CN114294178A - Method, system, electronic device and medium for guaranteeing operation of learning data requested by wind turbine generator - Google Patents
Method, system, electronic device and medium for guaranteeing operation of learning data requested by wind turbine generator Download PDFInfo
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- CN114294178A CN114294178A CN202111359202.1A CN202111359202A CN114294178A CN 114294178 A CN114294178 A CN 114294178A CN 202111359202 A CN202111359202 A CN 202111359202A CN 114294178 A CN114294178 A CN 114294178A
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/72—Wind turbines with rotation axis in wind direction
Abstract
The invention provides a method for guaranteeing the operation of a wind turbine generator by requesting learning data, belongs to the technical field of wind turbine generator operation control, and can at least partially solve the problem of the reliability of the existing wind turbine generator. The method for requesting learning data to keep sub-health operation of the wind turbine generator comprises the following steps: s1, generating a learning data request sent to a learned wind turbine generator which has an association relation with the learning wind turbine generator; s2, learning data returned by the learned wind turbine generator in response to the learning data request are obtained; and S3, generating a functional instruction for controlling the normal operation of the learning wind turbine generator set based on the returned learning data.
Description
Technical Field
The method belongs to the technical field of wind turbine generator operation control, and particularly relates to a method, a system, electronic equipment and a medium for requesting learning data guarantee operation of a wind turbine generator.
Background
In extreme cases, when various sensors used for maintaining normal operation of the wind turbine equipment and redundant sensors of the wind turbine equipment break down. The wind turbine generator is shut down in order to protect the wind turbine generator.
However, the operation and maintenance of offshore power generation and the operation and maintenance of land power generation are greatly different, and the operation of going out of the sea may not be performed for one month. Therefore, the maintenance cannot be performed in time, which finally causes the wind turbine to be in a shutdown state for a long time, and finally causes the reliability and the power generation amount of the wind turbine to be reduced.
The existing wind generating set generally depends on the design of redundant sensing equipment to generate redundant sensing data, and does not have the redundant sensing data of other channels. Therefore, the reliability of the conventional wind turbine generator system needs to be further improved.
Disclosure of Invention
The invention aims to improve the reliability and the power generation capacity of an offshore wind turbine, and provides a method, a system, electronic equipment and a medium for requesting learning data security of the wind turbine.
One aspect of the invention provides a method for requesting learning data to guarantee operation of a wind turbine generator.
The wind turbine generator requests to learn a data security method,
the method comprises the following steps:
s1, generating a learning data request sent to a learned wind turbine generator which has an association relation with the learning wind turbine generator;
s2, learning data returned by the learned wind turbine generator in response to the learning data request are obtained;
and S3, generating a functional instruction for controlling the normal operation of the learning wind turbine generator set based on the returned learning data.
Further, in the above-mentioned case,
further comprising the steps of:
s0, judging whether the learning wind turbine generator is in an abnormal state or not based on the operation data of the learning wind turbine generator;
s0 is performed before S1 or before S3.
Further, the learning of the operation data of the wind turbine includes: and learning at least 1 of wind direction data, wind speed data and environment temperature data respectively output by a wind direction sensor, a wind speed sensor and an environment temperature sensor of the wind turbine generator.
And when at least 1 of the wind direction data, the wind speed data and the environment temperature data is abnormal, judging that the learning wind turbine generator is in an abnormal state.
Further, the learning data request includes: at least 1 of a wind direction data request, a wind speed data request, and an ambient temperature data request.
Further, the returned learning data includes: and at least 1 of wind direction data, wind speed data and environment temperature data which are respectively output by a wind direction sensor, a wind speed sensor and an environment temperature sensor of the learned wind turbine generator.
Further, the learning wind turbine generator and the learned wind turbine generator belong to adjacent wind turbine generators closest to the geographical distance.
Furthermore, the learning wind turbine and the learned wind turbine belong to 2 groups of wind turbines in the same wind area under the same wind plant.
In another aspect of the present invention, an electronic apparatus includes:
one or more processors;
the storage unit is used for storing one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors can realize the method for requesting learning data to guarantee operation of the wind turbine generator.
In another aspect of the present invention, a computer-readable storage medium, having a computer program stored thereon,
the computer program can realize the method for guaranteeing the operation of the request learning data of the wind turbine generator when being executed by the processor.
In another aspect of the present invention, a wind turbine request learning data insurance system is provided, including: at least 1 of the learning wind turbine and the learned wind turbine;
learning the wind turbine generator includes:
a device or module that generates a request for learning data to be sent to a learned wind turbine that is associated with a learning wind turbine,
a device or module to obtain learning data returned by the learned wind turbine in response to the learning data request,
equipment or a module for generating a functional instruction for controlling normal operation of the learning wind turbine generator based on the returned learning data;
the learned wind turbine includes:
and responding to the learning data request and returning the learning data to the equipment or the module of the learning wind turbine generator.
In another aspect of the invention, a wind turbine request learning data security system is provided,
the method comprises the following steps: at least 1 of the learning control platform, the learning wind turbine generator and the learned wind turbine generator;
the learning control platform comprises:
a device or module that generates a request for learning data to be sent to a learned wind turbine that is associated with a learning wind turbine,
a device or module to obtain learning data returned by the learned wind turbine in response to the learning data request,
forwarding the returned learning data to a learning wind turbine generator or generating a functional instruction for controlling the normal operation of the learning wind turbine generator based on the returned learning data;
learning the wind turbine generator includes:
generating a functional instruction for controlling the normal operation of the learning wind turbine generator or equipment or a module for receiving the functional instruction based on the returned learning data;
the learned wind turbine includes:
and responding to the learning data request and returning the learning data to the equipment or the module of the learning wind turbine generator.
According to the method for guaranteeing the operation of the request learning data of the wind turbine generator, digital type redundancy is provided for the abnormal wind turbine generator by means of other related wind turbine generators, a digital redundancy channel is provided for the abnormal wind turbine generator besides a self equipment redundancy channel, the reliability of the wind turbine generator is improved, the wind turbine generator can still operate in a sub-healthy mode depending on the data of other wind turbine generators under the abnormal condition, and the power generation loss is reduced.
When the unit of the invention breaks down, the reliability of the unit can be improved by sensing the wind speed, the wind direction, the ambient temperature and the like by means of the adjacent unit except the redundancy of the unit.
Drawings
FIG. 1 is a schematic structural view of the present invention;
FIG. 2 is a schematic structural diagram according to embodiment 2 of the present invention;
FIG. 3 is another schematic structural view of embodiment 2 of the present invention;
FIG. 4 is a schematic flow diagram of a learning-based wind turbine and a learned wind turbine according to the present invention.
FIG. 5 is a schematic flow diagram of the present invention based on a learning wind turbine, a learned wind turbine, and a learning control platform.
FIG. 6 is another flow diagram of the present invention based on a learning wind turbine and a learned wind turbine and a learning control platform.
Detailed Description
In order to make the technical solutions of the present invention/invention better understood, the present invention/invention is further described in detail below with reference to the accompanying drawings and the detailed description.
Example 1: as shown in fig. 1, for a wind turbine generator, various conventional sensors are provided, such as a wind speed sensor, a wind direction sensor, and an ambient temperature sensor, sensing data generated by these sensors is generally used to ensure normal operation of the wind turbine generator, for example, yaw control is performed depending on wind speed and wind direction, that is, the wind turbine generator performs function calculation based on the sensing data of the wind speed sensor, the wind direction sensor, and the ambient temperature sensor, and finally outputs a functional instruction (yaw instruction, etc.) for ensuring normal operation of the wind turbine generator. Therefore, once the sensors fail, the normal operation of the wind turbine cannot be guaranteed, and in order to solve the problem and improve the reliability of the wind turbine, redundant sensors, such as a redundant wind speed sensor, a redundant wind direction sensor, and a redundant ambient temperature sensor, are generally arranged; these redundant sensors are typically enabled upon failure of a conventional sensor. In extreme environments, however, conventional sensors and redundant sensors often fail together. Thus, this hardware-dependent redundancy is not reliable. As shown in FIG. 1, the present invention introduces externally learned data for wind turbines, which are derived from associated other wind turbines, thus forming an external redundancy design. The conventional sensor and the redundant sensor form an internal redundant part of the wind turbine, and the external learning data form an external redundant part of the wind turbine.
Based on the above, embodiment 1 provides a wind turbine, where the wind turbine may obtain external learning data, the external learning data is used to form a redundant relationship with sensing data, and the external learning data, the sensing data of the redundant sensor, and the sensing data of the conventional sensor are all used as basis data for generating a functional instruction for controlling normal operation of the wind turbine.
Example 2:
FIG. 2, FIG. 3, FIG. 4, FIG. 5, FIG. 6,
the embodiment provides a learning wind turbine.
Learning the wind turbine generator:
learning data returned by the learned wind turbine in response to the learning data request is obtained,
and generating a functional instruction for controlling the normal operation of the learning wind turbine generator based on the returned learning data.
Further, before learning the wind turbine generator to obtain the learning data, the following actions can be completed: judging whether the learning wind turbine generator is in an abnormal state or not based on the operation data of the learning wind turbine generator; and generating a learning data request which is sent to the learned wind turbine which has an association relation with the learning wind turbine.
The embodiment provides a learned wind turbine.
The learned wind turbine:
and responding to the learning data request and returning the learning data to the learning wind turbine generator.
The embodiment provides a learning control platform.
A learning control platform:
judging whether the learning wind turbine is in an abnormal state or not based on the operation data of the learning wind turbine,
generating a learning data request transmitted to a learned wind turbine having an association relationship with the learning wind turbine,
learning data returned by the learned wind turbine in response to the learning data request is obtained,
forwarding the returned learning data to a learning wind turbine generator or generating a functional instruction for controlling the normal operation of the learning wind turbine generator based on the returned learning data;
the combination of the learning wind turbine, the learned wind turbine and the learning control platform forms a wind turbine learning data request security system, and the following embodiments are provided
Example 2.1
With reference to figure 2 of the drawings,
the wind turbine generator requests to learn a data security system,
the method comprises the following steps: at least 1 of the learning wind turbine and the learned wind turbine (2 parts are shown in figure 2 and participate);
learning the wind turbine generator includes:
a device or module that determines whether the learning wind turbine is in an abnormal state based on the operational data of the learning wind turbine,
a device or module that generates a request for learning data to be sent to a learned wind turbine that is associated with a learning wind turbine,
a device or module to obtain learning data returned by the learned wind turbine in response to the learning data request,
equipment or a module for generating a functional instruction for controlling normal operation of the learning wind turbine generator based on the returned learning data;
the learned wind turbine includes:
and responding to the learning data request and returning the learning data to the equipment or the module of the learning wind turbine generator.
Example 2.2
With reference to figure 3 of the drawings,
the wind turbine generator requests to learn a data security system,
the method comprises the following steps: at least 1 of the learning control platform, the learning wind turbine generator and the learned wind turbine generator (3 parts are shown in figure 3 and all participate);
the learning control platform comprises:
a device or module that determines whether the learning wind turbine is in an abnormal state based on the operational data of the learning wind turbine,
a device or module that generates a request for learning data to be sent to a learned wind turbine that is associated with a learning wind turbine,
a device or module to obtain learning data returned by the learned wind turbine in response to the learning data request,
forwarding the returned learning data to a learning wind turbine generator or generating a functional instruction for controlling the normal operation of the learning wind turbine generator based on the returned learning data;
learning the wind turbine generator includes:
generating a functional instruction for controlling the normal operation of the learning wind turbine generator or equipment or a module for receiving the functional instruction based on the returned learning data;
the learned wind turbine includes:
and responding to the learning data request and returning the learning data to the equipment or the module of the learning wind turbine generator.
Example 3
Referring to figures 4, 5 and 6,
one aspect of the invention provides a method for requesting learning data to guarantee operation of a wind turbine generator.
The wind turbine generator requests to learn a data security method,
the method comprises the following steps:
s0, judging whether the learning wind turbine generator is in an abnormal state or not based on the operation data of the learning wind turbine generator, if so, turning to S1,
s1, generating a learning data request sent to a learned wind turbine generator which has an association relation with the learning wind turbine generator;
s2, learning data returned by the learned wind turbine generator in response to the learning data request are obtained;
and S3, generating a functional instruction for controlling the normal operation of the learning wind turbine generator set based on the returned learning data.
One aspect of the invention provides a method for requesting learning data to guarantee operation of a wind turbine generator.
The wind turbine generator requests to learn a data security method,
the method comprises the following steps:
s1, generating a learning data request sent to a learned wind turbine generator which has an association relation with the learning wind turbine generator;
s2, learning data returned by the learned wind turbine generator in response to the learning data request are obtained;
s0, judging whether the learning wind turbine generator is in an abnormal state or not based on the operation data of the learning wind turbine generator, and if so, turning to S3;
and S3, generating a functional instruction for controlling the normal operation of the learning wind turbine generator set based on the returned learning data.
According to fig. 4, 5 and 6, the executing device of the above steps may be one or more of a learning wind turbine, a learned wind turbine and a learning control platform.
Further, the learning of the operation data of the wind turbine includes: and learning at least 1 of wind direction data, wind speed data and environment temperature data respectively output by a wind direction sensor, a wind speed sensor and an environment temperature sensor of the wind turbine generator.
And when at least 1 of the wind direction data, the wind speed data and the environment temperature data is abnormal, judging that the learning wind turbine generator is in an abnormal state.
Further, the learning data request includes: at least 1 of a wind direction data request, a wind speed data request, and an ambient temperature data request.
Further, the returned learning data includes: and at least 1 of wind direction data, wind speed data and environment temperature data which are respectively output by a wind direction sensor, a wind speed sensor and an environment temperature sensor of the learned wind turbine generator.
Further, the learning wind turbine generator and the learned wind turbine generator belong to adjacent wind turbine generators closest to the geographical distance.
Furthermore, the learning wind turbine and the learned wind turbine belong to 2 groups of wind turbines in the same wind area under the same wind plant.
The association relation between the learning wind turbine generator and the learned wind turbine generator can set the associated learned wind turbine generator through an HMI (human machine interface) module of the learning fan, and set the IP (Internet protocol) of the learned wind turbine generator, so that the learning wind turbine generator can provide the IP of the learned wind turbine generator to send a learning data request when the learning wind turbine generator is abnormal.
After the learning wind turbine generator receives the returned learning data (at least 1 of the wind direction data, the wind speed data and the environment temperature data respectively output by the wind direction sensor, the wind speed sensor and the environment temperature sensor of the learned wind turbine generator), validity verification can be carried out, and after the verification, the learning data are stored as redundancy quantity to be called by a subsequent functional instruction generating program.
In this embodiment, S1 and S2 may be executed in advance or in real time, which is beneficial to acquiring the learning data for redundancy in advance, so that when the learning wind turbine is abnormal, the determination of S0 is made, and then S4 is directly executed to call the learning data for redundancy to generate the functional instruction.
If an abnormality occurs in the learning wind turbine, S0 may be executed, and then S1, S2, and S3 may be executed, that is, the steps of requesting learning data, obtaining responsive learning data, and generating a functional command based on the redundant learning data may be executed.
In addition, the first and second substrates are,
and the learning wind turbine generator and the learned wind turbine generator are interacted by using a MoubusTcp protocol.
The data accuracy of the learning data should be not less than 10 HZ.
This embodiment can be when this study wind turbine generator's sensor damages, the unit does not carry out the shut down, and the suggestion informs the owner, and this study wind turbine generator switches over the learning data assurance unit sub-health operation that uses adjacent unit.
Example 4
In another aspect of the present invention, an electronic apparatus includes:
one or more processors;
the storage unit is used for storing one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors can realize the method for requesting learning data to guarantee operation of the wind turbine generator.
Example 5
In another aspect of the present invention, a computer-readable storage medium, having a computer program stored thereon,
the computer program can realize the method for guaranteeing the operation of the request learning data of the wind turbine generator when being executed by the processor.
The computer readable medium may be embodied in the apparatus, device, system, or device of the invention or may be separate.
The computer readable storage medium may be any tangible medium that can contain or store a program, and may be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, more specific examples of which include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, an optical fiber, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.
The computer readable storage medium may also include a propagated data signal with computer readable program code embodied therein, for example, in a non-transitory form, such as in a carrier wave or in a carrier wave, wherein the carrier wave is any suitable carrier wave or carrier wave for carrying the program code.
For example:
taking a plurality of wind power generation sets as an example,
setting target learning units of the wind turbine A as a wind turbine B, a wind turbine C and a wind turbine D through an HMI (human machine interface) module of the wind turbine A; (one or more of other wind turbines connected with the wind turbine A are selected as target learning turbines)
When the sensor of the wind turbine generator A breaks down, the data learning range of the wind turbine generator A is set to include: wind speed data and wind direction data; and prompting the main wind speed sensor and the wind direction sensor to have faults;
the wind turbine generator A sends a data request of wind speed data and wind direction data to the wind turbine generator B, the wind turbine generator C and the wind turbine generator D according to the data reading frequency of the wind speed sensor and the wind direction sensor;
the wind turbine generator A receives wind speed data and wind direction data returned by the wind turbine generator B, the wind turbine generator C and the wind turbine generator D, and an average value of the three wind speed data and the three wind direction data is obtained;
and taking the average wind speed data and the average wind direction data as the wind speed data and the wind direction data of the wind turbine A, and maintaining the sub-health state of the wind turbine A to continuously operate.
Another example is:
taking a plurality of wind power generation sets as an example,
taking the wind turbine generator E as an example, setting a target learning generator of the wind turbine generator E as a wind turbine generator F and a wind turbine generator G, setting a target learning generator of the wind turbine generator F as the wind turbine generator E and the wind turbine generator G, setting a target learning generator of the wind turbine generator H as the wind turbine generator E and the wind turbine generator H, and setting a target learning generator of the wind turbine generator G as the wind turbine generator E and the wind turbine generator H through a learning control platform;
the data learning range of each wind turbine set through the learning control platform comprises: ambient temperature data, wind speed data, wind direction data; setting a data request period to be 30 minutes;
when the wind turbine generator set does not detect faults, each wind turbine generator set sends data requests of environment data, temperature data, wind speed data and wind direction data to a target learning machine set every 30 minutes;
setting the serious error threshold values of the environmental data, the temperature data, the wind speed data and the wind direction data to be 50;
calculating the average value of the data returned by each target learning unit as a data learning result; comparing the data learning result with the detection data of the local unit, and judging that the local wind turbine unit has a fault when the difference value between the learning result and the detection data of the local unit is more than 50; such as: and when the difference value between the temperature data detected by the unit and the learning result of the temperature data is 60, determining that the temperature sensor of the unit breaks down.
If the following is found through the judgment mode and/or other electric structure judgment: three or more sensors in an environment sensor, a temperature sensor, a wind speed sensor and a wind direction sensor of a certain unit have faults, the unit is shut down, and the name of the sensor with the fault in the unit is prompted;
if the number of the fault sensors of a certain unit is less than three, setting the data learning range of the wind turbine generator to include the data measured by the fault sensors of the wind turbine generator, and prompting the owner of the name of the faulty sensor;
when an environmental sensor of the wind turbine generator E breaks down, the wind turbine generator E sends an environmental data request to the wind turbine generator F and the wind turbine generator G according to the data reading frequency of the environmental sensor; the wind turbine generator E receives environmental data returned by the wind turbine generator F and the wind turbine generator G, validity verification is carried out on the returned environmental data, and after the data are verified to be valid, the average value of the two environmental data is obtained; and if the data verification is invalid, retransmitting the data verification request.
And taking the average environmental data as the environmental data of the wind turbine generator E to maintain the sub-health state of the wind turbine generator E to continue running.
In principle, when each unit is subjected to data interaction with a target learning unit, switches of each unit are directly interacted; and the interaction can be indirect through the central control machine.
On-site operation and maintenance personnel can modify various parameters such as a target learning unit, a data learning range, a data request period, a serious error threshold value of each data and the like in real time through the HMI module on each wind turbine.
It will be understood that the above embodiments are merely exemplary embodiments taken to illustrate the principles of the invention/inventions, which are not limited thereto. It will be apparent to those skilled in the art that various modifications and improvements can be made without departing from the spirit and substance of the invention, and these modifications and improvements are also considered to be within the scope of the invention.
Claims (10)
1. A method for guaranteeing the operation of a wind turbine generator for requesting learning data is characterized in that,
the method comprises the following steps:
s1, generating a learning data request sent to a learned wind turbine generator which has an association relation with the learning wind turbine generator;
s2, learning data returned by the learned wind turbine generator in response to the learning data request are obtained;
and S3, generating a functional instruction for controlling the normal operation of the learning wind turbine generator set based on the returned learning data.
2. The wind turbine request learning data insurance method according to claim 1,
further comprising the steps of:
s0, judging whether the learning wind turbine generator is in an abnormal state or not based on the operation data of the learning wind turbine generator;
s0 is performed before S1 or before S3.
3. The wind turbine request learning data insurance method according to claim 2,
learning the operating data of the wind turbine includes: learning at least 1 of wind direction data, wind speed data and environment temperature data respectively output by a wind direction sensor, a wind speed sensor and an environment temperature sensor of the wind turbine generator;
and when at least 1 of the wind direction data, the wind speed data and the environment temperature data is abnormal, judging that the learning wind turbine generator is in an abnormal state.
4. The wind turbine request learning data insurance method according to claim 1,
the learning data request includes: at least 1 of a wind direction data request, a wind speed data request, and an ambient temperature data request.
5. The wind turbine request learning data insurance method according to claim 1,
the returned learning data includes: and at least 1 of wind direction data, wind speed data and environment temperature data which are respectively output by a wind direction sensor, a wind speed sensor and an environment temperature sensor of the learned wind turbine generator.
6. The wind turbine request learning data insurance method according to claim 1,
the learning wind turbine and the learned wind turbine belong to adjacent wind turbines with the nearest geographic distance or the learning wind turbine and the learned wind turbine belong to 2 wind turbines in the same wind area under the same wind plant.
7. An electronic device, comprising:
one or more processors;
a storage unit configured to store one or more programs, which when executed by the one or more processors, enable the one or more processors to implement the wind turbine request learning data insurance method according to any one of claims 1 to 6.
8. A computer-readable storage medium having stored thereon a computer program, characterized in that,
the computer program, when executed by a processor, is capable of implementing a wind turbine generator request learning data security method according to any one of claims 1 to 6.
9. A wind turbine request learning data security system is characterized in that,
the method comprises the following steps: at least 1 of the learning wind turbine and the learned wind turbine;
learning the wind turbine generator includes:
a device or module that generates a request for learning data to be sent to a learned wind turbine that is associated with a learning wind turbine,
a device or module to obtain learning data returned by the learned wind turbine in response to the learning data request,
equipment or a module for generating a functional instruction for controlling normal operation of the learning wind turbine generator based on the returned learning data;
the learned wind turbine includes:
and responding to the learning data request and returning the learning data to the equipment or the module of the learning wind turbine generator.
10. A wind turbine request learning data security system is characterized in that,
the method comprises the following steps: at least 1 of the learning control platform, the learning wind turbine generator and the learned wind turbine generator;
the learning control platform comprises:
a device or module that generates a request for learning data to be sent to a learned wind turbine that is associated with a learning wind turbine,
a device or module to obtain learning data returned by the learned wind turbine in response to the learning data request,
forwarding the returned learning data to a learning wind turbine generator or generating a functional instruction for controlling the normal operation of the learning wind turbine generator based on the returned learning data;
learning the wind turbine generator includes:
generating a functional instruction for controlling the normal operation of the learning wind turbine generator or equipment or a module for receiving the functional instruction based on the returned learning data;
the learned wind turbine includes:
and responding to the learning data request and returning the learning data to the equipment or the module of the learning wind turbine generator.
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