A method and system for condition monitoring of offshore wind farms

Abstract

The application provides a state monitoring method and system for offshore wind farms, which is based on meteorological satellite monitoring data of the sea area where the offshore wind farm is located to determine the corresponding environmental state information of the sea area, so as to determine all target power generation wind turbines that may have fault events inside the offshore wind farm; based on the distribution position information of all target power generation wind turbines, directional radar scanning is performed on the corresponding area inside the offshore wind farm to obtain radar echo data of the corresponding area, so as to identify abnormal power generation wind turbines that stop power generation operation inside the offshore wind farm; and based on real-time power generation state data of all power generation wind turbines inside the offshore wind farm that maintain power generation operation, power generation wind turbines that are not in a full load state are identified, and the power generation wind turbines that are not in a full load state are adjusted in working state in combination with power adjustable capacity information and environmental state information of the corresponding power generation wind turbines, so that the offshore wind farm can maintain stable and continuous power transmission to the outside in the case of power generation wind turbine failure.

Description

Technical Field

[0001] This invention relates to the field of wind farm monitoring, and more particularly to a method and system for condition monitoring of offshore wind farms. Background Technology

[0002] Wind power, as a clean energy source, has been widely used. However, wind resources suffer from uneven distribution and instability. To ensure the normal and continuous operation of wind power generation, it is necessary to select suitable areas for wind farms. As the boundary between land and sea, the nearshore area possesses stable and considerable wind resources year-round, making it the primary method for wind power generation. Offshore wind farms consist of several wind turbines distributed at different locations near the coast. This makes the turbines susceptible to interference from waves, especially during severe weather such as typhoons or storm surges, when the intensity and height of waves increase, threatening the structure of the turbines. This can cause turbines to be knocked over or their rotors to be damaged, leading to malfunctions. To ensure the normal and stable operation of offshore wind farms, continuous and targeted monitoring is necessary to promptly detect turbine failures. Summary of the Invention

[0003] The purpose of this invention is to provide a method and system for monitoring the status of offshore wind farms. Based on meteorological satellite monitoring data of the sea area where the offshore wind farm is located, it determines the corresponding environmental status information of the sea area, thereby identifying all target wind turbines within the offshore wind farm that may experience malfunctions. It then identifies and locates wind turbines within the offshore wind farm that may malfunction due to the marine environment. Based on the distribution location information of all target wind turbines, it performs directional radar scanning on the corresponding areas within the offshore wind farm to obtain radar echo data of the corresponding areas, thereby identifying abnormal wind turbines within the offshore wind farm that have stopped generating electricity. Furthermore, based on the real-time power generation status data of all wind turbines maintaining power generation within the offshore wind farm, it identifies wind turbines that are not operating at full capacity. Combining the power adjustable capacity information and environmental status information of the corresponding wind turbines, it adjusts the operating status of wind turbines that are not operating at full capacity, accurately identifies faulty wind turbines, and adaptively schedules the power generation intensity of wind turbines, ensuring that the offshore wind farm can still maintain stable and continuous power transmission even in the event of wind turbine malfunctions.

[0004] This invention is achieved through the following technical solution:

[0005] A condition monitoring method for offshore wind farms includes:

[0006] Meteorological satellite monitoring data of the sea area where the offshore wind farm is located is used to determine the environmental status information of the sea area; based on the environmental status information, all target wind turbines that may experience failure events within the offshore wind farm are identified, and the locations of all target wind turbines are calibrated.

[0007] Based on the distribution location information of all target wind turbines within the offshore wind farm, directional radar scanning is performed on the corresponding areas within the offshore wind farm to obtain radar echo data for the corresponding areas; the radar echo data is analyzed to identify abnormal wind turbines within the offshore wind farm that have stopped generating electricity.

[0008] Based on the real-time power generation status data of all wind turbines maintaining power generation operation within the offshore wind farm, wind turbines that are not in full-load power generation are identified; then, based on the power adjustable capacity information of wind turbines that are not in full-load power generation and the environmental status information, the operating status of wind turbines that are not in full-load power generation is adjusted.

[0009] Optionally, meteorological satellite monitoring data of the sea area where the offshore wind farm is located is used to determine the environmental status information corresponding to the sea area; based on the environmental status information, all target wind turbines that may experience fault events within the offshore wind farm are identified, and the locations of all target wind turbines are calibrated, including:

[0010] Meteorological satellite remote sensing image data of the sea area where the offshore wind farm is located is acquired, and the meteorological satellite remote sensing image data is analyzed to obtain the air pressure change distribution information of the sea area; based on the air pressure change distribution information and the tidal rise and fall status information of the sea area, the wind speed status information and wave status information of the sea area are determined, and these are used as the environmental status information.

[0011] Based on the wind speed information, it is determined whether the blades of all the wind turbines inside the offshore wind farm are in a state of extreme rotation; if so, the corresponding wind turbine is identified as a target wind turbine that may experience a failure event. Based on the wave information, it is determined whether the rotors of all the wind turbines inside the offshore wind farm are impacted by waves; if so, the corresponding wind turbine is identified as a target wind turbine that may experience a failure event, and the positions of all target wind turbines are calibrated.

[0012] Optionally, based on the environmental state information, all target wind turbines within the offshore wind farm that may experience fault events are identified, including:

[0013] Step S1: Let the salt spray concentration of the offshore wind farm be C, and the exposure time of the wind turbines at the offshore wind farm from installation to the current salt spray concentration environment be t. Then, the influence factor of salt spray on the wind turbines is:

[0014]

[0015] In the above formula (1), Y(t) is the influence factor of salt spray on the wind turbine, k is the salt spray corrosion constant, which is determined according to the environment of the offshore wind farm, e is the natural constant, and T C This is the preset salt spray corrosion time constant;

[0016] Step S2, let the generator's time from installation time T0 to the current time T t Let T(i) be the ambient temperature at any time i, where i is greater than T0 and less than T0. t If the integer is an integer, then the influence factor of temperature change on the wind turbine is:

[0017]

[0018] In formula (2) above, W(t) is the influence factor of temperature change on the wind turbine, f is the temperature sensitivity coefficient of the wind turbine, and its value is greater than 0 and less than 0.2. b The preset standard temperature value;

[0019] Step S3: Based on the calculation results of steps S1 and S2 above, determine the failure index of the generator wind turbine failure event.

[0020] Z(t)=αW(t)+(1-α) Y(t) (3)

[0021] In the above formula (3), Z(t) is the fault index of the generator wind turbine failure event, and α is the adjustment coefficient, which takes a value greater than 0 and less than 1.

[0022] When Z(t) is greater than 0.6, it indicates that the generating wind turbine belongs to the target generating wind turbine that may experience a failure event;

[0023] When Z(t) is less than or equal to 0.6, it indicates that the generating wind turbine is not among the target generating wind turbines that may experience a failure event.

[0024] Optionally, based on the distribution location information of all target wind turbines within the offshore wind farm, a directional radar scan is performed on the corresponding area within the offshore wind farm to obtain radar echo data for the corresponding area; the radar echo data is analyzed to identify abnormal wind turbines within the offshore wind farm that have ceased power generation, including:

[0025] Based on the distribution location information of all target wind turbines within the offshore wind farm, the relative distance and relative azimuth of each target wind turbine to the onshore radar are determined; based on the relative distance and relative azimuth, the distribution range of the corresponding area for the onshore radar to perform directional radar scanning within the offshore wind farm is determined, thereby performing directional radar scanning on the corresponding area and obtaining radar echo data for the corresponding area.

[0026] The radar echo data is analyzed for echo intensity characteristics and echo reflection frequency characteristics to obtain radar echo intensity distribution information and echo reflection frequency change information. Based on the intensity distribution information, abnormal wind turbines that have collapsed and stopped generating electricity within the offshore wind farm are identified. Based on the echo reflection frequency change information, abnormal wind turbines that have stopped generating electricity within the offshore wind farm due to rotor stoppage are identified.

[0027] Optionally, based on the real-time power generation status data of all wind turbines maintaining power generation operation within the offshore wind farm, wind turbines not operating at full load are identified; then, based on the power adjustable capacity information of the wind turbines not operating at full load and the environmental status information, the operating status of the wind turbines not operating at full load is adjusted, including:

[0028] Based on all abnormal wind turbines that have stopped generating power within the offshore wind farm, identify all wind turbines that are still generating power within the offshore wind farm, and obtain real-time power generation status data for each of these wind turbines. Analyze the real-time power generation status data to determine the peak power generation of each wind turbine that is still generating power. If the peak power generation exceeds a preset power generation threshold, determine that the wind turbine is in a full-load power generation state; otherwise, determine that the wind turbine is not in a full-load power generation state.

[0029] Based on the average actual power generation and rated power generation of the wind turbines not at full power generation capacity, determine the adjustable power capacity information of the wind turbines not at full power generation capacity; based on the adjustable power capacity information and the wind speed information corresponding to the sea area, adjust the maximum allowable impeller rotation speed of the wind turbines not at full power generation capacity.

[0030] A condition monitoring system for offshore wind farms, comprising:

[0031] The marine environment status determination module is used to determine the environmental status information of the marine area corresponding to the meteorological satellite monitoring data of the marine area where the offshore wind farm is located.

[0032] The first wind turbine identification module is used to identify all target wind turbines that may experience malfunctions inside the offshore wind farm based on the environmental status information, and to mark the location of all target wind turbines.

[0033] The directional radar scanning module is used to perform directional radar scanning on the corresponding area inside the offshore wind farm based on the distribution location information of all target wind turbines inside the offshore wind farm, and obtain radar echo data of the corresponding area.

[0034] The second wind turbine identification module is used to analyze the radar echo data and identify abnormal wind turbines that have stopped generating electricity inside the offshore wind farm.

[0035] The third wind turbine identification module is used to identify wind turbines that are not in full power generation capacity based on the real-time power generation status data of all wind turbines maintaining power generation operation inside the offshore wind farm.

[0036] The wind turbine operation adjustment module is used to adjust the operating status of wind turbines that are not in full-load operation based on the adjustable power capacity information of wind turbines that are not in full-load operation and the environmental status information.

[0037] Optionally, the marine environment status determination module is used to determine the environmental status information corresponding to the marine area based on meteorological satellite monitoring data of the marine area where the offshore wind farm is located, including:

[0038] Meteorological satellite remote sensing image data of the sea area where the offshore wind farm is located is acquired, and the meteorological satellite remote sensing image data is analyzed to obtain the air pressure change distribution information of the sea area; based on the air pressure change distribution information and the tidal rise and fall status information of the sea area, the wind speed status information and wave status information of the sea area are determined, and these are used as the environmental status information.

[0039] The first wind turbine identification module is used to determine all target wind turbines that may experience malfunctions within the offshore wind farm based on the environmental state information, and to locate all target wind turbines, including:

[0040] Based on the wind speed information, it is determined whether the blades of all the wind turbines inside the offshore wind farm are in a state of extreme rotation; if so, the corresponding wind turbine is identified as a target wind turbine that may experience a failure event. Based on the wave information, it is determined whether the rotors of all the wind turbines inside the offshore wind farm are impacted by waves; if so, the corresponding wind turbine is identified as a target wind turbine that may experience a failure event, and the positions of all target wind turbines are calibrated.

[0041] Optionally, the directional radar scanning module is used to perform directional radar scanning on a corresponding area within the offshore wind farm based on the distribution location information of all target wind turbines within the offshore wind farm, to obtain radar echo data for the corresponding area, including:

[0042] Based on the distribution location information of all target wind turbines within the offshore wind farm, the relative distance and relative azimuth of each target wind turbine to the onshore radar are determined; based on the relative distance and relative azimuth, the distribution range of the corresponding area for the onshore radar to perform directional radar scanning within the offshore wind farm is determined, thereby performing directional radar scanning on the corresponding area and obtaining radar echo data for the corresponding area.

[0043] The second wind turbine identification module is used to analyze the radar echo data and identify abnormal wind turbines that have stopped generating electricity within the offshore wind farm, including:

[0044] The radar echo data is analyzed for echo intensity characteristics and echo reflection frequency characteristics to obtain radar echo intensity distribution information and echo reflection frequency change information. Based on the intensity distribution information, abnormal wind turbines that have collapsed and stopped generating electricity within the offshore wind farm are identified. Based on the echo reflection frequency change information, abnormal wind turbines that have stopped generating electricity within the offshore wind farm due to rotor stoppage are identified.

[0045] Optionally, the third wind turbine identification module is used to identify wind turbines that are not operating at full capacity based on real-time power generation status data of all wind turbines maintaining power generation operation within the offshore wind farm, including:

[0046] Based on all abnormal wind turbines that have stopped generating power within the offshore wind farm, identify all wind turbines that are still generating power within the offshore wind farm, and obtain real-time power generation status data for each of these wind turbines. Analyze the real-time power generation status data to determine the peak power generation of each wind turbine that is still generating power. If the peak power generation exceeds a preset power generation threshold, determine that the wind turbine is in a full-load power generation state; otherwise, determine that the wind turbine is not in a full-load power generation state.

[0047] The wind turbine operation adjustment module is used to adjust the operating status of wind turbines that are not at full power generation capacity based on the adjustable capacity information of the wind turbines not at full power generation capacity and the environmental status information, including:

[0048] Based on the average actual power generation and rated power generation of the wind turbines not at full power generation capacity, determine the adjustable power capacity information of the wind turbines not at full power generation capacity; based on the adjustable power capacity information and the wind speed information corresponding to the sea area, adjust the maximum allowable impeller rotation speed of the wind turbines not at full power generation capacity.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] This application provides a method and system for monitoring the status of offshore wind farms. Based on meteorological satellite monitoring data of the sea area where the offshore wind farm is located, it determines the corresponding environmental status information of the sea area, thereby identifying all target wind turbines within the offshore wind farm that may experience malfunctions. It identifies and locates wind turbines within the offshore wind farm that may malfunction due to the marine environment. Based on the distribution location information of all target wind turbines, it performs directional radar scanning on the corresponding areas within the offshore wind farm to obtain radar echo data of the corresponding areas, thereby identifying abnormal wind turbines within the offshore wind farm that have stopped generating electricity. Furthermore, based on the real-time power generation status data of all wind turbines maintaining power generation within the offshore wind farm, it identifies wind turbines that are not operating at full capacity. Combining the power adjustable capacity information and environmental status information of the corresponding wind turbines, it adjusts the operating status of wind turbines that are not operating at full capacity, accurately identifies faulty wind turbines, and adaptively schedules the power generation intensity of wind turbines, ensuring that the offshore wind farm can still maintain stable and continuous power transmission even in the event of wind turbine malfunctions. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0052] Figure 1 This is a flowchart illustrating a condition monitoring method for offshore wind farms provided by the present invention.

[0053] Figure 2 This is a schematic diagram of a condition monitoring system for offshore wind farms provided by the present invention. Detailed Implementation

[0054] To make the above-mentioned objectives, features, and advantages of this application more apparent and understandable, the specific embodiments of this application will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, it should be noted that, for ease of description, only the parts relevant to this application are shown in the accompanying drawings, not the entire structure. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of this application.

[0055] The terms “comprising” and “having”, and any variations thereof, used in this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the steps or units listed, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus.

[0056] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0057] Please see Figure 1 As shown in the figure, an embodiment of this application provides a condition monitoring method for offshore wind farms. The condition monitoring method for offshore wind farms includes:

[0058] Meteorological satellite monitoring data of the sea area where the offshore wind farm is located is used to determine the corresponding environmental status information of the sea area; based on the environmental status information, all target wind turbines that may experience failure events within the offshore wind farm are identified, and the locations of all target wind turbines are marked.

[0059] Based on the distribution location information of all target wind turbines within the offshore wind farm, directional radar scanning is performed on the corresponding areas within the offshore wind farm to obtain radar echo data for the corresponding areas; the radar echo data is then analyzed to identify abnormal wind turbines within the offshore wind farm that have ceased power generation.

[0060] Based on the real-time power generation status data of all the wind turbines maintaining power generation operation within the offshore wind farm, wind turbines that are not in full-load power generation are identified; then, based on the power adjustable capacity information of the wind turbines that are not in full-load power generation and the environmental status information, the working status of the wind turbines that are not in full-load power generation is adjusted.

[0061] The beneficial effects of the above embodiments are as follows: the method for monitoring the status of offshore wind farms is based on meteorological satellite monitoring data of the sea area where the offshore wind farm is located to determine the environmental status information of the corresponding sea area, thereby identifying all target wind turbines that may experience failure events within the offshore wind farm, and marking and locating wind turbines within the offshore wind farm that may fail due to the influence of the sea environment; based on the distribution location information of all target wind turbines, directional radar scanning is performed on the corresponding area within the offshore wind farm to obtain radar echo data of the corresponding area, thereby identifying abnormal wind turbines within the offshore wind farm that have stopped generating electricity; furthermore, based on the real-time power generation status data of all wind turbines within the offshore wind farm that are maintaining power generation operation, wind turbines that are not in full-load power generation state are identified, and combined with the power adjustable capacity information and environmental status information of the corresponding wind turbines, the working status of wind turbines that are not in full-load power generation state is adjusted, accurately identifying faulty wind turbines, and adaptively scheduling the power generation intensity of wind turbines, so that the offshore wind farm can still maintain stable and continuous power transmission even in the event of wind turbine failure.

[0062] In another embodiment, meteorological satellite monitoring data of the sea area where the offshore wind farm is located is used to determine the corresponding environmental status information of the sea area; based on the environmental status information, all target wind turbines that may experience fault events within the offshore wind farm are identified, and the locations of all target wind turbines are calibrated, including:

[0063] Meteorological satellite remote sensing image data of the sea area where the offshore wind farm is located is acquired, and the meteorological satellite remote sensing image data is analyzed to obtain the air pressure change distribution information of the sea area; based on the air pressure change distribution information and the tidal rise and fall status information of the sea area, the wind speed status information and wave status information of the sea area are determined, which are used as the environmental status information.

[0064] Based on the wind speed information, it is determined whether the blades of all the wind turbines in the offshore wind farm are in the limit rotation state; if so, the corresponding wind turbine is identified as the target wind turbine that may experience a failure event. Based on the wave information, it is determined whether the rotors of all the wind turbines in the offshore wind farm are impacted by waves; if so, the corresponding wind turbine is identified as the target wind turbine that may experience a failure event, and the positions of all target wind turbines are marked.

[0065] The beneficial effects of the above embodiments are that offshore wind farms include several wind turbines installed in nearshore areas. Nearshore areas are located at the boundary between land and sea, and are more significantly affected by severe weather such as typhoons and storm surges. Especially during spring tides, the wind turbines are subjected to even greater forces from the impact of ocean waves. When the intensity of the waves is high or the wave height exceeds the height of the wind turbines, problems such as the turbines tipping over or the impellers being damaged may occur. To comprehensively identify all wind turbines within an offshore wind farm, meteorological satellite remote sensing image data of the sea area where the offshore wind farm is located is analyzed to obtain information on the distribution of air pressure changes in that sea area. The wind speed in this sea area is related to the gradient distribution of air pressure changes; the higher the wind speed, the greater the intensity and height of the corresponding waves. Based on this air pressure change distribution information and the tidal fluctuation information of the sea area, the corresponding wind speed and wave status information are determined. This allows for accurate identification of the wind and wave effects on the offshore wind farm, providing a reliable basis for subsequent judgments on whether wind turbine malfunctions will occur. The wind speed information can be, but is not limited to, the wind speed at different locations within the offshore wind farm, and the wave status information can be, but is not limited to, the wave height distribution information formed within the offshore wind farm. Based on the wind speed information, it is determined whether the blades of all the wind turbines within the offshore wind farm are at their limit of rotation, i.e., whether the wind force acting on the impeller surface of each turbine will cause the average rotational speed of the impeller to exceed the currently limited maximum rotational speed. Based on the wave information, it is also determined whether the impellers of all the wind turbines within the offshore wind farm are being impacted by waves, i.e., whether the impellers of each turbine are being impacted by waves (this can be achieved by determining the magnitude of the difference between the impeller's height and the wave height). This identifies all target wind turbines within the offshore wind farm that may experience malfunctions, and further positions all target wind turbines, defining the corresponding area within the offshore wind farm for subsequent directional radar scanning, thus improving the reliability of directional radar scanning.

[0066] In another embodiment, based on the environmental state information, all target wind turbines within the offshore wind farm that may experience malfunctions are identified, including:

[0067] Step S1: Let the salt spray concentration of the offshore wind farm be C, and the exposure time of the wind turbines at the offshore wind farm from installation to the current salt spray concentration environment be t. Then, the influence factor of salt spray on the wind turbines is:

[0068]

[0069] In the above formula (1), Y(t) is the influence factor of salt spray on the wind turbine, k is the salt spray corrosion constant, which is determined according to the environment of the offshore wind farm, e is the natural constant, and T C This is the preset salt spray corrosion time constant;

[0070] Step S2, let the generator's time from installation time T0 to the current time T t Let T(i) be the ambient temperature at any time i, where i is greater than T0 and less than T0. t If the integer is an integer, then the influence factor of temperature change on the wind turbine is:

[0071]

[0072] In formula (2) above, W(t) is the influence factor of temperature change on the wind turbine, f is the temperature sensitivity coefficient of the wind turbine, and its value is greater than 0 and less than 0.2. b The preset standard temperature value;

[0073] Step S3: Based on the calculation results of steps S1 and S2 above, determine the failure index of the generator wind turbine failure event.

[0074] Z(t)=αW(t)+(1-α) Y(t) (3)

[0075] In the above formula (3), Z(t) is the fault index of the generator wind turbine failure event, and α is the adjustment coefficient, which takes a value greater than 0 and less than 1.

[0076] When Z(t) is greater than 0.6, it indicates that the generating wind turbine belongs to the target generating wind turbine that may experience a failure event;

[0077] When Z(t) is less than or equal to 0.6, it indicates that the generating wind turbine is not among the target generating wind turbines that may experience a failure event.

[0078] The beneficial effects of the above embodiments, besides wind speed and ocean waves, also include the potential for wind turbine malfunctions caused by salt spray and temperature changes. Salt spray can accelerate the corrosion of the wind turbine's metal structure, affecting its mechanical integrity, while temperature changes can alter the material properties of the wind turbine, leading to malfunctions. Therefore, when determining whether a wind turbine is likely to malfunction, salt spray and temperature information are also considered to assess the likelihood of a malfunction, preventing situations where significant economic losses occur due to a lack of proactive measures to address malfunctions caused by salt spray and temperature factors. Accurate assessment of the potential for malfunctions based on salt spray and temperature changes avoids situations where significant economic losses occur due to a lack of proactive measures to address malfunctions caused by salt spray and temperature factors.

[0079] In another embodiment, based on the distribution location information of all target wind turbines within the offshore wind farm, a directional radar scan is performed on the corresponding area within the offshore wind farm to obtain radar echo data for the corresponding area; the radar echo data is analyzed to identify abnormal wind turbines within the offshore wind farm that have ceased power generation, including:

[0080] Based on the distribution location information of all target wind turbines within the offshore wind farm, the relative distance and relative azimuth of each target wind turbine to the land-based radar are determined. Based on the relative distance and relative azimuth, the distribution range of the corresponding area for the land-based radar to perform directional radar scanning within the offshore wind farm is determined, thereby performing directional radar scanning on the corresponding area and obtaining radar echo data for the corresponding area.

[0081] The radar echo data is analyzed for echo intensity characteristics and echo reflection frequency characteristics to obtain radar echo intensity distribution information and echo reflection frequency variation information. Based on the intensity distribution information, abnormal wind turbines that have collapsed and stopped generating electricity within the offshore wind farm are identified. Based on the echo reflection frequency variation information, abnormal wind turbines that have stopped rotating and stopped generating electricity within the offshore wind farm are identified.

[0082] The beneficial effects of the above embodiments are that, in order to ensure real-time, weather-independent detection of all wind turbines within an offshore wind farm, a scanning radar is typically installed on the coastal land corresponding to the sea area where the offshore wind farm is located. This scanning radar performs directional scanning of all target wind turbines. Specifically, based on the distribution location information of all target wind turbines within the offshore wind farm, the relative distance and relative azimuth of each target wind turbine to the land-based radar are determined. This determines the corresponding area distribution range for the land-based radar to perform directional radar scanning within the offshore wind farm, enabling the land-based radar to perform directional radar scanning of the corresponding area and obtain radar echo data for that area. During the directional radar scan of a corresponding area by the land-based radar, if a wind turbine within that area tilts over, the corresponding radar echo intensity will decrease. If the rotor of a wind turbine within that area stops rotating, the radar echo cannot be reflected back periodically. In this case, the radar echo data is analyzed for echo intensity characteristics and echo reflection frequency characteristics to obtain radar echo intensity distribution information and echo reflection frequency variation information. Based on this intensity distribution information, abnormal wind turbines that have tilted and stopped generating electricity within the offshore wind farm are identified. Based on the echo reflection frequency variation information, abnormal wind turbines that have stopped generating electricity due to rotor cessation are identified. This allows for accurate identification of abnormal wind turbines, providing a precise target range for subsequent adjustments to the operating status of the corresponding wind turbines within the offshore wind farm.

[0083] In another embodiment, based on real-time power generation status data of all wind turbines maintaining power generation operation within the offshore wind farm, wind turbines not operating at full load are identified; then, based on the power adjustable capacity information of the wind turbines not operating at full load and the environmental status information, the operating status of the wind turbines not operating at full load is adjusted, including:

[0084] Based on all abnormal wind turbines that have stopped generating power within the offshore wind farm, identify all wind turbines that are still generating power within the offshore wind farm and obtain real-time power generation status data for each of these turbines. Analyze this real-time power generation status data to determine the peak power generation of each wind turbine that is still generating power. If the peak power generation exceeds a preset power generation threshold, determine that the wind turbine is in full-load power generation; otherwise, determine that the wind turbine is not in full-load power generation.

[0085] Based on the average actual power generation and rated power generation of wind turbines not operating at full load, determine the adjustable power capacity information of wind turbines not operating at full load; based on this adjustable power capacity information and the wind speed information corresponding to the sea area, adjust the maximum allowable impeller rotation speed of wind turbines not operating at full load.

[0086] The beneficial effects of the above embodiments are as follows: Based on all abnormal wind turbines within the offshore wind farm whose rotors have stopped rotating and thus ceased power generation, all wind turbines within the offshore wind farm are screened to determine those that are maintaining power generation. The real-time power generation status data of each wind turbine maintaining power generation is analyzed to determine its peak power output. Then, a threshold comparison is performed on these peak power outputs to determine whether the wind turbine is in a full-load state. If the wind turbine is in a full-load state, it indicates that the wind turbine cannot increase its power output; if the wind turbine is not in a full-load state, it indicates that the wind turbine can further increase its power output. Furthermore, based on the average actual power output and rated power output of the wind turbines not in a full-load state, the adjustable power capacity information of the wind turbines not in a full-load state is determined, i.e., the power output value that the wind turbines not in a full-load state can further increase. Based on the adjustable power capacity information and the wind speed information corresponding to the sea area, the maximum allowable rotor speed of the wind turbines that are not in full power generation capacity is adjusted. For example, the maximum allowable rotor speed of the wind turbines that are not in full power generation capacity is increased, thereby removing the rotor speed restriction on the wind turbines that are not in full power generation capacity. This allows the wind turbines that are not in full power generation capacity to further increase their rotor speed, thereby making up for the power generation gap caused by the failure of the wind turbines. This ensures that the offshore wind farm can still maintain stable and continuous power transmission even in the event of a wind turbine failure.

[0087] Please see Figure 2 As shown in the figure, an embodiment of this application provides a condition monitoring system for offshore wind farms. The condition monitoring system for offshore wind farms includes:

[0088] The marine environment status determination module is used to determine the environmental status information of the marine area where the offshore wind farm is located based on meteorological satellite monitoring data.

[0089] The first wind turbine identification module is used to identify all target wind turbines that may experience malfunctions within the offshore wind farm based on the environmental status information, and to mark the location of all target wind turbines.

[0090] The directional radar scanning module is used to perform directional radar scanning of the corresponding area inside the offshore wind farm based on the distribution location information of all target wind turbines inside the offshore wind farm, and obtain the radar echo data of the corresponding area.

[0091] The second wind turbine identification module is used to analyze the radar echo data and identify abnormal wind turbines that have stopped generating electricity inside the offshore wind farm.

[0092] The third wind turbine identification module is used to identify wind turbines that are not in full power generation based on the real-time power generation status data of all wind turbines that maintain power generation operation within the offshore wind farm.

[0093] The wind turbine operation adjustment module is used to adjust the operating status of wind turbines that are not in full-load operation based on the adjustable capacity information of the wind turbines that are not in full-load operation and the environmental status information.

[0094] The beneficial effects of the above embodiments are as follows: the offshore wind farm status monitoring system determines the environmental status information of the corresponding sea area based on meteorological satellite monitoring data of the sea area where the offshore wind farm is located, thereby identifying all target wind turbines that may experience failure events within the offshore wind farm, and marking and locating wind turbines that may fail due to the influence of the sea environment; based on the distribution location information of all target wind turbines, directional radar scanning is performed on the corresponding area within the offshore wind farm to obtain radar echo data of the corresponding area, thereby identifying abnormal wind turbines that have stopped generating electricity within the offshore wind farm; furthermore, based on the real-time power generation status data of all wind turbines maintaining power generation operation within the offshore wind farm, wind turbines that are not in full-load power generation state are identified, and combined with the power adjustable capacity information and environmental status information of the corresponding wind turbines, the working status of wind turbines that are not in full-load power generation state is adjusted, accurately identifying faulty wind turbines, and adaptively scheduling the power generation intensity of wind turbines, so that the offshore wind farm can still maintain stable and continuous power transmission even in the event of wind turbine failure.

[0095] In another embodiment, the marine environmental status determination module is used to determine the environmental status information corresponding to the marine area based on meteorological satellite monitoring data of the marine area where the offshore wind farm is located, including:

[0096] Meteorological satellite remote sensing image data of the sea area where the offshore wind farm is located is acquired, and the meteorological satellite remote sensing image data is analyzed to obtain the air pressure change distribution information of the sea area; based on the air pressure change distribution information and the tidal rise and fall status information of the sea area, the wind speed status information and wave status information of the sea area are determined, which are used as the environmental status information.

[0097] The first wind turbine identification module is used to identify all target wind turbines within the offshore wind farm that may experience malfunctions based on the environmental status information, and to locate all target wind turbines, including:

[0098] Based on the wind speed information, it is determined whether the blades of all the wind turbines in the offshore wind farm are in the limit rotation state; if so, the corresponding wind turbine is identified as the target wind turbine that may experience a failure event. Based on the wave information, it is determined whether the rotors of all the wind turbines in the offshore wind farm are impacted by waves; if so, the corresponding wind turbine is identified as the target wind turbine that may experience a failure event, and the positions of all target wind turbines are marked.

[0099] The beneficial effects of the above embodiments are that offshore wind farms include several wind turbines installed in nearshore areas. Nearshore areas are located at the boundary between land and sea, and are more significantly affected by severe weather such as typhoons and storm surges. Especially during spring tides, the wind turbines are subjected to even greater forces from the impact of ocean waves. When the intensity of the waves is high or the wave height exceeds the height of the wind turbines, problems such as the turbines tipping over or the impellers being damaged may occur. To comprehensively identify all wind turbines within an offshore wind farm, meteorological satellite remote sensing image data of the sea area where the offshore wind farm is located is analyzed to obtain information on the distribution of air pressure changes in that sea area. The wind speed in this sea area is related to the gradient distribution of air pressure changes; the higher the wind speed, the greater the intensity and height of the corresponding waves. Based on this air pressure change distribution information and the tidal fluctuation information of the sea area, the corresponding wind speed and wave status information are determined. This allows for accurate identification of the wind and wave effects on the offshore wind farm, providing a reliable basis for subsequent judgments on whether wind turbine malfunctions will occur. The wind speed information can be, but is not limited to, the wind speed at different locations within the offshore wind farm, and the wave status information can be, but is not limited to, the wave height distribution information formed within the offshore wind farm. Based on the wind speed information, it is determined whether the blades of all the wind turbines within the offshore wind farm are at their limit of rotation, i.e., whether the wind force acting on the impeller surface of each turbine will cause the average rotational speed of the impeller to exceed the currently limited maximum rotational speed. Based on the wave information, it is also determined whether the impellers of all the wind turbines within the offshore wind farm are being impacted by waves, i.e., whether the impellers of each turbine are being impacted by waves (this can be achieved by determining the magnitude of the difference between the impeller's height and the wave height). This identifies all target wind turbines within the offshore wind farm that may experience malfunctions, and further positions all target wind turbines, defining the corresponding area within the offshore wind farm for subsequent directional radar scanning, thus improving the reliability of directional radar scanning.

[0100] In another embodiment, the directional radar scanning module is used to perform directional radar scanning on a corresponding area within the offshore wind farm based on the distribution location information of all target wind turbines within the offshore wind farm, to obtain radar echo data of the corresponding area, including:

[0101] Based on the distribution location information of all target wind turbines within the offshore wind farm, the relative distance and relative azimuth of each target wind turbine to the land-based radar are determined. Based on the relative distance and relative azimuth, the distribution range of the corresponding area for the land-based radar to perform directional radar scanning within the offshore wind farm is determined, thereby performing directional radar scanning on the corresponding area and obtaining radar echo data for the corresponding area.

[0102] The second wind turbine identification module is used to analyze the radar echo data to identify abnormal wind turbines that have stopped generating electricity within the offshore wind farm, including:

[0103] The radar echo data is analyzed for echo intensity characteristics and echo reflection frequency characteristics to obtain radar echo intensity distribution information and echo reflection frequency variation information. Based on the intensity distribution information, abnormal wind turbines that have collapsed and stopped generating electricity within the offshore wind farm are identified. Based on the echo reflection frequency variation information, abnormal wind turbines that have stopped rotating and stopped generating electricity within the offshore wind farm are identified.

[0104] The beneficial effects of the above embodiments are that, in order to ensure real-time, weather-independent detection of all wind turbines within an offshore wind farm, a scanning radar is typically installed on the coastal land corresponding to the sea area where the offshore wind farm is located. This scanning radar performs directional scanning of all target wind turbines. Specifically, based on the distribution location information of all target wind turbines within the offshore wind farm, the relative distance and relative azimuth of each target wind turbine to the land-based radar are determined. This determines the corresponding area distribution range for the land-based radar to perform directional radar scanning within the offshore wind farm, enabling the land-based radar to perform directional radar scanning of the corresponding area and obtain radar echo data for that area. During the directional radar scan of a corresponding area by the land-based radar, if a wind turbine within that area tilts over, the corresponding radar echo intensity will decrease. If the rotor of a wind turbine within that area stops rotating, the radar echo cannot be reflected back periodically. In this case, the radar echo data is analyzed for echo intensity characteristics and echo reflection frequency characteristics to obtain radar echo intensity distribution information and echo reflection frequency variation information. Based on this intensity distribution information, abnormal wind turbines that have tilted and stopped generating electricity within the offshore wind farm are identified. Based on the echo reflection frequency variation information, abnormal wind turbines that have stopped generating electricity due to rotor cessation are identified. This allows for accurate identification of abnormal wind turbines, providing a precise target range for subsequent adjustments to the operating status of the corresponding wind turbines within the offshore wind farm.

[0105] In another embodiment, the third wind turbine identification module is used to identify wind turbines that are not operating at full capacity based on real-time power generation status data of all wind turbines maintaining power generation operation within the offshore wind farm, including:

[0106] Based on all abnormal wind turbines that have stopped generating power within the offshore wind farm, identify all wind turbines that are still generating power within the offshore wind farm and obtain real-time power generation status data for each of these turbines. Analyze this real-time power generation status data to determine the peak power generation of each wind turbine that is still generating power. If the peak power generation exceeds a preset power generation threshold, determine that the wind turbine is in full-load power generation; otherwise, determine that the wind turbine is not in full-load power generation.

[0107] This wind turbine operation adjustment module is used to adjust the operating status of wind turbines that are not operating at full load based on the adjustable capacity information of the wind turbines and the environmental status information. This includes:

[0108] Based on the average actual power generation and rated power generation of wind turbines not operating at full load, determine the adjustable power capacity information of wind turbines not operating at full load; based on this adjustable power capacity information and the wind speed information corresponding to the sea area, adjust the maximum allowable impeller rotation speed of wind turbines not operating at full load.

[0109] The beneficial effects of the above embodiments are as follows: Based on all abnormal wind turbines within the offshore wind farm whose rotors have stopped rotating and thus ceased power generation, all wind turbines within the offshore wind farm are screened to determine those that are maintaining power generation. The real-time power generation status data of each wind turbine maintaining power generation is analyzed to determine its peak power output. Then, a threshold comparison is performed on these peak power outputs to determine whether the wind turbine is in a full-load state. If the wind turbine is in a full-load state, it indicates that the wind turbine cannot increase its power output; if the wind turbine is not in a full-load state, it indicates that the wind turbine can further increase its power output. Furthermore, based on the average actual power output and rated power output of the wind turbines not in a full-load state, the adjustable power capacity information of the wind turbines not in a full-load state is determined, i.e., the power output value that the wind turbines not in a full-load state can further increase. Based on the adjustable power capacity information and the wind speed information corresponding to the sea area, the maximum allowable rotor speed of the wind turbines that are not in full power generation capacity is adjusted. For example, the maximum allowable rotor speed of the wind turbines that are not in full power generation capacity is increased, thereby removing the rotor speed restriction on the wind turbines that are not in full power generation capacity. This allows the wind turbines that are not in full power generation capacity to further increase their rotor speed, thereby making up for the power generation gap caused by the failure of the wind turbines. This ensures that the offshore wind farm can still maintain stable and continuous power transmission even in the event of a wind turbine failure.

[0110] In summary, this method and system for monitoring the condition of offshore wind farms uses meteorological satellite monitoring data of the sea area where the offshore wind farm is located to determine the corresponding environmental status information of the sea area. This allows for the identification of all target wind turbines within the offshore wind farm that may experience malfunctions, and the identification and location of wind turbines that may malfunction due to the influence of the sea environment. Based on the distribution location information of all target wind turbines, directional radar scanning is performed on the corresponding areas within the offshore wind farm to obtain radar echo data of the corresponding areas, thereby identifying abnormal wind turbines that have stopped generating electricity. Furthermore, based on the real-time power generation status data of all wind turbines maintaining power generation within the offshore wind farm, wind turbines that are not operating at full capacity are identified. Combining the power adjustable capacity information and environmental status information of the corresponding wind turbines, the operating status of wind turbines that are not operating at full capacity is adjusted. This accurately identifies faulty wind turbines and adaptively schedules the power generation intensity of wind turbines, ensuring that the offshore wind farm can still maintain stable and continuous power transmission even in the event of wind turbine malfunctions.

[0111] The above is only one specific embodiment of the present invention, and any improvements made based on the concept of the present invention shall be considered within the scope of protection of the present invention.

Claims

1. A condition monitoring method for offshore wind farms, characterized in that, include: Meteorological satellite monitoring data of the sea area where offshore wind farms are located are used to determine the environmental status information of the sea area. Based on the environmental status information, all target wind turbines that may experience malfunctions within the offshore wind farm are identified, and the locations of all target wind turbines are calibrated. Based on the distribution location information of all target wind turbines within the offshore wind farm, directional radar scanning is performed on the corresponding areas within the offshore wind farm to obtain radar echo data for the corresponding areas. The radar echo data is analyzed to identify abnormal wind turbines within the offshore wind farm that have stopped generating electricity. Based on the real-time power generation status data of all wind turbines maintaining power generation operation within the offshore wind farm, wind turbines that are not in full-load power generation are identified; then, based on the power adjustable capacity information of wind turbines that are not in full-load power generation and the environmental status information, the operating status of wind turbines that are not in full-load power generation is adjusted.

2. The condition monitoring method for offshore wind farms as described in claim 1, characterized in that: Meteorological satellite monitoring data of the sea area where the offshore wind farm is located is used to determine the corresponding environmental status information of the sea area; based on the environmental status information, all target wind turbines within the offshore wind farm that may experience malfunctions are identified, and the locations of all target wind turbines are calibrated, including: Meteorological satellite remote sensing image data of the sea area where the offshore wind farm is located is acquired, and the meteorological satellite remote sensing image data is analyzed to obtain the air pressure change distribution information of the sea area; based on the air pressure change distribution information and the tidal rise and fall status information of the sea area, the wind speed status information and wave status information of the sea area are determined, and these are used as the environmental status information. Based on the wind speed information, it is determined whether the blades of all the wind turbines inside the offshore wind farm are in a state of extreme rotation; if so, the corresponding wind turbine is identified as a target wind turbine that may experience a failure event. Based on the wave information, it is determined whether the rotors of all the wind turbines inside the offshore wind farm are impacted by waves; if so, the corresponding wind turbine is identified as a target wind turbine that may experience a failure event, and the positions of all target wind turbines are calibrated.

3. The condition monitoring method for offshore wind farms as described in claim 1, characterized in that: Based on the environmental state information, all target wind turbines within the offshore wind farm that may experience malfunctions are identified, including: Step S1: Let the salt spray concentration of the offshore wind farm be C, and the exposure time of the wind turbines at the offshore wind farm from installation to the current salt spray concentration environment be t. Then, the influence factor of salt spray on the wind turbines is: In the above formula (1), Y(t) is the influence factor of salt spray on the wind turbine, k is the salt spray corrosion constant, which is determined according to the environment of the offshore wind farm, e is the natural constant, and T C This is the preset salt spray corrosion time constant; Step S2, let the generator's time from installation time T0 to the current time T t Let T(i) be the ambient temperature at any time i, where i is greater than T0 and less than T0. t If the integer is an integer, then the influence factor of temperature change on the wind turbine is: In formula (2) above, W(t) is the influence factor of temperature change on the wind turbine, f is the temperature sensitivity coefficient of the wind turbine, and its value is greater than 0 and less than 0.

2. b The preset standard temperature value; Step S3: Based on the calculation results of steps S1 and S2 above, determine the failure index of the generator wind turbine failure event. Z(t)=αW(t)+(1-α) Y(t) (3) In the above formula (3), Z(t) is the fault index of the generator wind turbine failure event, and α is the adjustment coefficient, which takes a value greater than 0 and less than 1. When Z(t) is greater than 0.6, it indicates that the generating wind turbine belongs to the target generating wind turbine that may experience a failure event; When Z(t) is less than or equal to 0.6, it indicates that the generating wind turbine is not among the target generating wind turbines that may experience a failure event.

4. The condition monitoring method for offshore wind farms as described in claim 1, characterized in that: Based on the distribution location information of all target wind turbines within the offshore wind farm, directional radar scanning is performed on the corresponding area within the offshore wind farm to obtain radar echo data for the corresponding area. Analyzing the radar echo data to identify abnormal wind turbines that have ceased power generation within the offshore wind farm includes: Based on the distribution location information of all target wind turbines within the offshore wind farm, the relative distance and relative orientation of each target wind turbine to the onshore radar are determined. Based on the relative distance and the relative azimuth, the distribution range of the corresponding area for the directional radar scan of the offshore wind farm by the land radar is determined, thereby performing directional radar scan of the corresponding area and obtaining radar echo data of the corresponding area. The radar echo data is analyzed for echo intensity characteristics and echo reflection frequency characteristics to obtain radar echo intensity distribution information and echo reflection frequency change information. Based on the intensity distribution information, abnormal wind turbines that have collapsed and stopped generating electricity within the offshore wind farm are identified. Based on the echo reflection frequency change information, abnormal wind turbines that have stopped generating electricity within the offshore wind farm due to rotor stoppage are identified.

5. The condition monitoring method for offshore wind farms as described in claim 1, characterized in that: Based on real-time power generation status data of all wind turbines maintaining power generation operation within the offshore wind farm, wind turbines not operating at full load are identified; then, based on the power adjustable capacity information of the wind turbines not operating at full load and the environmental status information, the operating status of the wind turbines not operating at full load is adjusted, including: Based on all abnormal wind turbines that have stopped generating power within the offshore wind farm, identify all wind turbines that are still generating power within the offshore wind farm, and obtain real-time power generation status data for each of these wind turbines. Analyze the real-time power generation status data to determine the peak power generation of each wind turbine that is still generating power. If the peak power generation exceeds a preset power generation threshold, determine that the wind turbine is in a full-load power generation state; otherwise, determine that the wind turbine is not in a full-load power generation state. Based on the average actual power generation and rated power generation of the wind turbines not at full power generation capacity, determine the adjustable power capacity information of the wind turbines not at full power generation capacity; based on the adjustable power capacity information and the wind speed information corresponding to the sea area, adjust the maximum allowable impeller rotation speed of the wind turbines not at full power generation capacity.

6. A condition monitoring system for offshore wind farms, characterized in that, include: The marine environment status determination module is used to determine the environmental status information of the marine area corresponding to the meteorological satellite monitoring data of the marine area where the offshore wind farm is located. The first wind turbine identification module is used to identify all target wind turbines that may experience malfunctions inside the offshore wind farm based on the environmental status information, and to mark the location of all target wind turbines. The directional radar scanning module is used to perform directional radar scanning on the corresponding area inside the offshore wind farm based on the distribution location information of all target wind turbines inside the offshore wind farm, and obtain radar echo data of the corresponding area. The second wind turbine identification module is used to analyze the radar echo data and identify abnormal wind turbines that have stopped generating electricity inside the offshore wind farm. The third wind turbine identification module is used to identify wind turbines that are not in full power generation based on the real-time power generation status data of all wind turbines maintaining power generation operation inside the offshore wind farm. The wind turbine operation adjustment module is used to adjust the operating status of wind turbines that are not in full-load operation based on the adjustable power capacity information of wind turbines that are not in full-load operation and the environmental status information.

7. The condition monitoring system for offshore wind farms as described in claim 6, characterized in that: The marine environment status determination module is used to determine the environmental status information corresponding to the marine area based on meteorological satellite monitoring data of the marine area where the offshore wind farm is located, including: Meteorological satellite remote sensing image data of the sea area where the offshore wind farm is located is acquired, and the meteorological satellite remote sensing image data is analyzed to obtain the air pressure change distribution information of the sea area; based on the air pressure change distribution information and the tidal rise and fall status information of the sea area, the wind speed status information and wave status information of the sea area are determined, and these are used as the environmental status information. The first wind turbine identification module is used to determine all target wind turbines that may experience malfunctions within the offshore wind farm based on the environmental state information, and to locate all target wind turbines, including: Based on the wind speed information, it is determined whether the blades of all the wind turbines inside the offshore wind farm are in a state of extreme rotation; if so, the corresponding wind turbine is identified as a target wind turbine that may experience a failure event. Based on the wave information, it is determined whether the rotors of all the wind turbines inside the offshore wind farm are impacted by waves; if so, the corresponding wind turbine is identified as a target wind turbine that may experience a failure event, and the positions of all target wind turbines are calibrated.

8. The condition monitoring system for offshore wind farms as described in claim 6, characterized in that: The directional radar scanning module is used to perform directional radar scanning on a corresponding area within the offshore wind farm based on the distribution location information of all target wind turbines within the offshore wind farm, and to obtain radar echo data for the corresponding area, including: Based on the distribution location information of all target wind turbines within the offshore wind farm, the relative distance and relative azimuth of each target wind turbine to the onshore radar are determined; based on the relative distance and relative azimuth, the distribution range of the corresponding area for the onshore radar to perform directional radar scanning within the offshore wind farm is determined, thereby performing directional radar scanning on the corresponding area and obtaining radar echo data for the corresponding area. The second wind turbine identification module is used to analyze the radar echo data and identify abnormal wind turbines that have stopped generating electricity inside the offshore wind farm, including: The radar echo data is analyzed for echo intensity characteristics and echo reflection frequency characteristics to obtain radar echo intensity distribution information and echo reflection frequency change information. Based on the intensity distribution information, abnormal wind turbines that have collapsed and stopped generating electricity within the offshore wind farm are identified. Based on the echo reflection frequency change information, abnormal wind turbines that have stopped generating electricity within the offshore wind farm due to rotor stoppage are identified.

9. The condition monitoring system for offshore wind farms as described in claim 6, characterized in that: The third wind turbine identification module is used to identify wind turbines that are not operating at full capacity based on real-time power generation status data of all wind turbines maintaining power generation operation within the offshore wind farm, including: Based on all abnormal wind turbines that have stopped generating power within the offshore wind farm, identify all wind turbines that are still generating power within the offshore wind farm, and obtain real-time power generation status data for each of these wind turbines. Analyze the real-time power generation status data to determine the peak power generation of each wind turbine that is still generating power. If the peak power generation exceeds a preset power generation threshold, determine that the wind turbine is in a full-load power generation state; otherwise, determine that the wind turbine is not in a full-load power generation state. The wind turbine operation adjustment module is used to adjust the operating status of wind turbines that are not at full power generation capacity based on the adjustable capacity information of the wind turbines not at full power generation capacity and the environmental status information, including: Based on the average actual power generation and rated power generation of the wind turbines not at full power generation capacity, determine the adjustable power capacity information of the wind turbines not at full power generation capacity; based on the adjustable power capacity information and the wind speed information corresponding to the sea area, adjust the maximum allowable impeller rotation speed of the wind turbines not at full power generation capacity.

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