An improved pattern-based wind power output fluctuation analysis method

Abstract

The application discloses a wind power output fluctuation analysis method based on an improved motif, which comprises the following steps: converting a wind power output time sequence into a network domain from a time domain through a horizontal visual graph method, corresponding sampling points in the wind power output time sequence to nodes in the network, and constructing a time sequence accompanying network about the wind power output; solving a three-node motif contained in the wind power output time sequence accompanying network according to the wind power output time sequence accompanying network, and subdividing the three-node motif according to a corresponding wind power output amplitude relative size relationship of the three-node motif in the network domain; and analyzing fluctuation characteristics of the wind power output through the amplitude relative size relationship according to the three-node motif distribution after the subdivision.

Description

Technical Field

[0001] This invention relates to the field of electrical engineering technology, and in particular to an analytical method for studying wind power output fluctuations. Background Technology

[0002] Wind power generation is one of the most mature new energy power generation technologies in the power system, capable of providing timely power supply during peak electricity demand periods. However, wind power generation is largely dependent on uncertainties such as weather and geography, resulting in uncertainty and randomness in wind power output. Fluctuations in wind power output pose a serious threat to local voltage stability and the systemic safety operation of the entire power system. Therefore, focusing on the characteristics of wind power output will greatly assist in the operation and dispatch of the power grid. Summary of the Invention

[0003] To address the problems existing in the prior art, this invention proposes a wind power output fluctuation analysis method based on an improved model, using the concepts of system evolution and complex network technology.

[0004] To achieve the above objectives, the present invention adopts the following technical solution:

[0005] A first aspect of this invention provides a method for analyzing wind power output fluctuations based on an improved model, the method specifically including the following steps:

[0006] (1) Convert the wind power output time series from the time domain to the network domain using the horizontal visualization method, and correspond the sampling points in the wind power output time series to the nodes in the network to construct a time series associated network for wind power output.

[0007] (2) Based on the wind power output time series associated network obtained in step (1), solve the three-node module contained in the wind power output time series associated network, and subdivide the three-node module by the relative magnitude of the wind power output amplitude corresponding to the three-node module in the network domain.

[0008] (3) Based on the distribution of the three-node modules after subdivision in step (2), analyze the fluctuation characteristics of wind power output through the relative magnitude relationship of amplitude.

[0009] Further, step (1) specifically includes:

[0010] For a wind power output time series with N sampling points, l={x i} i＝1,...,N Each sampling point in the wind power output time series is considered as a node in the wind power output time series associated network. From the perspective of the wind power output magnitude at the sampling point, if two sampling points x i and x jIf a horizontal line can be drawn connecting these two sampling points, and the magnitude of this horizontal line is greater than the wind power output of all other sampling points between these two points, then there exists an edge connecting the nodes corresponding to these two sampling points in the network domain; that is, sampling point x i and x j Satisfying the formula:

[0011]

[0012] Furthermore, step (2) specifically includes the following sub-steps:

[0013] (2.1) Treat the wind power output time series associated network as an undirected and unweighted network;

[0014] (2.2) Solve the three-node module contained in the wind power output time series associated network by using the Z-score standard score;

[0015] (2.3) Divide the amplitude of the wind power output time series into T levels, and further subdivide the three-node module according to the amplitude level.

[0016] Furthermore, the calculation formula for step (2.2) is as follows:

[0017]

[0018] in, and The number of three-node subgraphs i represents the number of nodes in the wind power output time series associated network and the number of nodes and edges in the random network that has the same number of nodes and edges as the wind power output time series associated network, respectively. and They are respectively The mean and standard deviation;

[0019] If it is generally considered that (N) is satisfied real -N rand >0.1N rand Then this three-node subgraph can be considered as a three-node module in the wind power output time series associated network.

[0020] Furthermore, the amplitude of the wind power output time series is divided into T levels, where the number of levels can be determined by the required amplitude resolution r, i.e., satisfying:

[0021]

[0022] Furthermore, the relative magnitude relationships (MR) of amplitude levels are divided into 5 categories, namely:

[0023]

[0024] In this context, ⊙ indicates that two nodes belong to the same image level. This indicates that the two nodes do not belong to the same image level.

[0025] Based on the relative magnitude relationship (MR) of the amplitude levels, the three-node phantoms are further subdivided into 15 types.

[0026] Furthermore, the method also includes: combining the fluctuation characteristics of wind power output obtained by analyzing the subdivided three-node phantom with the actual fluctuation characteristics of wind power output to verify the effectiveness of the proposed method in analyzing the fluctuation characteristics of wind power output.

[0027] A second aspect of the present invention provides an electronic device, including a memory and a processor, wherein the memory is coupled to the processor; wherein the memory is used to store program data, and the processor is used to execute the program data to implement the above-described wind power output fluctuation analysis method based on the improved model.

[0028] A third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described wind power output fluctuation analysis method based on an improved model.

[0029] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention proposes a wind power output fluctuation analysis method based on an improved model. It transforms the wind power output time series from the time domain to the network domain using a horizontal visualization method, constructs a time series-associated network for wind power output, solves for the three-node models contained in the wind power output time series-associated network, subdivides the three-node models based on the wind power output amplitude, and analyzes the fluctuation characteristics of wind power output based on the distribution of the subdivided three-node models. This invention determines the similarity of wind power output by transforming the time series into a whole. It improves upon the traditional method that treats each moment's data as a single entity, instead using the whole as the unit for correlation judgment. This invention provides a supplement to traditional fluctuation analysis methods. Attached Figure Description

[0030] Figure 1 This is a flowchart of the method of the present invention;

[0031] Figure 2 A flowchart for converting time series data into a network;

[0032] Figure 3 This is a schematic diagram illustrating the correspondence between time series and phantoms;

[0033] Figure 4 The correspondence between phantom distribution and intraday load sequence;

[0034] Figure 5This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0035] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of methods consistent with some aspects of the invention as detailed in the appended claims.

[0036] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The singular forms “a,” “the,” and “the” used in this invention and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0037] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments. Unless otherwise specified, the features of the following embodiments and implementation methods can be combined with each other.

[0038] This invention proposes a method for analyzing wind power output fluctuations based on an improved model, such as... Figure 1 As shown, the specific steps include:

[0039] (1) The wind power output time series is converted from the time domain to the network domain using the horizontal visualization method. The sampling points in the wind power output time series are matched with the nodes in the network to construct a time series associated network for wind power output.

[0040] Specifically, such as Figure 2 As shown, for a wind power output time series l = {x} containing N sampling points i} i＝1,...,N Each sampling point in the wind power output time series is considered as a node in the wind power output time series associated network. From the perspective of the wind power output magnitude at the sampling point, if two sampling points x i and x j If a horizontal line can be drawn connecting these two sampling points, and the magnitude of this horizontal line is greater than the wind power output of all other sampling points between these two points, then there exists an edge connecting the nodes corresponding to these two sampling points in the network domain; that is, sampling point x i and x j Satisfying the formula:

[0041]

[0042] (2) Based on the wind power output time series associated network obtained in step (1), solve the three-node module contained in the wind power output time series associated network, and subdivide the three-node module by the relative magnitude relationship of the wind power output amplitude corresponding to the three-node module in the network domain.

[0043] Furthermore, step (2) specifically includes the following sub-steps:

[0044] (2.1) The wind power output time series associated network obtained by the horizontal visual diagram method is an undirected and unweighted network G = (V, E, A), where the node set of the undirected and unweighted network is V = {v1, v2, ..., v...}. N The number of nodes is the same as the number of sampling points, and the edge set E = {e1, e2, ..., e} m Let A be the adjacency matrix of an undirected, unweighted network G. If two nodes v in an undirected, unweighted network G... i and v j If there is a connection between two edges, it is denoted as v. i :v j Then the adjacency matrix A of the undirected, unweighted network G can be defined as:

[0045]

[0046] The elements in the adjacency matrix A can be defined as follows:

[0047]

[0048] (2.2) Solve for the three-node module contained in the wind power output time series associated network using the Z-score standard score. The Z-score standard score satisfies the following definition:

[0049]

[0050] in, and The number of three-node subgraphs i represents the number of nodes in the wind power output time series associated network and the number of nodes and edges in the random network that has the same number of nodes and edges as the wind power output time series associated network, respectively. and They are respectively The mean and standard deviation. If the number of three-node subgraphs N in the wind power output time series associated network... real Much greater than the number N of three-node subgraphs in a random network. rand It is generally considered that satisfying (N) real -N rand >0.1N rand Then this three-node subgraph can be considered as a three-node module in the wind power output time series associated network.

[0051] (2.3) Divide the amplitude of the wind power output time series into T levels. The number of levels can be determined by the required amplitude resolution r, i.e., satisfying:

[0052]

[0053] The three-node model of the wind power output time series associated network is obtained from (2.2). The three nodes are denoted as A, B, and C in chronological order. Based on the relative magnitude relationship of the amplitude levels of the sampling points corresponding to the wind power output time series of these three nodes, the relative magnitude relationship MR of the amplitude levels can be divided into 5 categories, starting from whether there are nodes with the same amplitude level:

[0054]

[0055] In this context, ⊙ indicates that two nodes belong to the same image level. This indicates that the two nodes do not belong to the same amplitude level.

[0056] Three-node phantoms can be divided into four categories based on the different connections and the order of the two sides. These categories represent: A and B have at least one horizontal occlusion point, and A and C can see each other without obstruction; A and B, and B and C can both see each other without obstruction, but B occludes A and C; A and B, B and C, and A and C can all see each other without obstruction; B and C have at least one horizontal occlusion point, and A and C can see each other without obstruction. Since the first and fourth cases are symmetrical, after merging a symmetrical case, we can divide them into three categories of three-node phantoms, such as... Figure 3 As shown. Based on the relative magnitude relationships (MR) of the amplitude levels, the three-node phantoms are further subdivided into 15 types, denoted as follows: and and Both correspond to a trend of first decreasing and then increasing, but the differences between the two lie in... It requires that all three node pairs can see each other without obstruction, and generally represents a global trend. More attention is paid to the local area. This indicates a trend of first rising and then falling, or monotonically increasing or decreasing. Specifically, A0 represents a fluctuation with a sharp drop followed by a sharp rise; A1 represents a fluctuation with a gradual decrease followed by a sharp rise; A2 represents a fluctuation with a sharp drop followed by a sharp rise, eventually recovering to the initial amplitude level; A3 represents a fluctuation with a sharp drop followed by a gradual rise; and A4 represents a fluctuation with a gradual decrease followed by a gradual rise, with a small fluctuation range. B0 represents a large rise, a large fall, or a fluctuation with a sharp rise followed by a sharp drop; B1 represents a fluctuation with a gradual rise followed by a sharp rise, a gradual decrease followed by a sharp drop, or a gradual rise followed by a sharp drop; B2 represents a fluctuation with a sharp rise followed by a sharp drop, eventually recovering to the initial amplitude level; and B3 represents a fluctuation with a sharp rise followed by a gradual rise, a sharp drop followed by a gradual rise, or a gradual decrease followed by a gradual rise. The fluctuations are categorized as follows: B4 represents a gradual rise, a gradual fall, and a gradual rise followed by a gradual fall, with a small fluctuation range; C0 represents a sudden drop followed by a sudden rise, with each sample value being a local maximum; C1 represents a gradual drop followed by a sudden rise, with each sample value being a local maximum; C2 represents a sudden drop followed by a sudden rise, with each sample value being a local maximum, and eventually recovering to the initial amplitude level; C3 represents a sudden drop followed by a gradual rise, with each sample value being a local maximum; and C4 represents a gradual drop followed by a gradual rise, with each sample value being a local maximum, with a small fluctuation range.

[0057] (3) Based on the distribution of the three-node modules after subdivision in step (2), analyze the fluctuation characteristics of wind power output through the relative magnitude relationship of amplitude.

[0058] The method of this invention further includes: combining the fluctuation characteristics of wind power output obtained through the analysis of the subdivided three-node phantoms with the actual fluctuation characteristics of wind power output to verify the effectiveness of the proposed method in analyzing the fluctuation characteristics of wind power output. After verifying the effectiveness, the proportion of the subdivided three-node phantoms is used as the fluctuation characteristics of wind power output, providing a basis for subsequent wind power dispatch.

[0059] like Figure 4As shown, the phantom distribution heatmap uses January data from a specific wind farm as an example to calculate the phantom distribution over the 31 days of that month. The vertical axis of the heatmap represents the number of days, and the horizontal axis represents the phantom type. The values ​​in the heatmap are the normalized number of phantoms, ranging from 0 to 1. The arrows indicate the correspondence between the typical daily fluctuation curve and the phantom distribution on that day. Specifically, on day 12, the phantom B4 value is significantly higher than other phantoms, while the numbers of A4, C4, A0, and A2 gradually decrease, indicating that the overall wind power output on day 12 is relatively stable with few drastic fluctuations. Observing the actual wind power output on day 12, this is consistent with the phantom analysis. As a control group for day 12, day 19 also shows a large B4 value, as well as large A4 and C4 values. This indicates that the wind power output on day 19 is very stable with only slight fluctuations, and the actual wind power output curve on day 19 shows very little fluctuation, consistent with the analysis results. Using days 11, 17, 21, and 22 as a control group, day 11 showed higher A2 and B0 values, with A0, B1, and B4 values ​​gradually decreasing, indicating significant fluctuations in wind power output on day 11. Day 17 also showed relatively high A2 and B0 values, but the B4 value was higher than that of day 11, suggesting that while the wind power output fluctuations on day 17 were dramatic, they were relatively milder compared to day 11. Compared to days 11 and 17, the B0 value on day 21 was smaller, indicating fewer large fluctuations in wind power output on day 21. The B4 value on day 22 was the highest among the four days, indicating that there were many periods of slight fluctuations and the fewest periods of significant increases or decreases in wind power output. The results of the model-based wind power output analysis for these four days are consistent with the actual wind power output curves, verifying the effectiveness of the proposed method in describing wind power output.

[0060] Accordingly, this application also provides an electronic device, comprising: one or more processors; a memory for storing one or more programs; and, when the one or more programs are executed by the one or more processors, causing the one or more processors to implement the wind power output fluctuation analysis method based on the improved model as described above. Figure 5 The diagram shown is a hardware structure diagram of any device with data processing capabilities for the wind power output fluctuation analysis method based on the improved model provided in this embodiment of the invention, except for... Figure 5 In addition to the processor, memory, and network interface shown, any data processing device in the embodiment may also include other hardware depending on the actual function of the data processing device, which will not be described in detail here.

[0061] Accordingly, this application also provides a computer-readable storage medium storing computer instructions, which, when executed by a processor, implement the wind power output fluctuation analysis method based on the improved model described above. The computer-readable storage medium can be an internal storage unit of any data processing device as described in any of the foregoing embodiments, such as a hard disk or memory. The computer-readable storage medium can also be an external storage device, such as a plug-in hard disk, smart media card (SMC), SD card, flash card, etc., equipped on the device. Furthermore, the computer-readable storage medium can include both internal storage units of any data processing device and external storage devices. The computer-readable storage medium is used to store the computer program and other programs and data required by the data processing device, and can also be used to temporarily store data that has been output or will be output.

[0062] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only.

[0063] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope.

Claims

1. A method for analyzing wind power output fluctuations based on an improved model, characterized in that, The method specifically includes the following steps: (1) Convert the wind power output time series from the time domain to the network domain using the horizontal visualization method, and match the sampling points in the wind power output time series with the nodes in the network to construct a time series associated network for wind power output; (2) Based on the wind power output time series associated network obtained in step (1), solve for the three-node modules contained in the wind power output time series associated network, and further subdivide the three-node modules based on the relative magnitudes of the wind power output amplitudes corresponding to the three-node modules in the network domain; including: The amplitude of the wind power output time series is divided into There are several levels, where the number of levels can be determined by the required amplitude resolution. Decision, i.e. satisfaction: ； The relative magnitudes of the amplitude levels Divided into 5 categories, namely: ； in, This indicates that the two nodes belong to the same image level. This indicates that the two nodes do not belong to the same image level. Combining the relative magnitudes of the amplitude levels The three-node model is further subdivided into 15 types; (3) Based on the distribution of the three-node modules after subdivision in step (2), analyze the fluctuation characteristics of wind power output through the relative magnitude relationship of amplitude.

2. The wind power output fluctuation analysis method based on the improved model according to claim 1, characterized in that, The specific steps (1) are as follows: For a wind power output time series containing N sampling points Each sampling point in the wind power output time series is considered as a node in the wind power output time series associated network. From the perspective of the wind power output magnitude at the sampling point, if two sampling points... and If a horizontal line can be drawn connecting these two sampling points, and the magnitude of this horizontal line is greater than the wind power output of all other sampling points between these two points, then there exists an edge connecting the nodes corresponding to these two sampling points in the network domain; that is, the sampling points... and Satisfying the formula: 。 3. The wind power output fluctuation analysis method based on the improved model according to claim 1, characterized in that, Step (2) specifically includes the following sub-steps: (2.1) Treat the wind power output time series associated network as an undirected and unweighted network; (2.2) Solve for the three-node module contained in the wind power output time series associated network by using the Z-score standard score; (2.3) Divide the amplitude of the wind power output time series into The three-node module is further subdivided into several levels based on the amplitude level.

4. The wind power output fluctuation analysis method based on the improved model according to claim 3, characterized in that, The calculation formula for step (2.2) is as follows: ； in, and Three-node subgraphs representing the wind power output time series associated network and the random network with the same number of nodes and edges as the wind power output time series associated network, respectively. quantity, and They are respectively The mean and standard deviation; If it is generally considered to be satisfied Then this three-node subgraph can be considered as a three-node module in the wind power output time series associated network.

5. The wind power output fluctuation analysis method based on the improved model according to claim 1, characterized in that, The method further includes: combining the fluctuation characteristics of wind power output obtained by analyzing the subdivided three-node phantom with the actual fluctuation characteristics of wind power output to verify the effectiveness of the proposed method in analyzing the fluctuation characteristics of wind power output.

6. An electronic device comprising a memory and a processor, characterized in that, The memory is coupled to the processor; wherein the memory is used to store program data, and the processor is used to execute the program data to implement the wind power output fluctuation analysis method based on the improved model as described in any one of claims 1-5.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the wind power output fluctuation analysis method based on the improved model as described in any one of claims 1-5.

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