Offshore wind power direct current transmission control method and system

Abstract

The invention relates to the technical field of electric power engineering, in particular to an offshore wind power direct-current transmission control method and system, and the method comprises the steps: constructing a machine learning or artificial intelligence algorithm model, training the model through historical data to predict the change trend of the output power of a wind power plant, and dynamically adjusting a second threshold value according to real-time information and a model prediction result; comparing the power value sent by the wind power plant and obtained in real time with the dynamically adjusted second threshold value, and performing system mode switching according to a comparison result; in the mode switching process, a smooth transition strategy is designed, parameters are adjusted step by step, a state monitoring and feedback mechanism is introduced, the system state is tracked in real time, and the transition strategy is adjusted according to the real-time feedback of the system state. By dynamically adjusting the second threshold value, frequent mode switching of the system caused by power fluctuation of the wind power plant is reduced, and the stability and reliability of the system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power engineering, and in particular to an offshore wind power direct current transmission control method and system. Background Art

[0002] The transmission system of offshore wind power pays special attention to the sending-end converter station. The traditional converter station topology generally uses thyristor-based diode rectifiers. For example, diode rectifiers are small in size and have low losses, and can transmit ultra-high-power offshore wind power. However, since diode rectifiers have no control dimension and are unidirectional transmission topologies, they cannot provide stable AC power for offshore wind farms, nor can they provide black start energy. If a diode rectifier is used as the offshore converter station topology alone, not only is it necessary to equip an auxiliary cable for wind farm startup to provide black start energy, but the wind turbines in the wind farm also need to change the original grid-following control strategy to a grid-forming control strategy. At the same time, the diode rectifier also needs to be equipped with additional filters and reactive power compensation devices, making it difficult to implement the solution of using diodes alone as offshore converter stations.

[0003] Existing publication number CN113452061A discloses an offshore wind power DC transmission system and a control method thereof, wherein the AC side of a diode rectifier and the AC side of a voltage source converter of the system are respectively connected to an offshore power grid via transformers connected thereto, and the AC side of the voltage source converter is connected in parallel with the AC side of the diode rectifier; the DC side of the voltage source converter is connected in series with the DC side of the diode rectifier, and the DC side of the voltage source converter and the DC side of the diode rectifier are connected via a DC transmission line formed by a submarine DC cable to transmit electric energy to an onshore converter station; one end of the auxiliary cable is connected to the AC output side of the offshore power grid, and the other end is connected to the AC side of the onshore power grid or other wind farms that have been put into operation, so as to provide black start energy for the offshore wind farm. During operation, the power value generated by the wind farm and the rated power value of the offshore wind power DC transmission system are obtained; it is determined whether the power value generated by the wind farm is lower than the second threshold of the rated power value of the offshore wind power DC transmission system; if the power value generated by the wind farm is lower than the second threshold of the rated power value of the offshore wind power DC transmission system, the diode rectifier is controlled to stop transmitting active power, and the onshore converter station and the voltage source converter are controlled to adopt a constant voltage control mode; if the power value generated by the wind farm is not lower than the second threshold of the rated power value of the offshore wind power DC transmission system, the onshore converter station is controlled to adopt a constant current control mode, and the AC side voltage of the offshore wind power DC transmission system is adjusted in real time according to the power value generated by the offshore wind farm to adjust the active power transmitted by the diode rectifier. The converter station is small in size and is suitable for applications where offshore wind power DC is transmitted. The wind farm does not need to change the original grid-following control strategy, and has a wider range of applications.

[0004] However, during use of the above-mentioned offshore wind power DC transmission system and control method thereof, when the power value emitted by the wind farm approaches the second threshold, the system may frequently switch between different control modes, which not only increases the difficulty of control but also may affect the response speed and stability of the system. Summary of the invention

[0005] The object of the present invention is to provide an offshore wind power direct current transmission control method and system, which solves the problem that during the use of the existing offshore wind power direct current transmission system and its control method, when the power value emitted by the wind farm approaches a second threshold, the system may frequently switch between different control modes, which not only increases the difficulty of control but also may affect the response speed and stability of the system.

[0006] To achieve the above object, the present invention provides an offshore wind power direct current transmission control method, comprising the following steps:

[0007] Obtain the power value generated by the wind farm in real time, obtain the rated power value of the offshore wind power DC transmission system, and collect historical data and real-time information on the wind farm's output power;

[0008] Build a machine learning or artificial intelligence algorithm model, use historical data to train the model to predict the changing trend of wind farm output power, and dynamically adjust the second threshold value based on real-time information and model prediction results;

[0009] Comparing the power value generated by the wind farm obtained in real time with the second threshold value adjusted dynamically, and switching the system mode according to the comparison result;

[0010] During the mode switching process, a smooth transition strategy is designed to gradually adjust parameters, and a state monitoring and feedback mechanism is introduced to track the system status in real time and adjust the transition strategy based on the real-time feedback of the system status.

[0011] The steps of obtaining the power value generated by the wind farm in real time, obtaining the rated power value of the offshore wind power DC transmission system, and collecting historical data and real-time information on the output power of the wind farm, further include:

[0012] Power monitoring sensors are installed on each wind turbine in the wind farm to monitor and record the power value generated by the wind turbine in real time through the sensors;

[0013] Find and record the rated power value of the system from the preliminary design documents, technical specifications, equipment manuals or key equipment of the offshore wind power DC transmission system;

[0014] Extracting wind farm output power data for a past period of time from a data storage system or database of the wind farm;

[0015] Obtain current meteorological information in real time through meteorological monitoring stations or meteorological databases, and maintain communication with the power grid dispatching center or relevant departments to obtain power grid information in real time.

[0016] Among them, a machine learning or artificial intelligence algorithm model is constructed, and the model is trained using historical data to predict the change trend of the wind farm output power, and the second threshold is dynamically adjusted according to the real-time information and the model prediction result. The steps also include:

[0017] Preprocess historical data, including data cleaning, data normalization or standardization, feature selection or extraction;

[0018] Build corresponding machine learning or artificial intelligence models based on the selected algorithms and data characteristics;

[0019] Train the model using historical data.

[0020] Among them, a machine learning or artificial intelligence algorithm model is constructed, and the model is trained using historical data to predict the change trend of the wind farm output power, and the second threshold is dynamically adjusted according to the real-time information and the model prediction result. The steps also include:

[0021] Input the real-time wind farm output power data, meteorological information or grid stability requirements into the trained model for real-time prediction;

[0022] Analyze the changing trend of wind farm output power based on the model's prediction results;

[0023] Taking into account the fluctuation range of wind farm output power, the margin of system rated power, grid stability requirements and weather forecast factors, the second threshold is dynamically adjusted according to the forecast results;

[0024] The adjusted second threshold is updated to the system.

[0025] The power value generated by the wind farm obtained in real time is compared with the second threshold value after dynamic adjustment, and the system mode is switched according to the comparison result. The step also includes:

[0026] If the power value generated by the wind farm is lower than the second threshold, the diode rectifier is controlled to stop transmitting active power, and the onshore converter station and the voltage source converter are controlled to adopt a constant voltage control mode;

[0027] If the power value generated by the wind farm is not lower than the second threshold, the onshore converter station is controlled to adopt a constant current control mode, and the AC side voltage of the offshore wind power DC transmission system is adjusted in real time according to the power value generated by the offshore wind farm to adjust the active power transmitted by the diode rectifier.

[0028] In the process of mode switching, a smooth transition strategy is designed, parameters are gradually adjusted, and a state monitoring and feedback mechanism is introduced to track the system state in real time, and the transition strategy is adjusted according to the real-time feedback of the system state. The steps also include:

[0029] Install status monitoring equipment at key locations of the system to reflect changes in system status in real time;

[0030] Set thresholds and alarm mechanisms to promptly issue alarm signals when system status data exceeds the normal range;

[0031] Analyze the system's operating status and stability based on the monitored system status data. If any abnormality is found, immediately initiate the emergency plan and take measures to ensure the safe and stable operation of the system.

[0032] Dynamically adjust the transition strategy based on real-time feedback on system status. If potential risks or unstable factors are found during the transition process, optimize the transition plan in a timely manner.

[0033] An offshore wind power direct current transmission control system is applied to the offshore wind power direct current transmission control method.

[0034] The invention discloses an offshore wind power direct current transmission control method and system, which collects historical data of wind farm output power, including real-time information such as meteorological conditions, wind speed, and wind direction, for training machine learning models by acquiring the power value emitted by the wind farm and the rated power value of the offshore wind power direct current transmission system in real time. The historical data is used to train the machine learning model. A machine learning or artificial intelligence algorithm model, such as a neural network, a support vector machine, etc., is constructed, and the model is trained using historical data to predict the changing trend of the wind farm output power. The second threshold is dynamically adjusted according to the real-time information and the model prediction results to reduce the frequent mode switching caused by power fluctuations. The wind farm power value acquired in real time is compared with the second threshold after dynamic adjustment. When the power value approaches or exceeds the threshold, the system mode switching is triggered, a smooth transition strategy is designed, and the system parameters are gradually adjusted to ensure the stability and safety during the mode switching process. A state monitoring device is introduced to track the system state in real time. According to the real-time feedback of the system state, the transition strategy is dynamically adjusted to further optimize the mode switching process. By dynamically adjusting the second threshold, the frequent mode switching of the system caused by the power fluctuation of the wind farm is reduced, and the stability and reliability of the system are improved. The predictive ability of the machine learning model enables the system to perceive the changing trend of wind farm power in advance, so as to adjust parameters in advance and improve the response speed of the system. The introduction of state monitoring and feedback mechanism enables the system to dynamically adjust the transition strategy according to the real-time state, optimize the control strategy and improve the control accuracy. Through the smooth transition strategy, the system oscillation and instability during the mode switching process are reduced, and the overall performance of the system is improved. The strategy of dynamically adjusting thresholds and parameters enables the system to better adapt to different meteorological conditions and wind speed changes, and enhances the adaptability and robustness of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art are briefly introduced below.

[0036] Figure 1 It is a flowchart of the steps of the offshore wind power direct current transmission control method according to the first embodiment of the present invention.

[0037] Figure 2 It is a flowchart of the steps of constructing a machine learning or artificial intelligence algorithm model in the first embodiment of the present invention, using historical data to train the model to predict the changing trend of the wind farm output power, and dynamically adjusting the second threshold value based on real-time information and model prediction results. DETAILED DESCRIPTION

[0038] Embodiments of the present invention are described in detail below. Examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, but should not be construed as limiting the present invention.

[0039] The first embodiment of the present application is:

[0040] See also Figure 1 and Figure 2 ,in, Figure 1 It is a flowchart of the steps of the offshore wind power direct current transmission control method according to the first embodiment of the present invention. Figure 2 It is a flowchart of the steps of constructing a machine learning or artificial intelligence algorithm model in the first embodiment of the present invention, using historical data to train the model to predict the changing trend of the wind farm output power, and dynamically adjusting the second threshold value based on real-time information and model prediction results.

[0041] The present invention provides an offshore wind power direct current transmission control method, comprising the following steps:

[0042] S101: obtaining the power value generated by the wind farm in real time, obtaining the rated power value of the offshore wind power DC transmission system, and collecting historical data and real-time information on the wind farm output power;

[0043] Specifically, power monitoring sensors are installed on each wind turbine in the wind farm. These sensors can monitor and record the power value emitted by the wind turbine in real time. The power monitoring data on each wind turbine is summarized, and preliminary processing and verification are performed to ensure the accuracy and completeness of the data. The rated power value of the system is found and recorded from the preliminary design documents, technical specifications or equipment manuals of the offshore wind power DC transmission system. The rated power value marked on the nameplate of the key equipment of the offshore wind power DC transmission system, such as transformers and converters, is found and confirmed. The output power data of the wind farm in the past period of time, such as one year, half a year or quarter, is extracted from the data storage system or database of the wind farm. The collected historical data is cleaned and sorted, and abnormal values, duplicate values ​​or invalid data are removed to ensure the accuracy and reliability of the data. The cleaned historical data is processed by statistical analysis, trend analysis or data mining to extract useful information, and the processed data is archived and stored. The current wind speed, wind direction, temperature and other meteorological information are obtained in real time through the meteorological monitoring station or meteorological database. This information is of great significance for predicting the output power of the wind farm and formulating control strategies. Maintain communication with the power grid dispatch center or relevant departments to obtain real-time information such as the stability requirements of the power grid, load forecast information, and power grid operation mode. This information is of great significance to ensure the coordinated operation of wind farms and power grids and to ensure the safety and stability of the power system. The collected real-time information is stored in the system database and updated regularly to ensure the timeliness and accuracy of the information. Real-time acquisition of the power value generated by the wind farm, the rated power value of the offshore wind power DC transmission system, and the collection of historical data and real-time information on the output power of the wind farm provide strong data support for the subsequent control strategy formulation and optimization.

[0044] S102: construct a machine learning or artificial intelligence algorithm model, use historical data to train the model to predict the change trend of the wind farm output power, and dynamically adjust the second threshold value according to the real-time information and the model prediction result;

[0045] S1021: Preprocess historical data, including data cleaning, data normalization or standardization, feature selection or extraction;

[0046] S1022: Build corresponding machine learning or artificial intelligence models based on the selected algorithms and data characteristics;

[0047] S1023: Use historical data to train the model;

[0048] S1024: inputting the real-time acquired wind farm output power data, meteorological information or grid stability requirements into the trained model to perform real-time prediction;

[0049] S1025: Analyze the change trend of wind farm output power according to the prediction results of the model;

[0050] S1026: comprehensively considering the fluctuation range of the wind farm output power, the margin of the system rated power, the grid stability requirements and weather forecast factors, and dynamically adjusting the second threshold value according to the forecast result;

[0051] S1027: Update the adjusted second threshold to the system.

[0052] Specifically, according to the needs of wind farm output power prediction and data characteristics, select appropriate machine learning or artificial intelligence algorithms. Common algorithms include support vector machines (SVM), neural networks, such as BP neural networks, convolutional neural networks CNN, recurrent neural networks RNN and its variants LSTM, random forests, gradient boosting trees, etc. Preprocess historical data, including data cleaning (removing outliers, missing values, etc.), data normalization or standardization, feature selection or extraction, etc., to improve the efficiency and accuracy of model training. According to the selected algorithm and data characteristics, build the corresponding machine learning or artificial intelligence model. This includes setting the parameters of the model, defining the input and output variables, selecting the appropriate loss function and optimization algorithm, etc. Divide the historical data into a training set and a test set (or validation set). Usually, the training set is used to train the model, and the test set is used to evaluate the performance of the model. Use the training set data to train the model, and adjust the parameters of the model through iterative optimization algorithms (such as gradient descent, stochastic gradient descent, Adam, etc.) to make the prediction results of the model as close as possible to the actual data. The performance of the trained model is evaluated using the test set data. Common evaluation indicators include mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), etc. Based on the evaluation results, the model can be tuned, such as adjusting model parameters, increasing the amount of data, and improving feature engineering. The real-time wind farm output power data, meteorological information, grid stability requirements, etc. are input into the trained model for real-time prediction. According to the prediction results of the model, the change trend of the wind farm output power is analyzed, including the power fluctuation range, peak and valley values ​​in the future. Considering the fluctuation range of the wind farm output power, the margin of the system rated power, the grid stability requirements, and weather forecasts, the second threshold is dynamically adjusted according to the prediction results. This can be achieved by setting certain rules or algorithms, such as adjusting the threshold according to the average value, standard deviation, or confidence interval of the predicted power. The adjusted second threshold is updated to the control system and stored in the database for subsequent control strategy formulation and optimization.

[0053] S103: comparing the power value generated by the wind farm obtained in real time with the second threshold value after dynamic adjustment, and switching the system mode according to the comparison result;

[0054] Specifically, when the power value emitted by the wind farm is lower than the second threshold, the system adopts the following control strategy: Control the diode rectifier: stop transmitting active power. This means that when the output power of the wind farm is low, in order to reduce system losses and maintain system stability, the diode rectifier will no longer transmit active power. Onshore converter station and voltage source converter control: both adopt constant voltage control mode. The constant voltage control mode can ensure the stability of the system voltage and help maintain the stable operation of the entire offshore wind power DC transmission system. When the power value emitted by the wind farm is not lower than the second threshold, the system adopts the following control strategy: Onshore converter station control: adopt constant current control mode. The constant current control mode can ensure the stability of the current transmitted by the system and help regulate the power transmission of the offshore wind power DC transmission system. Adjust the AC side voltage: adjust the AC side voltage of the offshore wind power DC transmission system in real time according to the power value emitted by the offshore wind farm. This step is to further adjust the active power transmitted by the diode rectifier so that the system can be dynamically adjusted according to the actual output power of the wind farm. Diode rectifier control: While regulating the voltage on the AC side, the diode rectifier will transmit the corresponding active power according to the actual situation. This ensures that the system can perform flexible power transmission according to the output power of the wind farm. Flexible control of the offshore wind power DC transmission system is achieved, improving the stability and efficiency of the system. At the same time, the volatility and uncertainty of the wind farm output power are also taken into account, so that the system can better adapt to the actual operating conditions.

[0055] S104: During the mode switching process, a smooth transition strategy is designed, parameters are gradually adjusted, and a state monitoring and feedback mechanism is introduced to track the system state in real time, and the transition strategy is adjusted according to the real-time feedback of the system state.

[0056] Specifically, we should understand the structure, working principle and interaction between the components of the offshore wind power DC transmission system in detail, and analyze the system characteristics under different working modes, including the change rules of key parameters such as voltage, current and power. We should clarify the target state of mode switching, including the target voltage, current, power and other parameter values, and set a reasonable transition time and parameter change range during the transition process according to the system characteristics and stability requirements. We should design the steps and sequence of parameter adjustment to ensure that the parameter changes are stable and will not cause system oscillation, determine the time interval and parameter change amount of each step, and form a complete transition plan. According to the formulated transition plan, we should gradually adjust the system parameters. During the adjustment process, we should pay close attention to the changes in the system state to ensure that the parameter adjustment is within the controllable range. We should use high-precision measuring instruments to monitor the system's voltage, current, power and other parameters in real time, and record the data of parameter changes for subsequent analysis and optimization. According to the parameter changes monitored in real time, we should evaluate the execution effect of the transition plan. If we find that the parameter changes are abnormal or the system stability is affected, we should adjust the transition plan in time to ensure that the transition process is smooth. We should install status monitoring equipment such as voltage sensors, current sensors, temperature sensors, etc. at key positions of the system to ensure that the monitoring equipment can reflect the changes in the system state in real time and accurately. The monitored system status data is transmitted to the system in real time, and the threshold and alarm mechanism are set. When the system status data exceeds the normal range, the alarm signal is issued in time. According to the monitored system status data, the operating status and stability of the system are analyzed. If an abnormal situation is found, the emergency plan is immediately activated and necessary measures are taken to ensure the safe and stable operation of the system. According to the real-time feedback of the system status, the transition strategy is dynamically adjusted. If potential risks or unstable factors are found in the transition process, the transition plan is optimized in time to ensure a smooth and safe transition process.

[0057] A method for controlling direct current transmission of offshore wind power using the present embodiment reduces the frequent mode switching of the system caused by power fluctuations in the wind farm by dynamically adjusting the second threshold, thereby improving the stability and reliability of the system. The predictive capability of the machine learning model enables the system to perceive the changing trend of wind farm power in advance, thereby adjusting parameters in advance and improving the response speed of the system. The introduction of a state monitoring and feedback mechanism enables the system to dynamically adjust the transition strategy according to the real-time state, optimizes the control strategy, and improves the control accuracy. Through the smooth transition strategy, the system oscillation and instability during the mode switching process are reduced, and the overall performance of the system is improved. The strategy of dynamically adjusting thresholds and parameters enables the system to better adapt to different meteorological conditions and wind speed changes, enhancing the adaptability and robustness of the system.

[0058] The second embodiment of the present application is:

[0059] On the basis of the first embodiment, the offshore wind power DC transmission control system of this embodiment is applied to the offshore wind power DC transmission control method, and is the offshore wind power DC transmission system disclosed in the publication number CN113452061A, which not only utilizes the advantages of large transmission power, small loss and small volume of the diode rectifier, but also utilizes the function of the voltage source converter to provide a stable AC power supply, filter out the harmonics generated by the diode rectifier, and provide reactive power for the diode rectifier. The diode rectifier and the voltage source converter are connected in series on the DC side, which reduces the voltage level of each converter, that is, reduces the electrical insulation distance of the key parts in each converter, thereby reducing the volume of the converter station, and makes the topology have a higher power density, while reducing the construction cost of the wind farm, and the wind farm does not need to change the original grid-following control strategy, and the scope of application is wider. This is the prior art and will not be described in detail here.

[0060] What is disclosed above is only one or more preferred embodiments of the present application, and cannot be used to limit the scope of rights of the present application. Ordinary technicians in this field can understand that all or part of the processes of implementing the above embodiments and equivalent changes made according to the claims of the present application are still within the scope covered by the present application.

Claims

1. A method for controlling offshore wind power direct current transmission, characterized in that: The following steps are involved: Obtain the power value generated by the wind farm in real time, obtain the rated power value of the offshore wind power DC transmission system, and collect historical data and real-time information on the wind farm's output power; Build a machine learning or artificial intelligence algorithm model, use historical data to train the model to predict the changing trend of wind farm output power, and dynamically adjust the second threshold value based on real-time information and model prediction results; Comparing the power value generated by the wind farm obtained in real time with the second threshold value adjusted dynamically, and switching the system mode according to the comparison result; During the mode switching process, a smooth transition strategy is designed to gradually adjust parameters, and a state monitoring and feedback mechanism is introduced to track the system status in real time and adjust the transition strategy based on the real-time feedback of the system status.

2. The offshore wind power direct current transmission control method according to claim 1, characterized in that: Real-time acquisition of the power value generated by the wind farm, acquisition of the rated power value of the offshore wind power DC transmission system, and collection of historical data and real-time information on the wind farm output power, the steps further include: Power monitoring sensors are installed on each wind turbine in the wind farm to monitor and record the power value generated by the wind turbine in real time through the sensors; Find and record the rated power value of the system from the preliminary design documents, technical specifications, equipment manuals or key equipment of the offshore wind power DC transmission system; Extracting wind farm output power data for a past period of time from a data storage system or database of the wind farm; Obtain current meteorological information in real time through meteorological monitoring stations or meteorological databases, and maintain communication with the power grid dispatching center or relevant departments to obtain power grid information in real time.

3. The offshore wind power direct current transmission control method according to claim 2, characterized in that: Constructing a machine learning or artificial intelligence algorithm model, using historical data to train the model to predict the change trend of the wind farm output power, and dynamically adjusting the second threshold value according to the real-time information and the model prediction results, the steps also include: Preprocess historical data, including data cleaning, data normalization or standardization, feature selection or extraction; Build corresponding machine learning or artificial intelligence models based on the selected algorithms and data characteristics; Use historical data to train the model.

4. The offshore wind power direct current transmission control method according to claim 3, characterized in that: Constructing a machine learning or artificial intelligence algorithm model, using historical data to train the model to predict the change trend of the wind farm output power, and dynamically adjusting the second threshold value according to the real-time information and the model prediction results, the steps also include: Input the real-time wind farm output power data, meteorological information or grid stability requirements into the trained model for real-time prediction; Analyze the changing trend of wind farm output power based on the model's prediction results; Taking into account the fluctuation range of wind farm output power, the margin of system rated power, grid stability requirements and weather forecast factors, the second threshold is dynamically adjusted according to the forecast results; The adjusted second threshold is updated into the system.

5. The offshore wind power direct current transmission control method according to claim 4, characterized in that: The power value generated by the wind farm obtained in real time is compared with the second threshold value after dynamic adjustment, and the system mode is switched according to the comparison result. The step further includes: If the power value generated by the wind farm is lower than the second threshold, the diode rectifier is controlled to stop transmitting active power, and the onshore converter station and the voltage source converter are controlled to adopt a constant voltage control mode; If the power value generated by the wind farm is not lower than the second threshold, the onshore converter station is controlled to adopt a constant current control mode, and the AC side voltage of the offshore wind power DC transmission system is adjusted in real time according to the power value generated by the offshore wind farm to adjust the active power transmitted by the diode rectifier.

6. The offshore wind power direct current transmission control method according to claim 5, characterized in that: During the mode switching process, a smooth transition strategy is designed, parameters are gradually adjusted, and a state monitoring and feedback mechanism is introduced to track the system state in real time, and the transition strategy is adjusted according to the real-time feedback of the system state. The steps also include: Install status monitoring equipment at key locations of the system to reflect changes in system status in real time; Set thresholds and alarm mechanisms to promptly issue alarm signals when system status data exceeds the normal range; Analyze the system's operating status and stability based on the monitored system status data. If any abnormality is found, immediately initiate the emergency plan and take measures to ensure the safe and stable operation of the system. Dynamically adjust the transition strategy based on real-time feedback on system status. If potential risks or unstable factors are found during the transition process, optimize the transition plan in a timely manner.

7. An offshore wind power direct current transmission control system, applied to an offshore wind power direct current transmission control method as claimed in any one of claims 1 to 6.

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