A method and system for operating and maintaining offshore wind turbine groups

Abstract

The application discloses a kind of offshore wind turbine group operation and maintenance resource regulation methods and systems, method includes steps: S1.Dynamically identify the wind turbine unit needing repair in operation and maintenance period, form the wind turbine group set to be operated and maintained;S2.Fuzzy C means clustering algorithm is used to divide the wind turbine group set to be operated and maintained into k clusters, generate k dynamic operation and maintenance centers;S3.Construct the multi-objective offshore wind power operation and maintenance mother-ship path planning mathematical model with the minimum operation and maintenance cost, the shortest operation and maintenance time and the minimum wind turbine power generation loss as the goal, and add constraint conditions;S4.By changing the cluster number k value, form multiple different operation and maintenance schemes, solve the mathematical model based on mixed integer linear programming, obtain the optimal operation and maintenance center number and operation and maintenance mother-ship path.The application can significantly reduce the total operation and maintenance cost, shorten the operation and maintenance response time, reduce the power generation loss, improve the operation and maintenance window utilization efficiency and resource scheduling flexibility.

Description

Technical Field

[0001] This invention mainly relates to the field of wind power technology, specifically to a method and system for regulating and controlling the operation and maintenance resources of offshore wind turbine clusters. Background Technology

[0002] As offshore wind farms continue to expand, the complexity and cost pressures of operation and maintenance are rising sharply, and traditional operation and maintenance models are facing multiple technical bottlenecks. First, offshore units are exposed to corrosive environments with high salt spray and high humidity for extended periods, leading to a significant reduction in equipment reliability and lifespan, and an increase in the frequency of operation and maintenance. Second, harsh sea conditions result in an effective operation and maintenance window of less than 180 days per year, with highly concentrated tasks and significant time pressure. Third, existing scheduling strategies are inefficient; the traditional single-vessel operation and maintenance model involves long vessel round-trip times to port, resulting in low effective operating rates and serious waste of resources.

[0003] Current research suffers from three main shortcomings: first, its reliance on static scheduling mechanisms based on fixed operation and maintenance (O&M) bases fails to meet the distributed O&M needs of large-scale wind farms; second, it neglects vessel load limits (personnel, spare parts) and strict time window constraints; and third, it lacks multi-objective collaborative optimization of O&M costs, time windows, and power generation losses. Especially with the expansion of wind farms, these deficiencies lead to a surge in O&M costs and delayed fault response, necessitating a dynamic, multi-constraint collaborative O&M resource regulation method to overcome the limitations of existing technologies. Summary of the Invention

[0004] To address the technical problems existing in the prior art, this invention provides a method and system for regulating the operation and maintenance resources of offshore wind turbine clusters, which significantly reduces total operation and maintenance costs, shortens operation and maintenance response time, reduces power generation loss, and improves the efficiency of operation and maintenance window utilization and the flexibility of resource scheduling.

[0005] To solve the above-mentioned technical problems, the technical solution proposed by this invention is as follows:

[0006] A method for regulating and controlling operation and maintenance resources of offshore wind turbine clusters includes the following steps:

[0007] S1. Based on multi-objective task scheduling, opportunistic operation and maintenance decision-making under multiple operating conditions, and historical operation and maintenance data, dynamically identify wind turbine units that need maintenance within the operation and maintenance cycle, and form a set of wind turbine groups to be maintained; the set of wind turbine groups to be maintained includes wind turbine groups to be corrected and wind turbine groups to be prevented.

[0008] S2. Based on the spatial layout characteristics of the wind turbine group, the fuzzy C-means clustering algorithm is used to divide the set of wind turbine groups to be maintained obtained in step S1 into k clusters, and generate corresponding k dynamic maintenance centers.

[0009] S3. Based on the dynamic operation and maintenance center generated in S2, construct a multi-objective offshore wind power operation and maintenance mother ship path planning mathematical model with the goals of minimizing operation and maintenance costs, minimizing operation and maintenance time, and minimizing wind turbine power generation loss. Add operation and maintenance mother ship path constraints, operation and maintenance daughter ship path constraints, operation and maintenance daughter ship load and personnel restrictions, and operation and maintenance time window constraints.

[0010] S4. By changing the number of clusters k, multiple different operation and maintenance schemes are formed. The mathematical model in S3 is solved based on mixed integer linear programming to obtain the optimal number of operation and maintenance centers and the operation and maintenance mother ship path.

[0011] Preferably, in step S1, the specific process of dynamically identifying wind turbine units that need maintenance during the operation and maintenance cycle is as follows: real-time monitoring of wind turbine units based on the operating data and operation and maintenance records of offshore wind turbines; if a fault warning is detected for a wind turbine unit, the wind turbine is included in the wind turbine group to be corrected; if the wind turbine unit reaches the threshold for preventive operation and maintenance, it is included in the wind turbine group to be preventively maintained; the wind turbine group to be corrected and the wind turbine group to be preventively maintained are merged into a set of wind turbine groups to be maintained.

[0012] Preferably, in step S2, the specific process of using the fuzzy C-means clustering algorithm to divide the set of wind turbines to be maintained obtained in step S1 into k clusters and generating the corresponding k dynamic maintenance centers is as follows:

[0013] S201. Set the number of clusters k Fuzziness index m, maximum number of iterations T and convergence threshold ω Randomly generate an initial membership matrix that satisfies the constraints;

[0014] S202. Calculate the cluster center matrix based on the current membership matrix;

[0015] S203. Update the membership matrix based on the current cluster centers;

[0016] S204. Calculate the difference between the membership matrices in the two iterations. When the difference is less than the convergence threshold... ω Or the number of iterations reaches T If the iteration terminates, output the final membership matrix and dynamic operation and maintenance center; otherwise, return to step S202 to continue the iteration.

[0017] Preferably, in step S3, the functional expression of the mathematical model for the path planning of the multi-objective offshore wind power operation and maintenance mother-daughter vessel is:

[0018]

[0019] in, This refers to the penalty costs incurred for wind turbine units not being maintained within the optimal maintenance period. Penalty cost per unit Indicates the number of days of delayed maintenance. For the first n A collection of wind turbines that require maintenance within an operations and maintenance center;

[0020] This indicates the sailing cost of the maintenance mother ship. This indicates the unit sailing cost of the mother ship. For the first The operations and maintenance center to the first The distance between the operation and maintenance centers k Number of operation and maintenance centers; and All represent operation and maintenance bases;

[0021] This indicates the sailing cost of the maintenance sub-vessel. This indicates the unit sailing cost of the maintenance sub-vessel. For the first n A collection of wind turbines requiring maintenance within an operations and maintenance center. for Inner i The fan to the first j The distance between the fans;

[0022] This represents the cost of maintenance personnel throughout the entire maintenance cycle. This indicates the daily wage of the maintenance personnel. Indicates the number of maintenance personnel. This indicates the number of days of operation and maintenance.

[0023] Preferably, in step S3, the path constraint condition for the maintenance mother ship is:

[0024] Within the same operation and maintenance cycle, the maintenance mother ship can only be located at one operation and maintenance center;

[0025] Each operations and maintenance center can only be accessed once.

[0026] Preferably, in step S3, the path constraint condition for the maintenance sub-ship is:

[0027] The maintenance sub-vessel and the maintenance mother vessel are on the same path as the maintenance center;

[0028] The sub-ship flow conservation constraint means that the number of maintenance sub-ships entering the same wind turbine is equal to the number of maintenance sub-ships leaving.

[0029] Each wind turbine can only be maintained once;

[0030] The starting and ending points of the maintenance sub-vessels are both the maintenance center.

[0031] Preferably, in step S3, the load capacity and personnel restrictions of the maintenance sub-vessel are as follows:

[0032] The number of maintenance personnel carried by the maintenance sub-vessel shall not exceed the maximum carrying capacity;

[0033] The spare parts carried by the maintenance sub-vessel shall not exceed the maximum carrying weight.

[0034] Preferably, in step S3, the maintenance time window constraint is: the sum of the sailing time of the maintenance mother ship, the sailing time of the maintenance daughter ship, the transfer time, and the maintenance time of the wind turbine is less than the daily maintenance working time.

[0035] Preferably, in step S4, after the model is solved, the results are compared... k The total operation and maintenance cost is calculated based on the minimum total operation and maintenance cost, and the final offshore wind power operation and maintenance mother ship route is selected based on this minimum cost.

[0036] The present invention also discloses an offshore wind turbine cluster operation and maintenance resource control system, including a memory and a processor connected to each other. The memory stores a computer program, and the computer program executes the steps of the method described above when run by the processor.

[0037] Compared with the prior art, the advantages of the present invention are as follows:

[0038] This invention generates a distributed operation and maintenance center through dynamic clustering, and combines multi-objective path planning and multi-constraint collaborative optimization to achieve intelligent control of operation and maintenance resources for offshore wind turbine clusters. The method of this invention effectively overcomes the technical bottlenecks of traditional static scheduling mode, such as poor adaptability to large-scale wind farms, neglect of ship load and time window constraints, and lack of multi-objective collaborative optimization. It significantly reduces the total operation and maintenance cost, shortens the operation and maintenance response time, reduces power generation loss, improves the efficiency of operation and maintenance window utilization and resource scheduling flexibility, and provides efficient and reliable decision support for the operation and maintenance management of large-scale offshore wind farms. Attached Figure Description

[0039] Figure 1 This is a flowchart of the offshore wind turbine group operation and maintenance resource regulation method according to an embodiment of the present invention.

[0040] Figure 2 This is a clustering result diagram of wind turbine units with 2 operation and maintenance centers in an embodiment of the present invention.

[0041] Figure 3 This is a clustering result diagram of wind turbine units with 3 operation and maintenance centers in an embodiment of the present invention.

[0042] Figure 4 This is a clustering result diagram of wind turbine units with 4 operation and maintenance centers in an embodiment of the present invention.

[0043] Figure 5This is a diagram showing the path planning results for the mother and daughter ships in this embodiment of the invention when the cluster center is 2.

[0044] Figure 6 This is a diagram showing the path planning results for the mother and daughter ships in this embodiment of the invention when the cluster center is 3.

[0045] Figure 7 This is a diagram showing the path planning results for the mother and daughter ships in this embodiment of the invention when the cluster center is 4. Detailed Implementation

[0046] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0047] like Figure 1 As shown in the figure, the offshore wind turbine cluster operation and maintenance resource regulation method provided in this embodiment of the invention includes the following steps:

[0048] S1. Based on multi-objective task scheduling, opportunistic operation and maintenance decision-making under multiple operating conditions, and historical operation and maintenance data, the wind turbine units that need maintenance within the operation and maintenance cycle are dynamically identified through real-time monitoring and preventive service life prediction, forming a set of wind turbine groups to be maintained; the set of wind turbine groups to be maintained includes wind turbine groups to be corrected and wind turbine groups to be prevented.

[0049] Specifically, in offshore wind power operation and maintenance, heavy and large components typically have long design lifespans, and their failures are usually caused by unforeseen accidents rather than routine wear and tear or aging. Furthermore, their large size and weight limit accessibility and operability, resulting in lengthy maintenance times. Therefore, operation and maintenance planning primarily focuses on scheduling the operation and maintenance of small and medium-sized components of wind turbine units.

[0050] The specific method for dynamically identifying wind turbines that need maintenance during the operation and maintenance cycle is as follows: real-time monitoring of wind turbines is carried out based on the operating data and maintenance records of offshore wind turbines; if a fault warning is detected for a wind turbine, the turbine is included in the wind turbine group to be corrected; if the wind turbine reaches the threshold for preventive maintenance, it is included in the wind turbine group to be prevented, and the two are merged into a wind turbine group to be maintained.

[0051] S2. Based on the spatial layout characteristics of the wind turbine group, the fuzzy C-means clustering algorithm (FCM) is used to divide the set of wind turbine groups to be maintained obtained in step S1 into k clusters, and generate the corresponding k dynamic maintenance centers.

[0052] The objective function used to partition the units to be maintained is the FCM algorithm. for:

[0053]

[0054] in, For wind turbine coordinates, For the coordinates of the operations and maintenance center, n For the number of wind turbines, k For the number of clusters, For the first i The first fan is for the first j Membership degree of each cluster center m U is the fuzzy index; U is the membership matrix; V is the cluster center matrix.

[0055] Specifically, adopt FCM The algorithm for classifying servers into maintenance groups includes the following steps:

[0056] S201. Set the number of clusters k Fuzzy Index Maximum number of iterations and convergence threshold ω Randomly generate the initial membership matrix;

[0057] S202. Calculate the cluster center matrix based on the current membership matrix:

[0058] S203. Update the membership matrix based on the current cluster centers:

[0059] S204. Calculate the difference between the membership matrices in the two iterations. ,when The iteration terminates when the maximum number of iterations is reached, and the final membership matrix is ​​output. With Dynamic Operation and Maintenance Center Otherwise, return to step S302 and continue iterating;

[0060] in The calculation formula is as follows:

[0061]

[0062] in Let be the membership degree in the t-th iteration; Let be the membership degree in the (t+1)th iteration.

[0063] S3. Based on the dynamic operation and maintenance center generated in S2, a multi-objective offshore wind power operation and maintenance mother and daughter ship path planning mathematical model is constructed with the objectives of minimizing operation and maintenance costs, minimizing operation and maintenance time, and minimizing wind turbine power generation loss. The model incorporates path constraints for the operation and maintenance mother ship, path constraints for the operation and maintenance daughter ship, load and personnel restrictions for the operation and maintenance daughter ship, and operation and maintenance time window constraints. The multi-objective offshore wind power operation and maintenance mother and daughter ship path planning mathematical model is based on the dynamic operation and maintenance center generated in S2 for path planning.

[0064] Specifically, the operation and maintenance activities mainly consist of small to medium-sized activities within preventative, routine, and corrective operations and maintenance. The maintenance sub-vessels are used to transfer maintenance personnel, spare parts, and equipment to the wind turbines awaiting maintenance. Due to their large size and high fuel consumption, the maintenance mother vessel stays at the maintenance base and maintenance center, while the maintenance sub-vessels are used to visit the wind turbines and carry out maintenance.

[0065] When the maintenance mother ship is located at a maintenance center, the maintenance activities can be viewed as multiple separate delivery and pickup processes, with maintenance vessels shuttling between the wind turbine to be maintained and the mother ship. Within a certain time period, the maintenance vessel first receives maintenance personnel, spare parts, and equipment from the maintenance mother ship, then proceeds to the wind turbine to be maintained according to a planned route. After completing the maintenance task, the maintenance vessel returns to the maintenance mother ship.

[0066] During the maintenance mother ship route, the maintenance mother ship only moves between the maintenance base and the dynamic maintenance center, while the maintenance daughter ships travel back and forth between the dynamic maintenance center and the wind turbine to perform maintenance tasks.

[0067] Specifically, a mathematical model for path planning of multi-objective offshore wind power operation and maintenance mother-daughter vessels. The function expression is:

[0068]

[0069] in, This refers to the penalty costs incurred for wind turbine units not being maintained within the optimal maintenance period. Penalty cost per unit Indicates the number of days of delayed maintenance. For the first n A collection of wind turbines that require maintenance within an operations and maintenance center;

[0070] This indicates the sailing cost of the maintenance mother ship. This indicates the unit sailing cost of the mother ship. For the first The operations and maintenance center to the first The distance between the operation and maintenance centers;

[0071] This indicates the sailing cost of the maintenance sub-vessel. This indicates the unit sailing cost of the maintenance sub-vessel. For the first n A collection of wind turbines requiring maintenance within an operations and maintenance center. for Inner i The fan to the first j The distance between the fans;

[0072] This represents the cost of maintenance personnel throughout the entire maintenance cycle. This indicates the daily wage of the maintenance personnel. Indicates the number of maintenance personnel. This indicates the number of days of operation and maintenance.

[0073] When planning the maintenance mothership for offshore wind turbine clusters, it is necessary to ensure that the mothership can only be located at one maintenance center within the same maintenance cycle, and that each maintenance center can only be visited once. Therefore, the path constraint condition for the maintenance mothership is as follows:

[0074] ;

[0075] ;

[0076] in, Let them be decision variables, defined as:

[0077] ;

[0078] in T This refers to the number of days of operation and maintenance.

[0079] When planning the operation and maintenance sub-vessels for offshore wind turbine clusters, the path constraints for the operation and maintenance sub-vessels are as follows:

[0080] Firstly, within the same maintenance cycle, it is necessary to ensure that the maintenance sub-vessel and the maintenance mother vessel are on the same maintenance center path:

[0081] For two-dimensional decision variables, defined as:

[0082]

[0083] Secondly, there is the flow conservation constraint for the sub-vessels. During the operation and maintenance cycle, it is necessary to ensure that the number of operation and maintenance sub-vessels entering the same wind turbine is equal to the number of operation and maintenance sub-vessels leaving.

[0084]

[0085] in , For two-dimensional decision variables, defined as:

[0086]

[0087]

[0088] in This represents the total number of maintenance sub-vessels that have departed from wind turbine i. This indicates the total number of maintenance sub-vessels that have entered wind turbine i;

[0089] Secondly, ensure that each wind turbine can only be maintained once:

[0090]

[0091] This formula further limits the maintenance to all maintenance centers within the maintenance cycle, ensuring that each wind turbine can only be maintained once, thus avoiding duplicate maintenance.

[0092] Finally, the starting and ending points of the maintenance sub-vessels are both constrained to be the maintenance center:

[0093]

[0094] Among them, when the maintenance mother ship is in the maintenance center At that time, the itinerary set of the maintenance sub-vessels was as follows ; , These are two-dimensional decision variables, defined as follows:

[0095]

[0096]

[0097] When planning the operation and maintenance (O&M) routes for offshore wind turbine clusters, the specifications of the O&M sub-vessels are fixed within the same O&M cycle. Therefore, it is necessary to constrain their load capacity and personnel. The load capacity and personnel restrictions for the O&M sub-vessels are as follows:

[0098]

[0099]

[0100] in, For the maintenance of the fan i Number of personnel required For the maintenance of the fan i Required spare parts weight Q max The maximum number of maintenance personnel that the maintenance sub-vessel can carry. W max This is the maximum weight of spare parts that the maintenance sub-vessel can carry.

[0101] In offshore wind power operation and maintenance (O&M), daily working hours are limited, assuming a daily working time of 11 hours. During O&M activities, the O&M mother ship needs a certain sailing time to reach the O&M center, then dispatches O&M sub-ships to perform O&M tasks. After completing the day's O&M tasks, they need to return to the O&M mother ship for rest before continuing O&M tasks the next day. Therefore, it is necessary to ensure that the time for the last O&M task of the day to be completed and returned is within the daily O&M working hours. Thus, the constraints on the offshore wind power O&M time window mainly include the sailing time of the O&M mother ship, the sailing time of the O&M sub-ships, the O&M resource transfer time, and the wind turbine O&M time needing to be less than the daily O&M working hours. Therefore, the O&M time window constraint is:

[0102]

[0103] in, For the maintenance mothership in the t sailing time per day, For the maintenance of the sub-ship in the first t sailing time per day, For the first t Daily operation and maintenance resource transfer time, For the maintenance of the fan i Time required.

[0104] S4. By changing the number of clusters k, multiple different operation and maintenance schemes are formed. The mathematical model in S3 is solved based on mixed integer linear programming to obtain the optimal number of operation and maintenance centers and the operation and maintenance mother ship path.

[0105] Specifically, by changing the number of clusters k The system generates several different operation and maintenance (O&M) schemes and uses mixed-integer linear programming to derive the optimal number of O&M centers and the optimal O&M mother-daughter ship path. Since each wind turbine cluster requires at least one O&M center and the O&M cycle is fixed, a specific number of cluster centers is set. k Between the number of wind turbine clusters and the operation and maintenance cycle.

[0106] Comparison of each k The total operation and maintenance cost is calculated based on the minimum total operation and maintenance cost, and the final offshore wind power operation and maintenance mother ship route is selected based on this minimum cost.

[0107] This invention generates a distributed operation and maintenance center through dynamic clustering, and combines multi-objective path planning and multi-constraint collaborative optimization to achieve intelligent control of operation and maintenance resources for offshore wind turbine clusters. The method of this invention effectively overcomes the technical bottlenecks of traditional static scheduling mode, such as poor adaptability to large-scale wind farms, neglect of ship load and time window constraints, and lack of multi-objective collaborative optimization. It significantly reduces the total operation and maintenance cost, shortens the operation and maintenance response time, reduces power generation loss, improves the efficiency of operation and maintenance window utilization and resource scheduling flexibility, and provides efficient and reliable decision support for the operation and maintenance management of large-scale offshore wind farms.

[0108] To verify the offshore wind turbine cluster operation and maintenance resource control method of this embodiment, simulation verification was conducted. All simulations and optimizations of the methods in this embodiment were performed using MATLAB software R2021b on a PC with a 2.8-GHz CPU and 32-GB RAM environment. Currently, mainstream offshore wind turbine clusters generally adopt a regular array layout structure. This layout significantly reduces the difficulty of locating offshore wind turbines, allowing operation and maintenance personnel to quickly plan inspection routes and effectively improve fault diagnosis and equipment operation and maintenance efficiency. The wind speed, wave height, and operation and maintenance data used in this embodiment are from historical data of an offshore wind turbine cluster in Guangdong. A wind farm containing 30 wind turbine units was built in the simulation environment to fully verify the effectiveness of the proposed operation and maintenance mother-daughter vessel path planning model.

[0109] Based on the method of this embodiment, the characteristics of wind turbine units are extracted using the FCM clustering algorithm according to the spatial location and layout information of offshore wind turbine clusters, and the wind turbine units are then clustered and divided. Figure 2 , Figure 3 and Figure 4 The simulation results for FCM clustering of wind turbine clusters are presented, showing the results as the number of operation and maintenance (O&M) centers increases from 2 to 4. When there are 2 O&M centers, the wind turbine clusters are divided into two clusters with a large distance between cluster centers, which may lead to excessively long single O&M paths and increased O&M vessel travel costs. As the number of O&M centers increases, the average service distance between wind turbines in each cluster decreases, and the O&M vessel travel costs decrease accordingly. Based on the simulation results of the above FCM clustering algorithm, Figure 5 , Figure 6 and Figure 7This paper presents simulation results of the path planning for maintenance mother and daughter ships using a mixed-integer linear programming algorithm when the number of maintenance centers is 2-4. Table 1 shows the maintenance costs of the maintenance mother and daughter ship path planning under different numbers of maintenance centers. Based on the data in Table 1, the total maintenance cost is lowest when the number of maintenance centers is 3. This is a 5.8% reduction compared to the path planning scheme with 2 maintenance centers and a 9.8% reduction compared to the scheme with 4 maintenance centers. When there are 2 maintenance centers, the maintenance mother ship needs to cover a wide area of ​​offshore wind turbine clusters, significantly increasing the path for the maintenance daughter ships, leading to increased sailing costs and maintenance time. Furthermore, because the wind turbines are sparsely distributed at the maintenance centers, the maintenance daughter ships need to make multiple round trips, further increasing maintenance costs. When there are 3 maintenance centers, by evenly distributing the offshore wind turbine clusters, the maintenance sailing time of the maintenance daughter ships is significantly reduced, effectively balancing maintenance resources and path complexity. When there are 4 operation and maintenance centers, although the operation and maintenance sailing time of the operation and maintenance sub-vessels is further shortened, the difficulty of coordination and scheduling between operation and maintenance centers increases, resulting in reduced operation and maintenance efficiency and thus increased operation and maintenance costs.

[0110] Table 1: Operation and maintenance costs under different operation and maintenance centers

[0111]

[0112] This invention dynamically generates an operation and maintenance center, coordinates ship load limits and time window constraints, optimizes the scheduling strategy of operation and maintenance mother and daughter ships, and minimizes the total cost of multi-objective path planning, including operation and maintenance costs, time, and power generation losses.

[0113] This invention also discloses an offshore wind turbine cluster operation and maintenance resource control system, including an interconnected memory and a processor. The memory stores a computer program, which, when run by the processor, executes the steps of the method described above. The control system of this invention, corresponding to the control method described above, also possesses the advantages described above.

[0114] The present invention can implement all or part of the processes in the methods of the above embodiments, or it can be implemented by hardware related to computer program instructions. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of the above method embodiments. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable storage medium includes: any entity or device capable of carrying computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. The memory is used to store computer programs and / or modules. The processor implements various functions by running or executing the computer programs and / or modules stored in the memory, and by calling data stored in the memory. The memory may include high-speed random access memory, as well as non-volatile memory, such as hard disks, RAM, plug-in hard disks, smart media cards (SMC), secure digital (SD) cards, flash cards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0115] The above are merely preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should be considered within the scope of protection of the present invention.

Claims

1. A method for operating and maintaining a group of offshore wind turbines, characterized in that, The method comprises the steps of: S1. Based on multi-target task scheduling, multi-working condition opportunity operation and maintenance decision and historical operation and maintenance data, dynamically identifying wind turbines needing maintenance in an operation and maintenance period to form a wind turbine group set to be operated and maintained; the wind turbine group set to be operated and maintained comprises a wind turbine group to be corrective operation and maintenance and a wind turbine group to be preventive operation and maintenance; S2. According to the spatial layout characteristics of the wind turbine group, the fuzzy C-means clustering algorithm is used to divide the wind turbine group set to be operated and maintained obtained in step S1 into k clusters, and k dynamic operation and maintenance centers are generated; S3. Based on the dynamic operation and maintenance centers generated in S2, a multi-objective offshore wind power operation and maintenance mother-son ship path planning mathematical model is constructed, taking the minimization of operation and maintenance cost, the minimization of operation and maintenance time and the minimization of wind turbine power generation loss as the objectives, and operation and maintenance mother ship path constraints, operation and maintenance son ship path constraints, operation and maintenance son ship load and personnel constraints and operation and maintenance time window constraints are added; S4. By changing the clustering number k value, a plurality of different operation and maintenance schemes are formed, the mathematical model in S3 is solved based on mixed integer linear programming, and the optimal operation and maintenance center number and operation and maintenance mother-son ship path are obtained. In step S1, the specific process of dynamically identifying wind turbines needing maintenance in an operation and maintenance period is as follows: real-time monitoring of wind turbines is performed according to the operation data and operation records of offshore wind turbines; if a wind turbine sends out a fault warning, the wind turbine is listed in the wind turbine group to be corrective operation and maintenance; If a wind turbine reaches a preventive operation and maintenance threshold, it is listed in the wind turbine group to be preventive operation and maintenance; The wind turbine group to be corrective operation and maintenance and the wind turbine group to be preventive operation and maintenance are combined into the wind turbine group set to be operated and maintained.

2. The offshore wind turbine cluster O&M resource regulation method according to claim 1, characterized in that, In step S2, the specific process of using the fuzzy C-means clustering algorithm to divide the wind turbine group set to be operated and maintained obtained in step S1 into k clusters and generate k dynamic operation and maintenance centers is as follows: S201. Set the number of clusters k , fuzzy index m, maximum number of iterations and convergence threshold ω , randomly generate an initial membership matrix satisfying the constraint conditions; S202. Calculate the clustering center matrix according to the current membership matrix; S203. Update the membership matrix according to the current clustering center; S204. Calculate the difference between the two iteration membership matrices, when the difference is less than the convergence threshold ω or the number of iterations reaches the maximum number of iterations, terminate the iteration, output the final membership matrix and the dynamic operation and maintenance center; otherwise, return to step S202 to continue iteration.

3. The offshore wind turbine cluster O&M resource regulation method according to claim 1 or 2, characterized in that, In step S3, the function expression of the multi-objective offshore wind power operation and maintenance mother-son ship path planning mathematical model is as follows: wherein, represents the penalty cost generated by the wind turbine not being maintained within the optimal maintenance period, is the unit penalty cost, represents the number of days of delayed maintenance, is the set of wind turbines in the n maintenance center that need to be maintained; represents the sailing cost of the operation mother ship, represents the unit sailing cost of the operation mother ship, is the first is the distance from the first operation center to the second operation center, is the distance from the first operation center to the second operation center, k is the number of operation centers; and both represent the operation base; represents a sailing cost of the operation and maintenance sub-ship, represents a unit sailing cost of the operation and maintenance sub-ship, is the first n fan set in the operation and maintenance center that needs to be maintained, is the first fan in the operation and maintenance center, i is the distance from the first fan to the j second fan. represents the cost of the operation and maintenance personnel in the whole operation and maintenance period, represents the daily salary of the operation and maintenance personnel, represents the number of operation and maintenance personnel, represents the operation and maintenance days.

4. The offshore wind turbine cluster O&M resource regulation method according to claim 1 or 2, characterized in that, In step S3, the operation and maintenance mother ship path constraint condition is as follows: In the same operation and maintenance period, the operation and maintenance mother ship can only be located at one operation and maintenance center; Each operation and maintenance center can only be visited once.

5. The offshore wind turbine cluster O&M resource regulation method according to claim 1 or 2, characterized in that, In step S3, the path constraint condition of the operation and maintenance son ship is as follows: The operation and maintenance son ship and the operation and maintenance mother ship are on the same operation and maintenance center path; The son ship flow conservation constraint, i.e., the number of operation and maintenance son ships entering the same wind turbine is equal to the number of operation and maintenance son ships leaving the same wind turbine; Each wind turbine can only be operated once; The starting point and the end point of the operation and maintenance son ship are both operation and maintenance centers.

6. The offshore wind turbine cluster O&M resource regulation method according to claim 1 or 2, characterized in that, In step S3, the operation and maintenance son ship load and personnel constraint condition is as follows: The number of operation and maintenance personnel carried by the operation and maintenance son ship does not exceed the maximum carrying capacity; The weight of spare parts carried by the operation and maintenance son ship does not exceed the maximum carrying weight.

7. The offshore wind turbine cluster O&M resource regulation method according to claim 1 or 2, characterized in that, In step S3, the operation and maintenance time window constraint condition is as follows: the sum of the sailing time of the operation and maintenance mother ship, the sailing time of the operation and maintenance son ship, the transfer time and the operation and maintenance time of the wind turbine is less than the daily operation and maintenance working time.

8. The offshore wind turbine cluster O&M resource regulation method according to claim 1 or 2, characterized in that, In step S4, after the model is solved, the total operation and maintenance costs under each value are compared, and the minimum total operation and maintenance cost is selected as the final offshore wind power operation and maintenance sub-mother ship path. k value are compared, and the minimum total operation and maintenance cost is selected as the final offshore wind power operation and maintenance sub-mother ship path.

9. An offshore wind turbine group operation and maintenance resource regulation system comprising a memory and a processor connected to each other, and a computer program is stored on the memory, characterized in that, The computer program, when executed by a processor, performs the steps of the method of any one of claims 1-8.

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