Offshore wind turbine generator operation adjusting system and method based on wind speed prediction

Abstract

The invention discloses an offshore wind turbine generator operation regulation system and method based on wind speed prediction, and belongs to the technical field of wind turbine generator operation regulation, and the system comprises a power system stability analysis module which obtains a power system stability characteristic signal through comprehensive analysis; the initial predicted wind speed output module is used for outputting an initial predicted wind speed based on the trained linear regression model; the operation adjusting threshold value matching module is used for matching the predicted wind speed error item, obtaining a corrected predicted wind speed in combination with the initial predicted wind speed and matching the operation adjusting threshold value of the offshore wind turbine generator; the operation adjustment deviation item matching module is used for matching the operation adjustment deviation item of the offshore wind turbine generator; the wind turbine generator operation adjustment judgment module is used for determining an operation characteristic factor of the offshore wind turbine generator in combination with the operation adjustment deviation item of the offshore wind turbine generator and judging whether the offshore wind turbine generator needs to be subjected to operation adjustment in combination with an operation adjustment threshold value of the offshore wind turbine generator; the adjustment accuracy of the wind turbine generator is improved, and the operation efficiency and stability of the wind turbine generator are improved.

Description

Technical Field

[0001] The present invention belongs to the field of wind turbine operation regulation, and in particular relates to an offshore wind turbine operation regulation system and method based on wind speed prediction. Background Art

[0002] With increasing concern about environmental issues associated with traditional fossil fuels (such as greenhouse gas emissions and air pollution), sustainable energy development is being pursued. Offshore wind power, as a clean, renewable energy source with abundant reserves, offers enormous potential for energy transition. Offshore wind farms offer advantages such as high wind speeds, stable wind resources, and no land occupation. The output power of wind turbines is closely related to wind speed, a natural factor characterized by intermittent and fluctuating wind speeds. This characteristic results in unstable wind turbine output power, negatively impacting grid stability indicators such as frequency and voltage. Offshore wind turbines are expensive to construct and operate. To improve the economic viability of these projects, it is necessary to maximize wind turbine operating efficiency, fully utilize offshore wind energy resources, and adjust their operating status in a timely manner. Accurate wind speed forecasts allow for pre-adjustment of wind turbine operating parameters, such as blade angle and generator speed, to ensure optimal wind energy capture efficiency at varying wind speeds.

[0003] At present, there are still some deficiencies in the research on the operation and regulation of offshore wind turbines based on wind speed prediction. Specifically, the traditional wind speed prediction and wind turbine operation and regulation are not well coordinated and optimized. The regulation of wind turbines is single and the dynamic adaptability is insufficient, resulting in reduced power generation efficiency and the destruction of the reactive power balance of the power grid, which further affects the voltage stability of the power grid and reduces the power quality of the power grid. Summary of the Invention

[0004] The present invention addresses the problem of existing intelligent monitoring and control systems failing to efficiently and effectively utilize resources. A system and method for regulating the operation of offshore wind turbines based on wind speed prediction is proposed. The initial predicted wind speed output module utilizes historical wind speed data and a trained linear regression model to output an initial predicted wind speed. The operation regulation threshold matching module analyzes wind speed influencing characteristics and matches the predicted wind speed error term to obtain a corrected predicted wind speed. The offshore wind turbine operation regulation threshold is matched with power system stability characteristic signals, making the setting of the operation regulation threshold more reasonable. This not only accounts for the uncertainty of wind speed prediction but also takes into account the stability requirements of the power system, facilitating accurate regulation of the wind turbine under varying wind speeds and grid conditions. The operation regulation deviation matching module analyzes the historical characteristics of the offshore wind turbines, taking into account individual differences between wind turbines, ensuring that subsequent operation regulation decisions are more accurately tailored to the actual turbine conditions. The need for operation regulation is determined by combining the actual operating characteristics of the offshore wind turbine, the operation regulation deviation term, and the operation regulation threshold, thereby improving the accuracy of wind turbine regulation and enhancing the operational efficiency and stability of the wind turbine.

[0005] To solve the above technical problems, the present invention provides an offshore wind turbine operation adjustment system based on wind speed prediction, comprising:

[0006] The power system stability analysis module is used to obtain power system stability characteristic data and conduct comprehensive analysis to obtain power system stability characteristic signals;

[0007] The initial predicted wind speed output module is used to obtain historical wind speed data and output the initial predicted wind speed based on the trained linear regression model;

[0008] An operation adjustment threshold matching module is used to analyze the wind speed impact characteristics, match the predicted wind speed error term, combine the initial predicted wind speed, obtain a revised predicted wind speed, and match the offshore wind turbine operation adjustment threshold based on the revised predicted wind speed and the power system stability characteristic signal;

[0009] An operation adjustment deviation item matching module is used to analyze the historical characteristics of offshore wind turbines and match the operation adjustment deviation items of offshore wind turbines;

[0010] The wind turbine operation adjustment judgment module is used to analyze the actual operation characteristics of the offshore wind turbine, determine the offshore wind turbine operation characteristic factor in combination with the offshore wind turbine operation adjustment deviation item, and judge whether the offshore wind turbine needs to be operated and adjusted in combination with the offshore wind turbine operation adjustment threshold.

[0011] Preferably, the power system stability analysis module specifically includes:

[0012] Acquiring power system stability characteristic data, wherein the power system stability characteristic data specifically includes a power system frequency deviation rate, a power system voltage amplitude deviation rate, and a power system voltage fluctuation frequency;

[0013] Based on the power system stability characteristic data, a power system stability characteristic signal is obtained through comprehensive analysis.

[0014] Preferably, the trained linear regression model is specifically expressed as follows:

[0015]

[0016] Where y is the initial predicted wind speed, a0 is the intercept, a1 is the slope, b is the first error term, n is the number of historical wind speeds, and lsf is the initial predicted wind speed. j is the jth historical wind speed.

[0017] Preferably, the operation adjustment threshold matching module includes:

[0018] Acquire wind speed impact characteristic data, wherein the wind speed impact characteristic data specifically includes ambient temperature, ambient air pressure, and ambient altitude;

[0019] Based on the wind speed impact characteristic data, a comprehensive analysis is performed to obtain a wind speed impact characteristic factor;

[0020] The wind speed influencing characteristic factor is stored as a first designated label, the first designated label is compared with each set label stored in a database to obtain a set label corresponding to the first designated label, and a predicted wind speed error item corresponding to the set label stored in the database is obtained;

[0021] Accumulating the initial predicted wind speed and the predicted wind speed error term to obtain a corrected predicted wind speed;

[0022] The corrected predicted wind speed and power system stability characteristic signal is stored as a second designated tag, the second designated tag is compared with each set tag stored in a database, a set tag corresponding to the second designated tag is obtained, and the offshore wind turbine operation adjustment threshold corresponding to the set tag stored in the database is obtained.

[0023] Preferably, the wind speed impact characteristic factor is specifically expressed as:

[0024]

[0025] Where δ is the wind speed influence characteristic factor, wd is the ambient temperature, qy is the ambient air pressure, hbg is the ambient altitude, ε1 is the compensation factor of the set wd, ε2 is the compensation factor of the set qy, ε3 is the compensation factor of the set hbg, and e is a natural constant.

[0026] Preferably, the operation adjustment deviation item matching module specifically includes:

[0027] Acquire historical characteristic data of the offshore wind turbine generator set, wherein the historical characteristic data of the offshore wind turbine generator set specifically includes the historical service life of the wind turbine generator set, the historical power coefficient of the wind turbine generator set, and the historical abnormal shutdown times of the wind turbine generator set;

[0028] Based on the historical characteristic data of the offshore wind turbine generator set, a comprehensive analysis is performed to obtain a historical characteristic factor of the offshore wind turbine generator set;

[0029] The historical characteristic factor of the offshore wind turbine is stored as a third designated tag, the third designated tag is compared with each setting tag stored in a database to obtain a setting tag corresponding to the third designated tag, and the offshore wind turbine operation adjustment deviation item corresponding to the setting tag stored in the database is obtained.

[0030] Preferably, the wind turbine operation adjustment judgment module specifically includes:

[0031] Acquiring actual operating characteristic data of the offshore wind turbine generator set, wherein the actual operating characteristic data of the offshore wind turbine generator set specifically includes a power factor of the wind turbine generator set, a speed deviation rate of the generator of the wind turbine generator set, and a power output deviation rate of the wind turbine generator set;

[0032] Based on the actual operation characteristic data of the offshore wind turbine generator set and combined with the operation adjustment deviation item of the offshore wind turbine generator set, a comprehensive analysis is performed to obtain the operation characteristic factor of the offshore wind turbine generator set;

[0033] Compare the offshore wind turbine operation characteristic factors with the offshore wind turbine operation regulation thresholds;

[0034] If the offshore wind turbine operation characteristic factor is not lower than the offshore wind turbine operation adjustment threshold, the offshore wind turbine corresponding to the offshore wind turbine operation characteristic factor does not need to be adjusted in operation;

[0035] If the operating characteristic factor of the offshore wind turbine group is lower than the operating adjustment threshold of the offshore wind turbine group, the offshore wind turbine group corresponding to the operating characteristic factor of the offshore wind turbine group needs to be operated and adjusted, and an operation adjustment database corresponding to the corrected predicted wind speed is obtained, and the operating characteristic factor of the offshore wind turbine group is stored as a designated label. The designated label is compared with each set label stored in the operation adjustment database to obtain the set label corresponding to the designated label, and the offshore wind turbine group operation adjustment plan corresponding to the set label stored in the operation adjustment database is obtained.

[0036] Preferably, the operating characteristic factor of the offshore wind turbine is specifically expressed as:

[0037]

[0038] Where B is the operating characteristic factor of the offshore wind turbine, B0 is the operating adjustment deviation term of the offshore wind turbine, gys is the power factor of the wind turbine, zsp is the generator speed deviation rate of the wind turbine, gls is the power output deviation rate of the wind turbine, τ1 is the compensation factor of the set gys, τ2 is the compensation factor of the set zsp, and τ3 is the compensation factor of the set gls.

[0039] On the other hand, the present application also discloses an offshore wind turbine operation adjustment method based on wind speed prediction, which is applied to the aforementioned offshore wind turbine operation adjustment system based on wind speed prediction, and includes the following steps:

[0040] S1. Obtain power system stability characteristic data and obtain power system stability characteristic signals through comprehensive analysis;

[0041] S2. Obtain historical wind speed data and output the initial predicted wind speed based on the trained linear regression model;

[0042] S3. Analyze the wind speed impact characteristics, match the predicted wind speed error term, combine it with the initial predicted wind speed, obtain a revised predicted wind speed, and match the offshore wind turbine operation adjustment threshold based on the revised predicted wind speed and the power system stability characteristic signal;

[0043] S4. Analyze the historical characteristics of offshore wind turbines and match the operation adjustment deviation items of offshore wind turbines;

[0044] S5. Analyze the actual operating characteristics of the offshore wind turbine generator set, determine the operating characteristic factor of the offshore wind turbine generator set in combination with the operating adjustment deviation item of the offshore wind turbine generator set, and determine whether the offshore wind turbine generator set needs to be adjusted in combination with the operating adjustment threshold of the offshore wind turbine generator set.

[0045] Beneficial effects of the present invention:

[0046] 1. This solution uses historical wind speed data and a trained linear regression model to output the initial predicted wind speed through the initial predicted wind speed output module. The operation adjustment threshold matching module analyzes wind speed influencing characteristics and matches the predicted wind speed error term to obtain a corrected predicted wind speed. The operation adjustment threshold is matched to the offshore wind turbine using the power system stability characteristic signal, making the setting of the operation adjustment threshold more reasonable. This not only takes into account the uncertainty of wind speed prediction but also the stability requirements of the power system. This helps the wind turbine to accurately adjust under different wind speed and grid conditions. The operation adjustment deviation matching module analyzes the historical characteristics of the offshore wind turbine and takes into account the individual differences of different wind turbines, making subsequent operation adjustment decisions more accurate to the actual unit conditions. The actual operating characteristics of the offshore wind turbine, the operation adjustment deviation term, and the operation adjustment threshold are combined to determine whether operation adjustment is necessary, thereby improving the accuracy of wind turbine adjustment and enhancing the operational efficiency and stability of the wind turbine.

[0047] 2. By acquiring historical wind speed data, this solution can uncover potential patterns in wind speed changes over time. Historical wind speed data contains information on wind speeds under the influence of various past meteorological conditions and time factors. For example, by analyzing wind speed changes in different seasons and time periods, it is possible to discover possible cyclical patterns in wind speeds in the morning and evening of the day, and in different seasons of the year. Linear regression models can use these patterns to establish a correlation between historical wind speeds and predicted wind speeds. The initial predicted wind speed provides a basic reference for subsequent more accurate wind speed forecasts and offshore wind turbine operation adjustments. Even if this initial forecast has certain errors, it can reflect the general trend of wind speed changes to a certain extent, providing a starting point for subsequent corrections and optimizations based on other factors.

[0048] 3. This solution comprehensively considers the impact of various natural factors on wind speed by acquiring wind speed-influencing data such as ambient temperature, ambient air pressure, and ambient altitude. Changes in ambient temperature cause changes in air density, which in turn affects wind speed. Ambient pressure differences are one of the driving factors of wind, with pressure gradients driving air flow and forming wind. Wind speed distribution patterns vary at different altitudes, with higher altitudes typically having higher wind speeds than lower altitudes. The wind speed-influencing factor derived from these combined analyses can more comprehensively reflect actual conditions, ensuring that predicted wind speeds are closer to actual wind speeds.

[0049] 4. This solution uses the wind speed influencing characteristic factor as the basis for matching the predicted wind speed error term, and then corrects the initial predicted wind speed, which can achieve dynamic adjustment of the wind speed forecast. Using the corrected predicted wind speed and the power system stability characteristic signal as the basis for matching the operation adjustment threshold can better coordinate the operation of the wind turbine and the stability of the power system. The offshore environment is complex and changeable, and considering a variety of wind speed influencing characteristic data can enable the system to better adapt to this environment. Whether it is changes in the weather system or differences in geographical location, by comprehensively analyzing these factors and correcting the predicted wind speed and matching the operation adjustment threshold, wind turbines can operate more reasonably under different environmental conditions.

[0050] 5. This solution captures actual operational characteristic data, including wind turbine power factor, generator speed deviation rate, and power output deviation rate, to provide a comprehensive and detailed understanding of the wind turbine's current operating status. The power factor reflects the wind turbine's contribution to grid power quality and its reactive power compensation. The generator speed deviation rate indicates the stable operation of the wind turbine's power system, as speed deviation affects power generation frequency and efficiency. The power output deviation rate directly demonstrates the difference between the wind turbine's output power and the expected or standard value, which is crucial for evaluating power generation performance. A comprehensive analysis, combined with the offshore wind turbine's operational regulation deviation items, fully accounts for the individual differences of each wind turbine.

[0051] 6. In this solution, because different wind turbines have different historical usage and performance degradation, the operational adjustment deviation term reflects the impact of these differences on current operations. The operational characteristic factor, derived by combining the operational adjustment deviation term with actual operational characteristic data, serves as the basis for determining whether a wind turbine requires operational adjustment. This makes the determination more accurate, ensuring optimal operation of the wind turbine and improving operational efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 This is a schematic diagram of system module connections of the present invention.

[0053] Figure 2 Schematic diagram of the method steps of the present invention. DETAILED DESCRIPTION

[0054] Example 1: Figure 1-Figure 2 As shown, the offshore wind turbine operation regulation system based on wind speed prediction includes: a power system stability analysis module, an initial predicted wind speed output module, an operation regulation threshold matching module, an operation regulation deviation item matching module and a wind turbine operation regulation judgment module.

[0055] The power system stability analysis module is used to obtain power system stability characteristic data and obtain power system stability characteristic signals through comprehensive analysis.

[0056] The specific analysis process is as follows: obtaining power system stability characteristic data, which specifically includes the power system frequency deviation rate, the power system voltage amplitude deviation rate, and the power system voltage fluctuation frequency; based on the obtained power system stability characteristic data, a comprehensive analysis is performed to obtain the power system stability characteristic signal, which is used as the analysis basis for matching the operating adjustment threshold of the offshore wind turbine.

[0057] The power system frequency deviation rate refers to the ratio of the absolute value of the difference between the actual operating frequency and the rated frequency of the power system to the rated frequency. The power system voltage amplitude deviation rate refers to the ratio of the absolute value of the difference between the actual voltage amplitude and the rated voltage amplitude at a point in the power system to the rated voltage amplitude. The power system voltage fluctuation frequency refers to the number of voltage amplitude fluctuations per unit time.

[0058] The power system stability characteristic signals derived from comprehensive analysis provide an accurate reference for wind turbine operation and regulation. Different stability characteristic signals reflect different aspects of the power system's status. Using these signals to match operational regulation thresholds can lead to more precise regulation strategies. The reactive power regulation speed of wind turbines is determined based on the frequency of voltage fluctuations, ensuring regulation more closely aligned with the actual needs of the power system. As power grids expand in size and complexity, offshore wind turbines need to better adapt to various grid conditions. Using power system stability characteristic signals as a basis for analysis, wind turbines can adaptively adjust operational regulation thresholds under varying grid stability conditions, optimizing their output power, reactive power, and other parameters for better integration into grid operations. This reduces operational stress on wind turbine equipment. Using power system stability characteristic signals as a reference, wind turbines can operate under relatively stable conditions when system conditions permit, reducing equipment fatigue and damage caused by frequent or inappropriate regulation, thereby extending equipment life.

[0059] The specific analysis process of the power system stability characteristic signal is as follows:

[0060]

[0061] Where ω is the power system stability characteristic signal, plp is the power system frequency deviation rate, yfp is the power system voltage amplitude deviation rate, ybd is the power system voltage fluctuation frequency, μ1 is the compensation factor of the set plp, μ2 is the compensation factor of the set yfp, μ3 is the compensation factor of the set ybd, and e is a natural constant.

[0062] The parameters in this embodiment are all normalized before calculation, for example, plp, yfp, and ybd are normalized.

[0063] The power system frequency deviation rate, voltage amplitude deviation rate, and voltage fluctuation frequency indicators reflect the operating status of the power system from different perspectives. The frequency deviation rate primarily relates to the balance between power generation and consumption, the voltage amplitude deviation rate reflects reactive power balance and voltage support capacity, and the voltage fluctuation frequency is related to the load characteristics and dynamic disturbances in the system. Calculating the power system stability characteristic signal allows for a comprehensive and systematic assessment of power system stability, avoiding the one-sidedness that can occur when relying solely on a single indicator.

[0064] It should be explained that the compensation factors of PLP, YFP, and YBD set above are obtained from the database. Based on historical data, a mapping set of historically measured power system frequency deviation rate, power system voltage amplitude deviation rate, power system voltage fluctuation frequency and compensation factors of PLP, YFP, and YBD is established to obtain the compensation factors of PLP, YFP, and YBD corresponding to the current PLP, YFP, and YBD.

[0065] It should be noted that ε1, ε2, ε3, σ1, σ2, σ3, τ1, τ2, and τ3 mentioned below are also obtained through a mapping set of historical data and compensation factors established in the database, that is, the corresponding compensation factors are obtained based on the current data.

[0066] The initial predicted wind speed output module is used to obtain historical wind speed data and output the initial predicted wind speed based on the trained linear regression model.

[0067] The specific analysis process is: obtain historical wind speed data, get each historical wind speed, and record the jth historical wind speed as lsf j , j is the number of each historical wind speed; the trained linear regression model can be expressed as

[0068]

[0069] Where y is the initial predicted wind speed, a0 is the intercept, a1 is the slope, b is the first error term, and n is the number of historical wind speeds. Output the initial predicted wind speed. For example, if the historical wind speeds include the wind speeds from 10:00 AM to 12:00 PM on each day of the historical date, then the predicted y is the initial predicted wind speed from 10:00 AM to 12:00 PM on the current date.

[0070] Obtaining historical wind speed data can reveal potential patterns in wind speed changes over time. Historical wind speed data contains information on wind speeds influenced by various past meteorological conditions and time factors. For example, by analyzing wind speed variations across seasons and time periods, we can identify potential cyclical patterns in wind speeds at different times of the day and throughout the year. Linear regression models can leverage these patterns to correlate historical wind speeds with predicted wind speeds. The initial predicted wind speed provides a baseline for more accurate subsequent wind speed forecasts and offshore wind turbine operation adjustments. Even if this initial forecast has some error, it can still reflect the general trend of wind speed changes, providing a starting point for subsequent corrections and optimization based on other factors. The slope a1 represents the change in predicted wind speed for each unit change in historical wind speed; the intercept a0 can be understood as the baseline predicted wind speed value when the historical wind speed is zero.

[0071] The operation adjustment threshold matching module is used to analyze the wind speed impact characteristics, match the predicted wind speed error term, combine the initial predicted wind speed, obtain the corrected predicted wind speed, and match the operation adjustment threshold of the offshore wind turbine based on the corrected predicted wind speed and the power system stability characteristic signal.

[0072] The specific analysis process is as follows: obtaining wind speed impact characteristic data, which specifically includes ambient temperature, ambient air pressure, and ambient altitude; based on the obtained wind speed impact characteristic data, comprehensively analyzing to obtain wind speed impact characteristic factors, which are used as the analysis basis for matching predicted wind speed error items; storing the wind speed impact characteristic factors as designated tags, comparing the designated tags with the set tags stored in the database to obtain the set tags corresponding to the designated tags, and obtaining the predicted wind speed error items corresponding to the set tags stored in the database; accumulating the initial predicted wind speed and the predicted wind speed error items to obtain the corrected predicted wind speed; storing the corrected predicted wind speed and the power system stability characteristic signal as designated tags, comparing the designated tags with the set tags stored in the database to obtain the set tags corresponding to the designated tags, and obtaining the offshore wind turbine operation adjustment threshold corresponding to the set tags stored in the database.

[0073] Wind speed affects the characteristic factor. The specific analysis process is as follows:

[0074]

[0075] Where δ is the wind speed influence characteristic factor, wd is the ambient temperature, qy is the ambient air pressure, hbg is the ambient altitude, ε1 is the compensation factor of the set wd, ε2 is the compensation factor of the set qy, and ε3 is the compensation factor of the set hbg.

[0076] By acquiring wind speed-influencing characteristics such as ambient temperature, ambient air pressure, and ambient altitude, we can comprehensively consider the impact of various natural factors on wind speed. Changes in ambient temperature cause changes in air density, which in turn affects wind speed. Ambient pressure differences are one of the driving factors in wind generation, with pressure gradients driving air movement and forming wind. Wind speed distribution patterns vary at different altitudes, with higher altitudes typically experiencing higher wind speeds than lower altitudes. The wind speed-influencing characteristic factors derived from these combined analyses can more comprehensively reflect actual conditions, ensuring that predicted wind speeds are closer to the true wind speed.

[0077] Using the wind speed influencing characteristic factor as the basis for matching the predicted wind speed error term, and then correcting the initial predicted wind speed, dynamic adjustment of the wind speed forecast can be achieved. Using the corrected predicted wind speed and the power system stability characteristic signal together as the basis for matching the operational adjustment threshold, the operation of the wind turbine can be better coordinated with the stability of the power system. The offshore environment is complex and changeable, and considering a variety of wind speed influencing characteristic data can help the system better adapt to this environment. Whether it is changes in the weather system or differences in geographical location, by comprehensively analyzing these factors and correcting the predicted wind speed and matching the operational adjustment threshold, wind turbines can operate more rationally under different environmental conditions.

[0078] The operation adjustment deviation item matching module is used to analyze the historical characteristics of offshore wind turbines and match the operation adjustment deviation items of offshore wind turbines.

[0079] The specific analysis process is as follows: obtaining historical characteristic data of offshore wind turbines, which specifically includes the historical service years of the wind turbines, the historical power coefficient of the wind turbines, and the historical number of abnormal shutdowns of the wind turbines; based on the obtained historical characteristic data of the offshore wind turbines, a comprehensive analysis is performed to obtain the historical characteristic factors of the offshore wind turbines, which are used as the analysis basis for matching the operation adjustment deviation items of the offshore wind turbines; the historical characteristic factors of the offshore wind turbines are stored as designated tags, and the designated tags are compared with the set tags stored in the database to obtain the set tags corresponding to the designated tags, and obtain the operation adjustment deviation items of the offshore wind turbines corresponding to the set tags stored in the database.

[0080] The historical service life of a wind turbine refers to the length of time from the time the wind turbine was first put into operation to the current moment, usually calculated in years. The historical power coefficient of a wind turbine refers to the efficiency ratio of wind energy converted into electrical energy. The historical abnormal shutdown number of a wind turbine refers to the cumulative number of unplanned shutdowns during the operation of the wind turbine due to faults, protective actions, external environmental factors (such as extreme weather and power grid failures), etc.

[0081] The specific analysis process of historical characteristic factors of offshore wind turbines is as follows:

[0082]

[0083] Where A is the historical characteristic factor of the offshore wind turbine, syn is the historical service life of the wind turbine, glx is the historical power coefficient of the wind turbine, tjc is the historical number of abnormal shutdowns of the wind turbine, σ1 is the compensation factor of the set syn, σ2 is the compensation factor of the set glx, and σ3 is the compensation factor of the set tjc.

[0084] By acquiring historical characteristic data such as the wind turbine's historical service life, historical power coefficient, and historical abnormal shutdown times, we can fully understand the wind turbine's past operation. For example, the historical service life reflects the unit's degree of aging, the historical power coefficient reflects the unit's past power generation efficiency changes, and the historical abnormal shutdown times are an important indicator of the unit's reliability and stability.

[0085] The historical characteristic factors derived from these factors can more accurately assess the individual differences of each wind turbine. Using these historical characteristic factors as the basis for matching operational adjustment deviations, we can customize an operational adjustment strategy that better suits the actual conditions of each wind turbine. Wind turbines with different historical characteristics require different adjustment deviations to optimize their operation.

[0086] The wind turbine operation adjustment judgment module is used to analyze the actual operation characteristics of the offshore wind turbine, determine the offshore wind turbine operation characteristic factor in combination with the offshore wind turbine operation adjustment deviation item, and judge whether the offshore wind turbine needs to be operated and adjusted in combination with the offshore wind turbine operation adjustment threshold.

[0087] The specific analysis process is as follows: obtaining the actual operating characteristic data of the offshore wind turbine, which specifically includes the wind turbine power factor, the wind turbine generator speed deviation rate, and the wind turbine power output deviation rate; based on the obtained actual operating characteristic data of the offshore wind turbine, combined with the offshore wind turbine operation adjustment deviation item, a comprehensive analysis is performed to obtain the offshore wind turbine operation characteristic factor, which is used as the analysis basis for judging whether the offshore wind turbine needs to be adjusted in operation.

[0088] The power factor of a wind turbine is the ratio of the active power to the apparent power in the turbine's AC circuit. The generator speed deviation rate is the ratio of the absolute value of the difference between the actual and rated speeds to the rated speed. The power output deviation rate is the ratio of the absolute value of the difference between the actual and expected output power to the expected output power.

[0089] The specific analysis process of the operating characteristic factors of offshore wind turbines is as follows:

[0090]

[0091] Where B is the operating characteristic factor of the offshore wind turbine, B0 is the operating adjustment deviation term of the offshore wind turbine, gys is the power factor of the wind turbine, zsp is the generator speed deviation rate of the wind turbine, gls is the power output deviation rate of the wind turbine, τ1 is the compensation factor of the set gys, τ2 is the compensation factor of the set zsp, and τ3 is the compensation factor of the set gls.

[0092] Obtaining actual operational characteristic data such as wind turbine power factor, generator speed deviation rate, and power output deviation rate provides a comprehensive and detailed understanding of the wind turbine's current operating status. The power factor reflects the wind turbine's contribution to grid power quality and its reactive power compensation. The generator speed deviation rate reflects the stable operation of the wind turbine's power system, as speed deviation affects power generation frequency and efficiency. The power output deviation rate directly displays the difference between the wind turbine's output power and the expected or standard value, which is crucial for evaluating power generation performance. A comprehensive analysis combined with the offshore wind turbine's operational regulation deviation items can fully account for the individual differences of each wind turbine.

[0093] Because different wind turbines have different historical usage and performance degradation, the operational adjustment deviation term reflects the impact of these differences on current operation. The operational characteristic factor, derived by combining the operational adjustment deviation term with actual operational characteristic data, serves as the basis for determining whether a wind turbine requires operational adjustment. This makes the determination more accurate, ensuring optimal operation of the wind turbine and improving operational efficiency.

[0094] Compare the offshore wind turbine operation characteristic factor with the offshore wind turbine operation adjustment threshold; if the offshore wind turbine operation characteristic factor is not lower than the offshore wind turbine operation adjustment threshold, then the offshore wind turbine corresponding to the offshore wind turbine operation characteristic factor does not need to be operated; if the offshore wind turbine operation characteristic factor is lower than the offshore wind turbine operation adjustment threshold, then the offshore wind turbine corresponding to the offshore wind turbine operation characteristic factor needs to be operated, obtain the operation adjustment database corresponding to the corrected predicted wind speed, store the offshore wind turbine operation characteristic factor as a designated label, compare the designated label with each set label stored in the operation adjustment database, obtain the set label corresponding to the designated label, and obtain the offshore wind turbine operation adjustment plan corresponding to the set label stored in the operation adjustment database.

[0095] By comparing the operational characteristic factor with the operational adjustment threshold, it is possible to clearly determine whether a wind turbine requires operational adjustment. This comparison provides a clear criterion for determining wind turbine operational adjustments, avoiding over- or under-adjustment. The operational characteristic factor is derived by combining the wind turbine's actual operational characteristic data with the operational adjustment deviation term. It comprehensively considers individual differences between wind turbines (such as historical service life and historical power coefficient) and real-time operating status (such as current power factor and speed deviation rate). This comparison enables a decision on whether adjustment is necessary based on the specific circumstances of each wind turbine, making adjustment decisions more accurate and reasonable. Adjusting the wind turbine based on the accurately determined and matched operational adjustment plan can restore the wind turbine to its optimal or reasonable operating state. For example, by implementing a corresponding adjustment plan (such as adjusting the blade pitch angle or inverter control parameters) to stabilize the speed, the wind turbine's generating frequency can be maintained, power generation efficiency can be improved, and the risk of equipment wear and failure caused by poor operating conditions can be reduced.

[0096] Example 2: In a specific embodiment, the power system frequency deviation rate is 0.02, the power system voltage amplitude deviation rate is 0.03, the power system voltage fluctuation frequency is 5 times per month, the compensation factor of the power system frequency deviation rate is 0.5, the compensation factor of the power system voltage amplitude deviation rate is 0.6, and the compensation factor of the power system voltage fluctuation frequency is 0.4. The calculated power system stability characteristic signal is 0.1655.

[0097] Obtain historical wind speed data and, based on the trained linear regression model, output an initial predicted wind speed of 10 meters per second.

[0098] The ambient temperature is 20 degrees Celsius, the ambient air pressure is 1010 hPa, the ambient altitude is 100 meters, the compensation factor of the ambient temperature is 0.8, the compensation factor of the ambient air pressure is 0.7, and the compensation factor of the ambient altitude is 0.9. The calculated wind speed influence characteristic factor is 31.517.

[0099] The predicted wind speed error term is matched to 2 meters per second. Combined with the initial predicted wind speed of 10 meters per second, the corrected predicted wind speed is 12 meters per second. Based on the corrected predicted wind speed and the power system stability characteristic signal, the operating adjustment threshold of the offshore wind turbine is matched to 2.5.

[0100] The historical service life of the wind turbine is 5 years, the historical power coefficient of the wind turbine is 0.4, the historical number of abnormal shutdowns of the wind turbine is 3 times, the compensation factor of the historical service life of the wind turbine is 0.3, the compensation factor of the historical power coefficient of the wind turbine is 0.5, and the compensation factor of the historical number of abnormal shutdowns of the wind turbine is 0.4. The historical characteristic factor of the offshore wind turbine is calculated to be 0.668.

[0101] The operation adjustment deviation item matched to the offshore wind turbine is 0.5.

[0102] The power factor of the wind turbine is 0.9, the speed deviation rate of the wind turbine generator is 0.01, the power output deviation rate of the wind turbine is 0.02, the compensation factor of the wind turbine power factor is 0.7, the compensation factor of the wind turbine generator speed deviation rate is 0.8, and the compensation factor of the wind turbine power output deviation rate is 0.6. The calculated operating characteristic factor of the offshore wind turbine is 2.828.

[0103] The offshore wind turbine operation characteristic factor of 2.828 is not lower than the offshore wind turbine operation adjustment threshold of 2.5, and the offshore wind turbine does not need to be operated.

[0104] Reference Figure 2 As shown, the second aspect of the present invention provides an offshore wind turbine operation adjustment method based on wind speed prediction, comprising the following steps: acquiring power system stability characteristic data, and comprehensively analyzing to obtain power system stability characteristic signals.

[0105] Obtain historical wind speed data and output the initial predicted wind speed based on the trained linear regression model.

[0106] The wind speed influencing characteristics are analyzed, the predicted wind speed error term is matched, and the initial predicted wind speed is combined to obtain the corrected predicted wind speed. The operation adjustment threshold of the offshore wind turbine is matched based on the corrected predicted wind speed and the power system stability characteristic signal.

[0107] Analyze the historical characteristics of offshore wind turbines and match the operation adjustment deviation items of offshore wind turbines.

[0108] The actual operating characteristics of offshore wind turbines are analyzed, and the operating characteristic factors of offshore wind turbines are determined in combination with the operating adjustment deviation items of offshore wind turbines. In combination with the operating adjustment thresholds of offshore wind turbines, it is determined whether the offshore wind turbines need to be operated.

Claims

1. An offshore wind turbine operation adjustment system based on wind speed prediction, characterized in that: include: The power system stability analysis module is used to obtain power system stability characteristic data and conduct comprehensive analysis to obtain power system stability characteristic signals; The initial predicted wind speed output module is used to obtain historical wind speed data and output the initial predicted wind speed based on the trained linear regression model; An operation adjustment threshold matching module is used to analyze the wind speed impact characteristics, match the predicted wind speed error term, combine the initial predicted wind speed, obtain a revised predicted wind speed, and match the offshore wind turbine operation adjustment threshold based on the revised predicted wind speed and the power system stability characteristic signal; An operation adjustment deviation item matching module is used to analyze the historical characteristics of offshore wind turbines and match the operation adjustment deviation items of offshore wind turbines; The wind turbine operation adjustment judgment module is used to analyze the actual operation characteristics of the offshore wind turbine, determine the offshore wind turbine operation characteristic factor in combination with the offshore wind turbine operation adjustment deviation item, and judge whether the offshore wind turbine needs to be operated and adjusted in combination with the offshore wind turbine operation adjustment threshold.

2. The offshore wind turbine operation adjustment system based on wind speed prediction according to claim 1 is characterized in that: The power system stability analysis module specifically includes: Acquiring power system stability characteristic data, wherein the power system stability characteristic data specifically includes a power system frequency deviation rate, a power system voltage amplitude deviation rate, and a power system voltage fluctuation frequency; Based on the power system stability characteristic data, a power system stability characteristic signal is obtained through comprehensive analysis.

3. The offshore wind turbine operation adjustment system based on wind speed prediction according to claim 1 is characterized in that: The trained linear regression model is specifically expressed as follows: Where y is the initial predicted wind speed, a0 is the intercept, a1 is the slope, b is the first error term, n is the number of historical wind speeds, and lsf is the initial predicted wind speed. j is the jth historical wind speed.

4. The offshore wind turbine operation adjustment system based on wind speed prediction according to claim 1 is characterized in that: The operation adjustment threshold matching module includes: Acquire wind speed impact characteristic data, wherein the wind speed impact characteristic data specifically includes ambient temperature, ambient air pressure, and ambient altitude; Based on the wind speed impact characteristic data, a comprehensive analysis is performed to obtain a wind speed impact characteristic factor; The wind speed influencing characteristic factor is stored as a first designated label, the first designated label is compared with each set label stored in a database to obtain a set label corresponding to the first designated label, and a predicted wind speed error item corresponding to the set label stored in the database is obtained; Accumulating the initial predicted wind speed and the predicted wind speed error term to obtain a corrected predicted wind speed; The corrected predicted wind speed and power system stability characteristic signal is stored as a second designated tag, the second designated tag is compared with each set tag stored in a database, a set tag corresponding to the second designated tag is obtained, and the offshore wind turbine operation adjustment threshold corresponding to the set tag stored in the database is obtained.

5. The offshore wind turbine operation adjustment system based on wind speed prediction according to claim 4 is characterized in that: The wind speed impact characteristic factor is specifically expressed as: Where δ is the wind speed influence characteristic factor, wd is the ambient temperature, qy is the ambient air pressure, hbg is the ambient altitude, ε1 is the compensation factor of the set wd, ε2 is the compensation factor of the set qy, ε3 is the compensation factor of the set hbg, and e is a natural constant.

6. The offshore wind turbine operation adjustment system based on wind speed prediction according to claim 1, characterized in that: The operation adjustment deviation item matching module specifically includes: Acquire historical characteristic data of the offshore wind turbine generator set, wherein the historical characteristic data of the offshore wind turbine generator set specifically includes the historical service life of the wind turbine generator set, the historical power coefficient of the wind turbine generator set, and the historical abnormal shutdown times of the wind turbine generator set; Based on the historical characteristic data of the offshore wind turbine generator set, a comprehensive analysis is performed to obtain a historical characteristic factor of the offshore wind turbine generator set; The historical characteristic factor of the offshore wind turbine is stored as a third designated tag, the third designated tag is compared with each setting tag stored in a database to obtain a setting tag corresponding to the third designated tag, and the offshore wind turbine operation adjustment deviation item corresponding to the setting tag stored in the database is obtained.

7. The offshore wind turbine operation adjustment system based on wind speed prediction according to claim 1, characterized in that: The wind turbine operation adjustment judgment module specifically includes: Acquiring actual operating characteristic data of the offshore wind turbine generator set, wherein the actual operating characteristic data of the offshore wind turbine generator set specifically includes a power factor of the wind turbine generator set, a speed deviation rate of the generator of the wind turbine generator set, and a power output deviation rate of the wind turbine generator set; Based on the actual operation characteristic data of the offshore wind turbine generator set and combined with the operation adjustment deviation item of the offshore wind turbine generator set, a comprehensive analysis is performed to obtain the operation characteristic factor of the offshore wind turbine generator set; Compare the offshore wind turbine operation characteristic factors with the offshore wind turbine operation regulation thresholds; If the offshore wind turbine operation characteristic factor is not lower than the offshore wind turbine operation adjustment threshold, the offshore wind turbine corresponding to the offshore wind turbine operation characteristic factor does not need to be adjusted in operation; If the operating characteristic factor of the offshore wind turbine group is lower than the operating adjustment threshold of the offshore wind turbine group, the offshore wind turbine group corresponding to the operating characteristic factor of the offshore wind turbine group needs to be operated and adjusted, and an operation adjustment database corresponding to the corrected predicted wind speed is obtained, and the operating characteristic factor of the offshore wind turbine group is stored as a designated label. The designated label is compared with each set label stored in the operation adjustment database to obtain the set label corresponding to the designated label, and the offshore wind turbine group operation adjustment plan corresponding to the set label stored in the operation adjustment database is obtained.

8. The offshore wind turbine operation adjustment system based on wind speed prediction according to claim 7 is characterized in that: The operating characteristic factor of the offshore wind turbine is specifically expressed as: Where B is the operating characteristic factor of the offshore wind turbine, B0 is the operating adjustment deviation term of the offshore wind turbine, gys is the power factor of the wind turbine, zsp is the generator speed deviation rate of the wind turbine, gls is the power output deviation rate of the wind turbine, τ1 is the compensation factor of the set gys, τ2 is the compensation factor of the set zsp, and τ3 is the compensation factor of the set gls.

9. A method for regulating the operation of an offshore wind turbine generator system based on wind speed prediction, applied to the offshore wind turbine generator system based on wind speed prediction as claimed in any one of claims 1 to 8, comprising the following steps: S1. Obtain power system stability characteristic data and obtain power system stability characteristic signals through comprehensive analysis; S2. Obtain historical wind speed data and output the initial predicted wind speed based on the trained linear regression model; S3. Analyze the wind speed impact characteristics, match the predicted wind speed error term, combine it with the initial predicted wind speed, obtain a revised predicted wind speed, and match the offshore wind turbine operation adjustment threshold based on the revised predicted wind speed and the power system stability characteristic signal; S4. Analyze the historical characteristics of offshore wind turbines and match the operation adjustment deviation items of offshore wind turbines; S5. Analyze the actual operating characteristics of the offshore wind turbine generator set, determine the operating characteristic factor of the offshore wind turbine generator set in combination with the operating adjustment deviation item of the offshore wind turbine generator set, and determine whether the offshore wind turbine generator set needs to be adjusted in combination with the operating adjustment threshold of the offshore wind turbine generator set.

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