CN116231645A - Offshore wind farm power generation amount calculation method, calculation system and calculation terminal - Google Patents
Offshore wind farm power generation amount calculation method, calculation system and calculation terminal Download PDFInfo
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Abstract
The invention provides a method, a system and a terminal for calculating the power generation amount of an offshore wind farm, and relates to the technical field of offshore wind power generation, wherein ocean wave data and wind resource data of a preset time sequence are obtained; calculating the relation between sea surface roughness and sea waves by adopting a TY01 scheme to obtain time sequence sea surface roughness data; dividing the time series sea surface roughness data into n classes according to the size; the atmospheric stability is divided into six types by adopting a temperature gradient method; combining n types of time series sea surface roughness and six types of atmosphere stability combinations into 6 multiplied by n atmosphere stability-roughness combinations; constructing a CFD flow field model; calculating time sequence segment power data; and integrating statistical calculation to obtain the generated energy of each wind turbine generator in the offshore wind farm, and obtaining the generated energy result of the whole offshore wind farm after integration. The invention ensures that the calculation of the generated energy of the offshore wind power plant is more accurate, and has higher reference value for site selection, fan arrangement and investment benefit evaluation of the offshore wind power plant.
Description
Technical Field
The invention relates to the technical field of offshore wind power generation, in particular to a method, a system and a terminal for calculating the generated energy of an offshore wind farm based on time sequence wave data.
Background
As offshore wind power is growing more and more rapidly, large offshore wind farms have been or are being built in many areas. Particularly in some areas with longer coastlines and richer offshore wind energy resources, accurate assessment of wind energy resources is required for investment construction of offshore wind farms.
Sea surface roughness is an important factor affecting offshore wind energy assessment, and is not only dependent on wind speed, but also related to sea waves, the intensity and propagation of which affect the distribution of sea surface roughness. The sea wave is used as a real-time dynamic variable, is not taken into consideration in the calculation of the power generation amount of the traditional offshore wind farm, reduces the precision of offshore wind energy resource assessment, and causes larger deviation in the calculation of the power generation amount of the offshore wind farm.
Disclosure of Invention
The invention provides a method for calculating the power generation amount of an offshore wind farm, which realizes the calculation of the power generation amount of the offshore wind farm under the condition of considering real-time dynamic sea waves.
The method for calculating the generating capacity of the offshore wind farm comprises the following steps:
step one, acquiring sea wave data of a preset time sequence;
step two, acquiring wind resource data synchronous with the time sequence of sea wave data;
step three, according to the time series sea wave data, calculating the relation between sea surface roughness and sea waves by adopting a TY01 scheme to obtain the time series sea surface roughness data;
dividing the time series sea surface roughness data into n classes according to the size;
fifthly, according to time series air temperature data in the wind resource data, adopting a temperature gradient method to divide the atmospheric stability into six types;
step six, combining n types of time series sea surface roughness and six types of atmosphere stability combinations into 6 multiplied by n atmosphere stability-roughness combinations;
step seven, constructing 6 multiplied by n CFD flow field models;
step eight, calculating power data of the time sequence segment;
and step nine, arranging and combining the power data of the 6 Xn time sequence segments according to the time sequence, integrating and calculating to obtain the generated energy of each wind turbine generator in the offshore wind farm, and obtaining the generated energy result of the whole offshore wind farm after integration.
It should be further noted that, in the first step, time series sea wave data of one complete year or more is obtained through collection of an observation station or simulation of a SWAN mode;
the sea wave data includes: effective wave height and spectral peak period.
In the second step, wind resource data in the same period as the time sequence sea wave data is obtained through the actual measurement data of the wind measuring tower or the WRF simulation; the wind resource data includes: time series wind speed, wind direction, air temperature and air pressure data at different heights from the sea surface.
It should be further noted that the method for classifying the atmospheric stability-roughness combination includes:
dividing the calculated time series sea surface roughness data into n types according to the size to obtain n time series sections containing the sea surface roughness data, constructing the correlation between roughness and atmospheric stability, wherein each type of time series sea surface roughness data has different atmospheric stability, and combining six types of atmospheric stability combinations into 6×n atmospheric stability-roughness combinations.
It should be further noted that, the method for constructing 6×n CFD flow field models in the seventh step includes:
and modeling an offshore wind farm by adopting wind resource CFD software, and respectively modeling CFD flow fields by using the 6 Xn 'atmospheric stability-roughness' combinations, wherein each atmospheric stability-roughness combination corresponds to one CFD flow field model respectively, and 6 Xn CFD flow field models are obtained in total.
It should be further noted that the wind resource CFD software includes WindSim software, WT software, or WAsP software.
It should be further noted that, the time sequence segment power data calculating method in the step eight includes:
dividing wind resource data into 6 Xn time series sections according to the 6 Xn atmospheric stability-roughness combinations, inputting the 6 Xn wind resource time series section data into a CFD flow field model corresponding to the same atmospheric stability-roughness combinations according to a one-to-one correspondence of the time series sections, and obtaining the 6 Xn time series section power data of each wind turbine generator set of the offshore wind power plant through calculation.
It should be further noted that the sea surface roughness calculation formula corresponding to the step three TY01 scheme is,
wherein ,z 0 for the sea surface roughness,h s in order for the wave height to be effective,L p in order for the wavelength to be effective,vis the coefficient of dynamic viscosity of the material,
is the friction speed;
effective wavelengthL p The solution formula of (2) isL p =gT p /(2
)×T p, wherein ,gthe acceleration of the gravity is that,T p is the period of the spectrum peak;
friction speed
The solution formula of (2) is +.>
, wherein ,C 10 as a coefficient of resistance (f) of the material,U 10 wind speed at sea level 10m height;
coefficient of resistanceC 10 The solution formula of (2) isC 10 =0.5
×10 -3, wherein ,U 10 is the wind speed at sea level 10 m.
The invention also provides a system for calculating the generating capacity of the offshore wind farm, which comprises: the device comprises a data acquisition module, a sea surface roughness calculation module, a sea surface roughness classification module, an atmosphere stability classification module, a stability roughness combination module, a model construction module, a power data calculation module and a power generation amount calculation module;
the data acquisition module is used for acquiring sea wave data of a preset time sequence; acquiring wind resource data synchronous with the time sequence of sea wave data;
the sea surface roughness calculation module is used for calculating the relation between the sea surface roughness and sea waves by adopting a TY01 scheme according to the sea wave data of the time sequence to obtain sea surface roughness data of the time sequence;
the sea surface roughness classification module is used for classifying the time series sea surface roughness data into n classes according to the size;
the atmospheric stability classification module is used for classifying the atmospheric stability into six types by adopting a temperature gradient method according to time sequence air temperature data in the wind resource data;
the stability roughness combination module is used for combining n types of time series sea surface roughness and six types of atmosphere stability combinations into 6 multiplied by n atmosphere stability-roughness combinations;
the model building module is used for building 6×n CFD flow field models;
the power data calculation module is used for calculating time sequence segment power data;
the power generation amount calculation module is used for arranging and combining the power data of the 6 Xn time series segments according to the time sequence, integrating and calculating the power generation amount of each wind turbine generator in the offshore wind farm, and obtaining the power generation amount result of the whole offshore wind farm after integration.
The invention also provides a computing terminal which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor realizes the steps of the offshore wind farm power generation amount computing method when executing the program.
From the above technical scheme, the invention has the following advantages:
according to the method for calculating the generating capacity of the offshore wind farm based on the time sequence wave data, the time sequence wave data and the time sequence wind resource data are collected and processed, the time sequence sea surface roughness is calculated according to the sea surface roughness calculation method which changes along with the wave, the time sequence sea surface roughness is classified, the atmospheric stability is classified by using a temperature gradient method, the CFD flow fields are respectively modeled according to the combination of the atmospheric stability and the roughness, the time sequence power data of each wind turbine under each CFD flow field model is calculated, the generating capacity of each wind turbine is obtained according to the time sequence sequencing, and therefore the generating capacity of the whole offshore wind farm is calculated. The method not only fully considers the influence of the change of the time sequence wave data on the sea surface roughness so as to influence the generated energy of the offshore wind farm, but also combines different atmospheric stabilities, so that the generated energy assessment of the offshore wind farm is more accurate, and has higher reference value for site selection, fan arrangement and investment benefit assessment of the offshore wind farm.
Drawings
In order to more clearly illustrate the technical solutions of the present invention, the drawings that are needed in the description will be briefly introduced below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a method for calculating the power generation capacity of an offshore wind farm based on time series wave data;
FIG. 2 is a schematic diagram of an offshore wind farm power generation calculation system.
Detailed Description
As shown in fig. 1, the present invention provides a basic idea of the present invention, which is illustrated only schematically in the diagrams provided in a method for calculating the power generation amount of an offshore wind farm based on time-series sea wave data, and the method for calculating the power generation amount of the offshore wind farm can acquire and process associated data based on artificial intelligence technology. The method for calculating the generating capacity of the offshore wind farm utilizes a digital computer or a machine controlled by the digital computer to simulate, extend and expand the intelligence of people, sense the environment, acquire knowledge and acquire the theory, the method, the technology and the application device of the best result by using the knowledge. The method for calculating the generating capacity of the offshore wind farm can also be combined with machine learning simulation or realize the learning behavior of human beings so as to acquire new knowledge or skills, and reorganize the existing knowledge structure to continuously improve the performance of the offshore wind farm for calculating the generating capacity. The calculation of the power generation amount of the offshore wind farm enables a computer to have an intelligent fundamental approach, and the method is applied to various fields of artificial intelligence.
FIG. 1 is a flow chart of a preferred embodiment of the offshore wind farm power generation calculation method of the present invention based on time series sea wave data. The method for calculating the generating capacity of the offshore wind farm based on the time sequence sea wave data is applied to one or more calculation terminals, wherein the calculation terminals are equipment capable of automatically carrying out numerical calculation and/or information processing according to preset or stored instructions, and hardware of the equipment comprises, but is not limited to, a microprocessor, an Application-specific integrated circuit (SpecificIntegratedCircuit, ASIC), a programmable gate array (Field-ProgrammableGate Array, FPGA), a digital processor (DigitalSignalProcessor, DSP), embedded equipment and the like.
The computing terminal may be any electronic product that can interact with a user, such as a personal computer, tablet computer, smart phone, personal digital assistant (PersonalDigitalAssistant, PDA), interactive web television (InternetProtocolTelevision, IPTV), smart wearable device, etc.
The computing terminal may also include network devices and/or user devices. Wherein the network device includes, but is not limited to, a single network server, a server group made up of multiple network servers, or a cloud based on cloud computing (CloudComputing) made up of a large number of hosts or network servers.
The network in which the computing terminal is located includes, but is not limited to, the internet, a wide area network, a metropolitan area network, a local area network, a virtual private network (VirtualPrivateNetwork, VPN), and the like.
The method disclosed by the invention is described in detail below with reference to FIG. 1, can be applied to calculation and analysis of the generated energy of the offshore wind farm based on time series wave data, and has higher reference value for site selection, fan arrangement and investment benefit evaluation of the offshore wind farm.
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, a flowchart of a method for calculating the power generation capacity of an offshore wind farm according to an embodiment is shown, and the method includes the following steps:
1) Collecting or SWAN mode simulation through an observation station to obtain time series sea wave data of a whole year or more, wherein the data elements comprise: effective wave height and spectrum peak period.
2) Wind resource data synchronous with the time sequence wave data is obtained through wind tower measured data or WRF mesoscale numerical simulation, and the data elements comprise: time series wind speed, wind direction, air temperature, air pressure data at different heights from the sea surface, including but not limited to 10m height from the sea surface and wind turbine hub height.
3) According to the time sequence sea wave data, calculating the relation between sea surface roughness and sea waves by adopting a TY01 scheme to obtain time sequence sea surface roughness data;
the sea surface roughness calculation formula corresponding to the TY01 scheme is as follows,
wherein ,z 0 for the sea surface roughness,h s in order for the wave height to be effective,L p in order for the wavelength to be effective,vis the coefficient of dynamic viscosity of the material,
is the friction speed;
effective wavelengthL p The solution formula of (2) isL p =gT p /(2
)×T p, wherein ,gthe acceleration of the gravity is that,T p is the period of the spectrum peak;
friction speed
The solution formula of (2) is +.>
, wherein ,C 10 as a coefficient of resistance (f) of the material,U 10 wind speed at sea level 10m height;
coefficient of resistanceC 10 The solution formula of (2) isC 10 =0.5
×10 -3, wherein ,U 10 is the wind speed at sea level 10 m.
4) And classifying the time series sea surface roughness data into n classes according to the size.
5) According to the time sequence air temperature data, adopting a temperature gradient method to divide the atmospheric stability into A, B, C, D, E, F six types;
the dividing standard of the atmospheric stability in the temperature gradient method is as follows:
wherein ,ΔTDelta is the temperature difference between two height layersZLetters A, B, C, D, E and F represent the levels of strong, unstable, weak, neutral, relatively stable, and stable six types of atmospheric stability, respectively, for the difference in height of the two height layers.
6) In view of the independent relationship between roughness and atmospheric stability, each type of time series sea surface roughness data can have different atmospheric stability, so that the six types of atmospheric stability combinations are combined into 6×n atmospheric stability-roughness combinations.
7) And carrying out CFD flow field modeling on the offshore wind farm by adopting wind resource CFD software, and respectively carrying out CFD flow field modeling through the 6×n atmosphere stability-roughness combinations, so that each atmosphere stability-roughness combination corresponds to one CFD flow field model respectively, and 6×n CFD flow field models are obtained.
The wind resource CFD software includes WindSim, WT, WAsP and the like.
8) Dividing wind resource data into 6 Xn time sequence segments according to the 6 Xn atmospheric stability-roughness combinations, respectively inputting the time sequence segments into CFD flow field models corresponding to the same atmospheric stability-roughness combinations according to a one-to-one correspondence of the time sequence segments, and calculating to obtain 6 Xn time sequence segment power data of each wind turbine generator set of the offshore wind power plant;
9) And (3) arranging and combining the power data of the 6 Xn time sequence segments according to time sequence, integrating statistical calculation to obtain the generated energy of each wind turbine generator in the offshore wind farm, and obtaining the generated energy result of the whole offshore wind farm after integration.
According to the method, time series sea surface roughness is calculated by adopting a sea surface roughness calculation method which changes along with sea waves through collecting and processing time series sea wave data and time series wind resource data, and is classified, atmospheric stability is classified by utilizing a temperature gradient method, CFD flow fields are respectively modeled according to the combination of the atmospheric stability and the roughness, time series power data of each wind turbine generator under each CFD flow field model is calculated, and generated energy of each wind turbine generator is obtained through sequencing according to time sequence, so that generated energy of the whole offshore wind power plant is calculated. The method not only fully considers the influence of the change of the time sequence wave data on the sea surface roughness so as to influence the generated energy of the offshore wind farm, but also combines different atmospheric stabilities, so that the generated energy assessment of the offshore wind farm is more accurate, and has higher reference value for site selection, fan arrangement and investment benefit assessment of the offshore wind farm.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present invention.
As shown in fig. 2, the following is an embodiment of an offshore wind farm power generation amount calculation system provided by an embodiment of the present disclosure, which belongs to the same inventive concept as the offshore wind farm power generation amount calculation method of the above embodiments, and details of which are not described in detail in the embodiment of the offshore wind farm power generation amount calculation system may be referred to the embodiment of the offshore wind farm power generation amount calculation method.
The offshore wind farm power generation amount calculation system comprises: the device comprises a data acquisition module, a sea surface roughness calculation module, a sea surface roughness classification module, an atmosphere stability classification module, a stability roughness combination module, a model construction module, a power data calculation module and a power generation amount calculation module;
the data acquisition module is used for acquiring sea wave data of a preset time sequence; acquiring wind resource data synchronous with the time sequence of sea wave data;
the sea surface roughness calculation module is used for calculating the relation between the sea surface roughness and sea waves by adopting a TY01 scheme according to the sea wave data of the time sequence to obtain sea surface roughness data of the time sequence;
the sea surface roughness classification module is used for classifying the time series sea surface roughness data into n classes according to the size;
the atmospheric stability classification module is used for classifying the atmospheric stability into six types by adopting a temperature gradient method according to time sequence air temperature data in the wind resource data;
the stability roughness combination module is used for combining n types of time series sea surface roughness and six types of atmosphere stability combinations into 6 multiplied by n atmosphere stability-roughness combinations;
the model building module is used for building 6×n CFD flow field models;
the power data calculation module is used for calculating time sequence segment power data;
the power generation amount calculation module is used for arranging and combining the power data of the 6 Xn time series segments according to the time sequence, integrating and calculating the power generation amount of each wind turbine generator in the offshore wind farm, and obtaining the power generation amount result of the whole offshore wind farm after integration.
The units and algorithm steps of each example described in the embodiments disclosed in the offshore wind farm power generation calculation method and calculation system provided by the invention can be implemented in electronic hardware, computer software or a combination of the two, and in order to clearly illustrate the interchangeability of hardware and software, the components and steps of each example have been generally described in terms of functions in the above description. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The flowcharts and block diagrams in the figures of the offshore wind farm power generation calculation method and computing system illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. Two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Those skilled in the art will appreciate that the aspects of the method and system for calculating the power generation capacity of an offshore wind farm provided by the invention can be implemented as a system, method or program product. Accordingly, various aspects of the disclosure may be embodied in the following forms, namely: an entirely hardware embodiment, an entirely software embodiment (including firmware, micro-code, etc.) or an embodiment combining hardware and software aspects may be referred to herein as a "circuit," module "or" system.
In the offshore wind farm power generation calculation method and calculation system provided by the present invention, the computer program code for performing the operations of the present disclosure may be written in one or more programming languages, including, but not limited to, object oriented programming languages such as Java, smalltalk, C ++, and conventional procedural programming languages, such as the "C" language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or power server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A method for calculating the power generation capacity of an offshore wind farm, the method comprising:
step one, acquiring sea wave data of a preset time sequence;
step two, acquiring wind resource data synchronous with the time sequence of sea wave data;
step three, according to the time series sea wave data, calculating the relation between sea surface roughness and sea waves by adopting a TY01 scheme to obtain the time series sea surface roughness data;
dividing the time series sea surface roughness data into n classes according to the size;
fifthly, according to time series air temperature data in the wind resource data, adopting a temperature gradient method to divide the atmospheric stability into six types;
step six, combining n types of time series sea surface roughness and six types of atmosphere stability combinations into 6 multiplied by n atmosphere stability-roughness combinations;
step seven, constructing 6 multiplied by n CFD flow field models;
step eight, calculating power data of the time sequence segment;
and step nine, arranging and combining the power data of the 6 Xn time sequence segments according to the time sequence, integrating and calculating to obtain the generated energy of each wind turbine generator in the offshore wind farm, and obtaining the generated energy result of the whole offshore wind farm after integration.
2. A method for calculating the power generation capacity of an offshore wind farm according to claim 1,
step one, collecting or SWAN mode simulation through an observation station to obtain time sequence sea wave data of more than one whole year;
the sea wave data includes: effective wave height and spectral peak period.
3. A method for calculating the power generation capacity of an offshore wind farm according to claim 1,
step two, wind resource data synchronous with the time sequence wave data is obtained through wind tower measured data or WRF simulation; the wind resource data includes: time series wind speed, wind direction, air temperature and air pressure data at different heights from the sea surface.
4. A method for calculating the power generation capacity of an offshore wind farm according to claim 1,
the method for classifying the atmospheric stability-roughness combination comprises the following steps:
dividing the calculated time series sea surface roughness data into n types according to the size to obtain n time series sections containing the sea surface roughness data, constructing the correlation between roughness and atmospheric stability, wherein each type of time series sea surface roughness data has different atmospheric stability, and combining six types of atmospheric stability combinations into 6×n atmospheric stability-roughness combinations.
5. A method for calculating the power generation capacity of an offshore wind farm according to claim 1,
the method for constructing the 6 multiplied by n CFD flow field model in the seventh step comprises the following steps:
and modeling an offshore wind farm by adopting wind resource CFD software, and respectively modeling CFD flow fields by using the 6 Xn 'atmospheric stability-roughness' combinations, wherein each atmospheric stability-roughness combination corresponds to one CFD flow field model respectively, and 6 Xn CFD flow field models are obtained in total.
6. The offshore wind farm power generation calculation method of claim 5, wherein the wind resource CFD software comprises WindSim software, WT software, or WAsP software.
7. The offshore wind farm power generation amount calculation method according to claim 6, wherein the time series segment power data calculation method in step eight includes:
dividing wind resource data into 6 Xn time series sections according to the 6 Xn atmospheric stability-roughness combinations, inputting the 6 Xn wind resource time series section data into a CFD flow field model corresponding to the same atmospheric stability-roughness combinations according to a one-to-one correspondence of the time series sections, and obtaining the 6 Xn time series section power data of each wind turbine generator set of the offshore wind power plant through calculation.
8. The method for calculating the power generation amount of the offshore wind farm according to claim 1, wherein the sea surface roughness calculation formula corresponding to the step three TY01 scheme is as follows,
wherein ,z 0 for the sea surface roughness,h s in order for the wave height to be effective,L p in order for the wavelength to be effective,vis the coefficient of dynamic viscosity of the material,
is the friction speed;
effective wavelengthL p The solution formula of (2) isL p =gT p /(2
)×T p, wherein ,gthe acceleration of the gravity is that,T p is the period of the spectrum peak;
friction speed
The solution formula of (2) is +.>
, wherein ,C 10 as a coefficient of resistance (f) of the material,U 10 wind speed at sea level 10m height;
coefficient of resistanceC 10 The solution formula of (2) isC 10 =0.5
×10 -3, wherein ,U 10 is the wind speed at sea level 10 m.
9. An offshore wind farm power generation amount calculation system, characterized in that the system adopts the offshore wind farm power generation amount calculation method according to any one of claims 1 to 8;
the system comprises: the device comprises a data acquisition module, a sea surface roughness calculation module, a sea surface roughness classification module, an atmosphere stability classification module, a stability roughness combination module, a model construction module, a power data calculation module and a power generation amount calculation module;
the data acquisition module is used for acquiring sea wave data of a preset time sequence; acquiring wind resource data synchronous with the time sequence of sea wave data;
the sea surface roughness calculation module is used for calculating the relation between the sea surface roughness and sea waves by adopting a TY01 scheme according to the sea wave data of the time sequence to obtain sea surface roughness data of the time sequence;
the sea surface roughness classification module is used for classifying the time series sea surface roughness data into n classes according to the size;
the atmospheric stability classification module is used for classifying the atmospheric stability into six types by adopting a temperature gradient method according to time sequence air temperature data in the wind resource data;
the stability roughness combination module is used for combining n types of time series sea surface roughness and six types of atmosphere stability combinations into 6 multiplied by n atmosphere stability-roughness combinations;
the model building module is used for building 6×n CFD flow field models;
the power data calculation module is used for calculating time sequence segment power data;
the power generation amount calculation module is used for arranging and combining the power data of the 6 Xn time series segments according to the time sequence, integrating and calculating the power generation amount of each wind turbine generator in the offshore wind farm, and obtaining the power generation amount result of the whole offshore wind farm after integration.
10. A computing terminal comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor, when executing the program, implements the steps of the offshore wind farm power generation calculation method of any of claims 1 to 8.
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