CN116027107B - Resonance detection method and device for large wind power generation grid-connected system - Google Patents
Resonance detection method and device for large wind power generation grid-connected system Download PDFInfo
- Publication number
- CN116027107B CN116027107B CN202310001680.8A CN202310001680A CN116027107B CN 116027107 B CN116027107 B CN 116027107B CN 202310001680 A CN202310001680 A CN 202310001680A CN 116027107 B CN116027107 B CN 116027107B
- Authority
- CN
- China
- Prior art keywords
- grid
- power generation
- wind power
- wavelet
- function
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000010248 power generation Methods 0.000 title claims abstract description 57
- 238000001514 detection method Methods 0.000 title claims abstract description 49
- 230000009466 transformation Effects 0.000 claims abstract description 40
- 238000000034 method Methods 0.000 claims abstract description 29
- 230000006870 function Effects 0.000 claims description 58
- 238000004458 analytical method Methods 0.000 claims description 28
- 238000004590 computer program Methods 0.000 claims description 22
- 230000035772 mutation Effects 0.000 claims description 18
- 238000009499 grossing Methods 0.000 claims description 12
- 230000003044 adaptive effect Effects 0.000 claims description 7
- 230000000694 effects Effects 0.000 abstract description 11
- 238000010586 diagram Methods 0.000 description 15
- 238000012545 processing Methods 0.000 description 6
- 230000000630 rising effect Effects 0.000 description 5
- 238000006243 chemical reaction Methods 0.000 description 4
- 230000021615 conjugation Effects 0.000 description 4
- 238000004422 calculation algorithm Methods 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 238000005070 sampling Methods 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 2
- 238000000354 decomposition reaction Methods 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 230000003238 somatosensory effect Effects 0.000 description 1
Classifications
-
- 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
Landscapes
- Wind Motors (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention provides a resonance detection method and device of a large wind power generation grid-connected system, wherein the method comprises the following steps: analyzing grid-connected voltage signals of a large-scale wind power generation grid-connected system by adopting wavelet transformation, dividing the grid-connected voltage signals into different sub-band signals, and extracting the sub-band with the largest fluctuation range; the extension of the left end point and the right end point of the sub-band with the maximum fluctuation amplitude is realized by improving the self-adaptive waveform matching extension, and the extended signal is obtained; and analyzing the extended signals by using Hilbert yellow transformation to obtain the resonant frequency and amplitude of the large wind power generation grid-connected system. The method can effectively determine the amplitude and the frequency of the resonance signal in the large wind power generation grid-connected system, improve the end effect of HHT and improve the detection speed and the detection precision.
Description
Technical Field
The invention relates to the technical field of wind power generation, in particular to a resonance detection method and device of a large wind power generation grid-connected system.
Background
Along with the rapid development of wind energy and electric energy, wind power is connected in a large scale, and more power electronic devices such as inverters are used, so that the power grid becomes a complex high-order LC network, harmonic content is more complex, the power grid and the power electronic devices interact with each other, a resonance phenomenon is easy to occur in the system, the safe and stable operation of the power grid is seriously damaged, and therefore, the research on the resonance detection method of the large-scale wind power generation grid-connected system has important significance. At present, no related research is available for resonance detection of a large-scale wind power generation grid-connected system.
Disclosure of Invention
In view of the above, the present invention provides a resonance detection method and apparatus for a large wind power grid-connected system to solve at least one of the above-mentioned problems.
In order to achieve the above purpose, the present invention adopts the following scheme:
According to a first aspect of the present invention, an embodiment of the present invention provides a resonance detection method for a large wind power generation grid-connected system, where the method includes: analyzing grid-connected voltage signals of a large-scale wind power generation grid-connected system by adopting wavelet transformation (Wavelet Transform, WT), dividing the grid-connected voltage signals into different sub-band signals, and extracting the sub-band with the largest fluctuation range; the extension of the left end point and the right end point of the sub-band with the maximum fluctuation amplitude is realized by improving the self-adaptive waveform matching extension, and the extended signal is obtained; and analyzing the extended signal by using Hilbert yellow Transform (HHT) to obtain the resonant frequency and amplitude of the large wind power generation grid-connected system.
Preferably, in the method provided by the embodiment of the present invention, analyzing a grid-connected voltage signal of a large wind power generation grid-connected system by using wavelet transformation, dividing the grid-connected voltage signal into different subband signals, and extracting a subband with the largest fluctuation range includes: j layers of db40 wavelet analysis are carried out on grid-connected voltage signals of the large wind power generation grid-connected system, the grid-connected voltage signals are divided into low-frequency signals and high-frequency signals, and sub-bands with maximum fluctuation amplitude are extracted.
Preferably, in the method provided by the embodiment of the present invention, performing j-layer db40 wavelet analysis on a grid-connected voltage signal of a large wind power generation grid-connected system includes: the first derivative of the smoothing function h (t) is selected as a wavelet function to carry out wavelet transformation on the grid-connected voltage signal, and the wavelet transformation of the grid-connected voltage signal is as follows:
Wherein s (t) is a grid-connected voltage signal, h a (t) is a telescopic function of a smoothing function h (t), a is a scale, phi is a scale function, phi is a wavelet basis function, h is a low-pass filter, g is a high-pass filter, x represents conjugation, and W a s (t) is a wavelet transformation function of s (t) under the scale a.
Preferably, the extracting the sub-band with the largest fluctuation range in the method provided by the embodiment of the present invention includes: the mutation quantity of the grid-connected voltage signal s (t) is determined by acquiring the local extreme point of the wavelet transformation function W a s (t) under the scale a, and the sub-band with the largest mutation quantity is selected as the sub-band with the largest fluctuation amplitude.
According to a third aspect of the present invention, an embodiment of the present invention further provides a resonance detection device of a large wind power generation grid-connected system, where the device includes: the system comprises a wavelet transformation unit, a sampling unit and a sampling unit, wherein the wavelet transformation unit is used for analyzing grid-connected voltage signals of a large-scale wind power generation grid-connected system by adopting wavelet transformation, dividing the grid-connected voltage signals into different sub-band signals and extracting sub-bands with the largest fluctuation amplitude; the extension unit is used for realizing extension of the left end point and the right end point of the sub-band with the maximum fluctuation amplitude by improving the adaptive waveform matching extension, so as to obtain an extended signal; and the resonance analysis unit is used for analyzing the extended signals by utilizing Hilbert yellow transformation to obtain the resonance frequency and amplitude of the large wind power generation grid-connected system.
Preferably, the wavelet transform unit provided in the embodiment of the present invention is specifically used for: j layers of db40 wavelet analysis are carried out on grid-connected voltage signals of the large wind power generation grid-connected system, the grid-connected voltage signals are divided into low-frequency signals and high-frequency signals, and sub-bands with maximum fluctuation amplitude are extracted.
Preferably, the performing, by the wavelet transformation unit provided by the embodiment of the present invention, j layers of db40 wavelet analysis on a grid-connected voltage signal of a large wind power generation grid-connected system includes: the first derivative of the smoothing function h (t) is selected as a wavelet function to carry out wavelet transformation on the grid-connected voltage signal, and the wavelet transformation of the grid-connected voltage signal is as follows:
Wherein s (t) is a grid-connected voltage signal, h a (t) is a telescopic function of a smoothing function h (t), a is a scale, phi is a scale function, phi is a wavelet basis function, h is a low-pass filter, g is a high-pass filter, x represents conjugation, and W a s (t) is a wavelet transformation function of s (t) under the scale a.
Preferably, the extracting, by the wavelet transform unit provided by the embodiment of the present invention, a subband with the largest fluctuation range includes: the mutation quantity of the grid-connected voltage signal s (t) is determined by acquiring the local extreme point of the wavelet transformation function W a s (t) under the scale a, and the sub-band with the largest mutation quantity is selected as the sub-band with the largest fluctuation amplitude.
According to a third aspect of the present invention, there is also provided an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the above method when executing the computer program.
According to a fourth aspect of the present invention, embodiments of the present invention also provide a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the above-described method.
According to a fifth aspect of the invention, embodiments of the invention also provide a computer program product comprising computer programs/instructions which, when executed by a processor, implement the steps of the above method.
The resonance detection method and device for the large wind power generation grid-connected system provided by the invention have the characteristics of multiple resolutions, have the advantages in the aspect of detecting the mutation points, namely, the frequency band of the resonance signal is the frequency band with the largest amplitude fluctuation extracted by the WT, only the frequency band is required to be subjected to the next analysis, and other frequency bands are not required to be further analyzed, so that the detection speed can be improved. The HHT with improved self-adaptive waveform matching extension not only considers waveform depth, but also considers waveform rising time and waveform falling time on the basis of the existing waveform matching extension method, so that the matching waveform is more accurate, the endpoint effect of Hilbert yellow conversion is improved, and the detection precision can be improved. Compared with the traditional WT and HHT, the resonance detection method of the large wind power generation grid-connected system based on the combination of the WT and the improved HHT can not only effectively determine the amplitude and the frequency of a resonance signal, but also improve the end effect of the HHT and increase the detection speed and the detection precision.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. In the drawings:
FIG. 1 is a schematic flow chart of a resonance detection method of a large wind power generation grid-connected system provided by an embodiment of the application;
FIG. 2 is a schematic diagram of an improved adaptive waveform matching extension provided by an embodiment of the present application;
FIG. 3 is a schematic waveform diagram of a grid-connected voltage containing a resonance signal in a large wind power generation grid-connected system according to an embodiment of the present application;
FIG. 4 is a graphical representation of the analysis of the grid-tie voltage signal of FIG. 3 based on a combination of WT and modified HHT;
fig. 5 is a schematic diagram of the analysis result of the modified HHT analysis of the high-frequency signal cd 4;
FIG. 6 is a schematic structural diagram of a resonance detection device of a large wind power generation grid-connected system according to an embodiment of the present application;
fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention will be described in further detail with reference to the accompanying drawings. The exemplary embodiments of the present invention and their descriptions herein are for the purpose of explaining the present invention, but are not to be construed as limiting the invention.
Fig. 1 is a schematic flow chart of a resonance detection method of a large wind power generation grid-connected system according to an embodiment of the present application, where the method includes the following steps:
Step S101: the method comprises the steps of analyzing grid-connected voltage signals of a large-scale wind power generation grid-connected system by wavelet transformation, dividing the grid-connected voltage signals into different sub-band signals, and extracting the sub-band with the largest fluctuation range.
Preferably, the step further specifically may include: j layers of db40 wavelet analysis are carried out on grid-connected voltage signals of the large wind power generation grid-connected system, the grid-connected voltage signals are divided into low-frequency signals and high-frequency signals, and sub-bands with maximum fluctuation amplitude are extracted. In the embodiment, when resonance detection is performed, the subsequent resonance detection analysis is performed only for the sub-band with the largest fluctuation range, and the analysis is not performed on other frequency bands, so that the speed of resonance detection can be greatly improved.
Further preferably, the j-layer db40 wavelet analysis on the grid-connected voltage signal of the large wind power generation grid-connected system may include: the first derivative of the smoothing function h (t) is selected as a wavelet function to carry out wavelet transformation on the grid-connected voltage signal s (t), and then the wavelet transformation of the grid-connected voltage signal s (t) is represented by the following formula (1):
Wherein s (t) is a grid-connected voltage signal, h a (t) is a telescopic function of a smoothing function h (t), a is a scale, phi is a scale function, phi is a wavelet basis function, h is a low-pass filter, g is a high-pass filter, x represents conjugation, and W a s (t) is a wavelet transformation function of s (t) under the scale a.
As can be seen from the above formula (1), the detection of the mutation amount of the wavelet transformation is based on the following: the wavelet transform function W a s (t) is expressed as a first derivative of the signal s (t) smoothed by h a (t) at the scale a, and for a specific scale a, the mutation amount of s (t) corresponds to the local extremum point of W a s (t), so that the mutation amount of the grid-connected voltage signal s (t) is determined by acquiring the local extremum point of the wavelet transform function W a s (t) at the scale a, and the sub-band with the largest mutation amount can be selected as the sub-band s d (t) with the largest fluctuation amplitude.
Step S102: and the extension of the left end point and the right end point of the sub-band with the maximum fluctuation amplitude is realized by improving the self-adaptive waveform matching extension, so that the extended signal is obtained.
The schematic diagram of the improved adaptive waveform matching extension is shown in FIG. 2, wherein the left boundary point and the corresponding time of the sub-band signal s d (t) are k 1 and k 1 respectivelyThe first maximum from the left end point and the corresponding time are i 1 and/>, respectivelyThe first minimum and the corresponding time are j 1 and/>, respectivelyRise time t up1 and fall time t down1 between the signature waveforms. And selecting a k 1-i1-j1 triangular wave from the characteristic waveforms of the waveform matching method. The matching waveform is a k i-ii-ji triangular wave, and the left boundary point and the corresponding time of the matching waveform are k i and/>, respectivelyThe first maximum from the left end point and the corresponding time are i i and/>, respectivelyThe first minimum and the corresponding time are j i and/>, respectivelyRise time t upi and fall time t downi between matching waveforms. The data before the matching waveform (after the matching waveform) is used as the waveform extension of s d (t) in the left extension, so that the natural trend of the signal is satisfied.
It can be seen that the existing waveform matching error only considers the magnitudes of three points k, i and j, and the waveform matching error algorithm adopted by the invention also considers the waveform depth and the rising and falling time of the waveform, so that the matching waveform is more accurate, and the end point effect of HHT is improved.
Based on the principle of the adaptive waveform matching continuation, the adaptive waveform matching continuation algorithm of the present invention may include the following steps:
Step a: calculating the time corresponding to the boundary point k i of the self-adaptive matching waveform Specific/>The calculation formula of (2) is as follows:
Step b: the matching error Δ (i) between the characteristic waveform and the matching waveform is found by the following formula (3), where Δ 1 (i) represents the amplitude error between the waveforms, and Δ 2 (i) is the error of the rise time and the fall time between the waveforms.
W 1、W2 is two constants, whose calculation formula is as follows (4), ||represents a modulus:
Normalizing the match error with W 1、W2 to obtain a formula (5) as in delta 2 (i), bringing a new delta 2 (i) into delta (i), obtaining a normalized match error delta (i) as in formula (6), wherein,
And searching the waveform with the smallest matching error among all the matching errors as a final matching waveform.
Step c: and selecting a data waveform before the final matching waveform, namely a continuation waveform, and extending the data waveform to the left end point of the grid-connected voltage signal.
Step d: the right end point extension of the signal is realized according to the same method, and the extended signal is s d' (t);
Step S103: and analyzing the extended signal by using Hilbert yellow transformation to obtain the resonant frequency and amplitude of the large wind power generation grid-connected system.
Specifically, this step may be implemented by the following sub-steps:
Step S1031: empirical mode decomposition (EMPIRICAL MODE DECOMPOSITION, EMD) is performed on the extended signal s d' (t) to obtain a finite number of natural mode functions c i and a trend term r, wherein i is a variable, and the natural mode functions must satisfy the following two conditions: (1) The number of the extreme points is equal to or different from the zero point number; (2) The local mean of the upper envelope defined by the maxima and the lower envelope defined by the minima is zero. s d' (t) is shown in formula (7):
step S1032: performing Hilbert transform (Hilbert) on each natural mode function c i to obtain instantaneous frequency and instantaneous amplitude of each subharmonic, including:
For each inherent mode function c i, performing Hilbert transform to obtain Hilbert transform of c i The following are provided:
obtaining the instantaneous amplitude a (t) of the harmonic wave:
and phase θ (t) of the harmonic:
instantaneous frequency f (t) of the harmonic:
According to the resonance detection method of the large wind power generation grid-connected system, the WT has the characteristic of multiple resolutions, the advantages are achieved in the aspect of detecting the mutation points, the sub-frequency band with the largest amplitude fluctuation extracted by the WT is the frequency band where the resonance signal is located, the frequency band is only required to be analyzed in the next step, and other frequency bands are not required to be analyzed further, so that the detection speed can be improved. The HHT with improved self-adaptive waveform matching extension not only considers waveform depth, but also considers waveform rising time and waveform falling time on the basis of the existing waveform matching extension method, so that the matching waveform is more accurate, the endpoint effect of Hilbert yellow conversion is improved, and the detection precision can be improved. Compared with the traditional WT and HHT, the resonance detection method of the large wind power generation grid-connected system based on the combination of the WT and the improved HHT can not only effectively determine the amplitude and the frequency of a resonance signal, but also improve the end effect of the HHT and increase the detection speed and the detection precision.
The following describes a method for detecting resonance of the large-scale wind power generation grid-connected system and beneficial effects thereof by using a specific embodiment:
fig. 3 is a schematic waveform diagram of a grid-connected voltage containing a resonance signal in the large-scale wind power generation grid-connected system according to the embodiment of the application. As shown in fig. 4, the result of the analysis based on the combination of WT and modified HHT is that the sampling frequency is 12800Hz, and after the wavelet transform analysis in step S101, cd1-cd7 in fig. 4 is a high-frequency signal, ca7 is a low-frequency signal, and the frequency ranges of cd1-cd7 are [3200Hz,6400Hz],[1600Hz,3200Hz],[800Hz,1600Hz],[400Hz,800Hz],[200Hz,400Hz],[100Hz,200Hz],[50Hz,100Hz],ca7 respectively [0Hz,50Hz ].
As can be seen from FIG. 4, the amplitude fluctuation of cd4 is the largest, i.e., the sub-band where the resonant signal is located is cd4, and the frequency band is [400Hz,800Hz ]. The high-frequency signal cd4 was subjected to modified HHT analysis, and the analysis result is shown in fig. 5.
From FIG. 5, it can be seen that the resonance frequency is 707.14Hz and the amplitude is 27.11V. Simulation proves that the end point effect is effectively improved by improving the HHT algorithm, and the frequency and the amplitude of the resonance voltage in the large wind power generation grid-connected system can be accurately detected.
Fig. 6 is a schematic structural diagram of a resonance detection device of a large wind power generation grid-connected system according to an embodiment of the present application, where the device includes: wavelet transform unit 610, extension unit 620 and resonance analysis unit 630, wherein extension unit 620 is connected to wavelet transform unit 610 and resonance analysis unit 630, respectively.
The wavelet transformation unit 610 is configured to analyze a grid-connected voltage signal of a large-scale wind power generation grid-connected system by using wavelet transformation, divide the grid-connected voltage signal into different subband signals, and extract a subband with the largest fluctuation range.
The extension unit 620 is configured to extend the left endpoint and the right endpoint of the sub-band with the largest fluctuation range by improving adaptive waveform matching extension, so as to obtain the extended signal.
The resonance analysis unit 630 is configured to analyze the extended signal by using hilbert yellow transform, so as to obtain a resonance frequency and an amplitude of the large-scale wind power generation grid-connected system.
Preferably, the wavelet transform unit 610 may specifically be configured to: j layers of db40 wavelet analysis are carried out on grid-connected voltage signals of the large wind power generation grid-connected system, the grid-connected voltage signals are divided into low-frequency signals and high-frequency signals, and sub-bands with maximum fluctuation amplitude are extracted.
Further preferably, the performing, by the wavelet transformation unit 610, j layers of db40 wavelet analysis on the grid-connected voltage signal of the large wind power generation grid-connected system includes: the first derivative of the smoothing function h (t) is selected as a wavelet function to carry out wavelet transformation on the grid-connected voltage signal, and the wavelet transformation of the grid-connected voltage signal is as follows:
Wherein s (t) is a grid-connected voltage signal, h a (t) is a telescopic function of a smoothing function h (t), a is a scale, phi is a scale function, phi is a wavelet basis function, h is a low-pass filter, g is a high-pass filter, x represents conjugation, and W a s (t) is a wavelet transformation function of s (t) under the scale a.
Further preferably, the extracting the sub-band having the largest fluctuation range by the wavelet transform unit 610 includes: the mutation quantity of the grid-connected voltage signal s (t) is determined by acquiring the local extreme point of the wavelet transformation function W a s (t) under the scale a, and the sub-band with the largest mutation quantity is selected as the sub-band with the largest fluctuation amplitude.
The detailed description of each unit may refer to the corresponding description of the foregoing method embodiment, and will not be repeated herein.
The resonance detection device of the large wind power generation grid-connected system provided by the invention has the characteristics of multiple resolutions, has the advantages in the aspect of detecting the mutation points, namely, the frequency band of the resonance signal is the frequency band with the largest amplitude fluctuation extracted by the WT, only the frequency band is required to be subjected to the next analysis, and other frequency bands are not required to be further analyzed, so that the detection speed can be improved. The HHT with improved self-adaptive waveform matching extension not only considers waveform depth, but also considers waveform rising time and waveform falling time on the basis of the existing waveform matching extension method, so that the matching waveform is more accurate, the endpoint effect of Hilbert yellow conversion is improved, and the detection precision can be improved. Compared with the traditional WT and HHT, the resonance detection method of the large wind power generation grid-connected system based on the combination of the WT and the improved HHT can not only effectively determine the amplitude and the frequency of a resonance signal, but also improve the end effect of the HHT and increase the detection speed and the detection precision.
Fig. 7 is a schematic diagram of an electronic device according to an embodiment of the present invention. The electronic device shown in fig. 7 is a general-purpose data processing apparatus comprising a general-purpose computer hardware structure including at least a processor 801 and a memory 802. The processor 801 and the memory 802 are connected by a bus 803. The memory 802 is adapted to store one or more instructions or programs executable by the processor 801. The one or more instructions or programs are executed by the processor 801 to implement the steps in the source network load store interactive method described above.
The processor 801 may be a separate microprocessor or a collection of one or more microprocessors. Thus, the processor 801 performs the process of processing data and controlling other devices by executing the commands stored in the memory 802, thereby executing the method flow of the embodiment of the present invention as described above. The bus 803 connects the above-described components together, while connecting the above-described components to a display controller 804 and a display device and an input/output (I/O) device 805. Input/output (I/O) devices 805 may be a mouse, keyboard, modem, network interface, touch input device, somatosensory input device, printer, and other devices known in the art. Typically, input/output (I/O) devices 805 are connected to the system through input/output (I/O) controllers 806.
The memory 802 may store software components such as an operating system, communication modules, interaction modules, and application programs, among others. Each of the modules and applications described above corresponds to a set of executable program instructions that perform one or more functions and methods described in the embodiments of the invention.
The embodiment of the invention also provides a computer readable storage medium, on which a computer program is stored, which when being executed by a processor, is used for realizing the steps of the resonance detection method of the large wind power generation grid-connected system.
The embodiment of the invention also provides a computer program product, which comprises a computer program/instruction, wherein the computer program/instruction realizes the steps of the resonance detection method of the large wind power generation grid-connected system when being executed by a processor.
In summary, the resonance detection method and device for the large wind power generation grid-connected system provided by the invention have the characteristics of multiple resolutions, have advantages in the aspect of detecting mutation points, namely, the frequency band of the resonance signal is the frequency band of the sub-frequency band with the largest amplitude fluctuation extracted by the WT, only the frequency band needs to be analyzed in the next step, and other frequency bands do not need to be analyzed further, so that the detection speed can be improved. The HHT with improved self-adaptive waveform matching extension not only considers waveform depth, but also considers waveform rising time and waveform falling time on the basis of the existing waveform matching extension method, so that the matching waveform is more accurate, the endpoint effect of Hilbert yellow conversion is improved, and the detection precision can be improved. Compared with the traditional WT and HHT, the resonance detection method of the large wind power generation grid-connected system based on the combination of the WT and the improved HHT can not only effectively determine the amplitude and the frequency of a resonance signal, but also improve the end effect of the HHT and increase the detection speed and the detection precision.
Preferred embodiments of the present invention are described above with reference to the accompanying drawings. The many features and advantages of the embodiments are apparent from the detailed specification, and thus, it is intended by the appended claims to cover all such features and advantages of the embodiments which fall within the true spirit and scope thereof. Further, since numerous modifications and changes will readily occur to those skilled in the art, it is not desired to limit the embodiments of the invention to the exact construction and operation illustrated and described, and accordingly, all suitable modifications and equivalents may be resorted to, falling within the scope thereof.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the scope of the invention, but to limit the invention to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (7)
1. The resonance detection method of the large wind power generation grid-connected system is characterized by comprising the following steps of:
performing j-layer db40 wavelet analysis on a grid-connected voltage signal of a large wind power generation grid-connected system, dividing the grid-connected voltage signal into a low-frequency signal and a high-frequency signal, determining the mutation quantity of the grid-connected voltage signal s (t) by acquiring a local extremum point of a wavelet transformation function W a s (t) under a scale a, and selecting a sub-band with the maximum mutation quantity as the sub-band with the maximum fluctuation amplitude;
The extension of the left end point and the right end point of the sub-band with the maximum fluctuation amplitude is realized by improving the self-adaptive waveform matching extension, and the extended signal is obtained;
And analyzing the extended signal by using Hilbert yellow transformation to obtain the resonant frequency and amplitude of the large wind power generation grid-connected system.
2. The resonance detection method for a large wind power generation grid-connected system according to claim 1, wherein the j-layer db40 wavelet analysis of the grid-connected voltage signal of the large wind power generation grid-connected system comprises:
the first derivative of the smoothing function h (t) is selected as a wavelet function to carry out wavelet transformation on the grid-connected voltage signal, and the wavelet transformation of the grid-connected voltage signal is as follows:
Wherein s (t) is a grid-connected voltage signal, h a (t) is a telescopic function of a smoothing function h (t), a is a scale, f is a scale function, y is a wavelet basis function, h is a low-pass filter, g is a high-pass filter, W a s (t) is a wavelet transform function of s (t) under a scale a.
3. A resonance detection device for a large wind power generation grid-connected system, the device comprising:
The wavelet transformation unit is used for carrying out j-layer db40 wavelet analysis on a grid-connected voltage signal of the large-scale wind power generation grid-connected system, dividing the grid-connected voltage signal into a low-frequency signal and a high-frequency signal, determining the mutation quantity of the grid-connected voltage signal s (t) by acquiring the local extremum point of a wavelet transformation function W a s (t) under a scale a, and selecting a sub-band with the maximum mutation quantity as the sub-band with the maximum fluctuation amplitude;
The extension unit is used for realizing extension of the left end point and the right end point of the sub-band with the maximum fluctuation amplitude by improving the adaptive waveform matching extension, so as to obtain an extended signal;
and the resonance analysis unit is used for analyzing the extended signals by utilizing Hilbert yellow transformation to obtain the resonance frequency and amplitude of the large wind power generation grid-connected system.
4. The resonance detection apparatus for a large wind power generation grid-connected system according to claim 3, wherein the wavelet transformation unit performs j-layer db40 wavelet analysis on a grid-connected voltage signal of the large wind power generation grid-connected system, comprising:
the first derivative of the smoothing function h (t) is selected as a wavelet function to carry out wavelet transformation on the grid-connected voltage signal, and the wavelet transformation of the grid-connected voltage signal is as follows:
Wherein s (t) is a grid-connected voltage signal, h a (t) is a telescopic function of a smoothing function h (t), a is a scale, f is a scale function, y is a wavelet basis function, h is a low-pass filter, g is a high-pass filter, W a s (t) is a wavelet transform function of s (t) under a scale a.
5. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method of any of claims 1 to 2 when the computer program is executed by the processor.
6. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method according to any one of claims 1 to 2.
7. A computer program product comprising computer programs/instructions which, when executed by a processor, implement the steps of the method of any one of claims 1 to 2.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310001680.8A CN116027107B (en) | 2023-01-03 | 2023-01-03 | Resonance detection method and device for large wind power generation grid-connected system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310001680.8A CN116027107B (en) | 2023-01-03 | 2023-01-03 | Resonance detection method and device for large wind power generation grid-connected system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116027107A CN116027107A (en) | 2023-04-28 |
| CN116027107B true CN116027107B (en) | 2024-04-26 |
Family
ID=86078902
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310001680.8A Active CN116027107B (en) | 2023-01-03 | 2023-01-03 | Resonance detection method and device for large wind power generation grid-connected system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116027107B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5983162A (en) * | 1996-08-12 | 1999-11-09 | The United States Of America As Represented By The Administrator Of The National Aeronautics And Space Administration | Computer implemented empirical mode decomposition method, apparatus and article of manufacture |
| CN104237683A (en) * | 2014-09-01 | 2014-12-24 | 国家电网公司 | Ferromagnetic resonance fault detection method for isolated neutral system |
| CN106707021A (en) * | 2016-08-14 | 2017-05-24 | 华北电力大学（保定） | Resonance detection method of distributed photovoltaic power generation cluster grid-connected system |
| CN115015636A (en) * | 2022-05-27 | 2022-09-06 | 华北电力科学研究院有限责任公司 | Resonance detection method and device for AC/DC hybrid micro-grid |
| CN115097207A (en) * | 2022-05-27 | 2022-09-23 | 华北电力科学研究院有限责任公司 | Resonance detection method and device for AC/DC hybrid micro-grid |
-
2023
- 2023-01-03 CN CN202310001680.8A patent/CN116027107B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5983162A (en) * | 1996-08-12 | 1999-11-09 | The United States Of America As Represented By The Administrator Of The National Aeronautics And Space Administration | Computer implemented empirical mode decomposition method, apparatus and article of manufacture |
| CN104237683A (en) * | 2014-09-01 | 2014-12-24 | 国家电网公司 | Ferromagnetic resonance fault detection method for isolated neutral system |
| CN106707021A (en) * | 2016-08-14 | 2017-05-24 | 华北电力大学（保定） | Resonance detection method of distributed photovoltaic power generation cluster grid-connected system |
| CN115015636A (en) * | 2022-05-27 | 2022-09-06 | 华北电力科学研究院有限责任公司 | Resonance detection method and device for AC/DC hybrid micro-grid |
| CN115097207A (en) * | 2022-05-27 | 2022-09-23 | 华北电力科学研究院有限责任公司 | Resonance detection method and device for AC/DC hybrid micro-grid |
Non-Patent Citations (2)
| Title |
|---|
| 一种改善HHT端点效应的新方法及其在电能质量中的应用;苏玉香 等;电力自动化设备;20081110;28(11);第40-45页 * |
| 基于小波变换和HHT的光伏并网谐振检测方法;高静 等;电力科学与技术学报;20171228;32(04);第31-37页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116027107A (en) | 2023-04-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Huang | Hilbert-Huang transform and its applications | |
| CN109164489A (en) | A kind of earthquake fluid prediction technique based on VMD Yu TK energy operator | |
| CN112446323B (en) | HHT harmonic analysis method based on improved EMD modal aliasing and endpoint effect | |
| CN104050147B (en) | Time-domain signal is converted into into the method and system of frequency-region signal | |
| CN104407198A (en) | Method and system for detecting SAG signal in DVR device | |
| CN112362966A (en) | Power grid harmonic detection method and device based on fusion of wavelet packet transformation and STFT | |
| CN116027107B (en) | Resonance detection method and device for large wind power generation grid-connected system | |
| CN103647550A (en) | Phase-locked loop method for dynamic voltage reactive compensation | |
| CN103543331B (en) | A kind of method calculating electric signal harmonic wave and m-Acetyl chlorophosphonazo | |
| US20190331721A1 (en) | Noise spectrum analysis for electronic device | |
| He et al. | Downsampling-based synchrosqueezing transform and its applications on large-scale vibration data | |
| CN108090270A (en) | A kind of transient oscillation parameter identification method based on morphologic filtering and blind source separating | |
| CN117347043A (en) | Method, device, equipment and storage medium for extracting instantaneous rotating speed of rotary machine | |
| CN105429157B (en) | A kind of intelligent oscillation mode recognition methods based on Prony analyses | |
| CN108334822B (en) | Kalman and modified wavelet transform filtering method based on electric vehicle charging nonlinear load characteristics | |
| CN109617051B (en) | New energy power system low-frequency oscillation parameter identification method | |
| Nicolae et al. | Using computational techniques to improve the accuracy of Stationary Wavelet Transform analysis | |
| Zhu et al. | An improved Hilbert vibration decomposition method for analysis of rotor fault signals | |
| CN110377927A (en) | A kind of pumping plant unit rotor method for monitoring state based on MATLAB emulation | |
| CN110531137A (en) | A kind of power quality adjusts, voltage transient variation detection method and dynamic electric voltage recovery device | |
| CN110636053B (en) | Network attack detection method based on local mean decomposition and support vector machine | |
| CN114358041A (en) | Characteristic waveform extraction method and analysis method based on hybrid algorithm | |
| CN117572139A (en) | Resonance detection method and device for active power distribution network | |
| CN112986680A (en) | Coordinate transformation-based voltage sag rapid detection method and system | |
| CN102288803A (en) | Method for detecting transient disturbance of electric energy quality in real time |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |