CN115081742A - Ultra-short-term power prediction method for distributed wind power plant and related equipment - Google Patents
Ultra-short-term power prediction method for distributed wind power plant and related equipment Download PDFInfo
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Abstract
The application provides an ultra-short-term power prediction method and related equipment for a distributed wind power plant, wherein the method comprises the following steps: acquiring current ultra-short-term operation information of each virtual anemometry point; according to the current ultra-short-term operation information and a pre-trained prediction model, predicting to obtain the wind speed of the virtual wind measuring point in a future preset time period; calculating to obtain the expected ultra-short-term power of the virtual wind measuring point according to the wind speed in the future preset time period; and calculating the expected ultra-short-term power of the distributed wind power plant according to the expected ultra-short-term power of all the virtual wind measuring points. According to the method and the device, wind speed prediction and power calculation are performed on the basis of a virtual wind measurement technology, and the accuracy of ultra-short-term power prediction is improved.
Description
Technical Field
The application relates to the technical field of wind power prediction, in particular to an ultra-short-term power prediction method and related equipment for a distributed wind power plant.
Background
The problems of energy crisis, environmental pollution, climate change and the like cause that new energy power generation technologies represented by wind power generation are widely concerned, and the importance of wind power prediction is increasingly prominent.
In the distributed wind power plant, the distribution areas of the fans are wide, the terrain of the areas is complex and various, the wind speed environments of different fans have great difference in time and space, and great errors exist when wind power ultra-short-term power prediction is directly carried out by singly adopting the average wind speed or sampling point wind speed of the wind power plant.
Disclosure of Invention
In view of the above, the present application aims to provide a method for ultra-short term power prediction of a distributed wind farm and a related device.
Based on the above purpose, the present application provides a method for ultra-short term power prediction of a distributed wind farm, where the distributed wind farm includes a plurality of virtual wind measuring points, including:
acquiring current ultra-short-term operation information of each virtual anemometry point;
predicting to obtain the wind speed of the virtual wind measuring point in a future preset time period according to the current ultra-short term operation information and a pre-trained prediction model;
calculating to obtain the expected ultra-short-term power of the virtual wind measuring point according to the wind speed in the future preset time period;
and calculating the expected ultra-short-term power of the distributed wind power plant according to the expected ultra-short-term power of all the virtual wind measuring points.
Further, the ultra-short term operation information includes wind speed, power, air temperature, weather type, pressure, wind direction and humidity.
Further, the pre-training comprises:
acquiring training sample data and test sample data;
constructing a self-organizing competitive neural network;
training the self-organizing competitive neural network by adopting the training sample data;
testing the trained self-organizing competitive neural network by adopting the test sample data to obtain a test result;
responding to the test result not meeting the preset condition, and performing iterative training on the self-organizing competitive neural network by adopting the training sample data;
and completing the pre-training until the test result meets the preset condition to obtain the prediction model.
Further, the calculating the expected ultra-short-term power of the virtual wind measuring point according to the wind speed in the future preset time period includes:
acquiring historical wind speed and power data of the virtual wind measuring point;
carrying out nonlinear fitting on the historical wind speed and power data by utilizing a generalized error distribution model to obtain an output characteristic curve of the virtual wind measuring point;
and substituting the wind speed into the output characteristic curve to obtain the expected ultra-short-term power.
Further, the calculating the expected ultra-short term power of the distributed wind farm according to the expected ultra-short term powers of all the virtual wind measuring points includes:
wherein:
the expected ultra-short-term power of the distributed wind power plant; the number of virtual wind measuring points of the M distributed wind power plants;
the expected ultra-short-term power of the ith virtual anemometry point.
Further, after the acquiring training sample data and test sample data, the method includes:
and carrying out normalization processing on the acquired training sample data and the acquired test sample data.
Further, the acquiring training sample data and test sample data includes:
obtaining historical ultra-short term operation information of the virtual wind measuring point;
and dividing the historical ultra-short-term operation information into the training sample data and the test sample data.
Based on the same inventive concept, the application also provides an ultra-short-term power prediction device of a distributed wind power plant, wherein the distributed wind power plant comprises a plurality of virtual wind measuring points, and the ultra-short-term power prediction device comprises:
the acquisition module is configured to acquire current ultra-short-term operation information of each virtual wind measuring point;
the wind speed prediction module is configured to predict the wind speed of the virtual wind measuring point in a future preset time period according to the current ultra-short-term operation information and a pre-trained prediction model;
a first calculation module configured to calculate an expected ultra-short-term power of the virtual wind measuring point according to the wind speed in the future preset time period;
and the second calculation module is configured to calculate the expected ultra-short-term power of the distributed wind power plant according to the expected ultra-short-term power of all the virtual wind measuring points. Based on the same inventive concept, the present application further provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable by the processor, wherein the processor implements the method as described above when executing the computer program.
Based on the same inventive concept, the present application also provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the method as described above.
As can be seen from the above, according to the ultra-short-term power prediction method for the distributed wind power plant and the related device, the virtual wind measuring point is established on the distributed wind power plant, the wind speed prediction model is constructed, and on the basis, ultra-short-term power calculation is performed based on the virtual wind measuring technology, so that the error of ultra-short-term power prediction of the distributed wind power plant is reduced.
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In order to more clearly illustrate the technical solutions in the present application or the related art, the drawings needed to be used in the description of the embodiments or the related art will be briefly introduced below, and it is obvious that the drawings in the following description are only embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a schematic flow chart of an ultra-short-term power prediction method for a distributed wind farm according to an embodiment of the present application;
FIG. 2 is a schematic flow chart of pre-training in an embodiment of the present application;
fig. 3 is a schematic structural diagram of an ultra-short term power prediction apparatus of a distributed wind farm according to an embodiment of the present application;
fig. 4 is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is further described in detail below with reference to the accompanying drawings in combination with specific embodiments.
It should be noted that technical terms or scientific terms used in the embodiments of the present application should have a general meaning as understood by those having ordinary skill in the art to which the present application belongs, unless otherwise defined. The use of the terms "comprising" or "including" and the like in the embodiments of the present application, means that the element or item appearing before the term covers the element or item listed after the term and its equivalents, without excluding other elements or items. The terms "connected" or "coupled" and the like are not restricted to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", and the like are used merely to indicate relative positional relationships, and when the absolute position of the object being described is changed, the relative positional relationships may also be changed accordingly.
In the distributed wind power plant, the distribution areas of fans are wide, the terrain of the areas is complex and various, and the wind speeds of different fans are greatly different in time and space, so that the wind power prediction is directly carried out by singly adopting the average wind speed or sampling point wind speed of the wind power plant in the prior art, which causes a large error.
Therefore, a method with low prediction error still needs to be developed to perform ultra-short term power prediction of a distributed wind farm.
The ultra-short-term power prediction of the distributed wind power plant is specifically to predict active power of the distributed wind power plant in a period of time in the future, wherein the ultra-short term in the application refers to 15 minutes to 4 hours.
Embodiments of the present application are described in detail below with reference to the accompanying drawings.
The application provides an ultra-short-term power prediction method for a distributed wind power plant, wherein the distributed wind power plant comprises a plurality of virtual wind measuring points, and with reference to a figure 1, the method comprises the following steps:
and step S101, acquiring the current ultra-short term operation information of each virtual wind measuring point.
Specifically, a plurality of virtual wind measuring points are arranged based on the condition of a plurality of fans in the distributed wind power plant, each virtual wind measuring point corresponds to a plurality of fans, and the plurality of virtual wind measuring points correspond to all fans in the distributed wind power plant, so that each virtual wind measuring point is a subordinate unit of the distributed wind power plant. And acquiring the current ultra-short term operation information of each virtual anemometry point through the sampling data and the weather data of each virtual anemometry point.
And S102, predicting to obtain the wind speed of the virtual wind measuring point in a future preset time period according to the current ultra-short term operation information and a pre-trained prediction model.
Specifically, in this embodiment, the future preset time period is set to 4 hours, then the current ultra-short term operation information of each virtual wind measuring point is input into a pre-trained prediction model, and the prediction model obtains the wind speed of each virtual wind measuring point in the future preset time period, that is, the wind speed in the future 4 hours through calculation and prediction. It should be noted that the setting of the future preset time period to 4 hours in this embodiment is only an exemplary illustration, and the specific value of the future preset time period may be adjusted according to the actual wind speed prediction requirement.
And S103, calculating the expected ultra-short-term power of the virtual wind measuring point according to the wind speed in the future preset time period.
Specifically, the wind speed of each virtual wind measuring point in the future preset time period is substituted into the corresponding output characteristic curve of the virtual wind measuring point to obtain the expected ultra-short-term power of each virtual wind measuring point.
And step S104, calculating the expected ultra-short-term power of the distributed wind power plant according to the expected ultra-short-term power of all the virtual wind measuring points.
Specifically, the expected ultra-short-term power of the distributed wind power plant is obtained by summing on the basis of obtaining the expected ultra-short-term power of each virtual wind measuring point.
In the embodiment, the distributed wind power plant is divided into a plurality of virtual wind measuring points through a virtual wind measuring technology, the wind speed and power of each virtual wind measuring point are predicted on the basis, and the power of the distributed wind power plant is further predicted.
In some embodiments, the ultra-short term operational information includes wind speed, power, air temperature, weather type, pressure, wind direction, and humidity.
Specifically, the wind speed and the power are obtained through the virtual wind measuring point, and the air temperature, the weather type, the pressure, the wind direction and the humidity are obtained through weather data.
In some embodiments, referring to fig. 2, the pre-training comprises:
step S201, training sample data and test sample data are acquired.
Specifically, the training sample data and the test sample data are data of different virtual wind measuring points in different periods.
Step S202, a self-organizing competitive neural network is constructed.
Specifically, the level of the self-organizing competitive neural network is constructed based on the number of factors influencing and predicting the wind speed, if the number of the factors is m, the self-organizing competitive neural network containing m layers is constructed, the sample of an input layer is X, and the input of the i neuron of the k layer is X
Output is as
Of 1 at
The weight coefficient between the neuron j of the layer to the neuron i of the k-th layer is
According to its threshold value
Setting an initial value for the weight coefficient
And completing the construction of the self-organizing competitive neural network.
Step S203, training the self-organizing competitive neural network by adopting the training sample data.
Specifically, the training sample data is input into the constructed self-organizing competitive neural network, the self-organizing competitive neural network is trained, and after training, each layer of neurons in the self-organizing competitive neural network correspondingly generates a corresponding weight coefficient, so that the self-organizing competitive neural network with the weight coefficient is obtained.
Step S204, testing the trained self-organizing competitive neural network by adopting the test sample data to obtain the pre-trained test result.
Specifically, the test sample data is input into the self-organizing competitive neural network which is trained to generate corresponding weight coefficients to obtain prediction data, and error calculation is performed on the prediction data and real data corresponding to the prediction data in the test sample data to obtain the pre-trained test result.
Step S205, in response to that the test result does not satisfy a preset condition, performing iterative training on the self-organizing competitive neural network by using the training sample data until the test result satisfies the preset condition, and completing the pre-training to obtain the prediction model.
Specifically, if the pre-trained test result does not satisfy the preset condition, that is, the test result does not satisfy the accuracy requirement of the wind speed prediction error, the method returns to step S203, continues to train the self-organizing competitive neural network, and updates the weight coefficient to generate a new self-organizing competitive neural network.
And if the test result of the pre-training meets the preset condition, namely the test result meets the precision requirement of the wind speed prediction error, finishing the pre-training, and obtaining the prediction model.
In some embodiments, the calculating the expected ultra-short-term power of the virtual wind measuring point according to the wind speed in the future preset time period includes:
acquiring historical wind speed and power data of the virtual wind measuring point;
carrying out nonlinear fitting on the historical wind speed and power data by utilizing a generalized error distribution model to obtain an output characteristic curve of the virtual wind measuring point;
and substituting the wind speed into the output characteristic curve to obtain the expected ultra-short-term power.
Specifically, the output characteristic curve includes:
wherein: v is the wind speed of the fan,
the cut-in wind speed of the fan is obtained;
the rated wind speed of the fan;
the rated power of the fan;
，
，
and
respectively, are constant coefficients.
Although the constant coefficient in the output characteristic curve can be obtained by the factory parameters of the fan, the factory parameters of the fan change with time according to different operating environments of the fan, and cannot be calculated by the factory parameters of the fanCalculating the power of the fan, so that the embodiment performs nonlinear fitting on the historical wind speed and power data of the virtual wind measuring point by using a generalized error distribution model, and introduces an intelligent optimization algorithm to the output characteristic curve
，
，
And
，
，
and
optimizing to obtain an optimal parameter combination so as to minimize the error of the obtained expected ultra-short-term power, thus obtaining an output characteristic curve of the virtual wind measuring point, substituting the wind speed into the expected ultra-short-term power obtained by calculating the output characteristic curve, and obtaining the expected ultra-short-term power error of the virtual wind measuring point by calculating in such a way.
In some embodiments, the calculating the expected ultra-short term power of the distributed wind farm according to the expected ultra-short term power of all the virtual wind measuring points includes:
wherein:
the expected ultra-short-term power of the distributed wind power plant; the number of virtual wind measuring points of the M distributed wind power plants;
the expected ultra-short-term power of the ith virtual anemometry point.
Specifically, after the predicted power of each virtual wind measuring point in the distributed wind power plant is obtained, the expected ultra-short-term power of the distributed wind power plant is obtained by using a summation formula.
In some embodiments, after said obtaining training sample data and test sample data, comprising:
and carrying out normalization processing on the acquired training sample data and the acquired test sample data.
Specifically, the normalization processing is performed on the acquired training sample data and the test sample data, so that the sample data can be generalized and unified, and the convergence in the subsequent training process is accelerated.
In some embodiments, said obtaining training sample data and test sample data comprises:
obtaining historical ultra-short term operation information of the virtual wind measuring point;
and dividing the historical ultra-short-term operation information into the training sample data and the test sample data.
Specifically, the training sample data and the test sample data are both historical data from the virtual wind measuring point at different times, and the input data of the training sample data and the test sample data are as follows: the output data of the training sample data and the test sample data are the wind speed of the virtual wind measuring point relative to a future preset time period of the certain time point. Therefore, the self-organizing competitive neural network is trained based on historical actual data, and the prediction accuracy of the prediction model can be improved.
In some embodiments, the ultra-short-term operation information of each virtual wind measuring point in the distributed wind farm is obtained, the trained prediction model is input to obtain the predicted wind speed of each virtual wind measuring point, the wind speed of each virtual wind measuring point in the future preset time period is input to a constructed output characteristic curve corresponding to the virtual wind measuring point to obtain the expected ultra-short-term power of each virtual wind measuring point, the actual power of each virtual wind measuring point at the corresponding time is collected, and the predicted power and the actual power are subjected to error calculation, wherein the formula is as follows:
wherein: i is the number of virtual wind measuring points;
predicted power for the ith virtual anemometry point:
is the actual power of the ith virtual anemometry point.
Through calculation, the power accuracy of the distributed wind power plant obtained by prediction in the embodiment is 79.6%, which is greater than the requirement of 75% of the national power grid.
It should be noted that the method of the embodiment of the present application may be executed by a single device, such as a computer or a server. The method of the embodiment can also be applied to a distributed scene and is completed by the mutual cooperation of a plurality of devices. In such a distributed scenario, one of the multiple devices may only perform one or more steps of the method of the embodiment, and the multiple devices interact with each other to complete the method.
It should be noted that the above describes some embodiments of the present application. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments described above and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
Based on the same inventive concept, corresponding to any of the above embodiments, the present application further provides an ultra-short term power prediction apparatus for a distributed wind farm, and referring to fig. 3, the distributed wind farm includes a plurality of virtual wind measurement points, including:
an obtaining module 101 configured to obtain current ultra-short-term operation information of each virtual wind measuring point;
a wind speed prediction module 102 configured to predict a wind speed within a preset time period in the future of the virtual wind measuring point according to the current ultra-short term operation information and a pre-trained prediction model;
a first calculating module 103 configured to calculate an expected ultra-short-term power of the virtual wind measuring point according to the wind speed in the future preset time period;
a second calculating module 104 configured to calculate the expected ultra-short term power of the decentralized wind farm according to the expected ultra-short term power of all the virtual wind measuring points.
In some embodiments, the ultra-short term operational information includes wind speed, power, air temperature, weather type, pressure, wind direction, and humidity.
In some embodiments, the pre-training comprises:
acquiring training sample data and test sample data;
constructing a self-organizing competitive neural network;
training the self-organizing competitive neural network by adopting the training sample data;
testing the trained self-organizing competitive neural network by adopting the test sample data to obtain a test result;
responding to the test result not meeting the preset condition, and performing iterative training on the self-organizing competitive neural network by adopting the training sample data;
and completing the pre-training until the test result meets the preset condition to obtain the prediction model.
In some embodiments, the first computing module 103 is further configured to:
acquiring historical wind speed and power data of the virtual wind measuring point;
carrying out nonlinear fitting on the historical wind speed and power data by utilizing a generalized error distribution model to obtain an output characteristic curve of the virtual wind measuring point;
and substituting the wind speed into the output characteristic curve to obtain the expected ultra-short-term power.
In some embodiments, the second computing module 104 is further configured to:
wherein:
the expected ultra-short-term power of the distributed wind power plant; the number of virtual wind measuring points of the M distributed wind power plants;
the expected ultra-short-term power of the ith virtual anemometry point.
In some embodiments, after said obtaining training sample data and test sample data, comprising:
and carrying out normalization processing on the acquired training sample data and the acquired test sample data.
In some embodiments, the obtaining training sample data and test sample data comprises:
obtaining historical ultra-short term operation information of the virtual anemometry point;
and dividing the historical ultra-short-term operation information into the training sample data and the test sample data.
For convenience of description, the above devices are described as being divided into various modules by functions, and are described separately. Of course, the functionality of the various modules may be implemented in the same one or more software and/or hardware implementations as the present application.
The device of the foregoing embodiment is used to implement the ultra-short-term power prediction method of the distributed wind farm in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiment, which are not described herein again.
Based on the same inventive concept, corresponding to the method of any embodiment, the application further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the program, the method for ultra-short term power prediction of a distributed wind farm according to any embodiment is implemented.
Fig. 4 is a schematic diagram illustrating a more specific hardware structure of an electronic device according to this embodiment, where the electronic device may include: a processor 1010, a memory 1020, an input/output interface 1030, a communication interface 1040, and a bus 1050. Wherein the processor 1010, memory 1020, input/output interface 1030, and communication interface 1040 are communicatively coupled to each other within the device via bus 1050.
The processor 1010 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits, and is configured to execute related programs to implement the technical solutions provided in the embodiments of the present disclosure.
The Memory 1020 may be implemented in the form of a ROM (Read Only Memory), a RAM (Random Access Memory), a static storage device, a dynamic storage device, or the like. The memory 1020 may store an operating system and other application programs, and when the technical solution provided by the embodiments of the present specification is implemented by software or firmware, the relevant program codes are stored in the memory 1020 and called to be executed by the processor 1010.
The input/output interface 1030 is used for connecting an input/output module to input and output information. The i/o module may be configured as a component in a device (not shown) or may be external to the device to provide a corresponding function. Wherein the input devices may include a keyboard, mouse, touch screen, microphone, various sensors, etc., and the output devices may include a display, speaker, vibrator, indicator light, etc.
The communication interface 1040 is used for connecting a communication module (not shown in the drawings) to implement communication interaction between the present apparatus and other apparatuses. The communication module can realize communication in a wired mode (such as USB, network cable and the like) and also can realize communication in a wireless mode (such as mobile network, WIFI, Bluetooth and the like).
It should be noted that although the above-mentioned device only shows the processor 1010, the memory 1020, the input/output interface 1030, the communication interface 1040 and the bus 1050, in a specific implementation, the device may also include other components necessary for normal operation. In addition, those skilled in the art will appreciate that the above-described apparatus may also include only those components necessary to implement the embodiments of the present description, and not necessarily all of the components shown in the figures.
The electronic device of the foregoing embodiment is used to implement the ultra-short-term power prediction method of the distributed wind farm in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiment, which are not described herein again.
Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present application further provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the method for ultra-short term power prediction of a distributed wind farm according to any of the above embodiments.
Computer-readable media of the present embodiments, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device.
The computer instructions stored in the storage medium of the above embodiment are used to enable the computer to execute the ultra-short-term power prediction method for a distributed wind farm according to any of the above embodiments, and have the beneficial effects of corresponding method embodiments, and will not be described herein again.
Those of ordinary skill in the art will understand that: the discussion of any embodiment above is meant to be exemplary only, and is not intended to intimate that the scope of the disclosure, including the claims, is limited to these examples; within the context of the present application, technical features in the above embodiments or in different embodiments may also be combined, steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present application described above, which are not provided in detail for the sake of brevity.
In addition, well-known power/ground connections to Integrated Circuit (IC) chips and other components may or may not be shown in the provided figures for simplicity of illustration and discussion, and so as not to obscure the embodiments of the application. Furthermore, devices may be shown in block diagram form in order to avoid obscuring embodiments of the application, and this also takes into account the fact that specifics with respect to implementation of such block diagram devices are highly dependent upon the platform within which the embodiments of the application are to be implemented (i.e., specifics should be well within purview of one skilled in the art). Where specific details (e.g., circuits) are set forth in order to describe example embodiments of the application, it should be apparent to one skilled in the art that the embodiments of the application can be practiced without, or with variation of, these specific details. Accordingly, the description is to be regarded as illustrative instead of restrictive.
While the present application has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art in light of the foregoing description. For example, other memory architectures (e.g., dynamic ram (dram)) may use the embodiments discussed.
The present embodiments are intended to embrace all such alternatives, modifications and variances which fall within the broad scope of the appended claims. Therefore, any omissions, modifications, substitutions, improvements, and the like that may be made without departing from the spirit and principles of the embodiments of the present application are intended to be included within the scope of the present application.
Claims (10)
1. An ultra-short-term power prediction method for a distributed wind farm, wherein the distributed wind farm comprises a plurality of virtual wind measuring points, and the method comprises the following steps:
acquiring current ultra-short-term operation information of each virtual anemometry point;
predicting to obtain the wind speed of the virtual wind measuring point in a future preset time period according to the current ultra-short term operation information and a pre-trained prediction model;
calculating to obtain the expected ultra-short-term power of the virtual wind measuring point according to the wind speed in the future preset time period;
and calculating the expected ultra-short-term power of the distributed wind power plant according to the expected ultra-short-term power of all the virtual wind measuring points.
2. The method of claim 1, wherein the ultra-short term operating information includes wind speed, power, air temperature, weather type, pressure, wind direction, and humidity.
3. The method of claim 1, wherein the pre-training comprises:
acquiring training sample data and test sample data;
constructing a self-organizing competitive neural network;
training the self-organizing competitive neural network by adopting the training sample data;
testing the trained self-organizing competitive neural network by adopting the test sample data to obtain a test result;
responding to the test result not meeting the preset condition, and performing iterative training on the self-organizing competitive neural network by adopting the training sample data;
and completing the pre-training until the test result meets the preset condition to obtain the prediction model.
4. The method of claim 1, wherein the calculating the expected ultra-short-term power of the virtual wind measuring point according to the wind speed in the future preset time period comprises:
acquiring historical wind speed and power data of the virtual wind measuring point;
carrying out nonlinear fitting on the historical wind speed and power data by utilizing a generalized error distribution model to obtain an output characteristic curve of the virtual wind measuring point;
and substituting the wind speed into the output characteristic curve to obtain the expected ultra-short-term power.
5. The method of claim 1, wherein calculating the expected ultra-short term power of the decentralized wind farm from the expected ultra-short term power of all the virtual wind measuring points comprises:
wherein:
the expected ultra-short-term power of the distributed wind power plant; the number of virtual wind measuring points of the M distributed wind power plants;
the expected ultra-short-term power of the ith virtual anemometry point.
6. The method of claim 3, wherein after said obtaining training sample data and test sample data, comprising:
and carrying out normalization processing on the acquired training sample data and the acquired test sample data.
7. The method of claim 3, wherein obtaining training sample data and test sample data comprises:
obtaining historical ultra-short term operation information of the virtual wind measuring point;
and dividing the historical ultra-short-term operation information into the training sample data and the test sample data.
8. An ultra-short term power prediction device for a decentralized wind farm, wherein the decentralized wind farm comprises a plurality of virtual wind measuring points, comprising:
the acquisition module is configured to acquire current ultra-short-term operation information of each virtual wind measuring point;
the wind speed prediction module is configured to predict the wind speed of the virtual wind measuring point in a future preset time period according to the current ultra-short-term operation information and a pre-trained prediction model;
a first calculation module configured to calculate an expected ultra-short-term power of the virtual wind measuring point according to the wind speed in the future preset time period;
and the second calculation module is configured to calculate the expected ultra-short-term power of the distributed wind power plant according to the expected ultra-short-term power of all the virtual wind measuring points.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method according to any of claims 1 to 7 when executing the program.
10. A non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method of any one of claims 1 to 7.
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