A method and device for increasing energy of offshore wind farm coordinated network control

Abstract

The application discloses a kind of offshore wind farm energy-increasing coordination network control method and device, method includes S1, wind resource and unit operation data synchronous acquisition and preprocessing;S2, accurate analysis of full-condition wake characteristics based on adaptive Gaussian wake model;S3, unit-level multi-time scale power prediction based on wake analysis result;S4, real-time receiving of grid dispatching instruction and constraint boundary determination;S5, multi-objective collaborative optimization dispatching instruction generation;S6, control instruction issuing and whole-process closed-loop execution.The device includes wind resource monitoring module, wind turbine monitoring module, grid instruction receiving module, wake analysis module, power prediction module and energy coordination control module, the application constructs whole-process integrated closed-loop control link, realizes "energy-increasing" and "coordination network" dual target, and power prediction accuracy is significantly better than conventional method.

Description

Technical Field

[0001] This invention relates to the field of new energy power generation technology, and in particular to the field of offshore wind farm control and grid connection optimization technology, specifically to an offshore wind farm energy enhancement and grid coordination control method and device. Background Technology

[0002] Wind power generation is one of the most technologically mature and scalable renewable energy generation methods. Offshore wind power, with its stable resource conditions and proximity to load centers, has become a major direction for wind power development worldwide in recent years. However, the intermittent, random, and fluctuating nature of wind resources leads to fluctuations in wind power output, causing corresponding voltage fluctuations at the grid connection point of large wind farms. Furthermore, the large reactive power generated by submarine cables can easily cause the grid connection voltage of offshore wind farms to exceed limits. Therefore, effective reactive power control is crucial for ensuring the stability of the grid connection voltage and the entire power plant for offshore wind farms.

[0003] The existing technology has the following problems:

[0004] The existing large-scale development of offshore wind power has brought about wake interference problems (traditional wake losses reach more than 12.5%).

[0005] Existing Jensen wake models lack accuracy, with ultra-short-term power prediction RMSE generally exceeding 18%.

[0006] The stand-alone operation mode lacks cluster collaborative control capabilities;

[0007] It is impossible to achieve full-level collaborative optimization of wind turbines, farms, and networks. Summary of the Invention

[0008] To overcome the shortcomings of existing technologies, such as insufficient wake analysis accuracy, incomplete control links, single control objectives, and inability to simultaneously address energy enhancement and grid coordination, this invention provides a full-process, integrated offshore wind farm energy enhancement and grid coordination control method and device.

[0009] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0010] A method for grid-connected control to enhance the energy output of offshore wind farms includes the following steps:

[0011] S1. Synchronous acquisition and preprocessing of wind resources and unit operation data;

[0012] S2. Accurate analysis of wake characteristics under all operating conditions based on adaptive Gaussian wake model;

[0013] S3. Unit-level multi-timescale power prediction based on wake analysis results;

[0014] S4. Real-time reception of power grid dispatch instructions and determination of constraint boundaries;

[0015] S5. Generation of multi-objective collaborative optimization scheduling instructions;

[0016] S6. Control command issuance and closed-loop execution of the entire process.

[0017] As a preferred technical solution of the present invention, step S1 is specifically as follows:

[0018] S11, Data Acquisition:

[0019] The wind farm's SCADA system uses distributed data acquisition nodes to synchronously collect wind resource and turbine operation data.

[0020] S12, Data Preprocessing:

[0021] Outliers are removed from the collected raw data using the 3σ criterion, missing data is filled using linear interpolation, and wind resource and unit operation data are aligned to millisecond-level time sequence. After completing the data standardization process, a standardized dataset is output and transmitted to steps S2, S3, and S4 respectively.

[0022] As a preferred technical solution of the present invention, step S2 is specifically as follows:

[0023] S21. Wake width calculation:

[0024] The relevant parameters of the wake width are calculated using equations (1) and (2):

[0025] ;

[0026] ;

[0027] in, The diameter of the wind turbine, The thrust coefficient is the rated thrust coefficient. The initial wake width, The wake attenuation coefficient is... This is the distance along the axis of the wind turbine;

[0028] S22. Calculation of speed loss under normal operating conditions:

[0029] The axial velocity deficit at any position in the wake region of the wind turbine under normal operating conditions is calculated using equation (3):

[0030] ;

[0031] in, Radial distance of the wake cross section; Wake width;

[0032] S23. Calculation of wake deflection and speed loss under yaw conditions:

[0033] For units with yaw angles, the lateral offset of the wake centerline is calculated using equation (4), and the speed loss under yaw conditions is calculated using equation (5).

[0034] ;

[0035] ;

[0036] in, This is the lateral offset of the wake centerline. This refers to the yaw angle of the wind turbine. The height coordinates of the calculation point are used to determine the relative position of that point within the vertical spread range of the wake;

[0037] S24. Calculation of additional turbulence intensity:

[0038] The additional turbulence intensity in the wake region is calculated using equation (6):

[0039] ;

[0040] in, To add turbulence intensity to the wake, The original turbulence intensity of the incoming flow.

[0041] As a preferred technical solution of the present invention, step S3 is specifically as follows:

[0042] Based on the analytical results of each unit obtained from S2, and combined with the wind turbine power curve model, power predictions were completed for two time scales:

[0043] Ultra-short-term power prediction: The prediction time scale is 1 minute to 15 minutes, the prediction step is 1 minute, and the maximum, minimum and average power prediction values ​​for each unit are output every minute.

[0044] Ultra-short-term power prediction: The prediction time scale is 15 minutes to 4 hours, the prediction step is 15 minutes, and it matches the update cycle of the power grid dispatch command. It outputs the power prediction range and the assessment results of the adjustability of each unit.

[0045] As a preferred technical solution of the present invention, step S4 is specifically as follows:

[0046] S41. Receive the total active power dispatch instructions for wind farms issued by the power grid dispatch system in real time through the power dispatch data network. The instruction update cycle is 15 minutes. At the same time, receive the switching status signals of the power grid dispatch.

[0047] S42. Using the received power dispatch command from the power grid as a hard constraint, calculate the power deviation between the current total output of the wind farm and the dispatch command using equation (7). Meanwhile, the power deviation is calculated using equation (8). Apply rate of change constraints:

[0048] (7);

[0049] (8);

[0050] in, This represents the total output deviation of the wind farm. This represents the current actual total output of the wind farm. The power grid dispatcher issues a total active power command.

[0051] As a preferred technical solution of the present invention: in step S41, the subsequent step S5 optimization scheduling step can only be executed when the power grid dispatch is in operation.

[0052] As a preferred technical solution of the present invention, step S5 is specifically as follows:

[0053] Using the power regulation target determined in step S4 as a constraint, and taking the unit power fluctuation coefficient, wind curtailment rate, and power regulation margin as core evaluation indicators, combined with the power prediction results in step S3 and the real-time operating status of the units in S1, cluster collaborative control commands are generated. The specific process is as follows:

[0054] S51. First, calculate the core evaluation indicators to provide a basis for scheduling decisions:

[0055] Based on the wake analysis results of step S2, active wake deflection control is achieved by optimizing the yaw angle of the front wind turbine units, thereby reducing the wake loss of the rear units.

[0056] S52. Finally, the active power adjustment command and yaw angle optimization command of each unit are generated to form a complete cluster collaborative control command set.

[0057] As a preferred technical solution of the present invention, step S6 is specifically as follows:

[0058] S61. The collaborative control command generated in step S5 is sent to the control cabinet of each wind turbine in real time via the wind farm industrial Ethernet. The control cabinet controls the turbine to perform the corresponding power regulation and yaw angle adjustment actions.

[0059] S62. Synchronously collect the operating data of each unit after execution and the feedback results of the total output of the wind farm, complete the closed-loop verification of the current control cycle, and evaluate the tracking accuracy of the grid dispatch command and the effect of power generation improvement.

[0060] S63. Input the feedback data of this control cycle into the standardized dataset of step S1, start the iterative optimization of the next control cycle, and realize closed-loop control of the entire process.

[0061] A power enhancement and grid coordination control device for offshore wind farms includes a wind resource monitoring module, a wind turbine monitoring module, a grid command receiving module, a wake analysis module, a power prediction module, and an energy coordination and regulation module, wherein:

[0062] Wind resource monitoring module: Used to collect meteorological parameters such as wind speed, wind direction, temperature, and air pressure from the wind farm SCADA system in real time from the wind measurement towers in the offshore wind farm, and transmit them to the wake analysis module and power prediction module;

[0063] The wind turbine monitoring module is used to collect the operating status parameters of each wind turbine in the wind farm in real time from the wind farm SCADA system, including wind speed, rotational speed, real-time power, and yaw angle, and transmit them to the wake analysis module, power prediction module and energy coordination and control module.

[0064] The power grid command receiving module is used to receive wind farm power dispatch commands issued by the power grid dispatch system in real time and transmit them to the energy coordinated control module.

[0065] Wake analysis module: Built-in wake model, wake velocity linear superposition model and turbulence field square sum superposition model, used to calculate the wake velocity distribution, turbulence intensity distribution and wake superposition effect of each wind turbine under normal operation and yaw conditions based on wind resources and wind turbine operating parameters, and output wake analysis results to power prediction module and energy collaborative control module.

[0066] Power prediction module: Receives and, based on wake analysis results and combined with the wind turbine power curve model, realizes ultra-short-term and ultra-short-term power prediction at the wind farm turbine level, and outputs the power prediction results to the energy coordination and control module.

[0067] Energy Coordinated Regulation Module: It has a built-in multi-objective coordinated optimization scheduling strategy. It takes the power grid power scheduling command as a constraint and the power fluctuation coefficient, wind curtailment rate and power regulation margin as core evaluation indicators. It combines the power prediction results and the real-time operating status of wind turbine units to generate wind turbine cluster coordinated control commands and send them to each wind turbine unit for execution.

[0068] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0069] This invention constructs an integrated closed-loop control link throughout the entire process, which not only achieves the dual goals of "energy enhancement" and "network cooperation", but also significantly reduces wake loss and improves power prediction accuracy significantly compared to traditional methods.

[0070] Core technology role:

[0071] 1. Significantly reduces wake loss and increases power generation:

[0072] An adaptive Gaussian wake model is used to accurately analyze the wake characteristics under all operating conditions, replacing the traditional Jensen model which has insufficient accuracy. This can reduce wake loss from 12.5% ​​to 7.8%. By actively optimizing the yaw angle of the front-row units, wake deflection control is achieved, which significantly reduces the disturbance to the rear-row units and improves the overall power generation efficiency.

[0073] 2. Power prediction accuracy has been significantly improved, supporting precise scheduling:

[0074] Based on wake analysis results, unit-level multi-timescale power prediction is achieved, with ultra-short-term prediction RMSE as low as 5.8% and ultra-short-term prediction RMSE as low as 8.6%, which is far superior to traditional methods, providing a reliable basis for power grid dispatch and improving the accuracy of dispatch command tracking.

[0075] 3. Achieve synergy between wind turbines, farms, and the grid to enhance grid connection stability:

[0076] Using grid dispatch instructions as hard constraints, it strictly limits power deviation and rate of change, smooths out wind power output fluctuations, solves the problem of unstable grid connection voltage caused by intermittency and fluctuation of offshore wind power, adapts to the scenario of large reactive power of submarine cable charging, and ensures safe and stable grid connection.

[0077] 4. Closed-loop execution throughout the entire process, with strong real-time control:

[0078] With a control cycle as short as 1 minute, optimized calculations take little time, and instructions are issued in milliseconds and fed back in seconds, forming a closed loop of acquisition, analysis, prediction, scheduling, execution, and feedback. This meets the real-time control requirements of offshore wind farms and supports long-term stable automatic operation.

[0079] Typical application scenarios

[0080] 1. Large-scale offshore wind farm cluster control:

[0081] It is suitable for offshore wind farms of 300MW and above, and solves the problems of wake superposition and uneven power output caused by dense arrangement of multiple turbines, so as to achieve coordinated optimization of all units in the field.

[0082] 2. Grid dispatch-friendly grid connection control:

[0083] It is compatible with provincial / regional power grid dispatching systems, supports 15-minute cycle dispatching command response, and meets the technical requirements of power grid for wind power active power regulation, power smoothing, and fault support.

[0084] 3. High-precision operation adaptation under complex sea conditions:

[0085] For marine environments characterized by high salt spray, strong turbulence, and variable wind direction, the model parameters can be calibrated on-site to maintain high-precision wake calculation and power prediction, thereby improving control robustness under complex operating conditions.

[0086] 4. Digital Operation and Maintenance of Smart Wind Farms:

[0087] It can be integrated into the existing wind farm SCADA system without large-scale hardware modifications, quickly achieving dual upgrades of increased capacity and grid cooperation, reducing operation and maintenance costs and wind curtailment rate. Attached Figure Description

[0088] Figure 1 This is a flowchart of the offshore wind farm energy enhancement and grid control method in an embodiment of the present invention;

[0089] Figure 2 This is a flowchart of the wake model calculation process in an embodiment of the present invention;

[0090] Figure 3 This is a block diagram of the multi-objective optimization scheduling logic in an embodiment of the present invention;

[0091] Figure 4 This is a schematic diagram of the information flow of the hardware system in an embodiment of the present invention. Detailed Implementation

[0092] The present invention will be further illustrated below with reference to the accompanying drawings and specific embodiments. It should be understood that the following specific embodiments are for illustrative purposes only and are not intended to limit the scope of the invention.

[0093] like Figure 1 As shown, the present invention proposes a method for grid-connected control of offshore wind farms to enhance energy efficiency, comprising the following steps:

[0094] S1. Synchronous acquisition and preprocessing of wind resources and unit operation data;

[0095] S2. Accurate analysis of wake characteristics under all operating conditions based on adaptive Gaussian wake model;

[0096] S3. Unit-level multi-timescale power prediction based on wake analysis results;

[0097] S4. Real-time reception of power grid dispatch instructions and determination of constraint boundaries;

[0098] S5. Generation of multi-objective collaborative optimization scheduling instructions;

[0099] S6. Control command issuance and closed-loop execution of the entire process.

[0100] The present invention will now be described in detail with reference to specific embodiments:

[0101] This embodiment applies the proposed offshore wind farm capacity enhancement and grid coordination control method to a 900MW offshore wind farm in the Jiangsu coastal area of ​​my country. The wind farm is equipped with 180 5MW wind turbine units. The wind turbine units have a rated power of 5MW, a hub height of 95m, a rotor diameter of 186m, a starting wind speed of 3m / s, a rated wind speed of 13m / s, a cut-out wind speed of 25m / s, and a thrust coefficient of 0.8339 under rated operating conditions.

[0102] In this embodiment, the control cycle of the offshore wind farm capacity enhancement and grid coordination control method is set to 1 minute, and the grid dispatch command update cycle is 15 minutes. The specific implementation is as follows:

[0103] S1. Synchronous acquisition and preprocessing of wind resource and unit operation data:

[0104] Two types of core data are collected simultaneously through the distributed acquisition nodes of the wind farm SCADA system, with a sampling period of 1 minute.

[0105] The collected raw data is preprocessed, outliers are removed using the 3σ criterion, missing data is filled using linear interpolation, the two types of data are aligned in millisecond time series, and after data standardization, a standardized dataset is output and transmitted to the wake analysis stage in step S2, the power prediction stage in step S3, and the optimization scheduling stage in step S4, respectively.

[0106] S2. Precise analysis of wake characteristics under all operating conditions based on wake model:

[0107] Wake width calculation:

[0108] The wake width related parameters are calculated using equations (1) and (2). In this embodiment, the rotor diameter is... =186m, rated thrust coefficient =0.8339, the initial wake width is calculated. =152.7m; the wake attenuation coefficient k was calibrated to 0.048 based on field measurement data, which is suitable for the sea and sea conditions of this wind farm.

[0109] ;

[0110] ;

[0111] in, The diameter of the wind turbine, The thrust coefficient is the rated thrust coefficient. The initial wake width, The wake attenuation coefficient is... This is the distance along the axis of the wind turbine;

[0112] Calculation of speed loss under normal operating conditions:

[0113] The axial velocity deficit at any position in the wake region of the wind turbine under normal operating conditions is calculated using equation (3):

[0114] ;

[0115] in, Radial distance of the wake cross section; Wake width;

[0116] Calculation of wake deflection and speed loss under yaw conditions:

[0117] For units with yaw angles, the lateral offset of the wake centerline is calculated using equation (4), and the speed deficit under yaw conditions is calculated using equation (5), thus achieving accurate quantification of the wake characteristics under yaw conditions:

[0118] ;

[0119] ;

[0120] in, This is the lateral offset of the wake centerline. This refers to the yaw angle of the wind turbine. The height coordinates of the calculation point are used to determine the relative position of that point within the vertical spread range of the wake;

[0121] Additional turbulence intensity calculation:

[0122] The additional turbulence intensity in the wake region is calculated using equation (6), fully considering the impact of the wake on the turbulence intensity of the unit:

[0123] ;

[0124] in, To add turbulence intensity to the wake, The original turbulence intensity of the incoming flow.

[0125] In this embodiment, the wake model was validated in the field. Measurement points were set at positions 3D, 5D, 7D, and 10D downstream of the wind turbine. The relative errors between the model-predicted wind speed and the measured wind speed were all within 2%, meeting the accuracy requirements for engineering applications. The validation results are shown in Table 1.

[0126] Measurement point location Measured wind speed (m / s) Model-predicted wind speed (m / s) Relative error (%) 3D downstream of wind turbine 9.2 9.05 1.63 5D downstream of wind turbine 9.8 9.72 0.82 Downstream of wind turbine 7D 10.1 10.25 1.49 10D downstream of the wind turbine 10.5 10.68 1.71

[0127] Table 1

[0128] S3. Unit-level multi-timescale power prediction based on wake analysis results:

[0129] Based on the inflow wind speeds of each unit obtained from S2, and combined with the wind turbine's factory power curve model, power predictions were completed for two time scales:

[0130] Ultra-short-term power prediction: The prediction time scale is 1 minute to 15 minutes, the prediction step is 1 minute, and the maximum, minimum and average power prediction values ​​for each unit are output every minute.

[0131] Ultra-short-term power prediction: The prediction time scale is 15 minutes to 4 hours, the prediction step is 15 minutes, and it matches the update cycle of the power grid dispatch command. It outputs the power prediction range and the assessment results of the adjustability of each unit.

[0132] In this embodiment, the full-year operating data of the wind farm in 2024 was selected for verification. The RMSE of the ultra-short-term power prediction of this device was 5.8%, and the prediction accuracy reached 94.2%; the RMSE of the ultra-short-term power prediction was 8.6%, which is far better than the prediction effect of the traditional Jensen wake model. The comparison results are shown in Table 2:

[0133] Prediction methods Ultra-short-term RMSE (%) Ultra-short-term RMSE (%) Traditional method (Jensen) 12.5 18.3 Method of the present invention 5.8 8.6

[0134] Table 2

[0135] S4. Real-time reception of power grid dispatch instructions and determination of constraint boundaries:

[0136] Through the power dispatch data network, the system receives the total active power dispatch instructions for wind farms issued by the power grid dispatch system in real time. The instruction update cycle is 15 minutes. At the same time, it receives the grid dispatch status signal. Subsequent optimized dispatch steps are only executed when the grid dispatch is in operation.

[0137] Using the received power dispatch command from the power grid as a hard constraint, the power deviation between the current total output of the wind farm and the dispatch command is calculated using equation (7). Meanwhile, the power deviation is calculated using equation (8). Apply rate of change constraints:

[0138] (7);

[0139] (8);

[0140] in, This represents the total output deviation of the wind farm. This represents the current actual total output of the wind farm. The power grid dispatcher issues a total active power command.

[0141] In this embodiment, the power change limit for a single control cycle of the wind farm is set to 10% of the installed capacity, i.e., 90MW, to determine the power regulation target and constraint boundary for the current control cycle.

[0142] S5. Generation of multi-objective collaborative optimization scheduling instructions:

[0143] Using the power regulation target determined in S4 as a constraint, and taking the unit power fluctuation coefficient, wind curtailment rate, and power regulation margin as core evaluation indicators, combined with the power prediction results in S3 and the real-time operating status of the units in S1, cluster collaborative control commands are generated. The specific process is as follows:

[0144] First, calculate the core evaluation indicators to provide a basis for scheduling decisions:

[0145] Based on the S2 wake analysis results, active wake deflection control is achieved by optimizing the yaw angle of the front wind turbines, reducing wake losses of the rear turbines, and increasing the total power generation of the entire site.

[0146] In this embodiment, the optimization time for solving the optimal yaw angle is only 18 seconds per control cycle, which meets the real-time requirement of a 15-minute control cycle.

[0147] Finally, active power adjustment commands and yaw angle optimization commands for each unit are generated, forming a complete cluster collaborative control command set.

[0148] S6. Control command issuance and closed-loop execution of the entire process:

[0149] The collaborative control commands generated by S5 are sent to the control cabinets of each wind turbine in real time via the wind farm industrial Ethernet. The turbines then execute the corresponding power regulation and yaw angle adjustment actions. Simultaneously, the operating data after the turbines execute the commands and the feedback results of the total output of the wind farm are collected to complete the closed-loop verification of the current control cycle and evaluate the tracking accuracy of the grid dispatch commands and the effect of power generation improvement.

[0150] The feedback data from the current control cycle is input into the standardized dataset of S1 to initiate iterative optimization for the next control cycle, thereby achieving closed-loop control of the entire process.

[0151] In this embodiment, the 900MW offshore wind farm is subjected to collaborative optimization control using this device. The results are compared with the traditional independent operation mode, as shown in Table 3:

[0152] Operating mode Annual electricity generation (GWh) Wake loss (%) Equivalent full-load hours (h) Independent operation 2456.8 12.5 2730 This method is collaboratively optimized. 2712.5 7.8 3014

[0153] Table 3

[0154] The verification results show that after adopting the method of this invention, the wake loss of the wind farm is reduced from 12.5% ​​to 7.8%, the equivalent full-load operating hours are increased by 284 hours, and the annual power generation is increased by 255.7 GWh. Based on the offshore wind power grid connection price of RMB 0.302 / kWh, the annual electricity sales revenue increases by more than RMB 20.43 million. At the same time, it can save 927,000 tons of standard coal annually, and reduce carbon dioxide emissions by 2.31 million tons, sulfur dioxide emissions by 70,000 tons, and nitrogen oxide emissions by 35,000 tons, demonstrating outstanding economic and ecological benefits.

[0155] This invention also provides an apparatus for implementing the above-mentioned offshore wind farm energy enhancement and grid control method, comprising a hardware support system and a software functional system built on a B / S architecture. The software functional system is deployed on the host server of the hardware support system, wherein the software functional system built on the B / S architecture includes:

[0156] The wind resource monitoring module is used to collect meteorological parameters such as wind speed, wind direction, temperature, and air pressure from the wind farm's SCADA system in real time, and transmit them to the wake analysis module and power prediction module.

[0157] The wind turbine monitoring module is used to collect the operating status parameters of each wind turbine in the wind farm in real time from the wind farm SCADA system, including wind speed, rotational speed, real-time power, and yaw angle, and transmit them to the wake analysis module, power prediction module and energy coordination and control module.

[0158] The power grid command receiving module is used to receive wind farm power dispatch commands issued by the power grid dispatch system in real time and transmit them to the energy coordinated control module.

[0159] The wake analysis module has built-in wake model, wake velocity linear superposition model and turbulence field square sum superposition model. It is used to calculate the wake velocity distribution, turbulence intensity distribution and wake superposition effect of each wind turbine under normal operation and yaw conditions based on wind resources and wind turbine operating parameters, and output the wake analysis results to the power prediction module and energy collaborative control module.

[0160] The power prediction module is used to realize ultra-short-term and ultra-short-term power prediction at the wind farm unit level based on the wake analysis results and combined with the wind turbine power curve model, and outputs the power prediction results to the energy coordination and control module.

[0161] The energy coordinated regulation module has a built-in multi-objective coordinated optimization scheduling strategy. It takes the power grid power scheduling command as a constraint and the power fluctuation coefficient, wind curtailment rate and power regulation margin as core evaluation indicators. Combining the power prediction results and the real-time operating status of the wind turbines, it generates wind turbine cluster coordinated control commands and sends them to each wind turbine for execution, realizing three-level multi-objective coordinated optimization of wind-turbine-farm.

[0162] The hardware support system includes a host server, industrial switches, Ethernet communication units, data storage units, and security protection units, providing computation, communication, storage, and security protection support for the entire process of control method execution.

[0163] It should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention in any other way. Any modifications or equivalent changes made based on the technical essence of the present invention shall still fall within the scope of protection claimed by the present invention.

Claims

1. A method for grid-connected control to enhance the energy output of offshore wind farms, characterized in that, Includes the following steps: S1. Synchronous acquisition and preprocessing of wind resources and unit operation data; S2. Accurate analysis of wake characteristics under all operating conditions based on adaptive Gaussian wake model; S3. Unit-level multi-timescale power prediction based on wake analysis results; S4. Real-time reception of power grid dispatch instructions and determination of constraint boundaries; S5. Generation of multi-objective collaborative optimization scheduling instructions; S6. Control command issuance and closed-loop execution of the entire process.

2. The offshore wind farm energy enhancement and grid control method according to claim 1, characterized in that, The specific steps of S1 are as follows: S11, Data Acquisition: The wind farm's SCADA system uses distributed data acquisition nodes to synchronously collect wind resource and turbine operation data. S12, Data Preprocessing: Outliers are removed from the collected raw data using the 3σ criterion, missing data is filled using linear interpolation, and wind resource and unit operation data are aligned to millisecond-level time sequence. After completing the data standardization process, a standardized dataset is output and transmitted to steps S2, S3, and S4 respectively.

3. The offshore wind farm energy enhancement and grid control method according to claim 1, characterized in that, Step S2 is as follows: S21. Wake width calculation: The relevant parameters of the wake width are calculated using equations (1) and (2): ； ； in, The diameter of the wind turbine, The thrust coefficient is the rated thrust coefficient. The initial wake width, The wake attenuation coefficient is... This is the distance along the axis of the wind turbine; S22. Calculation of speed loss under normal operating conditions: The axial velocity deficit at any position in the wake region of the wind turbine under normal operating conditions is calculated using equation (3): ； in, Radial distance of the wake cross section; Wake width; S23. Calculation of wake deflection and speed loss under yaw conditions: For units with yaw angles, the lateral offset of the wake centerline is calculated using equation (4), and the speed loss under yaw conditions is calculated using equation (5). ； ； in, This represents the lateral offset of the wake centerline. This refers to the yaw angle of the wind turbine. The height coordinates of the calculation point are used to determine the relative position of that point within the vertical spread range of the wake; S24. Calculation of additional turbulence intensity: The additional turbulence intensity in the wake region is calculated using equation (6): ； in, To add turbulence intensity to the wake, The original turbulence intensity of the incoming flow.

4. The offshore wind farm energy enhancement and grid control method according to claim 1, characterized in that, Step S3 is as follows: Based on the analytical results of each unit obtained from S2, and combined with the wind turbine power curve model, power predictions were completed for two time scales: Ultra-short-term power prediction: The prediction time scale is 1 minute to 15 minutes, the prediction step is 1 minute, and the maximum, minimum and average power prediction values ​​for each unit are output every minute. Ultra-short-term power prediction: The prediction time scale is 15 minutes to 4 hours, the prediction step is 15 minutes, and it matches the update cycle of the power grid dispatch command. It outputs the power prediction range and the assessment results of the adjustability of each unit.

5. The offshore wind farm energy enhancement and grid control method according to claim 1, characterized in that, Step S4 is as follows: S41. Receive the total active power dispatch instructions for wind farms issued by the power grid dispatch system in real time through the power dispatch data network. The instruction update cycle is 15 minutes. At the same time, receive the switching status signals of the power grid dispatch. S42. Using the received power dispatch command from the power grid as a hard constraint, calculate the power deviation between the current total output of the wind farm and the dispatch command using equation (7). Meanwhile, the power deviation is calculated using equation (8). Apply rate of change constraints: （7）； （8）； in, This represents the total output deviation of the wind farm. This represents the current actual total output of the wind farm. The power grid dispatcher issues a total active power command.

6. The offshore wind farm energy enhancement and grid control method according to claim 5, characterized in that, In step S41, the subsequent optimized scheduling steps in step S5 can only be executed when the power grid dispatch is in operation.

7. The offshore wind farm energy enhancement and grid control method according to claim 1, characterized in that, Step S5 is as follows: Using the power regulation target determined in step S4 as a constraint, and taking the unit power fluctuation coefficient, wind curtailment rate, and power regulation margin as core evaluation indicators, combined with the power prediction results in step S3 and the real-time operating status of the units in S1, cluster collaborative control commands are generated. The specific process is as follows: S51. First, calculate the core evaluation indicators to provide a basis for scheduling decisions: Based on the wake analysis results of step S2, active wake deflection control is achieved by optimizing the yaw angle of the front wind turbine units, thereby reducing the wake loss of the rear units. S52. Finally, the active power adjustment command and yaw angle optimization command of each unit are generated to form a complete cluster collaborative control command set.

8. The offshore wind farm energy enhancement and grid control method according to claim 1, characterized in that, Step S6 is as follows: S61. The collaborative control command generated in step S5 is sent to the control cabinet of each wind turbine in real time via the wind farm industrial Ethernet. The control cabinet controls the turbine to perform the corresponding power regulation and yaw angle adjustment actions. S62. Synchronously collect the operating data of each unit after execution and the feedback results of the total output of the wind farm, complete the closed-loop verification of the current control cycle, and evaluate the tracking accuracy of the grid dispatch command and the effect of power generation improvement. S63. Input the feedback data of this control cycle into the standardized dataset of step S1, start the iterative optimization of the next control cycle, and realize closed-loop control of the entire process.

9. A grid-connected control device for enhancing the energy efficiency of an offshore wind farm according to any one of claims 1-8, characterized in that, It includes a wind resource monitoring module, a wind turbine monitoring module, a power grid command receiving module, a wake analysis module, a power prediction module, and an energy coordination and control module, among which: Wind resource monitoring module: Used to collect meteorological parameters such as wind speed, wind direction, temperature, and air pressure from the wind farm SCADA system in real time from the wind measurement towers in the offshore wind farm, and transmit them to the wake analysis module and power prediction module; The wind turbine monitoring module is used to collect the operating status parameters of each wind turbine in the wind farm in real time from the wind farm SCADA system, including wind speed, rotational speed, real-time power, and yaw angle, and transmit them to the wake analysis module, power prediction module and energy coordination and control module. The power grid command receiving module is used to receive wind farm power dispatch commands issued by the power grid dispatch system in real time and transmit them to the energy coordinated control module. Wake analysis module: Built-in wake model, wake velocity linear superposition model and turbulence field square sum superposition model, used to calculate the wake velocity distribution, turbulence intensity distribution and wake superposition effect of each wind turbine under normal operation and yaw conditions based on wind resources and wind turbine operating parameters, and output wake analysis results to power prediction module and energy collaborative control module. Power prediction module: Receives and, based on wake analysis results and combined with the wind turbine power curve model, realizes ultra-short-term and ultra-short-term power prediction at the wind farm turbine level, and outputs the power prediction results to the energy coordination and control module. Energy Coordinated Regulation Module: It has a built-in multi-objective coordinated optimization scheduling strategy. It takes the power grid power scheduling command as a constraint and the power fluctuation coefficient, wind curtailment rate and power regulation margin as core evaluation indicators. It combines the power prediction results and the real-time operating status of wind turbine units to generate wind turbine cluster coordinated control commands and send them to each wind turbine unit for execution.

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