CN113572152B - Wind turbine generator oscillation mode and influence factor correlation analysis method based on FP-growth - Google Patents
Wind turbine generator oscillation mode and influence factor correlation analysis method based on FP-growth Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/002—Flicker reduction, e.g. compensation of flicker introduced by non-linear load
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
- G06F18/232—Non-hierarchical techniques
- G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/381—Dispersed generators
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/28—The renewable source being wind energy
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/76—Power conversion electric or electronic aspects
Abstract
The invention belongs to the technical field of wind power system oscillation mode analysis, and discloses a wind turbine generator oscillation mode and influence factor association analysis method based on FP-growth, which comprises the following steps: collecting output power, voltage and wind speed data of a wind turbine generator during operation; segmenting the collected output power, voltage and wind speed data of the wind turbine generator; analyzing an oscillation mode contained in the data segment by adopting a Pony algorithm; clustering the wind speed and the voltage average value by using a spectral clustering algorithm; adopting an FP-growth algorithm to mine association rules of wind speed-voltage clustering and oscillation modes; based on the analysis result of the association rule, the oscillation mode is predicted by utilizing the wind speed-voltage clustering. According to the embodiment of the invention, the correlation analysis can be carried out on the oscillation mode of the wind turbine by utilizing the running big data of the wind turbine, a complex physical model is not required to be established, the calculation speed is high, and a reference is provided for the identification of the oscillation mode of the wind turbine.
Description
Technical Field
The invention belongs to the technical field of analysis of oscillation modes of wind power systems, and particularly relates to a correlation analysis method of wind speed, voltage clustering and oscillation modes during operation of a wind turbine generator.
Background
The combustion of fossil fuels is the production of CO 2 As a result, the conventional power system is gradually transformed into a high-proportion renewable energy power system, and new energy represented by wind energy is beginning to be connected into the power grid on a large scale. Compared with the traditional fossil energy power generation mode, the wind energy has the advantages of cleanness, high efficiency, reproducibility and the like, but the output has stronger uncertainty, and the oscillation problem can be caused when the wind energy is connected with a power grid. With the continuous increase of the permeability of wind power, the problem of interaction between a wind turbine and a power grid is getting more and more attention.
Research shows that the oscillation caused by the interaction between the wind turbine and the power grid is generally active power oscillation, and can be divided into different oscillation modes according to frequencies, such as low-frequency oscillation, synchronous control interaction (SSCI), subsynchronous oscillation (SSO), subsynchronous oscillation (SSR) and the like. Since the oscillation mechanisms of different oscillation modes are different, the premise of performing oscillation suppression is to perform oscillation mode identification. The traditional method generally identifies the types of the oscillation modes in a modeling and simulation mode, however, because the causes of the oscillation modes of the wind turbine generator are complex, the influence of factors such as voltage, wind speed and the like on the oscillation modes is difficult to comprehensively consider in physical modeling.
Disclosure of Invention
The embodiment of the invention provides a wind turbine generator oscillation mode and influence factor correlation analysis method based on FP-growth, which aims to solve the problem that the influence of factors such as voltage, wind speed and the like on the oscillation mode is difficult to comprehensively consider in the physical modeling of the existing method. The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview and is intended to neither identify key/critical elements nor delineate the scope of such embodiments. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.
According to a first aspect of the embodiment of the invention, a wind turbine generator oscillation mode and influence factor correlation analysis method based on FP-growth is provided.
In one embodiment, a method for analyzing correlation between oscillation modes and influence factors of a wind turbine generator based on FP-growth comprises the following steps:
step S1, collecting output power, voltage and wind speed data of a wind turbine generator during operation;
s2, segmenting the acquired output power, voltage and wind speed data of the wind turbine generator;
s3, analyzing an oscillation mode contained in the data segment by adopting a Pony algorithm;
s4, clustering the wind speed and the voltage mean value by using a spectral clustering algorithm;
s5, adopting an FP-growth algorithm to mine association rules of wind speed-voltage clusters and oscillation modes;
and S6, predicting the oscillation mode by utilizing the wind speed-voltage clustering based on the analysis result of the association rule.
Optionally, in the step S1, data acquisition is performed on output power, voltage and wind speed of the wind turbine generator set when the wind turbine generator set is running with M as a sampling frequency, where M is greater than or equal to 100Hz.
Optionally, in step S2, the collected output power, voltage and wind speed data of the wind turbine generator are segmented according to the wind speed variation amplitude.
Optionally, in the step S2, the step of segmenting the collected output power, voltage and wind speed data of the wind turbine generator set according to the wind speed variation amplitude specifically includes:
step S201, creating a data segment, sequentially reading wind speed values, writing the wind speed values into the data segment, and storing the data segment when the wind speed change in the data segment is greater than delta, wherein delta is a wind speed change threshold value;
step S202, newly creating a next data segment, continuing to read wind speed data, and repeating the wind speed change judgment conditions in the step S201 until all data are read, so as to obtain N groups of wind speed data;
step S203, aligning the output power data, the voltage data and the wind speed data, and dividing the output power data and the voltage data into N segments according to the dividing mode of the wind speed data to obtain output power data segments { P } 1 ,P 2 ,…,P N { U of voltage data segment } 1 ,U 2 ,…,U N Sum of wind velocity data segment { W } 1 ,W 2 ,…,W N }。
Optionally, the wind speed variation threshold delta is set to 0.05m/s.
Optionally, in step S3, a Pony algorithm is used to perform signal decomposition on each segment of output power data, so as to obtain an oscillation mode included in the output power data segment.
Optionally, in step S3, a power data segment is subjected to signal decomposition by using a pon algorithm to obtain an oscillation mode included in the power data segment, and a rule of correspondence between a signal decomposition result of the power data segment and the oscillation mode is shown in the following table:
| the frequency of the signal is in the range of/Hz | Mode of oscillation/Hz | Oscillating modal class labels |
| [0.1—1.8] | 0.1～1.8 | 1 |
| [1.82—2.02] | 1.92 | 2 |
| [4.17—4.57] | 4.37 | 3 |
| [11.91—12.91] | 12.41 | 4 |
| [22.4—23.6] | 23 | 5 |
| [44.57—46.17] | 45.37 | 6 |
| [77.5—80.5] | 79 | 7 |
Optionally, in the step S4, an average value of the wind speed and the voltage in each segment of data is obtained, an optimal cluster number of the wind speed and the voltage average value is determined by using CH indexes, and the wind speed and the voltage average value are clustered by using a spectral clustering algorithm to obtain a wind speed-voltage cluster.
Optionally, in the step S4, the step of determining the optimal cluster number of the wind speed and the voltage mean value by using the CH index specifically includes:
calculating the average value of wind speed and voltage in each segment of data, and carrying out multiple spectral clustering on the average value of wind speed and the average value of voltage, wherein the clustering number k is a natural number in the interval [ a, b ]; and calculating CH indexes corresponding to each clustering result, wherein the formula is as follows:
wherein k is the number of clusters; m is the number of samples; b (B) k 、W k Respectively an inter-cluster covariance matrix and an intra-cluster covariance matrix; tr (·) is the trace of the matrix;
and K corresponding to the maximum CH score is the optimal cluster number K.
Alternatively, the parameter a is set to 2 and the parameter b is set to 10.
Optionally, in step S4, a spectral clustering algorithm is used to gather the wind speed and the voltage average value into K types, so as to obtain a wind speed-voltage clustering label.
Optionally, in the step S5, the step of mining association rules between the wind speed-voltage cluster and the oscillation mode by using an FP-growth algorithm specifically includes:
the front item of the control item is a wind speed-voltage average value clustering label of a data section, and the rear item is an oscillation mode label contained in the data section, so that N items are obtained;
setting a minimum support S and a minimum confidence C, and mining frequent item sets of the wind speed-voltage clusters and the oscillation modes of the data segments by using an FP-growth algorithm, so as to obtain a wind speed-voltage cluster and the oscillation mode association rule meeting the minimum support and the minimum confidence.
Optionally, in the step S6, based on the analysis result of the association rule, the step of predicting the oscillation mode by using the wind speed-voltage cluster specifically includes:
the front item of the control item is a wind speed-voltage average value clustering label of the current data section, the rear item is an oscillation mode label contained in the next data section, and N-1 items are obtained;
setting a minimum support degree S and a minimum confidence degree C, and mining frequent item sets of a current data segment wind speed-voltage cluster and a next data segment oscillation mode by using an FP-growth algorithm, so as to obtain an association rule of the current data segment wind speed-voltage cluster and the next data segment oscillation mode, which meets the minimum support degree and the minimum confidence degree, and then predicting the oscillation mode of the next data segment based on the rule and a wind speed-voltage cluster result of the current data segment.
Alternatively, the minimum support S is set to 0.4.
Optionally, the minimum confidence level C is set to 0.6.
According to a second aspect of embodiments of the present invention, a computer device is provided.
In some embodiments, the computer device comprises a memory storing a computer program and a processor implementing the steps of the above method when the processor executes the computer program.
The technical scheme provided by the embodiment of the invention can have the following beneficial effects:
according to the method, the system and the device, the oscillation modes of the wind turbine can be subjected to correlation analysis by utilizing the running big data of the wind turbine, a complex physical model is not required to be established, the calculation speed is high, and a reference is provided for identifying the oscillation modes of the wind turbine.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
FIG. 1 is a flowchart illustrating a method for analyzing the correlation of a wind turbine oscillation mode and influence factors based on FP-growth according to an exemplary embodiment;
FIG. 2 is a flowchart illustrating the analytical method steps S2 according to an exemplary embodiment;
fig. 3 is a schematic diagram of a computer device according to an exemplary embodiment.
Detailed Description
The following description and the drawings sufficiently illustrate specific embodiments herein to enable those skilled in the art to practice them. Portions and features of some embodiments may be included in, or substituted for, those of others. The scope of the embodiments herein includes the full scope of the claims, as well as all available equivalents of the claims. The terms "first," "second," and the like herein are used merely to distinguish one element from another element and do not require or imply any actual relationship or order between the elements. Indeed the first element could also be termed a second element and vice versa. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a structure, apparatus, or device that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such structure, apparatus, or device. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a structure, apparatus or device comprising the element. Various embodiments are described herein in a progressive manner, each embodiment focusing on differences from other embodiments, and identical and similar parts between the various embodiments are sufficient to be seen with each other.
The terms "longitudinal," "transverse," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like herein refer to an orientation or positional relationship based on that shown in the drawings, merely for ease of description herein and to simplify the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operate in a particular orientation, and thus are not to be construed as limiting the invention. In the description herein, unless otherwise specified and limited, the terms "mounted," "connected," and "coupled" are to be construed broadly, and may be, for example, mechanically or electrically coupled, may be in communication with each other within two elements, may be directly coupled, or may be indirectly coupled through an intermediary, as would be apparent to one of ordinary skill in the art.
Herein, unless otherwise indicated, the term "plurality" means two or more.
Herein, the character "/" indicates that the front and rear objects are an or relationship. For example, A/B represents: a or B.
Herein, the term "and/or" is an association relation describing an object, meaning that three relations may exist. For example, a and/or B, represent: a or B, or, A and B.
FIG. 1 shows an alternative embodiment of the FP-growth based wind turbine oscillation mode and influence factor correlation analysis method of the invention.
In the alternative embodiment, the method for analyzing the correlation between the oscillation mode of the wind turbine generator and the influence factors based on the FP-growth comprises the following steps: step S1, collecting output power, voltage and wind speed data of a wind turbine generator during operation; s2, segmenting the acquired output power, voltage and wind speed data of the wind turbine generator; s3, analyzing an oscillation mode contained in the data segment by adopting a Pony algorithm; s4, clustering the wind speed and the voltage mean value by using a spectral clustering algorithm; s5, adopting an FP-growth algorithm to mine association rules of wind speed-voltage clusters and oscillation modes; and S6, predicting the oscillation mode by utilizing the wind speed-voltage clustering based on the analysis result of the association rule.
Optionally, in the step S1, M is used as a sampling frequency, and data acquisition is performed on the output power P, the voltage U and the wind speed W when the wind turbine generator actually operates, where the sampling frequency M is set to be greater than or equal to 100Hz.
In one embodiment, in the step S2, the collected output power, voltage and wind speed data of the wind turbine are segmented according to the wind speed variation amplitude.
In one embodiment, as shown in fig. 2, in the step S2, the step of segmenting the collected output power, voltage and wind speed data of the wind turbine according to the wind speed variation amplitude specifically includes:
step S201, creating a data segment, sequentially reading wind speed values, writing the wind speed values into the data segment, and storing the data segment when the wind speed change in the data segment is greater than delta, wherein delta is a wind speed change threshold value;
step S202, newly creating a next data segment, continuing to read wind speed data, and repeating the wind speed change judgment conditions in the step S201 until all data are read, so as to obtain N groups of wind speed data;
step S203, aligning the output power data, the voltage data and the wind speed data, and dividing the output power data and the voltage data into N segments according to the dividing mode of the wind speed data to obtain output power data segments { P } 1 ,P 2 ,…,P N { U of voltage data segment } 1 ,U 2 ,…,U N Sum of wind velocity data segment { W } 1 ,W 2 ,…,W N }。
Alternatively, the wind speed variation threshold delta is set to 0.05m/s.
In one embodiment, in the step S3, a pon algorithm is used to perform signal decomposition on each segment of output power data, so as to obtain an oscillation mode included in the output power data segment.
In one embodiment, in the step S3, a power data segment is subjected to signal decomposition by using a pon algorithm to obtain an oscillation mode included in the power data segment, and a rule of correspondence between a signal decomposition result of the power data segment and the oscillation mode is shown in the following table.
TABLE 1
In one embodiment, in the step S4, an average value of the wind speed and the voltage in each piece of data is obtained, an optimal cluster number of the wind speed and the voltage average value is determined by using CH (Calinski-Harabasz) indexes, and the wind speed and the voltage average value are clustered by using a spectral clustering algorithm to obtain a wind speed-voltage cluster.
In one embodiment, in the step S4, the step of determining the optimal cluster number of the wind speed and the voltage average by using the CH index specifically includes:
calculating the average value of wind speed and voltage in each segment of data, and carrying out multiple spectral clustering on the average value of wind speed and voltage, wherein the clustering number k is a natural number in the interval [ a, b ]; and calculating CH indexes corresponding to each clustering result, wherein the formula is as follows:
wherein k is the number of clusters; m is the number of samples; b (B) k 、W k Respectively an inter-cluster covariance matrix and an intra-cluster covariance matrix; tr (·) is the trace of the matrix. And K corresponding to the maximum CH score is the optimal cluster number K.
Alternatively, the parameter a is set to 2 and the parameter b is set to 10.
In one embodiment, in the step S4, the wind speed and the voltage average value are clustered into K types by using a spectral clustering algorithm, so as to obtain a wind speed-voltage clustering label.
In one embodiment, in the step S5, the step of mining association rules between the wind speed-voltage cluster and the oscillation mode by using the FP-growth algorithm specifically includes: the front item of the control item is a wind speed-voltage average value clustering label of a data section, and the rear item is an oscillation mode label contained in the data section, so that N items are obtained; setting a minimum support S and a minimum confidence C, and mining frequent item sets of the wind speed-voltage clusters and the oscillation modes of the data segments by using an FP-growth algorithm, so as to obtain a wind speed-voltage cluster and the oscillation mode association rule meeting the minimum support and the minimum confidence.
In one embodiment, in the step S6, the step of predicting the oscillation mode by using the wind speed-voltage cluster based on the analysis result of the association rule specifically includes: the front item of the control item is a wind speed-voltage average value clustering label of the current data section, the rear item is an oscillation mode label contained in the next data section, and N-1 items are obtained; setting a minimum support degree S and a minimum confidence degree C, and mining frequent item sets of a current data segment wind speed-voltage cluster and a next data segment oscillation mode by using an FP-growth algorithm, so as to obtain an association rule of the current data segment wind speed-voltage cluster and the next data segment oscillation mode, which meets the minimum support degree and the minimum confidence degree, and then predicting the oscillation mode of the next data segment based on the rule and a wind speed-voltage cluster result of the current data segment.
Alternatively, the minimum support S is set to 0.4.
Optionally, the minimum confidence level C is set to 0.6.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 3. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used to store static information and dynamic information data. The network interface of the computer device is used for communicating with an external terminal through a network connection. Which computer program, when being executed by a processor, carries out the steps of the above-mentioned method embodiments.
It will be appreciated by those skilled in the art that the structure shown in FIG. 3 is merely a block diagram of some of the structures associated with the present inventive arrangements and is not limiting of the computer device to which the present inventive arrangements may be applied, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In an embodiment, a computer device is also provided, comprising a memory and a processor, the memory having stored therein a computer program, the processor performing the steps of the above-described method embodiments when the computer program is executed.
In one embodiment, a computer readable storage medium is provided, on which a computer program is stored which, when executed by a processor, carries out the steps of the method embodiments described above.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, or the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static RandomAccess Memory, SRAM) or dynamic random access memory (Dynamic RandomAccess Memory, DRAM), and the like.
The present invention is not limited to the structure that has been described above and shown in the drawings, and various modifications and changes can be made without departing from the scope thereof. The scope of the invention is limited only by the appended claims.
Claims (14)
1. A wind turbine generator oscillation mode and influence factor association analysis method based on FP-growth is characterized by comprising the following steps:
step S1, collecting output power, voltage and wind speed data of a wind turbine generator during operation;
s2, segmenting the acquired output power, voltage and wind speed data of the wind turbine generator;
s3, analyzing an oscillation mode contained in the data segment by adopting a Pony algorithm;
s4, clustering the wind speed and the voltage mean value by using a spectral clustering algorithm;
s5, adopting an FP-growth algorithm to mine association rules of wind speed-voltage clusters and oscillation modes, wherein the association rules comprise:
the front item of the control item is a wind speed-voltage average value clustering label of a data section, and the rear item is an oscillation mode label contained in the data section, so that N items are obtained;
setting a minimum support degree S and a minimum confidence degree C, and mining frequent item sets of a data segment wind speed-voltage cluster and an oscillation mode by using an FP-growth algorithm so as to obtain a wind speed-voltage cluster and oscillation mode association rule meeting the minimum support degree and the minimum confidence degree;
step S6, based on the analysis result of the association rule, predicting the oscillation mode by utilizing the wind speed-voltage clustering, wherein the step comprises the following steps:
the front item of the control item is a wind speed-voltage average value clustering label of the current data section, the rear item is an oscillation mode label contained in the next data section, and N-1 items are obtained;
setting a minimum support degree S and a minimum confidence degree C, and mining frequent item sets of a current data segment wind speed-voltage cluster and a next data segment oscillation mode by using an FP-growth algorithm, so as to obtain an association rule of the current data segment wind speed-voltage cluster and the next data segment oscillation mode, which meets the minimum support degree and the minimum confidence degree, and then predicting the oscillation mode of the next data segment based on the rule and a wind speed-voltage cluster result of the current data segment.
2. The method for analyzing the correlation between the oscillation mode and the influence factor of the wind turbine generator based on the FP-growth is disclosed in claim 1,
in the step S1, data acquisition is carried out on output power, voltage and wind speed when the wind turbine generator runs by taking M as a sampling frequency, wherein M is greater than or equal to 100Hz.
3. The method for analyzing the correlation between the oscillation mode and the influence factor of the wind turbine generator based on the FP-growth is disclosed in claim 1,
in the step S2, the collected output power, voltage and wind speed data of the wind turbine generator are segmented according to the wind speed variation amplitude.
4. The method for analyzing the correlation between the oscillation mode and the influence factor of the wind turbine generator based on the FP-growth is characterized in that,
in the step S2, the step of segmenting the collected output power, voltage and wind speed data of the wind turbine generator according to the wind speed variation amplitude specifically includes:
step S201, creating a data segment, sequentially reading wind speed values, writing the wind speed values into the data segment, and storing the data segment when the wind speed change in the data segment is greater than delta, wherein delta is a wind speed change threshold value;
step S202, newly creating a next data segment, continuing to read wind speed data, and repeating the wind speed change judgment conditions in the step S201 until all data are read, so as to obtain N groups of wind speed data;
step S203, aligning the output power data, the voltage data and the wind speed data, and dividing the output power data and the voltage data into N segments according to the dividing mode of the wind speed data to obtain output power data segments { P } 1 ,P 2 ,…,P N { U of voltage data segment } 1 ,U 2 ,…,U N Sum of wind velocity data segment { W } 1 ,W 2 ,…,W N }。
5. The method for analyzing the correlation between the oscillation mode of the wind turbine and the influence factors based on the FP-growth is characterized in that the wind speed change threshold delta is set to be 0.05m/s.
6. The method for analyzing the correlation between the oscillation mode and the influence factor of the wind turbine generator based on the FP-growth is disclosed in claim 1,
in the step S3, a power data segment is subjected to signal decomposition by adopting a pon algorithm, so as to obtain an oscillation mode contained in the output power data segment.
7. The method for analyzing the correlation between the oscillation mode and the influence factor of the wind turbine generator based on the FP-growth of claim 6, wherein the method comprises the steps of,
in the step S3, a power data segment is subjected to signal decomposition by adopting a pon algorithm to obtain an oscillation mode contained in the power data segment, and a rule of correspondence between a signal decomposition result of the power data segment and the oscillation mode is shown in the following table:
。
8. The method for analyzing the correlation between the oscillation mode and the influence factor of the wind turbine generator based on the FP-growth is disclosed in claim 1,
in the step S4, the average value of the wind speed and the voltage in each section of data is calculated, the CH index is adopted to determine the optimal clustering number of the wind speed and the voltage average value, and the spectral clustering algorithm is used for clustering the wind speed and the voltage average value to obtain the wind speed-voltage clustering.
9. The method for analyzing the correlation between the oscillation mode and the influence factor of the wind turbine generator based on the FP-growth of claim 8, wherein the method comprises the steps of,
in the step S4, the step of determining the optimal cluster number of the wind speed and the voltage mean value by using the CH index specifically includes:
calculating the average value of wind speed and voltage in each segment of data, and carrying out multiple spectral clustering on the average value of wind speed and the average value of voltage, wherein the clustering number k is a natural number in the interval [ a, b ]; and calculating CH indexes corresponding to each clustering result, wherein the formula is as follows:
wherein k is the number of clusters; m is the number of samples; b (B) k 、W k Respectively an inter-cluster covariance matrix and an intra-cluster covariance matrix; tr (·) is the trace of the matrix;
and K corresponding to the maximum CH score is the optimal cluster number K.
10. The method for analyzing the correlation between the oscillation mode and the influence factor of the wind turbine generator based on the FP-growth of claim 9, wherein the method comprises the steps of,
setting parameter a to 2 and setting parameter b to 10.
11. The method for analyzing the correlation between the oscillation mode and the influence factor of the wind turbine generator based on the FP-growth of claim 8, wherein the method comprises the steps of,
in the step S4, the wind speed and the voltage average value are clustered into K types by using a spectral clustering algorithm, and a wind speed-voltage clustering label is obtained.
12. The method for analyzing the correlation between the oscillation mode and the influence factor of the wind turbine generator based on the FP-growth is disclosed in claim 1,
the minimum support S is set to 0.4.
13. The method for analyzing the correlation between the oscillation mode and the influence factor of the wind turbine generator based on the FP-growth is disclosed in claim 1,
the minimum confidence level C is set to 0.6.
14. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any one of claims 1 to 13 when the computer program is executed.
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