New energy power fluctuation modeling method, device, equipment, storage medium and product

Abstract

The invention provides a new energy power fluctuation modeling method and device, equipment, a storage medium and a product, and the method comprises the steps: determining a sampling rate and time duration required by modeling according to a modeling demand, carrying out the collection of a new energy power generation system according to the sampling rate and time duration, and obtaining an output power sequence under the sampling rate and time duration; decomposing the output power sequence in an empirical mode decomposition mode to obtain a plurality of intrinsic mode function components; and reconstructing the plurality of intrinsic mode function components based on a sampling rate to obtain power fluctuation components under different time scales. According to the scheme, the new energy power fluctuation condition can be efficiently and accurately reflected, and the stability and reliability of a power generation system are improved.

Description

Technical Field

[0001] The present invention relates to the field of renewable energy power generation control technology, and in particular to a renewable energy power fluctuation modeling method, device, equipment, storage medium and product. Background Art

[0002] With the rapid increase in installed capacity of renewable energy, the proportion of renewable energy power generation in the power system has gradually increased. However, renewable energy power generation has the characteristics of intermittent, volatile and random, and its power fluctuations have brought great challenges to the stable operation of the power grid.

[0003] In the existing power system, the modeling of renewable energy power fluctuations mainly adopts statistical methods or simulation methods in time domain and frequency domain. The use of statistical methods to establish a probability distribution model cannot describe the renewable energy fluctuation process as an organic physical process, and ignores the dynamic description of the changing trends of its different fluctuation forms; the time series simulation method in the time domain ignores the law of the change of the intrinsic fluctuation frequency of renewable energy power over time, and fails to reveal the physical characteristics and inherent properties of renewable energy power fluctuations; the spectrum change method based on the frequency domain is only suitable for the decomposition of composite signals whose sub-signals do not have non-stationary characteristics, and the characteristics of the fluctuation components of renewable energy power at different time scales cannot be accurately described from the spectrum alone. It can be understood that these methods often have certain limitations in the time resolution and data coverage required for modeling renewable energy power fluctuations when dealing with the complex fluctuation characteristics of renewable energy power.

[0004] Therefore, the existing technology cannot efficiently and accurately reflect the power fluctuation of new energy, and the stability and reliability of the power generation system are poor. Summary of the invention

[0005] The present invention provides a new energy power fluctuation modeling method, device, equipment, storage medium and product, which are used to solve the defects in the prior art that the new energy power fluctuation situation cannot be predicted efficiently and accurately, and the stability and reliability of the power generation system are poor. It realizes the establishment of a new energy power fluctuation model based on empirical mode decomposition that efficiently and accurately reflects the power fluctuation characteristics of different time scales, thereby improving the stability and reliability of the power generation system.

[0006] The present invention provides a new energy power fluctuation modeling method, comprising the following steps.

[0007] The sampling rate and time length required for modeling are determined according to the modeling requirements, and the new energy power generation system is sampled according to the sampling rate and the time length to obtain the output power sequence under the sampling rate and the time length.

[0008] The output power sequence is decomposed by empirical mode decomposition to obtain a plurality of intrinsic mode function components.

[0009] Based on the sampling rate, the multiple intrinsic mode function components are reconstructed to obtain power fluctuation components at different time scales.

[0010] According to a new energy power fluctuation modeling method provided by the present invention, the output power sequence is decomposed by empirical mode decomposition to obtain multiple intrinsic mode function components, including the following steps.

[0011] The output power sequence is used as an input signal.

[0012] The local maximum points and the local minimum points of the input signal are calculated, and all the local maximum points and all the local minimum points are connected respectively to obtain an upper envelope of the input signal and a lower envelope of the input signal.

[0013] The function value corresponding to the upper envelope of the input signal and the function value of the lower envelope of the input signal at each time point are calculated to obtain an instantaneous average value, and the instantaneous average values ​​at all time points are connected to obtain an instantaneous average line.

[0014] The instantaneous average is subtracted from the input signal to obtain an intermediate processed signal.

[0015] Determine whether the intermediate processed signal satisfies an intrinsic mode function condition; wherein the intrinsic mode function condition includes: within the entire time range of the intermediate processed signal, the number of local extreme points and zero-crossing points must be equal or differ by at most one; and, at any time point, the average values ​​of the upper envelope of the intermediate processed signal and the lower envelope of the intermediate processed signal are zero.

[0016] If the intermediate processed signal does not satisfy the intrinsic mode function condition, the intermediate processed signal is used as the input signal, and the step of calculating the local maximum point and the local minimum point of the input signal is returned to be executed.

[0017] If the intermediate processed signal satisfies the intrinsic mode function condition, the intermediate processed signal is stored as an intrinsic mode function component, and the intermediate processed signal is subtracted from the input signal to obtain a residual sequence signal; it is determined whether the residual sequence signal is a remainder; if the residual sequence signal is not a remainder, the residual sequence signal is used as the input signal, and the step of calculating the local maximum point and the local minimum point of the input signal is returned to execute; otherwise, it is determined that the output power sequence has completed decomposition, and the residual sequence signal is used as the residual component.

[0018] According to a new energy power fluctuation modeling method provided by the present invention, the multiple intrinsic mode function components are reconstructed based on the sampling rate to obtain power fluctuation components at different time scales, including the following steps.

[0019] The power spectrum density corresponding to each intrinsic mode function component is calculated based on the sampling rate.

[0020] Each intrinsic mode function component is analyzed according to the power spectrum density corresponding to each intrinsic mode function component to determine the time scale characteristics of each intrinsic mode function component.

[0021] Determine different time scale groups based on reconstruction requirements.

[0022] According to the time scale characteristics of each intrinsic mode function component, the time scale group to which each intrinsic mode function component belongs is determined.

[0023] The intrinsic mode function components belonging to the same time scale group are added together to obtain the power fluctuation components at different time scales.

[0024] According to a new energy power fluctuation modeling method provided by the present invention, the step of connecting all local maximum points and all local minimum points to obtain an upper envelope of the input signal and a lower envelope of the input signal specifically includes: A cubic spline interpolation method is applied to connect all local maximum points and all local minimum points respectively, so as to obtain an upper envelope of the input signal and a lower envelope of the input signal.

[0025] According to a new energy power fluctuation modeling method provided by the present invention, before decomposing the output power sequence by empirical mode decomposition to obtain multiple intrinsic mode function components, the method also includes: The output power sequence is preprocessed; wherein the preprocessing includes at least one of the following: missing value processing, outlier detection and processing, noise removal, normalization or standardization, data interpolation, data smoothing, data conversion, and feature extraction.

[0026] According to a new energy power fluctuation modeling method provided by the present invention, after reconstructing the multiple intrinsic mode function components based on the sampling rate to obtain power fluctuation components at different time scales, the method further includes: A new energy power fluctuation report is generated according to the power fluctuation components at different time scales and the output power sequence, and the new energy power fluctuation report is sent to a user.

[0027] The present invention also provides a new energy power fluctuation modeling device, comprising the following modules.

[0028] The acquisition module is used to determine the sampling rate and time length required for modeling according to the modeling requirements, and to collect data from the new energy power generation system according to the sampling rate and the time length to obtain the output power sequence under the sampling rate and the time length.

[0029] The decomposition module is used to decompose the output power sequence by empirical mode decomposition to obtain multiple intrinsic mode function components.

[0030] A reconstruction module is used to reconstruct the multiple intrinsic mode function components based on the sampling rate to obtain power fluctuation components at different time scales.

[0031] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein when the processor executes the computer program, it implements any of the above-mentioned new energy power fluctuation modeling methods.

[0032] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-mentioned new energy power fluctuation modeling methods.

[0033] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements any of the above-mentioned new energy power fluctuation modeling methods.

[0034] The method, device, equipment, storage medium and product for modeling new energy power fluctuations provided by the present invention establish a new energy power fluctuation model that efficiently and accurately reflects the power fluctuation characteristics of different time scales through empirical mode decomposition. Empirical mode decomposition is an adaptive signal time-frequency processing method, which decomposes the signal according to the time scale characteristics of the data itself, without the need to pre-set any basis functions. When processing nonlinear and non-stationary data, this method is completely adaptive. The new energy power fluctuation itself is nonlinear and non-stationary. On the one hand, the use of empirical mode decomposition can perform principal component analysis, and the separated components can characterize the characteristic information of different time scales; on the other hand, its adaptive time-frequency analysis characteristics can better reflect the inherent fluctuation characteristics of the new energy power itself. Therefore, the new energy power fluctuation modeling method provided by the present invention can efficiently and accurately reflect the new energy power fluctuation situation, and improve the stability and reliability of the power generation system. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0036] Figure 1 This is one of the flow charts of the new energy power fluctuation modeling method provided by the present invention.

[0037] Figure 2 This is the second flow chart of the new energy power fluctuation modeling method provided by the present invention.

[0038] Figure 3 It is a structural schematic diagram of the new energy power fluctuation modeling device provided by the present invention.

[0039] Figure 4 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] It should be noted that the terms "including" and "having" and any variations thereof in the specification of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0042] The following specific embodiments are used to describe in detail the technical solution of the present application and how the technical solution of the present application solves the above technical problems. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. Figure 1 and Figure 2 The new energy power fluctuation modeling method of the present invention is described.

[0043] In practical applications, the execution subject of the new energy power fluctuation modeling method can be a new energy power fluctuation modeling device. There are many ways to implement the new energy power fluctuation modeling device, for example, it can be implemented through a computer program, such as application software, etc.; or, for example, a chip, etc. It can also be implemented as a medium storing relevant computer programs, such as a U disk, a cloud disk, etc.; or, it can also be implemented through a physical device integrated or installed with relevant computer programs, such as a server, an intelligent device, etc.

[0044] Figure 1 This is one of the flow charts of the new energy power fluctuation modeling method provided by the present invention, such as Figure 1 As shown, the method includes the following: step 101 to step 104.

[0045] Step 101: Determine the sampling rate and time length required for modeling according to modeling requirements.

[0046] The sampling rate refers to the frequency at which a continuous signal is sampled per unit time. The sampling rate determines the temporal resolution of the digitized signal, that is, the degree to which the signal details can be captured. For example, the sampling rate is measured in Hertz (Hz), which represents the number of samples collected per second. For example, a sampling rate of 44.1kHz means that 44,100 sample data are collected per second. The time length refers to the duration of data collection, that is, the time span from the start to the end of data collection. For example, the sampling rate can be expressed as , the time length can be expressed as .

[0047] In practical applications, modeling requirements include but are not limited to modeling objectives, signal characteristics, data storage and processing capabilities, economy and usability, system response requirements, data availability, user-defined design, etc. For example, the sampling rate and time length are determined by determining the modeling objectives, such as predicting short-term power fluctuations or analyzing long-term trends, which will determine the required sampling rate and time length. For example, considering the characteristics of new energy power signals, such as new energy power signals are wind power output or photovoltaic output, the appropriate sampling rate is determined according to the fluctuation frequency and amplitude of wind power output or photovoltaic output. For example, the amount of data that can be processed is determined based on the available storage space and computing resources, and the sampling rate and time length are determined based on the amount of data that can be processed. Specifically, the process of determining the sampling rate and time length needs to comprehensively consider the above factors, and needs to be appropriately adjusted to obtain the sampling rate and time length required for modeling.

[0048] Step 102: collect data from the new energy power generation system according to the sampling rate and time length to obtain an output power sequence under the sampling rate and time length.

[0049] Among them, the output power series refers to a series of time series that record the output power values ​​of the renewable energy power generation system at different time points. The output power series describes in detail the power output of the power generation system over time and is an important data basis for analyzing and predicting the performance of the power generation system.

[0050] It is understandable that each data point of the output power sequence includes a timestamp and an output power value. In practical applications, the output power of the renewable energy power generation system is regularly collected according to the sampling rate and time length to obtain the output power sequence. For example, the output power sequence can be expressed as .

[0051] It should be noted that the present invention does not specifically limit the renewable energy power generation system. For example, the renewable energy power generation system may be a wind power generation system, a photovoltaic power generation system, a geothermal power generation system, a tidal power generation system, a biomass power generation system, a hydropower generation system, etc.

[0052] Accordingly, the above-mentioned output power sequence includes but is not limited to a wind output power sequence, a photovoltaic output power sequence, a geothermal output power sequence, a tidal power output power sequence, a biomass output power sequence, and a hydropower output power sequence.

[0053] Optionally, in one example, after step 102, the method further includes: preprocessing the output power sequence, the preprocessing including but not limited to: missing value processing, outlier detection and processing, noise removal, normalization or standardization, data interpolation, data smoothing, data conversion, feature extraction, etc.

[0054] It can be understood that after step 102, preprocessing the output power sequence can ensure the accuracy and reliability of the output power sequence, and can determine that the output power sequence is more suitable for subsequent analysis and modeling work, thereby improving the accuracy and reliability of new energy power fluctuation modeling.

[0055] Step 103: Decompose the output power sequence by empirical mode decomposition to obtain multiple intrinsic mode function components.

[0056] Among them, Empirical Mode Decomposition (EMD) is an adaptive, nonlinear and non-stationary time series analysis method. Empirical Mode Decomposition can decompose a complex time series signal into a series of simple Intrinsic Mode Functions (IMFs) with a single frequency and a final residual term.

[0057] It can be understood that empirical mode decomposition is an adaptive method that does not require a preset basis function and can handle nonlinear and non-stationary signals. It is particularly suitable for the analysis of renewable energy power generation output power series. Furthermore, empirical mode decomposition allows multi-scale analysis of the output power series, decomposing complex signals into a series of simple eigenmode functions, each of which represents fluctuations on different time scales. The decomposed eigenmode functions help to identify and extract features and patterns in renewable energy power generation output power series, such as periodic fluctuations, instantaneous spikes, etc. Furthermore, empirical mode decomposition can effectively separate the trend and periodic components of the signal, which helps to better understand the long-term trend and short-term fluctuations of renewable energy power generation output.

[0058] In practical applications, the empirical mode decomposition method is to decompose the output power sequence into multiple intrinsic mode function components and a residual component by calculating the average value of the upper and lower envelopes of the output power sequence.

[0059] Specifically, in a possible implementation, Figure 2 This is the second flow chart of the new energy power fluctuation modeling method provided by the present invention, such as Figure 2 As shown, the above step 103 includes: steps 201 to 210.

[0060] Step 201: Use the output power sequence as an input signal.

[0061] Specifically, the output power sequence As input signal .

[0062] Step 202: Calculate the local maximum points and local minimum points of the input signal, and connect all the local maximum points and all the local minimum points respectively to obtain the upper envelope of the input signal and the lower envelope of the input signal.

[0063] Specifically, scanning the entire input signal The output power values ​​corresponding to each time point identify all local maximum points and local minimum points. Among them, the local maximum point is the output power value higher than the output power value of its adjacent points, and the local minimum point is the output power value lower than the output power value of its adjacent points. Further, the local maximum points and local minimum points are marked.

[0064] Furthermore, all local maximum points are connected to obtain the upper envelope of the input signal; all local minimum points are connected to obtain the lower envelope of the input signal. The upper envelope of , the input signal The lower envelope of .

[0065] As an example, in a possible implementation manner, in the above step 202, all local maximum points and all local minimum points are connected respectively to obtain an upper envelope of the input signal and a lower envelope of the input signal, which specifically includes: The cubic spline interpolation method is applied to connect all the local maximum points and all the local minimum points respectively, and the upper envelope of the input signal and the lower envelope of the input signal are obtained.

[0066] Among them, Cubic Spline Interpolation is a commonly used interpolation method in numerical analysis, which approximates a given data set by constructing a series of cubic polynomials. The main feature of this method is to construct a cubic polynomial between every two data points, so that the entire interpolation function smoothly transitions between data points and has continuous first-order and second-order derivatives.

[0067] Step 203: Calculate the mean of the function value corresponding to the upper envelope of the input signal and the function value of the lower envelope of the input signal at each time point to obtain an instantaneous average value, and connect the instantaneous average values ​​at all time points to obtain an instantaneous average line.

[0068] Specifically, the instantaneous average line can be expressed as , the calculation formula of the instantaneous average is as follows: Step 204: Subtract the instantaneous average line from the input signal to obtain an intermediate processed signal.

[0069] Specifically, from the input signal Subtract the instantaneous average , get the intermediate processed signal , intermediate processing signal The expression is as follows: In practice, the intermediate processing signal It may be an eigenmode function. Further, we can judge Whether it is an intrinsic mode function and perform subsequent processing.

[0070] Step 205: Determine whether the intermediate processed signal satisfies the intrinsic mode function condition.

[0071] The intrinsic mode function conditions include: the number of local extreme points and zero-crossing points of the intermediate processing signal in the entire time range must be equal or differ by at most one; and at any time point, the average value of the upper envelope of the intermediate processing signal and the lower envelope of the intermediate processing signal is zero.

[0072] In practice, during the Empirical Mode Decomposition (EMD) process, an intermediate processing signal To be identified as an intrinsic mode function (IMF), the above mentioned intrinsic mode function conditions must be met. It can be understood that on the one hand, the intermediate processed signal The number of local maxima and local minima of must be equal or differ by at most one. This condition ensures that the signal is monotonic or has a simple oscillatory behavior. On the other hand, at any time point, the intermediate processed signal The upper envelope and the intermediate processed signal The lower envelope of must have a mean value of zero. This means that the intermediate processed signal The upper envelope and the intermediate processed signal The average values ​​of the lower envelope at each time point should cancel each other out, thus satisfying the definition of an intrinsic mode function (IMF). These conditions ensure that each intrinsic mode function (IMF) is a simple oscillation mode that can represent fluctuations on a specific time scale in the original signal.

[0073] It should be noted that in the process of decomposing the output power sequence, the intermediate processing signal is determined Whether it is an intrinsic mode function, mainly check the intermediate processing signal The upper envelope and the intermediate processed signal Whether the mean of the lower envelope of is always 0, this condition is also called the stopping criterion. However, it is difficult to make its mean strictly equal to 0 in actual decomposition, and it can only be infinitely close to zero.

[0074] As an example, in one possible implementation, the Cauchy convergence criterion is used as the stopping criterion to determine Is it an eigenmode function? k The stopping criterion of signal decomposition in iterations can be expressed as: in, It is The intermediate processed signal obtained by the iteration is It is k The intermediate processed signal obtained by the iteration is T is the length of time. Specifically, The value of measures the difference between the processed signals in two consecutive iterations relative to the signal in the previous iteration. When it is between 0.2 and 0.3, it indicates that the change of the intermediate processing signal is small enough, and it can be considered that the intermediate processing signal has converged to an intrinsic mode function (IMF). At this point, the further screening process can be terminated. It can be understood that The screening is terminated when the value is between 0.2 and 0.3, which can ensure the stability of IMF and make IMF have the physical meaning of corresponding power fluctuation.

[0075] Step 206 : If the intermediate processed signal does not satisfy the intrinsic mode function condition, the intermediate processed signal is used as the input signal, and the process returns to step 202 .

[0076] It is understandable that the intermediate processing signal If the intrinsic mode function is not satisfied, the intermediate processed signal as the input signal (i.e., regarded as the original sequence), and returns to step 202, and re-executes step 202 to step 206 until the intermediate processed signal is obtained. Satisfy the eigenmode function.

[0077] Step 207: If the intermediate processed signal satisfies the intrinsic mode function condition, the intermediate processed signal is stored as an intrinsic mode function component, and the intermediate processed signal is subtracted from the input signal to obtain a residual sequence signal.

[0078] Step 208: Determine whether the remaining sequence signal is a remainder.

[0079] Step 209 : If the remaining sequence signal is not a remainder, the remaining sequence signal is used as an input signal, and the process returns to step 202 .

[0080] Step 210: If the remaining sequence signal is a remainder, it is determined that the output power sequence has completed decomposition, and the remaining sequence signal is used as a residual component.

[0081] For example, the first intermediate processed signal that satisfies the intrinsic mode function condition is obtained, and the intermediate processed signal is used as the first intrinsic mode function component , then from the input signal Subtract the intermediate processed signal Get the remaining sequence signal , .

[0082] Further, the remaining sequence signal is determined Is it a remainder? If the remaining sequence signal If it is not a remainder, the remaining sequence signal As input signal , that is, another order , and returns to step 202, executes the above steps, and decomposes the second intrinsic mode function component in sequence , the third eigenmode function component , ... until N IMF components , until the remaining sequence signal If it becomes a monotone sequence or has only one extreme point, the decomposition ends and the sequence signal as the residual component.

[0083] Specifically, merging the decomposed components together will give the output power sequence: , the expression is as follows: in, is the output power sequence, i.e. the original input signal, For the i The intrinsic mode function components represent the output power sequence A simple oscillating pattern on a specific time scale. is the residual component, that is, the residual term after the decomposition process is completed. It represents the remaining signal part after all the intrinsic mode function components are extracted, usually the low-frequency trend or background noise of the signal. It could be the trend of the data or a constant value. N is the number of intrinsic mode function components obtained by decomposition.

[0084] It can be understood that empirical mode decomposition does not require a preset basis function, can be adaptively decomposed according to the characteristics of the signal itself, and can realize adaptive decomposition of the output power sequence. Furthermore, empirical mode decomposition is a multi-scale analysis tool that can decompose complex signals into a series of intrinsic mode function components with different time scales, which helps to identify and analyze the local characteristics of the signal. Furthermore, through the intrinsic mode function components obtained by decomposition, key features in the signal, such as periodicity, trend and random fluctuations, can be extracted, providing a basis for further analysis and modeling. Further, the intrinsic mode function components obtained by empirical mode decomposition can be used to establish a more accurate prediction model and improve the prediction accuracy of renewable energy power generation output. Further, by analyzing the intrinsic mode function components obtained by decomposition, the trend and periodic components in the signal can be identified and analyzed. Further, the intrinsic mode function components obtained by empirical mode decomposition can be reconstructed to restore the original signal or analyze the signal components on a specific time scale. Therefore, through empirical mode decomposition, the complex output power sequence can be decomposed into a series of simple intrinsic mode function components and a residual term, which helps to better understand and analyze the output power fluctuations of renewable energy power generation systems, provide support for the stable operation and optimal scheduling of power systems, and improve the stability and reliability of power generation systems.

[0085] Step 104 : reconstruct multiple intrinsic mode function components based on the sampling rate to obtain power fluctuation components at different time scales.

[0086] As an example, in a possible implementation, the above step 104 includes: Calculate the power spectral density corresponding to each intrinsic mode function component based on the sampling rate; Analyze each intrinsic mode function component according to the power spectrum density corresponding to each intrinsic mode function component to determine the time scale characteristics of each intrinsic mode function component; Determine different time scale groups based on reconstruction requirements; According to the time scale characteristics of each intrinsic mode function component, the time scale group to which each intrinsic mode function component belongs is determined; The intrinsic mode function components belonging to the same time scale group are added together to obtain the power fluctuation components at different time scales.

[0087] Specifically, for the eigenmode function components , based on the sampling rate , for the eigenmode function components Perform a discrete Fourier transform (DFT) or a fast Fourier transform (FFT) to obtain the power spectral density , power spectral density It can be expressed by the following formula: in, F represents Fourier transform.

[0088] Furthermore, each eigenmode function component is analyzed The corresponding power spectral density , to determine the eigenmode function components The main oscillation frequency For example, the power spectral density can be identified by The peak value in the , power spectral density The frequency corresponding to the peak in represents the eigenmode function component The main oscillation frequency .

[0089] Furthermore, each eigenmode function component is determined Time scale characteristics , time scale characteristics The main oscillation frequency can be It can be calculated by the reciprocal of , which can be expressed as: in, is the power spectral density The frequency corresponding to the peak.

[0090] Furthermore, according to the time scale characteristics , the eigenmode function components with similar time scales Furthermore, the intrinsic mode function components with similar time scales are added together to reconstruct the power fluctuation components at different time scales.

[0091] For example, the eigenmode function components (smaller ones) representing short-term fluctuations can be ) and the intrinsic mode function components representing long-term trends (larger ) are grouped separately. Accordingly, the intrinsic mode function components of the short-term time scale and the long-term time scale are added together to obtain the power fluctuation components under the short-term time scale and the long-term time scale: in, represents the power fluctuation component on the short-term time scale, Represents the power fluctuation component on the long-term time scale.

[0092] In this implementation, multiple intrinsic mode function components are reconstructed based on the sampling rate to obtain power fluctuation components at different time scales, which can provide an in-depth understanding of the power fluctuations of new energy sources and provide strong support for the planning, operation and optimization of the power system, thereby improving the efficiency and reliability of the entire power system.

[0093] In addition, in a possible implementation manner, after the above step 104, the above method further includes: Generate a new energy power fluctuation report based on the power fluctuation components and output power sequences at different time scales, and send the new energy power fluctuation report to the user.

[0094] In this implementation, a new energy power fluctuation report is generated based on the power fluctuation components and output power sequences at different time scales, and the new energy power fluctuation report is sent to the user, providing the user with valuable information, facilitating subsequent control and adjustment of the power system, and improving the stability and reliability of the power system.

[0095] In addition, in a possible implementation, the new energy power fluctuation model established by the above-mentioned new energy power fluctuation modeling method is integrated into the power system to realize automated power fluctuation analysis and prediction.

[0096] In addition, in a possible implementation manner, after the above step 104, the above method further includes: According to the power fluctuation components at different time scales and the above-mentioned output power sequence, the renewable energy power fluctuation modeling method is verified, and the renewable energy power fluctuation modeling method is optimized based on the verification results.

[0097] In the new energy power fluctuation modeling method provided in this embodiment, the sampling rate and time length required for modeling are determined according to the modeling requirements, and the new energy power generation system is collected according to the sampling rate and the time length to obtain the output power sequence under the sampling rate and the time length; the output power sequence is decomposed by the empirical mode decomposition method to obtain multiple intrinsic mode function components; based on the sampling rate and the time length, the multiple intrinsic mode function components are reconstructed to obtain power fluctuation components under different time scales. The scheme of this embodiment establishes a new energy power fluctuation model that efficiently and accurately reflects the power fluctuation characteristics of different time scales through empirical mode decomposition. Empirical mode decomposition is an adaptive signal time-frequency processing method, which performs signal decomposition based on the time scale characteristics of the data itself, without presetting any basis function, and the method is completely adaptive when processing nonlinear non-stationary data. The new energy power fluctuation itself is nonlinear and non-stationary. On the one hand, the empirical mode decomposition can be used to perform principal component analysis, and the separated components can characterize the characteristic information of different time scales; on the other hand, its adaptive time-frequency analysis characteristics can better reflect the inherent fluctuation characteristics of the new energy power itself. Therefore, the new energy power fluctuation modeling method provided by the present invention can efficiently and accurately reflect the new energy power fluctuation situation, and improve the stability and reliability of the power generation system.

[0098] The new energy power fluctuation modeling device provided by the present invention is described below. The new energy power fluctuation modeling device described below and the new energy power fluctuation modeling method described above can be referenced to each other.

[0099] Figure 3 Schematic diagram of the structure of the new energy power fluctuation modeling device provided by the present invention. Figure 3 As shown, the new energy power fluctuation modeling device includes a collection module 31 , a decomposition module 32 and a reconstruction module 33 .

[0100] The acquisition module 31 is used to determine the sampling rate and time length required for modeling according to the modeling requirements, and to collect data from the new energy power generation system according to the sampling rate and the time length to obtain the output power sequence under the sampling rate and time length.

[0101] The sampling rate refers to the frequency at which a continuous signal is sampled per unit time. The sampling rate determines the temporal resolution of the digitized signal, that is, the degree to which the signal details can be captured. The time length refers to the duration of data collection, that is, the time span from the start to the end of data collection. For example, the sampling rate can be expressed as , the time length can be expressed as .

[0102] In practical applications, modeling requirements include but are not limited to modeling objectives, signal characteristics, data storage and processing capabilities, economy and usability, system response requirements, data availability, user-defined design, etc. For example, the sampling rate and time length are determined by determining the modeling objectives, such as predicting short-term power fluctuations or analyzing long-term trends, which will determine the required sampling rate and time length. For example, considering the characteristics of new energy power signals, such as new energy power signals are wind power output or photovoltaic output, the appropriate sampling rate is determined according to the fluctuation frequency and amplitude of wind power output or photovoltaic output. For example, the amount of data that can be processed is determined based on the available storage space and computing resources, and the sampling rate and time length are determined based on the amount of data that can be processed. Specifically, the process of determining the sampling rate and time length needs to comprehensively consider the above factors, and needs to be appropriately adjusted to obtain the sampling rate and time length required for modeling.

[0103] Among them, the output power series refers to a series of time series that record the output power values ​​of the renewable energy power generation system at different time points. The output power series describes in detail the power output of the power generation system over time and is an important data basis for analyzing and predicting the performance of the power generation system.

[0104] It is understandable that each data point of the output power sequence includes a timestamp and an output power value. In practical applications, the output power of the renewable energy power generation system is regularly collected according to the sampling rate and time length to obtain the output power sequence. For example, the output power sequence can be expressed as .

[0105] It should be noted that the present invention does not specifically limit the renewable energy power generation system. For example, the renewable energy power generation system may be a wind power generation system, a photovoltaic power generation system, a geothermal power generation system, a tidal power generation system, a biomass power generation system, a hydropower generation system, etc.

[0106] Accordingly, the above-mentioned output power sequence includes but is not limited to a wind output power sequence, a photovoltaic output power sequence, a geothermal output power sequence, a tidal power output power sequence, a biomass output power sequence, and a hydropower output power sequence.

[0107] Optionally, in one example, the above-mentioned device also includes: a preprocessing module for preprocessing the output power sequence, and the preprocessing includes but is not limited to: missing value processing, outlier detection and processing, noise removal, normalization or standardization, data interpolation, data smoothing, data conversion, feature extraction, etc.

[0108] It can be understood that the preprocessing module preprocesses the output power sequence to ensure the accuracy and reliability of the output power sequence, and can determine that the output power sequence is more suitable for subsequent analysis and modeling work, thereby improving the accuracy and reliability of new energy power fluctuation modeling.

[0109] Combination Figure 3 The decomposition module 32 is used to decompose the output power sequence by empirical mode decomposition to obtain multiple intrinsic mode function components.

[0110] Among them, empirical mode decomposition (EMD) is an adaptive, nonlinear and non-stationary time series analysis method. EMD can decompose a complex time series signal into a series of simple intrinsic mode functions (IMFs) with a single frequency and a final residual term.

[0111] It can be understood that empirical mode decomposition is an adaptive method that does not require a preset basis function and can handle nonlinear and non-stationary signals. It is particularly suitable for the analysis of renewable energy power generation output power series. Furthermore, empirical mode decomposition allows multi-scale analysis of the output power series, decomposing complex signals into a series of simple eigenmode functions, each of which represents fluctuations on different time scales. The decomposed eigenmode functions help to identify and extract features and patterns in renewable energy power generation output power series, such as periodic fluctuations, instantaneous spikes, etc. Furthermore, empirical mode decomposition can effectively separate the trend and periodic components of the signal, which helps to better understand the long-term trend and short-term fluctuations of renewable energy power generation output.

[0112] In practical applications, the empirical mode decomposition method is to decompose the output power sequence into multiple intrinsic mode function components and a residual component by calculating the average value of the upper and lower envelopes of the output power sequence.

[0113] Specifically, in a possible implementation manner, the decomposition module 32 is specifically used to: The output power sequence is taken as the input signal; Calculate the local maximum points and local minimum points of the input signal, and connect all the local maximum points and all the local minimum points respectively to obtain the upper envelope of the input signal and the lower envelope of the input signal; Calculate the mean of the function value corresponding to the upper envelope of the input signal and the function value of the lower envelope of the input signal at each time point to obtain the instantaneous average value, and connect the instantaneous average values ​​at all time points to obtain the instantaneous average line; Subtract the instantaneous average from the input signal to obtain the intermediate processed signal; Determine whether the intermediate processed signal satisfies the intrinsic mode function condition; wherein the intrinsic mode function condition includes: the number of local extreme points and zero crossing points of the intermediate processed signal in the entire time range must be equal or differ by at most one; and at any time point, the average value of the upper envelope of the intermediate processed signal and the lower envelope of the intermediate processed signal is zero; If the intermediate processed signal does not satisfy the intrinsic mode function condition, the intermediate processed signal is used as the input signal, and the above steps of calculating the local maximum point and the local minimum point of the input signal are returned to be executed; If the intermediate processed signal satisfies the intrinsic mode function condition, the intermediate processed signal is stored as an intrinsic mode function component, and the intermediate processed signal is subtracted from the input signal to obtain a residual sequence signal; Determine whether the remaining sequence signal is a remainder; If the remaining sequence signal is not a remainder, the remaining sequence signal is used as the input signal, and the above steps of calculating the local maximum point and the local minimum point of the input signal are returned to be executed; If the remaining sequence signal is a remainder, it is determined that the output power sequence is decomposed and the remaining sequence signal is used as the residual component.

[0114] Specifically, the output power sequence As input signal .

[0115] Furthermore, scanning the entire input signal The output power values ​​corresponding to each time point identify all local maximum points and local minimum points. Among them, the local maximum point is the output power value higher than the output power value of its adjacent points, and the local minimum point is the output power value lower than the output power value of its adjacent points. Further, the local maximum points and local minimum points are marked.

[0116] Furthermore, all local maximum points are connected to obtain the upper envelope of the input signal; all local minimum points are connected to obtain the lower envelope of the input signal. The upper envelope of , the input signal The lower envelope of .

[0117] As an example, in a possible implementation manner, the decomposition module 32 is used to connect all local maximum points and all local minimum points respectively to obtain an upper envelope of the input signal and a lower envelope of the input signal, specifically for: The cubic spline interpolation method is applied to connect all the local maximum points and all the local minimum points respectively, and the upper envelope of the input signal and the lower envelope of the input signal are obtained.

[0118] Among them, Cubic Spline Interpolation is a commonly used interpolation method in numerical analysis, which approximates a given data set by constructing a series of cubic polynomials. The main feature of this method is to construct a cubic polynomial between every two data points, so that the entire interpolation function smoothly transitions between data points and has continuous first-order and second-order derivatives.

[0119] Furthermore, the instantaneous average can be expressed as , the calculation formula of the instantaneous average is as follows: Furthermore, from the input signal Subtract the instantaneous average , get the intermediate processed signal , intermediate processing signal The expression is as follows: In practice, the intermediate processing signal It may be an eigenmode function. Further, we can judge Whether it is an intrinsic mode function and perform subsequent processing.

[0120] The intrinsic mode function conditions include: the number of local extreme points and zero-crossing points of the intermediate processing signal in the entire time range must be equal or differ by at most one; and at any time point, the average value of the upper envelope of the intermediate processing signal and the lower envelope of the intermediate processing signal is zero.

[0121] In practice, during the Empirical Mode Decomposition (EMD) process, an intermediate processing signal To be identified as an intrinsic mode function (IMF), the above mentioned intrinsic mode function conditions must be met. It can be understood that on the one hand, the intermediate processed signal The number of local maxima and local minima of must be equal or differ by at most one. This condition ensures that the signal is monotonic or has a simple oscillatory behavior. On the other hand, at any time point, the intermediate processed signal The upper envelope and the intermediate processed signal The lower envelope of must have a mean value of zero. This means that the intermediate processed signal The upper envelope and the intermediate processed signal The average values ​​of the lower envelope at each time point should cancel each other out, thus satisfying the definition of an intrinsic mode function (IMF). These conditions ensure that each intrinsic mode function (IMF) is a simple oscillation mode that can represent fluctuations on a specific time scale in the original signal.

[0122] It should be noted that in the process of decomposing the output power sequence, the intermediate processing signal is determined Whether it is an intrinsic mode function, mainly check the intermediate processing signal The upper envelope and the intermediate processed signal Whether the mean of the lower envelope of is always 0, this condition is also called the stopping criterion. However, it is difficult to make its mean strictly equal to 0 in actual decomposition, and it can only be infinitely close to zero.

[0123] As an example, in one possible implementation, the Cauchy convergence criterion is used as the stopping criterion to determine Is it an eigenmode function? k The stopping criterion of signal decomposition in iterations can be expressed as: in, It is The intermediate processed signal obtained by the iteration is It is k The intermediate processed signal obtained by the iteration is T is the length of time. Specifically, The value of measures the difference between the processed signals in two consecutive iterations relative to the signal in the previous iteration. When it is between 0.2 and 0.3, it indicates that the change of the intermediate processing signal is small enough, and it can be considered that the intermediate processing signal has converged to an intrinsic mode function (IMF). At this point, the further screening process can be terminated. It can be understood that The screening is terminated when the value is between 0.2 and 0.3, which can ensure the stability of IMF and make IMF have the physical meaning of corresponding power fluctuation.

[0124] It is understandable that the intermediate processing signal If the intrinsic mode function is not satisfied, the intermediate processed signal As the input signal (i.e., regarded as the original sequence), and return to the steps of calculating the local maximum and local minimum points of the input signal, and re-execute the above steps until the intermediate processed signal is obtained Satisfy the eigenmode function.

[0125] For example, the first intermediate processed signal that satisfies the intrinsic mode function condition is obtained, and the intermediate processed signal is used as the first intrinsic mode function component , then from the input signal Subtract the intermediate processed signal Get the remaining sequence signal , .

[0126] Further, the remaining sequence signal is determined Is it a remainder? If the remaining sequence signal If it is not a remainder, the remaining sequence signal As input signal , that is, another order , and return to the steps of calculating the local maximum and local minimum points of the input signal, re-execute the above steps, and decompose the second intrinsic mode function component in sequence , the third eigenmode function component , ... until N IMF components , until the remaining sequence signal If it becomes a monotone sequence or has only one extreme point, the decomposition ends and the sequence signal as the residual component.

[0127] Specifically, merging the decomposed components together will give the output power sequence: , the expression is as follows: in, is the output power sequence, i.e. the original input signal, For the i The intrinsic mode function components represent the output power sequence A simple oscillating pattern on a specific time scale. is the residual component, that is, the residual term after the decomposition process is completed. It represents the remaining signal part after all the intrinsic mode function components are extracted, usually the low-frequency trend or background noise of the signal. It could be the trend of the data or a constant value. N is the number of intrinsic mode function components obtained by decomposition.

[0128] It can be understood that empirical mode decomposition does not require a preset basis function, can be adaptively decomposed according to the characteristics of the signal itself, and can realize adaptive decomposition of the output power sequence. Furthermore, empirical mode decomposition is a multi-scale analysis tool that can decompose complex signals into a series of intrinsic mode function components with different time scales, which helps to identify and analyze the local characteristics of the signal. Furthermore, through the intrinsic mode function components obtained by decomposition, key features in the signal, such as periodicity, trend and random fluctuations, can be extracted, providing a basis for further analysis and modeling. Further, the intrinsic mode function components obtained by empirical mode decomposition can be used to establish a more accurate prediction model and improve the prediction accuracy of renewable energy power generation output. Further, by analyzing the intrinsic mode function components obtained by decomposition, the trend and periodic components in the signal can be identified and analyzed. Further, the intrinsic mode function components obtained by empirical mode decomposition can be reconstructed to restore the original signal or analyze the signal components on a specific time scale. Therefore, through empirical mode decomposition, the complex output power sequence can be decomposed into a series of simple intrinsic mode function components and a residual term, which helps to better understand and analyze the output power fluctuations of renewable energy power generation systems, provide support for the stable operation and optimal scheduling of power systems, and improve the stability and reliability of power generation systems.

[0129] Combination Figure 3 The reconstruction module 33 is used to reconstruct multiple intrinsic mode function components based on the sampling rate to obtain power fluctuation components at different time scales.

[0130] As an example, in a possible implementation manner, the reconstruction module 33 is specifically used to: Calculate the power spectral density corresponding to each intrinsic mode function component based on the sampling rate; Analyze each intrinsic mode function component according to the power spectrum density corresponding to each intrinsic mode function component to determine the time scale characteristics of each intrinsic mode function component; Determine different time scale groups based on reconstruction requirements; According to the time scale characteristics of each intrinsic mode function component, the time scale group to which each intrinsic mode function component belongs is determined; The intrinsic mode function components belonging to the same time scale group are added together to obtain the power fluctuation components at different time scales.

[0131] Specifically, for the eigenmode function components , based on the sampling rate , for the eigenmode function components Perform a discrete Fourier transform (DFT) or a fast Fourier transform (FFT) to obtain the power spectral density , power spectral density It can be expressed by the following formula: in, F represents Fourier transform.

[0132] Furthermore, each eigenmode function component is analyzed The corresponding power spectral density , to determine the eigenmode function components The main oscillation frequency For example, the power spectral density can be identified by The peak value in the , power spectral density The frequency corresponding to the peak in represents the eigenmode function component The main oscillation frequency .

[0133] Furthermore, each eigenmode function component is determined Time scale characteristics , time scale characteristics The main oscillation frequency can be It can be calculated by the reciprocal of , which can be expressed as: in, is the power spectral density The frequency corresponding to the peak.

[0134] Furthermore, according to the time scale characteristics , the eigenmode function components with similar time scales Furthermore, the intrinsic mode function components with similar time scales are added together to reconstruct the power fluctuation components at different time scales.

[0135] For example, the eigenmode function components (smaller ones) representing short-term fluctuations can be ) and the intrinsic mode function components representing long-term trends (larger ) are grouped separately. Accordingly, the intrinsic mode function components of the short-term time scale and the long-term time scale are added together to obtain the power fluctuation components under the short-term time scale and the long-term time scale: in, represents the power fluctuation component on the short-term time scale, Represents the power fluctuation component on the long-term time scale.

[0136] In this implementation, multiple intrinsic mode function components are reconstructed based on the sampling rate to obtain power fluctuation components at different time scales, which can provide an in-depth understanding of the power fluctuations of new energy sources and provide strong support for the planning, operation and optimization of the power system, thereby improving the efficiency and reliability of the entire power system.

[0137] In addition, in a possible implementation, the above-mentioned device also includes: a report generation module, which is used to generate a new energy power fluctuation report according to the power fluctuation components and output power sequences at different time scales, and send the new energy power fluctuation report to the user.

[0138] In this implementation, a new energy power fluctuation report is generated based on the power fluctuation components and output power sequences at different time scales, and the new energy power fluctuation report is sent to the user, providing the user with valuable information, facilitating subsequent control and adjustment of the power system, and improving the stability and reliability of the power system.

[0139] In the new energy power fluctuation modeling device provided in this embodiment, the acquisition module determines the sampling rate and time length required for modeling according to the modeling requirements, and collects the new energy power generation system according to the sampling rate and the time length to obtain the output power sequence under the sampling rate and the time length; the decomposition module decomposes the output power sequence by empirical mode decomposition to obtain multiple intrinsic mode function components; the reconstruction module reconstructs multiple intrinsic mode function components based on the sampling rate and the time length to obtain power fluctuation components under different time scales. The scheme of this embodiment establishes a new energy power fluctuation model that efficiently and accurately reflects the power fluctuation characteristics of different time scales through empirical mode decomposition. Empirical mode decomposition is an adaptive signal time-frequency processing method, which performs signal decomposition based on the time scale characteristics of the data itself, without presetting any basis function. When processing nonlinear and non-stationary data, this method is completely adaptive. The power fluctuation of new energy itself is nonlinear and non-stationary. On the one hand, the use of empirical mode decomposition can perform principal component analysis, and the separated components can characterize the characteristic information of different time scales; on the other hand, its adaptive time-frequency analysis characteristics can better reflect the inherent fluctuation characteristics of the new energy power itself. Therefore, the new energy power fluctuation modeling method provided by the present invention can efficiently and accurately reflect the new energy power fluctuation situation, and improve the stability and reliability of the power generation system.

[0140] Figure 4 is a schematic diagram of the structure of the electronic device provided by the present invention, such as Figure 4As shown, the electronic device may include: a processor 410, a communication interface 420, a memory 430 and a communication bus 440, wherein the processor 410, the communication interface 420 and the memory 430 communicate with each other through the communication bus 440. The processor 410 may call the logic instructions in the memory 430 to execute the new energy power fluctuation modeling method, which includes: determining the sampling rate and time length required for modeling according to the modeling requirements, and collecting the new energy power generation system according to the sampling rate and the time length to obtain the output power sequence under the sampling rate and the time length; decomposing the output power sequence by the empirical mode decomposition method to obtain multiple intrinsic mode function components; based on the sampling rate and the time length, reconstructing the multiple intrinsic mode function components to obtain power fluctuation components under different time scales.

[0141] In addition, the logic instructions in the above-mentioned memory 430 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0142] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the new energy power fluctuation modeling method provided by the above methods. The method includes: determining the sampling rate and time length required for modeling according to the modeling requirements, and collecting data on the new energy power generation system according to the sampling rate and the time length to obtain an output power sequence under the sampling rate and the time length; decomposing the output power sequence by empirical mode decomposition to obtain multiple intrinsic mode function components; based on the sampling rate and the time length, reconstructing multiple intrinsic mode function components to obtain power fluctuation components under different time scales.

[0143] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, it is implemented to execute the new energy power fluctuation modeling method provided by the above-mentioned methods. The method includes: determining the sampling rate and time length required for modeling according to the modeling requirements, and collecting data on the new energy power generation system according to the sampling rate and the time length to obtain an output power sequence under the sampling rate and the time length; decomposing the output power sequence by empirical mode decomposition to obtain multiple intrinsic mode function components; based on the sampling rate and the time length, reconstructing the multiple intrinsic mode function components to obtain power fluctuation components under different time scales.

[0144] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0145] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0146] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A new energy power fluctuation modeling method, characterized in that: The method comprises: Determine the sampling rate and time length required for modeling according to the modeling requirements, and collect data from the new energy power generation system according to the sampling rate and the time length to obtain an output power sequence under the sampling rate and the time length; Decomposing the output power sequence by empirical mode decomposition to obtain multiple intrinsic mode function components; Based on the sampling rate, the multiple intrinsic mode function components are reconstructed to obtain power fluctuation components at different time scales.

2. The new energy power fluctuation modeling method according to claim 1 is characterized in that: The output power sequence is decomposed by an empirical mode decomposition method to obtain a plurality of intrinsic mode function components, including: Taking the output power sequence as an input signal; Calculating the local maximum points and the local minimum points of the input signal, and connecting all the local maximum points and all the local minimum points respectively to obtain the upper envelope of the input signal and the lower envelope of the input signal; Calculate the mean of the function value corresponding to the upper envelope of the input signal and the function value of the lower envelope of the input signal at each time point to obtain an instantaneous average value, and connect the instantaneous average values ​​at all time points to obtain an instantaneous average line; subtracting the instantaneous average from the input signal to obtain an intermediate processed signal; Determine whether the intermediate processed signal satisfies an intrinsic mode function condition; wherein the intrinsic mode function condition includes: the number of local extreme points and zero-crossing points of the intermediate processed signal in the entire time range must be equal or differ by at most one; and at any time point, the average value of the upper envelope of the intermediate processed signal and the lower envelope of the intermediate processed signal is zero; If the intermediate processed signal does not satisfy the intrinsic mode function condition, the intermediate processed signal is used as the input signal, and the step of calculating the local maximum point and the local minimum point of the input signal is returned to be executed; If the intermediate processed signal satisfies the intrinsic mode function condition, the intermediate processed signal is stored as an intrinsic mode function component, and the intermediate processed signal is subtracted from the input signal to obtain a residual sequence signal; it is determined whether the residual sequence signal is a remainder; if the residual sequence signal is not a remainder, the residual sequence signal is used as the input signal, and the step of calculating the local maximum point and the local minimum point of the input signal is returned to execute; otherwise, it is determined that the output power sequence has completed decomposition, and the residual sequence signal is used as the residual component.

3. The new energy power fluctuation modeling method according to claim 1 is characterized in that: The reconstructing the multiple intrinsic mode function components based on the sampling rate to obtain power fluctuation components at different time scales includes: Calculating the power spectral density corresponding to each intrinsic mode function component based on the sampling rate; Analyzing each intrinsic mode function component according to the power spectrum density corresponding to each intrinsic mode function component to determine the time scale characteristics of each intrinsic mode function component; Determine different time scale groups based on reconstruction requirements; Determining the time scale group to which each intrinsic mode function component belongs according to the time scale characteristics of each intrinsic mode function component; The intrinsic mode function components belonging to the same time scale group are added together to obtain the power fluctuation components at different time scales.

4. The new energy power fluctuation modeling method according to claim 2 is characterized in that: The step of respectively connecting all the local maximum points and all the local minimum points to obtain an upper envelope of the input signal and a lower envelope of the input signal specifically includes: A cubic spline interpolation method is applied to connect all local maximum points and all local minimum points respectively, so as to obtain an upper envelope of the input signal and a lower envelope of the input signal.

5. The new energy power fluctuation modeling method according to any one of claims 1 to 4, characterized in that: Before decomposing the output power sequence by means of empirical mode decomposition to obtain a plurality of intrinsic mode function components, the method further includes: The output power sequence is preprocessed, wherein the preprocessing includes at least one of the following: missing value processing, outlier detection and processing, noise removal, normalization or standardization, data interpolation, data smoothing, data conversion, and feature extraction.

6. The new energy power fluctuation modeling method according to any one of claims 1 to 4, characterized in that: After reconstructing the multiple intrinsic mode function components based on the sampling rate to obtain power fluctuation components at different time scales, the method further includes: A new energy power fluctuation report is generated according to the power fluctuation components at different time scales and the output power sequence, and the new energy power fluctuation report is sent to a user.

7. A new energy power fluctuation modeling device, characterized in that: The device comprises: The acquisition module is used to determine the sampling rate and time length required for modeling according to the modeling requirements, and to collect data from the new energy power generation system according to the sampling rate and the time length to obtain the output power sequence under the sampling rate and the time length; A decomposition module, used for decomposing the output power sequence by empirical mode decomposition to obtain multiple intrinsic mode function components; A reconstruction module is used to reconstruct the multiple intrinsic mode function components based on the sampling rate to obtain power fluctuation components at different time scales.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the new energy power fluctuation modeling method as described in any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the new energy power fluctuation modeling method as claimed in any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the new energy power fluctuation modeling method as claimed in any one of claims 1 to 6 is implemented.

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