Wind turbine generator heat dissipation technology transformation method and device and storage medium

Abstract

The invention provides a wind turbine generator heat dissipation technology transformation method and device and a storage medium. The method comprises the steps that whether a target wind turbine generator meets the technical transformation condition or not is judged according to the calculated electric quantity loss percentage; if it is judged that the target wind turbine generator set reaches the technical improvement condition, heat dissipation capability evaluation and heat dissipation simulation analysis are conducted on the target wind turbine generator set and the reference wind turbine generator set, and at least two candidate schemes are determined; performing weighted evaluation on the at least two candidate schemes, and taking the candidate scheme with the highest score as a technical improvement scheme; and performing technical transformation on the target wind turbine generator based on the technical transformation scheme. In the technical scheme provided by the embodiment of the invention, the candidate schemes are determined after the target wind turbine generator meeting the technical improvement condition is fully evaluated and analyzed, and the final technical improvement scheme is determined from the candidate schemes by comprehensively considering various factors in a weighted evaluation mode, so that the reasonability and practicability of the technical improvement scheme are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power generation, and in particular to a method, device and storage medium for transforming the heat dissipation technology of a wind turbine generator set. Background Art

[0002] In recent years, wind power, as a clean energy source, has gradually gained widespread attention. Wind turbines are a key component of wind power generation. To prevent dust or corrosive gases from entering the nacelle, the nacelle is generally designed to be enclosed. The nacelle contains high-power, heat-generating components such as the generator, gearbox, and inverter. These components generate significant heat during operation, and the temperature inside the nacelle can rise significantly, especially in high-temperature environments. Inadequate heat dissipation can shorten the service life of the equipment within the nacelle. In more serious cases, overheating can trigger power limiting or emergency shutdown. Therefore, the nacelle's heat dissipation capacity is crucial to the stable operation of the entire wind turbine.

[0003] In the related art, two solutions are commonly used to address the nacelle heat dissipation problem. The first is to create an opening in the fan, either directly or through a pre-existing opening (escape hatch) in the nacelle. A direct opening can increase the heat dissipation path and improve the internal heat dissipation rate of the fan. However, if the opening is not located appropriately, the nacelle temperature may drop too low during winter temperatures. Furthermore, if the nacelle is not properly sealed, dust from outside the nacelle can be drawn in and adsorbed in the heat dissipation paths of the gearbox and generator, affecting heat dissipation and shortening the service life of various components within the nacelle. To avoid affecting the performance of the installed heat dissipation paths, regular downtime inspections and maintenance are required, incurring significant maintenance costs. Dissipating heat through the pre-existing opening (escape hatch) in the nacelle does not damage the nacelle structure, but due to the fixed location of the pre-existing opening, it may have little effect on improving the nacelle's heat dissipation performance. The second solution is to increase the radiator power within the fan's existing ventilation area. However, in high-temperature environments, this has limited effectiveness in dissipating heat from heat-accumulating areas within the nacelle. Summary of the Invention

[0004] In view of the above problems, the embodiments of the present invention provide a method, device and storage medium for modifying the heat dissipation technology of a wind turbine, so as to solve the problem of low heat dissipation efficiency in the prior art of heat dissipation through openings or existing ventilation areas.

[0005] In a first aspect, an embodiment of the present invention provides a method for improving heat dissipation technology of a wind turbine generator system, the method comprising:

[0006] Determine whether the target wind turbine generator system meets the technical transformation conditions based on the calculated percentage of power loss;

[0007] If it is determined that the target wind turbine generator system meets the technical upgrade conditions, heat dissipation capacity evaluation and heat dissipation simulation analysis are performed on the target wind turbine generator system and a reference wind turbine generator system to determine at least two candidate solutions; the reference wind turbine generator system is a wind turbine generator system located in the same wind line as the target wind turbine generator system;

[0008] Conduct a weighted evaluation on the at least two candidate solutions, and select the candidate solution with the highest score as the technical improvement solution;

[0009] The target wind turbine generator system is technically transformed based on the technical transformation plan.

[0010] In a possible implementation, judging whether the target wind turbine generator set meets the technical transformation conditions based on the calculated percentage of power loss includes:

[0011] Calculate an estimated amount of power lost during the total power limit time period based on the rated power, power limit, and total power limit time period of the target wind turbine generator set;

[0012] Calculating the percentage of power loss based on the estimated power loss value and the actual total power generation;

[0013] Determining whether the percentage of power loss is greater than a preset percentage threshold;

[0014] If it is determined that the percentage of power loss is greater than the percentage threshold, it is determined that the target wind turbine generator system meets the technical transformation conditions;

[0015] If it is determined that the percentage of power loss is less than the percentage threshold, it is determined that the target wind turbine generator set does not meet the technical transformation conditions.

[0016] In a possible implementation, before determining whether the target wind turbine generator set meets the technical upgrade conditions based on the calculated percentage of power loss, the method further includes:

[0017] Import the monitoring data collected by the target wind turbine data acquisition and monitoring control SCADA system;

[0018] Determine and extract key data from the monitoring data based on configurable judgment rules;

[0019] Automatically diagnose cooling system faults based on the key data, generate fault diagnosis results, and generate a fault repair work order based on the fault diagnosis results.

[0020] In a possible implementation, before determining whether the target wind turbine generator set meets the technical upgrade conditions based on the calculated power loss percentage, the method further includes:

[0021] Query the latest maintenance time of the cooling system from the imported operation and maintenance data;

[0022] Determine whether the time difference between the latest maintenance time and the current time is greater than a preset maintenance time interval threshold;

[0023] If it is determined that the time difference is greater than the maintenance time interval threshold, a maintenance work order is generated;

[0024] If it is determined that the time difference is less than the maintenance time interval threshold, the step of determining whether the target wind turbine generator system meets the technical transformation conditions based on the calculated percentage of power loss within the power limit time period is performed.

[0025] In a possible implementation, before performing heat dissipation capability evaluation and heat dissipation simulation analysis on the target wind turbine generator set and the reference wind turbine generator set, the method further includes:

[0026] According to the matrix and weight of each color channel in the thermal image, the equivalent brightness value of the grayscale image is calculated;

[0027] generating a grayscale image corresponding to the thermal image according to the equivalent brightness value;

[0028] Performing large component area selection on the thermal image and the grayscale image to obtain a large component thermal image and a large component grayscale image;

[0029] Obtaining a maximum temperature value and a minimum temperature value in the thermal image of the large component, and a maximum brightness value and a minimum brightness value in the grayscale image of the large component;

[0030] Determining the linear relationship between the temperature value and the equivalent brightness value by the undetermined coefficient method;

[0031] The temperature value of each point is calculated according to the linear relationship and the equivalent brightness value of each point.

[0032] In a possible implementation, the heat dissipation capability evaluation and heat dissipation simulation analysis are performed on the target wind turbine and the reference wind turbine to determine at least two candidate solutions, including:

[0033] Calculating the distribution uniformity of the component surface temperature per minute based on the maximum temperature and the minimum temperature;

[0034] Calculate the component surface heat dissipation rate per minute based on the component surface average temperature at time t minute and the component surface average temperature at time t+1 minute;

[0035] Importing a constructed wind turbine physical model according to the size of the target wind turbine group and the size of the reference wind turbine group;

[0036] According to the corresponding relationship between time and boundary condition values, a target boundary condition value is inquired, wherein the target boundary condition value is the boundary condition value corresponding to the moment when the heat dissipation rate of the component surface is the highest;

[0037] Constructing a heat dissipation simulation model based on the target boundary condition value and the fan physical model;

[0038] Perform simulation analysis on the heat dissipation simulation model to determine at least two candidate solutions.

[0039] In a possible implementation, the performing simulation analysis on the heat dissipation simulation model to determine at least two candidate solutions includes:

[0040] Performing simulation analysis on the heat dissipation simulation model through different simulation schemes to obtain the temperature distribution of the target wind turbine group and the temperature distribution of the reference wind turbine group, wherein the temperature distribution refers to the temperature distribution of the vertical cross section in the direction of the transmission chain;

[0041] sorting the temperature distribution of the target wind turbine generator set and the temperature distribution of the reference wind turbine generator set based on the uniformity of the temperature distribution to obtain a uniformity distribution sequence;

[0042] Calculating a simulation score for each simulation scheme according to the uniform degree distribution sequence;

[0043] At least two solutions are selected in descending order of the simulation scores as candidate solutions.

[0044] In a possible implementation, performing a weighted evaluation on the at least two candidate solutions and selecting the candidate solution with the highest score as the technical improvement solution includes:

[0045] Performing a weighted calculation on the obtained expert evaluation score, cost evaluation score, safety evaluation score, and the simulation score to obtain a final score for the candidate solution;

[0046] The candidate solution with the highest final score is selected as the technical improvement solution.

[0047] In a second aspect, an embodiment of the present invention provides a device for improving heat dissipation technology of a wind turbine generator system, the device comprising:

[0048] A judgment module is used to judge whether the target wind turbine generator set meets the technical transformation conditions based on the calculated percentage of power loss;

[0049] If the judgment module determines that the target wind turbine generator set meets the technical transformation conditions, the determination module is triggered to perform heat dissipation capacity evaluation and heat dissipation simulation analysis on the target wind turbine generator set and a reference wind turbine generator set to determine at least two candidate solutions; the reference wind turbine generator set is a wind turbine generator set on the same wind line as the target wind turbine generator set;

[0050] An evaluation module, configured to perform a weighted evaluation on the at least two candidate solutions and select the candidate solution with the highest score as the technical improvement solution;

[0051] A technical transformation module is used to perform technical transformation on the target wind turbine generator set based on the technical transformation plan.

[0052] In a third aspect, an embodiment of the present invention further provides a computer-readable storage medium, which includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute the wind turbine heat dissipation technology transformation method as described in the first aspect or any possible implementation method of the first aspect.

[0053] In the technical solution provided by the embodiment of the present invention, candidate solutions are determined after a full evaluation and analysis of the target wind turbines that meet the technical transformation conditions. Then, through a weighted evaluation method, a variety of factors are comprehensively considered to determine the final technical transformation solution from the candidate solutions, thereby improving the rationality and practicality of the technical transformation solution.

[0054] In this embodiment of the present invention, there are three main reasons for high wind turbine temperatures: cooling system circuit or component failure, untimely maintenance or cleaning, and overloaded nacelle heat dissipation structures. This embodiment of the present invention allows for cooling system fault diagnosis and maintenance testing of the target wind turbine before optimizing the nacelle heat dissipation structure. After eliminating cooling system faults and maintenance causes, it further determines whether technical modifications are necessary for the target wind turbine. This allows for rapid identification of the cause of nacelle heat dissipation problems, improves the rationality of the nacelle heat dissipation process, and enhances the efficiency of heat dissipation problem resolution. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 The present invention provides a flow chart of a method for improving the heat dissipation technology of a wind turbine generator system.

[0056] Figure 2 The present invention provides a flow chart of a method for calculating a temperature value.

[0057] Figure 3 A schematic structural diagram of a wind turbine heat dissipation technology transformation device provided by an embodiment of the present invention.

[0058] Figure 4 A schematic diagram of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0059] To make the objectives, technical solutions, and advantages of the present invention more clear and understandable, the present invention is further described below with reference to the accompanying drawings and specific embodiments. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present invention.

[0060] Figure 1 A flow chart of a method for improving the heat dissipation technology of a wind turbine generator system provided by an embodiment of the present invention is shown in FIG. Figure 1 As shown, the method includes:

[0061] Step 101: Determine whether the target wind turbine generator set meets the technical upgrade conditions based on the calculated power loss percentage. If the target wind turbine generator set meets the technical upgrade conditions, proceed to step 102; if the target wind turbine generator set does not meet the technical upgrade conditions, the process ends.

[0062] In this step, based on the rated power, power limit and total power limit time period of the target wind turbine, the estimated value of the power loss within the total power limit time period is calculated; based on the estimated value of the power loss and the actual total power generation, the percentage of power loss is calculated; it is determined whether the percentage of power loss is greater than a preset percentage threshold; if it is determined that the percentage of power loss is greater than the percentage threshold, it is determined that the target wind turbine meets the technical transformation conditions; if it is determined that the percentage of power loss is less than the percentage threshold, it is determined that the target wind turbine does not meet the technical transformation conditions.

[0063] In the embodiment of the present invention, the calculation formula of the estimated value of power loss is:

[0064] Δh=(PP ′ )·Δt ′

[0065] Where Δh represents the estimated value of power loss (unit: kWh), P represents the rated power of the wind turbine (unit: kW), and P ′ Indicates the power limit (unit: kilowatt), Δt ′ Indicates the total power limit time period (unit: hours).

[0066] The formula for calculating the percentage of power loss is:

[0067]

[0068] Where k represents the percentage of power loss, h represents the actual total power generation (unit: kWh), and Δh represents the estimated value of power loss (unit: kWh).

[0069] In an embodiment of the present invention, before step 101, the method further includes: importing monitoring data collected by a supervisory control and data acquisition (SCADA) system of a target wind turbine; determining and extracting key data from the monitoring data based on configurable judgment rules; automatically diagnosing cooling system faults based on the key data, generating fault diagnosis results, and generating a fault repair work order based on the fault diagnosis results.

[0070] Specifically, the monitoring data includes basic operating data of the target wind turbine, as well as relevant fault data for the gearbox, gearbox cooling system, generator, and generator cooling system during a preset period before the fault. For example, the preset period is 20 minutes. For example, if the cooling system is a water-cooled system, the monitoring data includes wind speed, active power, ambient temperature, nacelle temperature, generator winding temperature, generator bearing temperature, generator coolant inlet temperature, generator coolant outlet temperature, gearbox inlet oil temperature, gearbox oil sump temperature, gearbox cooling water temperature, and gearbox bearing temperature.

[0071] For example, the judgment rule includes determining whether a temperature sensor has failed based on the gearbox bearing temperature and the gearbox oil sump temperature. The key data are the gearbox bearing temperature and the gearbox oil sump temperature. The automatic fault diagnosis process specifically includes: normalizing the gearbox bearing temperature and the gearbox oil sump temperature and performing first-order difference calculations to generate a bearing temperature series and an oil sump temperature series; calculating the average of the difference between the bearing temperature series and the oil sump temperature series; if the average value is greater than a preset difference difference threshold, it indicates that the bearing temperature sensor or the oil sump temperature sensor may be faulty. Faults include loose faults or false connection faults.

[0072] For example, a judgment rule might include determining whether a thermostatic valve is faulty based on the temperature difference between the gearbox oil sump temperature and the gearbox inlet oil temperature. The key data are the gearbox oil sump temperature and the gearbox inlet oil temperature. The automatic fault diagnosis process specifically includes the following: If the gearbox oil sump temperature is greater than a preset temperature threshold, the temperature difference is greater than a preset temperature difference threshold, and the cooling water pump is not running, then the thermostatic valve may be faulty. For example, the temperature threshold is 60°C.

[0073] In this embodiment of the present invention, cooling system faults are automatically diagnosed using configurable diagnostic rules. These rules can be edited and configured based on actual usage requirements, improving the flexibility and efficiency of fault diagnosis and avoiding the high reliance on manual experience during the diagnostic process. Furthermore, after the fault diagnosis is completed, a repair work order can be issued to on-site personnel based on the diagnostic results, improving the efficiency of cooling system repairs.

[0074] In an embodiment of the present invention, before step 101, the steps further include: querying the latest maintenance time of the cooling system from the imported operation and maintenance data; determining whether the time difference between the latest maintenance time and the current time is greater than a preset maintenance time interval threshold; generating a maintenance work order if the time difference is greater than the maintenance time interval threshold; and executing a step of determining whether the target wind turbine meets the technical upgrade conditions based on the calculated percentage of power loss within the power limit time period if the time difference is less than the maintenance time interval threshold. The operation and maintenance data includes operation and maintenance logs.

[0075] In an embodiment of the present invention, whether the cooling system needs periodic maintenance is determined by the time difference between the latest maintenance time and the current time, and when it is determined that the cooling system needs periodic maintenance, a maintenance work order is dispatched to on-site staff, thereby improving the maintenance efficiency of the cooling system.

[0076] Step 102: perform heat dissipation capability evaluation and heat dissipation simulation analysis on the target wind turbine generator set and the reference wind turbine generator set to determine at least two candidate solutions.

[0077] In this embodiment of the present invention, the reference wind turbine is located in the same wind direction as the target wind turbine. After the target wind turbine is determined to undergo technical modification, thermal imagers are installed inside the nacelles of the target and reference wind turbines to monitor the temperature of the gearbox and generator within the nacelles. Furthermore, thermistors are used to monitor the temperature at the entrances and exits of the nacelles to obtain the inlet and outlet temperatures, and wind speed measurement tools are used to monitor the wind speed to obtain the inlet and outlet wind speeds.

[0078] Figure 2 A flow chart of a temperature value calculation method provided by an embodiment of the present invention is as follows: Figure 2 As shown, the method includes:

[0079] Step S1: Calculate the equivalent brightness value of the grayscale image based on the matrix and weight of each color channel in the thermal image.

[0080] In this step, the thermal image generated by the thermal imager has three color channels, namely a red channel (ie, R channel), a green channel (ie, G channel), and a blue channel (ie, B channel).

[0081] The calculation formula for the equivalent brightness value is:

[0082] Y=ω r M r +ω g M g +ω b M b

[0083] Among them, Y represents the equivalent brightness value of the grayscale image, ω rRepresents the weight of the R channel, M r represents the matrix of the R channel, ω g represents the weight of the G channel, M g represents the matrix of G channel, ω b Indicates the weight of the B channel, M b Matrix representing the B channel.

[0084] Step S2: Generate a grayscale image corresponding to the thermal image according to the equivalent brightness value.

[0085] Step S3: Select a large component area from the thermal image and the grayscale image to obtain a large component thermal image and a large component grayscale image.

[0086] In this step, the large component area includes the generator area and the gearbox area, the large component thermal image is a thermal image of the large component area, and the large component grayscale image is a grayscale image of the large component area.

[0087] Step S4: Acquire the maximum temperature and minimum temperature in the thermal image of the large component, and the maximum brightness and minimum brightness in the grayscale image of the large component.

[0088] In this step, the maximum temperature and minimum temperature in the thermal image of the large component, as well as the maximum brightness and minimum brightness in the grayscale image of the large component, are obtained through the thermal imager software.

[0089] Step S5: Determine the linear relationship between the temperature value and the equivalent brightness value by using the undetermined coefficient method.

[0090] In this step, the linear relationship between the temperature value and the equivalent brightness value is:

[0091] T=kY+b

[0092]

[0093] b=T max -kY max

[0094] Where T represents the temperature, k represents the slope, b represents the intercept, and T max Indicates the maximum temperature, T min Indicates the minimum temperature, Y max Indicates the maximum brightness, Y min Indicates the minimum brightness value.

[0095] Step S6: Calculate the temperature value of each point based on the linear relationship and the equivalent brightness value of each point.

[0096] In an embodiment of the present invention, the distribution uniformity of the component surface temperature per minute is calculated based on the maximum temperature and the minimum temperature; and the component surface heat dissipation rate per minute is calculated based on the average distribution uniformity at time t minutes and the average distribution uniformity at time t+1 minutes.

[0097] In the embodiment of the present invention, the calculation formula for the uniformity of the surface temperature distribution of a component is:

[0098] T a =1 / (T max -T min )

[0099] Among them, T a Indicates the uniformity of the surface temperature distribution of the component, T max Indicates the maximum temperature, T min Indicates the minimum temperature.

[0100] The calculation formula for the heat dissipation rate of the component surface is:

[0101]

[0102] Among them, T b Indicates the heat dissipation rate of the component surface, Indicates the average surface temperature of the component at t+1 minute, Indicates the average surface temperature of the component at time t minutes.

[0103] In the embodiment of the present invention, the heat dissipation rate of the component surface can reflect the heat dissipation capability to a certain extent. The higher the heat dissipation rate of the component surface, the better the heat dissipation capability; the lower the heat dissipation rate of the component surface, the worse the heat dissipation capability.

[0104] In an embodiment of the present invention, a physical model of a wind turbine is constructed based on the size of a target wind turbine and the size of a reference wind turbine. A target boundary condition value is retrieved based on the correspondence between time and boundary condition values, where the target boundary condition value is the boundary condition value corresponding to the moment when the heat dissipation rate on the component surface is the highest. A heat dissipation simulation model is constructed based on the target boundary condition value and the physical model of the wind turbine. A simulation analysis is performed on the heat dissipation simulation model to determine at least two candidate solutions. Specifically, a correspondence between time and boundary condition values ​​is established by synchronizing the temperature value and the boundary condition values. Boundary condition values ​​include inlet and outlet wind speeds, inlet and outlet temperatures, and the average surface temperature of the component.

[0105] Specifically, the heat dissipation simulation model was simulated and analyzed using different simulation schemes to obtain the temperature distribution of the target wind turbine and the temperature distribution of the reference wind turbine. The temperature distribution refers to the temperature distribution of the vertical section in the direction of the transmission chain. Based on the uniformity of the temperature distribution, the temperature distribution of the target wind turbine and the temperature distribution of the reference wind turbine were sorted to obtain a uniformity distribution sequence. The simulation score of each simulation scheme was calculated based on the uniformity distribution sequence. At least two schemes were selected as candidate schemes in descending order of simulation scores. Among them, the simulation schemes include mainstream heat dissipation schemes such as downward rear exhaust, downward upper exhaust, downward side exhaust, side rear exhaust, and side upper exhaust.

[0106] As you can understand, wind turbines on the same wind line share the same dominant wind direction path, resulting in highly consistent airflow paths. They also share real-time meteorological data, resulting in highly consistent operating conditions for each wind turbine on the same wind line. Therefore, the temperature distribution of the reference wind turbine is a valuable reference for the target wind turbine.

[0107] In the embodiment of the present invention, the uniform degree distribution sequence is {q1, q2, q3...}, and the calculation formula of the simulation score is:

[0108] Q i =100*(q i -q min ) / (q max -q min )

[0109] Among them, Q i represents the simulation score, q i Indicates the temperature uniformity of the i-th wind turbine in the uniformity distribution sequence, q max Indicates the maximum temperature uniformity in the uniformity distribution sequence, q min Indicates the minimum value of temperature uniformity in the uniformity distribution sequence.

[0110] Step 103: Perform a weighted evaluation on at least two candidate solutions, and select the candidate solution with the highest score as the technical improvement solution.

[0111] In this step, the obtained expert evaluation scores, cost evaluation scores, safety evaluation scores, and simulation scores are weighted to obtain the final score of the candidate solution. The candidate solution with the highest final score is selected as the technical improvement solution. The expert evaluation score is the score of the technical improvement experts on the candidate solution, the cost evaluation score is the score of the candidate solution by the operations manager, and the safety evaluation score is the score of the candidate solution by the owner's representative.

[0112] The final score is calculated as follows:

[0113] P i =wQ Q i +w S S i +w C C i +w F F i

[0114] Among them, P i represents the final score of the i-th candidate solution, w Q represents the weight of the simulation score, Q i represents the simulation score of the i-th candidate solution, w S represents the weight of the expert evaluation score, S i represents the expert evaluation score of the i-th candidate solution, w C represents the weight of the cost assessment score, C i represents the cost evaluation score of the i-th candidate solution, w F Indicates the weight of the safety assessment score, F i Represents the safety assessment score of the i-th candidate solution. For example, when the final technical improvement solution is selected from two candidate solutions, i = 1, or i = 2.

[0115] Step 104: Perform technical transformation on the target wind turbine generator system based on the technical transformation plan.

[0116] In this step, after the target wind turbine is technically modified, the effect of the technical modification is analyzed. For wind turbines whose technical modification effect does not meet expectations, a secondary technical modification can be performed to improve the heat dissipation performance of the target wind turbine.

[0117] In the prior art, due to the difficulty in obtaining actual temperature data within the nacelle under high-temperature conditions, technical improvement plans are typically determined through two approaches. The first involves technical improvement experts directly developing a mass-produced cooling improvement plan based on prior technical experience. The second involves simulation engineers using physical modeling to simulate cooling based on different scenarios within a computer simulation environment. The solution with the best results is then selected as the technical improvement plan. Wind turbine operating conditions are complex and highly variable. Of these two approaches, the first lacks the theoretical support of physical models, while the second lacks practical experience in specific fields, resulting in limited controllable variables. Furthermore, neither approach considers the actual operating conditions of wind turbines. Nacelle cooling capacity is affected by numerous factors, and failure to capture certain factors may result in the improvement results falling short of expectations. For example, the gearbox and generator each have their own cooling systems. When a cooling system fails, the root cause must be identified and a solution developed. In practice, the trial-and-error cost of implementing technical improvement plans is also very high. Therefore, it is important to fully consider various factors when selecting a technical improvement plan to ensure its rationality.

[0118] In the technical solution provided by the embodiment of the present invention, candidate solutions are determined after a full evaluation and analysis of the target wind turbines that meet the technical transformation conditions. Then, through a weighted evaluation method, a variety of factors are comprehensively considered to determine the final technical transformation solution from the candidate solutions, thereby improving the rationality and practicality of the technical transformation solution.

[0119] In this embodiment of the present invention, there are three main reasons for high wind turbine temperatures: cooling system circuit or component failure, untimely maintenance or cleaning, and overloaded nacelle heat dissipation structures. This embodiment of the present invention allows for cooling system fault diagnosis and maintenance testing of the target wind turbine before optimizing the nacelle heat dissipation structure. After eliminating cooling system faults and maintenance causes, it further determines whether technical modifications are necessary for the target wind turbine. This allows for rapid identification of the cause of nacelle heat dissipation problems, improves the rationality of the nacelle heat dissipation process, and enhances the efficiency of heat dissipation problem resolution.

[0120] In the embodiment of the present invention, the data used include monitoring data of the SCADA system, operation and maintenance data, and surface temperature data of internal components of the cabin monitored by a far-infrared camera. It integrates comprehensive data of the inside and outside of the target wind turbine and manual maintenance, and can comprehensively analyze the heat dissipation capacity of the internal components of the cabin and the heat dissipation capacity of the structure, and the reliability of the analysis results is higher.

[0121] In this embodiment of the present invention, the evaluation process for technical improvement plans fully considers nacelle model simulation, expert experience, cost factors, and operational safety to determine the final technical improvement plan, improving its rationality and practicality. Furthermore, after the technical improvement plan is implemented, its effectiveness is analyzed, and secondary technical improvements are performed on wind turbines that do not achieve the expected results to improve the heat dissipation performance of the target wind turbines.

[0122] Figure 3 A schematic diagram of a wind turbine heat dissipation technology transformation device provided by an embodiment of the present invention is shown in FIG. Figure 3 As shown, the device includes a judgment module 11, a determination module 12, an evaluation module 13 and a technical transformation module 14. The judgment module 11 is used to judge whether the target wind turbine group meets the technical transformation conditions based on the calculated percentage of lost electricity; if the judgment module 11 judges that the target wind turbine group meets the technical transformation conditions, it triggers the determination module 12 to perform heat dissipation capacity evaluation and heat dissipation simulation analysis on the target wind turbine group and the reference wind turbine group, and determine at least two candidate solutions; the reference wind turbine group is a wind turbine group on the same wind line as the target wind turbine group; if the judgment module 11 judges that the target wind turbine group does not meet the technical transformation conditions, the process ends. The evaluation module 13 is used to perform a weighted evaluation on at least two candidate solutions, and use the candidate solution with the highest score as the technical transformation solution. The technical transformation module 14 is used to perform technical transformation on the target wind turbine group based on the technical transformation solution.

[0123] In an embodiment of the present invention, the judgment module 11 is specifically used to calculate an estimated value of power loss within the total power limit time period based on the rated power, power limit and total power limit time period of the target wind turbine generator set; calculate the percentage of power loss based on the estimated value of power loss and the actual total power generation; judge whether the percentage of power loss is greater than a preset percentage threshold; if it is judged that the percentage of power loss is greater than the percentage threshold, it is determined that the target wind turbine generator set meets the technical transformation conditions; if it is judged that the percentage of power loss is less than the percentage threshold, it is determined that the target wind turbine generator set does not meet the technical transformation conditions.

[0124] In an embodiment of the present invention, the device further includes a diagnostic module 15. The diagnostic module 15 is configured to import monitoring data collected by the target wind turbine generator system's data acquisition and supervisory control (SCADA) system; determine and extract key data from the monitoring data based on configurable judgment rules; automatically diagnose cooling system faults based on the key data, generate fault diagnosis results, and generate a fault repair work order based on the fault diagnosis results.

[0125] In an embodiment of the present invention, the device further includes a maintenance module 16. The maintenance module 16 is configured to query the latest maintenance time of the cooling system from the imported operation and maintenance data; determine whether the time difference between the latest maintenance time and the current time is greater than a preset maintenance time interval threshold; if it is determined that the time difference is greater than the maintenance time interval threshold, generate a maintenance work order; if it is determined that the time difference is less than the maintenance time interval threshold, execute the step of determining whether the target wind turbine meets the technical modification conditions based on the calculated percentage of power loss within the power limit time period.

[0126] In an embodiment of the present invention, the device further includes a calculation module 17. The calculation module 17 is configured to calculate an equivalent brightness value of the grayscale image based on the matrix and weight of each color channel in the thermal image; generate a grayscale image corresponding to the thermal image based on the equivalent brightness value; perform large component area selection on the thermal image and the grayscale image to obtain a large component thermal image and a large component grayscale image; obtain the maximum temperature and minimum temperature in the large component thermal image, as well as the maximum brightness and minimum brightness in the large component grayscale image; determine the linear relationship between the temperature value and the equivalent brightness value using the undetermined coefficient method; and calculate the temperature value of each point based on the linear relationship and the equivalent brightness value of each point.

[0127] In an embodiment of the present invention, the determination module 12 includes a first calculation unit, a second calculation unit, an import unit, a query unit, a construction unit and a determination unit. The first calculation unit is used to calculate the uniformity of the distribution of the component surface temperature per minute based on the maximum temperature and the minimum temperature. The second calculation unit is used to calculate the component surface heat dissipation rate per minute based on the average surface temperature of the component at time t minutes and the average surface temperature of the component at time t+1 minutes. The import unit is used to import the constructed wind turbine physical model based on the size of the target wind turbine group and the size of the reference wind turbine group. The query unit is used to query the target boundary condition value based on the correspondence between time and boundary condition values. The target boundary condition value is the boundary condition value corresponding to the moment when the component surface heat dissipation rate is the highest. The construction unit is used to construct a heat dissipation simulation model based on the target boundary condition value and the wind turbine physical model. The determination unit is used to perform simulation analysis on the heat dissipation simulation model to determine at least two candidate solutions.

[0128] In an embodiment of the present invention, the determination unit is specifically used to perform simulation analysis on the heat dissipation simulation model through different simulation schemes to obtain the temperature distribution of the target wind turbine group and the temperature distribution of the reference wind turbine group, where the temperature distribution refers to the temperature distribution of the vertical section in the direction of the transmission chain; based on the uniformity of the temperature distribution, the temperature distribution of the target wind turbine group and the temperature distribution of the reference wind turbine group are sorted to obtain a uniformity distribution sequence; the simulation score of each simulation scheme is calculated according to the uniformity distribution sequence; and at least two schemes are selected as candidate schemes in descending order of the simulation scores.

[0129] In the embodiment of the present invention, the evaluation module 13 is specifically used to perform weighted calculation on the obtained expert evaluation score, cost evaluation score, safety evaluation score and simulation score to obtain the final score of the candidate solution; and the candidate solution with the highest final score is used as the technical improvement solution.

[0130] In the technical solution provided by the embodiment of the present invention, candidate solutions are determined after a full evaluation and analysis of the target wind turbines that meet the technical transformation conditions. Then, through a weighted evaluation method, a variety of factors are comprehensively considered to determine the final technical transformation solution from the candidate solutions, thereby improving the rationality and practicality of the technical transformation solution.

[0131] In this embodiment of the present invention, there are three main reasons for high wind turbine temperatures: cooling system circuit or component failure, untimely maintenance or cleaning, and overloaded nacelle heat dissipation structures. This embodiment of the present invention allows for cooling system fault diagnosis and maintenance testing of the target wind turbine before optimizing the nacelle heat dissipation structure. After eliminating cooling system faults and maintenance causes, it further determines whether technical modifications are necessary for the target wind turbine. This allows for rapid identification of the cause of nacelle heat dissipation problems, improves the rationality of the nacelle heat dissipation process, and enhances the efficiency of heat dissipation problem resolution.

[0132] In the embodiment of the present invention, the data used include monitoring data of the SCADA system, operation and maintenance data, and surface temperature data of internal components of the cabin monitored by a far-infrared camera. It integrates comprehensive data of the inside and outside of the target wind turbine and manual maintenance, and can comprehensively analyze the heat dissipation capacity of the internal components of the cabin and the heat dissipation capacity of the structure, and the reliability of the analysis results is higher.

[0133] In this embodiment of the present invention, the evaluation process for technical improvement plans fully considers nacelle model simulation, expert experience, cost factors, and operational safety to determine the final technical improvement plan, improving its rationality and practicality. Furthermore, after the technical improvement plan is implemented, its effectiveness is analyzed, and secondary technical improvements are performed on wind turbines that do not achieve the expected results to improve the heat dissipation performance of the target wind turbines.

[0134] An embodiment of the present invention provides a computer-readable storage medium, which includes a stored program. When the program is running, the device where the computer-readable storage medium is located is controlled to execute the steps of the embodiment of the above-mentioned wind turbine heat dissipation technology transformation method. For a specific description, please refer to the embodiment of the above-mentioned wind turbine heat dissipation technology transformation method.

[0135] Figure 4 A schematic diagram of a computer device provided in an embodiment of the present invention is shown in FIG. Figure 4As shown, the computer device includes: a processor, a memory, and a computer program stored in the memory and executable by the processor. When the processor executes the computer program, the steps of the above-described method embodiments are implemented. Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-described apparatus embodiments are implemented.

[0136] Computer equipment may include but is not limited to a processor and a memory. It will be understood by those skilled in the art that Figure 4 The computer device is merely an example and does not limit the computer device. The computer device may include more or fewer components than shown in the figure, or different components.

[0137] The processor can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0138] Memory can be an internal storage unit of a computer device, such as a computer device's hard drive or memory. Memory can also be an external storage device of a computer device, such as a plug-in hard drive, a SmartMedia Card (SMC), a Secure Digital (SD) card, or a Flash Card. Memory can also include both internal storage units and external storage devices. Memory is used to store computer programs and other programs and data required by electronic devices.

[0139] The processor, memory, and I / O interface are connected via the I / O bus. A computer device has three I / O interfaces, which are connected to external devices, a display, and input devices, respectively.

[0140] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0141] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for improving heat dissipation technology of a wind turbine generator system, characterized in that: The method comprises: Determine whether the target wind turbine generator system meets the technical transformation conditions based on the calculated percentage of power loss; If it is determined that the target wind turbine generator system meets the technical upgrade conditions, heat dissipation capacity evaluation and heat dissipation simulation analysis are performed on the target wind turbine generator system and a reference wind turbine generator system to determine at least two candidate solutions; the reference wind turbine generator system is a wind turbine generator system located in the same wind line as the target wind turbine generator system; Conduct a weighted evaluation on the at least two candidate solutions, and select the candidate solution with the highest score as the technical improvement solution; The target wind turbine generator system is technically transformed based on the technical transformation plan.

2. The method according to claim 1, characterized in that The step of judging whether the target wind turbine generator set meets the technical transformation conditions based on the calculated percentage of power loss includes: Calculate an estimated amount of power lost during the total power limit time period based on the rated power, power limit, and total power limit time period of the target wind turbine generator set; Calculating the percentage of power loss based on the estimated power loss value and the actual total power generation; Determining whether the percentage of power loss is greater than a preset percentage threshold; If it is determined that the percentage of power loss is greater than the percentage threshold, it is determined that the target wind turbine generator system meets the technical transformation conditions; If it is determined that the percentage of power loss is less than the percentage threshold, it is determined that the target wind turbine generator set does not meet the technical transformation conditions.

3. The method according to claim 1, characterized in that Before determining whether the wind turbine generator set meets the technical transformation conditions based on the calculated power loss percentage, the method further includes: Import the monitoring data collected by the target wind turbine data acquisition and monitoring control SCADA system; Determine and extract key data from the monitoring data based on configurable decision rules; Automatically diagnose cooling system faults based on the key data, generate fault diagnosis results, and generate a fault repair work order based on the fault diagnosis results.

4. The method according to claim 1, wherein Before determining whether the target wind turbine generator set meets the technical transformation conditions based on the calculated power loss percentage, the method further includes: Query the latest maintenance time of the cooling system from the imported operation and maintenance data; Determine whether the time difference between the latest maintenance time and the current time is greater than a preset maintenance time interval threshold; If it is determined that the time difference is greater than the maintenance time interval threshold, a maintenance work order is generated; If it is determined that the time difference is less than the maintenance time interval threshold, the step of determining whether the target wind turbine generator system meets the technical transformation conditions based on the calculated percentage of power loss within the power limit time period is performed.

5. The method according to claim 1, wherein Before performing heat dissipation capacity evaluation and heat dissipation simulation analysis on the target wind turbine generator set and the reference wind turbine generator set, the method further includes: According to the matrix and weight of each color channel in the thermal image, the equivalent brightness value of the grayscale image is calculated; generating a grayscale image corresponding to the thermal image according to the equivalent brightness value; Performing large component area selection on the thermal image and the grayscale image to obtain a large component thermal image and a large component grayscale image; Obtaining a maximum temperature value and a minimum temperature value in the thermal image of the large component, and a maximum brightness value and a minimum brightness value in the grayscale image of the large component; Determining the linear relationship between the temperature value and the equivalent brightness value by the undetermined coefficient method; The temperature value of each point is calculated according to the linear relationship and the equivalent brightness value of each point.

6. The method according to claim 5, characterized in that The heat dissipation capability evaluation and heat dissipation simulation analysis of the target wind turbine and the reference wind turbine are performed to determine at least two candidate solutions, including: Calculating the distribution uniformity of the component surface temperature per minute based on the maximum temperature and the minimum temperature; Calculate the component surface heat dissipation rate per minute based on the component surface average temperature at time t minute and the component surface average temperature at time t+1 minute; Importing a constructed wind turbine physical model according to the size of the target wind turbine group and the size of the reference wind turbine group; According to the corresponding relationship between time and boundary condition values, a target boundary condition value is inquired, wherein the target boundary condition value is the boundary condition value corresponding to the moment when the heat dissipation rate of the component surface is the highest; Constructing a heat dissipation simulation model based on the target boundary condition value and the fan physical model; Perform simulation analysis on the heat dissipation simulation model to determine at least two candidate solutions.

7. The method according to claim 6, characterized in that The heat dissipation simulation model is simulated and analyzed to determine at least two candidate solutions, including: Performing simulation analysis on the heat dissipation simulation model through different simulation schemes to obtain the temperature distribution of the target wind turbine group and the temperature distribution of the reference wind turbine group, wherein the temperature distribution refers to the temperature distribution of the vertical cross section in the direction of the transmission chain; sorting the temperature distribution of the target wind turbine generator set and the temperature distribution of the reference wind turbine generator set based on the uniformity of the temperature distribution to obtain a uniformity distribution sequence; Calculating a simulation score for each simulation scheme according to the uniform degree distribution sequence; At least two solutions are selected in descending order of the simulation scores as candidate solutions.

8. The method according to claim 1, characterized in that The weighted evaluation of the at least two candidate solutions and selecting the candidate solution with the highest score as the technical improvement solution includes: Performing a weighted calculation on the obtained expert evaluation score, cost evaluation score, safety evaluation score, and the simulation score to obtain a final score for the candidate solution; The candidate solution with the highest final score is selected as the technical improvement solution.

9. A wind turbine heat dissipation technology transformation device, characterized in that: The device comprises: A judgment module is used to judge whether the target wind turbine generator set meets the technical transformation conditions based on the calculated percentage of power loss; If the judgment module determines that the target wind turbine generator set meets the technical transformation conditions, the determination module is triggered to perform heat dissipation capacity evaluation and heat dissipation simulation analysis on the target wind turbine generator set and a reference wind turbine generator set to determine at least two candidate solutions; the reference wind turbine generator set is a wind turbine generator set on the same wind line as the target wind turbine generator set; An evaluation module, configured to perform a weighted evaluation on the at least two candidate solutions and select the candidate solution with the highest score as the technical improvement solution; A technical transformation module is used to perform technical transformation on the target wind turbine generator set based on the technical transformation plan.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute the wind turbine heat dissipation technology transformation method according to any one of claims 1 to 8.

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