CN111932007A - Power prediction method and device for photovoltaic power station and storage medium - Google Patents

Power prediction method and device for photovoltaic power station and storage medium Download PDF

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CN111932007A
CN111932007A CN202010775268.8A CN202010775268A CN111932007A CN 111932007 A CN111932007 A CN 111932007A CN 202010775268 A CN202010775268 A CN 202010775268A CN 111932007 A CN111932007 A CN 111932007A
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刘勇
何国器
卢必娟
李华峰
杜增城
田景河
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Guangzhou Development New Energy Group Co.,Ltd.
Jiangmen Guangfa Fishery Photovoltaic Co ltd
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Abstract

The invention discloses a power prediction method, a power prediction device and a storage medium of a photovoltaic power station, wherein the method comprises the steps of obtaining historical power data of the photovoltaic power station in a first preset time period; calculating the historical power data according to a preset wavelet maximum layer decomposition formula to obtain a maximum layer approximate data sequence and a detail data sequence of each level; constructing a corresponding data sequence matrix by taking the maximum layer approximate data sequence and the detail data sequence of each level as input quantities; and reconstructing the data sequence matrix of each level according to a wavelet reconstruction algorithm to obtain a corresponding predicted power value. According to the power prediction method, the power prediction device and the storage medium of the photovoltaic power station, provided by the embodiment of the invention, the historical photovoltaic power data are processed by adopting a wavelet decomposition algorithm, and feature learning is carried out by combining neural network modeling, so that the accuracy of power prediction can be improved while the calculation complexity is reduced.

Description

Power prediction method and device for photovoltaic power station and storage medium
Technical Field
The invention relates to the technical field of solar energy, in particular to a power prediction method and device for a photovoltaic power station and a storage medium.
Background
The photovoltaic power generation is a technology for directly converting light energy into electric energy by utilizing the photovoltaic effect of a semiconductor interface, and due to the characteristics of fluctuation, intermittency, randomness and the like of solar illumination, the access of large-scale solar photovoltaic power generation has great influence on the stability of a power grid, and in order to enable a power dispatching department to dispatch the power according to the change of photovoltaic power generation in time, the power generation of a photovoltaic power station needs to be accurately predicted in the operation of the photovoltaic power station.
In the prior art, similar-day solar radiation distribution or similar-day photovoltaic power output is obtained through digitized weather forecast, but on one hand, the weather forecast contains more weather data types (including environmental parameters such as temperature and wind speed), which greatly increases the calculation complexity of photovoltaic forecast; on the other hand, compared with the actual result of solar irradiation distribution evaluation on similar days, the result of solar irradiation distribution evaluation on similar days has larger inherent error and cannot completely represent the similarity of solar irradiation space-time distribution, so that the existing power prediction method of the photovoltaic power station has higher calculation complexity and lower prediction precision, and is difficult to realize accurate prediction effect.
Disclosure of Invention
The invention provides a power prediction method, a power prediction device and a storage medium of a photovoltaic power station, which are used for solving the technical problems of higher calculation complexity and lower prediction accuracy of the conventional power prediction method of the photovoltaic power station.
An embodiment of the present invention provides a power prediction method for a photovoltaic power station, including:
acquiring historical power data of the photovoltaic power station in a first preset time period;
calculating the historical power data according to a preset wavelet maximum layer decomposition formula to obtain a maximum layer approximate data sequence and a detail data sequence of each level;
constructing a corresponding data sequence matrix by taking the maximum layer approximate data sequence and the detail data sequence of each level as input quantities;
and reconstructing the data sequence matrix of each level according to a wavelet reconstruction algorithm to obtain a corresponding predicted power value.
As one preferred scheme, the step of acquiring historical power data of the photovoltaic power station in a first preset time period specifically includes:
and acquiring second-level output power data of a photovoltaic inverter of the photovoltaic power station in the preset time period, and taking the second-level output power data as the historical power data.
As one of the preferable schemes, the wavelet maximum layer decomposition formula comprises:
Figure BDA0002617923020000021
wherein p issThe length of the time sequence of the generated power of the photovoltaic power station in a preset time period fsIn order to select the data length of the wavelet decomposition layer, maxL is the largest layer of the selected wavelet decomposition.
As one of the preferable schemes, the step of reconstructing the data sequence matrix of each level according to a wavelet reconstruction algorithm to obtain a corresponding predicted power value specifically includes:
inputting the data sequence matrix of each level into a convolutional neural network, so that the convolutional neural network generates a corresponding predicted data sequence within a second preset time period according to the data sequence matrix of each level;
and reconstructing the predicted data sequence according to a wavelet reconstruction algorithm to obtain the corresponding predicted power value.
Another embodiment of the invention provides a power prediction device of a photovoltaic power station, which comprises an acquisition module and a controller; the acquisition module is connected with the controller;
the controller is configured to:
acquiring historical power data of the photovoltaic power station in a first preset time period;
calculating the historical power data according to a preset wavelet maximum layer decomposition formula to obtain a maximum layer approximate data sequence and a detail data sequence of each level;
constructing a corresponding data sequence matrix by taking the maximum layer approximate data sequence and the detail data sequence of each level as input quantities;
and reconstructing the data sequence matrix of each level according to a wavelet reconstruction algorithm to obtain a corresponding predicted power value.
As one of the preferable solutions, the collection module includes a photovoltaic inverter, and the controller is further configured to:
and acquiring second-level output power data of the photovoltaic inverter in the preset time period, and taking the second-level output power data as the historical power data.
As one of the preferable schemes, the wavelet maximum layer decomposition formula comprises:
Figure BDA0002617923020000031
wherein p issThe length of the time sequence of the generated power of the photovoltaic power station in a preset time period fsIn order to select the data length of the wavelet decomposition layer, maxL is the largest layer of the selected wavelet decomposition.
As one of the preferable schemes, the controller is further configured to:
inputting the data sequence matrix of each level into a convolutional neural network, so that the convolutional neural network generates a corresponding predicted data sequence within a second preset time period according to the data sequence matrix of each level;
and reconstructing the predicted data sequence according to a wavelet reconstruction algorithm to obtain the corresponding predicted power value.
Yet another embodiment of the present invention provides a computer-readable storage medium comprising a stored computer program, wherein the computer program, when executed, controls an apparatus in which the computer-readable storage medium is located to perform the method for power prediction of a photovoltaic power plant as described above.
Compared with the prior art, the method and the device have the advantages that extra meteorological data parameters do not need to be acquired and calculated, the operation cost of the photovoltaic power station is effectively reduced, and the influence of inherent errors such as high-frequency components in similar days is eliminated by acquiring historical power data information of the photovoltaic power station for prediction. The whole power prediction method of the photovoltaic power station applies high-speed calculation processing service, combines discrete wavelet decomposition, constructs a neural network model for feature learning, and realizes decomposition, calculation and reconstruction of data signals, so that the system power of the photovoltaic power station in a preset period can be predicted, and the accuracy of power prediction can be improved while the calculation complexity is reduced.
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FIG. 1 is a schematic flow diagram of a method for power prediction of a photovoltaic power plant in one embodiment of the present invention;
figure 2 is a signal layer schematic of wavelet decomposition in one embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present application, the terms "first", "second", "third", etc. are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implying any number of technical features indicated. Thus, features defined as "first," "second," "third," etc. may explicitly or implicitly include one or more of the features. In the description of the present application, "a plurality" means two or more unless otherwise specified.
In the description of the present application, it is to be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present application can be understood in a specific case by those of ordinary skill in the art.
In the description of the present application, it is to be noted that, unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention, as those skilled in the art will recognize the specific meaning of the terms used in the present application in a particular context.
An embodiment of the present invention provides a method for predicting power of a photovoltaic power station, and specifically, please refer to fig. 1, where fig. 1 is a schematic flow diagram of the method for predicting power of a photovoltaic power station in one embodiment, where the method includes:
s1, acquiring historical power data of the photovoltaic power station in a first preset time period;
s2, calculating the historical power data according to a preset wavelet maximum layer decomposition formula to obtain a maximum layer approximate data sequence and a detail data sequence of each level;
s3, constructing a corresponding data sequence matrix by taking the maximum layer approximate data sequence and the detail data sequence of each level as input quantities;
and S4, reconstructing the data sequence matrix of each level according to a wavelet reconstruction algorithm to obtain a corresponding predicted power value.
It should be noted that the photovoltaic power generation change trend characteristic is obvious on a large time scale, and the photovoltaic output fluctuation characteristic is outstanding on a small time scale, which is different from the mode of predicting the photovoltaic power generation output power through meteorological parameters, extraterrestrial irradiation, a clear sky model, a satellite cloud picture, similar daily calculation and the like in the prior art, the invention can reduce the number of related sensor components and the operation load by only acquiring historical power data without depending on additional parameters, shorten the prediction interval and reduce the operation cost of a photovoltaic power station, more importantly, the invention decomposes the second-level solar irradiation, decomposes the original solar irradiation signal into a plurality of high-frequency and low-frequency sequences, respectively models each signal sequence by utilizing a neural network algorithm, models and learns each characteristic, and constructs the photovoltaic output power characteristic based on wavelet decomposition-neural network prediction-wavelet reconstruction, the method solves the problem of the output power prediction of the photovoltaic power station under the condition of no other meteorological parameters, and further can effectively predict the influence of the fluctuation of solar irradiation on the photovoltaic output.
The multi-resolution analysis of wavelet transform is to analyze signals according to the information of the signals appearing on different scales, so as to simulate the analysis process of human beings on the signals. A scaled signal refers to its best approximation at a certain resolution. The coarse scale is gradually transitioned to the fine scale by the Mallat algorithm, amplified and a more accurate representation of the given signal is obtained. A simple method of implementing multi-resolution analysis using Discrete Wavelet Transform (DWT) is defined as:
Figure BDA0002617923020000051
a and b are expressed as two integer variables that together determine the scaling and translation parameters of phi, T is a discrete time index, and T is the length of the signal f (T). According to the idea of multi-resolution analysis, the decomposition process of the signal actually reflects the relationship between the scale transformation corresponding to the scale multiplication and the wavelet transformation. In the case of a wavelet packet, it reflects the relationship between the subdivision of the wideband signal into smaller band signals. Specifically, referring to fig. 2, fig. 2 is a schematic diagram of a signal layer of wavelet decomposition in one embodiment, where j is denoted as a jth layer, V denotes a jth layer approximation component, and w denotes a jth layer detail component.
As one preferable scheme, in the embodiment, second-level output power data of a photovoltaic inverter of the photovoltaic power station in the preset time period is obtained, and the second-level output power data is used as the historical power data. The preset time period is preferably second-level power data in the past 1 hour, the photovoltaic power station output power of the second-level photovoltaic power in the past 1 hour is decomposed according to a Mallat algorithm through a Daubechies wavelet decomposition algorithm according to a wavelet maximum layer, and the calculation method of the decomposition maximum layer comprises the following steps:
Figure BDA0002617923020000061
wherein p issThe length of the time sequence of the generated power of the photovoltaic power station in a preset time period fsIn order to select the data length of the wavelet decomposition layer, maxL is the largest layer of the selected wavelet decomposition. Of course, the second-level photovoltaic power data of the past N hours can also be used as the preset time period, and N can be selected according to the predicted time interval and the length of the predicted time sequence.
As one preferable scheme, the step S4 is a step of reconstructing the data sequence matrix of each level according to a wavelet reconstruction algorithm to obtain a corresponding predicted power value, and specifically includes:
s41, inputting the data sequence matrix of each level into a convolutional neural network, so that the convolutional neural network generates a predicted data sequence within a corresponding second preset time period (the second preset time period corresponds to the first preset time period and is a next time interval of the first preset time period) according to the data sequence matrix of each level;
and S42, reconstructing the prediction data sequence according to a wavelet reconstruction algorithm to obtain the corresponding prediction power value.
It should be noted that after the wavelet decomposition step is completed, a maxL layer approximation sequence X with a time stamp of T is obtaineda，TMaxL layer detail sequence XdmLayer 1, maxL detail sequence Xdm-1…, first layer detail sequence Xd1(ii) a Then, a sequence matrix of each layer sequence is respectively established, and an input matrix M ═ X of the time sequence is established by taking the approximate sequence of the maxL layer as an examplea，T，Xa，T-1，…,Xa，1]X tagged with T +1 timea，T+1As an output matrix; and finally, performing feature learning through neural network modeling, taking a maxL layer approximate sequence as an example, constructing a neural network model, wherein the input is M, and the output is Xa，T+1And establishing a data set in the form of M-X according to past second-level data accumulated in a preset time period by the power of the photovoltaic system, training the neural network model, and respectively performing modeling training on detail parameters (V and other W layers) of other layers.
For convenience of explanation, taking the second-level power data of an inverter of a photovoltaic power station in a 60-second period as an example, a photovoltaic power second-level matrix is input:
Figure BDA0002617923020000071
matrix after wavelet transform with Vj-pLayers are as examples:
AC＝[ACT0,ACT1,ACT2,ACT3,ACT4]
so after the calculation of the convolutional neural network, the calculation process is
Figure BDA0002617923020000072
Wherein D is Vj-pThe CNN model is a 3-layer convolution plus pooling layer, 4-layer full-connection layer and dropointrate are 0.1, adam optimization gradient learning rate is 0.0001, and error loss is defined as an absolute error.
Therefore, according to the wavelet transformation inverse process, the predicted photovoltaic power is reconstructed, and the calculation process is as follows:
Figure BDA0002617923020000073
therefore, the output data of the photovoltaic power station in the past 1 hour is input, and the prediction calculation is realized in the neural network model through wavelet decomposition, so that the system power of the photovoltaic power station in the preset period can be predicted.
Another embodiment of the present invention provides a power prediction apparatus (not shown) for a photovoltaic power plant, comprising an acquisition module and a controller; the acquisition module is connected with the controller;
the controller is configured to:
acquiring historical power data of the photovoltaic power station in a first preset time period;
calculating the historical power data according to a preset wavelet maximum layer decomposition formula to obtain a maximum layer approximate data sequence and a detail data sequence of each level;
constructing a corresponding data sequence matrix by taking the maximum layer approximate data sequence and the detail data sequence of each level as input quantities;
and reconstructing the data sequence matrix of each level according to a wavelet reconstruction algorithm to obtain a corresponding predicted power value.
As one of the preferable solutions, the collection module includes a photovoltaic inverter, and the controller is further configured to:
and acquiring second-level output power data of the photovoltaic inverter of the photovoltaic power station in the preset time period, and taking the second-level output power data as the historical power data.
As one of the preferable schemes, the wavelet maximum layer decomposition formula comprises:
Figure BDA0002617923020000081
wherein p issThe length of the time sequence of the generated power of the photovoltaic power station in a preset time period fsIn order to select the data length of the wavelet decomposition layer, maxL is the largest layer of the selected wavelet decomposition.
As one of the preferable schemes, the controller is further configured to:
inputting the data sequence matrix of each level into a convolutional neural network, so that the convolutional neural network generates a corresponding predicted data sequence within a second preset time period according to the data sequence matrix of each level;
and reconstructing the predicted data sequence according to a wavelet reconstruction algorithm to obtain the corresponding predicted power value.
Yet another embodiment of the present invention provides a computer-readable storage medium comprising a stored computer program, wherein the computer program, when executed, controls an apparatus in which the computer-readable storage medium is located to perform the method for power prediction of a photovoltaic power plant as described above.
According to the power prediction method, the power prediction device and the storage medium of the photovoltaic power station, extra meteorological data parameters do not need to be acquired and calculated, the operation cost of the photovoltaic power station is effectively reduced, and the influence of inherent errors such as high-frequency components in similar days is eliminated by acquiring historical power data information of the photovoltaic power station for prediction. The whole power prediction method of the photovoltaic power station applies high-speed calculation processing service, combines discrete wavelet decomposition, constructs a neural network model for feature learning, and realizes decomposition, calculation and reconstruction of data signals, so that the system power of the photovoltaic power station in a preset period can be predicted, and the accuracy of power prediction can be improved while the calculation complexity is reduced.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.

Claims (9)

1. A power prediction method for a photovoltaic power station is characterized by comprising the following steps:
acquiring historical power data of the photovoltaic power station in a first preset time period;
calculating the historical power data according to a preset wavelet maximum layer decomposition formula to obtain a maximum layer approximate data sequence and a detail data sequence of each level;
constructing a corresponding data sequence matrix by taking the maximum layer approximate data sequence and the detail data sequence of each level as input quantities;
and reconstructing the data sequence matrix of each level according to a wavelet reconstruction algorithm to obtain a corresponding predicted power value.
2. The method for predicting power of a photovoltaic plant of claim 1, wherein the step of obtaining historical power data of the photovoltaic plant within a first preset time period comprises:
and acquiring second-level output power data of a photovoltaic inverter of the photovoltaic power station in the preset time period, and taking the second-level output power data as the historical power data.
3. The method for power prediction of a photovoltaic power plant of claim 1 wherein the wavelet maximum layer decomposition comprises:
Figure FDA0002617923010000011
wherein p issThe length of the time sequence of the generated power of the photovoltaic power station in a preset time period fsIn order to select the data length of the wavelet decomposition layer, maxL is the largest layer of the selected wavelet decomposition.
4. The method for power prediction of photovoltaic plants according to claim 3, characterized in that said step of reconstructing said data sequence matrix of each level according to a wavelet reconstruction algorithm to obtain a corresponding predicted power value comprises:
inputting the data sequence matrix of each level into a convolutional neural network, so that the convolutional neural network generates a corresponding predicted data sequence within a second preset time period according to the data sequence matrix of each level;
and reconstructing the predicted data sequence according to a wavelet reconstruction algorithm to obtain the corresponding predicted power value.
5. The power prediction device of the photovoltaic power station is characterized by comprising an acquisition module and a controller;
the acquisition module is connected with the controller;
the controller is configured to:
acquiring historical power data of the photovoltaic power station in a first preset time period;
calculating the historical power data according to a preset wavelet maximum layer decomposition formula to obtain a maximum layer approximate data sequence and a detail data sequence of each level;
constructing a corresponding data sequence matrix by taking the maximum layer approximate data sequence and the detail data sequence of each level as input quantities;
and reconstructing the data sequence matrix of each level according to a wavelet reconstruction algorithm to obtain a corresponding predicted power value.
6. The power prediction apparatus of a photovoltaic power plant of claim 5 wherein the collection module comprises a photovoltaic inverter, the controller further configured to:
and acquiring second-level output power data of the photovoltaic inverter in the preset time period, and taking the second-level output power data as the historical power data.
7. The power prediction apparatus of a photovoltaic power plant of claim 5 wherein the wavelet maximum layer decomposition comprises:
Figure FDA0002617923010000021
wherein p issThe length of the time sequence of the generated power of the photovoltaic power station in a preset time period fsIn order to select the data length of the wavelet decomposition layer, maxL is the largest layer of the selected wavelet decomposition.
8. The power prediction apparatus of a photovoltaic power plant of claim 7 wherein the controller is further configured to:
inputting the data sequence matrix of each level into a convolutional neural network, so that the convolutional neural network generates a corresponding predicted data sequence within a second preset time period according to the data sequence matrix of each level;
and reconstructing the predicted data sequence according to a wavelet reconstruction algorithm to obtain the corresponding predicted power value.
9. A computer-readable storage medium, comprising a stored computer program, wherein the computer program, when executed, controls an apparatus in which the computer-readable storage medium is located to perform the method of power prediction of a photovoltaic power plant of any one of claims 1 to 4.
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CN118551168A (en) * 2024-07-29 2024-08-27 国网浙江省电力有限公司乐清市供电公司 Method, device, equipment and medium for reconstructing missing power data of photovoltaic energy storage facilities
CN118551168B (en) * 2024-07-29 2024-10-11 国网浙江省电力有限公司乐清市供电公司 Method, device, equipment and medium for reconstructing missing power data of photovoltaic energy storage facilities

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