Near-wind-field power transmission line fitting operation state intelligent diagnosis method and system

Abstract

The invention provides a near-wind-field power transmission line hardware fitting operation state intelligent diagnosis method and system, and relates to the technical field of power transmission line operation monitoring. Comprising the steps of calculating a first influence factor and a second influence factor of a transmission line fitting use state; determining an initial diagnosis threshold of the use state of the power transmission line fitting; calculating a power transmission line hardware fitting use state diagnosis threshold value influence factor, and determining a power transmission line hardware fitting use state diagnosis threshold value; and acquiring a transmission line fitting operation characteristic data set, calculating a fitting operation characteristic evaluation value, and judging whether the transmission line fitting use state is qualified in combination with the transmission line fitting use state diagnosis threshold. According to the method, a comprehensive, dynamic and accurate power transmission line fitting use state diagnosis threshold value with a customized property is determined from the three aspects of environment microclimate, fitting characteristic data and near wind field wind power characteristics of the power transmission line fitting, and more scientific and reasonable fitting maintenance is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of power transmission line operation monitoring, and in particular relates to an intelligent diagnosis method and system for the operation status of hardware of a near-wind field power transmission line. Background Art

[0002] With the increasing demand for renewable energy, wind power generation has occupied an important position in the energy structure. Wind farms are generally located in remote areas with harsh climates, which poses challenges to the operation of transmission lines and their associated hardware. The operating environment of transmission lines near wind farms is usually accompanied by complex climatic conditions such as strong winds, high humidity, rain and snow, which may cause problems such as corrosion, mechanical fatigue or wear of hardware, thus affecting the overall safety and reliability of transmission lines.

[0003] With the development of technologies such as the Internet of Things, big data, and artificial intelligence, intelligent monitoring and diagnostic methods based on these technologies have become possible. By online monitoring of the operating status of the hardware of the power transmission line near the wind farm, combined with data analysis models, potential faults can be discovered in a timely manner to reduce the occurrence of unexpected power outages. By collecting transmission line operating data, especially parameters such as vibration and stress of hardware, a fault prediction model is constructed to achieve early warning of faults. This intelligent diagnostic method based on big data can accurately evaluate the health status of hardware and optimize maintenance decisions.

[0004] However, due to the different geographical locations and climate environments of different wind farms, the wind speeds near the wind farms are also different. Even for wind farms in the same geographical location, the above factors will be different in different time periods and seasons. These differences will have different impacts on the operating status of the transmission line hardware near the wind farm. If the above factors are not fully considered and only a unified transmission line usage status judgment threshold is formulated, the lack of personalized considerations will cause inaccurate diagnosis.

[0005] In the prior art, when the influence of a single factor is considered, the sudden fluctuation of the single factor often causes a short-term change in the state of the hardware, and the temporary change caused by the fluctuation is mistakenly judged as a fault, further causing inaccurate diagnosis.

[0006] In addition, there are still some deficiencies in the research on intelligent diagnosis of the operating status of transmission line fittings near wind farms. Specifically, the traditional transmission line fittings detection method mainly relies on regular manual inspections, which is inefficient and difficult to detect hidden dangers in a timely manner, and the dynamic response capability is insufficient. At the same time, the harsh environmental conditions increase the cost of manual inspections and increase the risks. During the entire life cycle of the transmission line, it is impossible to continuously and in real time monitor all lines and fittings, which means that some potential problems may develop and worsen in the gaps between manual inspections, resulting in hidden dangers not being dealt with in a timely manner for a long time, increasing the probability of failures. Manual detection cannot respond quickly to environmental changes, such as climate deterioration or external interference in the wind farm. Especially under extreme weather conditions, the status of the fittings may deteriorate rapidly, and manual inspections cannot achieve real-time monitoring and timely response. Dynamic monitoring is insufficient, delaying the opportunity for emergency treatment. Summary of the invention

[0007] In order to overcome the shortcomings of the above-mentioned prior art, the present invention provides an intelligent diagnosis method and system for the operating status of transmission line fittings near wind farms. From the three aspects of the environmental microclimate of the transmission line fittings, the characteristic data of the fittings, and the wind characteristics of the near wind farm, a comprehensive, dynamic, accurate, and customized transmission line fitting usage status diagnosis threshold is determined. Based on the threshold, it is determined whether the current usage status of the transmission line fittings is qualified, thereby achieving more scientific and reasonable fitting maintenance.

[0008] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions:

[0009] A first aspect of the present invention provides an intelligent diagnosis method for the operating status of hardware of a near-wind farm transmission line.

[0010] The intelligent diagnosis method for the operation status of the hardware of the power transmission line near the wind farm comprises the following steps:

[0011] Obtain the microclimate data set of the environment where the transmission line fittings are located, and calculate the first influencing factor of the use status of the transmission line fittings; obtain the characteristic data set of the transmission line fittings, and calculate the second influencing factor of the use status of the transmission line fittings;

[0012] The first influencing factor and the second influencing factor of the transmission line fitting usage status are used as the first comparison label, combined with the pre-stored first reference label and the corresponding diagnostic threshold, and the label comparison method is adopted to determine the initial diagnostic threshold of the transmission line fitting usage status;

[0013] Obtain a data set of wind force characteristics in a near wind field, calculate the influencing factor of the diagnostic threshold of the use status of transmission line hardware, use the influencing factor of the diagnostic threshold of the use status of transmission line hardware and the initial diagnostic threshold of the use status of transmission line hardware as the second comparison label, combine the pre-stored second reference label and the corresponding diagnostic threshold, and use the label comparison method to determine the diagnostic threshold of the use status of transmission line hardware;

[0014] Obtain the transmission line fittings operation characteristic data set, calculate the fittings operation characteristic evaluation value, and combine the transmission line fittings usage status diagnosis threshold to determine whether the transmission line fittings usage status is qualified.

[0015] A second aspect of the present invention provides an intelligent diagnosis system for the operating status of hardware of power transmission lines near wind farms.

[0016] The intelligent diagnosis system for the operation status of transmission line hardware near wind farms includes:

[0017] The first influencing factor and the second influencing factor determination module are configured to: obtain a microclimate data set of the environment where the transmission line fittings are located, and calculate a first influencing factor of the use status of the transmission line fittings; obtain a characteristic data set of the transmission line fittings, and calculate a second influencing factor of the use status of the transmission line fittings;

[0018] The initial diagnosis threshold comparison module is configured to: use the first influencing factor and the second influencing factor of the use status of the transmission line hardware as the first comparison label, combine the pre-stored first reference label and the corresponding diagnosis threshold, and use the label comparison method to determine the initial diagnosis threshold of the use status of the transmission line hardware;

[0019] The diagnostic threshold comparison module is configured to: obtain a wind force characteristic data set of a near wind field, calculate the diagnostic threshold influencing factor of the use status of transmission line hardware, use the diagnostic threshold influencing factor of the use status of transmission line hardware and the initial diagnostic threshold of the use status of transmission line hardware as the second comparison label, combine the pre-stored second reference label and the corresponding diagnostic threshold, and determine the diagnostic threshold of the use status of transmission line hardware by label comparison;

[0020] The judgment module is configured to: obtain a data set of operation characteristics of transmission line fittings, calculate an evaluation value of the operation characteristics of the fittings, and judge whether the use status of the transmission line fittings is qualified in combination with a diagnosis threshold of the use status of the transmission line fittings.

[0021] A third aspect of the present invention provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps of the intelligent diagnosis method for the operating status of near-wind farm transmission line hardware as described in the first aspect of the present invention.

[0022] The fourth aspect of the present invention provides an electronic device, including a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, the steps in the intelligent diagnosis method for the operating status of near-wind farm transmission line fittings as described in the first aspect of the present invention are implemented.

[0023] One or more of the above technical solutions have the following beneficial effects:

[0024] The present invention provides an intelligent diagnosis method and system for the operating status of hardware fittings of power transmission lines near wind farms. By providing an intelligent diagnosis method for the operating status of hardware fittings of power transmission lines near wind farms, regular monitoring and diagnosis can effectively improve the reliability of hardware fittings, reduce accidental damage caused by bad weather, and reduce the overall failure rate of power transmission lines. The microclimate environment where the hardware fittings of the transmission line are located is analyzed and hardware characteristic data is obtained to respectively determine the first and second influencing factors, so that the key parameters of the hardware operation can be fully grasped. The initial diagnosis threshold determined by combining these two types of influencing factors can more accurately evaluate the health status of the hardware and discover potential hidden dangers of faults. Factors such as wind speed and wind pressure directly affect the load and life of the hardware fittings of the power transmission line. Dynamic adjustment of the diagnosis threshold can ensure efficient status evaluation under different climatic conditions and avoid misjudgment. The hardware fittings are maintained only when necessary, avoiding the blindness of regular manual maintenance. At the same time, it can ensure that the hardware fittings are repaired in time when problems occur.

[0025] The present invention analyzes the microclimate of the environment where the transmission line hardware is located by setting a set time as a monitoring cycle in a near-wind environment. The wind field environment is complex and changeable. Microclimate conditions such as wind speed, humidity, temperature and precipitation will directly affect the stress and aging speed of the transmission line hardware. Regular monitoring of microclimate changes can dynamically grasp the impact of environmental factors on the hardware, which is helpful to timely discover abnormal changes. Periodically acquiring microclimate data can provide accurate environmental information for hardware fault diagnosis. Different microclimate conditions may cause different types of faults. High humidity may cause corrosion, while strong winds may cause vibration and fatigue damage. The use status diagnosis threshold of the transmission line hardware can be dynamically adjusted according to the real-time changes of the wind field environment, making the diagnosis more flexible and adapting to the influence of different environmental conditions.

[0026] The present invention analyzes the operating characteristics of transmission line hardware, combines the transmission line hardware usage status diagnosis threshold, determines whether the transmission line hardware usage status is qualified, and analyzes the operating characteristics of the hardware to more accurately determine whether the hardware is in normal operation, thereby avoiding misjudgment. The early warning mechanism can provide timely information feedback, which is helpful for maintenance before the fault develops to a serious stage, thereby greatly reducing the impact of the fault on the transmission system. Regular analysis of the operating characteristics can detect fatigue damage or minor abnormalities of the hardware in time, prevent small faults from turning into big problems, reduce the damage to the hardware caused by serious faults, and thus extend the service life of the hardware and the line.

[0027] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0029] Figure 1 It is a schematic diagram of the method flow of the present invention.

[0030] Figure 2 A flow chart of the steps for judging whether the use status of transmission line fittings is qualified. DETAILED DESCRIPTION

[0031] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0032] It should be noted that the terms used herein are for describing specific embodiments only and are not intended to be limiting of exemplary embodiments according to the present invention.

[0033] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0034] Embodiment 1

[0035] This embodiment provides a method for intelligently diagnosing the operating status of transmission line hardware near a wind farm, comprising the following steps:

[0036] In a near-wind environment, the microclimate of the environment where the transmission line fittings are located is analyzed with a set time as a monitoring cycle to determine the first influencing factor of the use status of the transmission line fittings;

[0037] Obtain a transmission line fitting feature data set, determine the second influencing factor of the transmission line fitting usage status, and determine the initial diagnosis threshold of the transmission line fitting usage status in combination with the first influencing factor of the transmission line fitting usage status;

[0038] Analyze the wind force characteristics of the nearby wind field, and determine the diagnostic threshold of the transmission line hardware usage status in combination with the initial diagnostic threshold of the transmission line hardware usage status;

[0039] Analyze the operation characteristics of transmission line fittings and judge whether the status of transmission line fittings is qualified by combining the diagnostic threshold of the status of transmission line fittings:

[0040] If the transmission line fittings are in good condition, the next monitoring cycle will begin; if the transmission line fittings are in unqualified condition, the transmission line fittings will be maintained, and the next monitoring cycle will begin after the maintenance of the unqualified transmission line fittings is completed.

[0041] As a further method, the first influencing factor of the use status of transmission line fittings is determined. The specific analysis process is as follows:

[0042] The microclimate dataset of the environment where the hardware is located is obtained, and the microclimate dataset of the environment where the hardware is located specifically includes the average ambient temperature, the ambient ultraviolet intensity, and the average ambient humidity. Based on the obtained microclimate dataset of the environment where the hardware is located, a comprehensive analysis is performed to obtain the first influencing factor of the usage status of the transmission line hardware. The first influencing factor of the usage status of the transmission line hardware is used as the analysis basis for determining the initial diagnostic threshold of the usage status of the transmission line hardware.

[0043] As a further method, the second influencing factor of the use status of transmission line fittings is determined. The specific analysis process is as follows:

[0044] A transmission line fittings characteristic data set is obtained, which specifically includes the service life of transmission line fittings, the mechanical strength of transmission line fittings, and the electrical conductivity of transmission line fittings. Based on the obtained transmission line fittings characteristic data set, a comprehensive analysis is performed to obtain the second influencing factor of the transmission line fittings usage status, which is used as the analysis basis for determining the initial diagnostic threshold of the transmission line fittings usage status.

[0045] As a further method, the initial diagnostic threshold of the transmission line fittings usage status is determined. The specific analysis process is as follows:

[0046] The first influencing factor of the transmission line fittings usage status and the second influencing factor of the transmission line fittings usage status are stored as designated tags, and the designated tags are compared with the initial diagnosis threshold of the transmission line fittings usage status corresponding to each designated tag stored in the database to obtain the initial diagnosis threshold of the transmission line fittings usage status corresponding to the designated tag.

[0047] As a further method, the wind characteristics of the near wind field are analyzed. The specific analysis process is as follows:

[0048] A near-wind field wind characteristic data set is obtained, which specifically includes the near-wind field wind energy density, the near-wind field average wind speed, the near-wind field turbulence intensity, and the near-wind field wind direction deviation angle. Based on the obtained near-wind field wind characteristic data set, a comprehensive analysis is performed to obtain the transmission line fitting usage status diagnosis threshold influencing factor, which is used as the analysis basis for determining the transmission line fitting usage status diagnosis threshold.

[0049] As a further method, the diagnostic threshold of the transmission line fittings usage status is determined. The specific analysis process is as follows:

[0050] The transmission line fitting usage status diagnosis threshold influencing factor and the transmission line fitting usage status initial diagnosis threshold are stored as designated tags, and the designated tags are compared with the transmission line fitting usage status diagnosis thresholds corresponding to the designated tags stored in the database to obtain the transmission line fitting usage status diagnosis thresholds corresponding to the designated tags.

[0051] As a further method, the operation characteristics of transmission line fittings are analyzed. The specific analysis process is as follows:

[0052] The transmission line fittings operation characteristic data set is obtained. Based on the obtained transmission line fittings operation characteristic data set, the fittings operation characteristic evaluation value is obtained through comprehensive analysis. The fittings operation characteristic evaluation value is used as the analysis basis for judging whether the transmission line fittings are in use.

[0053] As a further method, the transmission line hardware operation characteristic data set includes hardware tensile stress, hardware vibration frequency, hardware current load, total number of hardware cracks, and hardware surface salt spray deposition rate.

[0054] As a further method, to judge whether the use status of transmission line fittings is qualified, the specific analysis process is as follows:

[0055] Compare the transmission line fittings operation characteristic evaluation value with the transmission line fittings usage status diagnosis threshold; if the transmission line fittings operation characteristic evaluation value is not lower than the transmission line fittings usage status diagnosis threshold, then the transmission line fittings usage status corresponding to the transmission line fittings operation characteristic evaluation value is qualified and enters the next monitoring cycle; if the transmission line fittings operation characteristic evaluation value is lower than the transmission line fittings usage status diagnosis threshold, then the transmission line fittings usage status corresponding to the transmission line fittings operation characteristic evaluation value is unqualified and the transmission line fittings need to be maintained. After the maintenance of the transmission line fittings with unqualified usage status is completed, the next monitoring cycle is entered.

[0056] The specific implementation steps of the embodiments of the present invention will be explained in detail below with reference to the accompanying drawings.

[0057] Reference Figure 1 As shown, the present invention provides an intelligent diagnosis method for the operating status of transmission line fittings near wind fields, including: in a near wind field environment, with a set time as a monitoring cycle, analyzing the microclimate of the environment where the transmission line fittings are located, and determining the first influencing factor of the use status of the transmission line fittings.

[0058] The specific analysis process is as follows: obtain the microclimate dataset of the environment where the hardware is located, which specifically includes the average ambient temperature, ambient ultraviolet intensity, and average ambient humidity; based on the obtained microclimate dataset of the environment where the hardware is located, a comprehensive analysis is performed to obtain the first influencing factor of the usage status of the transmission line hardware. The first influencing factor of the usage status of the transmission line hardware is used as the analysis basis for determining the initial diagnostic threshold of the usage status of the transmission line hardware.

[0059] In a specific embodiment, the ambient average temperature refers to the average value of the air temperature in a certain area within a certain period of time, reflecting the overall heat situation in the area. For power transmission line fittings, temperature changes may affect the thermal expansion and contraction of the fittings. Long-term high temperature will accelerate material aging, and low temperature may cause material embrittlement. It can be obtained through a temperature sensor (such as a platinum resistance thermometer, a thermocouple, etc.) or a temperature monitoring module in a weather station. The ambient ultraviolet intensity indicates the intensity of ultraviolet radiation in the air, usually in milliwatts per square centimeter (mW / cm2). When the ultraviolet intensity is high, it will accelerate the aging and corrosion of the material on the surface of the fitting, especially the impact on metal and insulating materials. It is measured using an ultraviolet sensor (such as a UV sensor, an ultraviolet radiation sensor). The ambient average humidity refers to the water vapor content in the air, generally described by relative humidity (expressed as a percentage). When the humidity is high, the fittings are more susceptible to corrosion, especially in coastal or humid areas. Humidity changes have a greater impact on the material durability of the fittings, and are obtained using a humidity sensor (such as a capacitive humidity sensor, a temperature and humidity recorder).

[0060] In a specific embodiment, a temperature rise usually causes the evaporation of water in the air, increasing the water content of the air and thus increasing the humidity, especially in areas near water sources. However, in a high temperature environment, the relative humidity will decrease because the increase in temperature allows the air to accommodate more water. Although the total amount of water vapor in the air increases, the relative humidity (the saturation of water in the air) may decrease. The intensity of ultraviolet light has a certain indirect positive correlation with temperature. On sunny days, ultraviolet radiation is usually stronger and the temperature is relatively higher. On cloudy or foggy days, ultraviolet radiation is lower and the temperature is relatively lower. High humidity usually weakens the intensity of ultraviolet light because water vapor molecules scatter and absorb part of the ultraviolet light. Ultraviolet radiation is generally weaker in humid and cloudy weather.

[0061] In a specific embodiment, collecting microclimate data such as the average ambient temperature, ultraviolet intensity, and average humidity can accurately reflect the operating environment conditions of the hardware. These environmental factors directly affect the material properties and service life of the hardware. The first influencing factor obtained based on the analysis of these environmental factors can more accurately reflect the actual working status of the hardware and help set a more reasonable initial diagnostic threshold. The climatic conditions of each wind farm are different. Through comprehensive analysis of microclimate data, it is possible to set appropriate diagnostic thresholds for the use of hardware in a specific environment, thereby implementing customized maintenance strategies and avoiding excessive or insufficient maintenance. Considering factors such as ambient temperature, ultraviolet intensity, and humidity in the microclimate, the impact of these external conditions on the performance of hardware materials (such as metals, rubber, etc.) can be fully understood. Long-term high temperature or ultraviolet radiation will accelerate the aging of the hardware, and high humidity may cause corrosion. The initial diagnostic threshold set in combination with these factors can better ensure that the hardware is in good condition, thereby improving operational reliability.

[0062] In a specific embodiment, the first influencing factor of the use status of the transmission line hardware is analyzed in the following specific process:

[0063]

[0064] In the formula, α is the first influencing factor of the use status of transmission line fittings, wd is the average ambient temperature, qd is the ambient ultraviolet intensity, sd is the average ambient humidity, ε1 is the compensation factor of the set wd, ε2 is the compensation factor of the set qd, and ε3 is the compensation factor of the set sd.

[0065] It should be explained that the first influencing factor of the use status of the above-mentioned transmission line fittings is calculated through the average ambient temperature, ambient ultraviolet intensity, and average ambient humidity. WD, QD, and SD are normalized. Ambient temperature, ultraviolet rays, and humidity are the main external factors that cause the aging of fittings. These factors are calculated as the first influencing factor, which can accurately evaluate the impact of environmental conditions on fitting materials and help determine the degree of aging of fittings in specific environments. When the environmental impact is small, the first influencing factor will remain at a low level, allowing the diagnostic system to appropriately relax the detection frequency of fittings and reduce unnecessary maintenance work; while under harsh environmental conditions, the first influencing factor increases, and the system will increase the monitoring and maintenance frequency. When environmental factors have a significant impact on the status of fittings, the first influencing factor can reflect this change, so that the system can adjust the monitoring and maintenance strategy of fittings in time when an abnormal environment occurs to ensure that the fittings are in a safe operating state. Sudden fluctuations in the environment will cause short-term changes in the status of fittings. The dynamic adjustment of the first influencing factor based on environmental data can avoid misjudging temporary changes caused by environmental fluctuations as faults.

[0066] It should be explained that the compensation factors of wd, qd, and sd set above are obtained from the database. According to historical data, a mapping set of historically measured average ambient temperature, ambient ultraviolet intensity, ambient average humidity and compensation factors of wd, qd, and sd is established to obtain the compensation factors of wd, qd, and sd corresponding to the current wd, qd, and sd.

[0067] It should be noted that σ1, σ2, σ3, τ1, τ2, τ3, τ4, μ1, μ2, μ3, μ4, and μ5 mentioned below are also obtained through a mapping set of historical data and compensation factors established in a database, that is, the corresponding compensation factors are obtained according to the current data.

[0068] The characteristic data set of transmission line fittings is obtained, the second influencing factor of the usage status of transmission line fittings is determined, and the initial diagnosis threshold of the usage status of transmission line fittings is determined in combination with the first influencing factor of the usage status of transmission line fittings.

[0069] The specific analysis process is as follows: obtaining a transmission line fittings characteristic data set, which specifically includes the service life of transmission line fittings, the mechanical strength of transmission line fittings, and the electrical conductivity of transmission line fittings; based on the obtained transmission line fittings characteristic data set, a comprehensive analysis is performed to obtain the second influencing factor of the transmission line fittings usage status, and the second influencing factor of the transmission line fittings usage status is used as the analysis basis for determining the initial diagnostic threshold of the transmission line fittings usage status.

[0070] It needs to be explained that the above-mentioned service life refers to the number of years of operation of the transmission line hardware from the installation to the present time. The material of the hardware will gradually age, corrode or wear over time. The service life can be used as an important indicator to judge the life and degree of deterioration of the hardware. The service life is usually obtained through installation and maintenance records. Mechanical strength refers to the tolerance of the hardware when subjected to external forces. Mechanical strength usually involves its tensile, compressive and fatigue resistance, which is directly related to its stability in harsh environments (such as strong winds or ice and snow). Hardware with low mechanical strength is prone to deformation, loosening or even breaking, thereby affecting line safety. It is obtained based on a tensile tester. Conductivity refers to the electrical conductivity of the hardware material, usually in Siemens / meter (S / m). The higher the conductivity, the better the conductivity of the hardware and the higher the current transmission efficiency. Low conductivity will lead to increased losses during current transmission, which may cause local overheating, thereby affecting the life of the hardware and transmission stability. It is obtained based on a conductivity tester.

[0071] It needs to be explained that long-term exposure to the natural environment will cause the hardware to be affected by factors such as continuous mechanical stress, thermal expansion and contraction, causing fatigue and microcracks in the material. The decline in mechanical strength is a typical manifestation of hardware aging. The longer the service life, the easier it is for the mechanical strength to decrease. With the increase of years, oxidation and corrosion may occur on the contact surface of the hardware, resulting in reduced conductivity and lower current transmission efficiency. The increase in contact resistance will cause local overheating, further accelerating the degradation of the material and forming a vicious circle. The decline in mechanical strength may lead to a decrease in the tightness of the inside and contact parts of the hardware, resulting in poor contact and reduced current transmission efficiency. Especially in high stress or vibration environments, mechanical fatigue and conductivity loss may aggravate the decline in conductivity.

[0072] It needs to be explained that the above-mentioned service life, mechanical strength and electrical conductivity together constitute the key characteristics of the hardware status. The service life reflects the degree of aging of the hardware, the mechanical strength indicates its load-bearing capacity, and the electrical conductivity reflects its conductive performance. It can form a comprehensive evaluation of the operating status of the hardware to ensure a more scientific and systematic diagnosis. The second influencing factor obtained by analyzing the hardware characteristic data can provide a more accurate reference for the setting of the diagnostic threshold. The diagnostic threshold can more sensitively capture state changes, thereby improving the efficiency of fault identification. By analyzing the mechanical strength, electrical conductivity and other data of the hardware, its actual performance can be determined. If the hardware is in good condition, its service life can be extended. , avoid premature replacement; if the performance of the hardware is close to the threshold, it can be maintained or replaced in advance to extend the service life of other normal hardware. The comprehensive analysis of mechanical strength, conductivity and service life can effectively identify the fatigue degree and conductivity of the hardware, avoid failures caused by material degradation, looseness or decreased conductivity, ensure the safety of the hardware, and prevent breakage or short circuit caused by fatigue damage. The decrease in conductivity or mechanical strength of the hardware may cause uneven current distribution or loose structure, posing a threat to the safe operation of the line. The diagnostic threshold set by the second influencing factor can be identified and handled in the early stage of hardware degradation to avoid chain failures caused by local failures.

[0073] It needs to be explained that the second influencing factor of the use status of the above-mentioned transmission line hardware is analyzed in the following specific process:

[0074]

[0075] In the formula, β is the second influencing factor of the use status of transmission line fittings, nx is the service life of transmission line fittings, qd is the mechanical strength of transmission line fittings, dd is the conductivity of transmission line fittings, σ1 is the compensation factor of the set nx, σ2 is the compensation factor of the set qd, σ3 is the compensation factor of the set dd, and e is a natural constant.

[0076] It needs to be explained that the second influencing factor of the usage status of the above-mentioned transmission line fittings is calculated through the service life of the transmission line fittings, the mechanical strength of the transmission line fittings, and the electrical conductivity of the transmission line fittings. nx, qd, and dd are normalized. The service life, mechanical strength, and electrical conductivity cover the time aging, structural integrity, and conductive performance of the fittings. Through the comprehensive calculation of these factors, the second influencing factor can accurately reflect the aging status of the fittings, which is helpful to determine whether the fittings are still in a reliable state of use. The second influencing factor calculated comprehensively can identify possible failure trends in advance, enhance prediction accuracy, and issue early warnings. When the performance of the fittings begins to deteriorate, timely preventive maintenance measures based on the second influencing factor can slow down further degradation and extend the effective service life of the fittings. When the second influencing factor shows that the status of the fittings has deteriorated, maintenance can be arranged in time to avoid sudden failure of the equipment during use and improve the overall safety of the system.

[0077] Furthermore, the initial diagnosis threshold of the usage status of transmission line fittings is determined. The specific analysis process is: the first influencing factor of the usage status of transmission line fittings and the second influencing factor of the usage status of transmission line fittings are stored as designated tags, and the designated tag is compared with the initial diagnosis threshold of the usage status of transmission line fittings corresponding to each designated tag stored in the database to obtain the initial diagnosis threshold of the usage status of transmission line fittings corresponding to the designated tag.

[0078] In a specific embodiment, the influencing factors are stored as labels, and the initial diagnostic thresholds corresponding to similar labels can be quickly found from the database, avoiding the tedious recalculation process. The historical label data and the corresponding diagnostic thresholds stored in the database are used to ensure the reliability of the current diagnostic threshold. The historical data has been verified through actual operation and has a high reference value, which helps to reduce misjudgments and false alarms. Through label comparison, different diagnostic thresholds can be dynamically matched to flexibly adapt to different environments and changes in hardware status. According to the real-time changes in hardware characteristics, a more suitable threshold is dynamically selected for status judgment. Based on the labels and initial diagnostic thresholds in the database, it can be determined whether maintenance is needed for a specific state, avoiding excessive or insufficient maintenance caused by a unified threshold, making maintenance decisions more targeted, thereby reducing unnecessary maintenance work. The label comparison method based on the database can avoid errors caused by human subjective judgment, ensure the objectivity and consistency of the diagnosis process, and improve the credibility of the diagnosis results.

[0079] In this embodiment, the first reference diagnostic threshold corresponding to the first reference tag is determined by actual historical values:

[0080] The actual historical values ​​actually refer to the actual values ​​of the historical first influencing factors and the historical second influencing factors and the historical first benchmark diagnostic thresholds corresponding to the historical first influencing factors and the second influencing factors. The historical data of the transmission line hardware under different operating conditions are collected, including the actual values ​​of the historical first influencing factors and the historical second influencing factors and the historical first benchmark diagnostic thresholds corresponding to the historical first influencing factors and the second influencing factors. The actual values ​​of the historical first influencing factors, the second influencing factors and the historical first benchmark diagnostic thresholds corresponding to the historical first influencing factors and the second influencing factors are pre-stored in the first label comparison database to construct a one-to-one mapping set. The first benchmark label includes the first influencing factors and the second influencing factors of the historical transmission line hardware usage status with different values. The actual first influencing factors and the second influencing factors are marked as the first comparison label. The first comparison label is compared with the first benchmark label to screen out the first benchmark diagnostic threshold corresponding to the first benchmark label closest to the first comparison label. By comparing the similarity, the first benchmark diagnostic threshold corresponding to the first benchmark label closest to the first comparison label is screened out. The first benchmark diagnostic threshold corresponding to the first benchmark label closest to the first comparison label is the initial diagnostic threshold of the transmission line hardware usage status.

[0081] The wind characteristics of the near wind field are analyzed, and combined with the initial diagnostic threshold of the transmission line fittings usage status, the transmission line fittings usage status diagnostic threshold is determined.

[0082] The specific analysis process is as follows: obtaining a data set of wind characteristics in the near wind field, which specifically includes the wind energy density in the near wind field, the average wind speed in the near wind field, the turbulence intensity in the near wind field, and the wind direction deviation angle in the near wind field; based on the obtained data set of wind characteristics in the near wind field, a comprehensive analysis is performed to obtain the influencing factors of the diagnostic threshold of the usage status of transmission line fittings, and the influencing factors of the diagnostic threshold of the usage status of transmission line fittings are used as the analysis basis for determining the diagnostic threshold of the usage status of transmission line fittings.

[0083] It needs to be explained that the above-mentioned wind energy density is the size of wind energy per unit area, usually measured in watts per square meter, indicating the wind energy per square meter of cross-section. The wind energy density directly reflects the energy output potential of the wind farm. The higher the wind energy density, the richer the wind resources in the area, and the greater the mechanical load on the transmission line fittings. The wind energy density is usually calculated by the wind speed sensor and air pressure sensor in the wind measurement tower. The average wind speed refers to the average wind speed within a set time period (usually expressed in meters per second), indicating the overall wind speed level in the wind farm. The average wind speed reflects the intensity of the wind. A higher average wind speed will exert greater pressure on the transmission line fittings, making them more susceptible to fatigue and wear. The average wind speed is measured by an anemometer (such as a cup anemometer, ultrasonic anemometer) installed on the wind measurement tower. The equipment will continuously record the instantaneous wind speed and then calculate the average wind speed within the set period.

[0084] It needs to be explained that the above-mentioned turbulence intensity indicates the severity of wind speed fluctuations, usually expressed as a dimensionless value. The higher the turbulence intensity, the greater the fluctuation of wind speed, the more obvious the vibration and fatigue effects on hardware, which may lead to fatigue failure of mechanical structure or increased vibration. The turbulence intensity is calculated as the ratio of the standard deviation of wind speed to the average wind speed, usually expressed as a percentage (%). The turbulence intensity is usually obtained by an ultrasonic anemometer or a high-precision anemometer installed on a wind measurement tower. The wind direction deviation angle refers to the angular deviation between the actual wind direction and the dominant wind direction (usually the axial direction of the transmission line or wind turbine), usually in degrees. When the wind direction deviation angle is large, the hardware may be impacted by the lateral wind force, resulting in increased stress in the deviation direction, accelerated fatigue and damage, and the wind direction deviation angle is measured by a wind vane.

[0085] It needs to be explained that the above-mentioned high average wind speed will lead to higher wind energy density, indicating that the area has stronger wind energy resources. At the same time, it will also impose a greater load on the transmission line fittings, aggravating mechanical fatigue. When the average wind speed is high and the turbulence intensity is high, the wind speed fluctuation will cause uneven stress on the fittings, which is easy to aggravate its fatigue and loss; if the wind speed is stable but the turbulence intensity is low, the fatigue loss of the fittings will be small. When the wind direction deviation angle is large, the airflow will form a lateral impact and increase the turbulence intensity, causing the transmission line fittings to withstand vertical and horizontal stresses while also adapting to the lateral impact caused by changes in wind direction, thereby aggravating material fatigue. High wind energy density accompanied by low turbulence intensity is conducive to stable wind energy utilization, but if the turbulence intensity is high, despite the high wind energy density, the fittings will be subjected to irregular fluctuating loads, which may cause unstable mechanical stress. When the wind direction deviation angle is large, even if the wind energy density is high, the effective impact force of the wind will be weakened, but the fittings will still be affected by complex lateral and changing loads, making the overall load unstable.

[0086] It needs to be explained that the above-mentioned wind characteristics (such as wind speed, turbulence intensity, wind direction deviation, etc.) directly affect the mechanical load and stress state of the transmission line fittings. Comprehensive analysis of these wind characteristic data can more accurately reflect the usage status of the fittings in a specific wind field environment, thereby determining a more accurate diagnostic threshold and improving the accuracy of the diagnosis. The wind conditions in the near-wind field environment are complex and changeable. By analyzing the wind characteristic data, the diagnostic threshold can be dynamically adjusted to ensure that the diagnostic results can be updated in real time with the changes in wind field conditions, avoiding errors caused by fixed thresholds. Strong winds, turbulence and other factors in the near-wind field may cause instantaneous stress fluctuations in the fittings. If these wind characteristics are not considered, misjudgment of faults may occur. Analyzing wind characteristics can filter out short-term fluctuations caused by wind factors and reduce misjudgments. Wind characteristics directly affect the safety of transmission line fittings, especially under harsh conditions such as strong winds and turbulence. The fittings may be subjected to greater mechanical shocks. Reasonable diagnostic thresholds are set based on the influencing factors of wind characteristics, which can provide early warnings when fatigue or damage occurs in the fittings to prevent equipment failure.

[0087] It should be explained that the above-mentioned transmission line fittings usage status diagnosis threshold influencing factors, the specific analysis process is as follows:

[0088]

[0089] Wherein, γ is the influencing factor of the diagnosis threshold of the use status of transmission line hardware, md is the wind energy density in the near wind field, fs is the average wind speed in the near wind field, qd is the turbulence intensity in the near wind field, jd is the wind direction deviation angle in the near wind field, τ1 is the compensation factor of the set md, τ2 is the compensation factor of the set fs, τ3 is the compensation factor of the set qd, and τ4 is the compensation factor of the set jd.

[0090] It should be explained that the above-mentioned transmission line fittings diagnostic threshold influencing factors are calculated through wind energy density, average wind speed, turbulence intensity and wind direction deviation angle in the near wind field. MD, FS, QD and JD are normalized. Wind energy density, average wind speed, turbulence intensity and wind direction deviation angle jointly determine the mechanical load and stress condition of the fittings caused by the wind field environment. The influencing factors calculated based on these factors can accurately reflect the actual impact of the environment on the fittings status, which is helpful to set a more reasonable diagnostic threshold. The wind field environment is complex and changeable, and changes in wind speed, turbulence and wind direction will The influencing factors based on these dynamic environmental factors can significantly affect the stress condition of the hardware. The diagnostic threshold can be adjusted as the environment changes to ensure the accuracy of the status judgment. When the environmental load is low, the influencing factor will be relatively small, and the diagnostic threshold can be appropriately relaxed to avoid unnecessary maintenance. Under high load or extreme wind conditions, the threshold is more stringent to ensure that the equipment is maintained in a timely manner. Different wind farm environments have different forces on the hardware. The calculated influencing factors can help the diagnostic system identify higher-risk conditions and dynamically adjust the diagnostic threshold, so that early warnings can be issued when the hardware is actually subjected to a larger load.

[0091] Furthermore, the diagnostic threshold of the usage status of transmission line fittings is determined. The specific analysis process is: the influencing factor of the diagnostic threshold of the usage status of transmission line fittings and the initial diagnostic threshold of the usage status of transmission line fittings are stored as designated tags, and the designated tag is compared with the transmission line fitting usage status diagnostic threshold corresponding to each designated tag stored in the database to obtain the transmission line fitting usage status diagnostic threshold corresponding to the designated tag.

[0092] In a specific embodiment, by comparing the existing label data in the database, the diagnostic threshold under similar conditions can be accurately matched. The historical data provides a reference for the current judgment, so that the diagnostic threshold is more in line with the actual usage status and environmental impact of the current hardware. The label data in the database has been verified by actual operation and has a high degree of reliability. The comparison process not only makes the diagnostic threshold more scientific, but also utilizes verified empirical data to reduce the risk of misjudgment or deviation. The diagnostic threshold can be automatically adjusted with real-time changing data and working conditions. It has strong adaptability and is more in line with the use requirements of transmission line hardware in a changing environment. The diagnostic threshold obtained by comparing historical labels can achieve accurate judgment, and only necessary maintenance is performed when the status deviates, reducing resource waste, while ensuring the safety of the hardware status and enhancing the intelligence and automation level of the system.

[0093] In this embodiment, the second reference diagnostic threshold corresponding to the second reference tag is determined by actual historical values, specifically:

[0094] Collect historical data of transmission line fittings under different operating conditions, including historical transmission line fitting usage status diagnosis threshold influencing factors and transmission line fitting usage status initial diagnosis threshold, and actual values ​​of historical transmission line fitting usage status diagnosis threshold corresponding to the transmission line fitting usage status diagnosis threshold influencing factors and transmission line fitting usage status initial diagnosis threshold;

[0095] Pre-store the historical transmission line hardware usage status diagnosis threshold influencing factors and the transmission line hardware usage status initial diagnosis threshold, and the actual values ​​of the historical transmission line hardware usage status diagnosis threshold corresponding to the transmission line hardware usage status diagnosis threshold influencing factors and the transmission line hardware usage status initial diagnosis threshold into the second label comparison database;

[0096] The second reference label includes different values ​​of the transmission line hardware usage status diagnosis threshold influencing factors and the transmission line hardware usage status initial diagnosis threshold. The actual transmission line hardware usage status diagnosis threshold influencing factors and the transmission line hardware usage status initial diagnosis threshold are marked as second comparison labels. The second comparison label is compared with the second reference label, and the second reference diagnosis threshold corresponding to the second reference label closest to the first comparison label is screened out. By comparing the similarity, the second reference diagnosis threshold corresponding to the second reference label closest to the second comparison label is screened out. The second reference diagnosis threshold corresponding to the second reference label closest to the second comparison label is the transmission line hardware usage status diagnosis threshold.

[0097] Analyze the operating characteristics of transmission line fittings, and combine the diagnostic threshold of the transmission line fittings usage status to determine whether the transmission line fittings are in a qualified state: if the transmission line fittings are in a qualified state, enter the next monitoring cycle; if the transmission line fittings are in an unqualified state, maintain the transmission line fittings, and enter the next monitoring cycle after the maintenance of the unqualified transmission line fittings is completed.

[0098] The specific analysis process is as follows: obtaining a data set of transmission line fitting operation characteristics, and based on the obtained data set of transmission line fitting operation characteristics, obtaining a fitting operation characteristic evaluation value through comprehensive analysis. The fitting operation characteristic evaluation value is used as the analysis basis for judging whether the transmission line fitting usage status is qualified.

[0099] It needs to be explained that by integrating a variety of operating characteristic data (such as temperature, vibration frequency, current load, etc.), the judgment of the hardware status is more comprehensive, which can accurately reflect the actual status of the hardware and improve the accuracy of diagnosis. Through analysis of the actual measured data, the objectivity and accuracy of the judgment basis are ensured, and errors caused by lack of human experience or subjective judgment are avoided. The hardware data in the real-time monitoring and analysis environment are more adaptable and flexible, ensuring accurate diagnosis even in a changing environment. Pre-identification of potential faults can reduce line failures caused by hardware problems, ensure the reliability of transmission lines, and ensure stable power supply.

[0100] Furthermore, the transmission line hardware operation characteristic data set includes hardware tensile stress, hardware vibration frequency, hardware current load, total number of hardware cracks, and hardware surface salt spray deposition rate.

[0101] In a specific embodiment, the tensile stress of the hardware refers to the tensile stress generated when the hardware is subjected to external forces such as wire tension and wind load, which reflects the ability of the hardware to bear loads. It is monitored by installing a strain sensor or a stress sensor on the hardware to obtain real-time stress data. The vibration frequency of the hardware represents the vibration characteristics of the hardware under the action of external forces such as wind and airflow. A higher vibration frequency will accelerate the fatigue and loss of the hardware, which is recorded in real time by an acceleration sensor or a vibration sensor installed on the hardware. The current load of the hardware refers to the amount of current transmitted through the hardware, which directly affects its temperature and conductivity. The larger the current load, the more significant the impact on the thermal load of the hardware. A current sensor or a contact electrode is used. The resistance tester measures and evaluates the conductive state and heating conditions of the hardware by monitoring the current load. The total number of cracks in the hardware refers to the number of cracks on the surface or inside the hardware under the action of force, corrosion, etc. The increase in cracks will affect the mechanical strength and structural integrity of the hardware. The number of cracks is usually obtained by ultrasonic flaw detectors or X-ray detectors. These devices can detect tiny cracks inside the hardware to avoid structural failure. The salt spray deposition rate on the surface of the hardware reflects the amount of salt spray (especially in coastal or high-humidity areas) deposited on the surface of the hardware. When the salt spray deposition rate is high, the hardware is prone to corrosion, affecting its life. It is measured by salt spray sensors or surface corrosion detection equipment to determine the degree of salt spray corrosion on the hardware.

[0102] It needs to be explained that the above-mentioned tensile stress and vibration frequency are the mechanical reactions of the hardware under the action of external force. The increase in tensile stress will cause the hardware to bear a greater mechanical load, while high-frequency vibration will aggravate the fatigue of the hardware and material wear, especially in the stress concentration area. This fatigue effect can easily lead to the formation and expansion of tiny cracks. As the number of cracks increases, the overall strength of the hardware gradually decreases, making it more susceptible to structural damage under the combined action of tension and vibration. The current load reflects the conductive state and thermal load of the hardware. When the current load is large, the temperature of the hardware rises, and the thermal expansion effect will further aggravate the fatigue of the material. Combined with the tensile stress and vibration frequency, it will accelerate crack expansion and material aging. The increase in temperature will increase the reaction rate of surface salt spray, especially in coastal or high-humidity environments. Hardware with a higher salt spray deposition rate is more prone to corrosion, which accelerates crack formation and reduces conductivity.

[0103] Specifically, the operation characteristic evaluation value of transmission line hardware is analyzed as follows:

[0104]

[0105] Wherein, δ is the operating characteristic evaluation value of the transmission line fittings, yl is the tensile stress of the fittings, pl is the vibration frequency of the fittings, bl is the current load of the fittings, lw is the total number of cracks in the fittings, cj is the salt spray deposition rate on the surface of the fittings, μ1 is the compensation factor of the set yl, μ2 is the compensation factor of the set pl, μ3 is the compensation factor of the set bl, μ4 is the compensation factor of the set lw, and μ5 is the compensation factor of the set cj.

[0106] It needs to be explained that the above-mentioned transmission line fittings operation characteristic evaluation values ​​are calculated through fittings tensile stress, fittings vibration frequency, fittings current load, total number of fittings cracks, fittings surface salt spray deposition rate, yl, pl, bl, lw, cj are normalized, tensile stress, vibration frequency, current load, number of cracks and salt spray deposition rate cover the key mechanical, electrical and environmental characteristics of fittings, the evaluation value of these factors can comprehensively reflect the operating status of fittings, avoid misjudgment caused by a single indicator, the parameters are interrelated, and can jointly reveal potential failure trends, the combination of high tensile stress and vibration frequency is likely to cause an increase in cracks, while high salt spray deposition rate will accelerate corrosion, comprehensive evaluation can improve the accuracy of fault prediction, achieve early warning, maintenance through comprehensive evaluation values ​​in the early stage of state deterioration, help delay the degradation process of fittings, extend service life, regular monitoring and evaluation can take measures before small problems become major failures, the integration of multi-dimensional data makes the evaluation system flexible, ensuring the operation safety and diagnostic accuracy of fittings in a dynamic environment.

[0107] Further, such as Figure 2As shown, it is determined whether the use status of the transmission line fittings is qualified. The specific analysis process is: compare the transmission line fittings operation characteristic evaluation value with the transmission line fittings use status diagnosis threshold; if the transmission line fittings operation characteristic evaluation value is not lower than the transmission line fittings use status diagnosis threshold, then the transmission line fittings use status corresponding to the transmission line fittings operation characteristic evaluation value is qualified, and enter the next monitoring cycle; if the transmission line fittings operation characteristic evaluation value is lower than the transmission line fittings use status diagnosis threshold, then the transmission line fittings use status corresponding to the transmission line fittings operation characteristic evaluation value is unqualified, and the transmission line fittings need to be maintained. After the maintenance of the transmission line fittings with unqualified use status is completed, it enters the next monitoring cycle.

[0108] It needs to be explained that the above data-based approach ensures more accurate judgments, helps to identify unqualified hardware in a timely and accurate manner, and reduces the risk of underreporting or misjudgment. Timely maintenance when the assessment value is lower than the threshold can eliminate potential hidden dangers of failure, ensure that the hardware is always in a qualified state, and improve the reliability and safety of the system. By comparing the assessment value and the threshold, the maintenance strategy is optimized, and intervention is performed only when necessary, reducing the frequency of maintenance, saving time and resources, and improving the overall operation and maintenance efficiency. Timely maintenance of unqualified hardware effectively prevents power outages and safety hazards caused by hardware problems, and ensures the stable operation of the transmission system. Continuous data accumulation and comparative analysis can establish a long-term status monitoring model for the system, continuously optimize the diagnostic threshold and maintenance strategy, and achieve more intelligent operation and maintenance management.

[0109] Maintenance of transmission line fittings begins with surface cleaning and rust removal, especially cleaning of salt spray, dust and other pollutants accumulated on the surface of the fittings to prevent corrosion from worsening. Secondly, tightening inspection and reinforcement are carried out to ensure that the fittings remain stable when subjected to cable tension and external wind loads to prevent loosening or falling off. At the same time, the structure and material integrity of the fittings are tested, and non-destructive testing methods such as ultrasound or X-rays are used to detect internal cracks or minor damage to promptly repair potential hazards. In terms of electrical aspects, contact resistance tests and current load checks are carried out to ensure that the conductivity of the fittings is good and to avoid heating caused by excessive resistance. In addition, anti-corrosion coating repairs are carried out, especially in high humidity or salt spray environments. Re-coating of anti-corrosion materials can slow down the corrosion rate. Fittings with severe wear or structural problems need to be replaced to ensure the overall safety of the line.

[0110] In a specific embodiment, the average ambient temperature of the microclimate data set of the environment where the hardware is located is 30°C, the ambient ultraviolet intensity is 1.5mW / cm2, the average ambient humidity is 60%, the compensation factor of Wd is 0.8, the compensation factor of qd is 1.2, and the compensation factor of sd is 1.1. The first influencing factor of the use status of the transmission line hardware is calculated to be 111.73.

[0111] The service life of the transmission line fittings in the characteristic data set is 8 years, the mechanical strength is 95MPa, the conductivity is 5.8S / m, the compensation factor of nx is 1.5, the compensation factor of qd is 1.0, and the compensation factor of dd is 0.9. The second influencing factor of the transmission line fittings usage status is calculated to be 274.15, and the initial diagnostic threshold of the transmission line fittings usage status is determined to be 1.0.

[0112] The wind energy density in the near wind field wind characteristic data set is 450W / m2, the average wind speed is 12m / s, the turbulence intensity is 0.15, the wind direction deviation angle is 10 degrees, the compensation factor of md is 0.5, the compensation factor of fs is 1.3, the compensation factor of qd is 0.8, and the compensation factor of jd is 1.1. The influencing factor of the diagnostic threshold of the usage status of transmission line fittings is calculated to be -2.8876, and the diagnostic threshold of the usage status of transmission line fittings is determined to be -1.5.

[0113] The transmission line fittings operation characteristic data set has a tensile stress of 45MPa, a vibration frequency of 0.25Hz, a current load of 30A, a total number of cracks of 5, a salt spray deposition rate of 0.3%, a compensation factor of yl of 0.9, a compensation factor of pl of 1.1, a compensation factor of bl of 0.7, a compensation factor of lw of 1.2, and a compensation factor of cj of 1.0. The calculated operation characteristic evaluation value of the transmission line fittings is -1.77. Since the transmission line fittings operation characteristic evaluation value -1.77 is lower than the transmission line fittings usage status diagnosis threshold of -1.5, the transmission line fittings usage status corresponding to the transmission line fittings operation characteristic evaluation value is unqualified, and the transmission line fittings need to be maintained. After the maintenance of the transmission line fittings with unqualified usage status is completed, the next monitoring cycle is entered.

[0114] Embodiment 2

[0115] This embodiment discloses an intelligent diagnosis system for the operating status of hardware of power transmission lines near wind farms.

[0116] The intelligent diagnosis system for the operation status of transmission line hardware near wind farms includes:

[0117] The first influencing factor and the second influencing factor determination module are configured to: obtain a microclimate data set of the environment where the transmission line fittings are located, and calculate a first influencing factor of the use status of the transmission line fittings; obtain a characteristic data set of the transmission line fittings, and calculate a second influencing factor of the use status of the transmission line fittings;

[0118] The initial diagnosis threshold comparison module is configured to: use the first influencing factor and the second influencing factor of the use status of the transmission line hardware as the first comparison label, combine the pre-stored first reference label and the corresponding diagnosis threshold, and use the label comparison method to determine the initial diagnosis threshold of the use status of the transmission line hardware;

[0119] The diagnostic threshold comparison module is configured to: obtain a wind force characteristic data set of a near wind field, calculate the diagnostic threshold influencing factor of the use status of transmission line hardware, use the diagnostic threshold influencing factor of the use status of transmission line hardware and the initial diagnostic threshold of the use status of transmission line hardware as the second comparison label, combine the pre-stored second reference label and the corresponding diagnostic threshold, and determine the diagnostic threshold of the use status of transmission line hardware by label comparison;

[0120] The judgment module is configured to: obtain a data set of operation characteristics of transmission line fittings, calculate an evaluation value of the operation characteristics of the fittings, and judge whether the use status of the transmission line fittings is qualified in combination with a diagnosis threshold of the use status of the transmission line fittings.

[0121] Embodiment 3

[0122] The purpose of this embodiment is to provide a computer-readable storage medium.

[0123] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the method for intelligent diagnosis of the operating status of near-wind farm transmission line hardware as described in Example 1 of the present disclosure.

[0124] Embodiment 4

[0125] The purpose of this embodiment is to provide an electronic device.

[0126] An electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, the steps in the intelligent diagnosis method for the operating status of near-wind farm transmission line hardware as described in Example 1 of the present disclosure are implemented.

[0127] The steps involved in the apparatuses of the above embodiments 2, 3 and 4 correspond to the method embodiment 1, and the specific implementation methods can refer to the relevant description part of embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood to include any medium that can store, encode or carry an instruction set for execution by a processor and enable the processor to execute any method in the present invention.

[0128] Those skilled in the art should understand that the modules or steps of the present invention described above can be implemented by a general-purpose computer device, or alternatively, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

[0129] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. An intelligent diagnosis method for the operating status of power transmission line hardware near a wind farm, characterized in that: The following steps are involved: Obtain the microclimate data set of the environment where the transmission line fittings are located, and calculate the first influencing factor of the use status of the transmission line fittings; obtain the characteristic data set of the transmission line fittings, and calculate the second influencing factor of the use status of the transmission line fittings; The first influencing factor and the second influencing factor of the transmission line fitting usage status are used as the first comparison label, combined with the pre-stored first reference label and the corresponding diagnostic threshold, and the label comparison method is adopted to determine the initial diagnostic threshold of the transmission line fitting usage status; Obtain a data set of wind force characteristics in a near wind field, calculate the influencing factor of the diagnostic threshold of the use status of transmission line hardware, use the influencing factor of the diagnostic threshold of the use status of transmission line hardware and the initial diagnostic threshold of the use status of transmission line hardware as the second comparison label, combine the pre-stored second reference label and the corresponding diagnostic threshold, and use the label comparison method to determine the diagnostic threshold of the use status of transmission line hardware; Obtain the transmission line fittings operation characteristic data set, calculate the fittings operation characteristic evaluation value, and combine the transmission line fittings usage status diagnosis threshold to determine whether the transmission line fittings usage status is qualified.

2. The intelligent diagnosis method for the operation status of power transmission line hardware near wind farms according to claim 1 is characterized in that: Obtain the microclimate dataset of the environment where the transmission line fittings are located, and calculate the first influencing factor of the use status of the transmission line fittings, including: The microclimate data set of the environment where the hardware is located is obtained, including the average temperature, ultraviolet intensity and average humidity of the environment; The calculation formula of the first influencing factor of the use status of the transmission line hardware is: In the formula, α is the first influencing factor of the use status of transmission line fittings, wd is the average ambient temperature, qd is the ambient ultraviolet intensity, sd is the average ambient humidity, ε1 is the compensation factor of the set wd, ε2 is the compensation factor of the set qd, and ε3 is the compensation factor of the set sd.

3. The intelligent diagnosis method for the operation status of power transmission line hardware near wind farms according to claim 1 is characterized in that: Obtain the transmission line fittings feature data set and calculate the second influencing factor of the transmission line fittings usage status, including: The obtained transmission line fittings characteristic data set includes the service life of transmission line fittings, mechanical strength of transmission line fittings, and electrical conductivity of transmission line fittings; The calculation formula of the second influencing factor of the transmission line fittings usage status is: In the formula, β is the second influencing factor of the use status of transmission line fittings, nx is the service life of transmission line fittings, qd is the mechanical strength of transmission line fittings, dd is the conductivity of transmission line fittings, σ1 is the compensation factor of the set nx, σ2 is the compensation factor of the set qd, σ3 is the compensation factor of the set dd, and e is a natural constant.

4. The intelligent diagnosis method for the operation status of power transmission line hardware near wind farms according to claim 1, characterized in that: Obtain the wind force characteristic data set near the wind field and calculate the influencing factors of the diagnostic threshold of the transmission line hardware usage status, including: The acquired wind force characteristic data set of the near wind field includes wind energy density of the near wind field, average wind speed of the near wind field, turbulence intensity of the near wind field and wind direction deviation angle of the near wind field; The calculation formula of the transmission line fitting usage status diagnosis threshold influencing factor is: Wherein, γ is the influencing factor of the diagnosis threshold of the use status of transmission line hardware, md is the wind energy density in the near wind field, fs is the average wind speed in the near wind field, qd is the turbulence intensity in the near wind field, jd is the wind direction deviation angle in the near wind field, τ1 is the compensation factor of the set md, τ2 is the compensation factor of the set fs, τ3 is the compensation factor of the set qd, and τ4 is the compensation factor of the set jd.

5. The intelligent diagnosis method for the operation status of power transmission line hardware near wind farms according to claim 1 is characterized in that: Obtain the transmission line hardware operation characteristic data set and calculate the hardware operation characteristic evaluation value, including: The transmission line hardware operation characteristic data set includes hardware tensile stress, hardware vibration frequency, hardware current load, total number of hardware cracks and hardware surface salt spray deposition rate; The calculation formula of the hardware operation characteristic evaluation value is: Wherein, δ is the operating characteristic evaluation value of the transmission line fittings, yl is the tensile stress of the fittings, pl is the vibration frequency of the fittings, bl is the current load of the fittings, lw is the total number of cracks in the fittings, cj is the salt spray deposition rate on the surface of the fittings, μ1 is the compensation factor of the set yl, μ2 is the compensation factor of the set pl, μ3 is the compensation factor of the set bl, μ4 is the compensation factor of the set lw, μ5 is the compensation factor of the set cj, and e is a natural constant.

6. The intelligent diagnosis method for the operation status of power transmission line hardware near wind farms according to claim 1, characterized in that: Combined with the diagnostic threshold of the use status of transmission line fittings, it is judged whether the use status of transmission line fittings is qualified, including: Compare the hardware operation characteristic evaluation value with the transmission line hardware usage status diagnosis threshold: If the hardware operation characteristic evaluation value is not lower than the power transmission line hardware usage status diagnosis threshold, the corresponding power transmission line hardware usage status is judged to be qualified and enters the next monitoring cycle; If the evaluation value of the hardware operation characteristics is lower than the diagnostic threshold of the transmission line hardware usage status, the corresponding transmission line hardware usage status is judged to be unqualified, and the transmission line hardware is maintained. After the maintenance is completed, the next monitoring cycle begins.

7. The intelligent diagnosis method for the operation status of power transmission line hardware near wind farms according to claim 1 is characterized in that: Determine the initial diagnostic threshold for the use status of transmission line hardware, including: Establishing a first label comparison database, pre-storing a first reference label and a first reference diagnostic threshold corresponding to the first reference label, wherein the first reference label includes a first influencing factor and a second influencing factor of the use status of the power transmission line fittings with different values, and the first reference diagnostic threshold corresponding to the first reference label is determined by an actual historical value; Compare the first comparison tag with the first reference tag, and select the first reference diagnostic threshold corresponding to the first reference tag closest to the first comparison tag, so as to determine the initial diagnostic threshold of the use status of the power transmission line fittings; or, Establish a second label comparison database, pre-store a second reference label and a second reference diagnostic threshold corresponding to the second reference label, wherein the second reference label includes a transmission line hardware usage status diagnostic threshold influencing factor and a transmission line hardware usage status initial diagnostic threshold of different values, and the second reference diagnostic threshold corresponding to the second reference label is determined by an actual historical value; The second comparison tag is compared with the second reference tag, and the second reference diagnostic threshold corresponding to the second reference tag closest to the second comparison tag is screened out, so as to determine the transmission line fitting usage status diagnostic threshold.

8. Intelligent diagnosis system for the operation status of power transmission line hardware near wind farm, characterized by: include: The first influencing factor and the second influencing factor determination module are configured to: obtain a microclimate data set of the environment where the transmission line fittings are located, and calculate a first influencing factor of the use status of the transmission line fittings; obtain a characteristic data set of the transmission line fittings, and calculate a second influencing factor of the use status of the transmission line fittings; The initial diagnosis threshold comparison module is configured to: use the first influencing factor and the second influencing factor of the use status of the transmission line hardware as the first comparison label, combine the pre-stored first reference label and the corresponding diagnosis threshold, and use the label comparison method to determine the initial diagnosis threshold of the use status of the transmission line hardware; The diagnostic threshold comparison module is configured to: obtain a wind force characteristic data set of a near wind field, calculate the diagnostic threshold influencing factor of the use status of transmission line hardware, use the diagnostic threshold influencing factor of the use status of transmission line hardware and the initial diagnostic threshold of the use status of transmission line hardware as the second comparison label, combine the pre-stored second reference label and the corresponding diagnostic threshold, and determine the diagnostic threshold of the use status of transmission line hardware by label comparison; The judgment module is configured to: obtain a data set of operation characteristics of transmission line fittings, calculate an evaluation value of the operation characteristics of the fittings, and judge whether the use status of the transmission line fittings is qualified in combination with a diagnosis threshold of the use status of the transmission line fittings.

9. A computer-readable storage medium having a program stored thereon, characterized in that: When the program is executed by a processor, the steps of the intelligent diagnosis method for the operating status of near-wind farm transmission line hardware as described in any one of claims 1 to 7 are implemented.

10. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps in the intelligent diagnosis method for the operating status of near-wind farm transmission line hardware as described in any one of claims 1-7 are implemented.

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