CN113361100A - Wind speed estimation method, device, equipment and computer readable storage medium - Google Patents
Wind speed estimation method, device, equipment and computer readable storage medium Download PDFInfo
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Abstract
The embodiment of the invention provides a wind speed estimation method, a wind speed estimation device, wind speed estimation equipment and a computer readable storage medium, and relates to the field of wind speed detection. The method comprises the steps of establishing a correlation coefficient matrix of the fans by obtaining the total number of the fans of a wind field and historical wind speed data of each fan, obtaining a correlation fan set of the fans by the correlation coefficient matrix, establishing a whale swarm position coding rule and a fitness function of the fans according to the correlation fan set, optimizing the fitness function by a whale swarm algorithm to obtain an optimal solution of a whale swarm position, and estimating and modeling the wind speed of the fans according to the optimal solution of the whale swarm position and the whale swarm position coding rule of the fans. The wind speed can be accurately estimated by estimating and modeling the wind speed of the fan.
Description
Technical Field
The invention relates to the field of wind speed detection, in particular to a wind speed estimation method, a wind speed estimation device, wind speed estimation equipment and a computer readable storage medium.
Background
At present, most wind turbine generators in China are installed in field areas with large wind sand and cold climate or offshore areas with serious salt spray corrosion, anemoscope is easy to damage, failure rate is high, and wind speed data cannot be accurately acquired.
Disclosure of Invention
The invention aims to provide a wind speed estimation method, a wind speed estimation device, wind speed estimation equipment and a computer readable storage medium, which can accurately estimate wind speed.
In order to achieve the above purpose, the embodiment of the present invention adopts the following technical solutions:
in a first aspect, the present invention provides a wind speed estimation method applied to a wind turbine control system, including:
acquiring the total number n of fans, and numbering the fans, wherein the number is i, i belongs to {1,2,3,. n };
acquiring historical wind speed data X of each fan, and establishing a correlation coefficient matrix M of the fans according to the total number n of the fans and the historical wind speed data Xcor；
By a matrix M of correlation coefficientscorEstablishing a set phi of relevant fans of the fan Ii；
According to the related fan set phiiEstablishing whale shoal position coding rule P of fan No. iiAnd a fitness function max f (P)i)；
Optimizing the fitness function max f (P) by whale herd algorithmi) Obtaining the optimal solution P of the whale flock positioni GBest；
According to the optimal solution P of the whale flock positioni GBestAnd a whale shoal position coding rule P of the fan No. iiWind speed estimation modeling for No. i fan
Optionally, the historical wind speed data X is sliding average value data within a set time of the fan.
Optionally, a matrix of correlation coefficients McorThis can be established by the following equation:
wherein, corijThe coefficient of the correlation is represented by,
cov(Xi，Xj) Represents Xi，XjCovariance of (d), delta (X)i) Represents XiStandard deviation of, delta (X)j) Represents XjStandard deviation of (A), XiRepresenting historical wind speed data, X, for fan number ijRepresenting the historical wind speed data of a fan number j, n representing the total number of fans, i, j belonging to {1,2, 3.. n }.
Optionally, the set of relevant fans phi is established by the following formulai：
φiIf cor ═ j |)ij＞TcovAnd j ≠ i, i, j ∈ {1,2, 3.. n }
Wherein, TcovIndicating that a threshold is set.
Optionally, a whale shoal position coding rule P for establishing fan IiThe method comprises the following steps:
establishing a related fan weight influence vector W of the fan Ii＝[{Wij|j∈φi}]T；
Establishing a related fan deflection influence vector Wb of the fan Ii＝[{Wbij|j∈φi}]T；
Wherein, WijRepresents the influence of the wind speed estimation weight of the blower No. j on the blower No. i, WbijRepresenting the wind speed estimation paranoia influence of the j fan on the i fan;
establishing a whale swarm position coding rule of a fan No. i:
Pi＝flatten([Wi，Wbi])
wherein the flatten function represents flattening the array into a one-dimensional array.
Optionally, the fitness function max f (P) is established by the following formulai)：
Wherein, XiRepresenting historical wind speed data, X, for fan number ijRepresenting historical wind speed data for fan number j.
Optionally, the wind speed estimation modeling is performed on the fan No. i through the following formula
Wherein the content of the first and second substances,
denotes the actual wind speed, W, of fan # ji GBestRepresents the optimal solution of the influence vector of the related fan weight, Wbi GBestRepresents the optimal solution of the bias-execution influence vector of the related fan, Wij GBestRepresents the optimal solution of the influence of the wind speed estimation weight of the blower fan j on the wind speed estimation weight of the blower fan i, Wbij GBestAnd the optimal solution of the wind speed estimation deviation of the fan No. j to the fan No. i is shown.
In a second aspect, the present invention provides a wind speed estimation device, the device comprising:
the acquiring module is used for acquiring the total number n of the fans and historical wind speed data X of each fan, and numbering the fans, wherein the number is i, and i belongs to {1,2,3,. n };
a processing module for calculating a correlation coefficient matrix M of the fan according to the total number n of the fan and the historical wind speed data XcorBy a matrix M of correlation coefficientscorObtaining a relevant fan set phi of the fan IiAccording to the set phi of the related fansiEstablishing whale shoal position coding rule P of fan No. iiAnd a fitness function max f (P)i)；
An optimization module for passingOptimizing said fitness function max f (P)i) Obtaining the optimal solution P of the whale flock positioni GBest；
A modeling module for optimizing the solution P according to the whale flock positioni GBestWind speed estimation modeling for No. i fan
In a third aspect, the invention provides an electronic device comprising a memory storing a computer program and a processor implementing the steps of the wind speed estimation method when the processor executes the computer program.
In a fourth aspect, the invention provides a computer-readable storage medium having stored thereon a computer program which, when being executed by a processor, carries out the steps of the wind speed estimation method.
Compared with the prior art, the invention has the following beneficial effects:
the invention provides a wind speed estimation method, a wind speed estimation device, wind speed estimation equipment and a computer readable storage medium. The method comprises the steps of establishing a correlation coefficient matrix of the fans by obtaining the total number of the fans of a wind field and historical wind speed data of each fan, obtaining a correlation fan set of the fans by the correlation coefficient matrix, establishing a whale swarm position coding rule and a fitness function of the fans according to the correlation fan set, optimizing the fitness function by a whale swarm algorithm to obtain an optimal solution of a whale swarm position, and estimating and modeling the wind speed of the fans according to the optimal solution of the whale swarm position and the whale swarm position coding rule of the fans. The wind speed can be accurately estimated by estimating and modeling the wind speed of the fan.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a schematic view of an application scenario provided in an embodiment of the present invention;
FIG. 2 is a general flow chart of a wind speed estimation method according to an embodiment of the present invention;
FIG. 3 is a flowchart illustrating a circular execution of a wind speed estimation method according to an embodiment of the present invention;
FIG. 4 is a detailed flow chart of a whale swarm algorithm provided by the embodiment of the invention;
fig. 5 is a block diagram of a wind speed estimation device according to an embodiment of the present invention.
Reference numerals: 100-an electronic device; 110-wind speed estimation means; 111-an acquisition module; 112-a processing module; 113-an optimization module; 114-a modeling module; 120-a memory; 130-a processor; 140-a communication unit.
Detailed Description
At present, wind speed power generation is a relatively mature and relatively potential renewable energy technology. The wind speed generator set absorbs wind energy through the blades, converts the wind energy into mechanical energy, and converts the mechanical energy into electric energy through the generator, so that the wind speed is a dynamic parameter variable determining the working state of the wind speed generator.
Most wind turbine generators in China are installed in field areas with large wind sand and cold climate or offshore areas with serious salt spray corrosion, and anemoscope is easy to damage, so that wind speed cannot be accurately acquired, and therefore a reliable wind speed estimation method is needed to accurately estimate wind speed.
The problems existing in the prior art are all the results obtained after the inventor practices and researches, so that the discovery process of the problems and the solution proposed by the embodiment of the invention in the following for the problems are all the contributions of the inventor in the invention process.
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the 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.
It is to be noted that the term "comprises," "comprising," or any other variation thereof is intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
Some embodiments of the invention are described in detail below with reference to the accompanying drawings. It should be noted that the features of the embodiments of the present invention may be combined with each other without conflict.
The present embodiment provides an electronic device that can estimate wind speed. In one possible implementation, the electronic Device may be a user terminal, for example, but not limited to, a server, a smart phone, a Personal Computer (PC), a tablet PC, a Personal Digital Assistant (PDA), a Mobile Internet Device (MID), an image capture Device, and the like.
In another possible implementation manner, the electronic device may also be a server capable of communicating with the user terminal. The server can obtain the total number of the fans of the wind field and historical wind speed data of each fan, and carries out wind speed estimation modeling on the fans.
Please refer to fig. 1, which illustrates a schematic structure of the electronic device 100. The electronic device 100 comprises a wind speed estimation apparatus 110, a memory 120, a processor 130 and a communication unit 140.
The memory 120, processor 130, and communication unit 140 are electrically connected to each other directly or indirectly to enable data transmission or interaction. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The wind speed estimation device 110 includes at least one software function module which can be stored in the memory 120 in the form of software or Firmware (Firmware) or solidified in an Operating System (0S) of the electronic device 100. The processor 130 is used for executing executable modules stored in the memory 120, such as software functional modules and computer programs included in the wind speed estimation device 110. The wind speed estimation method is implemented when the computer executable instructions in the wind speed estimation device 110 are executed by the processor 130.
The Memory 120 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like. The memory 120 is used for storing a program, and the processor 130 executes the program after receiving the execution instruction. The communication unit 140 is used for transceiving data through a network.
The processor 130 may be an integrated circuit chip having signal processing capabilities. The Processor 130 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; but may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor 130 may be any conventional processor or the like.
It should be understood that the structure shown in fig. 1 is only a schematic structural diagram of the electronic device 100, and the electronic device 100 may also include more or fewer components than shown in fig. 1, or have a different configuration than shown in fig. 1. The components shown in fig. 1 may be implemented in hardware, software, or a combination thereof.
Referring to fig. 2, a wind speed estimation method according to an embodiment of the present invention is provided, and wind speed estimation modeling can be performed based on the wind speed estimation method. The wind speed estimation method may be performed by the electronic device 100 described in fig. 1, for example, may be performed by the processor 130 in the electronic device 100. The wind speed estimation method comprises the following steps:
step 101: and acquiring the total number n of the fans, and numbering the fans, wherein the number is i, i belongs to {1,2, 3.. n }.
Step 102: acquiring historical wind speed data X of each fan, and establishing a correlation coefficient matrix M of the fans according to the total number n of the fans and the historical wind speed data Xcor。
Step 103: by a matrix M of correlation coefficientscorEstablishing a set phi of relevant fans of the fan Ii。
Step 104: according to the related fan set phiiEstablishing whale shoal position coding rule P of fan No. iiAnd a fitness function maxf (P)i)。
Step 105: fitness function maxf (P) is optimized through whale swarm algorithmi) Obtaining the optimal solution P of the whale flock positioni GBest。
Step 106: optimal solution P according to whale flock positioni GBestWhale herd position coding rule P of fan No. iiWind speed estimation modeling for No. i fan
By the method, the wind speed can be accurately estimated. In practical application, an anemometer arranged in a fan can measure wind speed, one wind speed can be estimated through the method, and for a person skilled in the art, two wind speeds are used as references of wind speed values, so that the purpose of redundant protection is achieved.
In addition, if the built-in anemometer of the fan is in fault or damaged, a person skilled in the art can directly adopt the wind speed estimated by the method as a reference of the wind speed value, so that the wind speed value can replace the anemometer until the anemometer is overhauled or replaced. Particularly, in an offshore wind field, due to the fact that the overhaul cost is high when the anemoscope goes out of the ship, if the anemoscope is damaged, the anemoscope cannot be overhauled or replaced at once, a specific overhaul period is often provided, the anemoscope is overhauled or replaced only when the overhaul period comes, and in the process that the anemoscope is in a fault waiting overhaul period, the wind speed estimated by the method can be used as a reference of a wind speed value, so that wind field data can be better monitored.
Please refer to fig. 3 in combination, in order to perform the above steps in a circulating manner so that all the fans of the wind farm are optimized, a circulating performing step is further provided between step 102 and step 103, and includes:
step 107: the parameter i is set to 1.
After step 106, there are provided:
step 108: and judging whether i is equal to n, if so, turning to step 109, otherwise, setting i to i +1, and turning to step 103.
Step 109: and finishing the optimization process.
Optionally, the historical wind speed data X is sliding average value data within a set time of the fan.
Optionally, a matrix of correlation coefficients McorThis can be established by the following equation:
wherein, corijThe coefficient of the correlation is represented by,
cov(Xi，Xj) Represents Xi，XjCovariance of (d), delta (X)i) Represents XiStandard deviation of, delta (X)j) Represents XjStandard deviation of (A), XiRepresenting historical wind speed data, X, for fan number ijRepresenting the historical wind speed data of a fan number j, n representing the total number of fans, i, j belonging to {1,2, 3.. n }.
Optionally, the set of relevant fans phi is established by the following formulai：
φiIf cor ═ j |)ij＞TcovAnd j ≠ i, i, j ∈ {1,2,3,. n } },
wherein, TcovIndicating a set threshold, preferably Tcov∈[0.6，0.95]。
Further, the relevant fan set phi of the fan IiA set of fans φ may be formed:
φ＝{φ1，φ2，φ3，...φi...φn}
φ1indicating the relative fan set of fan # 1, #2Indicating the relative fan set of fan # 2, #3Indicating the relative fan set of fan # 3, phinRepresenting the relevant fan set of fan number n.
Optionally, a whale shoal position coding rule P of fan No. i is establishediThe method comprises the following steps:
establishing a related fan weight influence vector Wi of the fan I:
Wi＝[{Wij|j∈φi}]T；
establishing a related fan deflection influence vector Wb of the fan Ii：
Wbi＝[{Wbij|j∈φi}]T；
Wherein, WijRepresents the influence of the wind speed estimation weight of the blower No. j on the blower No. i, WbijRepresenting the wind speed estimation paranoia influence of the j fan on the i fan;
establishing a whale swarm position coding rule of a fan No. i:
Pi＝flatten([Wi，Wbi])
wherein the flatten function represents flattening the array into a one-dimensional array.
For example,
encoding rule P by whale shoal positioniEstablishing a related fan weight influence vector WiAnd the bias-actuating influence vector Wb of the related faniAnd the influence W of the wind speed estimation weight of the fan j on the fan iijAnd the influence Wb of the wind speed estimation deviation of the fan No. j on the fan No. iijThe link between them.
Further, the influence W of the wind speed of the fan j on the fan i can be estimated through the wind speed of the fan jijEstablishing a weight influence matrix W:
estimating deviation influence Wb on wind speed of fan I through fan jijEstablishing a bias execution influence matrix Wb:
optionally, the fitness function maxf (P) is established by the following formulai)：
Wherein, XiRepresenting historical wind speed data, X, for fan number ijRepresenting historical wind speed data for fan number j.
Optionally, the wind speed estimation modeling is performed on the fan I through the following formula
Wherein the content of the first and second substances,
denotes the actual wind speed, W, of fan # ji GBestRepresents the optimal solution of the influence vector of the related fan weight, Wbi GBestRepresents the optimal solution of the bias-execution influence vector of the related fan, Wij GBestRepresents the optimal solution of the influence of the wind speed estimation weight of the blower fan j on the wind speed estimation weight of the blower fan i, Wbij GBestAnd the optimal solution of the wind speed estimation deviation of the fan No. j to the fan No. i is shown.
Optimal solution P by whale flock positioni GBestAnd whale herd position coding rule PiAnd obtaining the optimal solution W of the influence vector of the weight of the related fani GBestOptimal solution Wb of deviation-caused influence vector of related fani GBestAnd the optimal solution W for the influence of the wind speed estimation weight of the blower fan j on the blower fan iij GBestAnd the optimal solution Wb of the wind speed estimation deviation of the fan j to the fan iij GBest。
Then passes through the actual wind speed of the No. j fan
Wind speed estimation modeling for No. i fan
To facilitate an understanding of embodiments of the present invention, reference is made to FIG. 4, which is incorporated herein by reference, and the whale herd algorithm is described in detail below.
The whale swarm algorithm specifically comprises the following steps:
step 201: and setting whale population algorithm parameters including a population size p, iteration times n, a stability threshold Ts and a field search radius r.
Step 202: initializing each whale individual to obtain an initial whale population omega, numbering the whale individuals in the initial whale population omega, wherein the number is k, k belongs to [1, 2,3, 4... ], p]And calculating the objective function value f of each whale individual, and calculating the objective function value fGBestIs set to 0.
Step 203: find the "superior and most recent" whale Y of whale k, if Y exists, execute step 204, otherwise execute step 209.
Wherein the "superior and nearest" whales are leading individuals of the current whales, and are whales which are closest to the current whales among all the whales superior to the current whales.
Step 204: a duplicate whale X of whale k is generated, and the X moves under the guidance of Y according to the following position update formula:
wherein x isi tDenotes the position of the ith element of X at the t-th iteration, Xi t+1Denotes the position of the ith element of X at the t +1 step iteration, yi tIndicating the position of the ith element of Y at the iteration of step t, dX，YRepresents the distance between X and Y,
represents 0 to
A random number in between.
Step 205: calculating the objective function value f (X) of X, if f (X) is greater than f (k), executing step 206, otherwise executing step 207.
Step 206: assigning X to k, and assigning an iteration counter omega of whale kk.cSet to 0, step 215 is performed.
Step 207: iterative counter omega for whale kk.cLess than Ts, will be Ωk.cPlus 1, step 215 is performed, otherwise step 205 is performed.
Step 208: the whale k is reinitialized, the objective function value f (X) of the whale k is calculated, and the step 215 is executed.
Step 209: a whale copy X 'of whale k is generated, a domain search is performed on X', and step 210 is performed.
Step 210: and calculating an objective function value f (X ') after the domain search, if f (X') is greater than f (k), executing step 211, otherwise, executing step 212.
Step 211: assigning X' to k, and assigning an iteration counter omega of whale kk.cSet to 0, step 215 is performed.
Step 212: iterative counter omega for whale kk.cLess than Ts, will be Ωk.cPlus 1, step 215 is performed, otherwise step 213 is performed.
Step 213: if the objective function value f (k) of whale k is greater than fGBestA 1 is to fGBestSet to f (k), the optimal solution GBest is set to k, and step 214 is performed, otherwise, f is not changedGBestAnd GBest, and step 214 is performed.
Step 214: the whale k is reinitialized, the objective function value f (k) of the whale k is calculated, and the step 215 is executed.
Step 215: and setting k to k +1, if k is less than the individual number | omega | of the whales, executing the step 203, and otherwise executing the step 216.
Step 216: and judging whether a termination condition is met, if not, executing the step 201, otherwise, executing the step 217.
Step 217: if the whale individual better than GBest exists in the population of the last generation, replacing GBest with the whale individual, and ending the single-round optimization of the whale population algorithm.
Referring to fig. 5, based on the foregoing implementation, an embodiment of the present invention further provides a wind speed estimation device 110, including:
the acquiring module 111 is used for acquiring the total number n of the fans and historical wind speed data X of each fan, and numbering the fans, wherein the number is i, and i belongs to {1,2,3,. n };
a processing module 112, configured to calculate a correlation coefficient of the fan according to the total number n of the fans and the historical wind speed data XMatrix McorBy a matrix M of correlation coefficientscorEstablishing a set phi of relevant fans of the fan IiAccording to the relative fan set phiiEstablishing whale shoal position coding rule P of fan No. iiAnd a fitness function maxf (P)i)；
An optimization module 113 for optimizing a fitness function maxf (P)i) Obtaining the optimal solution P of the whale flock positioni GBest；
A modeling module 114 for optimizing the solution P according to whale flock positioni GBestWind speed estimation modeling for No. i fan
The embodiment of the present invention further provides an electronic device 100, which includes a memory 120 and a processor 130, where the memory 120 stores a computer program, and the processor 130 implements the steps of the wind speed estimation method when executing the computer program.
The present embodiment also provides a computer readable storage medium having stored thereon a computer program which, when being executed by the processor 130, carries out the steps of the wind speed estimation method.
In summary, the present invention provides a wind speed estimation method, apparatus, device and computer readable storage medium. The method comprises the steps of establishing a correlation coefficient matrix of the fans by obtaining the total number of the fans of a wind field and historical wind speed data of each fan, obtaining a correlation fan set of the fans by the correlation coefficient matrix, establishing a whale swarm position coding rule and a fitness function of the fans according to the correlation fan set, optimizing the fitness function by a whale swarm algorithm to obtain an optimal solution of a whale swarm position, and estimating and modeling the wind speed of the fans according to the optimal solution of the whale swarm position and the whale swarm position coding rule of the fans. The wind speed can be accurately estimated by estimating and modeling the wind speed of the fan.
The above description is only for the specific embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. A wind speed estimation method, comprising:
acquiring the total number n of fans, and numbering the fans, wherein the number is i, i belongs to {1,2,3,. n };
acquiring historical wind speed data X of each fan, and establishing a correlation coefficient matrix M of the fans according to the total number n of the fans and the historical wind speed data Xcor；
By a matrix M of correlation coefficientscorEstablishing a set phi of relevant fans of the fan Ii；
According to the related fan set phiiEstablishing whale shoal position coding rule P of fan No. iiAnd a fitness function maxf (P)i)；
Optimizing the fitness function maxf (P) by whale herd algorithmi) Obtaining the optimal solution P of the whale flock positioni GBest；
According to the optimal solution P of the whale flock positioni GBestAnd a whale shoal position coding rule P of the fan No. iiWind speed estimation modeling for No. i fan
2. A wind speed estimation method according to claim 1, wherein the wind speed history data X is sliding average data within a set time period of the wind turbine.
3. A wind speed estimation method according to claim 1, wherein said correlation coefficient matrix M is established by the following formulacor：
Wherein, corijThe coefficient of the correlation is represented by,
cov(Xi,Xj) Represents Xi,XjCovariance of (d), delta (X)i) Represents XiStandard deviation of, delta (X)j) Represents XjStandard deviation of (A), XiRepresenting historical wind speed data, X, for fan number ijRepresenting the historical wind speed data of the fan number j, n representing the total number of fans, i, j e {1,2,3, … n }.
4. A wind speed estimation method according to claim 1, wherein said set of correlated wind turbines Φ is established by the following formulai：
φiIf cor ═ j |)ij>TcovAnd j ≠ i, i, j ∈ {1,2,3, … n } },
wherein, TcovIndicating that a threshold is set.
5. A wind speed estimation method according to claim 1, wherein the whale herd position coding rule P for fan i is establishediThe method comprises the following steps:
establishing a related fan weight influence vector W of the fan Ii：
Wi＝[{Wij|j∈φi}]T；
Establishing a related fan deflection influence vector Wb of the fan Ii：
Wbi＝[{Wbij|j∈φi}]T；
Wherein, WijRepresents the influence of the wind speed estimation weight of the blower No. j on the blower No. i, WbijRepresenting the wind speed estimation paranoia influence of the j fan on the i fan;
establishing a whale swarm position coding rule of a fan No. i:
Pi＝flatten([Wi，Wbi])
wherein the flatten function represents flattening the array into a one-dimensional array.
6. A wind speed estimation method according to claim 5, wherein said fitness function max f (Pmax) is established by the following formulai)：
Wherein, XiRepresenting historical wind speed data, X, for fan number ijRepresenting historical wind speed data for fan number j.
7. A wind speed estimation method according to claim 6, characterized in that the wind speed estimation modeling is performed for fan number i by the following formula
Wherein the content of the first and second substances,
denotes the actual wind speed, W, of fan # ji GBestRepresents the optimal solution of the influence vector of the related fan weight, Wbi GBestRepresents the optimal solution of the bias-execution influence vector of the related fan, Wij GBestRepresents the optimal solution of the influence of the wind speed estimation weight of the blower fan j on the wind speed estimation weight of the blower fan i, Wbij GBestAnd the optimal solution of the wind speed estimation deviation of the fan No. j to the fan No. i is shown.
8. A wind speed estimation device, characterized in that the device comprises:
the acquiring module is used for acquiring the total number n of the fans and historical wind speed data X of each fan, and numbering the fans, wherein the number is i, and i belongs to {1,2,3,. n };
a processing module for calculating a correlation coefficient matrix M of the fan according to the total number n of the fan and the historical wind speed data XcorBy a matrix M of correlation coefficientscorEstablishing a set phi of relevant fans of the fan IiAccording to the set phi of the related fansiEstablishing whale shoal position coding rule P of fan No. iiAnd a fitness function maxf (P)i)；
An optimization module for optimizing the fitness function maxf (P) by whale herd algorithmi) Obtaining the optimal solution P of the whale flock positioni GBest；
A modeling module for optimizing the solution P according to the whale flock positioni GBestWind speed estimation modeling for No. i fan
9. An electronic device, comprising a memory and a processor, the memory storing a computer program, wherein the processor, when executing the computer program, implements the steps of the method 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 according to any one of claims 1 to 7.
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