A remote monitoring and inspection system for wind turbine generator sets

Abstract

The present invention discloses a remote monitoring, inspection, and monitoring system for wind turbine generator sets, relating to the technical field of wind power generation monitoring system equipment. The system comprises a data acquisition unit, a state analysis unit, a health analysis unit, a fatigue analysis unit, and a processing unit. It analyzes the operating state of the wind turbine generator set step by step, achieving abnormality identification, health assessment, fatigue judgment, and power-off control. By constructing a multi-layered, nested operation assessment system centered on a power response index, a vibration-temperature response index, a fusion health state index, and a gearbox composite fatigue index, the present invention effectively addresses the problems of coarse state judgment granularity, a single response mechanism, and non-closed-loop fault logic in traditional systems. This system makes fault identification more sensitive, the response more hierarchical, and controllable, significantly enhancing the intelligent diagnostic capabilities and safety control level of the wind turbine remote monitoring system.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power generation monitoring system equipment, in particular to a wind power generator set remote monitoring and inspection station system. Background Art

[0002] With the rapid development of wind power technology, large-scale wind turbines have been widely deployed in coastal, mountainous, and offshore areas with abundant wind resources. Wind turbines are complex in structure and operate in a volatile environment. Their core components, such as blades, main shafts, gearboxes, generators, and bearings, are constantly exposed to high loads, high fatigue, severe vibration, and complex climatic conditions. These components are prone to operational anomalies such as wear, aging, loosening, and cracking. To ensure the operational safety and power generation efficiency of wind turbines, remote monitoring systems have emerged to enable real-time acquisition of wind turbine operating status, remote viewing, alarm response, and preliminary diagnosis.

[0003] In recent years, patrol and monitoring systems, as an extension of remote monitoring, have gradually been applied to unmanned wind farms to achieve auxiliary diagnosis and intelligent decision-making support for the operating status of key components. Existing monitoring systems mostly rely on SCADA platforms to collect basic signals such as voltage, current, wind speed, and power. Some systems deploy multi-modal sensors such as image, infrared, and sound to achieve visual perception of the external status of the unit. However, with the increase in the size and intelligence of units, relying solely on traditional parameter monitoring and abnormal alarms can no longer meet the actual operation and maintenance needs of fatigue evolution trends, health status changes, and core risk positioning of key components under complex working conditions. Therefore, how to realize intelligent patrol, health assessment and accurate early warning of wind turbines based on multi-source operation data has become an important technical problem to be solved in the field of wind power operation and maintenance.

[0004] The prior art, such as the invention patent application with announcement number: CN115693926A, discloses a remote monitoring and inspection system for wind turbines, which includes a host control platform, a monitoring and display terminal, and a data acquisition module. The host control platform stores critical values ​​of operating parameter data. The host control platform can compare the received operating data with the critical value. The critical value is input and stored inside the host control platform when in use. The critical value is the data value of the equipment when it is working normally, which is obtained based on the original detection data. When the detected data exceeds the critical value, the equipment fails. The data acquisition module includes an external tower warning module, an infrared detection module, a sound detection module, and a tower safety detection module. The characteristics of the intelligent patrol system are utilized to increase the patrol depth, expand the patrol range, strengthen background management, and unify the background; considering the analysis capabilities of the intelligent patrol system, etc., data analysis and management are strengthened, and system intelligent analysis and background analysis are strengthened. The system can realize global patrol scheduling of cameras.

[0005] Based on the above solution, it is found that the limitations of the existing technology include at least the following problems. The current remote monitoring system of wind turbines generally focuses on intuitive monitoring of external images, sounds or surface operating data. Although it can issue alarm signals when obvious abnormalities occur, it lacks the overall ability to conduct multi-level step-by-step analysis based on core operating parameters such as power response and bearing temperature rise. Especially when facing early potential faults or progressive fatigue processes, there is a lack of clear response paths and judgment logic, and technical shortcomings of abnormal signals are prone to occur, resulting in false alarms, missed judgments or delayed responses, which is not conducive to achieving accurate perception of the wind turbine operating status and dynamic closed-loop control. Summary of the Invention

[0006] In view of the shortcomings of the existing technology, the present invention provides a remote monitoring and inspection system for wind turbines, which solves the problem in the existing technology of lacking an intelligent step-by-step analysis and risk judgment mechanism for the internal status of wind turbines.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a remote monitoring and inspection station system for wind turbine generator sets, comprising: a data acquisition unit for acquiring power status data and bearing status data of the wind turbine generator set, and analyzing the power response index and vibration-temperature response index of the wind turbine generator set; a status analysis unit for judging and analyzing the power response index and vibration-temperature response index of the wind turbine generator set with preset power response evaluation intervals and vibration-temperature response evaluation intervals respectively; a health analysis unit for analyzing the fused health status index of the wind turbine generator set when there are abnormalities in the power response index and vibration-temperature response index of the wind turbine generator set, and comparing it with the preset health evaluation intervals The fatigue analysis unit is used to analyze the composite fatigue index of the gearbox of the wind turbine when the fusion health status index of the wind turbine is outside the preset health assessment interval, and to perform a judgment analysis with the preset fatigue assessment interval. If the composite fatigue index of the gearbox of the wind turbine is within the preset fatigue assessment interval, a health abnormality warning is sent. The processing unit is used to execute power-off measures on the wind turbine and send a gearbox abnormality alarm when the composite fatigue index of the gearbox of the wind turbine is outside the preset fatigue assessment interval.

[0008] Furthermore, the power status data includes actual output power and rated output power, and the bearing status data includes current bearing temperature value and set bearing temperature value.

[0009] Furthermore, the specific steps for obtaining the power response index of the wind turbine generator set are as follows: obtaining the regional wind speed value within the set area of ​​the wind turbine generator set; comprehensively analyzing the actual output power and rated output power of the wind turbine generator set in combination with the regional wind speed value within the set area of ​​the wind turbine generator set to obtain the power response index of the wind turbine generator set.

[0010] Furthermore, the specific steps for obtaining the vibration-temperature response index of the wind turbine generator set are as follows: obtaining the maximum allowable temperature rise value and vibration acceleration index of the bearing of the wind turbine generator set; comprehensively analyzing the current temperature value and bearing parameter temperature value of the wind turbine generator set in combination with the maximum allowable temperature rise value and vibration acceleration index of the bearing of the wind turbine generator set to obtain the vibration-temperature response index of the wind turbine generator set.

[0011] Furthermore, the specific steps for obtaining the vibration acceleration index of the wind turbine generator set are as follows: obtaining the current vibration acceleration values ​​of several vibration components of the wind turbine generator set; and performing a comprehensive analysis on the current vibration acceleration values ​​of several vibration components of the wind turbine generator set to obtain the vibration acceleration index of the wind turbine generator set.

[0012] Furthermore, the specific steps for analyzing the fusion health status index of the wind turbine generator set are as follows: obtain the rated wind speed value of the wind turbine generator set; input the actual output power, rated output power, vibration acceleration index, rated wind speed value, and regional wind speed value in the set area of ​​the wind turbine generator set into the preset health analysis model for health analysis to obtain the fusion health status index of the wind turbine generator set.

[0013] Furthermore, the health analysis model is specifically as follows: ;in, is the integrated health status index of the wind turbine generator set, is the actual output power of the wind turbine generator set, is the power influence coefficient stored in the database, is the regional wind speed value within the set area of ​​the wind turbine generator set, is the rated wind speed of the wind turbine generator set, is the wind speed influence coefficient stored in the database, is the vibration acceleration index of the wind turbine generator set, is the vibration response coefficient stored in the database, is the rated output power of the wind turbine generator set, is the vibration amplification factor stored in the database.

[0014] Furthermore, the specific steps for analyzing the composite fatigue index of the gearbox of the wind turbine generator set are as follows: obtaining the oil temperature value, oil temperature parameter set, speed comprehensive index, and speed parameter index in the gearbox of the wind turbine generator set, wherein the oil temperature parameter set includes a first oil temperature threshold and a second oil temperature threshold; inputting the actual output power, rated output power, oil temperature value in the gearbox, oil temperature parameter set, speed comprehensive index, and speed parameter index of the wind turbine generator set into a preset fatigue analysis model for fatigue analysis to obtain the composite fatigue index of the gearbox of the wind turbine generator set.

[0015] Furthermore, the specific steps for obtaining the comprehensive speed index of the wind turbine generator set are as follows: obtaining the main shaft speed value and the generator speed value of the wind turbine generator set; performing a comprehensive analysis on the main shaft speed value and the generator speed value of the wind turbine generator set to obtain the comprehensive speed index of the wind turbine generator set.

[0016] Furthermore, the fatigue analysis model is specifically as follows: ;in, is the composite fatigue index of the gearbox of the wind turbine generator set, is the oil temperature in the gearbox of the wind turbine generator set, is the low-temperature secondary thermal friction coefficient stored in the database, is the low temperature linear thermal friction coefficient stored in the database, is the low temperature base offset coefficient stored in the database, is the comprehensive speed index of the wind turbine generator set, is the speed parameter index of the wind turbine generator set, is the speed coupling coefficient stored in the database, is the first oil temperature threshold of the wind turbine generator set, is the medium temperature three-stage thermal friction coefficient stored in the database, is the medium temperature secondary thermal friction coefficient stored in the database, is the second oil temperature threshold of the wind turbine generator set, is the high temperature explosion benchmark coefficient stored in the database, is a natural constant, is the high temperature exponential growth coefficient stored in the database, is the actual output power of the wind turbine generator set, is the rated output power of the wind turbine generator set, is the high temperature load amplification factor stored in the database.

[0017] The present invention has the following beneficial effects:

[0018] (1) The remote monitoring and inspection station system of the wind turbine generator set has built a multi-layer nested operation evaluation system with power response index, vibration temperature response index, integrated health status index and gearbox composite fatigue index as the core. The system first calculates the power response index based on wind speed and power deviation, and calculates the vibration temperature response index by combining bearing temperature rise and vibration coupling. In any abnormality, it further integrates multi-source indicators to construct a health status index to form a middle-level risk judgment. Then, when the health status is abnormal, it introduces the fatigue modeling level, derives the gearbox composite fatigue index and triggers corresponding disposal. Each index has a logical dependency and data input inheritance, which prevents false alarms, missed alarms and unnecessary shutdowns. This structure effectively solves the problems of coarse state judgment granularity, single response mechanism and non-closed-loop fault logic in traditional systems, making fault identification more sensitive, response more hierarchical and controllable, and significantly improving the intelligent diagnosis capability and safety control level of the wind turbine remote monitoring system.

[0019] (2) The remote monitoring and inspection station system of the wind turbine generator set accurately constructs the power response index and the vibration temperature response index to identify the potential risks of the wind turbine generator set under complex operating conditions. The power response index establishes an exponential decay relationship through the actual output power, rated output power and regional wind speed, and introduces the wind speed response coefficient and wind speed adjustment coefficient obtained by training in the database, so as to effectively reflect the dynamic changes in wind energy utilization efficiency; the vibration temperature response index describes the nonlinear composite relationship between the bearing thermal load and mechanical shock through the current bearing temperature, reference temperature and allowable temperature rise, combined with the current vibration acceleration index. These two indicators no longer rely on a single threshold judgment, but are classified based on the modeling results and the set evaluation interval. They have trend, adjustability and strong interpretability, and can identify micro-anomalies that are difficult for traditional systems to detect, such as high wind speed and low power or light vibration but fast temperature rise, earlier. They greatly improve the recognition sensitivity of fault precursors and are an important reinforcement for the existing monitoring system.

[0020] (3) The remote monitoring and inspection system of the wind turbine generator set has realized the refined analysis and automatic power-off protection of the core transmission components under high-risk conditions by establishing a gearbox composite fatigue index model. The model takes oil temperature as the main axis variable and is divided into three sections: low temperature, medium temperature and high temperature. The quadratic polynomial, cubic polynomial and exponential models are used to simulate the oil temperature fatigue relationship respectively. At the same time, the speed comprehensive index, power normalization value and speed amplification coefficient are introduced to characterize the fatigue aggravation effect of long-term high-speed operation and high load. The coefficients of each section are fitted with historical data and stored in the database, which improves the adaptability and accuracy of the model. The system determines the level of the current index based on the set fatigue assessment interval. If it enters the high-risk area, the power-off mechanism is immediately triggered by the processing unit and the gearbox fault alarm is pushed, so that effective isolation is implemented before the fatigue reaches critical damage, avoiding risks such as transmission chain breakage, motor reverse twisting, and major shutdown accidents. This structure fills the gap of traditional system abnormality detection but no risk evolution reasoning, and realizes the whole process linkage control from risk identification to safe disposal.

[0021] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a block diagram of a remote monitoring and inspection station system for wind turbine generator sets according to the present invention.

[0023] Figure 2 The present invention provides a flow chart of the specific steps for obtaining the vibration-temperature response index of a wind turbine generator set in a remote monitoring and inspection station system for a wind turbine generator set.

[0024] Figure 3 This is a broken line graph of the current vibration acceleration of five vibrating components in a remote monitoring and inspection station system for a wind turbine generator set according to the present invention. DETAILED DESCRIPTION

[0025] See also Figure 1The embodiment of the present invention provides a technical solution: a remote monitoring and inspection station system for a wind turbine generator set, comprising: a data acquisition unit for acquiring power status data and bearing status data of the wind turbine generator set, and analyzing the power response index and vibration-temperature response index of the wind turbine generator set; a status analysis unit for judging and analyzing the power response index and vibration-temperature response index of the wind turbine generator set with preset power response evaluation intervals and vibration-temperature response evaluation intervals respectively; a health analysis unit for analyzing the fused health status index of the wind turbine generator set when an abnormality occurs in the power response index and vibration-temperature response index of the wind turbine generator set (that is, when any one of the power response index and the vibration-temperature response index is outside the corresponding evaluation interval), and comparing it with the preset The health assessment interval is used for judgment and analysis. If the fusion health status index of the wind turbine set is within the preset health assessment interval, no measures are taken; the fatigue analysis unit is used to analyze the composite fatigue index of the gearbox of the wind turbine set when the fusion health status index of the wind turbine set is outside the preset health assessment interval, and to perform judgment and analysis with the preset fatigue assessment interval. If the composite fatigue index of the gearbox of the wind turbine set is within the preset fatigue assessment interval, a health abnormality warning is sent to relevant staff; the processing unit is used to execute power-off measures on the wind turbine set when the composite fatigue index of the gearbox of the wind turbine set is outside the preset fatigue assessment interval, and to send a gearbox abnormality alarm to relevant staff.

[0026] The power status data includes the actual output power and the rated output power, and the bearing status data includes the current bearing temperature value and the bearing parameter temperature value.

[0027] The actual output power can be calculated in real time from the current and voltage of the generator.

[0028] The rated output power can be extracted from the wind turbine technical specification sheet or the equipment nameplate.

[0029] The current bearing temperature value can be measured and obtained through a temperature sensor.

[0030] The bearing parameter temperature value can be extracted from the wind turbine technical specification sheet or equipment nameplate.

[0031] Specifically, the specific steps for obtaining the power response index of a wind turbine generator set are as follows: obtaining the regional wind speed value within the set area of ​​the wind turbine generator set; comprehensively analyzing the actual output power and rated output power of the wind turbine generator set in combination with the regional wind speed value within the set area of ​​the wind turbine generator set to obtain the power response index of the wind turbine generator set.

[0032] Among them, the regional wind speed value can be measured and obtained by a wind speed sensor.

[0033] The specific formula for calculating the power response index of a wind turbine generator set is as follows: ;in, is the power response index of the wind turbine generator set, is the actual output power of the wind turbine generator set, is the rated output power of the wind turbine generator set, is the regional wind speed value within the set area of ​​the wind turbine generator set, is the wind speed response coefficient stored in the database, is the wind speed adjustment coefficient stored in the database.

[0034] It should be explained that the wind speed adjustment coefficient stored in the database , wind speed influence coefficient The specific acquisition steps are as follows: first, based on the actual wind speed values ​​and output power values ​​recorded during the long-term operation of the wind turbine generator set, a historical data comparison model of the wind speed and power response relationship is established. Then, a nonlinear fitting method (such as the least squares method to fit the tanh curve) is used to train multiple groups of wind speed-power samples to extract the response curvature and sensitivity parameters that best reflect the impact of wind speed changes on power deviation. The response amplitude of the fitting curve is , and the normalization coefficient of the wind speed adjustment on the calculation index weight is , and finally the results are stored in the database as standard model parameters for subsequent calculation and dynamic evaluation of real-time power response index.

[0035] In this embodiment, by introducing the power response index and its calculation method, a quantitative assessment of the wind energy utilization efficiency of the wind turbine generator set is achieved, significantly improving the system's sensitivity and response accuracy to the unit's operating status. Compared with the traditional extensive mode that only judges whether an abnormality is based on a single power threshold, the present invention comprehensively considers the relationship between actual output power, rated power and regional wind speed, and constructs an exponential nonlinear evaluation model that can more accurately reflect whether the power output is reasonable under wind speed changes. By introducing the wind speed response coefficient and wind speed adjustment coefficient and performing tanh-type curve fitting training based on historical operating data, adaptive modeling of the power change trend affected by wind speed is achieved, making the index not only universal but also adjustable to match the actual operating conditions of the specific unit. The final power response index can not only provide accurate judgment when the wind speed changes drastically and the operating boundary is fuzzy, but also serve as an important input variable for the fusion health status index, providing high-quality data support for the system's subsequent status assessment and early warning decision-making, effectively enhancing the system's intelligent analysis capabilities in early power anomaly identification and wind energy utilization efficiency fluctuation monitoring.

[0036] Specifically, if Figure 2As shown in FIG, the specific steps for obtaining the vibration-temperature response index of a wind turbine generator set are as follows: obtaining the maximum allowable temperature rise value and vibration acceleration index of the bearing of the wind turbine generator set; comprehensively analyzing the current temperature value and the bearing parameter temperature value of the wind turbine generator set in combination with the maximum allowable temperature rise value and the vibration acceleration index of the bearing of the wind turbine generator set to obtain the vibration-temperature response index of the wind turbine generator set.

[0037] Among them, the maximum allowable temperature rise of the bearing can be extracted from the technical specification sheet of the wind turbine or the equipment nameplate.

[0038] The specific steps for obtaining the vibration acceleration index of a wind turbine generator set are as follows: obtaining the current vibration acceleration values ​​of several vibrating components of the wind turbine generator set (such as bearings, gearboxes, etc.); performing a comprehensive analysis (i.e., mean analysis) on the current vibration acceleration values ​​of several vibrating components of the wind turbine generator set (such as bearings, gearboxes, etc.) to obtain the vibration acceleration index of the wind turbine generator set.

[0039] The specific implementation example of obtaining the vibration acceleration index of a wind turbine generator set is as follows. The following parameters are available, including the current vibration acceleration values ​​(m / s) of the five vibrating components (main shaft front bearing, main shaft rear bearing, gearbox input shaft section, gearbox output shaft section, and generator end fixed support). 2 ), the specific data are shown in Table 1 and Figure 3 As shown:

[0040] Table 1 Example of current vibration acceleration value data of five vibrating components

[0041] The first vibrating component Second vibrating component The third vibrating component The fourth vibrating component The fifth vibrating component Current vibration acceleration value 3.692 3.883 3.755 4.024 3.716

[0042] Performing mean analysis on the data in Table 1, we can get the vibration acceleration index of the wind turbine generator set to be approximately: 3.814m / s 2 .

[0043] The current vibration acceleration value can be measured and obtained by an acceleration sensor.

[0044] The specific formula for calculating the vibration temperature response index of a wind turbine generator set is as follows: ;in, is the vibration temperature response index of the wind turbine generator set, is the current temperature value of the bearing of the wind turbine generator set, The bearing temperature of the wind turbine is set. is the maximum allowable temperature rise of the bearing of the wind turbine generator set, is the vibration acceleration index of the wind turbine generator set, is the vibration adjustment coefficient stored in the database.

[0045] It should be explained that the vibration amplification factor stored in the database The specific acquisition steps are as follows: based on the coupling relationship between bearing temperature and vibration acceleration in the historical operation data of wind turbines, multiple groups of vibration acceleration values ​​and corresponding bearing temperature rise amplitudes under typical stable working conditions are first selected as samples, and the influence trend of vibration intensity on temperature rise change is calculated. Then, the amplification effect between vibration index and temperature response deviation is modeled using exponential or quadratic curve fitting method, and the amplification factor that best reflects the temperature rise amplification trend under high vibration conditions is extracted as , and finally the one with the best stability in the fitting results The coefficients are stored in the database and used as standard parameters in subsequent real-time vibration and temperature response index calculations.

[0046] The specific implementation example of calculating the vibration temperature response index of a wind turbine generator set is as follows. The following parameters are available:

[0047] The current temperature value of the wind turbine bearing is approximately: 68.742℃.

[0048] The bearing temperature of the wind turbine generator set is approximately 55.230℃.

[0049] The maximum allowable temperature rise of the bearings of wind turbines is approximately 25,000°C.

[0050] The vibration acceleration index of the wind turbine is approximately: 3.814m / s 2 .

[0051] The vibration adjustment coefficient stored in the database is approximately: 0.276.

[0052] Substituting the above data into the specific formula for calculating the vibration temperature response index of the wind turbine generator set, we obtain:

[0053] The vibration temperature response index of the wind turbine generator set = ((68.742-55.230) / 25.000)×(1+0.276×((3.814^2) / (1+3.814)))≈0.991.

[0054] In this implementation, a vibration-temperature response index (VTR) is constructed to accurately capture abnormal characteristics of wind turbine bearings under combined thermal and vibration conditions. This effectively addresses the inability of traditional monitoring systems to identify latent faults such as mild vibration, high temperature, or faster-than-expected temperature rise. The VTR uses the current bearing temperature, a reference temperature, and the maximum allowable temperature rise as basic thermal parameters. It also incorporates a vibration acceleration index calculated from the average vibration acceleration of multiple key components to reflect the indirect impact of mechanical shock on temperature rise. Compared to monitoring temperature or vibration alone, this index demonstrates increased sensitivity to system operating conditions and can dynamically identify combined anomalies caused by factors such as lubrication deterioration, bearing fatigue, or transient excitation. Furthermore, by incorporating a vibration amplification factor (VAM) trained using historical operating data, it quantifies the nonlinear effect of vibration amplitude on temperature rise trends, enabling the index to adaptively adjust to varying operating intensities. Ultimately, this index not only serves as a preemptive measure for identifying local anomalies but also provides key input for health assessment models, playing a significant role in early risk assessment, improving early warning accuracy, and reducing the cost of fault expansion.

[0055] Specifically, the specific steps for analyzing the fusion health status index of a wind turbine are as follows: obtain the rated wind speed value of the wind turbine; input the actual output power, rated output power, vibration acceleration index, rated wind speed value, and regional wind speed value in the set area of ​​the wind turbine into a preset health analysis model for health analysis to obtain the fusion health status index of the wind turbine.

[0056] Among them, the rated wind speed refers to the wind speed required for the wind turbine to output rated power.

[0057] The health analysis model is as follows: ;in, is the integrated health status index of the wind turbine generator set, is the actual output power of the wind turbine generator set, is the power influence coefficient stored in the database, is the regional wind speed value within the set area of ​​the wind turbine generator set, is the rated wind speed of the wind turbine generator set, is the wind speed influence coefficient stored in the database, is the vibration acceleration index of the wind turbine generator set, is the vibration response coefficient stored in the database, is the rated output power of the wind turbine generator set, is the vibration amplification factor stored in the database.

[0058] It should be explained that the power influence coefficient stored in the database , wind speed influence coefficient , vibration response coefficient , vibration amplification factor The specific steps for obtaining are as follows: based on the multivariate analysis of the response trend of each variable to the change of health status in the long-term operation data of the wind turbine generator set, first, by constructing a correlation regression model between the historical power, wind speed and vibration data and the health assessment results (such as fault frequency, health level), using machine learning regression, principal component analysis or least squares fitting methods, the sensitivity and amplification parameters of each variable to the health status deviation are extracted. The slope corresponding to the power change term in the regression model is , the weight corresponding to the wind speed normalization is , the influencing factor of the vibration index and health deviation fitting is , and the nonlinear amplification coefficient of the vibration square term in the healthy critical region on the overall exponential mutation is Finally, the above four items are solidified in the database as standard parameters for weight adjustment of real-time health fusion index.

[0059] In this implementation, a fused health status index is constructed to enable comprehensive analysis and unified health assessment of multiple wind turbine operating parameters, significantly improving the system's accuracy in identifying potential risks and intelligent decision-making capabilities under complex operating conditions. The index integrates multiple key variables, including the wind turbine's actual output power, rated power, regional wind speed, rated wind speed, and vibration acceleration index. It also incorporates the power influence coefficient, wind speed influence coefficient, vibration response coefficient, and vibration amplification coefficient, derived from historical operating data, as weight adjustment factors, effectively reflecting the true contribution of each variable to health status deviations. The established health analysis model not only assesses health fluctuations caused by abnormalities in a single indicator, but also enables early detection of minor anomalies in multiple factors with overlapping trends. This is particularly suitable for identifying complex hazards such as power reduction despite sufficient wind speed or sudden health changes despite normal vibration. Compared to traditional health assessment methods that rely on fixed thresholds or single variables, this fused index possesses stronger correlation analysis capabilities and trend insights, providing a scientific basis for decision-making regarding subsequent fatigue analysis and the need for intervention. It serves as a key supporting indicator for intelligent and adaptive wind turbine health assessment.

[0060] Specifically, the specific steps for analyzing the composite fatigue index of the gearbox of a wind turbine generator set are as follows: obtaining the oil temperature value, oil temperature parameter set, speed comprehensive index, and speed parameter index in the gearbox of the wind turbine generator set, where the oil temperature parameter set includes a first oil temperature threshold and a second oil temperature threshold; inputting the actual output power, rated output power, oil temperature value in the gearbox, oil temperature parameter set, speed comprehensive index, and speed parameter index of the wind turbine generator set into a preset fatigue analysis model for fatigue analysis to obtain the composite fatigue index of the gearbox of the wind turbine generator set.

[0061] Among them, the oil temperature value in the gearbox can be measured and obtained through thermocouples (K type, J type), PT100, and NTC temperature sensors.

[0062] The speed parameter index is obtained by performing mean analysis on the main shaft speed parameter value and the generator speed parameter value of the wind turbine generator set.

[0063] The specific steps for obtaining the comprehensive speed index of a wind turbine generator set are as follows: obtaining the main shaft speed value and the generator speed value of the wind turbine generator set; performing a comprehensive analysis (i.e., mean analysis) on the main shaft speed value and the generator speed value of the wind turbine generator set to obtain the comprehensive speed index of the wind turbine generator set.

[0064] Among them, the spindle speed value can be measured and obtained through Hall elements and magnetoelectric tachometers.

[0065] The generator speed value can be measured and obtained through a high-speed encoder, a magnetoelectric tachometer, or a Hall effect speed probe.

[0066] The fatigue analysis model is as follows: ;in, is the composite fatigue index of the gearbox of the wind turbine generator set, is the oil temperature in the gearbox of the wind turbine generator set, is the low-temperature secondary thermal friction coefficient stored in the database, is the low temperature linear thermal friction coefficient stored in the database, is the low temperature base offset coefficient stored in the database, is the comprehensive speed index of the wind turbine generator set, is the speed parameter index of the wind turbine generator set, is the speed coupling coefficient stored in the database, is the first oil temperature threshold of the wind turbine generator set, is the medium temperature three-stage thermal friction coefficient stored in the database, is the medium temperature secondary thermal friction coefficient stored in the database, is the second oil temperature threshold of the wind turbine generator set, is the high temperature explosion benchmark coefficient stored in the database, is a natural constant, and in this embodiment, its value is 2.71. is the high temperature exponential growth coefficient stored in the database, is the actual output power of the wind turbine generator set, is the rated output power of the wind turbine generator set, is the high temperature load amplification factor stored in the database.

[0067] It should be explained that the low-temperature secondary thermal wear coefficient stored in the database , low temperature linear thermal friction coefficient , low temperature foundation offset coefficient , speed coupling coefficient The specific acquisition steps are as follows: regression modeling is performed based on the historical operating data of the gearbox in the low oil temperature zone (temperature is lower than the first oil temperature threshold), sample data with the most correlation between oil temperature and fatigue loss trend in this temperature range is selected, a quadratic polynomial function is constructed with oil temperature as the independent variable and fatigue index as the dependent variable, and the coefficients of the quadratic term, linear term and constant term are extracted by the least squares fitting method as 、 、 At the same time, the coupling ratio between the corresponding speed and fatigue is constructed as a normalization factor, and the amplification effect coefficient of the average speed on fatigue growth is obtained as the speed coupling coefficient stored in the database.

[0068] Medium temperature three-stage thermal friction coefficient stored in the database , Medium temperature secondary thermal friction coefficient The specific acquisition steps are as follows: based on the gearbox oil temperature being in the intermediate temperature rise interval between the first oil temperature threshold and the second oil temperature threshold, the historical curve of the relationship between fatigue index and oil temperature rise in this temperature range is extracted, and a nonlinear cubic regression function is used for fitting, with oil temperature as the variable and fatigue increment as the response. The cubic term and quadratic term coefficients in the fitting curve are respectively used as and , thereby constructing a polynomial thermal fatigue enhancement model in the medium temperature stage. These two parameters reflect the nonlinear increasing trend of fatigue rate after the oil temperature enters the critical lubrication zone.

[0069] High temperature burst benchmark coefficients stored in the database , high temperature exponential growth coefficient , high temperature load amplification factor The specific acquisition steps are as follows: based on the historical operation samples where the gearbox oil temperature is higher than the second threshold range, the combined data of power output, oil temperature and fatigue accumulation are extracted, and the oil temperature-driven fatigue outbreak model is established through the exponential fitting function to obtain its growth starting base value ( ) and the high temperature exponential growth coefficient Then, the normalized power and fatigue change in the same interval are squared and regressed to obtain the amplification factor of the power load on fatigue enhancement, which is recorded as Finally, the above three items are used as response parameters of fatigue mutation behavior under extreme high temperature and stored in the database for real-time calculation and early warning judgment.

[0070] In this implementation plan, by constructing a gearbox composite fatigue index, a dynamic fatigue modeling and risk prediction mechanism for the core transmission components of wind turbines is established, effectively filling the technical gap in traditional systems where abnormalities can be perceived but fatigue is difficult to deduce. The index models key parameters such as oil temperature, power and speed in sections, and uses different function forms to fit the fatigue growth trend according to the oil temperature in different temperature zones (low temperature, medium temperature and high temperature). Polynomial models and exponential models are established respectively to accurately reflect the nonlinear evolution process of fatigue accumulation of gear meshing surface materials under different thermal conditions. At the same time, the normalized speed index constructed by the speed comprehensive index and the speed parameter value is used to predict the fatigue growth trend of the gear meshing surface. The difference term, combined with the speed coupling coefficient, introduces the shear stress effect brought by high-speed operation, making the model sensitive to the influence of long-term high-speed operation; the introduction of the power normalization term and the high-temperature load amplification coefficient makes the load-fatigue relationship exponentially amplified under extreme conditions, which is closer to the actual damage process. The coefficients of each section are derived from historical data training and fitting, which enhances the engineering adaptability and stability of the model. Finally, the index is used as a fine diagnostic tool after the health status is abnormal. It can not only realize the real-time quantitative judgment of the fatigue degree of the gearbox, but also provide a direct basis for whether to power off, significantly improving the system's fault response initiative and safety control level.

[0071] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0072] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A remote monitoring and inspection system for wind turbine generator sets, characterized in that: include: A data acquisition unit is used to acquire power status data and bearing status data of the wind turbine generator set, and analyze the power response index and vibration temperature response index of the wind turbine generator set; the power status data includes actual output power and rated output power, and the bearing status data includes current bearing temperature value and bearing parameter temperature value; The specific steps to obtain the vibration temperature response index of a wind turbine generator set are as follows: Obtain the maximum allowable temperature rise and vibration acceleration index of the bearings of the wind turbine generator set; The current bearing temperature value and the reference bearing temperature value of the wind turbine generator set are comprehensively analyzed in combination with the maximum allowable bearing temperature rise value and the vibration acceleration index of the wind turbine generator set to obtain the vibration temperature response index of the wind turbine generator set; A state analysis unit is used to judge and analyze the power response index and vibration-temperature response index of the wind turbine generator set with the preset power response evaluation interval and vibration-temperature response evaluation interval respectively; The health analysis unit is used to analyze the integrated health status index of the wind turbine generator set when there is an abnormality in the power response index and the vibration temperature response index of the wind turbine generator set, and make a judgment analysis with the preset health assessment interval. If the integrated health status index of the wind turbine generator set is within the preset health assessment interval, no measures are taken; The specific steps for analyzing the integrated health status index of a wind turbine are as follows: Get the rated wind speed value of the wind turbine generator set; The actual output power, rated output power, vibration acceleration index, rated wind speed value, and regional wind speed value within the set area of ​​the wind turbine are input into the preset health analysis model for health analysis to obtain the integrated health status index of the wind turbine; The health analysis model is as follows: ； in, is the integrated health status index of the wind turbine generator set, is the actual output power of the wind turbine generator set, is the power influence coefficient stored in the database, is the regional wind speed value within the set area of ​​the wind turbine generator set, is the rated wind speed of the wind turbine generator set, is the wind speed influence coefficient stored in the database, is the vibration acceleration index of the wind turbine generator set, is the vibration response coefficient stored in the database, is the rated output power of the wind turbine generator set, is the vibration amplification factor stored in the database; A fatigue analysis unit is used to analyze the composite fatigue index of the wind turbine's gearbox when the wind turbine's integrated health status index is outside the preset health assessment interval, and to perform a judgment analysis based on the preset fatigue assessment interval. If the composite fatigue index of the wind turbine's gearbox is within the preset fatigue assessment interval, a health abnormality warning is issued; The processing unit is used to execute power-off measures on the wind turbine generator set and send a gearbox abnormality alarm when the composite fatigue index of the gearbox of the wind turbine generator set is outside a preset fatigue assessment range.

2. The wind turbine generator remote monitoring and inspection system according to claim 1 is characterized in that: The specific steps to obtain the power response index of a wind turbine are as follows: Obtain the regional wind speed value within the set area of ​​the wind turbine generator set; The actual output power and rated output power of the wind turbine generator set are comprehensively analyzed in combination with the regional wind speed value in the set area of ​​the wind turbine generator set to obtain the power response index of the wind turbine generator set.

3. The wind turbine generator remote monitoring and inspection system according to claim 1 is characterized in that: The specific steps to obtain the vibration acceleration index of a wind turbine are as follows: Obtaining current vibration acceleration values ​​of several vibrating components of a wind turbine generator set; The current vibration acceleration values ​​of several vibrating components of the wind turbine generator set are comprehensively analyzed to obtain the vibration acceleration index of the wind turbine generator set.

4. The wind turbine generator remote monitoring and inspection system according to claim 1 is characterized in that: The specific steps for analyzing the composite fatigue index of the gearbox of a wind turbine are as follows: Obtaining an oil temperature value, an oil temperature parameter set, a speed comprehensive index, and a speed parameter index in a gearbox of a wind turbine generator set, wherein the oil temperature parameter set includes a first oil temperature threshold value and a second oil temperature threshold value; The actual output power, rated output power, gearbox oil temperature, oil temperature parameter set, speed comprehensive index, and speed parameter index of the wind turbine generator set are respectively input into a preset fatigue analysis model for fatigue analysis to obtain the gearbox composite fatigue index of the wind turbine generator set.

5. The wind turbine generator remote monitoring and inspection system according to claim 4 is characterized in that: The specific steps for obtaining the comprehensive speed index of a wind turbine are as follows: Obtain the main shaft speed value and generator speed value of the wind turbine generator set; A comprehensive analysis is performed on the main shaft speed value and the generator speed value of the wind turbine generator set to obtain the comprehensive speed index of the wind turbine generator set.

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