CN117748501B - Wind power prediction method and system for energy storage auxiliary black start - Google Patents
Wind power prediction method and system for energy storage auxiliary black start Download PDFInfo
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Abstract
The application belongs to the technical field of wind power, and particularly provides a wind power prediction method and a wind power prediction system for energy storage auxiliary black start, wherein the method comprises the steps of obtaining a wind speed sequence; decomposing the wind speed sequence by using a CEEMD algorithm to obtain a plurality of modal components; obtaining a corresponding wind speed component predicted value by using a first cyclic neural network model based on each modal component, and obtaining an error coefficient corresponding to each modal component based on each wind speed component predicted value and each modal component; reconstructing all modal components based on the positive and negative of each error coefficient to obtain a plurality of complementary modal components; obtaining a corresponding target wind speed component predicted value by using a second cyclic neural network model based on each complementary modal component; and obtaining a wind speed predicted value of a next sampling point of the current sampling point based on the predicted values of the target wind speed components, and further obtaining a wind power predicted value by utilizing the wind speed predicted value so as to participate in energy storage auxiliary black start when the power failure occurs. The method can improve the prediction accuracy of wind power.
Description
Technical Field
The application relates to the technical field of wind power, in particular to a wind power prediction method and a wind power prediction system for energy storage auxiliary black start.
Background
As the power grid scale increases, the consequences of the blackout accident become more serious; the traditional black start power supply in the windy and windy areas is insufficient under the constraint of natural resources. The wind-solar energy storage system is used as a black start power supply, so that the black start capability of the regional power grid can be improved. When the wind-solar energy storage power generation system is used as a black start power supply, the charge/discharge power constraint and the electric quantity constraint of the energy storage device are considered, and in the black start process, when the output of a wind power plant and a photovoltaic power station is insufficient or fluctuation is severe, the situation that the energy storage is overcharged and overdischarged possibly occurs, so that the energy storage cannot be continuously utilized, and the black start fails. Therefore, in order to better utilize the wind-solar energy storage power generation system to carry out black start after a power failure accident, continuous effective output probability wind power of the wind power plant needs to be estimated according to historical wind speed data. However, the conventional wind power prediction has a problem of poor prediction accuracy.
Disclosure of Invention
The present application aims to solve at least one of the technical problems in the related art to some extent.
Therefore, a first object of the present application is to provide a wind power prediction method for energy storage auxiliary black start to improve the wind power prediction accuracy.
A second object of the present application is to provide a wind power prediction system with energy storage auxiliary black start.
A third object of the present application is to propose an electronic device.
A fourth object of the present application is to propose a computer readable storage medium.
To achieve the above objective, an embodiment of a first aspect of the present application provides a wind power prediction method for energy storage assisted black start, including:
Acquiring a wind speed sequence, wherein the wind speed sequence comprises wind speed measurement values of a current sampling point and a plurality of historical sampling points;
decomposing the wind speed sequence by using a CEEMD algorithm to obtain a plurality of modal components;
Obtaining a corresponding wind speed component predicted value by using a first cyclic neural network model based on each modal component, and obtaining an error coefficient corresponding to each modal component based on each wind speed component predicted value and each modal component;
Reconstructing all modal components based on the positive and negative of each error coefficient to obtain a plurality of complementary modal components;
Obtaining a corresponding target wind speed component predicted value by using a second cyclic neural network model based on each complementary modal component;
and obtaining a wind speed predicted value of a next sampling point of the current sampling point based on each target wind speed component predicted value, and further obtaining a wind power predicted value by utilizing the wind speed predicted value so as to participate in energy storage auxiliary black start when a power failure occurs.
In the method according to the first aspect of the present application, each modal component consists of wind speed components obtained by decomposing wind speed measurement values of all sampling points of the wind speed sequence, and the number of wind speed components is equal to the number of sampling points of the wind speed sequence; setting parameters of a first cyclic neural network model, so that the first cyclic neural network model outputs a preset number of wind speed component predicted values, and the actual values corresponding to the preset number of wind speed component predicted values output by the model are the current sampling points in the input modal components and the same number of wind speed components before the current sampling points; the method for obtaining the corresponding wind speed component predicted value based on each modal component by using the first cyclic neural network model, obtaining the error coefficient corresponding to each modal component based on each wind speed component predicted value and each modal component comprises the following steps: inputting any modal component into a first cyclic neural network model to obtain a corresponding preset number of wind speed component predicted values; and obtaining error coefficients of the modal components based on the predicted values of the wind speed components of the preset quantity and the corresponding actual values, and further obtaining the error coefficients of the modal components.
In the method of the first aspect of the present application, the reconstructing all modal components to obtain a plurality of complementary modal components based on the positive and negative properties of each error coefficient includes: dividing all modal components into a non-negative array and a negative array based on the positive and negative of error coefficients of each modal component; according to the error coefficient, arranging the modal components in the non-negative array from large to small to obtain a target non-negative array, and arranging the modal components in the negative array from small to large to obtain a target negative array; and adding the modal components of the corresponding positions in the target non-negative array and the target negative array to obtain a plurality of complementary modal components.
In the method of the first aspect of the present application, parameters of the second cyclic neural network model are set so that the second cyclic neural network model outputs a target wind speed component predicted value of a sampling point next to the current sampling point; the obtaining a corresponding target wind speed component predicted value based on each complementary modal component by using a second cyclic neural network model comprises the following steps: and inputting the complementary modal component into a second cyclic neural network model for any complementary modal component to obtain a target wind speed component predicted value of a next sampling point of the corresponding current sampling point, and further obtaining the target wind speed component predicted value corresponding to each complementary modal component.
In the method of the first aspect of the present application, the obtaining the wind speed predicted value of the next sampling point of the current sampling point based on each target wind speed component predicted value includes: and summing the predicted values of the target wind speed components to obtain the predicted value of the wind speed of the next sampling point of the current sampling point.
In the method of the first aspect of the present application, the first recurrent neural network model and the second recurrent neural network model respectively employ a GRU model.
In the method of the first aspect of the present application, the error coefficients of the modal components satisfy: wherein/> For the error coefficient of the kth modal component, N is the preset number of model outputs corresponding to the kth modal component, y k,n is the nth wind speed component predicted value of the model outputs corresponding to the kth modal component, and s k,n is the actual value corresponding to y k,n.
To achieve the above object, according to a second aspect of the present application, there is provided a wind power prediction system for energy storage assisted black start, including:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring a wind speed sequence, and the wind speed sequence comprises wind speed measurement values of a current sampling point and a plurality of historical sampling points;
The decomposition module is used for decomposing the wind speed sequence by using a CEEMD algorithm to obtain a plurality of modal components;
the error calculation module is used for obtaining a corresponding wind speed component predicted value by utilizing the first cyclic neural network model based on each modal component and obtaining an error coefficient corresponding to each modal component based on each wind speed component predicted value and each modal component;
The reconstruction module is used for reconstructing all the modal components based on the positive and negative of each error coefficient to obtain a plurality of complementary modal components;
The prediction module is used for obtaining a corresponding target wind speed component predicted value by using a second cyclic neural network model based on each complementary modal component;
The control module is used for obtaining a wind speed predicted value of a next sampling point of the current sampling point based on each target wind speed component predicted value, and further obtaining a wind power predicted value by utilizing the wind speed predicted value so as to participate in energy storage auxiliary black start when a power failure occurs.
To achieve the above object, an embodiment of a third aspect of the present application provides an electronic device, including: a processor, and a memory communicatively coupled to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method according to the first aspect of the present application.
To achieve the above object, an embodiment of a fourth aspect of the present application provides a computer-readable storage medium having stored therein computer-executable instructions for implementing the method set forth in the first aspect of the present application when executed by a processor.
According to the wind power prediction method, the system, the electronic equipment and the storage medium for the energy storage auxiliary black start, a wind speed sequence is obtained, and the wind speed sequence comprises wind speed measurement values of a current sampling point and a plurality of historical sampling points; decomposing the wind speed sequence by using a CEEMD algorithm to obtain a plurality of modal components; obtaining a corresponding wind speed component predicted value by using a first cyclic neural network model based on each modal component, and obtaining an error coefficient corresponding to each modal component based on each wind speed component predicted value and each modal component; reconstructing all modal components based on the positive and negative of each error coefficient to obtain a plurality of complementary modal components; obtaining a corresponding target wind speed component predicted value by using a second cyclic neural network model based on each complementary modal component; and obtaining a wind speed predicted value of a next sampling point of the current sampling point based on the predicted values of the target wind speed components, and further obtaining a wind power predicted value by utilizing the wind speed predicted value so as to participate in energy storage auxiliary black start when a power failure occurs. Under the condition, after a plurality of modal components are obtained by using the CEEMD algorithm, a wind speed component predicted value is obtained by using a cyclic neural network model, so that error coefficients corresponding to the modal components are obtained, all the modal components are reconstructed by the positive and negative of the error coefficients to obtain a plurality of complementary modal components, and the wind speed predicted value of the next sampling point of the current sampling point is obtained by using the complementary modal components, so that the phenomenon that the wind speed predicted value is obtained by decomposing the components by using the traditional CEEMD algorithm like the prior art is avoided, the error of each component is reduced, the wind speed predicted precision is improved, and the wind power predicted precision is further improved.
Additional aspects and advantages of the application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the application.
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The foregoing and/or additional aspects and advantages of the application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:
Fig. 1 is a schematic flow chart of a wind power prediction method for energy storage auxiliary black start according to an embodiment of the present application;
FIG. 2 is a flowchart illustrating a wind speed prediction process according to an embodiment of the present application;
fig. 3 is a block diagram of a wind power prediction system with energy storage auxiliary black start according to an embodiment of the present application.
Detailed Description
Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative and intended to explain the present application and should not be construed as limiting the application.
The following describes a wind power prediction method and system for energy storage auxiliary black start according to an embodiment of the present application with reference to the accompanying drawings.
The embodiment of the application provides a wind power prediction method for energy storage auxiliary black start, which is used for improving the prediction accuracy of wind power. The embodiment of the application predicts the wind speed first and then obtains the required wind power predicted value by using the predicted wind speed. The specific process is as follows.
Fig. 1 is a schematic flow chart of a wind power prediction method for energy storage auxiliary black start according to an embodiment of the present application. FIG. 2 is a flowchart illustrating a wind speed prediction process according to an embodiment of the present application.
As shown in fig. 1, the wind power prediction method for energy storage auxiliary black start includes the following steps:
step S101, acquiring a wind speed sequence, wherein the wind speed sequence comprises wind speed measured values of a current sampling point and a plurality of historical sampling points.
Specifically, in step S101, the number of sampling points is set to be M, where the M sampling points include a current sampling point and M-1 historical sampling points, and the wind speed measurement values acquired at the M sampling points are acquired to obtain a required wind speed sequence (also referred to as an original wind speed sequence). In other words the number of wind speed measurements in the wind speed sequence is equal to M.
Step S102, decomposing the wind speed sequence by using CEEMD algorithm to obtain a plurality of modal components.
As will be readily appreciated, the CEEMD (Complementary Ensemble Empirical Mode Decomposition, complementary set empirical mode decomposition) algorithm is an adaptive signal processing method. The method comprises the steps of obtaining 1 group of new positive and negative mixed sequences by using 1 group of auxiliary white noise with opposite signs and original signals, then carrying out EMD (EMPIRICAL MODE DECOMPOSITION ) decomposition on each sequence, and finally decomposing the original signals (such as a wind speed sequence) into a limited number of modal components (INTRINSIC MODE FUNCTION, IMF) through multiple decomposition. In step S102, the number of modal components obtained by decomposing the wind speed sequence by using the CEEMD algorithm is K, where the kth modal component is denoted as IMF k, and K is 1~K.
Since the wind speed sequence includes M wind speed measurements, each modal component decomposed in step S102 may be regarded as a wind speed component decomposed from wind speed measurements at all sampling points of the wind speed sequence. The number of wind speed components is equal to the number of sampling points of the wind speed sequence. The sequence length of each modal component is equal to M, each subsequence in the modal components is a wind speed component, the sampling point corresponding to each modal component is consistent with the acquired wind speed sequence, and each wind speed component is obtained by decomposing a wind speed measured value at the corresponding sampling point.
Step S103, obtaining a corresponding wind speed component predicted value by using the first cyclic neural network model based on each modal component, and obtaining an error coefficient corresponding to each modal component based on each wind speed component predicted value and each modal component.
Specifically, in step S103, parameters of the first cyclic neural network model are set, so that the first cyclic neural network model outputs a preset number of wind speed component predicted values, and an actual value corresponding to the preset number of wind speed component predicted values output by the model is the current sampling point in the input modal component and the same number of wind speed components before the current sampling point; obtaining a corresponding wind speed component predicted value based on each modal component by using a first cyclic neural network model, and obtaining an error coefficient corresponding to each modal component based on each wind speed component predicted value and each modal component, including: inputting any modal component into a first cyclic neural network model to obtain a corresponding preset number of wind speed component predicted values; and obtaining error coefficients of the modal components based on the predicted values of the wind speed components of the preset quantity and the corresponding actual values, and further obtaining the error coefficients of the modal components.
Taking a first modal component IMF 1, a preset number N, and a sequence length of the modal components equal to M, wherein M is larger than N, as an example, the model is input into a first modal component IMF 1, the first modal component IMF 1 comprises M wind speed components at a current sampling point and M-1 historical sampling points, the modal components are input into a first cyclic neural network model to obtain N wind speed component predicted values, the actual values corresponding to the N wind speed component predicted values are wind speed components at the current sampling point and the previous N-1 historical sampling points in the first modal component IMF 1, and an error coefficient of the first modal component IMF 1 is obtained based on the N wind speed component predicted values and the corresponding N wind speed components。
In step S103, the error coefficients of the modal components satisfy: wherein/> And (3) taking N as the error coefficient of the kth modal component, the preset quantity of model outputs corresponding to the kth modal component, y k,n as the nth wind speed component predicted value of model outputs corresponding to the kth modal component, and N as 1-N. s k,n is the actual value corresponding to y k,n.
The first recurrent neural network model employs a gated recurrent unit network (gated recurrent unit, GRU) model in step S103. As can be easily understood, the GRU network is an improved model of an LSTM (Long Short-Term Memory) network, and by integrating a forgetting gate and an input gate into an updating gate, training parameters of the network are reduced to a certain extent, and meanwhile, the Memory of effective information can be ensured. The update gate and reset gate states are z t and r t, respectively, the input vector is x t, the hidden layer state is h t, and the mathematical expression is:
In the method, in the process of the invention, Is the input state at the time t and the hidden layer state at the last time/>Is a process vector of (2); /(I)Representing a sigmoid function; /(I)、/>Weight matrix representing input vector pair update gate and reset gate, respectively,/>、/>Respectively representing weight matrixes of hidden layer states on an update gate and a reset gate; /(I)A weight matrix representing hidden layer states versus process vectors; w represents a weight matrix of the input vector to the process vector; /(I)Representing an output weight matrix, b being a bias; /(I)The output at time t.
As shown in FIG. 2, in the building stage of the complementary sequence, the original wind speed sequence is decomposed by CEEMD to obtain K modal components IMF (IMF 1,...,IMFK), the K IMF components are respectively put into GRU model for prediction, and error coefficients of the modal components are obtained by using the obtained wind speed component predicted values and corresponding actual values (i.e.,...,/>）。
Step S104, reconstructing all modal components based on the positive and negative of each error coefficient to obtain a plurality of complementary modal components.
Specifically, in step S104, all the modal components are reconstructed based on the positive and negative of each error coefficient to obtain a plurality of complementary modal components, including: dividing all modal components into a non-negative array and a negative array based on the positive and negative of error coefficients of each modal component; according to the error coefficient, arranging the modal components in the non-negative array from large to small to obtain a target non-negative array, and arranging the modal components in the negative array from small to large to obtain a target negative array; and adding the modal components of the corresponding positions in the target non-negative array and the target negative array to obtain a plurality of complementary modal components.
Taking the number of modal components in the non-negative number group as L and the number of modal components in the negative number group as K-L as an example, dividing K error coefficients into two groups according to signs, wherein L error coefficients are greater than or equal to 0, K-L error coefficients are smaller than 0, arranging the modal components corresponding to the error coefficients greater than or equal to 0 according to the error coefficients from large to small to obtain a target non-negative number group [ IMF max-L+1,…,IMFmax-2,IMFmax-1,IMFmax ], arranging the modal components corresponding to the error coefficients smaller than 0 according to the error coefficients from small to large to obtain a target negative number group [ IMF min-K+L+1,…,IMFmin-2,IMFmin-1,IMFmin ], and adding the IMFs of the two corresponding positions of the two groups two by two until one group with smaller sequence length is added to form a plurality of complementary modal components [ H 1,H2,…,Hmax(L,K-L) ] (see FIG. 2). When the two sets of modal components are added, the modal components are added from the initial positions of the sets (i.e. from the IMF max-L+1+IMFmin-K+L+1) until the set with the smaller sequence length is added, and the rest modal components in the set with the larger sequence length are directly reserved.
Step S105, obtaining a corresponding target wind speed component predicted value by using a second cyclic neural network model based on each complementary modal component.
Specifically, in step S105, parameters of the second recurrent neural network model are set so that the second recurrent neural network model outputs a target wind speed component predicted value of a sampling point next to the current sampling point; obtaining a corresponding target wind speed component predicted value based on each complementary modal component by using a second cyclic neural network model, including: and inputting the complementary modal component into a second cyclic neural network model for any complementary modal component to obtain a target wind speed component predicted value of a next sampling point of the corresponding current sampling point, and further obtaining the target wind speed component predicted value corresponding to each complementary modal component.
In step S105, the second recurrent neural network model may employ a GRU model. As shown in fig. 2, in the prediction stage, the multiple complementary modal components [ H 1,H2,…,Hmax(L,K-L) ] obtained in step S104 are respectively input into the GRU model to obtain target wind speed component predicted values (i.e., Y 1 ', Y2 ',…, Ymax(L,K-L) ') corresponding to the complementary modal components.
Step S106, obtaining a wind speed predicted value of a next sampling point of the current sampling point based on each target wind speed component predicted value, and further obtaining a wind power predicted value by utilizing the wind speed predicted value so as to participate in energy storage auxiliary black start when a power failure occurs.
Specifically, in step S106, obtaining a wind speed predicted value of a sampling point next to the current sampling point based on each target wind speed component predicted value includes: and summing the predicted values of the target wind speed components to obtain the predicted value of the wind speed of the next sampling point of the current sampling point. As shown in fig. 2, all the target wind speed component predictors (i.e., Y 1 ', Y2 ',…, Ymax(L,K-L) ') are summed to obtain a wind speed prediction final result, which is the wind speed predictor of the next sampling point of the current sampling point.
In step S106, obtaining a wind power prediction value using the wind speed prediction value includes: wind power predictions are obtained based on wind speed predictions, fan rated output power, rated wind speed, cut-in wind speed, and cut-out wind speed. The relation between the wind speed and the wind power (namely the power generation power of the wind turbine) satisfies the following formula:
Wherein P r is the rated output power (kW) of the fan; v is the wind speed (m/s) at the height of the fan hub, and v r is the rated wind speed (m/s); v ci is the cut-in wind speed (m/s) and v co is the cut-out wind speed (m/s).
In step S106, the wind speed predicted value is regarded as the wind speed v at the height of the hub of the wind turbine, and the wind power predicted value P w is obtained according to the relation between the wind speed and the wind power.
To verify the effect of the method of the present application, verification is performed. Wherein the acquired experimental data (i.e. the raw wind speed sequence) comes from a wind farm with energy storage assisted black start capability. The sampling wind speed data is obtained every 15 minutes from day 1 of 9 in 2022 to day 14 of 9 in 2016. For each set of cases, the wind speed sequence is divided into a training set and a test set. Thus, 7 days of data were selected and a total of 672 data samples at 15 minute intervals were provided to train the predictive model; the next 96 data (corresponding to 1 day data) were used to test the performance of the proposed model.
In order to verify the performance of the proposed model of the present application, the proposed model was further evaluated by experiments. Wherein GRU, CEEMD-GRU is called reference model, and is used for comparison with the model proposed by the application. In the experiments of the present application, the mean absolute percentage error (Mean absolute percentage error, MAPE) and root mean square error (Root mean square error, RMSE) were chosen as the evaluation criteria for each model. In the experiments of the present application, the experimental results are shown in table 1.
Table 1 evaluation index table of each model
As shown in table 1, the model proposed by the present application has the smallest error coefficient when compared with all other reference models. Compared with the CEEMD-GRU model, the model provided by the application has the advantages that the error coefficients of 2 evaluation indexes are greatly reduced, so that the prediction performance can be effectively improved by the method provided by the application, and compared with the GRU model, the error coefficients of 2 evaluation indexes are greatly reduced, so that the prediction precision can be improved by data preprocessing.
In order to achieve the above embodiment, the present application further provides a wind power prediction system for energy storage auxiliary black start.
Fig. 3 is a block diagram of a wind power prediction system with energy storage auxiliary black start according to an embodiment of the present application.
As shown in fig. 3, the energy storage auxiliary black start wind power prediction system includes an acquisition module 11, a decomposition module 12, an error calculation module 13, a reconstruction module 14, a prediction module 15, and a control module 16, wherein:
An obtaining module 11, configured to obtain a wind speed sequence, where the wind speed sequence includes wind speed measurement values of a current sampling point and a plurality of historical sampling points;
A decomposition module 12, configured to decompose the wind speed sequence by using a CEEMD algorithm to obtain a plurality of modal components;
the error calculation module 13 is configured to obtain a corresponding wind speed component predicted value based on each modal component by using the first recurrent neural network model, and obtain an error coefficient corresponding to each modal component based on each wind speed component predicted value and each modal component;
a reconstruction module 14, configured to reconstruct all modal components based on the positive and negative of each error coefficient to obtain a plurality of complementary modal components;
a prediction module 15, configured to obtain a corresponding target wind speed component predicted value by using a second recurrent neural network model based on each complementary modal component;
The control module 16 is configured to obtain a wind speed predicted value of a next sampling point of the current sampling point based on the predicted values of the target wind speed components, and further obtain a wind power predicted value by using the wind speed predicted value, so that the wind power predicted value is used to participate in energy storage auxiliary black start when a power failure occurs.
Further, in one possible implementation manner of the embodiment of the present application, each modal component is composed of wind speed components obtained by decomposing wind speed measurement values of all sampling points of a wind speed sequence, the number of wind speed components is equal to the number of sampling points of the wind speed sequence, parameters of a first cyclic neural network model are set so that the first cyclic neural network model outputs a preset number of predicted values of wind speed components, and actual values corresponding to the preset number of predicted values of wind speed components output by the model are the current sampling points and previous wind speed components of the same number in the input modal components; the error calculation module 13 is specifically configured to: inputting any modal component into a first cyclic neural network model to obtain a corresponding preset number of wind speed component predicted values; and obtaining error coefficients of the modal components based on the predicted values of the wind speed components of the preset quantity and the corresponding actual values, and further obtaining the error coefficients of the modal components.
Further, in a possible implementation manner of the embodiment of the present application, the first recurrent neural network model and the second recurrent neural network model respectively adopt a GRU model.
Further, in one possible implementation manner of the embodiment of the present application, in the error calculation module 13, the error coefficient of each modal component satisfies: wherein/> For the error coefficient of the kth modal component, N is the preset number of model outputs corresponding to the kth modal component, y k,n is the nth wind speed component predicted value of the model outputs corresponding to the kth modal component, and s k,n is the actual value corresponding to y k,n.
Further, in one possible implementation of the embodiment of the present application, the reconstruction module 14 is specifically configured to: dividing all modal components into a non-negative array and a negative array based on the positive and negative of error coefficients of each modal component; according to the error coefficient, arranging the modal components in the non-negative array from large to small to obtain a target non-negative array, and arranging the modal components in the negative array from small to large to obtain a target negative array; and adding the modal components of the corresponding positions in the target non-negative array and the target negative array to obtain a plurality of complementary modal components.
Further, in one possible implementation manner of the embodiment of the present application, parameters of the second cyclic neural network model are set, so that the second cyclic neural network model outputs a target wind speed component predicted value of a next sampling point of the current sampling point; the prediction module 15 is specifically configured to: and inputting the complementary modal component into a second cyclic neural network model for any complementary modal component to obtain a target wind speed component predicted value of a next sampling point of the corresponding current sampling point, and further obtaining the target wind speed component predicted value corresponding to each complementary modal component.
Further, in one possible implementation of the embodiment of the present application, the control module 16 is specifically configured to: and summing the predicted values of the target wind speed components to obtain the predicted value of the wind speed of the next sampling point of the current sampling point.
It should be noted that the foregoing explanation of the embodiment of the wind power prediction method for energy storage auxiliary black start is also applicable to the wind power prediction system for energy storage auxiliary black start of this embodiment, and will not be repeated herein.
In the embodiment of the application, a wind speed sequence is obtained, wherein the wind speed sequence comprises wind speed measured values of a current sampling point and a plurality of historical sampling points; decomposing the wind speed sequence by using a CEEMD algorithm to obtain a plurality of modal components; obtaining a corresponding wind speed component predicted value by using a first cyclic neural network model based on each modal component, and obtaining an error coefficient corresponding to each modal component based on each wind speed component predicted value and each modal component; reconstructing all modal components based on the positive and negative of each error coefficient to obtain a plurality of complementary modal components; obtaining a corresponding target wind speed component predicted value by using a second cyclic neural network model based on each complementary modal component; and obtaining a wind speed predicted value of a next sampling point of the current sampling point based on the predicted values of the target wind speed components, and further obtaining a wind power predicted value by utilizing the wind speed predicted value so as to participate in energy storage auxiliary black start when a power failure occurs. Under the condition, after a plurality of modal components are obtained by using the CEEMD algorithm, a wind speed component predicted value is obtained by using a cyclic neural network model, so that error coefficients corresponding to the modal components are obtained, all the modal components are reconstructed by the positive and negative of the error coefficients to obtain a plurality of complementary modal components, and the wind speed predicted value of the next sampling point of the current sampling point is obtained by using the complementary modal components, so that the phenomenon that the wind speed predicted value is obtained by decomposing the components by using the traditional CEEMD algorithm like the prior art is avoided, the error of each component is reduced, the wind speed predicted precision is improved, and the wind power predicted precision is further improved. The method solves the problems that the sub-sequence decomposed by the traditional data decomposition technology (CEEMD) has larger error and can affect the prediction, and the combined model prediction is adopted to increase the running time, thereby improving the prediction precision of wind power.
In order to achieve the above embodiment, the present application further provides an electronic device, including: a processor, a memory communicatively coupled to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the methods provided by the previous embodiments.
In order to implement the above embodiment, the present application also proposes a computer-readable storage medium having stored therein computer-executable instructions, which when executed by a processor are configured to implement the method provided in the foregoing embodiment.
In order to implement the above embodiments, the present application also proposes a computer program product comprising a computer program which, when executed by a processor, implements the method provided by the above embodiments.
In the foregoing description of embodiments, reference has been made to the terms "one embodiment," "some embodiments," "example," "a particular example," or "some examples," etc., meaning 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, schematic representations of the above terms are not necessarily directed 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, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present application, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined 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 additional implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order from that shown or discussed, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present application.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing 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). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may 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 is to be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. As with the other embodiments, if implemented in hardware, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product.
The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like. While embodiments of the present application have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the application, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the application.
Claims (9)
1. The wind power prediction method for energy storage auxiliary black start is characterized by comprising the following steps of:
Acquiring a wind speed sequence, wherein the wind speed sequence comprises wind speed measurement values of a current sampling point and a plurality of historical sampling points;
decomposing the wind speed sequence by using a CEEMD algorithm to obtain a plurality of modal components;
Obtaining a corresponding wind speed component predicted value by using a first cyclic neural network model based on each modal component, and obtaining an error coefficient corresponding to each modal component based on each wind speed component predicted value and each modal component;
Reconstructing all modal components based on the positive and negative of each error coefficient to obtain a plurality of complementary modal components;
Obtaining a corresponding target wind speed component predicted value by using a second cyclic neural network model based on each complementary modal component;
Obtaining a wind speed predicted value of a next sampling point of the current sampling point based on each target wind speed component predicted value, and further obtaining a wind power predicted value by utilizing the wind speed predicted value so as to participate in energy storage auxiliary black start when a power failure occurs;
wherein reconstructing all modal components based on the positive and negative of each error coefficient to obtain a plurality of complementary modal components comprises:
dividing all modal components into a non-negative array and a negative array based on the positive and negative of error coefficients of each modal component;
According to the error coefficient, arranging the modal components in the non-negative array from large to small to obtain a target non-negative array, and arranging the modal components in the negative array from small to large to obtain a target negative array;
And adding the modal components of the corresponding positions in the target non-negative array and the target negative array to obtain a plurality of complementary modal components.
2. The energy storage assisted black start wind power prediction method according to claim 1, wherein each modal component consists of wind speed components obtained by decomposing wind speed measurement values of all sampling points of the wind speed sequence, and the number of wind speed components is equal to the number of sampling points of the wind speed sequence;
Setting parameters of a first cyclic neural network model, so that the first cyclic neural network model outputs a preset number of wind speed component predicted values, and the actual values corresponding to the preset number of wind speed component predicted values output by the model are the current sampling points in the input modal components and the same number of wind speed components before the current sampling points;
The method for obtaining the corresponding wind speed component predicted value based on each modal component by using the first cyclic neural network model, obtaining the error coefficient corresponding to each modal component based on each wind speed component predicted value and each modal component comprises the following steps:
inputting any modal component into a first cyclic neural network model to obtain a corresponding preset number of wind speed component predicted values; and obtaining error coefficients of the modal components based on the predicted values of the wind speed components of the preset quantity and the corresponding actual values, and further obtaining the error coefficients of the modal components.
3. The energy storage-assisted black-start wind power prediction method according to claim 1, wherein parameters of the second cyclic neural network model are set so that the second cyclic neural network model outputs a target wind speed component predicted value of a sampling point next to the current sampling point;
The obtaining a corresponding target wind speed component predicted value based on each complementary modal component by using a second cyclic neural network model comprises the following steps:
And inputting the complementary modal component into a second cyclic neural network model for any complementary modal component to obtain a target wind speed component predicted value of a next sampling point of the corresponding current sampling point, and further obtaining the target wind speed component predicted value corresponding to each complementary modal component.
4. A method of energy storage assisted black start wind power prediction according to claim 3, wherein the obtaining a wind speed prediction value for a next sampling point to a current sampling point based on each target wind speed component prediction value comprises:
and summing the predicted values of the target wind speed components to obtain the predicted value of the wind speed of the next sampling point of the current sampling point.
5. The energy storage assisted black start wind power prediction method of claim 1, wherein the first and second recurrent neural network models each employ a GRU model.
6. The energy storage assisted black start wind power prediction method according to claim 2, wherein error coefficients of each modal component satisfy: wherein/> For the error coefficient of the kth modal component, N is the preset number of model outputs corresponding to the kth modal component, y k,n is the nth wind speed component predicted value of the model outputs corresponding to the kth modal component, and s k,n is the actual value corresponding to y k,n.
7. An energy storage assisted black start wind power prediction system, comprising:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring a wind speed sequence, and the wind speed sequence comprises wind speed measurement values of a current sampling point and a plurality of historical sampling points;
The decomposition module is used for decomposing the wind speed sequence by using a CEEMD algorithm to obtain a plurality of modal components;
the error calculation module is used for obtaining a corresponding wind speed component predicted value by utilizing the first cyclic neural network model based on each modal component and obtaining an error coefficient corresponding to each modal component based on each wind speed component predicted value and each modal component;
The reconstruction module is used for reconstructing all the modal components based on the positive and negative of each error coefficient to obtain a plurality of complementary modal components;
The prediction module is used for obtaining a corresponding target wind speed component predicted value by using a second cyclic neural network model based on each complementary modal component;
the control module is used for obtaining a wind speed predicted value of a next sampling point of the current sampling point based on each target wind speed component predicted value, and further obtaining a wind power predicted value by utilizing the wind speed predicted value so as to participate in energy storage auxiliary black start when a power failure occurs;
wherein reconstructing all modal components based on the positive and negative of each error coefficient to obtain a plurality of complementary modal components comprises:
dividing all modal components into a non-negative array and a negative array based on the positive and negative of error coefficients of each modal component;
According to the error coefficient, arranging the modal components in the non-negative array from large to small to obtain a target non-negative array, and arranging the modal components in the negative array from small to large to obtain a target negative array;
And adding the modal components of the corresponding positions in the target non-negative array and the target negative array to obtain a plurality of complementary modal components.
8. An electronic device, comprising: a processor, and a memory communicatively coupled to the processor;
The memory stores computer-executable instructions;
the processor executes computer-executable instructions stored in the memory to implement the method of any one of claims 1-6.
9. A computer readable storage medium having stored therein computer executable instructions which when executed by a processor are adapted to carry out the method of any one of claims 1-6.
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| CN117972363B (en) * | 2024-03-29 | 2024-06-07 | 西安热工研究院有限公司 | Wind speed prediction method and system for energy storage auxiliary black start based on stability |
| CN117977583B (en) * | 2024-04-01 | 2024-07-12 | 西安热工研究院有限公司 | Energy storage auxiliary black start wind speed prediction method based on double-order error compensation |
| CN118523318B (en) * | 2024-07-19 | 2024-10-18 | 西安热工研究院有限公司 | Black-start wind power prediction method based on super-capacity energy storage |
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