CN113866493A - Method for measuring voltage fluctuation and flicker caused by wind power - Google Patents
Method for measuring voltage fluctuation and flicker caused by wind power Download PDFInfo
- Publication number
- CN113866493A CN113866493A CN202111238621.XA CN202111238621A CN113866493A CN 113866493 A CN113866493 A CN 113866493A CN 202111238621 A CN202111238621 A CN 202111238621A CN 113866493 A CN113866493 A CN 113866493A
- Authority
- CN
- China
- Prior art keywords
- inter
- harmonic
- wind power
- frequency
- voltage fluctuation
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 17
- 238000001228 spectrum Methods 0.000 claims abstract description 17
- 238000004458 analytical method Methods 0.000 claims abstract description 15
- 238000004364 calculation method Methods 0.000 claims abstract description 10
- 108010076504 Protein Sorting Signals Proteins 0.000 claims abstract description 8
- 238000005070 sampling Methods 0.000 claims abstract description 8
- 238000005259 measurement Methods 0.000 claims abstract description 7
- 238000012937 correction Methods 0.000 claims abstract description 4
- 230000035945 sensitivity Effects 0.000 claims abstract description 4
- 230000001360 synchronised effect Effects 0.000 claims abstract description 4
- 230000003595 spectral effect Effects 0.000 claims description 11
- 238000001914 filtration Methods 0.000 claims description 3
- 230000008447 perception Effects 0.000 claims description 3
- 238000010248 power generation Methods 0.000 claims 3
- 238000001514 detection method Methods 0.000 abstract description 8
- 238000011160 research Methods 0.000 description 4
- 238000007405 data analysis Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 238000011156 evaluation Methods 0.000 description 3
- 238000013500 data storage Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000009434 installation Methods 0.000 description 2
- 238000010223 real-time analysis Methods 0.000 description 2
- 230000004888 barrier function Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000003750 conditioning effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000010008 shearing Methods 0.000 description 1
- 238000010183 spectrum analysis Methods 0.000 description 1
- 238000003860 storage Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R19/00—Arrangements for measuring currents or voltages or for indicating presence or sign thereof
- G01R19/25—Arrangements for measuring currents or voltages or for indicating presence or sign thereof using digital measurement techniques
- G01R19/2506—Arrangements for conditioning or analysing measured signals, e.g. for indicating peak values ; Details concerning sampling, digitizing or waveform capturing
- G01R19/2509—Details concerning sampling, digitizing or waveform capturing
Abstract
The invention relates to a method for measuring voltage fluctuation and flicker caused by wind power, which comprises the following steps: step 1, performing power spectrum estimation on a wind power output voltage signal sequence after synchronous sampling by using a Burg algorithm pair based on optimal window weighting correction, and analyzing a power spectrum to accurately obtain the frequency of each inter-harmonic component; step 2, carrying out FFT analysis on the voltage sampling signal sequence according to the frequency of each inter-harmonic component obtained in the step 1 to obtain the amplitude and the phase of the real inter-harmonic; and 3, obtaining the measurement result of voltage fluctuation and flicker caused by wind power after the original signal passes through a vision sensitivity weighting filter to simulate lamp-eye-brain frequency response characteristic according to the amplitude and the phase of the obtained real inter-harmonic wave obtained by calculation in the step 2. The invention can improve the detection accuracy of voltage fluctuation and flicker caused by the wind power plant.
Description
Technical Field
The invention belongs to the technical field of power quality monitoring of power systems, and particularly relates to a method for measuring voltage fluctuation and flicker caused by wind power.
Background
The wind power generator set is influenced by factors such as wind shearing effect, tower shadow effect and the like, fluctuation of specific frequency occurs in output power, so that inter-harmonic waves of the specific frequency are generated, and the damage of voltage fluctuation and flicker caused by the inter-harmonic waves is also wide. Inter-harmonics are the root cause of voltage fluctuations and flicker in wind farms.
With the increasing scale of wind power installations, the influence of the wind power installations on the power quality of a power grid is more and more obvious, so that the requirements on inter-harmonic detection of a wind power plant and voltage fluctuation and flicker analysis caused by the inter-harmonic are higher and higher. The voltage flicker phenomenon caused by wind power is random and influenced in many aspects, and the voltage flicker calculation and evaluation research difficulty caused by the wind power is higher due to the fact that the frequency domain distribution universality and the amplitude of inter-harmonics are weak and the like.
Some contents related to inter-harmonic detection algorithm and flicker caused by inter-harmonics are mentioned in relevant national standards, but the method has limitations, resolution is not high, and detailed inter-harmonic parameters cannot be obtained.
Therefore, in view of the harmfulness of voltage fluctuation and flicker of the wind power plant and the lack of the current analysis means, research on calculation and evaluation of the voltage fluctuation and flicker of the wind power plant needs to be strengthened, and a method for measuring the voltage fluctuation and flicker caused by wind power is invented.
No prior art publications that are the same or similar to the present invention have been found by search.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, provides a method for measuring voltage fluctuation and flicker caused by wind power, and can improve the detection accuracy of the voltage fluctuation and flicker caused by a wind power plant.
The invention solves the practical problem by adopting the following technical scheme:
a method for measuring voltage fluctuation and flicker caused by wind power comprises the following steps:
step 1, performing power spectrum estimation on a wind power output voltage signal sequence after synchronous sampling by using a Burg algorithm pair based on optimal window weighting correction, and analyzing a power spectrum to accurately obtain the frequency of each inter-harmonic component;
step 2, carrying out FFT analysis on the voltage sampling signal sequence according to the frequency of each inter-harmonic component obtained in the step 1 to obtain the amplitude and the phase of the real inter-harmonic;
and 3, obtaining the measurement result of voltage fluctuation and flicker caused by wind power after the original signal passes through a vision sensitivity weighting filter to simulate lamp-eye-brain frequency response characteristic according to the amplitude and the phase of the obtained real inter-harmonic wave obtained by calculation in the step 2.
Further, the specific steps of step 1 include:
(1) for a discrete signal x (n), the representation as a p-th order AR model is:
in formula (1): η (n) is zero mean variance σ2White noise of (2). a isk(k 1.., p) is the coefficient of the AR model of order p.
(2) The AR power spectral density of signal x (n) is:
an AR model of order p is equivalent to a linear predictor of order p, the parameters of the AR model are the coefficients of the linear predictor, and the variance σ2Minimum prediction error power equal to order ppTherefore, the power spectrum formula is equivalent to:
therefore, only the minimum prediction error power ρ is obtainedpAnd model parameters akThe power spectrum of the signal can be obtained;
(3) AR model parameter solution
E in formulae (4), (5) and (6)m(n)、bm(n) are the forward and backward prediction errors, respectively, for an order of m;ρmthe predicted error power when the order is m; k is a radical ofmThe reflection coefficient is the order m.
Initial value: prediction error
Initial value e of front and back prediction error0(n)＝b0(n) ═ x (n). The filter coefficients are calculated according to the Burg algorithm:
calculating the prediction error power:
ρm＝(1-|km|2)ρm-1 (8)
computing output
Finally obtaining the minimum prediction error power rhopAnd model parameters ak。
The power spectral density is obtained by substituting formula (3), and the frequency f of each inter-harmonic component contained in the original data can be accurately obtained1,f2,f3,...,fn。
Further, the specific steps of step 2 include:
(1) let fa，fbIs f1、f2、f3、...、fnOf the 2 frequency components having the smallest difference;
determining the two closest frequency components in the signal, denoted faAnd fbThe difference f between the two frequenciesab＝|fa-fbThe frequency resolution Δ f of the spectral line should at least satisfy:
minimum number of data points N for Fourier analysis of a signalminComprises the following steps:
intercepting N data of an original signal, and meeting the following requirements: n is an integer multiple of 1024 and N>Nmin；
(2) Performing FFT analysis on the intercepted data x (n) to obtain the amplitude and the phase of each inter-harmonic;
because the maximum frequency range of human perception to flicker does not exceed 0.05-35 Hz, the frequency band of inter-harmonic waves concerned by the invention is 15-85 Hz; after filtering, only f with the inter-harmonic frequency band within the range of 15-85 Hz is reserved1、f2、f3、...、fnThe amplitude and phase of each inter-harmonic, i.e.:
ui(t)＝Ui sin(ωit+θi)
(3) the original signal can be expressed as:
in the formula, the first part is fundamental wave, and the second part is inter-harmonic wave.
Further, the specific steps of step 3 include:
(1) the original signal is passed through a perceptibility weighting filter to simulate a lamp-eye-brain frequency response characteristic as follows:
and obtaining the final measurement result values of voltage fluctuation and flicker caused by wind power:
in the formula: g is a constant of the gain, and G is a constant of the gain,
ω0i＝ω0-ωi，θ0i＝θ0-θi。
the invention has the advantages and beneficial effects that:
the invention combines the flicker problem with the inter-harmonic, starts with researching the frequency spectrum analysis algorithm of the inter-harmonic, improves the precision of inter-harmonic detection and measures the related parameters of the inter-harmonic, thereby improving the calculation precision and speed of the frequency domain algorithm for flicker calculation. The invention deeply researches the mechanism of the voltage flicker phenomenon caused by wind power, takes a flicker calculation method and a flicker evaluation standard as final targets, and systematically researches the voltage fluctuation and flicker problem caused by the wind power.
Drawings
FIG. 1 is a hardware configuration diagram of an inter-harmonic detection apparatus;
fig. 2 is a software configuration diagram of the inter-harmonic detection apparatus.
Detailed Description
The embodiments of the invention will be described in further detail below with reference to the accompanying drawings:
a method for measuring voltage fluctuation and flicker caused by wind power comprises the following steps:
step 1, performing power spectrum estimation on a wind power output voltage signal sequence after synchronous sampling by using a Burg algorithm pair based on optimal window weighting correction, and analyzing a power spectrum to accurately obtain the frequency of each inter-harmonic component;
the method is mainly applied to non-real-time analysis of wind field voltage fluctuation and flicker, the time window can be set to be longer, and the frequency of each inter-harmonic component can be accurately obtained through analysis of the power spectrum.
The specific steps of the step 1 are as follows:
(1) for a discrete signal x (n), the representation as a p-th order AR model is:
in formula (1): η (n) is zero mean variance σ2White noise of (2). a isk(k 1.., p) is the coefficient of the AR model of order p.
(2) The AR power spectral density of signal x (n) is:
an AR model of order p is equivalent to a linear predictor of order p. The parameters of the AR model are the coefficients of the linear predictor, the variance σ2Minimum prediction error power equal to order pp. So the power spectrum formula is equivalent to:
therefore, only the minimum prediction error power ρ is obtainedpAnd model parameters akThe power spectrum of the signal is obtained.
(4) AR model parameter solution
E in formulae (4), (5) and (6)m(n)、bm(n) are the forward and backward prediction errors, respectively, for an order of m; rhomThe predicted error power when the order is m; k is a radical ofmThe reflection coefficient is the order m.
Initial value: prediction error
Initial value e of front and back prediction error0(n)＝b0(n) ═ x (n). Calculating filter coefficients according to the Burg algorithm
Calculating prediction error power
ρm＝(1-|km|2)ρm-1 (8)
Computing output
Finally obtaining the minimum prediction error power rhopAnd model parameters ak。
The power spectral density is obtained by substituting formula (3), and the frequency f of each inter-harmonic component contained in the original data can be accurately obtained1,f2,f3,...,fn(but not the precise amplitude and phase of each frequency component).
Step 2, carrying out FFT analysis on the voltage sampling signal sequence according to the frequency of each inter-harmonic component obtained in the step 1 to obtain the amplitude and the phase of the real inter-harmonic;
the FFT analysis of inter-harmonic containing signals typically results in spectral leakage, which includes both long range leakage and short range leakage. The long-range leakage is mutual interference between signal spectrum side lobes caused by signal truncation and a small truncation window, the method is non-real-time analysis, and the window length can be large enough, so the long-range leakage can be ignored; short-range leakage refers to that due to unreasonable length of a truncation window, a barrier effect of a discrete spectrum is caused to generate a false inter-harmonic signal, and real inter-harmonics are hidden. According to the accurate inter-harmonic frequency obtained in the step 1, the method selects the appropriate length of the truncation window, so that the amplitude and the phase of the real inter-harmonic are obtained.
The specific steps of the step 2 comprise:
(1) let fa，fbIs f1、f2、f3、...、fnOf the 2 frequency components having the smallest difference.
Determining the two closest frequency components in the signal, denoted faAnd fbThe difference f between the two frequenciesab＝|fa-fbThe frequency resolution Δ f of the spectral lines should at least satisfy (take m spectral lines):
minimum number of data points N for Fourier analysis of a signalminComprises the following steps:
intercepting N data of an original signal, and meeting the following requirements: n is an integer multiple of 1024 and N>Nmin。
(2) Performing FFT analysis on the intercepted data x (n) to obtain the amplitude and the phase of each inter-harmonic;
because the maximum frequency range of human perception to flicker does not exceed 0.05-35 Hz, the frequency band of inter-harmonic waves concerned by the invention is 15-85 Hz; after filtering, only f with the inter-harmonic frequency band within the range of 15-85 Hz is reserved1、f2、f3、...、fnThe amplitude and phase of each inter-harmonic, i.e.:
ui(t)＝Ui sin(ωit+θi)
(3) the original signal can be expressed as:
in the formula, the first part is fundamental wave, and the second part is inter-harmonic wave.
And 3, obtaining the measurement result of voltage fluctuation and flicker caused by wind power after the original signal passes through a vision sensitivity weighting filter to simulate lamp-eye-brain frequency response characteristic according to the amplitude and the phase of the obtained real inter-harmonic wave obtained by calculation in the step 2.
The specific steps of the step 3 comprise:
(1) the original signal is passed through a perceptibility weighting filter to simulate a lamp-eye-brain frequency response characteristic as follows:
and obtaining the final measurement result values of voltage fluctuation and flicker caused by wind power:
in the formula: g is a constant of the gain, and G is a constant of the gain,
ω0i＝ω0-ωi，θ0i＝θ0-θi。
in the present embodiment, the present invention is explained in terms of a hardware configuration with reference to fig. 1. The voltage signal (1.1) and the current signal (1.2) firstly pass through a cabinet wiring terminal (1.3) and then are converted into analog signals (1.4), the analog signals pass through a sensor (1.5) and a signal conditioning circuit (1.6) and then are subjected to data acquisition by a data acquisition card (1.7), the acquired data are sent to an industrial personal computer (1.8), and data analysis, calculation and storage are carried out by a LabVIEW software program.
The inter-harmonic detection device adopts a concept design software system of hierarchical structure design, and the structure is shown in fig. 2. The software device is established on a hardware platform (2.2) of the virtual instrument, and the equipment management of the hardware platform is realized through a Windows NT operating system (2.3). Data (2.1) acquired by a data acquisition card is input into device software, and the device software consists of a PCI equipment driver (2.4), an NI-DAQ data acquisition operation support library (2.5), a data analysis subsystem (2.6), a data storage subsystem (2.7) and a user interface (2.8). The data analysis subsystem completes complex operation on the acquired data, the data storage subsystem is used for storing the acquired field data and analysis results, and the user interface provides interface elements such as curves, charts, reports, buttons, menus, shortcut keys and the like on the front panel for a user to realize humanized operation.
It should be emphasized that the examples described herein are illustrative and not restrictive, and thus the present invention includes, but is not limited to, those examples described in this detailed description, as well as other embodiments that can be derived from the teachings of the present invention by those skilled in the art and that are within the scope of the present invention.
Claims (4)
1. A method for measuring voltage fluctuation and flicker caused by wind power is characterized in that: the method comprises the following steps:
step 1, performing power spectrum estimation on a wind power output voltage signal sequence after synchronous sampling by using a Burg algorithm pair based on optimal window weighting correction, and analyzing a power spectrum to accurately obtain the frequency of each inter-harmonic component;
step 2, carrying out FFT analysis on the voltage sampling signal sequence according to the frequency of each inter-harmonic component obtained in the step 1 to obtain the amplitude and the phase of the real inter-harmonic;
and 3, obtaining the measurement result of voltage fluctuation and flicker caused by wind power after the original signal passes through a vision sensitivity weighting filter to simulate lamp-eye-brain frequency response characteristic according to the amplitude and the phase of the obtained real inter-harmonic wave obtained by calculation in the step 2.
2. The method for measuring voltage fluctuation and flicker caused by wind power generation as claimed in claim 1, wherein: the specific steps of the step 1 comprise:
(1) for a discrete signal x (n), the representation as a p-th order AR model is:
in formula (1): η (n) is zero mean variance σ2White noise of (2). a isk(k 1.., p) is the coefficient of the AR model of order p.
(2) The AR power spectral density of signal x (n) is:
an AR model of order p is equivalent to a linear predictor of order p, the parameters of the AR model are the coefficients of the linear predictor, and the variance σ2Minimum prediction error power equal to order ppTherefore, the power spectrum formula is equivalent to:
therefore, only the minimum prediction error power ρ is obtainedpAnd model parameters akThe power spectrum of the signal can be obtained;
(3) AR model parameter solution
E in formulae (4), (5) and (6)m(n)、bm(n) are the forward and backward prediction errors, respectively, for an order of m; rhomPrediction error for order mA differential power; k is a radical ofmThe reflection coefficient is the order m.
Initial value: prediction error
Initial value e of front and back prediction error0(n)＝b0(n) ═ x (n). The filter coefficients are calculated according to the Burg algorithm:
calculating the prediction error power:
ρm＝(1-|km|2)ρm-1 (8)
computing output
Finally obtaining the minimum prediction error power rhopAnd model parameters ak。
The power spectral density is obtained by substituting formula (3), and the frequency f of each inter-harmonic component contained in the original data can be accurately obtained1,f2,f3,...,fn。
3. The method for measuring voltage fluctuation and flicker caused by wind power generation as claimed in claim 1, wherein: the specific steps of the step 2 comprise:
(1) let fa，fbIs f1、f2、f3、...、fnOf the 2 frequency components having the smallest difference;
determining the two closest frequency components in the signal, denoted faAnd fbThe difference f between the two frequenciesab＝|fa-fbThe frequency resolution Δ f of the spectral line should at least satisfy:
minimum number of data points N for Fourier analysis of a signalminComprises the following steps:
intercepting N data of an original signal, and meeting the following requirements: n is an integer multiple of 1024 and N>Nmin；
(2) Performing FFT analysis on the intercepted data x (n) to obtain the amplitude and the phase of each inter-harmonic;
because the maximum frequency range of human perception to flicker does not exceed 0.05-35 Hz, the frequency band of inter-harmonic waves concerned by the invention is 15-85 Hz; after filtering, only f with the inter-harmonic frequency band within the range of 15-85 Hz is reserved1、f2、f3、...、fnThe amplitude and phase of each inter-harmonic, i.e.:
ui(t)＝Ui sin(ωit+θi)
(3) the original signal can be expressed as:
in the formula, the first part is fundamental wave, and the second part is inter-harmonic wave.
4. The method for measuring voltage fluctuation and flicker caused by wind power generation as claimed in claim 1, wherein: the specific steps of the step 3 comprise:
(1) the original signal is passed through a perceptibility weighting filter to simulate a lamp-eye-brain frequency response characteristic as follows:
and obtaining the final measurement result values of voltage fluctuation and flicker caused by wind power:
in the formula: g is a constant of the gain, and G is a constant of the gain,
ω0i＝ω0-ωi，θ0i＝θ0-θi。
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111238621.XA CN113866493A (en) | 2021-10-25 | 2021-10-25 | Method for measuring voltage fluctuation and flicker caused by wind power |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111238621.XA CN113866493A (en) | 2021-10-25 | 2021-10-25 | Method for measuring voltage fluctuation and flicker caused by wind power |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113866493A true CN113866493A (en) | 2021-12-31 |
Family
ID=78997717
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111238621.XA Pending CN113866493A (en) | 2021-10-25 | 2021-10-25 | Method for measuring voltage fluctuation and flicker caused by wind power |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113866493A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116865269A (en) * | 2023-09-01 | 2023-10-10 | 山东泰开电力电子有限公司 | Wind turbine generator system high harmonic compensation method and system |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102288807A (en) * | 2011-05-14 | 2011-12-21 | 苏州大学 | Method for measuring electric network voltage flicker |
| CN105223434A (en) * | 2015-08-27 | 2016-01-06 | 国网青海省电力公司电力科学研究院 | Quality of power supply mixing detection method |
-
2021
- 2021-10-25 CN CN202111238621.XA patent/CN113866493A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102288807A (en) * | 2011-05-14 | 2011-12-21 | 苏州大学 | Method for measuring electric network voltage flicker |
| CN105223434A (en) * | 2015-08-27 | 2016-01-06 | 国网青海省电力公司电力科学研究院 | Quality of power supply mixing detection method |
Non-Patent Citations (2)
| Title |
|---|
| 张全明 等: "基于频谱分析的间谐波闪变效应计算", 电力系统自动化, 10 May 2009 (2009-05-10), pages 67 - 70 * |
| 张惠娟 等: "基于AR模型的电力系统间谐波分析", 电工技术学报, 31 July 2010 (2010-07-31), pages 144 - 145 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116865269A (en) * | 2023-09-01 | 2023-10-10 | 山东泰开电力电子有限公司 | Wind turbine generator system high harmonic compensation method and system |
| CN116865269B (en) * | 2023-09-01 | 2023-11-21 | 山东泰开电力电子有限公司 | Wind turbine generator system high harmonic compensation method and system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Zygarlicki et al. | A reduced Prony's method in power-quality analysis—parameters selection | |
| CN106018956B (en) | A kind of power system frequency computational methods of adding window spectral line interpolation | |
| CN101216512A (en) | Non-sine periodic signal real time high precision detection method | |
| CN108535613B (en) | Voltage flicker parameter detection method based on combined window function | |
| CN108318852B (en) | Square wave influence test method for intelligent electric energy meter | |
| CN109633262A (en) | Three phase harmonic electric energy gauging method, device based on composite window multiline FFT | |
| CN109061302A (en) | A kind of wind power generator incorporated in power network group harmonic measure system converted based on EEMD and Hilbert | |
| CN108169540A (en) | A kind of measuring method of wind power generating set voltage flicker | |
| CN107064846A (en) | The sensitivity detection method and device of live testing apparatus for local discharge | |
| CN113866493A (en) | Method for measuring voltage fluctuation and flicker caused by wind power | |
| CN109581045B (en) | Inter-harmonic power metering method meeting IEC standard framework | |
| Jin et al. | A novel power harmonic analysis method based on Nuttall-Kaiser combination window double spectrum interpolated FFT algorithm | |
| Gallo et al. | Real-time digital multifunction instrument for power quality integrated indexes measurement | |
| Mei et al. | Wavelet packet transform and improved complete ensemble empirical mode decomposition with adaptive noise based power quality disturbance detection | |
| De Capua et al. | Measurement station performance optimization for testing of high efficiency variable speed drives | |
| CN109541304A (en) | The weak amplitude harmonic detecting method of power grid high order based on six minimum secondary lobe window interpolation | |
| CN112213560A (en) | High-precision power grid broadband signal measuring method based on Z-ADALINE | |
| CN110320400B (en) | Voltage flicker envelope parameter extraction method for quasi-synchronous sampling and improved energy operator | |
| Rodrigues et al. | Low-cost embedded measurement system for power quality frequency monitoring | |
| CN112946374B (en) | Three-phase unbalance detection method and device based on convolution window function | |
| CN114487589A (en) | Power grid broadband signal self-adaptive measurement method, device and system | |
| CN114184838A (en) | Power system harmonic detection method, system and medium based on SN mutual convolution window | |
| Liu et al. | An approach to power system harmonic analysis based on triple-line interpolation discrete Fourier transform | |
| Zhivomirov et al. | A method for single-tone frequency estimation | |
| Zhao et al. | Analysis of Real-time Noise Signal Characteristics of Power Transformer Based on All-phase Fast Fourier Transform |
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 |