Wind turbine operation performance monitoring system and method based on PLC hardware platform

Abstract

The application discloses a wind turbine operation performance monitoring system and method based on a PLC hardware platform, relates to the technical field of wind motor monitoring, and comprises the following steps: obtaining wind turbine operation performance evaluation data, obtaining historical operation data of the wind turbine, analyzing wind turbine operation performance evaluation indexes, analyzing wind turbine operation performance correction indexes, generating early warning information, determining unmanned aerial vehicle (UAV) inspection demand information, analyzing wind turbine UAV inspection verification indication coefficient, determining wind turbine remote early warning demand information, and adaptively adjusting the UAV inspection cycle. The application can more accurately monitor the operation performance of the wind turbine, timely find whether the wind turbine operation is abnormal, provide data basis for subsequent analysis, accurately warn the fault risk, effectively avoid fault deterioration, reduce maintenance cost and downtime, simultaneously optimize the UAV inspection strategy, improve the inspection efficiency and pertinence, reduce power generation loss, and improve power generation efficiency.

Description

Technical Field

[0001] This invention relates to the field of wind turbine monitoring technology, specifically to a wind turbine operation performance monitoring system and method based on a PLC hardware platform. Background Technology

[0002] With the increasing demand for clean energy, wind power, as a renewable energy source, is playing an increasingly important role in the energy sector. Wind turbines, as key equipment for converting wind energy into electricity, directly affect wind energy utilization efficiency, power generation stability, and equipment lifespan. During wind turbine operation, on the one hand, the harsh natural environment of wind farms significantly impacts their operation. On the other hand, wind turbines operate outdoors for extended periods, exposed to corrosive substances such as dust and salt spray, which can easily lead to corrosion and reduced insulation performance of electrical equipment, increasing the risk of equipment failure.

[0003] Existing technology, such as the invention patent with announcement number CN112032003B, is a method for monitoring the operational performance of large wind turbine units. The method includes the following steps: operational data acquisition and preprocessing; wind speed range division; operational area division; and performance anomaly determination. The method divides the operational area into normal operating areas and inefficient operating areas based on the shape of the scatter plot of wind speed ranges where inefficient data points tend to accumulate. The abnormal operational performance of the unit is determined based on two quantitative indicators: the area of ​​the inefficient area and the proportion of inefficient data.

[0004] Existing technologies, such as the invention patent with announcement number CN113107785B, are a method and device for real-time monitoring of abnormal power performance of wind turbine units, including the following steps: acquiring historical operating data of the wind turbine unit to be monitored and historical data of the anemometer tower of the wind farm in the same period; cleaning the historical operating data of the wind turbine unit to be monitored, removing invalid data, and retaining the data of the wind turbine unit under normal operating conditions; and constructing a multi-dimensional feature vector characterizing the power performance of the wind turbine unit, dividing the multi-dimensional feature vector into a model learning group and a model verification group.

[0005] As can be seen from the above solutions, current wind turbine performance monitoring systems have certain shortcomings in terms of effectiveness and accuracy. They often focus on data processing or feature construction for specific aspects, but in the actual operating environment of wind turbines, the performance of the turbines is affected by a variety of complex factors. The operating conditions of various components within the wind turbine are interconnected and work synergistically, while the variability of the external environment continuously interferes with the operation of the turbine. This complex coupling relationship means that monitoring based on only partial data or a single perspective is difficult to comprehensively and accurately grasp the true operating performance of the wind turbine, easily overlooking potential problems, failing to make accurate judgments on performance change trends in a timely manner, and potentially delaying maintenance, increasing the probability of sudden equipment failures, reducing the power generation efficiency and overall operational stability of the wind turbine, and affecting the reliability of the energy supply of the wind farm. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a wind turbine operation performance monitoring system and method based on a PLC hardware platform, which can effectively solve the problems mentioned in the background technology.

[0007] To achieve the above objectives, the present invention provides the following technical solution: The first aspect of the present invention provides a wind turbine operating performance monitoring system based on a PLC hardware platform, comprising:

[0008] The operation performance evaluation data acquisition module is used to collect the operating parameters of the wind turbine and input them into the central processing unit of the PLC hardware platform to analyze and process the operating parameters of the wind turbine and obtain the operation performance evaluation data of the wind turbine.

[0009] The wind turbine performance evaluation label determination module is used to simultaneously acquire historical operating data of wind turbines based on wind turbine operating performance evaluation data, and analyze and process the data to obtain wind turbine operating performance evaluation indicators.

[0010] The UAV inspection demand information determination module is used to acquire the basic operating parameters of the wind turbine, and analyze the wind turbine operating performance correction index in combination with the wind turbine operating performance evaluation index. Based on the wind turbine operating performance correction index, it generates early warning information and simultaneously determines the UAV inspection demand information, which includes inspection demand and no inspection required.

[0011] The UAV inspection and analysis module is used to collect wind turbine UAV inspection data when the UAV inspection demand information is determined to be a demand inspection, analyze the wind turbine UAV inspection verification index coefficient, and combine it with the wind turbine operation performance correction index to determine the wind turbine remote early warning demand information. Simultaneously, the UAV inspection cycle is adaptively adjusted based on the wind turbine UAV inspection verification index coefficient.

[0012] A second aspect of this invention provides a method for monitoring the operating performance of wind turbine generators based on a PLC hardware platform, comprising the following steps:

[0013] S1 collects the operating parameters of the wind turbine and inputs them into the central processing unit of the PLC hardware platform to analyze and process the operating parameters of the wind turbine and obtain the operating performance evaluation data of the wind turbine.

[0014] S2, based on the wind turbine operation performance evaluation data, simultaneously acquire the historical operation data of the wind turbine, analyze and process it to obtain the wind turbine operation performance evaluation index.

[0015] S3. Obtain the basic operating parameters of the wind turbine, and analyze the wind turbine operating performance correction index in combination with the wind turbine operating performance evaluation index. Based on the wind turbine operating performance correction index, generate early warning information, and simultaneously determine the drone inspection demand information, which includes inspection demand and no inspection required.

[0016] S4. When the drone inspection demand information is determined to be a demand inspection, collect the drone inspection data of the wind turbine, analyze the verification index coefficient of the drone inspection of the wind turbine, and combine it with the wind turbine operation performance correction index to determine the remote early warning demand information of the wind turbine. Simultaneously, based on the verification index coefficient of the drone inspection of the wind turbine, the drone inspection cycle is adaptively adjusted.

[0017] Compared with the prior art, the embodiments of the present invention have at least the following beneficial effects:

[0018] (1) This invention provides a wind turbine operation performance monitoring system and method based on a PLC hardware platform. Utilizing the powerful data processing capabilities of the PLC hardware platform, it achieves accurate acquisition and analysis of wind turbine operation parameters, enabling the acquisition of accurate operation performance evaluation data. This results in more precise monitoring of wind turbine operation performance, allowing for timely detection of any abnormalities in wind turbine operation and providing a reliable data foundation for subsequent analysis and decision-making. By comprehensively considering multiple factors to calculate operation performance correction indicators, it can accurately predict fault risks, identify potential problems in advance, effectively prevent fault escalation, reduce maintenance costs and downtime, and simultaneously optimize UAV inspection strategies, improving inspection efficiency and targeting, reducing power generation losses, and enhancing power generation efficiency.

[0019] (2) This invention obtains wind turbine operation performance evaluation indicators through analysis and processing, which can realize accurate judgment of the wind turbine operation status. With this, operation and maintenance personnel can more accurately understand the health status and performance trend of the wind turbine, and promptly discover potential performance decline or abnormal changes. Thus, they can take effective preventive measures before the failure occurs, accurately allocate resources, improve the reliability and stability of the wind turbine, extend the service life of the equipment, and ultimately improve the overall power generation efficiency and economic benefits of the wind farm.

[0020] (3) This invention generates early warning information based on wind turbine operating performance correction indicators and simultaneously determines UAV inspection needs, thereby improving the scientific nature of wind turbine performance monitoring. The early warning information generated based on operating performance correction indicators can promptly and accurately reflect whether the wind turbine's operating status is abnormal, enabling maintenance personnel to quickly identify potential risks, prevent further escalation of faults, effectively reduce the probability of sudden shutdowns, ensure continuous and stable power generation of wind turbines, and reduce power generation losses caused by shutdowns. At the same time, the reasonable determination of UAV inspection needs avoids over-inspection or under-inspection, achieving optimized allocation of inspection resources. When abnormal signs appear in the turbine's operating performance, UAV inspections are arranged in a timely manner, providing numerical basis for subsequent precise maintenance, further improving maintenance efficiency and quality, and ensuring the long-term efficient operation of wind turbines.

[0021] (4) This invention determines the remote early warning demand information of wind turbine units and calculates the remote early warning demand assessment index parameters to accurately measure the degree of remote early warning demand of wind turbine units. When the parameter is greater than or equal to the remote early warning demand assessment threshold, a remote early warning is issued in a timely manner, enabling operation and maintenance personnel to obtain information on potential serious faults of the units as soon as possible even when they are far away from the wind farm. This effectively avoids long-term shutdown of wind turbine units or aggravated equipment damage due to sudden faults. At the same time, the remote early warning mechanism helps to realize centralized management of wind farms, improve operation and maintenance management efficiency, and ensure that wind turbine units always maintain a safe, stable, and efficient operating state in complex and ever-changing operating environments.

[0022] 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

[0023] Figure 1 This is a schematic diagram of the system module connections of the present invention;

[0024] Figure 2 This is a schematic diagram of the method flow of the present invention. Detailed Implementation

[0025] 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.

[0026] Please see Figure 1 As shown, this embodiment of the invention provides a wind turbine operation performance monitoring system based on a PLC hardware platform, including:

[0027] The operation performance evaluation data acquisition module is used to collect the operating parameters of the wind turbine and input them into the central processing unit of the PLC hardware platform to analyze and process the operating parameters of the wind turbine and obtain the operation performance evaluation data of the wind turbine.

[0028] In this embodiment, the wind turbine operating performance evaluation data is obtained, and the specific method for obtaining the data is as follows:

[0029] Within the preset monitoring period, wind turbine operating parameters are collected, including the total wind energy of the wind turbine, the total electrical energy output of the wind turbine, the wind turbine voltage at each time point, the wind turbine current, the wind turbine power factor, the wind turbine yaw angle, the wind turbine rotor speed, and the wind direction of the wind farm.

[0030] It should be noted that the operating parameters of wind turbines can be obtained through the integrated sensing devices of the wind turbines.

[0031] By analyzing the wind turbine voltage, wind turbine current, and wind turbine power factor at each time point, the actual output power of the wind turbine at each time point is obtained. Based on the actual output power of the wind turbine at each time point, the variance of the actual output power of the wind turbine is calculated.

[0032] It should be noted that the actual output power of the wind turbine at each time point is equal to the voltage multiplied by the current and then by the power factor at that time point.

[0033] It should be noted that the specific method for obtaining the variance of the actual output power of wind turbines is as follows: Where a is the variance of the actual output power of the wind turbine, and α i Let represent the actual output power of the wind turbine at the i-th time point, where i is the number of each time point, i = 1, 2, ..., n, and n is the number of time points.

[0034] The power generation efficiency of a wind turbine is calculated based on the total wind energy and the total electrical energy output of the wind turbine.

[0035] It should be noted that the power generation efficiency of wind turbines is obtained in the following ways: Where b is the power generation efficiency of the wind turbine, b1 is the total wind energy of the wind turbine, and b2 is the total electrical energy output of the wind turbine.

[0036] Based on the yaw angle of the wind turbine and the wind direction of the wind farm at each time point, the yaw error rate of the wind turbine is calculated.

[0037] It is important to note that the yaw angle of a wind turbine refers to the yaw angle of the wind turbine nacelle relative to the wind direction.

[0038] It is important to note that the yaw error rate of wind turbines is obtained as follows: the difference between the yaw angle of the wind turbine and the wind direction of the wind farm at each time point is processed to obtain the yaw error angle at each time point. The absolute values ​​of the yaw error angles at each time point are accumulated to obtain the total yaw error angle within the monitoring period. The preset reference total yaw error angle is extracted from the database. The total yaw error angle within the monitoring period is subtracted from the reference total yaw error angle to obtain the total yaw error deviation angle. The total yaw error deviation angle is divided by the reference total yaw error angle to obtain the yaw error rate of the wind turbine.

[0039] The wind turbine rotor speed fluctuation rate is calculated based on the wind turbine rotor speed at each time point.

[0040] It should be noted that the wind turbine speed fluctuation rate is obtained as follows: the average wind turbine speed within the monitoring period is obtained by averaging the wind turbine rotor speed at each time point; the wind turbine rotor speed at each time point is compared with the average wind turbine rotor speed to obtain the wind turbine speed fluctuation amount at each time point; the standard deviation of the wind turbine speed fluctuation amount at each time point is calculated, and the standard deviation of the wind turbine speed fluctuation amount is recorded as the wind turbine speed fluctuation rate.

[0041] It should also be noted that the standard deviation of the wind turbine speed fluctuation at each time point is calculated as follows: Where d is the standard deviation of the wind turbine speed fluctuation, and β i Let represent the wind turbine speed fluctuation at the i-th time point, where i is the number of each time point, i = 1, 2, ..., n, and n is the number of time points.

[0042] The variance of actual output power of wind turbine, power generation efficiency of wind turbine, yaw error rate of wind turbine, and speed fluctuation rate of wind turbine are combined as the data for evaluating the operating performance of wind turbine.

[0043] The wind turbine performance evaluation label determination module is used to simultaneously acquire historical operating data of wind turbines based on wind turbine operating performance evaluation data, and analyze and process the data to obtain wind turbine operating performance evaluation indicators.

[0044] In this embodiment, the wind turbine operating performance evaluation index is obtained through analysis and processing. The specific analysis process is as follows:

[0045] Historical operating data of wind turbines include historical average power factor, historical average rotor speed, historical average nacelle vibration velocity, and historical average oil temperature.

[0046] It should be noted that the historical operating data of the wind turbines were extracted from the database to which the wind turbines belong.

[0047] Based on the wind turbine operation performance evaluation data and historical wind turbine operation data, the wind turbine operation performance evaluation index was obtained through analysis and processing.

[0048] The wind turbine operating performance evaluation index is used to characterize the operating performance of the wind turbine.

[0049] In a specific embodiment, the wind turbine operating performance evaluation indicators are obtained in the following ways:

[0050] Extract the reference operating performance evaluation data and reference historical operating data of wind turbines stored in the database. The reference operating performance evaluation data of wind turbines includes the reference output power variance, reference power generation efficiency, reference yaw error rate, and reference speed fluctuation rate of wind turbines.

[0051] The historical operating data of wind turbines includes the historical average power factor, the ideal historical average rotor speed, the historical average vibration velocity of the nacelle, and the historical average oil temperature.

[0052] It should be noted that the reference operating performance evaluation data and the reference historical operating data of wind turbines were obtained by collecting a large amount of operating performance evaluation data and historical operating data and averaging them before the database was established.

[0053]

[0054]

[0055] Where A represents the wind turbine operating performance evaluation index, A1 represents the wind turbine operating performance evaluation data characterization factor, and A2 represents the wind turbine historical operating data characterization factor. The weights of the characterization factors for wind turbine operation performance evaluation data. Here are the weights of the historical operating data representation factors for the wind turbine: a is the variance of the actual output power of the wind turbine, b is the power generation efficiency of the wind turbine, c is the yaw error rate of the wind turbine, d is the speed fluctuation rate of the wind turbine, f is the historical average power factor of the wind turbine, g is the historical average rotor speed of the wind turbine, h is the historical average nacelle vibration velocity of the wind turbine, j is the historical average oil temperature of the wind turbine, a0 is the variance of the reference output power of the wind turbine, b0 is the reference power generation efficiency of the wind turbine, c0 is the reference yaw error rate of the wind turbine, d0 is the reference speed fluctuation rate of the wind turbine, f0 is the reference historical average power factor of the wind turbine, g0 is the ideal historical average rotor speed of the wind turbine, h0 is the reference historical average nacelle vibration velocity of the wind turbine, j0 is the reference historical average oil temperature of the wind turbine, and e is the natural constant.

[0056] It should be noted that the weights of the wind turbine operation performance evaluation data representation factors and the historical operation data representation factors both range from 0 to 1. In specific embodiments, pre-set values ​​can be directly extracted from the database. For example, the collected wind turbine operation performance evaluation data representation factors, historical operation data representation factors, and their corresponding weights can be mapped and associated in the database. When weights are needed, the system inputs the current actual values ​​of the wind turbine operation performance evaluation data representation factors and historical operation data representation factors, and directly extracts the corresponding pre-set weights from the mapping and association system.

[0057] It should also be noted that the wind turbine performance evaluation indicators, derived from analysis and processing of wind turbine operating performance assessment data and historical operating data, take into account the interrelationships among these parameters. For example, the variance of actual output power reflects the stability of the wind turbine's output power. A large variance indicates drastic fluctuations in the wind turbine's output power, which may be caused by unstable wind speed, mechanical component failure, or improper control system adjustments. Such power fluctuations make it difficult for the wind turbine to operate under optimal conditions, thus reducing power generation efficiency. A large yaw error rate means that the wind turbine cannot accurately align with the wind direction, causing the rotor to fail to capture wind energy to the maximum extent. This will result in the actual output power of the wind turbine being lower than the optimal value, and due to the continuous changes in wind direction, the actual output power will fluctuate significantly, further increasing the variance of actual output power. The rotational speed fluctuation rate reflects the stability of the wind turbine's rotor speed. A large rotational speed fluctuation rate will cause fluctuations in the wind turbine's output power, as the wind turbine's output power is closely related to the rotor speed. A high yaw error rate will prevent the wind turbine from fully utilizing wind energy, thus reducing power generation efficiency. Large speed fluctuations are detrimental to the optimal operation of wind turbines, reducing power generation efficiency. A large yaw error rate leads to uneven force on the wind turbine rotor, causing unstable speed and increased speed fluctuations. The historical average power factor reflects the past power quality of the wind turbine. A low historical average power factor indicates potential issues such as insufficient reactive power compensation during past operation, which may affect the stability of the wind turbine's control system. An unstable control system may prevent the wind turbine from effectively regulating its output power during current operation, thus increasing the variance of the actual output power. The historical average oil temperature reflects the past performance of the wind turbine's cooling and lubrication systems. A high historical average oil temperature may indicate problems with the cooling system or poor lubrication. During current operation, this may affect the efficiency and reliability of mechanical components, leading to unstable wind turbine output power and increased variance of the actual output power. A low historical average power factor may suggest problems on the grid side or insufficient reactive power regulation capability of the wind turbine itself, which may affect the grid connection stability of the wind turbine and consequently, power generation efficiency. High historical average oil temperature may be caused by heat dissipation or lubrication issues, which can affect the working environment of internal components of the wind turbine and reduce power generation efficiency. A large yaw error rate prevents the wind turbine from effectively utilizing wind energy, resulting in unstable output power, which may impact the grid and affect the power factor. A large yaw error rate also leads to uneven stress on the rotor and unstable rotational speed, affecting the statistical analysis of historical average rotor speed. Large rotational speed fluctuations result in significant variations in internal energy losses and heat generation within the wind turbine, affecting oil temperature changes and making the historical average oil temperature unstable. For example, as the rotational speed increases, both mechanical and electrical losses in the wind turbine increase, and the oil temperature rises more rapidly.

[0058] In a specific embodiment, by analyzing and processing the wind turbine's operating performance evaluation indicators, it is possible to accurately judge the operating status of the wind turbine. This allows maintenance personnel to more accurately understand the health status and performance trends of the wind turbine, promptly detect potential performance degradation or abnormal changes, and thus take effective preventive measures before a failure occurs, accurately allocate resources, improve the reliability and stability of the wind turbine, extend the service life of the equipment, and ultimately improve the overall power generation efficiency and economic benefits of the wind farm.

[0059] The UAV inspection demand information determination module is used to acquire the basic operating parameters of the wind turbine, and analyze the wind turbine operating performance correction index in combination with the wind turbine operating performance evaluation index. Based on the wind turbine operating performance correction index, it generates early warning information and simultaneously determines the UAV inspection demand information, which includes inspection demand and no inspection required.

[0060] In this embodiment, the basic operating parameters of the wind turbine include the wind turbine equipment parameters and external environmental parameters, wherein:

[0061] The parameters of wind turbine equipment include the service life of the wind turbine, the total wear of bearings, and the average response time of the controller.

[0062] It should be noted that the service life of the wind turbines is extracted from the database to which the wind turbines belong.

[0063] The total bearing wear can be measured using a wind turbine main shaft sliding bearing wear monitoring sensor. The sensor converts the bearing operating status and wear amount into an electrical signal in real time through the monitoring contacts. This signal is then fed into the monitoring equipment via a signal cable to output the total bearing wear data.

[0064] The average response time of the controller can be collected and recorded by the wind turbine controller monitoring system.

[0065] External environmental parameters include the standard deviation of the daily average wind speed, the frequency of daily wind direction changes, and the altitude of the wind farm.

[0066] It should be noted that wind speed can be obtained using an anemometer, wind direction can be obtained using an anemometer, and altitude can be obtained using a barometric altimeter.

[0067] In this embodiment, the performance correction index of wind turbine generators is analyzed. The specific analysis process is as follows:

[0068] Based on the basic operating parameters of the wind turbine, the operating correction coefficient of the wind turbine is obtained through analysis and processing.

[0069] In a specific embodiment, the wind turbine operation correction coefficient is obtained through analysis and processing. The specific analysis method is as follows:

[0070] Based on the basic operating parameters of the wind turbine, the basic operating characteristic values ​​of the wind turbine are obtained through analysis and processing. These basic operating characteristic values ​​are used to characterize the degree of use of the wind turbine equipment and the degree of influence of the wind farm environment on the wind turbine.

[0071] In a specific embodiment, the basic operating characteristic values ​​of the wind turbine generator are obtained in the following way:

[0072] Extract the reference wind turbine equipment parameters and reference external environment parameters stored in the database. The reference wind turbine equipment parameters include the rated service life of the wind turbine, the total amount of bearing wear that can be tolerated, and the average response time of the reference controller.

[0073] The reference external environmental parameters include the ideal daily average wind speed standard deviation, the reference daily average wind direction change frequency, and the ideal altitude of the wind farm.

[0074]

[0075] Where B is the basic operating characteristic value of the wind turbine, k is the service life of the wind turbine, m is the total bearing wear of the wind turbine, p is the average response time of the controller of the wind turbine, q is the standard deviation of the daily average wind speed of the wind farm, r is the daily average wind direction change frequency of the wind farm, s is the altitude of the wind farm, k0 is the rated service life of the wind turbine, m0 is the total bearing wear that the wind turbine can withstand, p0 is the average response time of the reference controller of the wind turbine, q0 is the standard deviation of the ideal daily average wind speed of the wind farm, r0 is the reference daily average wind direction change frequency of the wind farm, s0 is the ideal altitude of the wind farm, and e is a natural constant.

[0076] It's important to understand that the softsign function is a built-in function in Python.

[0077] It should be noted that the wind turbine foundation operating characteristic values ​​obtained through analysis and processing of foundation operating parameters take into account the interrelationships between these parameters. For example, as the service life of the wind turbine increases, the operating time of each component increases, leading to accelerated wear accumulation. Bearings, as critical rotating components, experience accelerated wear due to continuous friction, vibration, and loads under different operating conditions during long-term operation. As the service life of the wind turbine increases, problems such as aging of electronic components and wiring gradually emerge. The controller, as the core of the wind turbine's control, experiences a decline in the performance of its internal electronic components over time. Increased total bearing wear leads to increased rotational imbalance and intensified vibration. This vibration is transmitted throughout the entire wind turbine structure, including the nacelle where the controller is installed. Strong vibrations may interfere with the normal operation of the controller's internal electronic components, such as loosening solder joints or causing poor contact in connectors, thus affecting the controller's signal transmission and processing speed, resulting in an increase in average response time. A large daily average wind speed standard deviation in a wind farm indicates significant wind speed fluctuations. This instability affects wind direction, making it more prone to change. Conversely, a high frequency of daily average wind direction changes also impacts wind speed distribution. Frequent wind direction changes disrupt airflow within the wind farm, causing interactions between air currents from different directions and resulting in more dispersed wind speed measurements, thus increasing the daily average wind speed standard deviation. Generally, higher altitudes lead to more complex atmospheric environments and more dramatic wind speed changes, typically resulting in a larger daily average wind speed standard deviation. Furthermore, the magnitude of the daily average wind speed standard deviation also affects the operating performance of wind turbines at different altitudes. A larger standard deviation means that wind turbines need to adjust operating parameters more frequently to adapt to wind speed changes, which can be more challenging at high altitudes. At high altitudes, atmospheric circulation is more complex, less affected by topography, but the scale of atmospheric flow is larger, making wind direction more susceptible to frequent changes caused by large-scale weather systems. External environmental factors such as wind speed and direction can affect bearing wear. In wind farms with large daily average wind speed standard deviations and frequent daily wind direction changes, the operating state of wind turbines is unstable, and the load on the bearings changes frequently in both direction and magnitude. This unstable load accelerates bearing wear, increasing the total amount of wear. Altitude also affects the bearing's operating environment. Low air pressure and low temperatures at high altitudes can alter the properties of lubricating oil, reducing its lubrication effect and increasing the bearing's coefficient of friction, leading to faster bearing wear.

[0078] In specific embodiments, analyzing the basic operational characteristic values ​​of wind turbine units can comprehensively reflect the utilization level of wind turbine equipment and the combined impact of the wind farm environment on its operating status. This helps to accurately locate potential problems that wind turbine units may face during operation, providing numerical basis for developing targeted maintenance strategies. This effectively reduces the probability of sudden equipment failures, extends the service life of wind turbine units, ensures the stable operation of the wind farm, improves wind energy utilization efficiency, and reduces power generation losses due to equipment failures. Simultaneously, it also helps to optimize the allocation of wind farm operation and maintenance resources, making operation and maintenance work more scientific and efficient, and ensuring that wind turbine units maintain good operating performance in complex and ever-changing environments.

[0079] Extract the correction coefficients corresponding to each basic operating characteristic value interval stored in the database, and map and extract the operating correction coefficients corresponding to the intervals where the basic operating characteristic values ​​of the wind turbine are located, and mark them as the operating correction coefficients of the wind turbine.

[0080] Based on the wind turbine operation correction coefficient and the wind turbine operation performance evaluation index, the wind turbine operation performance correction index is obtained through analysis and processing.

[0081] The wind turbine operating performance correction index is used to characterize the actual operating performance of the wind turbine after correction.

[0082] In specific embodiments, the basic operating characteristic values ​​of wind turbines encompass key information from multiple aspects, including equipment service life, bearing wear, controller response time, and wind farm environment. These factors are interrelated and have a complex impact on the operating performance of wind turbines. The operating correction coefficient is derived by comparing the basic operating characteristic values ​​with the characteristic values ​​under standard or ideal conditions in the database. It quantifies the performance deviations caused by these factors and accurately reflects the degree of difference between the wind turbine's current actual state and its ideal state, thereby allowing for targeted correction of the operating performance evaluation indicators. For example, when the service life is long or the bearing wear is severe, the operating correction coefficient will be adjusted accordingly, making the operating performance evaluation indicators more closely match the actual performance level of the wind turbine. This avoids incorrect judgments of the wind turbine's operating status due to the failure to consider these actual factors, thus providing a reliable basis for accurate assessment and effective maintenance. Based on the wind turbine operating correction coefficient and the wind turbine operating performance evaluation indicators, the wind turbine operating performance correction indicators are analyzed and processed to more accurately characterize the actual operating performance of the wind turbine after correction, more sensitively capture the true operating status of the wind turbine, promptly detect potential anomalies, and avoid misjudging the unit's status due to the limitations of a single evaluation indicator. It helps to provide early warning of fault risks, optimize the allocation of maintenance resources, reduce downtime, improve the stability, reliability and overall power generation efficiency of wind turbine operation, and ensure the continuous and efficient operation of wind farms.

[0083] In a specific embodiment, the wind turbine operating performance correction index is obtained in the following way:

[0084]

[0085] Where C is the wind turbine operating performance correction index, and A is the wind turbine operating performance evaluation index. is the wind turbine operation correction factor, and e is the natural constant.

[0086] In this embodiment, early warning information is generated based on the wind turbine operating performance correction index, and the drone inspection requirement information is determined simultaneously. The specific process is as follows:

[0087] Extract the preset first threshold and second threshold for wind turbine operation performance correction verification from the database.

[0088] It should be noted that the first threshold for wind turbine performance correction verification is lower than the second threshold for wind turbine performance correction verification.

[0089] It should be noted that the first threshold and the second threshold for wind turbine operation performance correction verification are pre-set in the database and are critical indicators used to measure whether the wind turbine operation status is within the normal range and whether corresponding maintenance or inspection measures need to be taken.

[0090] In one specific embodiment, there are multiple ways to set the first threshold and the second threshold for wind turbine operation performance correction verification. Various operational performance data of the wind turbine during long-term operation can be collected, and these data can be analyzed in detail to determine the boundary values ​​for normal operation and safe performance of the wind turbine under different conditions, as well as the boundary values ​​for wind turbine anomalies. These boundary values ​​can then be comprehensively processed, such as by averaging, weighted averaging, or by setting a certain safety margin according to actual conditions, to obtain the first and second thresholds for wind turbine operation performance correction verification.

[0091] The early warning information includes local early warnings that are needed and early warnings that are not needed.

[0092] If the wind turbine operating performance correction index is greater than or equal to the first threshold for wind turbine operating performance correction verification, the warning information will be marked as a local demand warning. If the wind turbine operating performance correction index is less than the first threshold for wind turbine operating performance correction verification, the warning information will be marked as no warning required.

[0093] If the wind turbine's operating performance correction index is greater than or equal to the first threshold for wind turbine operating performance correction verification, it indicates that the wind turbine's current operating performance is poor and has exceeded the acceptable range for normal operation. There may be a problem, and a timely local warning is needed so that on-site maintenance personnel can quickly take corresponding measures to inspect, repair, or adjust the wind turbine to prevent the problem from worsening and ensure that the wind turbine can be restored to normal operation as soon as possible, thus guaranteeing the stable power generation and overall operating efficiency of the wind farm.

[0094] If the wind turbine's operating performance correction index is less than the first threshold for wind turbine operating performance correction verification, it indicates that the wind turbine's current operating performance is in a relatively normal state. Although there may be some minor fluctuations or potential problems, they have not yet reached the level where an immediate local warning needs to be issued.

[0095] If the wind turbine's operating performance correction index is greater than or equal to the second threshold for wind turbine operating performance correction verification, then the wind turbine's drone inspection requirement information will be marked as requiring inspection. If the wind turbine's operating performance correction index is less than the second threshold for wind turbine operating performance correction verification, then the wind turbine's drone inspection requirement information will be marked as not requiring inspection.

[0096] If the wind turbine's operating performance correction index is greater than or equal to the second threshold for operating performance correction verification, it indicates that the wind turbine's operating performance has shown significant abnormalities or has a large potential risk. Conventional monitoring data and analysis alone may not be able to fully and accurately determine its internal condition. It is necessary to use drone inspections to conduct detailed checks on the wind turbine's external structure, such as whether there are cracks in the blades, whether the tower is tilted, and whether the anti-vibration hammers are displaced, so as to promptly identify potential safety hazards and equipment failures. This will provide data for subsequent precise repair and maintenance, ensure the safe and stable operation of the wind turbine, avoid serious equipment damage or shutdown accidents caused by the failure to detect potential problems in time, and ensure the normal power generation of the wind farm.

[0097] If the wind turbine's operating performance correction index is less than the second threshold for wind turbine operating performance correction verification, it means that although the current operating performance of the wind turbine may fluctuate to some extent, its operating status can be clearly understood through existing monitoring methods and performance evaluation, and there is no need to start drone inspection for the time being.

[0098] In a specific embodiment, by generating early warning information based on wind turbine operating performance correction indicators and simultaneously determining drone inspection needs, the scientific rigor of wind turbine performance monitoring can be improved. Early warning information generated based on operating performance correction indicators can promptly and accurately reflect whether the wind turbine's operating status is abnormal, enabling maintenance personnel to quickly identify potential risks, prevent further escalation of faults, effectively reduce the probability of sudden outages, ensure continuous and stable power generation from the wind turbine, and minimize power generation losses due to outages. Simultaneously, rationally determining drone inspection needs avoids over- or under-inspection, achieving optimized allocation of inspection resources. Timely drone inspections are arranged when abnormal signs of turbine operating performance appear, providing numerical data for subsequent precise maintenance, further improving maintenance efficiency and quality, and ensuring the long-term efficient operation of the wind turbine.

[0099] The UAV inspection and analysis module is used to collect wind turbine UAV inspection data when the UAV inspection demand information is determined to be a demand inspection, analyze the wind turbine UAV inspection verification index coefficient, and combine it with the wind turbine operation performance correction index to determine the wind turbine remote early warning demand information. Simultaneously, the UAV inspection cycle is adaptively adjusted based on the wind turbine UAV inspection verification index coefficient.

[0100] In this embodiment, the indicator coefficients for wind turbine drone inspection and verification are analyzed. The specific analysis process is as follows:

[0101] The data from drone inspections of wind turbines includes the average crack width of the wind turbine blades, the cumulative area of ​​surface defects, the tower tilt angle, and the displacement of the anti-vibration hammer.

[0102] In one specific embodiment, the average crack width of the blade is obtained as follows: A drone carrying a high-definition image acquisition device captures images of the wind turbine blade, obtaining image data containing complete information about the blade surface. Then, the captured images are preprocessed, including image enhancement and noise reduction, to improve image quality for subsequent analysis. Next, image recognition technology and edge detection algorithms are used to accurately identify the crack regions on the blade and extract the crack contour information. For multiple identified cracks, their maximum widths are measured separately. Finally, all crack widths are summed and averaged to obtain the average crack width of the blade.

[0103] The specific method for obtaining the cumulative surface defect area is as follows: A drone carrying a high-definition image acquisition device takes images of the wind turbine, ensuring coverage of the entire area where defects may exist, and accurately identifies various defect areas, separating the defective parts from the background. The area of ​​each segmented defect area is calculated, and the pixel area is converted into an actual area using methods such as pixel counting and combining parameters such as image resolution. Finally, the areas of all identified defective areas are summed to obtain the cumulative surface defect area of ​​the wind turbine.

[0104] The tower tilt angle is obtained as follows: A drone is used to capture images of the wind turbine tower, identifying and locating the top and bottom center points of the tower. A line connecting these two points is then obtained. Simultaneously, a horizontal line is acquired on the ground, thus determining the tower's deviation angle from the ground. Initial images of the wind turbine tower from the same location are extracted from the database to obtain the initial deviation angle between the tower and the ground. This initial deviation angle is then subtracted from the initial deviation angle to obtain the tower's tilt angle.

[0105] The displacement of the vibration damper is obtained as follows: The vibration damper of the wind turbine is photographed by a drone, the coordinates of the center point of the vibration damper are identified and located, the initial image of the vibration damper of the wind turbine at the same shooting location stored in the database is extracted simultaneously, the initial coordinates of the center point of the vibration damper are identified and located, and the displacement of the vibration damper is calculated based on the coordinates of the center point of the vibration damper and the initial coordinates of the center point of the vibration damper.

[0106] It is important to note that the same shooting location refers to a location where all factors affecting the content of the image, such as the shooting angle and shooting height, are exactly the same.

[0107] Extract wind turbine inspection data from the database using reference drones.

[0108] The reference data for wind turbine inspection by drones includes the average crack width of the reference blades, the cumulative defect area of ​​the reference surface, the tilt angle of the reference tower, and the displacement of the reference vibration damper.

[0109] Based on the wind turbine drone inspection data and the wind turbine reference drone inspection data, the wind turbine drone inspection verification index coefficient is obtained through comprehensive analysis and processing. The wind turbine drone inspection verification index coefficient is used to characterize the degree of external structural defects of the wind turbine during drone inspection.

[0110] In a specific embodiment, the indicator coefficients for wind turbine drone inspection and verification are obtained in the following way:

[0111]

[0112] Where D is the indicator coefficient for UAV inspection and verification of wind turbine units, t is the average crack width of the wind turbine blades, v is the cumulative surface defect area of ​​the wind turbine, w is the tower tilt angle of the wind turbine, z is the displacement of the vibration damper of the wind turbine, t0 is the reference average crack width of the wind turbine blades, v0 is the reference cumulative surface defect area of ​​the wind turbine, w0 is the reference tower tilt angle of the wind turbine, z0 is the reference vibration damper displacement of the wind turbine, x1 is the weight of the average crack width of the blades, x2 is the weight of the cumulative surface defect area, x3 is the weight of the tower tilt angle, x4 is the weight of the vibration damper displacement, and e is the natural constant.

[0113] It should be noted that the sigmoid function is a built-in function in Python.

[0114] It should be noted that the weights for the average blade crack width, cumulative surface defect area, tower tilt angle, and damper displacement are all within the range of 0-1. These preset values ​​can be directly extracted from the database. In one specific embodiment, the extraction method is as follows: The real-time monitored data on the average blade crack width, cumulative surface defect area, tower tilt angle, and damper displacement are organized and categorized. Based on the range of these data values ​​and their impact on the wind turbine's operational safety and performance, a corresponding mapping table is established in the database. When weights need to be extracted, the system accurately searches for and directly extracts the corresponding preset weights from the mapping table based on the current actual values ​​of the average blade crack width, cumulative surface defect area, tower tilt angle, and damper displacement.

[0115] It should also be noted that the verification index coefficients for wind turbine drone inspections, obtained through analysis and processing of data from wind turbine drone inspections, take into account the correlation between these parameters. For example, when the average crack width of the blades increases, the stress distribution on the blades changes during wind turbine operation. Cracks become more susceptible to airflow impact and erosion, leading to gradual peeling and wear of the material around the cracks, thus increasing the cumulative defect area on the surface. Changes in the average crack width of the blades affect the overall balance of the wind turbine. If the crack widths on multiple blades are uneven or the crack development degrees differ, it will lead to an imbalance in the mass distribution of the rotor. During wind turbine operation, this imbalance will generate unbalanced torques acting on the tower, causing the tower to bear uneven lateral forces, thereby changing the tower's tilt angle. When the average crack width of the blades increases, the vibration characteristics of the wind turbine will change, and the vibration amplitude and frequency may increase. Vibration dampers are devices used to reduce conductor vibration. When the vibration of the wind turbine is transmitted to the conductor system, the vibration dampers will correspondingly generate larger displacements to dissipate the vibration energy. For example, under normal circumstances, when blade cracks are small, the vibration of the wind turbine is relatively small, and the displacement of the vibration damper is also small. However, when the blade cracks expand and the vibration intensifies, the vibration damper will be driven to generate a larger displacement to suppress excessive vibration of the conductor, thus protecting the conductor and the entire wind turbine electrical system. A large cumulative surface defect area indicates that the overall structural integrity of the wind turbine has been compromised to some extent. This affects the center of gravity distribution of the wind turbine, causing it to deviate from its original design position. During wind turbine operation, this center of gravity shift generates additional overturning moments acting on the tower, leading to an increase in the tower's tilt angle. The increase in the cumulative surface defect area is usually related to changes in the overall vibration level of the wind turbine. When there are many surface defects, the aerodynamic performance of the wind turbine decreases, its operational stability deteriorates, and vibration intensifies. This vibration is transmitted to the conductor system, requiring the vibration damper to withstand greater vibration energy, thus increasing its displacement. When the tower tilt angle changes, it alters the conductor suspension angle and tension distribution. Changes in conductor tension directly affect the stress state of the vibration damper, causing its displacement to change.

[0116] In a specific embodiment, by analyzing the indicator coefficients of wind turbine drone inspections, the defect status of the wind turbine's external structure can be accurately and comprehensively grasped. This effectively avoids safety accidents caused by external structural problems, such as blade breakage and tower collapse, ensuring the safe and stable operation of the wind turbine. Simultaneously, it provides crucial data support for optimizing operation and maintenance strategies, improving operational efficiency and reducing costs. Furthermore, it helps to gain a deeper understanding of the structural change trends of the wind turbine during operation, predict potential faults in advance, reduce power generation losses due to downtime for maintenance, ensure continuous and efficient power generation of the wind farm, and improve the reliability of the wind farm.

[0117] In a specific embodiment, the inspection cycle of the UAV is adaptively adjusted based on the verification index coefficient of the wind turbine UAV inspection. The specific process is as follows:

[0118] Extract the preset drone inspection verification thresholds from the database.

[0119] It should be noted that the drone inspection verification threshold is a pre-set value in the database and is used to measure whether the drone inspection cycle of the wind turbine needs to be adjusted.

[0120] In one specific embodiment, there are several ways to set the drone inspection verification threshold. First, collect drone inspection data from numerous wind turbines under different operating stages, environmental conditions, and maintenance states. Perform detailed analysis on this data under these different scenarios to calculate the reasonable range boundary values ​​corresponding to the aforementioned inspection data while ensuring the safe and stable operation and good performance of the wind turbines. Taking these boundary values ​​into account, methods such as interval estimation in statistics, empirical formulas, or calculation methods based on risk assessment models can be used to obtain the drone inspection verification threshold.

[0121] The difference between the indicator coefficient and the threshold for drone inspection and verification of wind turbine units is used to obtain the deviation value for drone inspection and verification of wind turbine units.

[0122] It should be noted that the difference processing refers to subtracting the drone inspection and verification threshold from the indicator coefficient of the wind turbine drone inspection and verification. The result of the difference processing can be greater than zero, less than zero, or equal to zero.

[0123] Extract the inspection cycle adjustment value corresponding to each inspection verification deviation value range stored in the database, and map and extract the inspection cycle adjustment value corresponding to the range where the wind turbine drone inspection verification deviation value is located, and record it as the wind turbine drone inspection cycle adjustment value.

[0124] The inspection cycle of drones is adaptively adjusted based on the adjustment value of the inspection cycle of wind turbine drones.

[0125] In this embodiment, the remote early warning requirement information for wind turbine generators is determined, and the specific determination process is as follows:

[0126] The remote early warning requirement information for wind turbine units includes those requiring remote early warning and those not requiring remote early warning.

[0127] Extract the correction coefficients corresponding to the intervals of each UAV inspection verification indicator coefficient stored in the database, and map and extract the UAV inspection correction coefficients corresponding to the intervals of the wind turbine UAV inspection verification indicator coefficients, and mark them as wind turbine correction coefficients.

[0128] Based on the wind turbine correction coefficient and the wind turbine operating performance correction index, the remote early warning demand assessment index parameters of wind turbines are obtained through comprehensive analysis and processing.

[0129] The aforementioned remote early warning demand assessment index parameters for wind turbines are used to characterize the degree of remote early warning demand for wind turbines.

[0130] In a specific embodiment, the indicator parameters for assessing the remote early warning requirements of wind turbine units are obtained in the following way:

[0131]

[0132] Where F is the indicator parameter for assessing the remote early warning needs of wind turbine units, C is the wind turbine unit operating performance correction index, and α D is the correction factor for wind turbine units, and e is the natural constant.

[0133] It's important to understand that the softplus function is a built-in function in Python, where softplus(x) = lg(1 + e^x). x ).

[0134] In specific embodiments, the remote early warning requirement assessment indicators for wind turbines can comprehensively and accurately reflect the actual operating status and potential risk level of wind turbines. By timely and accurately assessing whether wind turbines require remote early warning, serious equipment damage and prolonged downtime accidents caused by undetected potential faults can be effectively avoided, ensuring the safe and stable operation of wind turbines and significantly improving the power generation reliability of wind farms. Simultaneously, it helps to rationally allocate operation and maintenance resources, plan maintenance work in advance, reduce operation and maintenance costs, and improve operation and maintenance efficiency.

[0135] Extract the preset remote early warning demand assessment threshold from the database.

[0136] It should be noted that the remote early warning demand assessment threshold is a pre-set threshold in the database and is used to measure whether wind turbines need remote early warning.

[0137] In one specific embodiment, the method for setting the remote early warning demand assessment threshold considers multiple factors. It can be comprehensively determined based on the design performance parameters of the wind turbine and a large amount of actual operational monitoring data. Under the influence of different wind conditions, environmental conditions, and turbine operating duration, data related to various operational performance assessment indicators and remote early warning demand assessment parameters of the wind turbine are collected. These data are analyzed in depth to identify the performance indicator boundary between stable operation and potential failure risk of the wind turbine under different operating conditions. Through statistical processing of these boundary point data, such as using cluster analysis and probability distribution statistics, a reasonable range is determined. Then, based on the wind farm's safety operation requirements and maintenance strategies, an appropriate value is selected as the remote early warning demand assessment threshold to ensure timely remote early warning when the turbine's operating state approaches or exceeds the safe range, thus guaranteeing the safe and stable operation of the wind turbine.

[0138] If the indicator parameter for remote early warning demand assessment of wind turbine units is greater than or equal to the threshold for remote early warning demand assessment, then the remote early warning demand information of wind turbine units will be marked as remote early warning demand and remote early warning will be issued.

[0139] If the remote early warning demand assessment indicator parameter of the wind turbine is greater than or equal to the remote early warning demand assessment threshold, it indicates that the current operating status of the wind turbine has deviated significantly from the normal range, and it faces a high potential risk. It is necessary to promptly issue a remote early warning signal to the relevant operation and maintenance personnel so that they can take corresponding measures quickly to avoid possible serious failures, ensure the safe, stable and efficient operation of the wind turbine, reduce energy waste caused by downtime due to failures, and also help improve the reliability of the entire wind farm.

[0140] If the indicator parameters for assessing the remote early warning requirement of wind turbine units are less than the threshold for assessing the remote early warning requirement, then the remote early warning requirement information for wind turbine units will be marked as not requiring remote early warning.

[0141] If the indicator parameters for remote early warning demand assessment of wind turbine units are less than the threshold for remote early warning demand assessment, it indicates that the current operating status of the wind turbine units is basically within the normal range, the various performance indicators are relatively stable, the potential risks they face are relatively low, and there is no need to immediately issue remote early warnings.

[0142] In a specific embodiment, by determining the remote early warning demand information of wind turbines and calculating the remote early warning demand assessment index parameters, the degree of remote early warning demand of wind turbines can be accurately measured. When the parameter is greater than or equal to the remote early warning demand assessment threshold, a remote early warning is issued in a timely manner, enabling operation and maintenance personnel to obtain information on potential serious faults of the units even when they are far away from the wind farm. This effectively avoids prolonged downtime of wind turbines or aggravated equipment damage due to sudden faults. At the same time, the remote early warning mechanism helps to realize centralized management of wind farms, improve operation and maintenance management efficiency, and ensure that wind turbines maintain a safe, stable, and efficient operating state in complex and ever-changing operating environments.

[0143] Please see Figure 2 As shown, this embodiment of the invention provides a method for monitoring the operating performance of wind turbine units based on a PLC hardware platform, including the following steps:

[0144] S1 collects the operating parameters of the wind turbine and inputs them into the central processing unit of the PLC hardware platform to analyze and process the operating parameters of the wind turbine and obtain the operating performance evaluation data of the wind turbine.

[0145] S2, based on the wind turbine operation performance evaluation data, simultaneously acquire the historical operation data of the wind turbine, analyze and process it to obtain the wind turbine operation performance evaluation index.

[0146] S3. Obtain the basic operating parameters of the wind turbine, and analyze the wind turbine operating performance correction index in combination with the wind turbine operating performance evaluation index. Based on the wind turbine operating performance correction index, generate early warning information, and simultaneously determine the drone inspection demand information, which includes inspection demand and no inspection required.

[0147] S4. When the drone inspection demand information is determined to be a demand inspection, collect the drone inspection data of the wind turbine, analyze the verification index coefficient of the drone inspection of the wind turbine, and combine it with the wind turbine operation performance correction index to determine the remote early warning demand information of the wind turbine. Simultaneously, based on the verification index coefficient of the drone inspection of the wind turbine, the drone inspection cycle is adaptively adjusted.

[0148] In a specific embodiment, a wind turbine operation performance monitoring system and method based on a PLC hardware platform are provided. Leveraging the powerful data processing capabilities of the PLC hardware platform, accurate acquisition and analysis of wind turbine operation parameters are achieved. This enables the acquisition of accurate operation performance evaluation data, resulting in more precise monitoring of wind turbine operation performance. It allows for timely detection of any abnormalities in wind turbine operation, providing a reliable data foundation for subsequent analysis and decision-making. By comprehensively considering multiple factors to calculate operation performance correction indicators, accurate early warning of fault risks can be provided, potential problems can be identified in advance, and fault escalation can be effectively prevented, reducing maintenance costs and downtime. Simultaneously, the UAV inspection strategy is optimized, improving inspection efficiency and targeting, reducing power generation loss, and increasing power generation efficiency.

[0149] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0150] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. The selection and detailed description of these embodiments in this specification are intended to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. Any modifications or variations that do not deviate from the structure of the invention or exceed the scope defined by the invention should fall within the protection scope of the invention.

Claims

1. A wind turbine operation performance monitoring system based on a PLC hardware platform, characterized in that: include: The operation performance evaluation data acquisition module is used to collect the operating parameters of the wind turbine and input them into the central processing unit of the PLC hardware platform to analyze and process the operating parameters of the wind turbine and obtain the operating performance evaluation data of the wind turbine. The operating parameters of the wind turbine include the total wind energy of the wind turbine, the total electrical energy output of the wind turbine, the voltage of the wind turbine at each time point, the current of the wind turbine, the power factor of the wind turbine, the yaw angle of the wind turbine, the rotor speed of the wind turbine, and the wind direction of the wind farm. The wind turbine performance evaluation label determination module is used to simultaneously acquire historical operating data of wind turbines based on wind turbine operating performance evaluation data, and analyze and process the data to obtain wind turbine operating performance evaluation indicators. The UAV inspection demand information determination module is used to acquire the basic operating parameters of the wind turbine, and analyze the wind turbine operating performance correction index in combination with the wind turbine operating performance evaluation index. Based on the wind turbine operating performance correction index, it generates early warning information and simultaneously determines the UAV inspection demand information. The UAV inspection demand information includes inspection demand and no inspection. The basic operating parameters of the wind turbine include wind turbine equipment parameters and external environmental parameters. The wind turbine equipment parameters include the service life of the wind turbine, the total bearing wear, and the average response time of the controller. The external environmental parameters include the daily average wind speed standard deviation, the daily average wind direction change frequency, and the altitude of the wind farm. The UAV inspection and analysis module is used to collect wind turbine UAV inspection data when the UAV inspection demand information is determined to be a demand inspection, analyze the wind turbine UAV inspection verification index coefficient, and combine it with the wind turbine operation performance correction index to determine the wind turbine remote early warning demand information. Simultaneously, the UAV inspection cycle is adaptively adjusted based on the wind turbine UAV inspection verification index coefficient.

2. The wind turbine operation performance monitoring system based on a PLC hardware platform according to claim 1, characterized in that: The specific method for obtaining the wind turbine operating performance evaluation data is as follows: Within the preset monitoring period, wind turbine operating parameters are collected; By analyzing the wind turbine voltage, wind turbine current and wind turbine power factor at each time point, the actual output power of the wind turbine at each time point is obtained. Based on the actual output power of the wind turbine at each time point, the variance of the actual output power of the wind turbine is calculated. The power generation efficiency of the wind turbine is calculated based on the total wind energy and the total electrical energy output of the wind turbine. Based on the yaw angle of the wind turbine and the wind direction of the wind farm at each time point, the yaw error rate of the wind turbine is calculated. Based on the wind turbine rotor speed at each time point, the wind turbine speed fluctuation rate is calculated. The variance of actual output power of wind turbine, power generation efficiency of wind turbine, yaw error rate of wind turbine, and speed fluctuation rate of wind turbine are combined as the data for evaluating the operating performance of wind turbine.

3. The wind turbine operation performance monitoring system based on a PLC hardware platform according to claim 2, characterized in that: The analysis and processing yielded wind turbine operating performance evaluation indicators, and the specific analysis process is as follows: Historical operating data of wind turbine units include historical average power factor, historical average rotor speed, historical average nacelle vibration velocity, and historical average oil temperature. Based on wind turbine operation performance evaluation data and historical wind turbine operation data, wind turbine operation performance evaluation indicators are obtained through analysis and processing. The wind turbine operating performance evaluation index is used to characterize the operating performance of the wind turbine.

4. The wind turbine operation performance monitoring system based on a PLC hardware platform according to claim 1, characterized in that: The analysis process for the wind turbine operating performance correction index is as follows: Based on the basic operating parameters of the wind turbine, the operating correction coefficient of the wind turbine is obtained through analysis and processing. Based on the wind turbine operation correction coefficient and the wind turbine operation performance evaluation index, the wind turbine operation performance correction index is obtained through analysis and processing. The wind turbine operating performance correction index is used to characterize the actual operating performance of the wind turbine after correction.

5. The wind turbine operation performance monitoring system based on a PLC hardware platform according to claim 4, characterized in that: The process of generating early warning information based on wind turbine operating performance correction indicators and simultaneously determining drone inspection needs is as follows: Extract the preset first threshold and second threshold for wind turbine operation performance correction verification from the database; The early warning information includes local early warnings that are required and those that are not required. If the wind turbine operating performance correction index is greater than or equal to the first threshold for wind turbine operating performance correction verification, the warning information will be marked as a local demand warning; if the wind turbine operating performance correction index is less than the first threshold for wind turbine operating performance correction verification, the warning information will be marked as no warning required. If the wind turbine's operating performance correction index is greater than or equal to the second threshold for wind turbine operating performance correction verification, then the wind turbine's drone inspection requirement information will be marked as requiring inspection. If the wind turbine's operating performance correction index is less than the second threshold for wind turbine operating performance correction verification, then the wind turbine's drone inspection requirement information will be marked as not requiring inspection.

6. The wind turbine operation performance monitoring system based on a PLC hardware platform according to claim 1, characterized in that: The analysis of the indicator coefficients for the unmanned aerial vehicle (UAV) inspection of wind turbines is as follows: The wind turbine drone inspection data includes the average crack width of the wind turbine blades, the cumulative surface defect area, the tower tilt angle, and the displacement of the anti-vibration hammer. Extract wind turbine inspection data from the database using reference drones; Based on the wind turbine drone inspection data and the wind turbine reference drone inspection data, the wind turbine drone inspection verification index coefficient is obtained through comprehensive analysis and processing. The wind turbine drone inspection verification index coefficient is used to characterize the degree of external structural defects of the wind turbine during drone inspection.

7. The wind turbine operation performance monitoring system based on a PLC hardware platform according to claim 6, characterized in that: The specific process for determining the remote early warning requirement information for wind turbine units is as follows: The remote early warning requirement information for wind turbine units includes those requiring remote early warning and those not requiring remote early warning. Extract the correction coefficients corresponding to the intervals of each UAV inspection verification indicator coefficient stored in the database, and map and extract the UAV inspection correction coefficients corresponding to the intervals of the wind turbine UAV inspection verification indicator coefficients, and mark them as wind turbine correction coefficients. Based on the wind turbine correction coefficient and the wind turbine operating performance correction index, the remote early warning demand assessment index parameters of wind turbines are obtained through comprehensive analysis and processing. The aforementioned remote early warning demand assessment index parameters for wind turbine units are used to characterize the degree of remote early warning demand for wind turbine units. Extract the preset remote early warning demand assessment threshold from the database; If the indicator parameter for remote early warning demand assessment of wind turbine units is greater than or equal to the remote early warning demand assessment threshold, then the remote early warning demand information of wind turbine units will be marked as remote early warning demand and remote early warning will be issued. If the indicator parameters for assessing the remote early warning requirement of wind turbine units are less than the threshold for assessing the remote early warning requirement, then the remote early warning requirement information for wind turbine units will be marked as not requiring remote early warning.

8. The wind turbine operation performance monitoring system based on a PLC hardware platform according to claim 7, characterized in that: The specific methods for obtaining the remote early warning demand assessment index parameters for wind turbine units are as follows: ； in, Indicator parameters for remote early warning requirements assessment of wind turbine units. Correction indicators for the operating performance of wind turbine units. is the correction factor for wind turbine units, and e is the natural constant.

9. A method for monitoring the operating performance of wind turbine units based on a PLC hardware platform, characterized in that: Includes the following steps: S1. Collect wind turbine operating parameters and input them into the central processing unit of the PLC hardware platform to analyze and process the wind turbine operating parameters and obtain wind turbine operating performance evaluation data. The wind turbine operating parameters include the total wind energy of the wind turbine, the total electrical energy output of the wind turbine, the wind turbine voltage at each time point, the wind turbine current, the wind turbine power factor, the wind turbine yaw angle, the wind turbine rotor speed, and the wind direction of the wind farm. S2. Based on the wind turbine operation performance evaluation data, the historical operation data of the wind turbine are acquired simultaneously, and the wind turbine operation performance evaluation index is obtained through analysis and processing. S3. Obtain the basic operating parameters of the wind turbine, and analyze the wind turbine operating performance correction index in combination with the wind turbine operating performance evaluation index. Based on the wind turbine operating performance correction index, generate early warning information and simultaneously determine the drone inspection demand information. The drone inspection demand information includes required inspection and no inspection required. The basic operating parameters of the wind turbine include wind turbine equipment parameters and external environmental parameters. The wind turbine equipment parameters include the service life of the wind turbine, the total bearing wear, and the average response time of the controller. The external environmental parameters include the daily average wind speed standard deviation, the daily average wind direction change frequency, and the altitude of the wind farm. S4. When the drone inspection demand information is determined to be a demand inspection, collect the drone inspection data of the wind turbine, analyze the verification index coefficient of the drone inspection of the wind turbine, and combine it with the wind turbine operation performance correction index to determine the remote early warning demand information of the wind turbine. Simultaneously, based on the verification index coefficient of the drone inspection of the wind turbine, the drone inspection cycle is adaptively adjusted.

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