Sector management method and system for risk operation sector identification based on follow-up grid

Abstract

The embodiment of the invention provides a sector management method and system for multi-evaluation-index high-risk operation sector identification based on a follow-up grid, and belongs to the technical field of wind power. The method comprises the following steps: processing wind resource data into a plurality of wind resource data points; based on the wind resource information and the unit response data, sector identification of the target unit is carried out, and boundary fitting is carried out on the identified sector; dividing each wind resource data point into the corresponding sector after boundary fitting, and obtaining a load evaluation result of the target unit corresponding to each sector after boundary fitting; and formulating a management scheme of each sector based on a load evaluation result, and executing the corresponding management scheme based on real-time wind resource state matching. According to the scheme, conventional sectors are replaced by more representative characteristic sectors, the unit is accurately managed, the problem of excessive or insufficient power limitation of the wind turbine unit is avoided, the power generation efficiency of the unit is maximized, and meanwhile the service life of equipment is prolonged.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power, and in particular to a sector management method based on identification of characteristic sectors of a follower grid and a sector management system based on identification of risky operating sectors of a follower grid. Background Art

[0002] As a clean and renewable energy source, wind power has been widely used around the world in recent years. However, one of the biggest challenges faced by wind turbines in operation is the high uncertainty of wind resources. The variability of wind resources, the volatility of turbulence intensity, and the variability of climate conditions will affect the operational safety and power generation efficiency of wind turbines. Traditional wind turbine designs are usually based on past wind resource forecast data, but these data often cannot accurately cover the operating environment of the turbine in the next 20 years, resulting in potential risks during the operation of the turbine.

[0003] Existing risk management solutions for wind turbines mostly rely on the division of regular sectors, that is, by dividing the entire wind farm into several fixed sectors and managing them according to the average wind speed or turbulence distribution in each sector. However, this approach has significant shortcomings. First, the distribution of turbulence intensity often spans multiple sectors, resulting in certain high-risk areas that cannot be effectively identified and processed. Secondly, these solutions usually only rely on the input data of wind resources, ignoring the operating response of the wind turbine itself, such as the vibration acceleration of key components. In addition, the sector management strategy of the existing solution is too static and cannot dynamically adapt to the long-term changes in wind resources and fluctuations in the health status of the unit during unit operation, resulting in reduced operating efficiency and potential safety hazards.

[0004] Therefore, the existing solutions are insufficient in wind resource management, unit response monitoring, and the flexibility of sector management strategies. There is an urgent need for a more accurate and dynamic method for identifying and managing high-risk operating areas of wind turbines to improve the operating safety and power generation efficiency of the units. Summary of the invention

[0005] The purpose of the embodiments of the present invention is to provide a sector management method and system for identifying high-risk operating sectors based on a dynamic grid and facing multiple evaluation indicators, so as to at least solve the problem that the existing solutions have insufficient flexibility in the sector management strategy for unit load exceeding the standard.

[0006] In order to achieve the above-mentioned purpose, the first aspect of the present invention provides a sector management method for risk operation sector identification based on a follower grid, the method comprising: collecting wind resource information and corresponding unit response data within a pre-defined historical period of the target unit, and processing the wind resource data into multiple wind resource data points; based on the wind resource information and the unit response data, performing sector identification of the target unit, and performing boundary fitting on the identified sector; dividing each wind resource data point into a corresponding sector after boundary fitting, and obtaining a load assessment result of the target unit of each sector after the corresponding boundary fitting; based on the load assessment result of the target unit of each sector, formulating a management plan for each sector, and executing the corresponding management plan based on the current wind resource status matching.

[0007] Optionally, the wind resource information includes wind speed information and turbulence intensity information; and the unit response data includes vibration acceleration of each preset key component and damage status information of each preset key component.

[0008] Optionally, if the current moment is within the first pre-defined historical period starting from the initial moment, the data set of current wind resource information and corresponding unit response data is an empty set; if the current moment is not within the first pre-defined historical period starting from the initial moment, the data set of current wind resource information and corresponding unit response data is the collected data within the previous pre-defined historical period.

[0009] Optionally, processing the wind resource data into multiple wind resource data points includes: performing wind resource data segmentation based on a time window of 600 seconds, and taking the corresponding wind resource data in each time window as a wind resource data point.

[0010] Optionally, the sector identification of the target unit is performed based on the wind resource information and the unit response data, and boundary fitting is performed on the identified sectors, including: performing feature extraction on the wind resource information and the unit response data, respectively, and generating a feature cloud map of the wind resource information and a feature cloud map of the unit response data based on the extracted features; based on a preset global threshold of the target unit, binarizing the feature cloud map of the wind resource information and the feature cloud map of the unit response data, respectively, and based on the binarization result, taking each white area as a candidate sector to obtain candidate sectors corresponding to the wind resource information and candidate sectors corresponding to the unit response data; performing boundary fitting on each candidate sector based on a curve within a preset number to obtain boundary information of each candidate sector.

[0011] Optionally, after performing boundary fitting on the identified sectors, the method further includes: judging whether there is an intersection area between the candidate sectors corresponding to the wind resource information and the candidate sectors corresponding to the unit response data based on the boundary information of the candidate sectors corresponding to the wind resource information and the boundary information of the candidate sectors corresponding to the unit response data; if so, judging the set priority of the intersection area, selecting the sector type of the area based on the set priority, and performing the segmentation of the candidate sectors corresponding to the wind resource information and the candidate sectors corresponding to the unit response data based on the straight line division principle based on the set results to obtain each identified sector with closed boundaries.

[0012] Optionally, each wind resource data point is divided into a corresponding sector after boundary fitting, and a load assessment result of the target unit in each sector after the corresponding boundary fitting is obtained, including: determining the sector to which each wind resource data point belongs, and dividing each wind resource data point into a corresponding sector based on the belonging relationship; based on the data points in each sector, respectively calculating the ultimate load and fatigue load in the corresponding sector, and using the calculation results as the load assessment result of the target unit in the corresponding sector; wherein, respectively calculating the ultimate load and fatigue load in the corresponding sector includes: using the load envelope value corresponding to all data points in the corresponding sector as the ultimate load, and accumulating the damage based on the fatigue load calculated separately for each data point point by point as a sector fatigue load measurement indicator; if there is no matching relationship between the wind resource grid points, executing a cubic interpolation algorithm to obtain the ultimate load and fatigue load at the corresponding moment.

[0013] Optionally, based on the load assessment results of each sector, a management plan for each sector is formulated, including: if the limit load in the load assessment results is greater than the design limit load of the target unit, a shutdown management plan or a speed limit management plan is formulated to activate the sector management strategy; conversely, if the limit load in the load assessment results is not greater than the design limit load of the target unit, the future total accumulated damage is determined based on the fatigue load in the load assessment results; if the future total accumulated damage is greater than the remaining damage of the target unit, the sector management plan is triggered; the sector management plan is executed to assign a corresponding management plan to each sector.

[0014] Optionally, the execution of the sector management plan allocates a corresponding management plan to each sector, including: determining the management plan corresponding to each sector based on the future total accumulated damage; wherein the management strategy of each sector is independent of each other; taking the minimum power generation loss as the optimization goal, performing optimization based on matching the current wind resource state with the corresponding future total accumulated damage, and obtaining the optimal management plan for the current wind resource state; wherein the management plan includes: any one or more of a normal power generation plan, a limited power operation plan, and a shutdown plan.

[0015] Optionally, the method also includes: predicting the remaining life of each preset key component based on the fatigue load in the load assessment result; determining the outlier value of each preset key component based on the remaining life of each preset key component; if the outlier value is greater than a preset outlier threshold, ignoring the remaining life influence of the preset key component when determining the sector management plan, and outputting prompt replacement information based on the preset key component.

[0016] The second aspect of the present invention provides a sector management system for risk operation sector identification based on a follower grid, the system comprising: a collection unit, used to collect wind resource information and corresponding unit response data within a pre-defined historical period of the target unit, and process the wind resource data into multiple wind resource data points; a processing unit, used to identify the sector of the target unit based on the wind resource information and the unit response data, and perform boundary fitting on the identified sector; an evaluation unit, used to divide each wind resource data point into a corresponding sector after boundary fitting, and obtain a load evaluation result of the target unit of each sector after the corresponding boundary fitting; an execution unit, used to formulate a management plan for each sector based on the load evaluation result of the target unit of each sector, and execute the corresponding management plan based on the current wind resource status matching.

[0017] On the other hand, the present invention provides a computer-readable storage medium having instructions stored thereon, which when executed on a computer enables the computer to execute the above-mentioned sector management method for risk operation sector identification based on a moving grid.

[0018] Through the above technical scheme, the scheme of the present invention collects the wind resource information and unit response data of the target unit in the historical period, and processes the wind resource data into multiple data points, so as to achieve more accurate sector identification. Based on these data points, the high-risk sectors in the operation of the unit can be effectively identified, and each sector can be accurately divided by boundary fitting technology. Furthermore, each wind resource data point is divided into the fitted sector, and a load assessment is performed to ensure that the risk status of each component is comprehensively analyzed and evaluated. Through the load assessment results of each component, a corresponding sector management plan can be formulated for the unit, including strategies such as normal operation, power restriction or shutdown. The technical effect of this technical scheme is reflected in the ability to manage the operating status of the wind turbine more dynamically and accurately, improve safety, avoid excessive or insufficient power restriction of wind turbines, and maximize the power generation efficiency of the unit, while extending the service life of the equipment.

[0019] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the embodiments of the present invention, but do not constitute a limitation on the embodiments of the present invention. In the accompanying drawings:

[0021] Figure 1 It is a flowchart of the steps of a sector management method for risk operation sector identification based on a follower grid provided by an embodiment of the present invention;

[0022] Figure 2 It is a schematic diagram of a sector management strategy provided by an embodiment of the present invention;

[0023] Figure 3 It is a system structure diagram of a sector management system for risk operation sector identification based on a follower grid provided in one embodiment of the present invention. DETAILED DESCRIPTION

[0024] The specific implementation of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the present invention, and is not used to limit the present invention.

[0025] Figure 1 FIG. 1 is a flow chart of a method for sector management based on risk operation sector identification of a moving grid provided by an embodiment of the present invention. Figure 1 As shown, an embodiment of the present invention provides a sector management method for risk operation sector identification based on a follower grid, the method comprising:

[0026] Step S10: collecting wind resource information and corresponding unit response data of a target unit within a predetermined historical period, and processing the wind resource data into a plurality of wind resource data points.

[0027] Specifically, the wind resource information includes wind speed information and turbulence intensity information; the unit response data includes vibration acceleration of each preset key component and damage status information of each preset key component.

[0028] In an embodiment of the present invention, by collecting wind resource information of the target wind turbine in a pre-defined historical period and the response data of the unit, basic data support is provided for subsequent sector division and management strategies. Specifically, the wind resource information includes wind speed information and turbulence intensity information, which reflect the wind speed changes and turbulence distribution in the environment where the unit is located. Wind speed information can help analyze the operating efficiency of the unit under different wind conditions, while turbulence intensity information can effectively evaluate the stability of the wind and further help identify high-risk operating areas.

[0029] In terms of unit response data, the data includes the vibration acceleration and damage status information of each preset key component. The vibration acceleration data can monitor the structural vibration of the unit under different wind resource conditions, especially under high turbulence conditions. The vibration acceleration of key components directly reflects the stress load of the unit. By monitoring the vibration acceleration of key components, it is easier to screen out characteristic sectors that affect the safety of the unit. At the same time, the damage status information can further quantify the health status of the unit components and, combined with the wind resource information, form a complete unit operation status assessment.

[0030] Based on the solution of the present invention, the technical effect of the solution is to screen typical characteristic sectors and then manage each sector more targetedly. This not only improves the safety of unit operation, but also optimizes the power generation efficiency and equipment service life of the wind turbine.

[0031] Preferably, if the current moment is within the first pre-defined historical period starting from the initial moment, the data set of the current wind resource information and the corresponding unit response data is an empty set; if the current moment is not within the first pre-defined historical period starting from the initial moment, the data set of the current wind resource information and the corresponding unit response data is the collected data within the previous pre-defined historical period.

[0032] In an embodiment of the present invention, the scheme provides a dynamic management and optimization strategy for the long-term operation of the wind turbine by collecting and processing wind resource information and unit response data in stages. In the first year after the installation of the wind turbine, the system does not adopt any power restriction strategy, but lays the foundation for subsequent management strategies by recording the wind resource information and the cumulative damage data of the unit during this year. Since the design life of a wind turbine is generally 20 years, the data of the first year is assumed to be representative data for the distribution of wind resources in the next 19 years. Although this assumption simplifies the complexity of wind resource changes, by using the data collected in the first year as the basis of the model, effective prediction and management of subsequent wind resource distribution can be achieved.

[0033] In the first year, the wind turbine operation data set is an empty set, because there is no historical data to refer to, so there is no power restriction on the operation status of the unit. This means that the first year is the period of full power operation of the wind turbine, and the system will try to obtain complete wind resource information, including environmental data such as wind speed and turbulence intensity, as well as response information of key components of the unit, such as vibration acceleration and damage status.

[0034] After entering the second year, the system will begin to formulate sector management strategies based on the data of the previous year (the first year). By using the wind resource data of the previous year, the system can divide different risk sectors for the first time and assign corresponding management strategies to each sector. These strategies include measures such as normal power generation, power restriction or shutdown to ensure that the units can be effectively protected in high-risk sectors. At the same time, the system dynamically adjusts and optimizes these management strategies on a monthly basis. The monthly optimization process is based on the distribution of wind resource data in the past year, combined with historical data similar to the current wind season as a reference to further improve the flexibility and accuracy of the strategy. Through this dynamic adjustment mechanism, the system can not only adapt to long-term changes in wind resources, but also continuously optimize management strategies based on actual operating data. For example, when it is found that certain key components frequently vibrate or are damaged in certain specific sectors, the system can adjust the power restriction or shutdown strategy accordingly to protect the unit from excessive load or potential failure.

[0035] Based on the scheme of the present invention, the scheme of the present invention provides a reliable foundation for data collection and management strategies in subsequent years through full-power operation without intervention in the first year. By performing sector management based on historical data, it is possible to effectively avoid safety hazards caused by not considering the response of the unit under different wind resource conditions. At the same time, the monthly dynamic adjustment and optimization mechanism ensures that the system can adapt to changes in wind resources and unit health status in real time. This refined management not only extends the service life of wind turbines, but also maximizes the power generation efficiency, ensuring the long-term economic benefits and safety of wind farms.

[0036] Specifically, the processing of the wind resource data into a plurality of wind resource data points includes: performing wind resource data segmentation based on a 600s time window, and taking the corresponding wind resource data in each time window as a wind resource data point.

[0037] In one possible implementation, the processing of wind resource data uses a fixed time window, for example, every 600 seconds is a fixed window. All wind resource data and unit response data are divided into intervals of 600 seconds to obtain corresponding wind resource data points. The fixed time window method is suitable for situations where wind resource fluctuations are relatively stable and the amplitude of changes is not large. In this case, the system can maintain a stable sampling frequency to ensure consistency in data processing and a simplified management process. Risk assessment and management of wind turbines can also be preset based on this fixed window data.

[0038] Based on the solution of the present invention, the advantage of this embodiment is that it is simple and easy to implement, and is suitable for areas with relatively stable wind resources or for unified analysis in the initial stage. The fixed time window avoids complex dynamic adjustment logic and is suitable for standard data collection and management under conventional wind resource environments. However, in the case of large fluctuations in wind resources, the fixed window may not be able to capture the risk of rapid changes.

[0039] Step S20: Based on the wind resource information and the unit response data, the sector of the target unit is identified, and boundary fitting is performed on the identified sector.

[0040] Specifically, feature extraction is performed on the wind resource information and the unit response data, respectively, and a feature cloud map of the wind resource information and a feature cloud map of the unit response data are generated based on the extracted features; based on a preset global threshold of the target unit, binarization processing is performed on the feature cloud map of the wind resource information and the feature cloud map of the unit response data, respectively, and based on the binarization processing result, each white area is used as a candidate sector to obtain the candidate sector corresponding to the wind resource information and the candidate sector corresponding to the unit response data; boundary fitting is performed on each candidate sector based on a curve within a preset number to obtain boundary information of each candidate sector.

[0041] In an embodiment of the present invention, significant features of wind resource changes and unit operating status are extracted through feature extraction technology, and these features can help the system more accurately identify the response of wind turbines under different wind resource conditions. After extracting the features, the system generates a feature cloud map of wind resource information and a feature cloud map of unit response data. The feature cloud map is a visual expression based on the feature extraction results, which can display the operating characteristics of wind turbines under different wind resource conditions. The wind resource feature cloud map reflects the distribution of wind conditions under different wind speeds and turbulence intensities, while the unit response feature cloud map reflects the vibration and damage of key components of wind turbines under different working conditions.

[0042] Furthermore, based on the preset global threshold of the target unit, the characteristic cloud maps of wind resource information and unit response data are binarized respectively. Binarization is to convert continuous characteristic data into binary form, that is, to divide the pixels in the characteristic cloud map into two categories: "high-risk area" and "low-risk area". Specifically, by setting a global threshold, the area greater than the threshold is marked as "white" (indicating high risk), and the area less than the threshold is marked as "black" (indicating low risk). This processing method can help the system more intuitively identify high-risk areas as candidate high-risk sectors.

[0043] Furthermore, after the binarization process is completed, all white areas are considered candidate sectors, which represent potential high-risk areas in wind resource information and unit response data. Subsequently, the system fits the boundaries of these candidate sectors according to the preset number of curves. Boundary fitting is to describe the boundaries of candidate sectors as mathematical curves using a curve fitting algorithm to accurately determine the scope of each sector. Usually 1 to 4 curves are used to fit the boundaries of each sector to ensure the clarity and accuracy of the boundaries. When a candidate sector is too large or spans multiple standard sectors, the system will further split or adjust the boundaries to ensure that each sector has a reasonable size and shape.

[0044] Based on the scheme of the present invention, by extracting and binarizing the features of wind resource information and unit response data, the system can accurately identify high-risk areas of wind turbines under different wind resource conditions. This method avoids the errors that may be caused by the traditional rule sector division method and improves the accuracy of wind turbine operation management. The system generates candidate sectors based on the different characteristics of wind resources and unit responses, and accurately describes the range of each sector through boundary fitting technology. This dynamic division method enables the system to adjust the sector division according to actual wind resource changes, flexibly adapt to different wind resource environments, and avoid the management failure problem caused by wind resource changes in traditional methods.

[0045] Furthermore, by accurately demarcating high-risk sectors, the system can formulate targeted management strategies for each sector, such as power restrictions, shutdowns, or adjustments to power output. This targeted management strategy can effectively reduce the loss of wind turbines under high-risk conditions, extend the service life of key components, and improve the overall operating safety and power generation efficiency of the units. Boundary fitting technology ensures that the boundaries of each sector are clear by using a curve fitting algorithm. This refined boundary division improves the system's management accuracy for each sector and avoids management confusion caused by overlapping or crossing boundaries of multiple sectors.

[0046] Specifically, after performing boundary fitting on the identified sectors, the method also includes: based on the boundary information of the candidate sectors corresponding to the wind resource information and the boundary information of the candidate sectors corresponding to the unit response data, determining whether there is an intersection area between the two; if so, determining the set priority of the intersection area, selecting the sector type of the area based on the set priority, and performing the segmentation of the candidate sectors corresponding to the wind resource information and the candidate sectors corresponding to the unit response data based on the straight line division principle based on the set results to obtain each identified sector with closed boundaries.

[0047] In an embodiment of the present invention, sector identification is not only performed by boundary fitting of wind resource information and unit response data respectively, but also further judges and processes the intersection area of ​​candidate sectors corresponding to these two types of data. First, the system generates boundary information of candidate sectors according to wind resource information and unit response data respectively, and compares the two to identify whether there is an intersection area. The intersection area usually means that in the same area, the high-risk area of ​​wind resources coincides with the vibration or damage state of key components of the unit, so further processing is required to determine the final sector division. After judging that there is an intersection area, the system will determine the sector type of the intersection area according to the preset priority rules. The setting of priority can be based on multiple factors, such as the impact of wind resources on the overall load of the unit or the severity of vibration acceleration in the unit response data. According to the priority level, the system selects which sector division method to use in the intersection area to ensure that the high risk of the area is effectively managed. The system divides the intersection area based on the principle of straight line division to ensure that the sector boundary is clear and closed. This straight line division method helps to avoid complex sector boundary overlap problems while maintaining the simplicity and consistency of sector management.

[0048] Based on the solution of the present invention, not only wind resource information is taken into account, but also high-risk areas of unit response data are included in the analysis to ensure that the division of each sector can reflect the comprehensive risk status. This method effectively improves the safety of wind turbines while avoiding management omissions in high-risk areas. The introduction of the straight line division principle also ensures the clarity of boundaries and simplifies subsequent sector management strategies.

[0049] Step S30: Divide each wind resource data point into a corresponding sector after boundary fitting, and obtain a load assessment result of the target unit in each sector after boundary fitting.

[0050] Specifically, determine the sector to which each wind resource data point belongs, and divide each wind resource data point into a corresponding sector based on the belonging relationship; based on the data points in each sector, respectively calculate the ultimate load and fatigue load in the corresponding sector, and use the calculation results as the load assessment results of the corresponding sector; wherein, respectively calculating the ultimate load and fatigue load in the corresponding sector includes: using the load envelope value corresponding to all data points in the corresponding sector as the ultimate load, and accumulating the damage based on the fatigue load calculated separately for each data point point by point as the sector fatigue load measurement index; if there is no matching relationship between the wind resource grid points, execute the cubic interpolation algorithm to obtain the ultimate load and fatigue load at the corresponding moment.

[0051] In the embodiment of the present invention, the sector to which each wind resource data point belongs is first determined. This step is based on the boundary fitting results completed in the previous stage, and each wind resource data point is associated with the corresponding sector through the spatial coordinate position or other corresponding identifiers. Then, the system divides these wind resource data points into corresponding sectors, which serves as the basis for load analysis of each sector.

[0052] Furthermore, after determining the wind resource data points in each sector, the system needs to evaluate the loads in these sectors. The evaluation process includes calculating the ultimate load and fatigue load in each sector. The ultimate load refers to the maximum load that a wind turbine can withstand under specific wind resource conditions; the fatigue load reflects the cumulative damage to the components of the unit under the action of long-term wind resources. In order to calculate these loads, the system will match the corresponding grid points in the load library according to each data point. If there is a matching grid point, the load envelope value of the grid point will be directly used as the ultimate load of the data point, and the fatigue load calculated based on the history and actual operating conditions of the data point will be accumulated as the fatigue load evaluation value of the sector.

[0053] Furthermore, for those data points that do not find a match on the load library grid points, the system will perform a cubic interpolation algorithm. Cubic interpolation is an accurate interpolation method that can calculate the load value of unknown data points through cubic polynomial fitting based on adjacent known grid points. This method ensures that relatively accurate limit loads and fatigue loads can still be obtained when data is insufficient or incomplete. Through the results of the interpolation calculation, the system can continue to evaluate the load conditions of these unmatched points to ensure the comprehensiveness of the evaluation.

[0054] Based on the solution of the present invention, by accurately dividing the wind resource data points into sectors after boundary fitting, and matching or interpolation calculations based on the load library, the solution ensures the accuracy of the ultimate load and fatigue load assessment in each sector. This precise load assessment helps to identify high-risk sectors and provide data support for subsequent management strategies. The introduction of the cubic interpolation algorithm makes it possible to obtain more accurate load values ​​through interpolation calculations even when the wind resource data points do not match the grid points of the load library. This flexibility ensures the robustness of the solution, that is, reliable assessment results can still be obtained in the case of insufficient data or large measurement errors.

[0055] Furthermore, the cumulative calculation of fatigue loads ensures the long-term safe operation of wind turbines. By superimposing the fatigue loads of each wind resource data point point by point and using it as the overall fatigue load assessment value of the sector, the system can effectively warn of potential structural problems and adjust the operation strategy when necessary. This solution can not only identify the extreme load risks under current wind resource conditions, but also identify long-term accumulated risks through fatigue load assessment. By evaluating the load conditions of each sector, the system can formulate appropriate power-limiting or shutdown strategies based on the evaluation results to ensure the safety and efficiency of wind turbines.

[0056] Step S40: Based on the load assessment results of the target units in each sector, a management plan for each sector is formulated, and the corresponding management plan is executed based on the current wind resource status.

[0057] Specifically, based on the load assessment results of each sector, a management plan for each sector is formulated, including: if the limit load in the load assessment results is greater than the design limit load of the target unit, a shutdown management plan or a speed limit management plan is formulated to start the sector management strategy; conversely, if the limit load in the load assessment results is not greater than the design limit load of the target unit, the future total accumulated damage is determined based on the fatigue load in the load assessment results; if the future total accumulated damage is greater than the remaining damage of the target unit, the sector management plan is triggered; the sector management plan is executed to assign a corresponding management plan to each sector.

[0058] Further, such as Figure 2 , the execution of the sector management plan, assigning a corresponding management plan to each sector, including: determining the management plan corresponding to each sector based on the future total accumulated damage; wherein the management strategy of each sector is independent of each other; taking the minimum power generation loss as the optimization goal, performing optimization based on matching the current wind resource state with the corresponding future total accumulated damage, and obtaining the optimal management plan for the current wind resource state; wherein the management plan includes: any one or more of a normal power generation plan, a power-limited operation plan, and a shutdown plan.

[0059] In an embodiment of the present invention, the system determines the operating status of the current sector through the load assessment result. If the limit load of a sector exceeds the design limit load capacity of the target unit, the system will immediately adopt a shutdown plan to prevent the wind turbine from continuing to operate under an overload state, thereby preventing damage to the unit or major failures. The triggering of the shutdown plan is mandatory when the limit load exceeds the design load capacity, in order to ensure the safety of the wind turbine under extreme wind resource conditions. If the limit load does not exceed the design load capacity, the system will further evaluate the fatigue load and predict the cumulative damage within a predetermined time in the future. If the prediction results show that the cumulative damage within a predetermined time in the future exceeds the remaining damage capacity of the wind turbine (i.e., the remaining life of the unit), the sector management strategy is triggered. At this time, the system will dynamically adjust the management plan of each sector according to the fatigue load conditions of each sector.

[0060] Furthermore, in the process of implementing the sector management strategy, the system will assign a suitable management plan to each sector. Specifically, the system first determines the remaining life constraint of each sector based on the accumulated damage within a predetermined time in the future. This step is very important as it determines the maximum tolerable load of each sector. Then, the system takes the minimum power generation loss as the optimization goal and looks for the optimal management plan based on the current wind resource status. In this process, the system will also add the remaining life constraints of each sector to ensure that the unit can operate under conditions of minimum loss. Through this optimization process, the system finally determines the optimal management plan for each sector and executes it to ensure the long-term stability of the unit.

[0061] In one possible implementation, a simple optimization problem is used to demonstrate how to find the optimal management solution through the optimization algorithm, using the Genetic Algorithm (GA). Assume that we have three sectors, each with different power generation and cumulative damage limits. Our goal is to maximize power generation while satisfying the remaining life constraints of each sector. Each sector has three management strategies:

[0062] 1) Normal power generation (100% power output);

[0063] 2) Limited power operation (50% power output);

[0064] 4) Shutdown (0% power output);

[0065] Each strategy has a different damage increment, assuming:

[0066] 1) The damage increment of normal power generation is 1 unit / hour

[0067] 2) The damage increment for limited power operation is 0.5 unit / hour

[0068] 3) The damage increment of downtime is 0 units / hour

[0069] The remaining life of each sector is constrained to 10 units of damage. The corresponding genetic algorithm process is:

[0070] 1) Initialize the population: Generate multiple random initial solutions (individuals), each of which represents a different strategy combination of the sector.

[0071] Individual 1: normal power generation (sector 1), limited power operation (sector 2), shutdown (sector 3);

[0072] Individual 2: limited power operation (sector 1), normal power generation (sector 2), shutdown (sector 3);

[0073] Other individual combinations;

[0074] 2) Calculate fitness: The fitness of each individual is calculated based on its power generation and whether it meets the damage limit. Assuming the calculation time is 10 hours, we calculate the power generation and cumulative damage of each strategy combination:

[0075] For individual 1: normal power generation (sector 1) + limited power operation (sector 2) + shutdown (sector 3) power generation: 100% × 10 hours + 50% × 10 hours + 0% = 10 + 5 + 0 = 15 units of power generation;

[0076] Damage increment: 1×10 hours + 0.5×10 hours + 0 = 10 + 5 = 15 damage units (exceeding the remaining life constraint of sector 2);

[0077] For individual 2: limited power operation (sector 1) + normal power generation (sector 2) + shutdown (sector 3) power generation: 50% × 0 hours + 100% × 10 hours + 0% = 5 + 10 + 0 = 15 units of power generation

[0078] Damage increment: 0.5×10 hours + 1×10 hours + 0 = 5 + 10 = 15 damage units (satisfying the remaining life constraints of all sectors)

[0079] Through these calculations, individual 2 satisfies the remaining life constraints of all sectors and therefore has a higher fitness.

[0080] 3) Selection, crossover, and mutation:

[0081] Selection: Based on fitness, individuals with high fitness are given priority for the next round of operations.

[0082] Crossover: Generate new individuals by cross-combining the strategies of individuals. For example, cross-combining individual 1 and individual 2 to generate new individual 3, combining different strategies.

[0083] Mutation: To avoid falling into a local optimal solution, some strategies of some individuals are randomly changed. For example, the strategy of a sector is changed from "normal power generation" to "limited power operation".

[0084] Iterative evolution: The process of calculating fitness, selection, crossover and mutation is repeated until the optimal solution is found or the termination condition is met (such as maximum power generation and meeting damage limits).

[0085] Through multiple iterations, the genetic algorithm may find a management solution that maximizes power generation and satisfies the remaining life constraints. Assume that the final optimal solution is: sector 1: limited power operation; sector 2: normal power generation; sector 3: shutdown. The solution generates 15 units of power in 10 hours, and the damage of all sectors does not exceed their remaining life limits.

[0086] Specifically, the management scheme includes three types: normal power generation scheme, power-limited operation scheme and shutdown scheme. The normal power generation scheme is applicable to sectors where the load assessment results are within the safe range and the cumulative damage is within the limit; the power-limited operation scheme is used for sectors where the cumulative damage is close to the upper limit of the remaining life but still within the acceptable range; and the shutdown scheme is used for high-risk sectors that have or are about to exceed the design load capacity or the cumulative damage exceeds the limit.

[0087] Based on the solution of the present invention, the technical solution can effectively identify the operating risks of each sector by analyzing the load assessment results in real time, especially ensuring the safety of wind turbines through shutdown plans under extreme conditions. The dual assessment mechanism of extreme load and fatigue load ensures that the unit can respond in time in high-risk situations to avoid catastrophic failures. By predicting future cumulative damage, the system can take measures in advance to prevent excessive wear and tear of key components. This life management method can not only extend the service life of the unit, but also warn of possible damage at critical moments, thereby avoiding sudden failures.

[0088] Furthermore, under the premise of ensuring safety, the system takes the minimum loss of power generation as the optimization goal, and achieves a balance between safety and efficiency through the allocation of reasonable management solutions. By dynamically adjusting the power generation mode of the sector, the system retains the power generation capacity to the maximum extent, while reducing unnecessary power restrictions or shutdowns. Matching management solutions based on the current wind resource status enables the system to flexibly adapt to different wind resource conditions. This dynamic adaptability improves the system's ability to cope with complex wind resource environments and ensures that wind turbines always operate in the optimal state under different environments. The combination of normal power generation, power-limited operation and shutdown solutions enables the system to take multi-level response measures under different conditions, thereby ensuring the safety of the unit and maintaining a high power generation efficiency. This flexible and diverse management solution provides a guarantee for the long-term operation of the wind farm.

[0089] Preferably, the method also includes: predicting the remaining life of each preset key component based on the fatigue load in the load assessment result; determining the outlier value of each preset key component based on the remaining life of each preset key component; if the outlier value is greater than the preset outlier value threshold, ignoring the remaining life influence of the preset key component when determining the sector management plan, and outputting prompt replacement information based on the preset key component.

[0090] In an embodiment of the present invention, the scheme of the present invention predicts the remaining life of each preset key component of the wind turbine generator set through the fatigue load in the load assessment result, so as to timely discover possible component loss problems in the operation and management of the wind turbine generator set. Specifically, the system first predicts the remaining life of each preset key component (such as the main bearing, pitch bearing, yaw bearing, etc.) according to the accumulation of fatigue loads. Then, the outlier value of each component is calculated based on the remaining life of these components, that is, whether the operating life of a certain component is significantly lower than that of other components.

[0091] When the outlier value of a preset key component is greater than the preset outlier threshold, it means that the remaining life of the component may be seriously insufficient and does not meet the normal operating conditions. In this case, the system will ignore the remaining life impact of the component when calculating the cumulative damage, that is, it will not use the component as a limiting factor for the overall life of the system to ensure the normal operation of other healthy components. At the same time, the system will issue an alarm message to the component, prompting maintenance personnel that the component needs to be repaired or replaced.

[0092] Based on the solution of the present invention, by predicting the remaining life of each key component individually, the system can more accurately understand the operating conditions of each component of the unit, avoiding the impact of excessive wear and tear of individual components on the operation of the entire unit. When the outlier value of a certain component exceeds the threshold, the system will automatically issue an alarm message to prompt relevant maintenance personnel to prevent failures during unit operation, thereby improving the safety and stability of the wind turbine. While ignoring the impact of the remaining life of high outlier components, the system can continue to perform normal cumulative damage calculations on other components to ensure that the efficiency of the overall power generation system is optimal and that the operation of the entire machine will not be affected by problems with individual components.

[0093] Figure 3 FIG. 1 is a system structure diagram of a sector management system for risk operation sector identification based on a follower grid provided by an embodiment of the present invention. Figure 3As shown, an embodiment of the present invention provides a sector management system for risk operation sector identification based on a follower grid, and the system includes: a collection unit, used to collect wind resource information and corresponding unit response data within a pre-defined historical period of the target unit, and process the wind resource data into multiple wind resource data points; a processing unit, used to identify the sector of the target unit based on the wind resource information and the unit response data, and perform boundary fitting on the identified sector; an evaluation unit, used to divide each wind resource data point into a corresponding sector after boundary fitting, and obtain a load evaluation result of each sector after the corresponding boundary fitting; an execution unit, used to formulate a management plan for each sector based on the load evaluation result of each sector, and execute the corresponding management plan based on the current wind resource status matching.

[0094] The embodiment of the present invention further provides a computer-readable storage medium, on which instructions are stored, which, when executed on a computer, enable the computer to execute the above-mentioned sector management method for risk operation sector identification based on a moving grid.

[0095] Those skilled in the art will understand that all or part of the steps in the method for implementing the above-mentioned embodiments can be completed by instructing the relevant hardware through a program, and the program is stored in a storage medium, including several instructions for making a single-chip microcomputer, a chip or a processor (processor) perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0096] The optional embodiments of the present invention are described in detail above in conjunction with the accompanying drawings. However, the embodiments of the present invention are not limited to the specific details in the above embodiments. Within the technical concept of the embodiments of the present invention, the technical scheme of the embodiments of the present invention can be subjected to a variety of simple modifications, and these simple modifications all belong to 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. In order to avoid unnecessary repetition, the embodiments of the present invention will not further describe various possible combinations.

[0097] In addition, various embodiments of the present invention may be arbitrarily combined, and as long as they do not violate the concept of the embodiments of the present invention, they should also be regarded as the contents disclosed in the embodiments of the present invention.

Claims

1. A sector management method for risk operation sector identification based on a follow-up grid, characterized in that: The method comprises: Collect wind resource information and corresponding unit response data within a pre-defined historical period of the target unit, and process the wind resource data into multiple wind resource data points; Based on wind resource information and unit response data, the target unit sector is identified, and boundary fitting is performed on the identified sector; Divide each wind resource data point into a corresponding sector after boundary fitting, and obtain a load assessment result of the target unit in each sector after boundary fitting; Based on the load assessment results of the target units in each sector, a management plan for each sector is formulated, and the corresponding management plan is executed based on the current wind resource status.

2. The method according to claim 1, characterized in that: The wind resource information includes wind speed information and turbulence intensity information; The unit response data includes the vibration acceleration of each preset key component and the damage status information of each preset key component.

3. The method according to claim 1, characterized in that If the current moment is within the first pre-defined historical period starting from the initial moment, the data set of the current wind resource information and the corresponding unit response data is an empty set; If the current moment is not within the first pre-defined historical period starting from the initial moment, the data set of the current wind resource information and the corresponding unit response data is the collected data within the previous pre-defined historical period.

4. The method according to claim 1, characterized in that: Processing the wind resource data into a plurality of wind resource data points includes: The wind resource data segmentation is performed based on a 600 s time window, and the corresponding wind resource data in each time window is taken as a wind resource data point.

5. The method according to claim 1, characterized in that: The step of identifying the sectors of the target unit based on the wind resource information and the unit response data and performing boundary fitting on the identified sectors includes: Perform feature extraction on the wind resource information and the unit response data respectively, and generate a feature cloud map of the wind resource information and a feature cloud map of the unit response data based on the extracted features; Based on the preset global threshold of the target unit, the characteristic cloud map of the wind resource information and the characteristic cloud map of the unit response data are binarized respectively, and each white area is used as a candidate sector based on the binarization result to obtain the candidate sector corresponding to the wind resource information and the candidate sector corresponding to the unit response data; Boundary fitting is performed on each candidate sector based on a preset number of curves to obtain boundary information of each candidate sector.

6. The method according to claim 5, characterized in that After performing boundary fitting on the identified sectors, the method further comprises: Based on the boundary information of the candidate sectors corresponding to the wind resource information and the boundary information of the candidate sectors corresponding to the unit response data, it is determined whether there is an intersection area between the two; If it exists, the setting priority of the intersection area is determined, the sector type of the area is selected based on the setting priority, and the candidate sectors corresponding to the wind resource information and the candidate sectors corresponding to the unit response data are segmented based on the straight line division principle based on the setting result to obtain the identified sectors with closed boundaries.

7. The method according to claim 1, characterized in that Divide each wind resource data point into the corresponding sector after boundary fitting, and obtain the load assessment results of the target unit in each sector after the corresponding boundary fitting, including: Determine the sector to which each wind resource data point belongs, and divide each wind resource data point into a corresponding sector based on the belonging relationship; Based on the data points in each sector, the limit load and fatigue load in the corresponding sector are calculated respectively, and the calculation results are used as the load assessment results of the target unit in the corresponding sector; The respectively calculating the limit load and fatigue load in the corresponding sectors includes: The load envelope value corresponding to all data points in the corresponding sector is taken as the limit load, and the fatigue load calculated based on each data point is accumulated point by point to calculate the damage as the sector fatigue load measurement index; If there is no matching relationship between the wind resource grid points, the cubic interpolation algorithm is executed to obtain the ultimate load and fatigue load at the corresponding moment.

8. The method according to claim 1, characterized in that Based on the load assessment results of each sector, a management plan for each sector is formulated, including: If the limit load in the load assessment result is greater than the design limit load of the target unit, formulate a shutdown management plan or a speed limit management plan to start the sector management strategy; On the contrary, if the limit load in the load assessment result is not greater than the design limit load of the target unit, the future total accumulated damage is determined based on the fatigue load in the load assessment result; If the total accumulated damage in the future is greater than the remaining damage of the target unit, the sector management scheme is triggered; The sector management scheme is executed to allocate a corresponding management scheme to each sector.

9. The method according to claim 8, characterized in that The executing the sector management scheme and allocating a corresponding management scheme to each sector includes: Determine the management plan for each sector based on the total cumulative damage in the future; The management strategy of each sector is independent of each other; Taking the minimum power generation loss as the optimization goal, the optimization is performed based on the current wind resource state matching the corresponding future total accumulated damage to obtain the optimal management plan for the current wind resource state; The management options include: Any one or more of the normal power generation scheme, the power-limited operation scheme and the shutdown scheme.

10. The method according to claim 8, characterized in that The method further comprises: Predict the remaining life of each preset key component based on the fatigue load in the load assessment results; Based on the remaining life of each preset key component, respectively determine the outlier value of each preset key component; If the outlier value is greater than a preset outlier threshold, the remaining life influence of the preset key component is ignored when determining the sector management plan, and prompt replacement information is output based on the preset key component.

11. A sector management system for risk operation sector identification based on a follow-up grid, characterized in that: The system comprises: A collection unit, used to collect wind resource information and corresponding unit response data within a pre-defined historical period of the target unit, and process the wind resource data into a plurality of wind resource data points; A processing unit, configured to identify sectors of a target unit based on wind resource information and unit response data, and perform boundary fitting on the identified sectors; An evaluation unit, used to divide each wind resource data point into a corresponding sector after boundary fitting, and obtain a load evaluation result of a target unit in each sector after boundary fitting; The execution unit is used to formulate a management plan for each sector based on the load assessment results of the target units in each sector, and to match and execute the corresponding management plan based on the current wind resource status.

12. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the sector management method for risk operation sector identification based on a moving grid as described in any one of claims 1 to 10.

Images