Wind turbine control algorithm optimization method, device and storage medium

Abstract

The application provides a wind turbine control algorithm optimization method and device and a storage medium, and belongs to the field of wind turbine control. The method comprises the following steps: obtaining typical operation data acted on by a target control algorithm; determining target simulation wind conditions according to the typical operation data; adjusting the target control algorithm according to the typical operation data; and simulating and testing the new target control algorithm by using the target simulation wind conditions until the gap between the simulation test data of the new target control algorithm and the control expected parameters is less than a first preset value, and the obtained new target control algorithm is an optimized target control algorithm. The target simulation wind conditions acted on by the target control algorithm are obtained by simulation, and the simulation test of the adjusted new target control algorithm is performed by using the target simulation wind conditions, so that the target control algorithm is optimized based on the typical wind conditions acted on by the target control algorithm, and the optimized target control algorithm can achieve a more ideal effect in actual application.

Description

Technical Field

[0001] This invention relates to the field of wind turbine generator control, specifically to a wind turbine generator control algorithm optimization method, a wind turbine generator control algorithm optimization device, and a machine-readable storage medium. Background Technology

[0002] When applying control algorithms to the field environment, it is often necessary to optimize and adjust the control parameters according to the program's response under the actual wind conditions to ensure that the control algorithm achieves the best control effect in actual field operation. At the same time, during the application of the algorithm, there are some special wind conditions at the field or at the machine location, and general control parameters cannot achieve good control effects under these special wind conditions. Therefore, more targeted and matched control parameters are needed to achieve good control effects for specific wind fields or machine locations.

[0003] Because many control algorithms have a certain impact on the safety of the generating unit, and actual wind conditions are quite random, it is very likely that the typical wind conditions required for the target control algorithm to function will not occur for a considerable period of time. Therefore, simulation is often used to optimize control parameters in practical applications. However, standard simulation conditions have significant limitations in optimizing and tuning parameters for specific algorithms. This is mainly manifested in the unpredictable differences between the unit's response under standard simulation conditions and the response under actual field conditions. Parameters that perform as expected by the algorithm under simulation conditions may not achieve the desired results in practical applications. Summary of the Invention

[0004] The purpose of this invention is to provide a method, device, and storage medium for optimizing wind turbine control algorithms. This method uses simulation to obtain the target simulated wind conditions under which the target control algorithm operates, and then uses the target simulated wind conditions to simulate and test the adjusted new target control algorithm. This achieves optimization of the target control algorithm based on typical wind conditions under which the target control algorithm operates, thereby ensuring that the optimized target control algorithm can achieve more ideal results in practical applications.

[0005] To achieve the above objectives, the first aspect of the present invention provides a wind turbine control algorithm optimization method, the wind turbine control algorithm optimization method comprising:

[0006] Obtain typical operational data of the target control algorithm;

[0007] Determine the target simulated wind conditions based on typical operational data;

[0008] The target control algorithm is adjusted based on typical operating data. The new target control algorithm is then tested using simulated wind conditions until the difference between the simulation test data of the new target control algorithm and the expected control parameters is less than the first preset value. The resulting new target control algorithm is the optimized target control algorithm.

[0009] Based on the above technical means, the target simulated wind conditions for the action of the target control algorithm are obtained by simulation. Then, the target simulated wind conditions are used to simulate and test the adjusted new target control algorithm. This realizes the optimization of the target control algorithm based on typical wind conditions, thereby ensuring that the optimized target control algorithm can achieve more ideal results in practical applications.

[0010] In this embodiment of the application, obtaining typical operational data of the target control algorithm includes:

[0011] Obtain actual runtime data during the execution of the target control algorithm;

[0012] Extract the operational data of the target control algorithm during the effective period from the actual operational data as the target operational data;

[0013] Typical operational data are obtained by integrating and extracting the target operational data.

[0014] Based on the above technical means, the operating data of the target control algorithm during the actual operation of the wind turbine is obtained from the recorded operating data. The typical operating data obtained by integrating these operating data can better characterize the wind conditions and operating status when the target control algorithm is in effect.

[0015] In this embodiment of the application, typical operational data is obtained by integrating and extracting the target operational data, including:

[0016] Statistically analyze the time-series data characteristics of each parameter in the operational data of each target;

[0017] The target operational data are grouped according to the data characteristics of each target operational data;

[0018] Typical operating data are determined based on the target operating data for each group.

[0019] Based on the above technical means, the time-series data characteristics of different target operation data are the key points of the algorithm operation within that time period. The change process of data characteristics in the same group is similar. By identifying the typical operation data that represents the key points of the group's characteristics, we can better characterize the wind conditions and operation status when the target control algorithm is in effect.

[0020] In this embodiment of the application, determining the target simulated wind conditions based on typical operating data includes:

[0021] Wind condition simulation is performed based on wind condition data from typical operational data.

[0022] Simulation tests of the target control algorithm were conducted using simulated wind conditions.

[0023] The simulation test results are compared with the key operating parameters in the typical operating data to adjust the settings of the wind condition simulation until the difference between the simulation test results and the key operating parameters in the typical operating data is less than the second preset value, and the current simulated wind condition is determined as the target simulated wind condition.

[0024] Based on the above technical means, wind condition simulation is performed based on wind condition data in typical operating data. The target control algorithm is used as a fixed quantity. The setting parameters of the wind condition simulation are adjusted to make the simulation test results approximate the key operating parameters in the typical operating data. Thus, the simulated wind condition in which the target control algorithm takes effect is obtained as the target simulated wind condition, so as to provide a simulated wind condition that is closer to the actual operating state for the target control algorithm to be optimized and adjusted.

[0025] In this embodiment of the application, wind condition simulation is performed based on wind condition data from typical operational data, including:

[0026] Set the initial settings parameters for wind condition simulation based on wind condition data from typical operating data;

[0027] Wind conditions were simulated using simulation software based on the initial settings.

[0028] By using the aforementioned technical means and setting the initial parameters for wind condition simulation based on wind condition data from typical operational data, the wind condition simulation process can be accelerated, and the target simulated wind condition that meets expectations can be simulated more quickly.

[0029] In this embodiment of the application, adjusting the target control algorithm based on typical operating data includes:

[0030] Adjust the target control algorithm based on the gap between the key operating parameters in typical operating data and the expected control parameters of the target control algorithm.

[0031] By comparing the expected control parameters with the actual key operating parameters using the aforementioned technical means, the target control algorithm can be adjusted in a more targeted manner.

[0032] In this embodiment of the application, the new target control algorithm is simulated and tested using target simulated wind conditions until the difference between the simulation test data of the new target control algorithm and the expected control parameters is less than a first preset value, including:

[0033] The new target control algorithm was tested using simulated wind conditions to obtain simulation test data;

[0034] The direction of the discrepancy is determined based on the gap between the simulation test data and the expected control parameters, as well as the gap between the key operating parameters in the typical operating data and the expected control parameters.

[0035] Adjust the target control algorithm according to the direction of difference;

[0036] Repeat the above steps until the difference between the simulation test data of the new target control algorithm and the expected control parameters is less than the first preset value.

[0037] Based on the above technical means, the direction of difference can be determined by the adjustment direction of the initial control algorithm and the difference between the simulation test data and the expected control parameters. Adjusting according to the direction of difference can more quickly optimize and obtain the optimized target control algorithm.

[0038] In this embodiment of the application, the direction of the difference is determined based on the gap between the simulation test data and the expected control parameters, as well as the gap between the key operating parameters in the typical operating data and the expected control parameters, including:

[0039] Calculate the first gap between the key operating parameters in typical operating data and the expected control parameters of the target control algorithm;

[0040] Calculate the second gap between the simulation test data and the expected control parameters;

[0041] Compare the absolute value of the first gap with the absolute value of the second gap;

[0042] If the absolute value of the first gap is less than the absolute value of the second gap, then determine whether the current first gap and the second gap have the same sign. If they have the same sign, then determine that the current adjustment direction of the target control algorithm is the direction of increasing the difference, and it needs to continue adjusting in the opposite direction of the current adjustment direction. If they have different signs, then determine that the current adjustment direction of the target control algorithm is the direction of decreasing the difference, and it needs to continue adjusting in the opposite direction of the current adjustment direction.

[0043] If the absolute value of the first difference is greater than the absolute value of the second difference, then determine whether the current first difference and the second difference have the same sign. If they have the same sign, then determine that the current adjustment direction of the target control algorithm is the difference reduction direction, and it needs to continue adjusting along the current adjustment direction. If they have different signs, then determine that the current adjustment direction of the target control algorithm is the difference reduction direction, and it needs to continue adjusting along the opposite direction of the current adjustment direction.

[0044] Based on the above technical means, it is possible to determine whether the current adjustment direction can optimize the algorithm based on whether the simulation test results obtained after the initial control algorithm adjustment are closer to or further away from the expectation, thereby obtaining the optimized target control algorithm more quickly.

[0045] A second aspect of this application provides a wind turbine control algorithm optimization device, the wind turbine control algorithm optimization device comprising:

[0046] Typical operational data acquisition unit, used to acquire typical operational data of the target control algorithm;

[0047] The target simulation wind condition determination unit is used to determine the target simulation wind condition based on typical operating data.

[0048] The target control algorithm adjustment unit is used to adjust the target control algorithm based on typical operating data.

[0049] The simulation test unit is used to simulate and test the new target control algorithm using the target simulated wind conditions until the difference between the simulation test data of the new target control algorithm and the expected control parameters is less than the first preset value. The resulting new target control algorithm is the optimized target control algorithm.

[0050] Based on the above technical means, the device uses simulation to obtain the target simulated wind conditions under which the target control algorithm is applied, and then uses the target simulated wind conditions to conduct simulation tests on the adjusted new target control algorithm. This enables the target control algorithm to be optimized based on typical wind conditions under which the target control algorithm is applied, thereby ensuring that the optimized target control algorithm can achieve more ideal results in practical applications.

[0051] A third aspect of this application provides a machine-readable storage medium storing instructions for causing a machine to execute the wind turbine control algorithm optimization method described above.

[0052] The above technical solution uses simulation to obtain the target simulated wind conditions under which the target control algorithm operates. Then, the target simulated wind conditions are used to simulate and test the adjusted new target control algorithm. This allows for the optimization of the target control algorithm based on typical wind conditions under which the target control algorithm operates, thereby ensuring that the optimized target control algorithm can achieve more ideal results in practical applications.

[0053] This method uses hardware testing and simulation to find the simulation condition that best approximates the actual response in the field under the current program. Under this simulation condition, the specific control algorithm is optimized and tested, making the optimization process simpler and more efficient, and the optimization can better present the expected control effect in the actual field.

[0054] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0055] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings:

[0056] Figure 1 This is a flowchart of a wind turbine control algorithm optimization method provided in one embodiment of the present invention;

[0057] Figure 2 This is a flowchart of the optimized implementation of the wind turbine control algorithm provided in one embodiment of the present invention;

[0058] Figure 3 This is a block diagram of a wind turbine control algorithm optimization device provided in one embodiment of the present invention. Detailed Implementation

[0059] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0060] To meet the actual operating conditions of wind farms, various control algorithms need to be added to the control system of wind turbine generators to solve different problems, such as overspeed prevention, prevention of exceeding clearance limits, or warnings for special operating conditions. However, obtaining the optimal algorithm parameters that both meet the safe operation of the generator and minimize power generation losses requires a lot of optimization and adjustment based on the actual wind conditions.

[0061] Considering the safety of unit operation and the randomness of on-site wind conditions, the optimization of control algorithms generally begins with obtaining the current optimal parameters through simulation before applying them to the field. When optimizing control algorithms through simulation, two points are crucial: 1. The reproduction of typical actual wind conditions under which the algorithm operates; 2. The performance of the initial parameters of the control algorithm under actual on-site wind conditions. Without knowing these two key issues, it is difficult to complete the optimization of the control algorithm in a short period of time.

[0062] To solve the above problems, this invention obtains typical simulation conditions that can represent the actual wind speed, enabling more efficient and faster optimization of the control algorithm.

[0063] Figure 1 This is a flowchart of a wind turbine control algorithm optimization method provided in one embodiment of the present invention. Figure 1 As shown, the wind turbine control algorithm optimization method includes:

[0064] S1: Obtain typical operational data of the target control algorithm. In this embodiment, the target control algorithm is the algorithm that needs to be optimized, such as algorithms for preventing overspeeding, preventing exceeding airspace limits, or providing early warnings for special operating conditions.

[0065] In this embodiment of the application, obtaining typical operational data of the target control algorithm includes:

[0066] In this embodiment, the actual operating data during the execution of the target control algorithm is obtained. This actual operating data refers to the data collected during the operation of the wind turbine unit running the target control algorithm, including wind condition data and key operating parameters. The actual operating data can be directly read from the wind turbine unit's operating database.

[0067] The target operating data is extracted from the actual operating data during the time period in which the target control algorithm is effective. In this embodiment, the time period in which the target control algorithm is effective refers to the period during which the target control algorithm is active or may be active. For example, the overspeed algorithm focuses on the period when the generator speed is high and close to the overspeed threshold.

[0068] Typical operational data are obtained by integrating and extracting the target operational data.

[0069] In this embodiment, the typical operational data obtained by integrating and extracting the target operational data specifically includes:

[0070] The time-series data characteristics of each parameter in the operational data of each target are statistically analyzed. For each selected data segment, the time-series changes of key wind conditions and operational parameters such as wind speed, wind direction, generator speed, output power, blade angle, and torque are observed and statistically analyzed, including magnitude, amplitude of change, duration of each stage, and the time correspondence of each parameter, so as to obtain the time-series data characteristics of each parameter.

[0071] The target operation data are grouped according to the data characteristics of each target operation data, and data with similar change processes are integrated into a group of data.

[0072] Typical operating data are determined based on each set of target operating data. Typical operating data refers to data that can characterize the wind conditions and key operating parameters in that set of target operating data.

[0073] In some embodiments, representative data can be identified from each set of target running data as typical data, or the characteristic time series after adjusting specific parameters according to the overall pattern can be used as typical running data. In some embodiments, the most representative 2-3 data points can be identified from each set of target running data as typical running data.

[0074] The time-series data characteristics of different target operation data are the key points of the algorithm operation within that time period. The change process of data characteristics in the same group is similar. By identifying the typical operation data that represents the key features of the group, we can better characterize the wind conditions and operation status when the target control algorithm is in effect.

[0075] By obtaining the actual operating data of the wind turbine during the recorded operation process, the operating data of the target control algorithm during the effective period can be obtained. The typical operating data obtained by integrating these operating data can better characterize the wind conditions and operating status when the target control algorithm is in effect.

[0076] S2: Determine the target simulated wind conditions based on typical operating data. In this embodiment, the target simulated wind conditions are the wind conditions when the simulated target control algorithm takes effect.

[0077] In this embodiment of the application, determining the target simulated wind conditions based on typical operating data includes:

[0078] 1) Perform wind condition simulation based on wind condition data from typical operational data, specifically:

[0079] First, the initial settings parameters for the wind condition simulation are set based on wind condition data from typical operational data. These initial settings parameters include: initial values ​​of wind speed and direction, amplitude of change, process of change, start time of change, and duration of change. Then, the simulation software is used to perform wind condition simulation based on the initial settings parameters.

[0080] Setting the initial parameters for wind condition simulation based on wind condition data from typical operational data can speed up the wind condition simulation process and more quickly simulate the target wind conditions that meet expectations.

[0081] 2) Simulate and test the target control algorithm using simulated wind conditions.

[0082] 3) Compare the simulation test results with the key operating parameters in the typical operating data to adjust the settings of the wind condition simulation until the difference between the simulation test results and the key operating parameters in the typical operating data is less than the second preset value. The current simulated wind condition is then determined as the target simulated wind condition. If the change process and characteristics of the key parameters differ significantly from those in the actual operating data, the settings of the simulated wind condition are adjusted and the test is repeated until the test results show that the difference between the change process and characteristics of the key operating parameters and those in the actual operating data is less than the second preset value. In this case, the current simulated wind condition is considered to reflect the wind condition when the target control algorithm takes effect. This wind condition is then determined as the target simulated wind condition for the target control algorithm program optimization. In this embodiment, the smaller the difference between the simulation test results and the key operating parameters in the typical operating data, the closer the simulated wind condition is to the wind condition in the typical operating data. Therefore, the second preset value can be set according to the requirements for the degree of algorithm optimization.

[0083] Wind condition simulation is performed based on wind condition data from typical operating data. The target control algorithm is used as a fixed variable. The settings parameters of the wind condition simulation are adjusted to make the simulation test results approximate the key operating parameters in the typical operating data. The simulated wind condition in which the target control algorithm takes effect is then used as the target simulated wind condition, so as to provide a simulated wind condition that is closer to the actual operating state for the target control algorithm to be optimized and adjusted.

[0084] S3: Adjust the target control algorithm based on typical operating data.

[0085] In this embodiment of the application, adjusting the target control algorithm based on typical operating data includes:

[0086] The target control algorithm is adjusted based on the discrepancy between the key operating parameters in typical operating data and the expected control parameters of the target control algorithm. The expected control parameters of the target control algorithm are the values ​​that the key operating parameters of the wind turbine are expected to achieve during the algorithm's operation. The key operating parameters in the typical operating data are the measured values ​​of these parameters during actual operation of the wind turbine under the target control algorithm. To ensure the target control algorithm achieves the desired effect in practical applications, it needs to be adjusted based on the discrepancy between the key operating parameters and the expected control parameters. This discrepancy can be between certain parameters or between the entire dataset.

[0087] By comparing the expected control parameters with the actual key operating parameters using the aforementioned technical means, the target control algorithm can be adjusted in a more targeted manner.

[0088] S4: Simulate and test the new target control algorithm using the target simulated wind conditions until the difference between the simulation test data of the new target control algorithm and the expected control parameters is less than the first preset value. The resulting new target control algorithm is the optimized target control algorithm, which specifically includes:

[0089] The new target control algorithm was tested using simulated wind conditions to obtain simulation test data;

[0090] The direction of the discrepancy is determined based on the gap between the simulation test data and the expected control parameters, as well as the gap between the key operating parameters in the typical operating data and the expected control parameters.

[0091] Adjust the target control algorithm according to the direction of difference;

[0092] Repeat the above steps until the difference between the simulation test data and the expected control parameters of the new target control algorithm is less than a first preset value. In this embodiment, the smaller the difference between the simulation test data and the expected control parameters, the more the new target control algorithm meets expectations. Therefore, the first preset value can be set according to the requirements for the algorithm optimization accuracy.

[0093] The direction of the difference can be determined by adjusting the direction of the initial control algorithm and the difference between the simulation test data and the expected control parameters. Adjusting the direction of the difference can lead to a faster optimization of the target control algorithm.

[0094] In this embodiment of the application, the direction of the difference is determined based on the gap between the simulation test data and the expected control parameters, as well as the gap between the key operating parameters in the typical operating data and the expected control parameters, including:

[0095] Calculate the first gap between the key operating parameters in typical operating data and the expected control parameters of the target control algorithm;

[0096] Calculate the second gap between the simulation test data and the expected control parameters;

[0097] Compare the absolute value of the first gap with the absolute value of the second gap;

[0098] If the absolute value of the first gap is less than the absolute value of the second gap, then determine whether the current first gap and the second gap have the same sign. If they have the same sign, then determine that the current adjustment direction of the target control algorithm is the direction of increasing the difference, and it needs to continue adjusting in the opposite direction of the current adjustment direction. If they have different signs, then determine that the current adjustment direction of the target control algorithm is the direction of decreasing the difference, and it needs to continue adjusting in the opposite direction of the current adjustment direction.

[0099] If the absolute value of the first difference is greater than the absolute value of the second difference, then determine whether the current first difference and the second difference have the same sign. If they have the same sign, then determine that the current adjustment direction of the target control algorithm is the difference reduction direction, and it needs to continue adjusting along the current adjustment direction. If they have different signs, then determine that the current adjustment direction of the target control algorithm is the difference reduction direction, and it needs to continue adjusting along the opposite direction of the current adjustment direction.

[0100] For example, assuming the absolute value of the first gap is 3 and the absolute value of the second gap is 6, it can be determined that the absolute value of the first gap is less than the absolute value of the second gap. If the first gap is -3 and the second gap is -6, it can be determined that the gap between the simulation test data of the new target control algorithm and the expected control parameters is getting larger and larger. It can be determined that the current adjustment direction of the target control algorithm is the direction of increasing difference. In order to reduce the gap between the simulation test data and the expected control parameters, it is necessary to continue adjusting in the opposite direction of the first adjustment. That is, if there is a first parameter, and the first adjustment increased the first parameter, then the first parameter needs to be reduced in the next adjustment, and the adjustment amount needs to be increased. The specific adjustment amount can be based on the ratio of the absolute value of the second gap to the change amount and the first... The relationship between the parameter and the change is calculated. As mentioned earlier, the first gap is -3, the second gap is -6, and the change from the first gap to the second gap is 3. The absolute value of the second gap is 6 / 3 = 2 times the change. Then, the adjustment amount of the first parameter is calculated based on the relationship between the first parameter and the change. If the first adjustment results in a reduction of the first parameter, then the first parameter needs to be increased during the next adjustment, and the adjustment amount needs to be increased. The absolute value of the second gap is 6 / 3 = 2 times the change. Then, the adjustment amount of the first parameter is calculated based on the relationship between the first parameter and the change until the gap between the simulation test data and the expected control parameter is less than the first preset value, that is, the absolute value of the second gap is less than the first preset value.

[0101] If the first gap is -3 and the second gap is +6, it can be determined that the gap between the simulation test data and the expected control parameters of the new target control algorithm first narrowed and then increased in the opposite direction, and the increase was greater than the absolute value of the first gap. At this time, in order to achieve the goal of narrowing the gap between the simulation test data and the expected control parameters, it is necessary to continue to adjust in the opposite direction of the first adjustment, and at the same time, it is necessary to reduce the adjustment amount to avoid excessive adjustment again. That is, if a first parameter exists, and the first adjustment increases the first parameter, then subsequent adjustments need to decrease the first parameter, and the adjustment amount needs to be reduced. The specific adjustment amount can be calculated based on the ratio of the absolute value of the second gap to the change amount and the relationship between the first parameter and the change amount. For example, if the first gap is -3 and the second gap is +6, the change amount between the first gap and the second gap is 9, and the ratio of the absolute value of the second gap to the change amount is 2 / 3, then the adjustment amount of the first parameter is calculated based on the relationship between the first parameter and the change amount. If the first adjustment decreases the first parameter, then subsequent adjustments need to increase the first parameter, and the adjustment amount needs to be reduced. The ratio of the absolute value of the second gap to the change amount is 6 / 9 = 2 / 3 times, then the adjustment amount of the first parameter is calculated based on the relationship between the first parameter and the change amount, until the difference between the simulation test data and the expected control parameter is less than the first preset value, that is, the absolute value of the second gap is less than the first preset value.

[0102] Assuming the absolute value of the first gap is 3 and the absolute value of the second gap is 2, it can be determined that the absolute value of the first gap is greater than the absolute value of the second gap. If the first gap is -3 and the second gap is -2, it can be determined that the gap between the simulation test data and the expected control parameters of the new target control algorithm is decreasing, and the current adjustment direction of the target control algorithm can be determined as the direction of difference reduction. In order to further reduce the gap between the simulation test data and the expected control parameters, it is necessary to continue adjusting along the first adjustment direction. That is, if there is a first parameter, and the first adjustment increased the first parameter, then the first parameter needs to be further increased in the next adjustment. The specific adjustment amount can be based on the ratio of the absolute value of the second gap to the change amount and the first parameter. The relationship between the number and the change is calculated. As mentioned earlier, the first gap is -3, the second gap is -2, and the change between the first gap and the second gap is 1. The absolute value of the second gap is 2 / 1 = 2 times the change. Then, the adjustment amount of the first parameter is calculated based on the relationship between the first parameter and the change. If the first adjustment reduces the first parameter, then the first parameter needs to be further reduced when adjusting again. The absolute value of the second gap is 2 / 1 = 2 times the change. Then, the adjustment amount of the first parameter is calculated based on the relationship between the first parameter and the change until the gap between the simulation test data and the expected control parameter is less than the first preset value, that is, the absolute value of the second gap is less than the first preset value.

[0103] If the first gap is -3 and the second gap is +2, it can be determined that the gap between the simulation test data and the expected control parameters of the new target control algorithm first narrowed and then increased in the opposite direction, and the increase was less than the absolute value of the first gap. At this time, in order to achieve the goal of narrowing the gap between the simulation test data and the expected control parameters, it is necessary to continue to adjust in the opposite direction of the first adjustment, and at the same time, it is necessary to reduce the adjustment amount to avoid excessive adjustment again. That is, if a first parameter exists, and the first adjustment increases the first parameter, then subsequent adjustments need to decrease the first parameter, and the adjustment amount needs to be reduced. The specific adjustment amount can be calculated based on the ratio of the absolute value of the second gap to the change amount and the relationship between the first parameter and the change amount. For example, if the first gap is -3 and the second gap is +2, the change amount between the first gap and the second gap is 5, and the ratio of the absolute value of the second gap to the change amount is 2 / 5, then the adjustment amount of the first parameter is calculated based on the relationship between the first parameter and the change amount. If the first adjustment decreases the first parameter, then subsequent adjustments need to increase the first parameter, and the adjustment amount needs to be reduced. The ratio of the absolute value of the second gap to the change amount is 2 / 5, then the adjustment amount of the first parameter is calculated based on the relationship between the first parameter and the change amount, until the difference between the simulation test data and the expected control parameter is less than the first preset value, that is, the absolute value of the second gap is less than the first preset value.

[0104] The simulation test results obtained after the initial control algorithm adjustment can be used to determine whether the current adjustment direction can optimize the algorithm, thus obtaining the optimized target control algorithm more quickly.

[0105] Based on the above technical means, the target simulated wind conditions for the action of the target control algorithm are obtained by simulation. Then, the target simulated wind conditions are used to simulate and test the adjusted new target control algorithm. This realizes the optimization of the target control algorithm based on typical wind conditions, thereby ensuring that the optimized target control algorithm can achieve more ideal results in practical applications.

[0106] In the actual implementation of the wind turbine control algorithm optimization method of this application, the target control algorithm or the adjusted target control algorithm needs to be loaded into the PLC controller for operation. The PLC controller needs to be the same as the PLC controller used in the field unit control system. At the same time, a host computer running wind turbine simulation software is also needed to simulate the target simulated wind conditions based on typical operating data.

[0107] like Figure 2As shown, the wind turbine control algorithm optimization method of this application first obtains the actual operating data during the operation of the target control algorithm, and then extracts typical operating data of the target control algorithm's operating time period from the actual operating data. The typical operating data includes wind condition data and key operating parameters. Then, wind condition simulation is performed based on the wind condition data in the typical operating data. Interactive simulation tests are conducted between the simulated wind conditions and the PLC running the target control algorithm to determine whether the simulation test results match the key operating parameters in the typical operating data. If they do not match, the settings of the wind condition simulation are adjusted until the simulation test results match the key operating parameters in the typical operating data. Finally, the target simulated wind conditions obtained from the simulation are used to optimize the target control algorithm. During the optimization of the target control algorithm, the target control algorithm is first adjusted based on the gap between the key operating parameters and the expected control parameters of the target control algorithm. The new target control algorithm is then reloaded into the PLC. Interactive simulation tests are conducted between the target simulated wind conditions and the PLC to determine whether the simulation test results match the expected control parameters of the target control algorithm. If they do not match, the target control algorithm is adjusted based on the gap between the simulation test data and the expected control parameters until the simulation test results match the expected control parameters of the target control algorithm. The target control algorithm optimization is then complete, and a new version of the control program is created for field application.

[0108] The second aspect of this application provides a wind turbine control algorithm optimization device, such as... Figure 3 As shown, the wind turbine control algorithm optimization device includes:

[0109] Typical operational data acquisition unit, used to acquire typical operational data of the target control algorithm;

[0110] The target simulation wind condition determination unit is used to determine the target simulation wind condition based on typical operating data.

[0111] The target control algorithm adjustment unit is used to adjust the target control algorithm based on typical operating data.

[0112] The simulation test unit is used to simulate and test the new target control algorithm using the target simulated wind conditions until the difference between the simulation test data of the new target control algorithm and the expected control parameters is less than the first preset value. The resulting new target control algorithm is the optimized target control algorithm.

[0113] Based on the above technical means, the device uses simulation to obtain the target simulated wind conditions under which the target control algorithm is applied, and then uses the target simulated wind conditions to conduct simulation tests on the adjusted new target control algorithm. This enables the target control algorithm to be optimized based on typical wind conditions under which the target control algorithm is applied, thereby ensuring that the optimized target control algorithm can achieve more ideal results in practical applications.

[0114] A third aspect of this application provides a machine-readable storage medium storing instructions for causing a machine to execute the wind turbine control algorithm optimization method described above.

[0115] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a microcontroller, chip, or processor to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0116] The optional embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the embodiments of the present invention are not limited to the specific details described above. Within the scope of the technical concept of the embodiments of the present invention, various simple modifications can be made to the technical solutions of the embodiments of the present invention, and these simple modifications all fall within the protection scope of the embodiments of the present invention. It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, the embodiments of the present invention will not further describe the various possible combinations.

[0117] Furthermore, various different embodiments of the present invention can be combined in any way, as long as they do not violate the spirit of the embodiments of the present invention, they should also be regarded as the content disclosed by the embodiments of the present invention.

Claims

1. A method for optimizing a wind turbine control algorithm, characterized in that, The wind turbine control algorithm optimization method includes: Obtain typical operational data of the target control algorithm; The target simulation wind conditions are determined based on typical operating data, including: Wind condition simulation is performed based on wind condition data from typical operational data. Interactive simulation tests were conducted using a PLC that simulates wind conditions and operates target control algorithms. The PLC used in the field unit control system is the same as the PLC used in the field unit control system. The simulation test results are compared with the key operating parameters in the typical operating data to adjust the settings of the wind condition simulation until the difference between the simulation test results and the key operating parameters in the typical operating data is less than the second preset value, and the current simulated wind condition is determined as the target simulated wind condition. The target control algorithm is adjusted based on typical operating data. Interactive simulation tests are conducted between the target simulated wind conditions and the PLC running the new target control algorithm until the difference between the simulation test data of the new target control algorithm and the expected control parameters is less than the first preset value. The resulting new target control algorithm is the optimized target control algorithm.

2. The wind turbine control algorithm optimization method according to claim 1, characterized in that, Obtain typical operational data for the target control algorithm, including: Obtain actual runtime data during the execution of the target control algorithm; Extract the operational data of the target control algorithm during the effective period from the actual operational data as the target operational data; Typical operational data are obtained by integrating and extracting the target operational data.

3. The wind turbine control algorithm optimization method according to claim 2, characterized in that, Typical operational data is obtained by integrating and extracting the target operational data, including: Statistically analyze the time-series data characteristics of each parameter in the operational data of each target; The target operational data are grouped according to the data characteristics of each target operational data; Typical operating data are determined based on the target operating data for each group.

4. The wind turbine control algorithm optimization method according to claim 1, characterized in that, Wind condition simulation is performed based on wind condition data from typical operational data, including: Set the initial settings parameters for wind condition simulation based on wind condition data from typical operating data; Wind conditions were simulated using simulation software based on the initial settings.

5. The wind turbine control algorithm optimization method according to claim 1, characterized in that, Adjust the target control algorithm based on typical operating data, including: Adjust the target control algorithm based on the gap between the key operating parameters in typical operating data and the expected control parameters of the target control algorithm.

6. The wind turbine control algorithm optimization method according to claim 5, characterized in that, Interactive simulation tests are conducted using the target simulated wind conditions and a PLC running the new target control algorithm until the difference between the simulation test data of the new target control algorithm and the expected control parameters is less than a first preset value, including: Interactive simulation tests were conducted using the target simulated wind conditions and a PLC running a new target control algorithm to obtain simulation test data; The direction of the discrepancy is determined based on the gap between the simulation test data and the expected control parameters, as well as the gap between the key operating parameters in the typical operating data and the expected control parameters. Adjust the target control algorithm according to the direction of difference; Repeat the above steps until the difference between the simulation test data of the new target control algorithm and the expected control parameters is less than the first preset value.

7. The wind turbine control algorithm optimization method according to claim 6, characterized in that, The direction of the discrepancy is determined based on the gap between the simulation test data and the expected control parameters, as well as the gap between the key operating parameters in the typical operating data and the expected control parameters, including: Calculate the first gap between the key operating parameters in typical operating data and the expected control parameters of the target control algorithm; Calculate the second gap between the simulation test data and the expected control parameters; Compare the absolute value of the first gap with the absolute value of the second gap; If the absolute value of the first gap is less than the absolute value of the second gap, then determine whether the current first gap and the second gap have the same sign. If they have the same sign, then determine that the current adjustment direction of the target control algorithm is the direction of increasing the difference, and it needs to continue adjusting in the opposite direction of the current adjustment direction. If they have different signs, then determine that the current adjustment direction of the target control algorithm is the direction of decreasing the difference, and it needs to continue adjusting in the opposite direction of the current adjustment direction. If the absolute value of the first difference is greater than the absolute value of the second difference, then determine whether the current first difference and the second difference have the same sign. If they have the same sign, then determine that the current adjustment direction of the target control algorithm is the difference reduction direction, and it needs to continue adjusting along the current adjustment direction. If they have different signs, then determine that the current adjustment direction of the target control algorithm is the difference reduction direction, and it needs to continue adjusting along the opposite direction of the current adjustment direction.

8. A wind turbine control algorithm optimization device, characterized in that, The wind turbine control algorithm optimization device includes: Typical operational data acquisition unit, used to acquire typical operational data of the target control algorithm; The target simulation wind condition determination unit is used to determine the target simulation wind condition based on typical operating data, including: Wind condition simulation is performed based on wind condition data from typical operational data. Interactive simulation tests were conducted using a PLC that simulates wind conditions and operates target control algorithms. The PLC used in the field unit control system is the same as the PLC used in the field unit control system. The simulation test results are compared with the key operating parameters in the typical operating data to adjust the settings of the wind condition simulation until the difference between the simulation test results and the key operating parameters in the typical operating data is less than the second preset value, and the current simulated wind condition is determined as the target simulated wind condition. The target control algorithm adjustment unit is used to adjust the target control algorithm based on typical operating data. The simulation test unit is used to conduct interactive simulation tests between the target simulated wind conditions and the PLC running the new target control algorithm until the difference between the simulation test data of the new target control algorithm and the expected control parameters is less than the first preset value. The resulting new target control algorithm is the optimized target control algorithm.

9. A machine-readable storage medium storing instructions thereon, characterized in that, This instruction is used to cause the machine to execute the wind turbine control algorithm optimization method as described in any one of claims 1-7.

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