CN115126654A - Collaborative optimization method and system for power generation performance of wind turbine generator - Google Patents
Collaborative optimization method and system for power generation performance of wind turbine generator Download PDFInfo
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
- F03D—WIND MOTORS
- F03D7/00—Controlling wind motors
- F03D7/02—Controlling wind motors the wind motors having rotation axis substantially parallel to the air flow entering the rotor
- F03D7/04—Automatic control; Regulation
- F03D7/042—Automatic control; Regulation by means of an electrical or electronic controller
- F03D7/043—Automatic control; Regulation by means of an electrical or electronic controller characterised by the type of control logic
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
- F03D—WIND MOTORS
- F03D7/00—Controlling wind motors
- F03D7/02—Controlling wind motors the wind motors having rotation axis substantially parallel to the air flow entering the rotor
- F03D7/0204—Controlling wind motors the wind motors having rotation axis substantially parallel to the air flow entering the rotor for orientation in relation to wind direction
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
- F03D—WIND MOTORS
- F03D7/00—Controlling wind motors
- F03D7/02—Controlling wind motors the wind motors having rotation axis substantially parallel to the air flow entering the rotor
- F03D7/022—Adjusting aerodynamic properties of the blades
- F03D7/0224—Adjusting blade pitch
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
- F03D—WIND MOTORS
- F03D7/00—Controlling wind motors
- F03D7/02—Controlling wind motors the wind motors having rotation axis substantially parallel to the air flow entering the rotor
- F03D7/0296—Controlling wind motors the wind motors having rotation axis substantially parallel to the air flow entering the rotor to prevent, counteract or reduce noise emissions
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
- F03D—WIND MOTORS
- F03D7/00—Controlling wind motors
- F03D7/02—Controlling wind motors the wind motors having rotation axis substantially parallel to the air flow entering the rotor
- F03D7/04—Automatic control; Regulation
- F03D7/042—Automatic control; Regulation by means of an electrical or electronic controller
- F03D7/047—Automatic control; Regulation by means of an electrical or electronic controller characterised by the controller architecture, e.g. multiple processors or data communications
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
- F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
- F05B2270/00—Control
- F05B2270/10—Purpose of the control system
- F05B2270/20—Purpose of the control system to optimise the performance of a machine
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
- F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
- F05B2270/00—Control
- F05B2270/70—Type of control algorithm
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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 collaborative optimization method and a collaborative optimization system for the power generation performance of a wind turbine generator, wherein the method comprises the following steps: acquiring real-time operation data of the wind turbine generator, which is acquired by a PLC (programmable logic controller), analyzing the mutual influence relationship between the functions to be optimized and the optimization functions of the wind turbine generator, determining the starting sequence of each optimization function according to the mutual influence relationship between the optimization functions, always starting the optimal gain air density adaptation function, performing optimal gain air density compensation on the real-time operation control of the wind turbine generator, starting each optimization function according to the starting sequence, and cooperatively optimizing the power generation performance of the wind turbine generator; wherein each optimization function comprises: the method comprises the following steps of impeller balance optimization, optimal gain optimization, paddle angle optimization and yaw optimization. The cooperative optimization method fully considers the relation among different factors influencing the power generation performance of the wind turbine generator, integrates the optimization functions, reasonably adjusts the power generation performance of the wind turbine generator, effectively improves the power generation amount of the wind turbine generator, and meanwhile reduces the optimization time of the wind turbine generator by orderly connecting the optimization functions.
Description
Technical Field
The invention relates to the technical field of wind power generation, in particular to a cooperative optimization method and a cooperative optimization system for the power generation performance of a wind turbine generator.
Background
Current mainstream wind generating set generally adopts wind direction, horizontal axis, three blade structural style, and its theory of operation is: the impeller of the generator set absorbs wind energy and converts the wind energy into electric energy. However, in the process of converting wind energy into mechanical energy by the impeller of the wind turbine, there are many factors affecting energy conversion, for example, the following factors are common: the imbalance of the impeller, the inaccurate execution of the Kopt (proportionality constant at the optimal rotating speed, also called as the optimal gain or the optimal torque gain) can not track the optimal tip speed ratio in the MPPT (Maximum Power Point Tracking) section, and the optimal blade angle execution deviation, the unit yaw deviation and the like caused by the difference between the manufacturing and theoretical wind energy absorption efficiency of the blade.
Based on this, a plurality of optimization strategy schemes are proposed in the industry, but the schemes corresponding to the factors generally aim at the problem itself, and the mutual influence relationship among the factors is often ignored in the actual execution, so that the accuracy and reliability of the single optimization strategy scheme in the actual execution are not high, and the purpose of efficiently improving the power generation performance of the wind turbine generator cannot be realized.
Disclosure of Invention
Therefore, the invention provides a collaborative optimization method and a collaborative optimization system for the power generation performance of the wind turbine generator, aiming at overcoming the defects in the prior art and improving the power generation performance of the wind turbine generator.
In a first aspect, the present invention provides a method for collaborative optimization of power generation performance of a wind turbine, including:
acquiring real-time operation data of the wind turbine generator, which is acquired by a PLC;
analyzing the mutual influence relationship between the to-be-optimized function and the optimized function of the wind turbine generator, wherein each optimized function comprises the following steps: the method comprises the following steps of impeller balance optimization, optimal gain optimization, paddle angle optimization and yaw optimization;
determining the starting sequence of each optimization function according to the mutual influence relationship of the optimization functions;
starting an optimal gain air density adaptation function all the time, and performing optimal gain air density compensation on the real-time operation control of the wind turbine generator;
and starting the optimization functions according to the starting sequence, and cooperatively optimizing the power generation performance of the wind turbine generator.
Optionally, the starting sequence of each optimization function is: yaw optimization, impeller balance optimization, optimal gain optimization and paddle angle optimization; wherein:
yaw optimization, optimal gain optimization and blade angle optimization are combined with the optimal gain air density adaptation function;
when the optimal gain optimization and the oar angle optimization are started, the optimal gain optimization is started first, and then the optimal gain optimization and the oar angle optimization are combined.
Optionally, the real-time operation data includes: wind speed, wind direction, rotational speed, nacelle acceleration, yaw state, pitch angle, power, windage yaw state, and air density.
Optionally, after the optimization functions are started according to the starting sequence and the power generation performance of the wind turbine generator is cooperatively optimized, the method further includes:
judging whether the current optimizing compensation configuration is equal to the uncompensated configuration;
if so, controlling the current optimization compensation configuration to be equal to the cooperative optimization compensation configuration;
if the judgment result is negative, controlling the current optimization compensation configuration to be equal to the uncompensated configuration;
judging whether the number of the statistical evaluation objects in the wind speed interval meets the preset number requirement or not;
and if so, determining the overall effect of the cooperative optimization according to the evaluation object.
Optionally, after determining whether the number of the statistical evaluation objects in the wind speed interval meets the preset number requirement, the method further includes:
if the judgment result is negative, judging whether the preset switching time length is reached;
if the judgment result is yes, returning to execute the step of judging whether the current optimizing compensation configuration is equal to the uncompensated configuration;
if not, returning to the step of judging whether the number of the statistical evaluation objects on the wind speed interval meets the preset number requirement or not.
Optionally, determining the overall effect of the collaborative optimization according to the evaluation object includes:
calculating the evaluation index after the wind turbine generator is subjected to collaborative optimization according to the evaluation object, wherein the evaluation index comprises the following steps: the power generation lifting proportion, the load reduction proportion and the impeller rotating speed-frequency doubling reduction proportion which correspond to the cooperative optimization compensation configuration are configured;
and determining the overall effect of the cooperative optimization based on the evaluation indexes.
Optionally, the evaluation object includes: power, impeller rotating speed first-frequency multiplication, blade structure load, tower bottom load, impeller rotating speed third-frequency multiplication and tower first-order amplitude.
In a second aspect, the present invention provides a system for collaborative optimization of power generation performance of a wind turbine, including:
the acquisition unit is used for acquiring real-time operation data of the wind turbine generator collected by the PLC;
the analysis unit is used for analyzing the mutual influence relationship between the functions to be optimized and the optimization functions of the wind turbine generator and determining the starting sequence of each optimization function according to the mutual influence relationship between the optimization functions; wherein each optimization function comprises: the method comprises the following steps of impeller balance optimization, optimal gain optimization, paddle angle optimization and yaw optimization;
the compensation unit is used for starting the optimal gain air density adaptation function all the time and carrying out optimal gain air density compensation on the real-time operation control of the wind turbine generator;
and the optimization unit is used for starting the optimization functions according to the starting sequence and cooperatively optimizing the power generation performance of the wind turbine generator.
In a third aspect, the present invention provides an edge computing device comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executable by the at least one processor to cause the at least one processor to perform the steps of the method for collaborative optimization of wind turbine generator system power generation performance according to the first aspect or any one of the optional embodiments of the first aspect.
In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the steps of the method for collaborative optimization of wind turbine generator performance according to the first aspect or any one of the optional embodiments of the first aspect.
The technical scheme of the invention has the following advantages:
the invention provides a collaborative optimization method and a collaborative optimization system for the power generation performance of a wind turbine generator, wherein the method comprises the following steps: acquiring real-time operation data of the wind turbine generator, which is acquired by a PLC (programmable logic controller), analyzing the mutual influence relationship between the functions to be optimized and the optimization functions of the wind turbine generator, determining the starting sequence of each optimization function according to the mutual influence relationship between the optimization functions, always starting the optimal gain air density adaptation function, performing optimal gain air density compensation on the real-time operation control of the wind turbine generator, starting each optimization function according to the starting sequence, and cooperatively optimizing the power generation performance of the wind turbine generator; wherein each optimization function comprises: the method comprises the following steps of impeller balance optimization, optimal gain optimization, paddle angle optimization and yaw optimization. The cooperative optimization method fully considers the relation among different factors influencing the power generation performance of the wind turbine generator, integrates the optimization functions, reasonably adjusts the power generation performance of the wind turbine generator, effectively improves the power generation amount of the wind turbine generator, and meanwhile reduces the optimization time of the wind turbine generator by orderly connecting the optimization functions.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of a method for collaborative optimization of power generation performance of a wind turbine generator according to an embodiment of the present invention;
fig. 2 is a flowchart of a specific example of a method for collaborative optimization of power generation performance of a wind turbine generator according to an embodiment of the present invention;
fig. 3 to fig. 4 are flowcharts of another specific example of the cooperative optimization method for the power generation performance of the wind turbine generator according to the embodiment of the present invention, respectively;
fig. 5 is a schematic structural diagram of a cooperative optimization system for power generation performance of a wind turbine generator according to an embodiment of the present invention;
fig. 6 is a schematic diagram of an edge computing device according to an embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Furthermore, the technical features involved in the different embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
The cooperative optimization method for the power generation performance of the wind turbine generator, provided by the embodiment of the invention, can effectively adjust the power generation performance of the wind turbine generator, so as to improve the power generation capacity of the wind turbine generator, and the flow chart is shown in fig. 1 and comprises the following steps:
and step S1, acquiring real-time operation data of the wind turbine generator collected by the PLC.
The PLC (Programmable Logic Controller) is used for acquiring real-time operation data of the wind turbine generator and effectively allocating PLC resources of the wind turbine generator, and the PLC used in the industry is equivalent to or close to a host of a compact computer, so that the PLC has the advantages of expansibility and reliability, can be widely applied to various industrial control fields at present, and can better improve the power generation performance of the wind turbine generator.
Specifically, the real-time operation data of the wind turbine generator may include: wind speed, wind direction, rotational speed, nacelle acceleration, yaw state, pitch angle, power, windage yaw state, air density, and the like. The operation data of the wind turbine generator is not limited to the above, and can be increased or reduced according to the actual application condition.
And step S2, analyzing the mutual influence relationship between the to-be-optimized function and the optimized function of the wind turbine generator.
After the step S1 is executed to obtain the real-time operation data of the wind turbine, it may be determined whether the wind turbine needs to be optimized according to the real-time operation data, and the function to be optimized of the wind turbine is analyzed at the same time, that is, what optimization processes need to be performed on the wind turbine, for example, if it is determined that there is no static or dynamic deviation to the wind, the yaw optimization process is not needed, but not limited thereto, and the method for analyzing the function to be optimized of the wind turbine is the same as that in the prior art, and is not listed one by one. Wherein, each optimization function may include: research and development personnel find that the optimization functions have mutual influence relations, for example, the power of the wind turbine generator is different and the optimal tip speed ratio is different under different impeller states, so that the optimal gain is different. Therefore, it is necessary to complete the impeller balance optimization first and then perform Kopt optimization.
And step S3, determining the starting sequence of each optimization function according to the mutual influence relationship of the optimization functions.
Since the optimization functions have an influence relationship with each other, the order of turning on the optimization functions can be determined according to the relationship, and step S5 is performed.
And step S4, starting the optimal gain air density adaptation function all the time, and performing optimal gain air density compensation on the real-time operation control of the wind turbine generator.
In the process of collaborative optimization of the wind turbine generator, the optimal gain air density adaptation (hereinafter referred to as Kopt air density adaptation) function is always started, and the optimal gain air density compensation is realized for the real-time operation control of the wind turbine generator. It should be noted that the execution sequence of step S4 is not limited to the sequence shown in fig. 1, and step S4 may be executed before step S2 or S3, as long as it is ensured that the optimum gain air density adaptation function is always turned on, and all that is within the protection scope of the present embodiment.
And step S5, starting the optimization functions according to the starting sequence, and cooperatively optimizing the power generation performance of the wind turbine generator.
According to the cooperative optimization method for the power generation performance of the wind turbine generator, the optimization functions are sequentially connected and scheduled, the power generation performance optimization process is integrated, cooperative power generation performance optimization is achieved, the power generation amount of the wind turbine generator can be effectively improved, and the time of the optimization process is shortened.
In a specific embodiment, in the above embodiment, the process of starting each optimization function to optimize the wind turbine generator is as follows:
(1) after the Kopt air density adaptation function is started, Kopt air density compensation is continuously performed. The compensation formula can be expressed as:
Kopt1＝Kopt0*Factor1；
wherein, Kopt1 represents the optimal gain after air density compensation, and Kopt0 represents the model design stage according to rho Design of The optimal gain is obtained, Factor1 is a Kopt compensation coefficient obtained based on the design and the measured air density, and the calculation formula is as follows: factor1 ═ ρ Measuring /ρ Design of ；ρ Measuring Measuring the air density, rho, for the unit Design of The specific value of the air density is preset and can be adjusted according to the application condition.
(2) The wind deviation compensation formula after yaw optimization is completed can be expressed as:
Yawerror1＝Yawerror0+△Yawerror；
wherein, Yawerror1 represents the deviation of the wind after optimization and correction, Yawerror0 represents the deviation of the wind obtained by actual measurement of the unit, and delta Yawerror is the deviation of the wind for additional compensation.
(3) The blade angle compensation formula of the three blades after the impeller balance optimization is completed can be expressed as follows:
Pitch1Demand1＝Pitch1Demand0+△P1；
Pitch2Demand1＝Pitch2Demand0+△P2；
Pitch3Demand1＝Pitch3Demand0+△P3；
wherein, Pitch1Demand1 represents the blade angle given value after the blade 1 impeller balance optimization correction, Pitch1Demand0 represents the blade angle given value before the wind turbine generator blade 1 is not compensated, and Δ P1 is the additional compensation blade angle of the blade 1; similarly, the Pitch2Demand1 represents a given Pitch angle value after the blade 2 impeller is balanced, optimized and corrected, the Pitch2Demand0 represents a given Pitch angle value before the wind turbine generator blade 2 is not compensated, and the delta P2 is an additional compensation Pitch angle of the blade 2; the Pitch3Demand1 represents a given Pitch angle value after the blade 3 impeller is balanced and optimized, the Pitch3Demand0 represents a given Pitch angle value before the wind turbine generator blade 3 is not compensated, and the delta P3 is an additional compensation Pitch angle of the blade 3.
(4) The blade angle compensation formula of the three blades after the blade angle optimization is completed can be expressed as follows:
Pitch1Demand2＝Pitch1Demand1+△P；
Pitch2Demand2＝Pitch2Demand1+△P；
Pitch3Demand2＝Pitch3Demand1+△P；
wherein, Pitch1Demand2 represents the blade angle given value after blade 1 blade angle optimization and correction, Pitch1Demand1 represents the blade angle given value after blade 1 impeller balance optimization and correction of the unit, Δ P is the blade additional compensation blade angle, the three blades of the wind turbine generator set have the same additional compensation blade angle, the optimization and correction of blade 2 and blade 3 are similar to blade 1, and are not described again.
(5) The compensation formula after the Kopt optimization is completed can be expressed as:
Kopt2＝Kopt1*Factor2；
wherein, Kopt2 represents the optimal gain after Kopt optimizing correction, Kopt1 represents the optimal gain after air density compensation, and Factor2 is the Kopt compensation coefficient obtained by Kopt optimizing.
It should be noted that, in each optimization process, parameters such as an additional compensation pitch angle and an additional compensation wind offset are determined according to an algorithm of each optimization function based on real-time operation information of the wind turbine generator, and in a specific embodiment, a specific process of analyzing the mutual influence relationship of the optimization functions in step S2 of the above embodiment is as follows.
The starting sequence of each optimization function is as follows: yaw optimization, impeller balance optimization, optimal gain optimization and paddle angle optimization; wherein: yaw optimization, optimal gain optimization and blade angle optimization are combined with the optimal gain air density adaptation function, when the optimal gain optimization and the blade angle optimization are started, the optimal gain optimization is started first, and then the optimal gain optimization and the blade angle optimization are combined. Based on the relationship between the optimization functions, if all the optimization functions need to be started, the flow chart is shown in fig. 2, that is, the connection order of the optimization functions is: firstly, starting yaw optimization and Kopt air density adaptation, then starting an impeller to balance optimization, and finally carrying out paddle angle and Kopt combined optimization and Kopt air density adaptation; and realizing the cooperative optimization of the wind turbine generator.
In a specific embodiment, after the step S5 is executed in the foregoing embodiment, the method further includes verifying the result of the collaborative optimization, and a flowchart of the method is shown in fig. 3, where the method includes:
in step S6, it is determined whether the current optimum compensation configuration is equal to the uncompensated configuration.
After the wind turbine generator is subjected to collaborative optimization, the current optimization compensation configuration and the uncompensated configuration can be compared, and the comparison content can include a Kopt compensation coefficient, an additional compensation wind offset, an additional compensation paddle angle, a blade additional compensation paddle angle and the like, but is not limited to the above. If so, go to step S7, otherwise go to step S8.
In step S7, the current optimization compensation configuration is controlled to be equal to the cooperative optimization compensation configuration.
And step S8, controlling the current optimizing compensation configuration to be equal to the uncompensated configuration.
The cooperative optimization compensation configuration and the uncompensated configuration may be set in advance, and are not specifically limited, for example, as shown in the following table:
| serial number | Optimization compensation configuration | Factor1 | △Yawerror | △P1 | △P2 | △P3 | △P | Factor2 |
| 1 | Non-compensated arrangement | 1 | 0 | 0 | 0 | 0 | 0 | 1 |
| 2 | Collaborative optimization configuration | 0.9 | 5 | -0.5 | 0 | 0 | 0.5 | 1.1 |
Therefore, based on the above steps S6-S8, the optimization results of the respective functions can be simultaneously applied and switched at a timing, that is, a timing compensation configuration switching effect is achieved. After the execution of step S7 or S8, step S9 is executed.
And step S9, judging whether the number of the statistical evaluation objects on the wind speed interval meets the preset number requirement.
In practical application, the wind speed interval and the preset number may be determined according to the situation, and are not limited. The evaluation object can be collected by the PLC, and can comprise: power, impeller rotating speed first-frequency multiplication, blade structure load, tower bottom load, impeller rotating speed third-frequency multiplication, tower first-order amplitude and the like. After the number of the statistical evaluation objects reaches the preset number requirement, step S10 is executed.
And step S10, determining the overall effect of the collaborative optimization according to the evaluation object.
Specifically, the process of determining the overall effect of the collaborative optimization is as follows: and calculating an evaluation index after the wind turbine generator is subjected to collaborative optimization according to the evaluation object, wherein the evaluation index comprises the following steps: the power generation lifting proportion, the load reduction proportion and the impeller rotating speed-frequency doubling reduction proportion which correspond to the cooperative optimization compensation configuration are configured; and determining the overall effect of the cooperative optimization based on the evaluation indexes. For example, the power generation boost ratio may be calculated according to the power amplitude, specifically, the power generation boost ratio is [ (power average value in the cooperative optimization configuration) - (power average value in the uncompensated configuration) ]/(power average value in the uncompensated configuration) ], and the calculation manners of other evaluation indexes are similar to this, and are not described again.
The cooperative optimization method for the power generation performance of the wind turbine generator set can verify the optimization effect of the cooperative optimization method, for example, the power generation amount at the designated wind speed section is increased, vibration or load of related components is reduced, and the like, so that the power generation amount is increased, and the method is favorable for reducing vibration of the wind turbine generator set, load of the related components, stall risk of operation of blades and the like. In a specific embodiment, after the step S9, if the determination result is negative, the following steps are performed.
Step S11, determine whether the preset switching time period is reached.
In practical application, the preset switching time period may be set according to an application condition, for example, the preset switching time period is set to 30 minutes. If the preset switching duration is reached, returning to execute the step of judging whether the current optimization compensation configuration is equal to the uncompensated configuration; if the preset switching duration is not reached, returning to the step of judging whether the number of the statistical evaluation objects in the wind speed interval meets the preset number requirement or not; the overall process flow can be seen in fig. 4.
As shown in fig. 5, based on the same inventive concept as the above method for collaborative optimization of the power generation performance of the wind turbine, an embodiment of the present invention further provides a system for collaborative optimization of the power generation performance of the wind turbine, where the system includes:
the acquisition unit 1 is used for acquiring real-time operation data of the wind turbine generator, which is acquired by the PLC; the analysis unit 2 is used for analyzing the mutual influence relationship between the functions to be optimized and the optimization functions of the wind turbine generator, and determining the starting sequence of each optimization function according to the mutual influence relationship between the optimization functions; wherein each optimization function comprises: the method comprises the following steps of impeller balance optimization, optimal gain optimization, paddle angle optimization and yaw optimization; the compensation unit 3 is used for starting the optimal gain air density adaptation function all the time and performing optimal gain air density compensation on real-time operation data; and the optimization unit 4 is used for starting the optimization functions according to the starting sequence and cooperatively optimizing the power generation performance of the wind turbine generator.
As shown in fig. 6, based on the same inventive concept as the above-mentioned collaborative optimization method for the power generation performance of the wind turbine, one or more embodiments of the present invention can also provide an edge computing device, including: at least one processor; and a memory communicatively coupled to the at least one processor, wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to perform the steps of the method for collaborative optimization of wind turbine generation performance according to at least one embodiment of the present disclosure. The detailed implementation process of the cooperative optimization method for the power generation performance of the wind turbine generator is described in detail in this specification, and is not described herein again. Based on this, the calculation, storage and scheduling of the collaborative optimization process can be completed on the edge computing device, and the output of the collaborative optimization is still executed by the PLC, so that the occupation of the PLC resources by the collaborative optimization function is reduced.
As shown in fig. 6, based on the same inventive concept as the above-mentioned collaborative optimization method for the power generation performance of the wind turbine, one or more embodiments of the present invention can also provide a computer-readable storage medium, on which a computer program is stored for non-transiently storing computer-executable instructions, which, when executed by a processor, implement the collaborative optimization method for the power generation performance of the wind turbine provided by at least one embodiment of the present invention. The detailed implementation process of the cooperative optimization method for the power generation performance of the wind turbine generator is described in detail in this specification, and is not described herein again.
The logic and/or steps represented in the flowcharts or otherwise described herein, such as an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable storage medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable storage medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer cartridge (magnetic device), a Random Access Memory (RAM), a Read-Only Memory (ROM), an Erasable Programmable Read-Only Memory (EPROM-Only Memory, or flash Memory), an optical fiber device, and a portable Compact Disc Read-Only Memory (CDROM). Additionally, the computer-readable storage medium may even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic Gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic Gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. This need not be, nor should it be exhaustive of all embodiments. And obvious variations or modifications derived therefrom are intended to be within the scope of the invention.
Claims (10)
1. A collaborative optimization method for the power generation performance of a wind turbine generator is characterized by comprising the following steps:
acquiring real-time operation data of the wind turbine generator, which is acquired by a PLC;
analyzing the mutual influence relationship between the to-be-optimized function and the optimized function of the wind turbine generator, wherein each optimized function comprises the following steps: the method comprises the following steps of impeller balance optimization, optimal gain optimization, paddle angle optimization and yaw optimization;
determining the starting sequence of each optimization function according to the mutual influence relationship of the optimization functions;
starting an optimal gain air density adaptive function all the time, and performing optimal gain air density compensation on the real-time operation control of the wind turbine generator;
and starting the optimization functions according to the starting sequence, and cooperatively optimizing the power generation performance of the wind turbine generator.
2. The cooperative optimization method for the power generation performance of the wind turbine generator set according to claim 1, wherein the starting sequence of each optimization function is as follows: yaw optimization, impeller balance optimization, optimal gain optimization and paddle angle optimization; wherein:
the yaw optimization, the optimal gain optimization and the pitch optimization are all combined with the optimal gain air density adaptation function;
when the optimal gain optimization and the paddle angle optimization are started, the optimal gain optimization is started first, and then the optimal gain optimization and the paddle angle optimization are combined.
3. The method for collaborative optimization of wind turbine generator system power generation performance according to claim 1 or2, wherein the real-time operation data includes: wind speed, wind direction, rotational speed, nacelle acceleration, yaw state, pitch angle, power, windage yaw state, and air density.
4. The method for collaborative optimization of the power generation performance of the wind turbine generator according to any one of claims 1 to 3, wherein after the starting of the optimization functions according to the starting sequence and collaborative optimization of the power generation performance of the wind turbine generator, the method further comprises:
judging whether the current optimizing compensation configuration is equal to the uncompensated configuration;
if so, controlling the current optimization compensation configuration to be equal to the cooperative optimization compensation configuration;
if the judgment result is negative, controlling the current optimization compensation configuration to be equal to the uncompensated configuration;
judging whether the number of the evaluation objects in the wind speed interval meets the preset number requirement or not;
and if so, determining the overall effect of the cooperative optimization according to the evaluation object.
5. The collaborative optimization method for the power generation performance of the wind turbine generator according to claim 4, wherein after judging whether the number of the statistical evaluation objects in the wind speed interval meets a preset number requirement, the method further comprises:
if the judgment result is negative, judging whether the preset switching time length is reached;
if yes, returning to the step of judging whether the current optimizing compensation configuration is equal to the uncompensated configuration;
if not, returning to the step of judging whether the number of the statistical evaluation objects in the wind speed interval meets the requirement of the preset number.
6. The collaborative optimization method for the power generation performance of the wind turbine generator set according to claim 4, wherein determining a collaborative optimization overall effect according to the evaluation object includes:
calculating an evaluation index after the wind turbine generator is subjected to collaborative optimization according to the evaluation object, wherein the evaluation index comprises the following steps: the power generation lifting proportion, the load reduction proportion and the impeller rotating speed-frequency doubling reduction proportion corresponding to the cooperative optimization compensation configuration are configured;
and determining the overall effect of the cooperative optimization based on the evaluation index.
7. The collaborative optimization method for the power generation performance of the wind turbine generator according to claim 6, wherein the evaluation object includes: power, impeller rotating speed first-frequency multiplication, blade structure load, tower bottom load, impeller rotating speed third-frequency multiplication and tower first-order amplitude.
8. The cooperative optimization system for the power generation performance of the wind turbine generator is characterized by comprising the following components:
the acquisition unit is used for acquiring real-time operation data of the wind turbine generator, which is acquired by the PLC;
the analysis unit is used for analyzing the mutual influence relationship between the functions to be optimized and the optimization functions of the wind turbine generator and determining the starting sequence of each optimization function according to the mutual influence relationship between the optimization functions; wherein each optimization function comprises: the method comprises the following steps of impeller balance optimization, optimal gain optimization, paddle angle optimization and yaw optimization;
the compensation unit is used for starting the optimal gain air density adaptation function all the time and carrying out optimal gain air density compensation on the real-time operation control of the wind turbine generator;
and the optimization unit is used for starting the optimization functions according to the starting sequence and cooperatively optimizing the power generation performance of the wind turbine generator.
9. An edge computing device, comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to perform the steps of the method for collaborative optimization of wind turbine generation performance according to any of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method for collaborative optimization of wind turbine generator performance according to any of claims 1-7.
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