with these complex structures; (3) In addition, in cases of severe icing, the on-site environment is often more severe, such as strong winds, blizzards, and low temperatures. Using mechanical de-icing methods is very dangerous. However, some thermal de-icing equipment (such as DC de-icing devices and laser de-icing systems) can be operated remotely. Workers can remotely start and control the de-icing process in a relatively safe control room based on real-time monitoring data of line icing (such as ice thickness and line temperature), which greatly reduces the safety risks to personnel. Therefore, mechanical de-icing can be used as an auxiliary means when the risk assessment status is high risk.

[0106] It is understandable that (1) mechanical de-icing tools (such as insulating rods for manual de-icing, pulley scrapers, de-icing robots, etc.) can directly act on the iced area and can accurately de-ic the area with severe local icing; however, mechanical de-icing methods may not be able to remove the tiny ice crystals on the surface of the line and the impurities inside the ice layer. These residual ice may cause icing problems again when the environment changes in the future; (2) thermal de-icing methods (such as short-circuit current de-icing, DC de-icing, laser de-icing) can quickly melt large areas of icing. Once the de-icing program is started, the ice layer can be melted and removed in a short time. This method is highly effective for long-distance transmission lines with thick ice buildup. Some thermal de-icing equipment (such as DC de-icing devices and laser de-icing systems) can be operated remotely. Staff can remotely start and control the de-icing process from the control room based on real-time monitoring of the ice buildup on the line, avoiding the risks of working on-site in harsh environments. Thermal de-icing can remove ice from line components such as conductors and insulators of various shapes. Whether it is a bent conductor or a complex-shaped fitting, as long as heat can be transferred to the iced area, the ice can be melted. Compared with the limitations of mechanical de-icing on complex structures, it has obvious advantages.

[0107] It is understandable that each transmission line has a corresponding transmission efficiency range when it is built. The preset transmission efficiency is determined based on its maximum and minimum transmission efficiency. Usually, the preset transmission efficiency = minimum transmission efficiency + 0.3 × (maximum transmission efficiency - minimum transmission efficiency).

[0108] Understandably, the risk assessment module categorizes risk assessment states into low, medium, and high risk levels based on whether preset distribution conditions are met and the level of transmission efficiency. This assessment method comprehensively and accurately reflects the potential impact of icing on transmission lines, avoiding ambiguity and uncertainty in risk assessment. This makes the understanding of icing risks clearer and provides a reliable basis for subsequent decision-making. Based on different risk assessment states, targeted de-icing methods are determined to ensure that transmission lines return to normal operation as quickly as possible, reducing the probability of power outages caused by icing. For low-risk states, natural de-icing is chosen to fully utilize natural conditions and avoid unnecessary investment of manpower and resources. For medium-risk states, mechanical de-icing is used to address icing problems promptly and effectively, preventing further deterioration. For high-risk states, a combination of thermal and mechanical de-icing methods is used, leveraging the advantages of thermal de-icing—its high efficiency and adaptability to complex icing structures—and the precise local treatment and emergency repair capabilities of mechanical de-icing, to quickly and thoroughly resolve severe icing problems.

[0109] Please see Figure 4 The diagram illustrates the steps of the transmission line icing risk assessment method according to an embodiment of the present invention. The present invention also provides a transmission line icing risk assessment method, including:

[0110] Step S1: Obtain historical icing data of the transmission line, and determine the characteristic time period and the icing trend of each characteristic time period based on the historical icing data.

[0111] Step S2: Determine the current icing trend and combine it with real-time temperature to determine the icing characterization tendency to determine whether to conduct an icing risk assessment.

[0112] Step S3: Based on the judgment result of the icing risk assessment, control the data acquisition module to start data acquisition;

[0113] Step S4: Perform spectrum analysis on the collected electromagnetic signals to determine the frequency distribution of electromagnetic interference generated by corona discharge and to determine whether the preset distribution conditions are met.

[0114] Step S5: Determine the risk assessment status based on whether the preset distribution conditions are met and the power transmission efficiency;

[0115] Step S6: Determine the subsequent de-icing method based on the risk assessment status.

[0116] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using dedicated hardware-based apparatus to perform the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0117] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0118] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A power line icing risk assessment system, characterized by, The method comprises the following steps: a data acquisition module, comprising a data searching unit for acquiring historical icing data of a power transmission line, and a data networking unit for acquiring real-time temperature of a location of the power transmission line, the historical icing data comprising historical icing time periods and corresponding historical icing thicknesses; a data collection module, comprising a spectrum collection unit for receiving electromagnetic signals generated by corona discharge, and a power meter monitoring unit for collecting power transmission efficiency; a spectrum analysis module connected to the spectrum collection unit, for performing spectrum analysis on the electromagnetic signals to determine frequency distribution of electromagnetic interference generated by corona discharge; wherein the frequency distribution is intensity distribution of electromagnetic waves at different frequencies; a data analysis module connected to the data acquisition module, for determining icing trend of each characteristic time period according to historical icing data, and determining icing representation tendency in combination with real-time temperature to determine whether to perform icing risk assessment; the data analysis module determines icing representation tendency in combination with the real-time temperature according to a determination result of the icing trend at the current time, comprising: if the time period corresponding to the current time is not a characteristic time period, then the icing representation tendency is no icing tendency; if the time period corresponding to the current time is a characteristic time period, then the icing representation tendency is determined according to the corresponding icing trend, wherein: if the icing trend is a severe icing trend, then it is determined that the icing representation tendency is a dominant icing tendency; if the icing trend is a mild icing trend, then the icing representation tendency is determined in combination with the real-time temperature, wherein: if the real-time temperature is lower than an icing temperature, then it is determined that the icing representation tendency is a dominant icing tendency; if the real-time temperature is higher than the icing temperature, then it is determined that the icing representation tendency is a recessive icing tendency; wherein the icing temperature is determined according to the freezing point of water and the pressure at the location of the power transmission line; wherein the icing trend comprises a severe icing trend and a mild icing trend, and the icing representation tendency comprises a dominant icing tendency and a recessive icing tendency; a risk assessment module connected to the data collection module, the spectrum analysis module and the data analysis module, respectively, for controlling the data collection module to start data collection according to whether to perform icing risk assessment, determining whether a preset distribution condition is met according to the frequency distribution, and determining a risk assessment state according to a determination result of whether the preset distribution condition is met and the power transmission efficiency to determine a subsequent deicing method; wherein the deicing method comprises natural deicing, thermal deicing and mechanical deicing.

2. The power line icing risk assessment system of claim 1, wherein, The spectrum analysis module performs spectrum analysis on the electromagnetic signals, and the spectrum analysis comprises windowing and filtering processing, power spectrum and unit conversion, and peak detection and frequency estimation.

3. The power line icing risk assessment system of claim 1, wherein, The data analysis module determines the historical icing time periods as characteristic time periods, and determines whether to merge corresponding two characteristic time periods according to a time interval between two adjacent characteristic time periods, comprising: if the time interval between two adjacent characteristic time periods is less than or equal to a preset time interval, then it is determined to merge the corresponding two characteristic time periods into one characteristic time period; If the time interval between two adjacent feature time periods is greater than the preset time interval, it is determined that the corresponding two feature time periods are not merged.

4. The power line icing risk assessment system of claim 3, wherein, The data analysis module determines the icing trend of the corresponding feature time period according to the average icing thickness of each feature time period, including, If the average icing thickness of the feature time period is greater than the preset icing thickness, it is determined that the icing trend of the corresponding feature time period is heavy icing trend. If the average icing thickness of the feature time period is less than or equal to the preset icing thickness, it is determined that the icing trend of the corresponding feature time period is light icing trend.

5. The power line icing risk assessment system of claim 1, wherein, The data analysis module determines to perform icing risk assessment according to the determination result that the icing representation tendency is dominant icing tendency, so that the risk assessment module controls the data acquisition module to start data acquisition.

6. The power line icing risk assessment system of claim 1, wherein, The risk assessment module determines whether the preset distribution condition is met according to the frequency distribution condition determined by the machine learning model. The preset distribution condition includes that the frequency center value of electromagnetic interference is within the preset range and the intensity gradually decreases on both sides of the frequency center value.

7. The power line icing risk assessment system of claim 6, wherein, The risk assessment module determines the frequency center value as the frequency at which the frequency intensity maximum value is located.

8. The power line icing risk assessment system of claim 1, wherein, The risk assessment module determines the risk assessment state according to the determination result of whether the preset distribution condition is met and the power transmission efficiency to determine the subsequent deicing method, including, If the preset distribution condition is not met and the power transmission efficiency is greater than or equal to the preset power transmission efficiency, it is determined that the risk assessment state is a low risk state, and the subsequent deicing method is natural deicing method. If the preset distribution condition is met or the power transmission efficiency is less than the preset power transmission efficiency, it is determined that the risk assessment state is a medium risk state, and the subsequent deicing method is mechanical deicing method. If the preset distribution condition is met and the power transmission efficiency is less than the preset power transmission efficiency, it is determined that the risk assessment state is a high risk state, and the subsequent deicing method is thermal deicing method and mechanical deicing method.

9. A power line icing risk assessment method applied to the power line icing risk assessment system according to any one of claims 1 to 8, characterized in that, Including, Obtain historical icing data of the power transmission line, and determine feature time periods and icing trends of each feature time period according to the historical icing data; Determine the icing trend of the current time, and combine the real-time temperature to determine the icing representation tendency to determine whether to perform icing risk assessment; Control the data acquisition module to start data acquisition according to the determination result of whether to perform icing risk assessment; Perform frequency spectrum analysis on the collected electromagnetic signals to determine the frequency distribution of the electromagnetic interference generated by corona discharge to determine whether the preset distribution condition is met; Determine the risk assessment state according to whether the preset distribution condition is met and the power transmission efficiency. Determine the subsequent deicing method according to the risk assessment state.

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