Operation and maintenance management system for intelligently monitoring wind power generation equipment in mountainous area

Abstract

The invention relates to the technical field of operation and maintenance management, and discloses an operation and maintenance management system for intelligently monitoring wind power generation equipment in a mountainous area, and the system comprises a data collection module which comprises a sensor unit and a storage unit; the sensor unit comprises a temperature sensor and is used for acquiring the environment temperature of a mountainous area and the operation temperature of wind power generation equipment; the storage unit is used for storing various data of the wind power generation equipment and the mountain environment; the operation analysis module is used for acquiring the temperature rise rate of the wind power generation equipment according to the data of the wind power generation equipment and the mountain environment; and the early warning triggering module is used for triggering aging early warning according to the obtained temperature rise rate of the wind power generation equipment and prompting maintenance personnel to perform corresponding maintenance on the wind power generation equipment. By acquiring the temperature smooth value and further calculating the temperature increase rate according to the temperature smooth value, temperature fluctuation interference in a high-load period is effectively filtered, and a tiny temperature rise signal caused by equipment aging in a low-load period is amplified.

Description

Technical Field

[0001] This invention relates to the field of operation and maintenance management technology, specifically to an intelligent operation and maintenance management system for monitoring wind power generation equipment in mountainous areas. Background Technology

[0002] Mountain wind power generation equipment refers to a power generation system used to convert wind energy into electrical energy in complex mountainous environments. Its design must be adapted to high altitudes, steep terrain, and variable climate conditions. The equipment consists of core components such as blades, towers, and generators. The blades capture wind energy and convert it into mechanical energy, which is then accelerated by a gearbox to drive the generator to generate electricity. Finally, the electricity is transmitted to the grid through a control system. In the health monitoring of wind power equipment, temperature monitoring is one of the most direct and critical technical means. By acquiring temperature data of the core parts of the wind power equipment, processing the data, assessing the operating status of the wind power equipment, establishing a threshold model, avoiding false alarms when alarms are triggered, and finally making maintenance decisions. However, threshold models are usually built from historical data, making it difficult to track the trend of equipment performance degradation in real time. For example, when the bearing of a wind power equipment experiences continuous wear, the initial slight temperature rise may be smoothed out by historical data and missed, and thus ignored until the wear becomes more and more serious and causes more obvious abnormalities, making maintenance work more troublesome. Therefore, this invention provides an intelligent monitoring and maintenance management system for wind power generation equipment in mountainous areas to address the shortcomings of existing technologies. Summary of the Invention

[0003] The purpose of this invention is to provide an intelligent operation and maintenance management system for monitoring wind power generation equipment in mountainous areas, which solves the problem in existing technologies where the threshold model for temperature monitoring is usually established based on historical data, making it difficult to track the trend of equipment performance degradation in real time. The objective of this invention can be achieved through the following technical solutions: A smart operation and maintenance management system for monitoring wind power generation equipment in mountainous areas includes: The data acquisition module includes a sensor unit and a storage unit; The sensor unit includes a temperature sensor for acquiring the ambient temperature in the mountainous area and the operating temperature of the wind power generation equipment. The storage unit is used to store various data related to wind power generation equipment and the mountainous environment; The operation analysis module is used to obtain the temperature rise rate of the wind power generation equipment based on various data of the wind power generation equipment and the mountainous environment; The early warning trigger module triggers an aging warning based on the acquired rate of temperature increase of the wind power generation equipment, prompting maintenance personnel to carry out corresponding maintenance on the wind power generation equipment.

[0004] Preferably, the method for obtaining the temperature rise rate of the wind power generation equipment is... The process is as follows: The operating load of the wind power generation equipment is calculated based on the acquired data on the wind power equipment and the mountainous environment. ; Based on the operating temperature and operating load of the wind power generation equipment Obtain temperature smoothing value of wind power generation equipment ; Based on the temperature smoothing value of wind power generation equipment Calculate and obtain the temperature rise rate of wind power generation equipment .

[0005] Preferably, the wind power generation equipment operates under load Obtained through the following methods:

[0006] in, For the first The temperature variation curve of the mountainous environment obtained by Tian To monitor the start time, For the end time of monitoring, The average temperature curve of the mountainous area over the past month. For temperature standard deviation, This is the temperature influence coefficient. For the first The number of times the wind power generation equipment yaws is obtained daily. This represents the average number of yaws by wind turbines over the past month. The standard deviation of the number of yaws of wind power generation equipment. This represents the historical maximum number of yaws for wind power generation equipment. This is the yaw effect coefficient. For the first Wind speed variation curves in mountainous areas obtained by Tian Average wind speed variation curve in mountainous areas over the past month. The standard deviation of wind speed variation This is the wind speed impact index.

[0007] Preferably, the temperature smoothing value of the wind power generation equipment Obtained through the following methods: in, For the first The maximum operating temperature of wind power equipment obtained from the day For the first The maximum operating temperature of wind power equipment obtained from the day For the first The maximum operating temperature of wind power generation equipment obtained in a day.

[0008] Preferably, the sliding average temperature rise rate of the wind power generation equipment Obtained through the following methods:

[0009] in, For the first Temperature smoothing value of wind power generation equipment on a given day. For the first Temperature smoothing value of wind power generation equipment on a given day. For the first The load on the wind power generation equipment of the day, This is the load impact factor. This is the temperature compensation coefficient.

[0010] Preferably, the aging warning is triggered in the following manner: when and At the same time, inspect the lubrication system and heat dissipation device of the wind power generation equipment; when and When necessary, stop the machine for maintenance and troubleshoot transmission connections such as gearbox and bearings; in, This is a dynamic threshold.

[0011] The dynamic threshold The adjustment method is as follows: Obtaining temperature influence factors Wind speed influencing factors Yaw frequency influencing factors , when And when hour, ; when Or when hour, .

[0012] The beneficial effects of this invention are: 1. This invention acquires various data of wind power generation equipment and mountain environment through a data acquisition module. The operation analysis module calculates the operating load of the wind power generation equipment using data including ambient temperature, yaw rate, and mountain wind speed. Then, it calculates the temperature smoothing value by comprehensively calculating the operating load of the wind power generation equipment and the maximum operating temperature of the equipment over three consecutive days. Based on the temperature smoothing value, the temperature rise rate is calculated. Finally, the early warning triggering module triggers an aging warning based on the trend and cumulative amount of the temperature rise rate. This effectively filters the temperature fluctuation interference during high load periods and amplifies the subtle temperature rise signal caused by equipment aging during low load periods.

[0013] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0014] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a system module diagram of an intelligent monitoring and maintenance management system for wind power generation equipment in mountainous areas, as described in this invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Please see Figure 1 As shown, this invention is an intelligent monitoring and maintenance management system for wind power generation equipment in mountainous areas, comprising: The data acquisition module includes a sensor unit and a storage unit; The sensor unit includes a temperature sensor, used to acquire the ambient temperature in mountainous areas and the operating temperature of wind power generation equipment; The storage unit is used to store various data of the wind power generation equipment and the mountain environment, including the operating temperature of the wind power generation equipment, the number of times the wind power generation equipment yaws, the ambient temperature, and the wind speed in the mountain area. The operation analysis module is used to obtain the temperature rise rate of the wind power generation equipment based on various data of the wind power generation equipment and the mountainous environment; The early warning trigger module triggers an aging warning based on the acquired rate of temperature increase of the wind power generation equipment, prompting maintenance personnel to carry out corresponding maintenance on the wind power generation equipment.

[0018] Specifically, the data acquisition module consists of a sensor unit and a storage unit. The sensor unit consists of multiple temperature sensors, mainly used to collect the ambient temperature of the mountainous area where the wind power generation equipment is located and the operating temperature of the wind power generation equipment. The operating temperature of the wind power generation equipment is mainly the temperature of the planetary gearbox. The planetary gearbox is one of the most important parts of the wind power generation equipment. It is mainly used to transmit the rotation of the wind turbine blades to the generator, thereby realizing wind power generation. The temperature inside the planetary gearbox directly reflects the operating status of the planetary gears. When the components inside the planetary gearbox age, the temperature will gradually increase with the gradual increase of operating time, thus affecting the operation of the wind power generation equipment.

[0019] In the sensor unit implementation of the data acquisition module, the temperature sensor adopts a combination of a high-precision thermocouple sensor (accuracy ±0.5℃) and an infrared thermal imager: the thermocouple sensor is pre-embedded in key heat-generating parts of the wind turbine equipment, such as the planetary gearbox bearing housing, generator stator winding, and converter heat dissipation module, and collects single-point temperature data in real time every 10 minutes; the infrared thermal imager is installed in the middle of the equipment tower and performs a thermal imaging scan of the entire gearbox every hour to obtain the surface temperature field distribution. For the complex mountainous environment, all sensors are equipped with IP68-level protective housings that are moisture-proof and dust-proof, and data transmission is achieved through armored optical fibers, ensuring stable operation within a temperature range of -40℃ to 85℃ and an environment with 95% humidity.

[0020] The storage unit adopts a distributed database architecture, consisting of local edge computing nodes and cloud servers: local nodes are deployed at the wind farm booster station and use solid-state drives to store nearly 7 days of raw monitoring data in real time, supporting data transmission resumed after network outages; the cloud server synchronizes data in real time through 4G / 5G networks and uses a distributed file system to store historical data.

[0021] Obtain the temperature rise rate of wind power generation equipment The process is as follows: The operating load of the wind power generation equipment was calculated based on the acquired data on the wind power equipment and the mountainous environment. ; Based on the operating temperature and operating load of the wind power generation equipment Obtain temperature smoothing value of wind power generation equipment ; Based on the temperature smoothing value of wind power generation equipment Calculate and obtain the temperature rise rate of wind power generation equipment .

[0022] Wind power equipment operating load The data is mainly based on the ambient temperature in the mountainous area, the number of yaws of the wind power generation equipment, and the wind speed in the mountainous area. Specifically, the ambient temperature in the mountainous area directly affects the wind power generation equipment, while the number of yaws and the wind speed in the mountainous area are two key factors in the aging of the wind power generation equipment. Specifically, wind power generation equipment is equipped with wind vanes, anemometers, yaw systems, and braking systems. When wind speeds increase in mountainous areas, the yaw system controls the rotation of the wind power generation equipment to ensure it operates normally. This allows the fan blades to withstand appropriate pressure. During yaw, the wind power generation equipment continues to rotate, resulting in greater wind pressure and a heavier load on the planetary gearbox. This further increases wear on internal components and contributes to equipment aging.

[0023] Wind power equipment operating load Obtained through the following methods:

[0024] in, For the first The temperature variation curve of the mountainous environment obtained by Tian To monitor the start time, For the end time of monitoring, The average temperature curve of the mountainous area over the past month. For temperature standard deviation, This is the temperature influence coefficient. For the first The number of times the wind power generation equipment yaws is obtained daily. This represents the average number of yaws by wind turbines over the past month. The standard deviation of the number of yaws of wind power generation equipment. This represents the historical maximum number of yaws for wind power generation equipment. This is the yaw effect coefficient. For the first Wind speed variation curves in mountainous areas obtained by Tian Average wind speed variation curve in mountainous areas over the past month. The standard deviation of wind speed variation This is the wind speed impact index.

[0025] Specifically, wind power operating load The data was obtained by comprehensively calculating the ambient temperature in the main mountainous areas, the yaw rate of wind power generation equipment, and the wind speed in the mountainous areas. First, the ambient temperature in the mountainous areas was collected by temperature sensors, and the ambient temperature change curve in the mountainous areas was calculated. Obtained from the average temperature curve of the mountainous area over the past month The difference was calculated and compared with the temperature standard deviation. Compare and combine weighting coefficients This amplifies the impact of mountain temperatures on wind power generation equipment; Curve reflecting the ambient temperature of the day Compared with the average temperature curve of the past 30 days The degree of deviation is determined by the fact that when a cold wave suddenly hits a mountainous area, the viscosity of the equipment lubricating oil increases, causing a surge in gearbox load. At this time, the impact of abnormal temperature on the load is amplified by a weighting coefficient. However, during the high-temperature period in summer, the default setting is used to avoid oversensitivity.

[0026] By using the obtained yaw count Compared with the average The difference is calculated and then compared with the standard deviation of the number of yaws. The impact factor is obtained through comparison. This impact factor is then calculated against the maximum number of yaws. Since the number of yaws significantly affects the internal wear and tear of wind turbine equipment, the calculation result is amplified by squaring and incorporating weighting coefficients. The impact of yaw rate on wind power generation equipment is highlighted. By obtaining the wind speed variation curve in mountainous areas Combined with the average curve of wind speed variation in mountainous areas Obtain the difference and compare it with the standard deviation of wind speed change. By comparing and obtaining the influencing factors, the impact of wind speed on wind power generation equipment is further amplified by combining the square method with the weighting coefficients. The above three relatively intuitive parameters provide a clear picture of the operating load of wind power equipment, while the monitoring start time... The monitoring ends at midnight every day. Starting at midnight on the second day, the monitoring will be conducted daily on a 24-hour cycle, recording and storing various data related to the wind power generation equipment and the mountainous environment. After the monitoring period ends, the acquired monitoring data will be recorded and stored.

[0027] Temperature influence coefficient Yaw influence coefficient Wind speed influence coefficient Obtained through the following methods respectively:

[0028]

[0029]

[0030] Among them, among them, This refers to the vibration amplitude at the blade tip of a wind turbine generator. The maximum ambient temperature. This represents the maximum wind speed in the mountainous area. This is the curve showing the variation of rated wind speed. The rotation factor is the ratio of wind speed to the number of rotations. This represents the maximum number of yaw gear rotations per day. This refers to the yaw angle of the wind turbine generator. Wind direction in the mountainous area For the first Standard deviation of daily wind speed variation For the first Average daily wind speed variation The average of the standard deviations of wind speed variation. For the output power of wind power generation equipment, This is the ratio of the blade tip linear velocity to the wind speed. The diameter of the blades in a wind turbine generator. This is the planetary gear transmission ratio. and All are weighting coefficients.

[0031] Specifically, regarding the temperature influence coefficient The greater the deviation of the ambient temperature from the rated value, the more significant the heat dissipation efficiency of the wind power generation equipment. Conversely, when the heat dissipation power of the wind power generation equipment decreases, it indicates that the temperature of the wind power generation equipment will rise. Regarding the yaw effect coefficient When a wind turbine yaws, it is mainly driven by the yaw system. The yaw system controls the rotation of the wind turbine by driving a large gear through multiple small gears. The direction of rotation, number of rotations, and angle of the yaw system are affected by the wind speed and direction in the mountainous area. The greater the wind speed and the more varied the wind speed, the larger the yaw angle will be, and thus the number of rotations of the gears in the yaw system will also increase. Regarding the wind speed impact index Specifically, when the wind speed in mountainous areas is high, it has a more direct impact on the vibration amplitude of the blade tip of the wind power generation equipment and the output power of the wind power generation equipment. The output power of the wind power generation equipment is directly affected by the planetary gear transmission ratio and the blade speed. Moreover, when the wind force is greater, the output power of the wind power generation equipment will gradually increase until the rated power stabilizes. By dynamically acquiring various impact indices, the operating load of wind power equipment is obtained in a dynamic scenario, thus enabling more flexible acquisition of the operating load of wind power equipment.

[0032] Temperature smoothing value of wind power generation equipment Obtained through the following methods: in, For the first The maximum operating temperature of wind power equipment obtained from the day For the first The maximum operating temperature of wind power equipment obtained from the day For the first The maximum operating temperature of wind power generation equipment obtained in a day.

[0033] Specifically, the temperature smoothing value of wind power generation equipment The data was obtained by weighting the maximum temperature of wind power equipment over three consecutive days based on load weight. , , For example, divide by the corresponding load respectively. , , The factors that are inversely proportional to the load are obtained, and the average value of the three factors is calculated by summing them. Dividing this average by the corresponding load is primarily to determine that higher loads generally result in higher temperatures, which is normal and is influenced by aging. However, when the load is low but the temperature is high, it indicates equipment aging or malfunction. This is achieved by considering the operating load of the wind power equipment. Temperature data is dynamically weighted to suppress the interference of temperature fluctuations during high-load periods on trend analysis, while amplifying the weight of temperature changes during low-load periods, so that the smoothed temperature series can more clearly expose the aging of the equipment itself.

[0034] When wind power equipment operates under heavy load, for example When it is 2, The value is 0.5. Due to the strong wind, the equipment continues to yaw, and the temperature of the planetary gears gradually increases. However, this temperature increase is mainly caused by short-term overload, and its weight is relatively low. When the wind power generation equipment is operating under normal load, for example When it is 1.2, The value is 0.833, which is relatively normal. However, when the wind power generation equipment is operating at a low load... When the value is 0.6 and there is no wind, at this time If the value is 1.667, then the weight is relatively large, which means that the internal temperature of the equipment is still high. Therefore, this high temperature is mostly caused by internal defects in the equipment.

[0035] For example, the The ambient temperature was 20 degrees Celsius. The operating load of the wind power generation equipment is 55 degrees Celsius. It is 0.6; No. The ambient temperature was 25 degrees Celsius. The operating load of the wind power generation equipment is 58 degrees Celsius. It is 1.2; No. The ambient temperature was 30 degrees Celsius. The operating load of the wind power generation equipment is 65 degrees Celsius. When it is 2.0, the first Temperature smoothing value of wind power generation equipment for: This allows for a more intuitive demonstration of what appears to be from... Heaven to the First Heaven to the First The temperature of wind power equipment continued to decrease, but it was significantly affected by changes in actual load.

[0036] And through Eliminate the direct impact of load on temperature rise, when When this happens, the calculated temperature rise rate automatically decreases to avoid misinterpreting normal overload temperature rise as equipment aging. At the same time, the temperature rise signal is amplified to capture subtle anomalies, thus reflecting the temperature rise caused by equipment aging.

[0037] Wind power equipment sliding average temperature rise rate Obtained through the following methods:

[0038] in, For the first Temperature smoothing value of wind power generation equipment on a given day. For the first Temperature smoothing value of wind power generation equipment on a given day. For the first The load on the wind power generation equipment of the day, This is the load impact factor. This is the temperature compensation coefficient.

[0039] Specifically, using the smoothed temperature values ​​of two adjacent days. , The difference is used as the original temperature rise rate, divided by the daily load index to eliminate the direct impact of load on temperature rise. When the load is higher, the temperature rise rate is further reduced, and then multiplied by the load influence coefficient. Adjusting weights under different loads, load impact coefficient Operating load of wind power generation equipment Regarding the operating load of wind power equipment When the load is large, for example when the wind power equipment is operating under load. hour, Finally, with temperature compensation item Summation corrects for calculation bias under extreme loads; Temperature compensation item The compensation is related to the ambient temperature in the mountainous area at the time, and is adjusted for extreme temperature differences. For example, when the temperature difference in the mountainous area is large in winter, the compensation is made to compensate for the impact of a sudden drop in ambient temperature on the heat dissipation of the equipment surface, avoiding misjudgments caused by changes in heat dissipation conditions. Conversely, when the temperature difference in the mountainous area is small, the compensation is reduced accordingly. hour, ,when hour, ,when hour, ; By using three methods—wind power load, dynamic adjustment of load weight, and temperature compensation—the true temperature increase trend caused by internal equipment defects is further highlighted.

[0040] Aging warnings are triggered in the following ways: Aging warnings are triggered in the following ways: when and At the same time, inspect the lubrication system and heat dissipation device of the wind power generation equipment; when and When necessary, stop the machine for maintenance and troubleshoot transmission connections such as gearbox and bearings; in, This is a dynamic threshold.

[0041] By acquiring the temperature rise trend and cumulative temperature rise value of wind power generation equipment, the degree of equipment aging can be graded. Specifically, when the temperature rise rate of wind power generation equipment shows an upward trend for five consecutive days, and the cumulative temperature rise is greater than [a certain value], the aging level of the equipment can be classified. When the condition is critical, it indicates that the lubrication system and cooling fan of the wind power equipment need maintenance. Further analysis of the gearbox lubricating oil deterioration can be conducted using online oil monitoring sensors. If the viscosity change rate is too high or the concentration of iron-based abrasive particles is too high, an oil change should be arranged immediately. When the temperature rise of wind power equipment shows an upward trend for three consecutive days, and the average temperature rise over these three days is greater than... When the wear of the planetary gearbox and bearing connection of the wind power generation equipment is relatively severe, vibration sensors can be used to collect the acceleration signal of the gearbox bearing housing to identify gear cracks or bearing peeling faults. If the amplitude of characteristic frequencies, such as gear meshing frequency or bearing fault characteristic frequency, exceeds three times the alarm threshold, the machine should be stopped immediately for inspection. The main shaft torque sensor data can also be checked at the same time. If the torque fluctuation coefficient is large, check for loose coupling bolts or misalignment of the gearbox input shaft. By combining short-term and medium-term trends, we can capture both rapidly developing serious faults such as accelerated temperature rise in the early stages of bearing seizure and gradual aging faults such as cumulative temperature rise caused by slow deterioration of lubricating grease. This provides clearer guidance for operation and maintenance personnel, avoids misjudgment or omission due to a single threshold, and reduces the risk of unplanned downtime, thereby improving the safety and economy of wind farm equipment operation in mountainous areas.

[0042] Dynamic threshold The adjustment method is as follows: Obtaining temperature influence factors Wind speed influencing factors Yaw frequency influencing factors , when And when hour, ; when Or when hour, .

[0043] Specifically, ambient temperature has a direct impact on wind power generation equipment. When the ambient temperature is high, the corresponding wind power generation equipment will naturally be hotter. Therefore, when the temperature influence factor is greater than the wind speed influence factor and the yaw rate influence factor, the temperature rise of the equipment is in a relatively normal situation, and the threshold can be appropriately increased. Conversely, wind speed and yaw rate have a more direct impact on the aging of wind power generation equipment. The more yaw rate, the more severe the wear on the gearbox. The higher the wind speed, the greater the vibration impact on various components of the wind power generation equipment. Therefore, when either the wind speed impact factor or the yaw impact factor is greater than the temperature impact factor, the threshold needs to be reduced to more accurately detect the aging of the equipment.

[0044] The above description is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined in the claims, they should all fall within the protection scope of the present invention.

Claims

1. A smart monitoring and maintenance management system for wind power generation equipment in mountainous areas, characterized in that, include: The data acquisition module includes a sensor unit and a storage unit; The sensor unit includes a temperature sensor for acquiring the ambient temperature in the mountainous area and the operating temperature of the wind power generation equipment. The storage unit is used to store various data related to wind power generation equipment and the mountainous environment; The operation analysis module is used to obtain the temperature rise rate of the wind power generation equipment based on various data of the wind power generation equipment and the mountain environment. It also calculates the wind power generation operation load by comprehensively considering the mountain environment temperature, the number of yaws of the wind power generation equipment, the influence coefficient of mountain wind speed and temperature, the yaw influence coefficient, and the wind speed influence coefficient. The smoothed temperature value of the wind power equipment is calculated based on the temperature of the wind power equipment over three consecutive days. The rate of temperature rise of wind power equipment is calculated by combining the difference between the temperature smoothing value of the current day and the temperature smoothing value of the adjacent day with the overall wind power operation load of the current day. The early warning trigger module triggers an aging warning based on the acquired rate of temperature increase of the wind power generation equipment, prompting maintenance personnel to carry out corresponding maintenance on the wind power generation equipment.

2. The intelligent monitoring and maintenance management system for wind power generation equipment in mountainous areas according to claim 1, characterized in that, The rate of temperature rise of the wind power generation equipment is obtained. The process is as follows: The operating load of the wind power generation equipment is calculated based on the acquired data on the wind power equipment and the mountainous environment. ; Based on the operating temperature and operating load of the wind power generation equipment Obtain temperature smoothing value of wind power generation equipment ; Based on the temperature smoothing value of wind power generation equipment Calculate and obtain the temperature rise rate of wind power generation equipment .

3. The intelligent monitoring and maintenance management system for wind power generation equipment in mountainous areas according to claim 2, characterized in that, The operating load of the wind power generation equipment Obtained through the following methods: ； in, For the first The temperature variation curve of the mountainous environment obtained by Tian To monitor the start time, For the end time of monitoring, The average temperature curve of the mountainous area over the past month. For temperature standard deviation, This is the temperature influence coefficient. For the first The number of times the wind power generation equipment yaws is obtained daily. This represents the average number of yaws by wind turbines over the past month. The standard deviation of the number of yaws of wind power generation equipment. This represents the historical maximum number of yaws for wind power generation equipment. This is the yaw effect coefficient. For the first Wind speed variation curves in mountainous areas obtained by Tian The average wind speed change curve in mountainous areas over the past month. The standard deviation of wind speed variation This is the wind speed impact index.

4. The intelligent monitoring and maintenance management system for wind power generation equipment in mountainous areas according to claim 2, characterized in that, The temperature smoothing value of the wind power generation equipment Obtained through the following methods: in, For the first The maximum operating temperature of wind power equipment obtained from the day For the first The maximum operating temperature of wind power equipment obtained from the day For the first The maximum operating temperature of wind power generation equipment obtained in a day.

5. The intelligent monitoring and maintenance management system for wind power generation equipment in mountainous areas according to claim 2, characterized in that, The sliding average temperature rise rate of the wind power generation equipment Obtained through the following methods: ； in, For the first Temperature smoothing value of wind power generation equipment on a given day. For the first Temperature smoothing value of wind power generation equipment on a given day. For the first The load on the wind power generation equipment of the day, This is the load impact factor. This is the temperature compensation coefficient.

6. The intelligent monitoring and maintenance management system for wind power generation equipment in mountainous areas according to claim 5, characterized in that, The aging warning is triggered in the following ways: when and At the same time, inspect the lubrication system and heat dissipation device of the wind power generation equipment; when and When necessary, stop the machine for maintenance and troubleshoot the gearbox, bearings, and transmission connections. in, This is a dynamic threshold.

7. The intelligent monitoring and maintenance management system for wind power generation equipment in mountainous areas according to claim 6, characterized in that, The dynamic threshold The adjustment method is as follows: Obtaining temperature influence factors Wind speed influencing factors Yaw frequency influencing factors , when And when hour, ; when Or when hour, .

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