CN113988398A - Wind turbine generator power prediction method and device, electronic equipment and storage medium - Google Patents
Wind turbine generator power prediction method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The disclosure provides a wind turbine generator power prediction method and device, electronic equipment and a storage medium, and belongs to the field of wind turbine generator power prediction. Wherein the method comprises the following steps: establishing a wind speed-wind power scatter diagram of the wind turbine generator by using historical wind speed data and wind power data of the wind turbine generator to be predicted; after abnormal scatter points in the scatter diagram are deleted, respectively normalizing wind speed data and wind power data corresponding to the remaining scatter points, and forming a sample data set by using the normalized wind speed data and wind power data; training a wind turbine generator power prediction model by using a sample data set to obtain a trained wind turbine generator power prediction model; and predicting the power of the wind turbine generator by using the trained wind turbine generator power prediction model. According to the wind power prediction model established by the method, data cleaning is not needed during prediction, the identification precision and efficiency can be considered at the same time, the wind power prediction precision can be improved, and the stable operation of a power grid is facilitated.
Description
Technical Field
The disclosure relates to the field of wind turbine generator power prediction, in particular to a wind turbine generator power prediction method and device, electronic equipment and a storage medium.
Background
Wind power in China develops rapidly, and with large-scale wind power integration, the proportion of wind power in the power market is larger and larger. Because wind power has the characteristics of intermittence, volatility and randomness, the accuracy of wind power prediction and the stability of wind power generation are seriously influenced by unstable and uncontrollable wind speed, the control of a wind generating set and the dispatching of a micro-grid are influenced, and the balance of power supply and demand is damaged. Therefore, it is essential to be able to accurately predict the wind power for the stability of the operation of the power system. The relation curve of the output power and the wind speed of the wind turbine is an important parameter of wind power and wind speed, and by combining the relation curve of the power and the wind speed and the predicted wind speed, the wind power plant and a power grid operation dispatcher can obtain the processing condition of the wind power plant at the future time, and the relation curve plays an important role in the control, maintenance and management work of the wind power plant. However, due to the influence of the abnormal data and the limited power data, the power curve directly established on the original data will greatly deviate from the normal power curve, and cannot be put into practical use. The method adopted at the present stage is to clean the wind speed-power data and then fit the power curve, but the data cleaning algorithm is often inefficient and is easy to generate the phenomenon of over-cleaning. Moreover, historical data acquired by the SCADA system is not completely data of the unit in normal operation, and a lot of abnormal data exist. Therefore, an accurate wind turbine generator power curve model is established, operation and maintenance scheduling of a wind power plant can be optimized, utilization efficiency of wind power is improved, and influence of wind power integration on stable operation of a power grid is reduced. The current wind turbine power curve modeling is generally divided into two parts: the wind speed and power data are cleaned by using a data cleaning algorithm, and then a modeling method is selected for power curve fitting, but the existing wind turbine power curve modeling takes longer time on cleaning abnormal data, and the adopted algorithms have lower efficiency and have certain limitations.
Disclosure of Invention
The purpose of the present disclosure is to overcome the disadvantages of the prior art, and provide a method and an apparatus for predicting wind turbine generator power, an electronic device, and a storage medium. According to the method, data cleaning is not needed during wind turbine generator power prediction, identification precision and efficiency can be considered simultaneously, wind turbine generator power prediction precision can be improved, and stable operation of a power grid is facilitated.
An embodiment of the first aspect of the present disclosure provides a wind turbine generator power prediction method, including:
acquiring wind speed data and wind power data of a wind turbine generator to be predicted in a set historical time period;
establishing a wind speed-wind power scatter diagram of the wind turbine generator set in the historical period by using the wind speed data and the wind power data, and deleting abnormal scatter points in the scatter diagram;
respectively normalizing the wind speed data and the wind power data corresponding to the remaining scatters in the scattergram, forming a sample by the normalized wind speed data and the normalized wind power data corresponding to each scatters, and forming a sample data set by all samples;
constructing a wind turbine generator power prediction model;
training the wind turbine generator power prediction model by using the sample data set to obtain a trained wind turbine generator power prediction model;
and predicting the power of the wind turbine generator by using the trained wind turbine generator power prediction model.
In a specific embodiment of the present disclosure, the wind speed data and wind power data of the set historical period are acquired from a data acquisition and monitoring control system SCADA, and the length of the historical period is one month to one year.
In one embodiment of the present disclosure, the abnormal scatter includes two types:
1) scattered points accumulated close to the abscissa axis of the scattered point diagram, wherein the wind power of the scattered points is lower than the normal wind power under the corresponding wind speed;
2) and the scatter points are distributed randomly and have low density.
In a specific embodiment of the present disclosure, the constructing a wind turbine generator power prediction model includes:
1) constructing a wind turbine generator power prediction model, wherein the model adopts a BP neural network model; the input of the model is normalized wind speed, and the output is normalized wind power;
2) determining an activation function of the model;
3) determining an adaptive robustness loss function, the expression of which is as follows:
the loss function is derived to:
in a specific embodiment of the present disclosure, the training the wind turbine power prediction model by using the sample data set to obtain a trained wind turbine power prediction model includes:
setting learning rate, training iteration times and model parameters, training the wind power prediction model by using the sample data set, and calculating Root Mean Square Error (RMSE) as an evaluation index of training;
and when the RMSE reaches the set threshold requirement, finishing the training to obtain a trained wind power prediction model.
In a specific embodiment of the present disclosure, the predicting the power of the wind turbine by using the trained wind turbine power prediction model includes:
acquiring wind speed data of the wind turbine generator at any time to be predicted, normalizing the wind speed data to obtain normalized wind speed, inputting the normalized wind speed into the trained wind power prediction model, and outputting a normalized wind power prediction value by the model;
and performing inverse normalization on the normalized wind power predicted value to obtain the wind power predicted value of the wind turbine generator at the moment to be predicted.
An embodiment of a second aspect of the present disclosure provides a wind turbine generator power prediction apparatus, including:
the data acquisition module is used for acquiring wind speed data and wind power data of the wind turbine generator to be predicted in a set historical time period;
the scatter diagram building module is used for building a wind speed-wind power scatter diagram of the wind turbine generator set in the historical time period by using the wind speed data and the wind power data, and deleting abnormal scatter points in the scatter diagram;
the sample data set construction module is used for respectively normalizing the wind speed data and the wind power data corresponding to the remaining scattergrams in the scattergram, forming the normalized wind speed data and the normalized wind power data corresponding to each scattergram into a sample, and forming all the samples into a sample data set;
the wind turbine generator power prediction model building module is used for building a wind turbine generator power prediction model;
the wind turbine power prediction model training module is used for training the wind turbine power prediction model by using the sample data set to obtain a trained wind turbine power prediction model;
and the wind turbine generator power prediction module is used for predicting the power of the wind turbine generator by using the trained wind turbine generator power prediction model.
An embodiment of a third aspect of the present disclosure provides an electronic device, including:
at least one processor; and a memory communicatively coupled to the at least one processor;
wherein the memory stores instructions executable by the at least one processor, the instructions being configured to perform a wind turbine power prediction method as described above.
A fourth aspect of the present disclosure is directed to a computer-readable storage medium storing computer instructions for causing a computer to perform the wind turbine power prediction method.
The characteristics and the beneficial effects of the disclosure are as follows:
compared with the prior method for predicting the wind speed by combining the power wind speed relation curve and the wind speed, the wind power prediction model based on the robust learning is established, data cleaning is not needed during training of the model, the identification precision and efficiency can be considered at the same time, the wind power prediction precision can be improved, and the stable operation of a power grid is facilitated.
Drawings
Fig. 1 is an overall flowchart of a wind turbine power prediction method in an embodiment of the present disclosure.
FIG. 2 is a wind turbine generator wind speed-wind power data scatter plot in a particular embodiment of the present disclosure.
Detailed description of the preferred embodiment
The present disclosure provides a method and an apparatus for predicting wind turbine generator power, an electronic device, and a storage medium, and the following describes the present disclosure in detail with reference to the accompanying drawings and specific embodiments.
An embodiment of the first aspect of the disclosure provides a method for predicting power of a wind turbine, an overall flow is shown in fig. 1, and the method includes the following specific steps:
1) data acquisition and monitoring control System (SCADA) data of a set historical time period of a wind turbine to be detected in a wind power plant are acquired. And the historical time period can be selected from the past month to the year, and the SCADA data mainly comprises wind speed and wind power parameters. In a specific embodiment of the present disclosure, the wind speed data includes: cut-in wind speed, rated wind speed and cut-out wind speed and wind speed data of all time points, wherein the data length is one month.
2) Based on the wind speed and the wind power obtained in the step (1), a wind speed-wind power scatter diagram of the wind turbine generator set is drawn before a model is trained to analyze abnormal state data of the wind turbine generator set and delete the abnormal state data, and all the remaining wind speed data and power data are respectively subjected to normalization preprocessing.
Wherein, a scatter diagram in one embodiment of the present disclosure is shown in fig. 2. When the abnormal state data of the wind turbine generator is analyzed, two types of abnormal data exist: one type is a pile-up type abnormal data point near a coordinate axis, which is far lower than the normal power under the wind speed, even close to 0; the second type is randomly distributed low-density scatter points in the scatter diagram. In fig. 2, two circles are the abnormal points screened by the present embodiment.
After the abnormal points in the scatter diagram are deleted, the wind speed and wind power data corresponding to the remaining scatter diagrams are respectively normalized, the wind speed and wind power corresponding to each scatter diagram after normalization form a sample, and all the samples form a sample data set.
3) Constructing a wind turbine generator power prediction model;
in the embodiment of the disclosure, the wind turbine generator power prediction model adopts a BP neural network model.
The parameter of the BP neural network model input layer is normalized wind speed, and the parameter of the output layer is normalized wind power; preferably, in a specific embodiment of the present disclosure, the number of input layer, middle two hidden layers, and output layer neurons is set to 1, 50, and 1, respectively.
Determining an activation function of the neural network, using a relu function within the hidden layer:
where x is the input to the neuron.
Using the tanh function on the output layer:
where x is the input to the neuron.
Determining an adaptive robustness loss function, wherein the loss function can be understood as a set of various robustness loss functions, so that different types of loss functions can be obtained by changing a parameter alpha in the adaptive robustness loss function, and a general formula of the loss function is as follows:
in equation (3), z is an error (f (z) -Y) between the true value and the predicted value, where α ∈ R is a shape parameter for controlling the robustness of the loss function, and c > 0 is a scale parameter for controlling the degree of bending of the loss function in the vicinity of z ═ 0. The probability density distribution formula of the adaptive robustness loss function is as follows:
as can be seen from formula (4), when α ≧ 0, p (z | μ, α, c) is defined. The formula for the loss function is thus expanded as:
the loss function is derived to:
4) and training a wind turbine generator power prediction model by using the sample data set.
And sequentially inputting all samples of the sample data set into the constructed wind power prediction model, wherein the input parameter is wind speed, the output parameter is wind power, and the Root Mean Square Error (RMSE) is calculated to serve as an evaluation index of the model.
After each training is finished, the model training can be adjusted by adjusting the learning rate, the iteration times and the hidden layer parameters until the RMSE data reaches the set threshold requirement (about 0.01 in the embodiment), and the training is finished to obtain the trained wind power prediction model.
5) Predicting wind power;
acquiring wind speed data of the wind turbine generator to be predicted at any time to be predicted, normalizing the wind speed data to obtain normalized wind speed, inputting the normalized wind speed into a trained wind power prediction model, and outputting a normalized wind power prediction value by the model.
And performing inverse normalization on the normalized wind power predicted value to obtain the wind power predicted value of the wind turbine generator at the moment to be predicted.
In order to implement the foregoing embodiment, an embodiment of a second aspect of the present disclosure provides a wind turbine generator power prediction apparatus, including: the data acquisition module is used for acquiring wind speed data and wind power data of the wind turbine generator to be predicted in a set historical time period; the scatter diagram building module is used for building a wind speed-wind power scatter diagram of the wind turbine generator set in the historical time period by using the wind speed data and the wind power data, and deleting abnormal scatter points in the scatter diagram;
the sample data set construction module is used for respectively normalizing the wind speed data and the wind power data corresponding to the remaining scattergrams in the scattergram, forming the normalized wind speed data and the normalized wind power data corresponding to each scattergram into a sample, and forming all the samples into a sample data set;
the wind turbine generator power prediction model building module is used for building a wind turbine generator power prediction model;
the wind turbine power prediction model training module is used for training the wind turbine power prediction model by using the sample data set to obtain a trained wind turbine power prediction model;
and the wind turbine generator power prediction module is used for predicting the power of the wind turbine generator by using the trained wind turbine generator power prediction model.
To achieve the above embodiments, an embodiment of a third aspect of the present disclosure provides an electronic device, including:
at least one processor; and a memory communicatively coupled to the at least one processor;
wherein the memory stores instructions executable by the at least one processor, the instructions being configured to perform a wind turbine power prediction method as described above.
To achieve the foregoing embodiments, a fourth aspect of the present disclosure provides a computer-readable storage medium storing computer instructions for causing a computer to execute the foregoing wind turbine power prediction method.
It should be noted that the computer readable medium in the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs, which when executed by the electronic device, cause the electronic device to execute a wind turbine power prediction method of the above embodiments.
Computer program code for carrying out operations for aspects of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and the scope of the preferred embodiments of the present application includes other implementations in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present application.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware that is related to instructions of a program, and the program may be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a separate product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.
Claims (9)
1. A wind turbine generator power prediction method is characterized by comprising the following steps:
acquiring wind speed data and wind power data of a wind turbine generator to be predicted in a set historical time period;
establishing a wind speed-wind power scatter diagram of the wind turbine generator set in the historical period by using the wind speed data and the wind power data, and deleting abnormal scatter points in the scatter diagram;
respectively normalizing the wind speed data and the wind power data corresponding to the remaining scatters in the scattergram, forming a sample by the normalized wind speed data and the normalized wind power data corresponding to each scatters, and forming a sample data set by all samples;
constructing a wind turbine generator power prediction model;
training the wind turbine generator power prediction model by using the sample data set to obtain a trained wind turbine generator power prediction model;
and predicting the power of the wind turbine generator by using the trained wind turbine generator power prediction model.
2. The method of claim 1, wherein the wind speed data and wind power data for the set historical period of time are obtained from a data acquisition and monitoring control System (SCADA), the historical period of time being one month to one year in length.
3. The method of claim 1, wherein the anomalous scatter includes two categories:
1) scattered points accumulated close to the abscissa axis of the scattered point diagram, wherein the wind power of the scattered points is lower than the normal wind power under the corresponding wind speed;
2) and the scatter points are distributed randomly and have low density.
4. The method of claim 1, wherein constructing the wind turbine power prediction model comprises:
1) constructing a wind turbine generator power prediction model, wherein the model adopts a BP neural network model; the input of the model is normalized wind speed, and the output is normalized wind power;
2) determining an activation function of the model;
3) determining an adaptive robustness loss function, the expression of which is as follows:
the loss function is derived to:
5. the method of claim 1, wherein training the wind turbine power prediction model using the sample data set to obtain a trained wind turbine power prediction model comprises:
setting learning rate, training iteration times and model parameters, training the wind power prediction model by using the sample data set, and calculating Root Mean Square Error (RMSE) as an evaluation index of training;
and when the RMSE reaches the set threshold requirement, finishing the training to obtain a trained wind power prediction model.
6. The method according to claim 1, wherein the predicting the power of the wind turbine by using the trained wind turbine power prediction model comprises:
acquiring wind speed data of the wind turbine generator at any time to be predicted, normalizing the wind speed data to obtain normalized wind speed, inputting the normalized wind speed into the trained wind power prediction model, and outputting a normalized wind power prediction value by the model;
and performing inverse normalization on the normalized wind power predicted value to obtain the wind power predicted value of the wind turbine generator at the moment to be predicted.
7. A wind turbine power prediction device, comprising:
the data acquisition module is used for acquiring wind speed data and wind power data of the wind turbine generator to be predicted in a set historical time period;
the scatter diagram building module is used for building a wind speed-wind power scatter diagram of the wind turbine generator set in the historical time period by using the wind speed data and the wind power data, and deleting abnormal scatter points in the scatter diagram;
the sample data set construction module is used for respectively normalizing the wind speed data and the wind power data corresponding to the remaining scattergrams in the scattergram, forming the normalized wind speed data and the normalized wind power data corresponding to each scattergram into a sample, and forming all the samples into a sample data set;
the wind turbine generator power prediction model building module is used for building a wind turbine generator power prediction model;
the wind turbine power prediction model training module is used for training the wind turbine power prediction model by using the sample data set to obtain a trained wind turbine power prediction model;
and the wind turbine generator power prediction module is used for predicting the power of the wind turbine generator by using the trained wind turbine generator power prediction model.
8. An electronic device, comprising:
at least one processor; and a memory communicatively coupled to the at least one processor;
wherein the memory stores instructions executable by the at least one processor, the instructions being arranged to perform the method of any of the preceding claims 1-6.
9. A computer-readable storage medium storing computer instructions for causing a computer to perform the method of any one of claims 1-6.
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