A Flexible DC Transmission Optimization Control Method Suitable for Offshore Wind Power Integration

Abstract

The present invention discloses an optimized control method for flexible DC power transmission applicable to the access of offshore wind power, which relates to the technical field of offshore power transmission control. The steps of this method include: collecting the longitude and latitude coordinates and the offshore distance of each wind farm, and collecting the environmental parameters of each wind farm; setting a prediction strategy for the complexity of the sea surface environment to obtain an index of the complexity of the marine environment; calculating the relative positions and mutual dependencies of each wind farm in the entire area according to the spatial distribution of the wind farms to obtain the position of the wind farm cluster, and calculating its neighborhood coupling factor; constructing a parameter allocation model for the wind farms, and formulating corresponding current transmission allocation strategies for each wind farm according to the marine environment complexity index and the neighborhood coupling factor of each wind farm. The present invention solves the problem that in far-offshore wind farms, communication delay and data loss seriously affect the real-time performance of the control system.

Description

Technical Field

[0001] The present invention relates to the technical field of offshore power transmission control, and specifically to an optimized control method for flexible DC power transmission applicable to the connection of offshore wind power. Background Technique

[0002] Since offshore wind farms are often far from the shore and have complex environments, their connection to onshore power grids and large-scale long-distance power transmission face a series of challenges. As an advanced power transmission technology for solving long-distance and multi-terminal interconnection problems, flexible DC power transmission has become an important technical means for the connection of offshore wind power due to its characteristics of flexible regulation, commutation without failure, and independent control of active and reactive power. However, in practical applications, there are still some deficiencies and technical problems in this technology:

[0003] A flexible DC power transmission system usually involves the coordinated control of an offshore converter station and an onshore converter station, as well as multiple units within the wind farm; high-precision and high-speed information interaction and coordination are required among the control units to ensure the stable operation of the entire system under fault and fluctuating conditions. Existing methods still have problems such as information delay and inconsistent control strategies in coordinating the AC side and the DC side, as well as the coordinated scheduling among control units at all levels, which will directly affect the power distribution and stable regulation of the system.

[0004] There are natural uncertainties in offshore wind farms. For example, environmental parameters such as wind speed, sea current, and wave have high randomness and time-variability. Existing control methods often rely on offline modeling or predictive control based on fixed parameters, making it difficult to capture real-time environmental changes in a timely manner, resulting in untimely responses of power transmission control strategies in the face of emergencies and even causing system instability. In addition, data acquisition and communication delay are also problems that need to be urgently solved in current practical engineering.

[0005] Therefore, the present invention provides an optimized control method for flexible DC power transmission applicable to the connection of offshore wind power. Summary of the Invention

[0006] The purpose of the present invention is to provide an optimized control method and system for flexible DC power transmission applicable to the connection of offshore wind power to solve the existing problems mentioned in the above background technique.

[0007] To achieve the above purpose, the present invention provides the following technical solution: An optimized control method for flexible DC power transmission applicable to the connection of offshore wind power, including the following steps:

[0008] S1. Collect the longitude and latitude coordinates and the distance from the shore of each wind farm, and collect the environmental parameters of each wind farm;

[0009] S2. Set a prediction strategy for the complexity of the sea surface environment to obtain an index of the complexity of the marine environment;

[0010] S3. Calculate the relative positions and mutual dependencies of each wind farm in the entire region based on the spatial distribution of the wind farms to obtain the positions of the wind farm clusters, and calculate their neighborhood coupling factors;

[0011] S4. Construct a wind farm parameter allocation model, and formulate corresponding current transmission allocation strategies for each wind farm according to the marine environment complexity index and neighborhood coupling factor of each wind farm.

[0012] A further improvement of the present invention lies in that the sea surface environment complexity prediction strategy includes a historical complexity coefficient construction sub-strategy, a perturbation factor construction sub-strategy, an underwater acoustic resonance index sub-strategy, and a marine environment complexity index calculation sub-strategy; the historical complexity coefficient construction sub-strategy includes collecting the energy utilization rate during the historical operation of each wind farm, and defining the historical complexity coefficient HCC through the normalized offshore distance and the normalized energy utilization rate; the perturbation factor construction sub-strategy includes recording sea surface environment data, where the sea surface environment data includes the sea surface wave height, wind speed, seawater temperature, and salt fog concentration of each wind farm, and mapping the sea surface environment data into a non-linear function to obtain the perturbation factor ref.

[0013] A further improvement of the present invention lies in that the underwater acoustic resonance index sub-strategy includes obtaining the sea wave excitation frequency band and the sea current excitation frequency band ; the specific process includes: collecting the historical sea wave periods, and obtaining the sea wave excitation frequency band by calculating the reciprocal of the mean of the collected historical sea wave periods; deploying an underwater acoustic sensor array to collect ocean ambient signals, and the specific process includes performing a fast Fourier transform on the ocean ambient signals to obtain the ocean sound spectrum S(f), dividing the spectrum into N frequency bands according to a set step size, calculating the correlation coefficients of all frequency bands with the perturbation factor, and extracting the frequency bands with correlation coefficients greater than the set correlation coefficient threshold as the sea current excitation frequency bands; combining the sea wave excitation frequency band and the sea current excitation frequency band to obtain the acoustic-electric coupling term Ari, and the calculation formula is: where, represents the i-th sea current excitation frequency band, and n represents the number of sea current excitation frequency bands.

[0014] A further improvement of the present invention lies in that the marine environment complexity index calculation sub-strategy constructs a marine environment complexity index by combining the historical complexity coefficient, the perturbation factor, and the acoustic-electric coupling term, and is specifically expressed as:

[0015] ;

[0016] where, represents the normalized acoustic-electric coupling term, represents the set acoustic-electric coupling gain, represents the non-linear mapping function.

[0017] A further improvement of the present invention lies in that the specific steps of step S3 include:

[0018] S31. Take the longitude and latitude coordinates of each wind farm as input vectors and send them into the wind farm zoning strategy to obtain M wind farm clusters;

[0019] S32. For each wind farm cluster β, calculate its neighborhood coupling factor , where is the Euclidean distance between wind farm α and β, α represents any wind farm in wind farm cluster β, represents the set characteristic scale parameter.

[0020] A further improvement of the present invention lies in that the wind farm zoning strategy includes a preliminary mapping output layer and an abstract clustering output layer; the specific steps include:

[0021] S311. The preliminary mapping output layer maps the original space coordinates to a two-dimensional space, and uses competitive learning to form K candidate wind farm clusters for adjacent wind farms;

[0022] S312. The abstract clustering output layer extracts the centers of the K candidate wind farm clusters;

[0023] S313. For each wind farm, find the center of the candidate wind farm cluster closest to it through the Euclidean distance, and let participate in the competitive update;

[0024] S314. Coordinately update the original cluster center of each wind farm and through the neighborhood function;

[0025] S315. When the absolute value of the difference between the marine environment complexity index of the updated wind farm cluster center and the mean value of the marine environment complexity index of the wind farm cluster is less than the set cluster difference threshold, stop the competition.

[0026] A further improvement of the present invention lies in that the wind farm parameter allocation model includes defining the current transmission allocation weight for each wind farm cluster, where represents the mean value of the marine environment complexity index of the β-th wind farm cluster; extract the next transmission-back cluster set of each wind farm cluster. At this time, calculate the current transmission allocation weight of each wind farm cluster in the next transmission-back cluster set, and allocate the transmission current of each wind farm cluster according to the current transmission allocation weight of each wind farm cluster in the next transmission-back cluster set.

[0027] The present invention is further improved in that the wind farm parameter allocation model also includes an adaptive marine environment complexity index updating sub-model; the adaptive marine environment complexity index updating sub-model extracts the mean value of the marine environment complexity index and the mean value of the acoustic-electric coupling term of the β-th wind farm cluster in the past year, and sets a target gain function F, which is expressed as ,in, represents the scale factor, Indicates the environmental benchmark threshold. = MECI, output the historical MECI median value of the β-th wind farm cluster, represents the Ari gain weight, Represents the gain function, integrates the target gain function into the wind farm controller, and uses discrete time Make adaptive adjustments.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] The present invention firstly realizes acoustic-electric coupling control by introducing underwater acoustic feedback, which can identify early warning signals of environmental changes and adjust the current transmission strategy of the wind farm through adaptive control, thereby reducing the risk under extreme working conditions and protecting the equipment from failure due to overload or abnormal environmental impact.

[0030] The neighborhood coupling factor between wind farms is introduced, and the dynamic weight of each node is calculated in real time using a distributed collaborative optimization algorithm, so that the current distribution strategy can fully reflect the relative position and cluster density of each wind farm in the entire network, which helps to reduce local operation risks while balancing the transmission load in the entire region and improving the overall stability and economy of the system.

[0031] By deploying edge computing units at each wind farm node, data preprocessing, indicator calculation and local decision-making are all completed on-site, and overall coordination is achieved through distributed algorithms, thereby greatly improving real-time and fault-tolerance capabilities, ensuring that the system can still maintain stable operation when communication anomalies or node failures occur. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 This is a flow chart of a flexible direct current transmission optimization control method applicable to offshore wind power access according to the present invention;

[0033] Figure 2 A flowchart of neighborhood coupling factor calculation of a flexible direct current transmission optimization control method suitable for offshore wind power access according to the present invention; DETAILED DESCRIPTION

[0034] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features therein are detailed descriptions of the technical solution of the present invention, rather than limitations on the technical solution of the present invention. Without conflict, the technical features in the embodiments of the present invention and the embodiments can be combined with each other.

[0035] The term "and / or" is merely a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " generally indicates that the associated objects before and after are in an "or" relationship.

[0036] Embodiment 1

[0037] Figure 1 The flowchart of an optimized control method for flexible DC transmission applicable to offshore wind power access disclosed in this embodiment is shown as follows, and the steps are as follows:

[0038] S1. Collect the longitude and latitude coordinates and the offshore distance of each wind farm, and collect the environmental parameters of each wind farm;

[0039] S2. Set the prediction strategy for the complexity of the sea surface environment to obtain the ocean environment complexity index;

[0040] S3. Calculate the relative position and mutual dependence of each wind farm in the entire area according to the spatial distribution of the wind farms to obtain the position of the wind farm cluster, and calculate its neighborhood coupling factor;

[0041] S4. Construct a wind farm parameter allocation model, and formulate corresponding current transmission allocation strategies for each wind farm according to the ocean environment complexity index and neighborhood coupling factor of each wind farm.

[0042] The prediction strategy for the complexity of the sea surface environment includes a historical complexity coefficient construction sub-strategy, a perturbation factor construction sub-strategy, an underwater acoustic resonance index sub-strategy, and an ocean environment complexity index calculation sub-strategy;

[0043] The historical complexity coefficient construction sub-strategy includes collecting the energy utilization rate during the historical operation of each wind farm, and defining the historical complexity coefficient HCC through the standardized offshore distance and the standardized energy utilization rate; , where represents the normalized value of the offshore distance, d represents the offshore distance, represents the normalized value of the average historical energy utilization rate, represents the average historical energy utilization rate, represents the adjustment parameter, which is used to amplify the non-linear effect of the ratio of the two;

[0044] The disturbance factor construction sub-strategy includes recording the sea surface wave height Hsu, wind speed Usw, seawater temperature and salt fog concentration Ssc of each wind farm, and mapping the sea surface environmental data into a non-linear function to obtain the disturbance factor ref; specifically expressed as:

[0045] ;

[0046] where a, b, c, d, e are design constants, and the non-linear and periodic characteristics of the sea conditions are captured through non-linear functions such as sin() and exp().

[0047] The underwater acoustic resonance index sub-strategy includes obtaining the sea wave excitation frequency band and the sea current excitation frequency band ; the specific process includes:

[0048] Collect historical sea wave periods, and obtain the sea wave excitation frequency band by calculating the reciprocal of the mean of the collected historical sea wave periods; affected by wind, tide and waves, the disturbance of sea waves usually also concentrates in the medium and low frequency bands;

[0049] Deploy an underwater acoustic sensor array to collect ocean ambient noise signals. The specific process includes performing a fast Fourier transform on the ocean ambient noise signals to obtain the ocean sound spectrum S(f), dividing the spectrum into N frequency bands at a set step size. Since the sea current is affected by parameters such as wave height, wind speed, seawater temperature and salt fog concentration, the sea current excitation frequency band is obtained by calculating the correlation coefficient between all frequency bands of the spectrum and the wind speed, calculating the correlation coefficient between all frequency bands and the disturbance factor, and extracting the frequency bands with a correlation coefficient greater than the set correlation coefficient threshold as the sea current excitation frequency band;

[0050] Combining the sea wave excitation frequency band and the sea current excitation frequency band to obtain the acoustic-electric coupling term Ari, and the calculation formula is: , where represents the i-th sea current excitation frequency band, and n represents the number of sea current excitation frequency bands.

[0051] The marine environmental complexity index calculation sub-strategy constructs a marine environmental complexity index by combining the historical complexity coefficient, disturbance factor and acoustic-electric coupling term. In the past, systems often only considered part of the indicators in real-time data or historical data in the transmission current distribution, ignoring the coupling effect between the two. The present invention first constructs a historical complexity coefficient HCC generated by the offshore distance and power generation utilization efficiency using historical data, and then combines the real-time environmental disturbance ref and acoustic feedback, and organically couples the two to form a comprehensive marine environmental complexity index, specifically expressed as:

[0052] ;

[0053] where represents the normalized acoustic-electric coupling term, represents the set acoustic-electric coupling gain, represents the non-linear mapping function, expressed as , used to convert the acoustic feedback into an adjustment term, represents the set reference value of the acoustic-electric coupling term.

[0054] Embodiment 2

[0055] Based on the technical solution of Embodiment 1, this embodiment provides the specific calculation process of the neighborhood coupling factor in step S3. Figure 2 The flowchart of calculating the neighborhood coupling factor of an optimized control method for flexible DC transmission applicable to offshore wind power access disclosed in this embodiment is shown as follows:

[0056] S31. Take the longitude and latitude coordinates of each wind farm as the input vector and send it into the wind farm zoning strategy to obtain M wind farm clusters, so that the wind farms within each cluster have a relatively close geographical distance and high similarity;

[0057] The wind farm zoning strategy includes a preliminary mapping output layer and an abstract clustering output layer; the specific steps are as follows:

[0058] S311. The preliminary mapping output layer maps the original space coordinates to a two-dimensional space, and uses competitive learning to form K candidate wind farm clusters for adjacent wind farms;

[0059] S312. The abstract clustering output layer extracts the centers of the K candidate wind farm clusters ;

[0060] S313. For each wind farm, find the center of the candidate wind farm cluster closest to it through the Euclidean distance , and let participate in the competitive update, and the update formula is:

[0061] ;

[0062] where represents the set learning rate that gradually decays with iteration. Only when the current input wind farm is closest to the center of a certain wind farm cluster candidate , will this center be selected as the "winner" and updated.

[0063] S314. To enable adjacent candidate centers to be activated simultaneously, perform collaborative update on the original cluster center of each wind farm and through the neighborhood function:

[0064] ;

[0065] Among them, represents the neighborhood function; the neighborhood function in the present invention is a Gaussian function.

[0066] S315. When the absolute value of the difference between the updated marine environment complexity index of the wind farm cluster center and the mean value of the marine environment complexity index of the wind farm cluster is less than the set cluster difference threshold, stop the competition.

[0067] The wind farm zoning strategy has the ability of adaptive learning, can dynamically adjust the clustering result of the wind farm cluster according to real-time data, so that a stable clustering center can be obtained even when the position data noise of the offshore wind farm is large, effectively reducing the system regulation error;

[0068] Using the clustering result of the wind farm cluster, a collaborative mechanism for different nodes within the region can be constructed to achieve distributed optimization, ensuring that when the risk of the local dense area is high, the transmission current in this area is automatically reduced, while fully exerting the output in the local sparse area to achieve global optimal allocation.

[0069] S32. For each wind farm cluster, name it with its cluster center β, and calculate its neighborhood coupling factor , where is the Euclidean distance between wind farm α and β, α represents any wind farm in wind farm cluster β, represents the set characteristic scale parameter, which is used to control the attenuation rate of the distance influence. The neighborhood coupling factor represents the superposition of exponential decay functions. The closer nodes contribute more to the factor, and the contribution of the distant nodes decays;

[0070] This embodiment comprehensively considers the spatial relationship between wind farms in a non-linear manner, extracts local density information, and each wind farm cluster can calculate through local information and communication with neighboring nodes, reducing the centralized computing pressure; and the neighborhood coupling factor can reflect the degree of mutual dependence within the local cluster, providing a basis for introducing collaborative control into the current distribution strategy.

[0071] Embodiment 3

[0072] Based on the technical solutions of Embodiment 1 and Embodiment 2, this embodiment provides the specific construction process of the wind farm parameter allocation model in step S4, and the steps are as follows:

[0073] The wind farm parameter allocation model includes defining the current transmission allocation weight of each wind farm cluster , where Denote the mean value of the marine environment complexity index of the β-th wind farm cluster; allocate the transmission current according to the current transmission distribution weights of the wind farm clusters through which the current return path of each wind farm cluster passes. For example, the A wind farm cluster can select Q wind farm clusters as the next return path points. At this time, calculate the current transmission distribution weights of these Q wind farm clusters, and calculate the proportion of the current transmission distribution weight of the λ-th sub-power plant cluster in the Q wind farm clusters in the total sum of the current transmission distribution weights of the Q wind farm clusters, and multiply it by the total amount of current transmission of the A wind farm cluster to obtain the transmission current of this wind farm cluster.

[0074] When the MECI is high, that is, the environmental complexity risk is large, the weight decreases; when the neighborhood coupling factor is high, it indicates that this wind farm cluster is located in a relatively dense area and there is a synergistic complementary effect. Therefore, reduce the single-point weight to achieve global collaborative allocation.

[0075] In the case of extreme environments or communication loss, each wind farm cluster may not be able to obtain global information. Therefore, the wind farm parameter allocation model is equipped with an adaptive marine environment complexity index update sub-model;

[0076] The adaptive marine environment complexity index update sub-model extracts the mean value of the marine environment complexity index and the mean value of the acoustic-electric coupling term of the β-th wind farm cluster in the past year, and sets a target gain function F, which is expressed as , where represents the scaling factor, which is used to adjust the influence of MECI on the output, represents the environmental benchmark threshold. In this way, the function F will automatically map the current environment and acoustic state to a desired coupling gain target value. When = MECI, output the historical MECI intermediate value of the β-th wind farm cluster, represents the Ari gain weight, which is used to amplify the sensitivity of Ari changes, represents the gain function, which is expressed as so that when Ari is large, there will be a sharp fluctuation;

[0077] Integrate the target gain function into the wind farm controller and adaptively adjust through discrete time; the adjustment process is expressed as: , where represents the learning rate, which determines the update amplitude. Selecting an appropriate can quickly respond to local environmental changes while maintaining the stability of the system. The update formula adopts the idea similar to discrete-time first-order filtering for smooth non-linear adjustment.

[0078] In this embodiment, a distributed collaborative network is constructed among wind farm clusters. Even in the case of partial communication interruption, each node can independently run the sub-model for updating the adaptive ocean environment complexity index and automatically fuse the information of each wind farm cluster when communication is restored to maintain global consistency.

[0079] The set values such as the threshold and weight can be set according to the default settings of the present invention or can be set by the operator himself.

[0080] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0081] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for realizing the functions specified in one Figure 1 flow or multiple flows and / or blocks Figure 1 block or multiple blocks.

[0082] These computer program instructions can also be stored in a computer-readable memory capable of guiding a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means realizes the functions specified in one Figure 1 flow or multiple flows and / or blocks Figure 1 block or multiple blocks.

[0083] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for realizing the functions specified in one Figure 1 flow or multiple flows and / or blocks Figure 1 block or multiple blocks.

[0084] The embodiments of the present invention have been described above in conjunction with the accompanying drawings. However, the present invention is not limited to the above specific embodiments. The above specific embodiments are merely illustrative rather than restrictive. Under the inspiration of the present invention, those of ordinary skill in the art can also make many forms without departing from the spirit of the present invention and the scope protected by the claims. All of these fall within the protection scope of the present invention.

Claims

1. A flexible DC transmission optimization control method applicable to the access of offshore wind power, characterized in that: It includes the following steps: S1. Collect the longitude and latitude coordinates and the offshore distance of each wind farm, and collect the environmental parameters of each wind farm; S2. Set the prediction strategy for the complexity of the sea surface environment to obtain the index of the complexity of the marine environment; S3. Calculate the relative position and mutual dependence of each wind farm in the entire region according to the spatial distribution of the wind farms to obtain the location of the wind farm cluster, and calculate its neighborhood coupling factor; S4. Construct a parameter allocation model for the wind farms, and formulate corresponding current transmission allocation strategies for each wind farm according to the marine environment complexity index and the neighborhood coupling factor of each wind farm; The prediction strategy for the complexity of the sea surface environment includes a historical complexity coefficient construction sub-strategy, a perturbation factor construction sub-strategy, an underwater acoustic resonance index sub-strategy, and a marine environment complexity index calculation sub-strategy; the historical complexity coefficient construction sub-strategy includes collecting the energy utilization rate during the historical operation of each wind farm, and defining the historical complexity coefficient HCC through the standardized offshore distance and the standardized energy utilization rate; The perturbation factor construction sub-strategy includes recording the sea surface environment data, where the sea surface environment data includes the sea surface wave height, wind speed, seawater temperature, and salt fog concentration of each wind farm, and mapping the sea surface environment data into a non-linear function to obtain the perturbation factor ref; The underwater acoustic resonance index sub-strategy includes obtaining the frequency bands excited by ocean waves and the frequency bands excited by ocean currents ; The specific process includes: collecting historical wave periods, and obtaining the wave excitation frequency band by calculating the reciprocal of the mean of the collected historical wave periods; deploying an underwater acoustic sensor array to collect ocean ambient signals. The specific process includes performing a fast Fourier transform on the ocean ambient signals to obtain the ocean sound spectrum S(f), dividing the spectrum into N frequency bands at a set step size, calculating the correlation coefficients of all frequency bands with the perturbation factor, and extracting the frequency bands with correlation coefficients greater than the set correlation coefficient threshold as the current excitation frequency bands; combining the wave excitation frequency band and the current excitation frequency band to obtain the acoustic-electric coupling term Ari, and the calculation formula is: , where represents the i-th current excitation frequency band, and n represents the number of current excitation frequency bands; The marine environment complexity index calculation sub-strategy constructs the marine environment complexity index by combining the historical complexity coefficient, the perturbation factor, and the sound-electricity coupling term, and is specifically expressed as: ； Among them, represents the normalized acoustic-electric coupling term, represents the set acoustic-electric coupling gain, represents the non-linear mapping function.

2. The optimized control method for flexible DC power transmission applicable to offshore wind power access according to claim 1, wherein: The specific steps of step S3 include: S31. Take the longitude and latitude coordinates of each wind farm as input vectors and send them into the wind farm zoning strategy to obtain M wind farm clusters; S32. For each wind farm cluster β, calculate its neighborhood coupling factor , where is the Euclidean distance between wind farm α and β, and α represents any wind farm in wind farm cluster β, represents the set characteristic scale parameter.

3. An optimized control method for flexible DC power transmission applicable to offshore wind power integration according to claim 2, characterized in that: The wind farm zoning strategy includes a preliminary mapping output layer and an abstract clustering output layer; The specific steps include: S311. The preliminary mapping output layer maps the original spatial coordinates to a two-dimensional space, and uses competitive learning to form K candidate wind farm clusters for adjacent wind farms; S312. The abstract clustering output layer extracts K candidate wind farm cluster centers ; S313. For each wind farm, find the candidate wind farm cluster center closest to it through the Euclidean distance , and let participate in the competitive update; S314. Coordinate the update of the original cluster center of each wind farm through the neighborhood function and perform collaborative update; S315. When the absolute value of the difference between the marine environment complexity index of the updated wind farm cluster center and the average value of the marine environment complexity index of this wind farm cluster is less than the set cluster difference threshold, stop the competition.

4. The optimized control method for flexible DC power transmission applicable to offshore wind power access according to claim 3, wherein: The wind farm parameter allocation model includes defining the current transmission allocation weight for each wind farm cluster , where represents the mean value of the marine environment complexity index of the β-th wind farm cluster; extract the next transmission cluster set of each wind farm cluster. At this time, calculate the current transmission allocation weight of each wind farm cluster in the next transmission cluster set, and allocate the transmission current of each wind farm cluster according to the current transmission allocation weight of each wind farm cluster in the next transmission cluster set.

5. The optimized control method for flexible DC power transmission applicable to the access of offshore wind power according to claim 4, wherein: The wind farm parameter allocation model further includes an adaptive marine environment complexity index update sub-model; the adaptive marine environment complexity index update sub-model extracts the mean value of the marine environment complexity index and the mean value of the acoustic-electric coupling term of the β-th wind farm cluster in the past year, and sets a target gain function F, expressed as , where represents the scale factor, represents the environmental benchmark threshold. When =MECI, the historical MECI intermediate value of the β-th wind farm cluster is output, represents the Ari gain weight, represents the gain function. The target gain function is integrated into the wind farm controller, and is adaptively adjusted through discrete time.

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