CN116151130A - Wind power plant maximum frequency damping coefficient calculation method, device, equipment and medium - Google Patents
Wind power plant maximum frequency damping coefficient calculation method, device, equipment and medium Download PDFInfo
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- 238000013016 damping Methods 0.000 title claims abstract description 55
- 238000004364 calculation method Methods 0.000 title claims abstract description 28
- 238000000034 method Methods 0.000 claims abstract description 42
- 238000007665 sagging Methods 0.000 claims abstract description 21
- 238000012549 training Methods 0.000 claims description 31
- 238000004590 computer program Methods 0.000 claims description 24
- 239000011159 matrix material Substances 0.000 claims description 21
- 239000013598 vector Substances 0.000 claims description 20
- 230000008569 process Effects 0.000 claims description 13
- 230000000630 rising effect Effects 0.000 claims description 9
- 238000010276 construction Methods 0.000 claims description 3
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- 230000006870 function Effects 0.000 description 8
- 238000010586 diagram Methods 0.000 description 5
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- 241000532123 Setia Species 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
- H02J3/241—The oscillation concerning frequency
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/381—Dispersed generators
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2113/00—Details relating to the application field
- G06F2113/06—Wind turbines or wind farms
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/28—The renewable source being wind energy
Abstract
The invention discloses a method, a device, equipment and a medium for calculating a maximum frequency damping coefficient of a wind power plant, which are used for measuring the real-time wind speed of the running wind power plant; inputting the real-time wind speed and the preset limit speed constraint of the fan into a pre-established dimension-ascending linear model for calculation, and outputting the calculated maximum integral sagging coefficient as the maximum frequency damping coefficient of the wind power plant; the dimension-lifting linear model comprises a one-to-one correspondence of a final rotating speed and a wind speed as input and an overall droop coefficient as output. The maximum frequency damping coefficient evaluation of the wind power plant can be realized rapidly and with high precision.
Description
Technical Field
The invention relates to the technical field of power systems, in particular to a method, a device, equipment and a medium for calculating a maximum frequency damping coefficient of a wind power plant.
Background
Most wind turbine generators are connected in a grid through a power electronic inverter device, the rotating speed and the system frequency of the wind turbine generators are in a decoupling state, natural inertia is not possessed, the large-scale access to a power grid to replace a thermal power generating unit can lead to system inertia reduction, and quick and effective response to system frequency change cannot be timely made. The wind turbine generator can perform frequency modulation by simulating the sagging characteristic and the inertia characteristic of the synchronous generator when participating in primary frequency modulation, the wind farm is used as a grid-connected main body, the sagging characteristic of a conventional power station is required to be integrally simulated during primary frequency modulation, and the grid-connected guidance rule is met. By adopting an output adjusting method of the power electronic converter, the kinetic energy stored in the fan blade can participate in primary frequency modulation by adjusting the active power set value of the converter. However, the control method can cause the change of the rotating speed of the fans to influence the safe operation, so that the power of the fans needs to be reasonably distributed to ensure the safe operation of each fan. In order to ensure the safety of the rotating speeds of all fans, before participating in primary frequency modulation, the frequency modulation capacity is firstly required to be evaluated, the limiting sagging slope of the fans is calculated for reporting, and then the regulation is executed according to the instruction fed back by a power grid.
However, due to the fact that the number of fans in the wind farm is large, dynamic characteristics are complex, the frequency modulation capacity evaluation model is a high-dimensional nonlinear differential algebraic equation set, and accurate resolving and solving are difficult. And in different wind speed states, the simulation calculation method needs to be carried out again to determine the frequency modulation capability, the calculation period is longer, and the accuracy of the calculation result is poor.
Disclosure of Invention
Aiming at the defects, the invention provides a method, a device, equipment and a medium for calculating the maximum frequency damping coefficient of a wind power plant, which can quickly and accurately evaluate the maximum frequency damping coefficient of the wind power plant.
The embodiment of the invention provides a method for calculating a maximum frequency damping coefficient of a wind farm, which comprises the following steps:
measuring the real-time wind speed of a running wind power plant;
inputting the real-time wind speed and the preset limit speed constraint of the fan into a pre-established dimension-ascending linear model for calculation, and outputting the calculated maximum integral sagging coefficient as the maximum frequency damping coefficient of the wind power plant; the dimension-lifting linear model comprises a one-to-one correspondence of a final rotating speed and a wind speed as input and an overall droop coefficient as output.
Preferably, the up-scaling linear model construction process specifically includes:
obtaining operation data of each fan rotor after the wind power plant participates in the frequency modulation process, and taking the operation data of each time as one sample data in a data set, wherein the operation data comprise the final rotating speed, the integral sagging coefficient and the wind speed of the wind power plant;
dividing samples in the dataset into input samples and output samples;
carrying out dimension lifting on the input sample to obtain a dimension-lifted input sample;
constructing an input sample set and an output sample set after dimension lifting required by least square calculation according to the input sample after dimension lifting and the output sample;
and training the linear training matrix in the dimension-lifting linear model through least square to obtain the dimension-lifting linear model.
As a preferable mode, the method is characterized in that the first sample in the input samplesiSample data
；
Employing the data setiIntegral sag factor of individual sample datak i As the first of the output samplesiSample data;
wherein ,
is the firstiThe rotational speed of the wind farm in the individual sample data after the primary frequency modulation process,/>
Is the firstiWind speed of the wind farm in the individual sample data.
Preferably, the input sample set
；
The output sample set
；
wherein wherein ,k i is the first of the output samplesiThe data of the individual samples are taken,
i=1, 2, …, n, n being the number of sample data in the dataset,ψ（X i ) Is the first of the input samples after the dimension riseiSample data->
Middle by->
Vector obtained by up-scaling, where the j-th dimension +.>
，c j Is input withSample ofX i A basis vector of the same dimension.
As a preferable scheme, the linear training matrix is
；
The dimension-increasing linear model is as follows:
wherein ,
for inputting sample set +.>
Matrix transpose of>
Is->
Y is the output sample set, +.>
The final rotating speed of the wind turbine after primary frequency modulation is +.>
Is wind speed of wind power plant, < >>
To at the same time
and />
The resulting vector is extended on the basis of the rising dimension.
Preferably, the maximum frequency damping coefficient
；
Wherein M is the followingA linear training matrix in an upwarp linear model,
for the said real-time wind speed,
for limiting the limit speed of the fan, < >>
Is at->
and />
The resulting vector is extended on the basis of the rising dimension.
As a preferred embodiment, the method further comprises:
and uploading the real-time wind speed, the limit rotation speed constraint of the fan and the maximum frequency damping coefficient to a power grid dispatching center to serve as sample data for training the dimension-ascending linear model.
The embodiment of the invention also provides a device for calculating the maximum frequency damping coefficient of the wind power plant, which comprises the following steps:
the wind speed measuring module is used for measuring the real-time wind speed of the running wind power plant;
the calculation module is used for inputting the real-time wind speed and the preset limit rotating speed constraint of the fan into a pre-established dimension-lifting linear model for calculation, and outputting the calculated maximum integral sagging coefficient as a maximum frequency damping coefficient of the wind power plant; the dimension-lifting linear model comprises a one-to-one correspondence of a final rotating speed and a wind speed as input and an overall droop coefficient as output.
Preferably, the calculating module builds the up-dimension linear model specifically including:
obtaining operation data of each fan rotor after the wind power plant participates in the frequency modulation process, and taking the operation data of each time as one sample data in a data set, wherein the operation data comprise the final rotating speed, the integral sagging coefficient and the wind speed of the wind power plant;
dividing samples in the dataset into input samples and output samples;
carrying out dimension lifting on the input sample to obtain a dimension-lifted input sample;
constructing an input sample set and an output sample set after dimension lifting required by least square calculation according to the input sample after dimension lifting and the output sample;
and training the linear training matrix in the dimension-lifting linear model through least square to obtain the dimension-lifting linear model.
As an improvement of the above, the first sample in the input samplesiSample data
；
Employing the data setiIntegral sag factor of individual sample datak i As the first of the output samplesiSample data;
wherein ,
is the firstiThe rotational speed of the wind farm in the individual sample data after the primary frequency modulation process,/>
Is the firstiWind speed of the wind farm in the individual sample data.
Preferably, the input sample set
；
The output sample set
；
wherein , wherein ,k i is the first of the output samplesiThe data of the individual samples are taken,
i=1, 2, …, n, n being the number of sample data in the dataset,ψ（X i ) Is the first of the input samples after the dimension riseiIndividual samplesData->
Middle by->
Vector obtained by up-scaling, where the j-th dimension +.>
，c j To and input samplesX i A basis vector of the same dimension.
As a preferable scheme, the linear training matrix is
；
The dimension-increasing linear model is as follows:
wherein ,
for inputting sample set +.>
Matrix transpose of>
Is->
Y is the output sample set, +.>
The final rotating speed of the wind turbine after primary frequency modulation is +.>
Is wind speed of wind power plant, < >>
To at the same time
and />
The resulting vector is extended on the basis of the rising dimension.
Preferably, the maximum frequency damping coefficient
；
Wherein M is a linear training matrix in the up-scaling linear model,
for the said real-time wind speed,
for limiting the limit speed of the fan, < >>
Is at->
and />
The resulting vector is extended on the basis of the rising dimension.
As a preferred embodiment, the method further comprises:
and uploading the real-time wind speed, the limit rotation speed constraint of the fan and the maximum frequency damping coefficient to a power grid dispatching center to serve as sample data for training the dimension-ascending linear model.
The embodiment of the invention also provides a terminal device, which comprises a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, wherein the method for calculating the maximum frequency damping coefficient of the wind farm according to any one of the above embodiments is realized when the processor executes the computer program.
The embodiment of the invention also provides a computer readable storage medium, which comprises a stored computer program, wherein when the computer program runs, equipment where the computer readable storage medium is located is controlled to execute the wind farm maximum frequency damping coefficient calculation method according to any one of the above embodiments.
The method, the device, the equipment and the medium for calculating the maximum frequency damping coefficient of the wind power plant are used for measuring the real-time wind speed of the running wind power plant; inputting the real-time wind speed and the preset limit speed constraint of the fan into a pre-established dimension-ascending linear model for calculation, and outputting the calculated maximum integral sagging coefficient as the maximum frequency damping coefficient of the wind power plant; the dimension-lifting linear model comprises a one-to-one correspondence of a final rotating speed and a wind speed as input and an overall droop coefficient as output. The maximum frequency damping coefficient evaluation of the wind power plant can be realized rapidly and with high precision.
Drawings
FIG. 1 is a schematic flow chart of a method for calculating a maximum frequency damping coefficient of a wind farm according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of a wind farm maximum frequency damping coefficient calculation device according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a terminal device according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, a flowchart of a method for calculating a maximum frequency damping coefficient of a wind farm according to an embodiment of the present invention is provided, and the method includes steps S1 to S2:
s1, measuring the real-time wind speed of a running wind power plant;
s2, inputting the real-time wind speed and the preset limit speed constraint of the fan into a pre-established dimension-ascending linear model for calculation, and outputting the calculated maximum integral sagging coefficient as a maximum frequency damping coefficient of the wind power plant; the dimension-lifting linear model comprises a one-to-one correspondence of a final rotating speed and a wind speed as input and an overall droop coefficient as output.
In the specific implementation of the embodiment, through a data monitoring platform of a wind power plant, each data of the wind power plant which is involved in primary frequency modulation historically is recorded, the integral sagging coefficient of the wind power plant is used as an output sample to train according to the final rotating speed of each fan rotor after the frequency modulation process and the data of the wind speed of the wind power plant, and an up-scaling linear model which comprises the one-to-one correspondence of the final rotating speed and the wind speed as input and the integral sagging coefficient as output is constructed.
After the dimension-increasing linear model is obtained, measuring the real-time wind speed of the running wind power plant, inputting the real-time wind speed and the preset limit rotating speed constraint of the fan into the dimension-increasing linear model for calculation, obtaining the maximum integral sagging coefficient through calculation, and outputting the obtained maximum integral sagging coefficient as the maximum frequency damping coefficient of the wind power plant.
The online evaluation process only needs wind speed measurement, and the calculation speed is extremely high, so that the time requirement of online evaluation can be met. The dependence on model parameters is avoided, and high-precision evaluation can be performed in a scene with incomplete or inaccurate model parameters of the wind power plant.
In another embodiment provided by the present invention, the up-scaling linear model construction process specifically includes:
obtaining operation data of each fan rotor after the wind power plant participates in the frequency modulation process, and taking the operation data of each time as one sample data in a data set, wherein the operation data comprise the final rotating speed, the integral sagging coefficient and the wind speed of the wind power plant;
dividing samples in the dataset into input samples and output samples;
carrying out dimension lifting on the input sample to obtain a dimension-lifted input sample;
constructing an input sample set and an output sample set after dimension lifting required by least square calculation according to the input sample after dimension lifting and the output sample;
and training the linear training matrix in the dimension-lifting linear model through least square to obtain the dimension-lifting linear model.
When the embodiment is implemented, through a data monitoring platform of a wind power plant, each time of operation data of the wind power plant participating in frequency modulation is recorded, each time of operation data is used as one sample data in a data set, and the operation data comprises: and after primary frequency modulation, the final rotating speed of the fan and the integral sagging coefficient of the wind power plant and the wind speed of the wind power plant. And storing the acquired operation data in a server, wherein 1000 groups of recorded sample data are obtained, and a data set consisting of 1000 groups of sample data is obtained.
Constructing a training sample set for data-driven least square model fitting according to the data set, and dividing sample data in the sample set into an input sample and an output sample;
carrying out dimension lifting on the input sample to obtain a dimension-lifted input sample;
according to the input sample after dimension lifting and the output sample, constructing an input sample set and an output sample set after dimension lifting, which are required by least square model calculation;
and constructing a linear training matrix in the ascending-dimension linear model through least square training, and constructing the obtained ascending-dimension linear model.
According to the embodiment, the wind power plant history is utilized to participate in the primary frequency modulation data training to obtain the ascending dimension linear model, so that the frequency modulation capacity is evaluated on line, dependence on model parameters is avoided, and high-precision evaluation can be performed in a scene where wind power plant model parameters are incomplete or inaccurate. The training set can construct a global model without covering a scene that the fan reaches the safe rotation speed limit, and then the evaluation result under the safe rotation speed limit is calculated through the model, so that the evaluation accuracy and reliability are stronger.
In a further embodiment of the present invention, the first input sample isiSample data
；
Employing the data setiAs the overall sagging coefficient of the individual sample dataOutput sample of the firstiFrequency damping coefficient of individual sample datak i ；
wherein ,
is the firstiThe rotational speed of the wind farm in the individual sample data after the primary frequency modulation process,/>
Is the firstiWind speed of the wind farm in the individual sample data.
In the implementation of this embodiment, the input samples are defined as:
wherein ,X i representing the first of the input samplesiThe data of the individual samples are taken,
is the firstiThe rotational speed of the wind farm in the individual sample data after the primary frequency modulation process,/>
Is the firstiWind speed of the wind farm in the individual sample data.
The first of the output samplesiSample data
Specifically the firstiThe overall droop coefficient of the individual sample data, i.e., the frequency damping coefficient in the ith sample.
In a further embodiment provided by the present invention, the input sample set
；
The output sample set
；
wherein , wherein ,k i is the first of the output samplesiThe data of the individual samples are taken,
i=1, 2, …, n, n being the number of sample data in the dataset,ψ（X i ) Is the first of the input samples after the dimension riseiSample data->
Middle by->
Vector obtained by up-scaling, where the j-th dimension +.>
，c j To and input samplesX i A basis vector of the same dimension.
In the implementation of this embodiment, the input sample is up-scaled to obtain the up-scaled input sample, and the first input sample isiSample data, its up-scaled sample dataX i,lift The definition is as follows:
wherein ,ψ（X i ) Is the first of the input samples after the dimension riseiSample data
Middle by->
Vector obtained by up-scaling, where the j-th dimension +.>
，c j To and input samplesX i The base vectors of the same dimension have completely random values.
Constructing an input sample set and an output sample set after the dimension rise required by the least square calculation, wherein the input sample set is defined as:
the output sample set after dimension up is defined as:
in the formula ,k i is the first of the output samplesiI=1, 2, …, n, n is the number of sample data in the dataset.
In yet another embodiment of the present invention, the linear training matrix is
；
The dimension-increasing linear model is as follows:
wherein ,
for inputting sample set +.>
Matrix transpose of>
Is->
Y is the output sample set, +.>
The final rotating speed of the wind turbine after primary frequency modulation is +.>
Is wind speed of wind power plant, < >>
To at the same time
and />
The resulting vector is extended on the basis of the rising dimension.
When the embodiment is implemented, a linear training matrix in the ascending-dimension linear model is constructed through least square training:
wherein ,
for inputting sample set +.>
Matrix transpose of>
Is->
Is pseudo-inverse of the matrix of (a), Y is the output sample set.
The constructed dimension-ascending linear model is as follows:
wherein ,
the final rotating speed of the wind turbine after primary frequency modulation is +.>
Is wind speed of wind power plant, < >>
To at the same time
and />
The resulting vector is extended on the basis of the rising dimension. Is that
In a further embodiment provided by the present invention, the maximum frequency damping coefficient
Wherein M is a linear training matrix in the up-scaling linear model,
for the said real-time wind speed,
for limiting the limit speed of the fan, < >>
Is at->
and />
The resulting vector is extended on the basis of the rising dimension.
In the implementation of the embodiment, the real-time wind speed of the actual running wind power plant is measured
Constraint according to limit speed of fan>
And the dimension-ascending linear model is used for calculating the maximum frequency damping coefficient:
in the formula ,
is at->
and />
And (3) expanding the obtained vector on the basis of the ascending dimension, wherein M is a linear training matrix in the ascending dimension linear model.
In yet another embodiment provided by the invention, the method further comprises:
and uploading the real-time wind speed, the limit rotation speed constraint of the fan and the maximum frequency damping coefficient to a power grid dispatching center to serve as sample data for training the dimension-ascending linear model.
When the embodiment is implemented, when the parameters of the wind power plant are changed, the dimension-lifting linear model needs to be updated and maintained;
and uploading the real-time wind speed, the limit rotation speed constraint of the fan and the maximum frequency damping coefficient to a power grid dispatching center as sample data of the dimension-lifting linear model training, and carrying out model training again to improve model accuracy.
According to the method, through state space transformation, the relation between the damping coefficient and the rotating speed of the fan and the wind speed are built through data-driven least square training, the wind speed is measured on line through an up-to-dimension linear model, and quick and high-precision assessment can be achieved.
The embodiment of the invention also provides a device for calculating the maximum frequency damping coefficient of the wind power plant, which is shown in fig. 2, and is a schematic structural diagram of the device for calculating the maximum frequency damping coefficient of the wind power plant, and the device comprises:
the wind speed measuring module is used for measuring the real-time wind speed of the running wind power plant;
the calculation module is used for inputting the real-time wind speed and the preset limit rotating speed constraint of the fan into a pre-established dimension-lifting linear model for calculation, and outputting the calculated maximum integral sagging coefficient as a maximum frequency damping coefficient of the wind power plant; the dimension-lifting linear model comprises a one-to-one correspondence of a final rotating speed and a wind speed as input and an overall droop coefficient as output.
It should be noted that, the wind farm maximum frequency damping coefficient calculating device provided by the embodiment of the present invention can execute the wind farm maximum frequency damping coefficient calculating method described in any embodiment of the foregoing embodiments, and specific functions of the wind farm maximum frequency damping coefficient calculating device are not described herein.
Referring to fig. 3, a schematic structural diagram of a terminal device according to an embodiment of the present invention is provided. The terminal device of this embodiment includes: a processor, a memory and a computer program stored in the memory and executable on the processor, such as a wind farm maximum frequency damping coefficient calculation program. The steps in the embodiment of the method for calculating the maximum frequency damping coefficient of each wind farm are implemented when the processor executes the computer program, for example, steps S1 to S2 shown in fig. 1. Alternatively, the processor may implement the functions of the modules in the above-described device embodiments when executing the computer program.
The computer program may be divided into one or more modules/units, which are stored in the memory and executed by the processor to accomplish the present invention, for example. The one or more modules/units may be a series of computer program instruction segments capable of performing the specified functions, which instruction segments are used for describing the execution of the computer program in the terminal device. For example, the computer program may be divided into modules, and specific functions of each module are not described herein.
The terminal equipment can be computing equipment such as a desktop computer, a notebook computer, a palm computer, a cloud server and the like. The terminal device may include, but is not limited to, a processor, a memory. It will be appreciated by those skilled in the art that the schematic diagram is merely an example of a terminal device and does not constitute a limitation of the terminal device, and may include more or less components than illustrated, or may combine certain components, or different components, e.g., the terminal device may further include an input-output device, a network access device, a bus, etc.
The processor may be a central processing unit (Central Processing Unit, CPU), other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, which is a control center of the terminal device, and which connects various parts of the entire terminal device using various interfaces and lines.
The memory may be used to store the computer program and/or module, and the processor may implement various functions of the terminal device by running or executing the computer program and/or module stored in the memory and invoking data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like; the storage data area may store data (such as audio data, phonebook, etc.) created according to the use of the handset, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart Media Card (SMC), secure Digital (SD) Card, flash Card (Flash Card), at least one disk storage device, flash memory device, or other volatile solid-state storage device.
Wherein the terminal device integrated modules/units may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as stand alone products. Based on such understanding, the present invention may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code, which may be in the form of code, object code, executable files, or in some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth.
While the foregoing is directed to the preferred embodiments of the present invention, it will be appreciated by those skilled in the art that changes and modifications may be made without departing from the principles of the invention, such changes and modifications are also intended to be within the scope of the invention.
Claims (10)
1. The method for calculating the maximum frequency damping coefficient of the wind farm is characterized by comprising the following steps of:
measuring the real-time wind speed of a running wind power plant;
inputting the real-time wind speed and the preset limit speed constraint of the fan into a pre-established dimension-ascending linear model for calculation, and outputting the calculated maximum integral sagging coefficient as the maximum frequency damping coefficient of the wind power plant; the dimension-lifting linear model comprises a one-to-one correspondence of a final rotating speed and a wind speed as input and an overall droop coefficient as output.
2. The method for calculating the maximum frequency damping coefficient of a wind farm according to claim 1, wherein the up-scaling linear model construction process specifically comprises:
obtaining operation data of each fan rotor after the wind power plant participates in the frequency modulation process, and taking the operation data of each time as one sample data in a data set, wherein the operation data comprise the final rotating speed, the integral sagging coefficient and the wind speed of the wind power plant;
dividing samples in the dataset into input samples and output samples;
carrying out dimension lifting on the input sample to obtain a dimension-lifted input sample;
constructing an input sample set and an output sample set after dimension lifting required by least square calculation according to the input sample after dimension lifting and the output sample;
and training the linear training matrix in the dimension-lifting linear model through least square to obtain the dimension-lifting linear model.
3. A method for calculating a maximum frequency damping coefficient of a wind farm according to claim 2, wherein the input sample is the firstiSample data
；
Employing the data setiIntegral sag factor of individual sample datak i As the first of the output samplesiSample data;
wherein ,
is the firstiThe rotational speed of the wind farm in the individual sample data after the primary frequency modulation process,/>
Is the firstiWind speed of the wind farm in the individual sample data.
4. A method for calculating a maximum frequency damping coefficient of a wind farm according to claim 2, wherein the input sample set
；
The output sample set
；
wherein ,k i is the first of the output samplesiThe data of the individual samples are taken,
i=1, 2, …, n, n is the aboveThe number of sample data in the data set,ψ（X i ) Is the first of the input samples after the dimension riseiSample data->
Middle by->
Vector obtained by up-scaling, where the j-th dimension +.>
，c j To and input samplesX i A basis vector of the same dimension.
5. The method for calculating a maximum frequency damping coefficient of a wind farm according to claim 2, wherein the linear training matrix is
；
The dimension-increasing linear model is as follows:
wherein ,
for inputting sample set +.>
Matrix transpose of>
Is->
Y is the output sample set, +.>
The final rotating speed of the wind turbine after primary frequency modulation is +.>
Is wind speed of wind power plant, < >>
Is at->
and />
The resulting vector is extended on the basis of the rising dimension.
6. A method for calculating a maximum frequency damping coefficient of a wind farm according to claim 1, wherein the maximum frequency damping coefficient
；
Wherein M is a linear training matrix in the up-scaling linear model,
for the said real-time wind speed,
for limiting the limit speed of the fan, < >>
Is at->
and />
The resulting vector is extended on the basis of the rising dimension.
7. The method for calculating a maximum frequency damping coefficient of a wind farm according to claim 1, wherein the method further comprises:
and uploading the real-time wind speed, the limit rotation speed constraint of the fan and the maximum frequency damping coefficient to a power grid dispatching center to serve as sample data for training the dimension-ascending linear model.
8. A wind farm maximum frequency damping coefficient calculation device, the device comprising:
the wind speed measuring module is used for measuring the real-time wind speed of the running wind power plant;
the calculation module is used for inputting the real-time wind speed and the preset limit rotating speed constraint of the fan into a pre-established dimension-lifting linear model for calculation, and outputting the calculated maximum integral sagging coefficient as a maximum frequency damping coefficient of the wind power plant; the dimension-lifting linear model comprises a one-to-one correspondence of a final rotating speed and a wind speed as input and an overall droop coefficient as output.
9. A terminal device comprising a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, the processor implementing the wind farm maximum frequency damping coefficient calculation method according to any of claims 1 to 7 when executing the computer program.
10. A computer readable storage medium, characterized in that the computer readable storage medium comprises a stored computer program, wherein the computer program when run controls a device in which the computer readable storage medium is located to perform the method of calculating a maximum frequency damping coefficient of a wind farm according to any of claims 1 to 7.
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