CN103995178A - Voltage sag detection method for S-transformation on basis of time-frequency gathering characteristic criteria - Google Patents
Voltage sag detection method for S-transformation on basis of time-frequency gathering characteristic criteria Download PDFInfo
- Publication number
- CN103995178A CN103995178A CN201410212549.7A CN201410212549A CN103995178A CN 103995178 A CN103995178 A CN 103995178A CN 201410212549 A CN201410212549 A CN 201410212549A CN 103995178 A CN103995178 A CN 103995178A
- Authority
- CN
- China
- Prior art keywords
- frequency
- time
- voltage
- signal
- value
- 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
Landscapes
- Measuring Frequencies, Analyzing Spectra (AREA)
Abstract
The invention discloses a voltage sag detection method for S-transformation on the basis of time-frequency gathering characteristic criteria. The method includes the first step of obtaining voltage sag disturbance signals to be detected generated in a power distribution network, the second step of conducting analog-digital conversion on an obtained voltage sag disturbance signal model, the third step of conducting optimized selection on gaussian time-frequency window width adjustment parameters in an improved S-transformation method according to time-frequency gathering characteristic criteria, substituting the gaussian time-frequency window width adjustment parameters selected according to empirical values, and conducting characteristic extraction, detection and analysis on digital signals to be detected in the second step through the improved S-transformation. Selection of condition parameters of a gaussian window function is optimized through the constraint condition that the value of a time-frequency gathering characteristic expression is maximized when the signals are analyzed through the improved S-transformation, and the improved S-transformation is applied to characteristic extraction, detection and analysis on voltage sags. The voltage sag detection method for S-transformation on the basis of time-frequency gathering characteristic criteria has the higher detection accuracy degree and signal denoising capacity on transient-state electric energy quality signals.
Description
Technical field
The present invention relates to a kind of electric voltage temporary drop detecting method based on time-frequency aggregation properties criterion S conversion, belong to transient power quality disturbance signal analysis field, the extraction of transient signal characteristic quantity while being particularly suitable for occurring voltage dip in power distribution network, detect and analysis.
Background technology
In recent years, voltage dip in various degree, rise temporarily, interrupt in short-term and the transient power quality disturbance problem such as transient oscillation to airport, bank, precise electronic components and parts manufacturing industry, computer network with serve the places such as Surveillance center and cause great economic loss.Administer transient power quality problem for taking rational measure, must the effective quality of power supply online monitoring system of Speed-up Establishment, and wherein the first step is also that a step of most critical detects timely and accurately, locates and analyzes various transient power quality disturbance signals exactly.
At present, transient power quality disturbance input and analytical approach are mainly comprised: short time discrete Fourier transform (Short Time Fourier Transform, STFT) detection method, wavelet transformation (wavelet transform, WT) detection method, S conversion (S-transform, ST) detection method etc.
Short Time Fourier Transform exists geneogenous deficiency all the time catching on jump signal such as fluctuation harmonic wave etc.: timi requirement is inaccurate, require each to analyze yardstick discrete form roughly the same and conversion does not have orthogonal expansion, be difficult to realize efficient computing.
Wavelet transformation theory is compared with Short Time Fourier Transform and S conversion indigestibility, computing complexity; Choosing of wavelet transformation result and wavelet function is closely related, under current research, also there is no to select accordingly the theoretical foundation of wavelet mother function, in most cases or according to forefathers' experience and achievement; In addition, wavelet transformation is difficult for differentiating to be changed to main disturbance (as voltage jumps, voltage dip) with temporal signatures.
Basic S transform method is proposed in 1996 by people such as Stockwell, and its thought is combination and the expansion to short time discrete Fourier transform and wavelet transformation.Compared with Short Time Fourier Transform, because basic S conversion has been incorporated into frequency in Gauss's window function, be difficult for definite problem so overcome STFT window function; Compared with wavelet transformation, the more directly perceived and easy to understand of the result of basic S conversion, and decompose than continuous wavelet transform at HFS more careful.But the defect of basic S conversion also clearly: no matter what feature signal to be analyzed has, the standard deviation of Gauss's window function is all the inverse of frequency, its variation tendency with frequency is constant, also be that Gauss's window function form is fixed, be difficult to ensure in thering is higher frequency resolution, to there is again higher temporal resolution, this has just limited its dirigibility and practicality in the time analyzing unlike signal object, has greatly limited basic S and has converted the advantage in complicated transient power quality disturbance signal analysis.
Many scholars improve the window function of basic S conversion for this reason, Gauss function in basic S conversion has been added to a time frequency window width adjusting parameter, by regulating this parameter, the time-frequency window features of the Gauss function in basic S conversion just can present various variation tendency thereupon, the changeless problem of Gauss function time frequency resolution variation tendency of having avoided basic S conversion has certain adaptability and dirigibility in application.But, at present choosing of this time frequency window width adjusting parameter adding is limited to uncertainty principle (uncertainty principle), the temporal resolution of signal and frequency resolution can not reach optimum simultaneously, must have a compromise.So the adjusting parameter at present Gauss's window function being added can only be according to the span of a large amount of experimental result preliminary judgement parameters, choose based on experience value again, can bring certain personal error to the detection analysis of transient power quality disturbance signal like this.
Summary of the invention
In order to meet, transient power quality disturbance signal to be carried out accurately, detected in time and analyzes, farthest bring into play basic S and convert the advantage in signal analysis, to basic S conversion and improve on the basis of Gauss's window Functional Analysis research of S conversion, the present invention proposes a kind of time-frequency aggregation properties criterion S conversion and improve one's methods transient power quality disturbance signal is extracted, detects and analyzed.
Technical scheme of the present invention is:
Based on an electric voltage temporary drop detecting method for time-frequency aggregation properties criterion S conversion, comprise the following steps:
Step 1: obtain the voltage Sag Disturbance measured signal occurring in power distribution network;
Step 2: the voltage Sag Disturbance signal model obtaining in step 1 is carried out to analog to digital conversion, comprise sampling, the discretize of signal, simulating signal is converted to digital signal;
Step 3: utilize time-frequency aggregation properties criterion that the Gauss's time frequency window width adjusting parameter in improvement S transform method is optimized and is chosen, replace Gauss's time frequency window width adjusting parameter of utilizing empirical value to choose, and utilize the digital signal to be measured in the S transfer pair step 2 after improving to carry out extraction, detection, the analysis of characteristic quantity, determine the characteristic quantity of voltage Sag Disturbance signal, comprise the saltus step of start-stop moment, duration, temporary range of decrease value, harmonic wave and the phase place of falling temporarily etc., for the effective Electric Power Quality On-line Monitor System of next step Speed-up Establishment is prepared.
Further, in step 3, described improvement S conversion comprises following part:
(1) basic S conversion improves thought
The one-dimensional signal that the people such as Stockwell provide
continuous S conversion may be defined as:
In formula, Gauss's window function can be seen following formula as:
In formula,
for timing signal sequence,
the time displacement factor,
be the standard deviation of window function, also referred to as time-frequency window scale factor, it is frequency
inverse, can be expressed as:
Because S conversion has been incorporated into frequency in Gauss's window function, show that S converts aspect signal processing compared with the advantage of short time discrete Fourier transform; But the defect of S conversion is also clearly: no matter signal to be analyzed has any feature, and the standard deviation of window function
be all the inverse of frequency, its variation tendency with frequency is constant, has just limited its dirigibility and practicality in the time analyzing unlike signal object, and meanwhile, the time-frequency aggregation properties of S conversion is also very poor.
The improvement S conversion expression adopting is as follows:
Due to burst
all with sampling time interval
for the cycle samples, therefore can make
,
,
, the discrete expression of the S that is improved conversion is as follows:
In formula,
for the Gauss's window function width adjusting parameter adding;
with
for discrete-time variable;
for discrete frequency variable;
for sampling number;
for the sampling period.
Time-frequency aggregation properties is the important indicator of weighing Time-Frequency Analysis Method quality.Improve in S conversion Gauss function and increased adjusting parameter, can improve S and convert with regard to a certain signal to be analyzed the time-frequency aggregation properties at whole time-frequency plane, and when signal is in the time that the time-frequency aggregation properties of whole time-frequency plane reaches the highest, be also that the adjusting parameter in Gauss's window function reaches optimal value.Otherwise, utilize signal to reach this constraint condition of mxm. at the time-frequency aggregation properties of whole time-frequency plane and choose Gauss's window function and regulate the optimal value of parameter.Concrete thought is: by the time-frequency aggregation PROBLEM DECOMPOSITION of whole time-frequency plane, to each discrete frequency place, as long as improved the time-frequency aggregation at each frequency place by certain approach, the time-frequency aggregation of whole time-frequency plane is also with regard to corresponding raising.And the approach that improves each frequency place time-frequency aggregation is exactly the Gauss's window width by optimizing each frequency place, be also that the adjusting parameter of optimization gauss window function realizes.Optimize and regulate parameter should make time-frequency aggregation properties measurement criterion expression formula
Value maximum.Obtain optimal adjustment parameter
afterwards, carry it into and improve in S mapping algorithm, can avoid choosing Gauss function because of human factor and regulate parameter, further improve improvement S and convert the precision aspect input.
(2) basic S conversion improves step
Utilize the optimization of time-frequency aggregation properties measurement criterion to choose the adjusting parameter of Gauss function in improvement S conversion
algorithm steps is as follows:
1) according to the discrete expression that improves S conversion, for
each in scope
be worth, calculate respectively the time-frequency distributions of signal to be analyzed
;
2) select arbitrarily a certain original frequency
, for each
value, brings respectively time-frequency aggregation properties measurement criterion expression formula into
middle calculating; Step 1) in
be exactly to regulate parameter in fact
value correspondence
time-frequency distributions is in selected frequency
the cross-sectional distribution at place, is designated as
;
3) for any selected frequency
, comparison step 2) obtain each
value, its maximal value is corresponding
value is exactly selected frequency
corresponding optimal adjustment parameter
, also:
4) repeating step 2) and 3), until calculate adjusting parameter optimization value corresponding to all frequencies
.
5) use after optimization
as regulating parameter to be brought in improved discrete S conversion, replace the adjusting parameter that empirical value is chosen
.
Further, aspect parameter arranges, step 1) in adjusting parameter in Gauss's window function
value can not be greater than 1.Because in the time carrying out emulation experiment, find, for a certain analytic signal, to suppose
, its Gauss's window can narrow in time domain direction, and Gauss's window function can, very close to Dirac function (Dirac function), also work as like this
value much larger than 1 o'clock, very narrow Gauss's window is just only applicable to the set for analyzing Dirac function or this class function, this just greatly limited Gauss's window function aspect analytic signal effect; And in the time of analytic signal, for
each in scope
value, all time-frequency distributions of signal that correspondence calculates
also can meet frequency edges requirement.
Step 2) in, for time-frequency aggregation properties measurement criterion expression formula
in parameter
,
for constant, and
span be set to
.Because suppose
, denominator
, for a certain original frequency
, what signal was detected like this is that a certain frequency is
time interval sequence, this time interval may be to be caused by given frequency, also may be caused by the Frequency point diffusion being adjacent, even if it is very delicate to be subject to like this impact of side frequency point, also can make time-frequency aggregation properties measurement criterion expression formula
value alter a great deal; On the other hand, if
or
, for step 1) in each
value,
will be close to a certain constant, like this for different
value, the time-frequency distributions value difference that calculates is different little, also can not filter out optimum
value.In addition, because detected signal is all likely with noise composition, the slight change in analytic process all can produce error very to whole result of calculation, through repetition test, selected
,
.
Further, in step 3, improve the voltage dip Characteristic Extraction of S mapping algorithm and the key step of determination method and can be summarized as described below:
1) voltage signal to be measured is improved to S transform operation, obtain the multiple time-frequency matrix after conversion, multiple time-frequency matrix is processed, separate depanning value matrix
and phasing matrix
;
2) by mould value matrix
obtain fundamental voltage amplitude curve map, thereby can calculate the temporary drawdown degree of voltage magnitude
;
3) by mould value matrix
obtain high frequency spectrum value and curve map, thereby can calculate voltage dip start-stop moment and duration;
4) by phasing matrix
can calculate voltage dip phase hit value
thereby, can determine the SPA sudden phase anomalies point of voltage dip signal.
Further, step 1 in claim 4)-4) the characteristic quantity concrete analysis of described voltage dip is as described below:
A. the detection of voltage dip amplitude
Voltage dip amplitude, there is the size of the voltage magnitude after decline suddenly in voltage, often with the temporary drawdown degree of voltage magnitude
(
; Wherein,
for falling temporarily the effective value of front voltage,
the effective value of voltage when falling temporarily) represent.By the modular matrix that improves S conversion
column vector corresponding to fundamental frequency can obtain the time dependent fundamental voltage amplitude curve of reflected signal fundamental voltage amplitude
.The temporary drawdown degree of voltage magnitude
can be by
calculate
B. the detection of voltage dip start-stop moment and voltage dip duration
By modular matrix
in be more than or equal to ten times of fundamental frequencies column vector corresponding to all high fdrequency components can obtain each high frequency amplitude characteristic of signal, these vectors with the high frequency spectrum and the matrix that have formed signal
,
The curve with forming of high frequency amplitude matrix can reflect the high fdrequency component rule over time of signal.The start-stop moment correspondence of voltage dip the sudden change moment of signal, and the sudden change moment of voltage signal causes the appearance of high fdrequency component.Simulation study is found, there is the moment and all have spike to exist the finish time through improving high frequency spectrum amplitude after the conversion of S mapping algorithm and curve at voltage dip in voltage dip signal, can accurately detect voltage dip start-stop moment, duration by these two spike points generation moment.
C. the detection of voltage dip phase hit
By phasing matrix
in extract fundamental frequency row phase vectors
, and then adjacent 2 phase increments of point-by-point comparison
, i.e. phase hit value, computing formula is:
In formula,
for length is
column vector,
, and
.
The invention has the beneficial effects as follows:
The time-frequency aggregation properties criterion S mapping algorithm that the present invention proposes is no matter be from time-frequency aggregation properties aspect, or from removing undesired signal aspect, be all obviously better than basic S mapping algorithm and improve S conversion (
value immobilizes) algorithm, prove that the S of the present invention's proposition converts the feasibility of improvement project;
Use the S transfer pair voltage dip signal after improving to detect analysis, can obtain characterizing the curve of voltage dip characteristic quantity, utilize these characteristic curvees can obtain more intuitively, exactly voltage the feature of falling temporarily occurs;
At the same time under the input condition containing voltage magnitude decline and phase hit, the time-frequency aggregation properties criterion S transform method that the present invention proposes can carry out the detection of voltage dip very effectively, and the temporary drawdown degree of voltage magnitude, the detection error of falling temporarily the one of transient characteristic quantity such as duration, phase hit be all less than basic S mapping algorithm and improve S conversion (
value immobilizes) detection method of algorithm; Improve S conversion affected by noise not remarkable compared with other two kinds of detection methods, ensured higher voltage dip input analysis accuracy and practicality;
In sum, the present invention, basic S conversion and improvement S are converted on the basis of Gauss's window Functional Analysis research, is incorporated into Gauss's window function by characterization signal time-frequency aggregation properties criterion and regulates in choosing of parameter.Improve value that S converts the time-frequency aggregation properties expression formula in analytic signal process and reach maximum this constraint condition optimization and choose the conditional parameter of Gauss's window function by utilization, avoid utilizing empirical value to regulate choosing of parameter, and the S conversion after improving is used for to extraction, the detection to voltage dip characteristic quantity and analyzes, example shows that the method has higher accuracy of detection and signal denoising ability aspect transient power quality signal.The present invention can effectively meet to transient power quality disturbance signal carry out accurately, in time detect with analyze, farthest brought into play basic S and converted the advantage in signal analysis.
Brief description of the drawings
Fig. 1 utilizes the optimization of time-frequency aggregation properties measurement criterion to choose the algorithm flow chart of the adjusting parameter of Gauss's time frequency window function in improvement S conversion;
Fig. 2 is in the embodiment of the present invention one, the time frequency distribution map of a certain given sophisticated signal:
Fig. 3 is in the embodiment of the present invention one, adopts the time frequency distribution map of basic S transform method;
Fig. 4 is in the embodiment of the present invention one, adopts the time frequency distribution map of improving S transform method;
Fig. 5 is in the embodiment of the present invention one, adopts the time frequency distribution map of time-frequency aggregation properties criterion S conversion of the present invention;
Fig. 6 be based on the present invention propose to be improved S mapping algorithm voltage dip Characteristic Extraction with detect analytical algorithm process flow diagram;
Fig. 7 is in the embodiment of the present invention two, produce the three-phase voltage sag disturbing signal being caused by short trouble in power distribution network for simulation, utilize the analysis chart of the voltage dip original signal of time-frequency aggregation properties criterion S transfer pair that the present invention a proposes phase voltage wherein;
Fig. 8 is in the embodiment of the present invention two, utilizes the analysis chart of the fundamental frequency amplitude signal of time-frequency aggregation properties criterion S transfer pair that the present invention a proposes phase voltage wherein;
Fig. 9 is in the embodiment of the present invention two, utilizes the high frequency amplitude of time-frequency aggregation properties criterion S transfer pair that the present invention a proposes phase voltage wherein and the analysis chart of curve;
Figure 10 is in the embodiment of the present invention two, utilizes the analysis chart of the fundamental phase curve of time-frequency aggregation properties criterion S transfer pair that the present invention a proposes phase voltage wherein;
Figure 11 is in the embodiment of the present invention two, through improving the sampled point-amplitude-frequency three-dimensional plot after S transform analysis.
Embodiment
Below in conjunction with accompanying drawing, the present invention is described in further detail.
Shown in Figure 1, a kind of method of utilizing the optimization of time-frequency aggregation properties measurement criterion to choose the adjusting parameter of Gauss's time frequency window function in existing improvement S conversion of the present invention, elaborates to this point following components of improving one's methods below.
(1) basic S conversion improves thought
The one-dimensional signal that the people such as Stockwell provide
continuous S conversion may be defined as:
In formula, Gauss's window function can be seen following formula as:
In formula,
for timing signal sequence,
the time displacement factor,
be the standard deviation of window function, also referred to as time-frequency window scale factor, it is frequency
inverse, can be expressed as:
Because basic S conversion has been incorporated into frequency in Gauss's window function, show that S converts aspect signal processing compared with the advantage of short time discrete Fourier transform; But the defect of S conversion is also clearly: no matter signal to be analyzed has any feature, and the standard deviation of window function
be all the inverse of frequency, its variation tendency with frequency is constant, has just limited its dirigibility and practicality in the time analyzing unlike signal object, and meanwhile, the time-frequency aggregation properties of S conversion is also very poor.
Improve S conversion expression as follows:
Due to burst
all with sampling time interval
for the cycle samples, therefore can make
,
,
, the discrete expression of the S that is improved conversion is as follows:
In formula,
with
for discrete-time variable;
for discrete frequency variable;
for sampling number;
for the sampling period.
Time-frequency aggregation properties is the important indicator of weighing Time-Frequency Analysis Method quality.Improve in S conversion Gauss function and increased adjusting parameter, can improve S and convert with regard to a certain signal to be analyzed the time-frequency aggregation properties at whole time-frequency plane, and when signal is in the time that the time-frequency aggregation properties of whole time-frequency plane reaches the highest, be also that the adjusting parameter in Gauss's window function reaches optimal value.Otherwise, utilize signal to reach this constraint condition of mxm. at the time-frequency aggregation properties of whole time-frequency plane and choose Gauss's window function and regulate the optimal value of parameter.Concrete thought is: by the time-frequency aggregation PROBLEM DECOMPOSITION of whole time-frequency plane, to each discrete frequency place, as long as improved the time-frequency aggregation at each frequency place by certain approach, the time-frequency aggregation of whole time-frequency plane is also with regard to corresponding raising.And the approach that improves each frequency place time-frequency aggregation is exactly the Gauss's window width by optimizing each frequency place, be also that the adjusting parameter of optimization gauss window function realizes.Optimize and regulate parameter should make time-frequency aggregation properties measurement criterion expression formula
Value maximum.Obtain optimal adjustment parameter
afterwards, carry it into and improve in S mapping algorithm, can avoid choosing Gauss function because of human factor and regulate parameter, further improve improvement S and convert the precision aspect input.
(2) basic S conversion improves step
Utilize the optimization of time-frequency aggregation properties measurement criterion to choose the adjusting parameter of Gauss function in improvement S conversion
algorithm steps is as follows:
1) according to the discrete expression that improves S conversion, for
each in scope
be worth, calculate respectively the time-frequency distributions of signal to be analyzed
;
2) select arbitrarily a certain original frequency
, for each
value, brings respectively time-frequency aggregation properties measurement criterion expression formula into
middle calculating; Step 1) in
be exactly to regulate parameter in fact
value correspondence
time-frequency distributions is in selected frequency
the cross-sectional distribution at place, is designated as
;
3) for any selected frequency
, comparison step 2) obtain each
value, its maximal value is corresponding
value is exactly selected frequency
corresponding optimal adjustment parameter
, also:
4) repeating step 2) and 3), until calculate adjusting parameter optimization value corresponding to all frequencies
.
5) use after optimization
as regulating parameter to be brought in improved discrete S conversion, replace the adjusting parameter that empirical value is chosen
.
Be described further as follows to above improvement step:
Wherein aspect parameter arranges, step 1) in adjusting parameter in Gauss's window function
value can not be greater than 1.Because in the time carrying out emulation experiment, find, for a certain analytic signal, to suppose
, its Gauss's window can narrow in time domain direction, and Gauss's window function can, very close to Dirac function (Dirac function), also work as like this
value much larger than 1 o'clock, very narrow Gauss's window is just only applicable to the set for analyzing Dirac function or this class function, this just greatly limited Gauss's window function aspect analytic signal effect; And in the time of analytic signal, for
each in scope
value, all time-frequency distributions of signal that correspondence calculates
also can meet frequency edges requirement.
Step 2) in, for time-frequency aggregation properties measurement criterion expression formula
in parameter
,
for constant, and
span be set to
.Because suppose
, denominator
, for a certain original frequency
, what signal was detected like this is that a certain frequency is
time interval sequence, this time interval may be to be caused by given frequency, also may be caused by the Frequency point diffusion being adjacent, even if it is very delicate to be subject to like this impact of side frequency point, also can make time-frequency aggregation properties measurement criterion expression formula
value alter a great deal; On the other hand, if
or
, for step 1) in each
value,
will be close to a certain constant, like this for different
value, the time-frequency distributions value difference that calculates is different little, also can not filter out optimum
value.In addition, because detected signal is all likely with noise composition, the slight change in analytic process all can produce error very to whole result of calculation, through repetition test, selected
,
.
Shown in Fig. 2-Fig. 5, in order to illustrate that basic S conversion that the present invention proposes improves the feasibility of algorithm and in the advantage aspect sophisticated signal analysis, below by basic S conversion, improve S conversion (
value immobilizes), the time-frequency aggregation properties criterion S mapping algorithm that proposes of the present invention is (after optimization
) a given signal is carried out to time frequency analysis, and analysis result is contrasted.
The signal that this example is considered is formed by stacking by two compositions, and first is multiple sinusoidal signal (sampling number is 256, and normalized frequency is 0.1); Second is Gauss's undesired signal (sampling number is 256, and normalized frequency is 0.3), and time and frequency have translation, and signal in later stage many places with obvious phase hit, original signal is as shown in Figure 2.First simulate the basic S conversion time-frequency distributions of this signal, as shown in Figure 3; Then regulating parameter
(emulation arranges adjusting parameter to get empirical value
) situation under, simulate this signal in the time-frequency distributions of improving under S conversion, as Fig. 4; Finally adopt the adjusting parameter optimization algorithm that the present invention introduces (
,
), Frequency point interval
time, the improvement S that simulates this signal converts time-frequency distributions, as shown in Figure 5.
Comparing result shown in Fig. 2-Fig. 5 is made to brief analysis as follows:
As shown in Figure 2, in the time Gauss's undesired signal in signal and phase hit not detected, this signal is exactly a simple sinusoidal signal again, its time-frequency distributions should be that frequency is a smooth straight line of carrier frequency in theory, and linear width is narrower, smoothness is higher, and it is better to illustrate for detection of the algorithm time-frequency aggregation properties of this signal, and the ability of removing undesired signal is stronger.Can find out from Fig. 3, Fig. 4, first two algorithm has all detected signal and has had phase hit, but phase hit point almost mixes with Gauss's distracter, cannot distinguish; And from Fig. 5, can clearly be seen that initial time and Gauss's distracter of phase hit appears in signal.Aspect time-frequency aggregation properties and anti-Gauss's undesired signal, Gauss's window function regulates parameter
fixing improvement S conversion is being better than basic S conversion aspect anti-Gauss's undesired signal ability, also smoother of time-frequency distributions straight line, but (being also the multiple sinusoidal signal stage that signal is in standard) time-frequency distributions straight line is too wide at head and the tail two ends, illustrate that time-frequency aggregation properties is desirable not enough, and the time-frequency aggregation properties criterion S mapping algorithm that the present invention proposes is no matter be from time-frequency aggregation properties aspect, or from removing undesired signal aspect, all obviously be better than other two kinds of algorithms, prove the feasibility of the S conversion improvement project of the present invention's proposition.
Shown in Figure 6, the characteristic quantity that reflecting voltage falls temporarily mainly comprises the saltus step of start-stop moment, duration, temporary range of decrease value and the phase place of falling temporarily.Use the S transfer pair voltage dip signal after improving to detect analysis, can obtain characterizing the curve of voltage dip characteristic quantity, utilize these characteristic curvees can obtain more intuitively, exactly voltage the feature of falling temporarily occurs.
Voltage Sag Disturbance signal is the multiple time-frequency matrix of two dimension through improved S transformation results, if directly utilize multiple time-frequency matrix to carry out extraction and the analysis of Transient Disturbance Signal, data volume is too huge, and the perturbation features amount extracting is easily submerged, and causes testing result accurate not.Therefore this example, in analytic process, has carried out asking modular arithmetic to multiple time-frequency matrix, and has isolated phasing matrix, then directly modular matrix and phasing matrix is carried out to the extraction and detection analysis of characteristic quantity, makes a concrete analysis of as follows:
A. the detection of voltage dip amplitude
Voltage dip amplitude, there is the size of the voltage magnitude after decline suddenly in voltage, often with the temporary drawdown degree of voltage magnitude
(
; Wherein,
for falling temporarily the effective value of front voltage,
the effective value of voltage when falling temporarily) represent.By the modular matrix that improves S conversion
column vector corresponding to fundamental frequency can obtain the time dependent fundamental voltage amplitude curve of reflected signal fundamental voltage amplitude
.The temporary drawdown degree of voltage magnitude
can be by
calculate
B. the detection of voltage dip start-stop moment and voltage dip duration
By modular matrix
in be more than or equal to ten times of fundamental frequencies column vector corresponding to all high fdrequency components can obtain each high frequency amplitude characteristic of signal, these vectors with the high frequency spectrum and the matrix that have formed signal
,
The curve with forming of high frequency amplitude matrix can reflect the high fdrequency component rule over time of signal.The start-stop moment correspondence of voltage dip the sudden change moment of signal, and the sudden change moment of voltage signal causes the appearance of high fdrequency component.Simulation study is found, there is the moment and all have spike to exist the finish time through improving high frequency spectrum amplitude after the conversion of S mapping algorithm and curve at voltage dip in voltage dip signal, can accurately detect voltage dip start-stop moment, duration by these two spike points generation moment.
C. the detection of voltage dip phase hit
By phasing matrix
in extract fundamental frequency row phase vectors
, and then adjacent 2 phase increments of point-by-point comparison
, i.e. phase hit value, computing formula is:
In formula,
for length is
column vector,
, and
.
Further, utilize the voltage dip Characteristic Extraction of the time-frequency aggregation properties criterion S mapping algorithm that the present invention proposes and the key step of determination method to can be summarized as described below:
1) voltage signal to be measured is improved to S transform operation, obtain the multiple time-frequency matrix after conversion, multiple time-frequency matrix is processed, separate depanning value matrix
and phasing matrix
;
2) by mould value matrix
obtain fundamental voltage amplitude curve map, thereby can calculate the temporary drawdown degree of voltage magnitude
;
3) by mould value matrix
obtain high frequency spectrum value and curve map, thereby can calculate voltage dip start-stop moment and duration;
4) by phasing matrix
can calculate voltage dip phase hit value
thereby, can determine the SPA sudden phase anomalies point of voltage dip signal.
Referring to Fig. 7-Figure 11, simulation produces a three-phase voltage sag signal being caused by short trouble, to wherein one use mutually the time-frequency aggregation properties criterion S conversion that the present invention proposes to study, as shown in Figure 7, algorithm parameter arranges as follows: sample frequency is made as 1.6KHz, and each cycle sampling number is 32; Improve in S mapping algorithm and optimize and regulate parameter to be set to
,
, Frequency point is set to
.The input parameter of emulation experiment is set as: system voltage is 220V, and frequency is 50Hz, and falling temporarily the duration is 0.1s, and the temporary drawdown degree of amplitude is 62%, in the saltus step of 150 and 300 these two sample point generation phase places, and the equal normalization of all units.Signal waveform (signal to noise ratio (S/N ratio) falls in primary voltage temporarily
) and improve S mapping algorithm and detect as shown in Figure 8,9, 10.Testing result is as described below:
By fundamental voltage amplitude curve map and modular matrix
value can solve falls front voltage magnitude temporarily
, obtain minimum the 198th sample point
, temporary drawdown degree
; By high frequency amplitude and curve and modular matrix
value can obtain obtaining respectively maximum value 0.0017 and 0.00158 at the 154th sampled point and the 298th sample point, under power frequency state, and 32 points of one-period sampling, the voltage dip duration is
; By fundamental phase curve and phasing matrix
be worth knownly, point out and obtain maximum value 0.1315 in the 150th and the 300th these two samplings, the saltus step of phase place has occurred at this two point voltage.The analysis result that has represented more visually the improvement S transfer pair voltage Sag Disturbance signal of this example employing from Figure 11 sampled point-frequency-amplitude 3 dimensional drawing is two-dimensional matrix.
Further, in order to verify that time-frequency aggregation properties criterion S transform method that the present invention proposes is promoting feasibility in the application of transient power quality input and simulating actual conditions better, above-mentioned voltage dip sample has been carried out based on S voltage of transformation, improved time-frequency aggregation properties criterion S change detection totally 80 simulation analysis that S converts, this example adopts, select one group of best testing result and contrast, comparing result is as shown in table 1; Add
white Gaussian noise undesired signal after, again detect contrast, it is as shown in table 2 that voltage dip duration under different noise is detected error comparing result.
Three kinds of method testing result contrasts of table 1
The detection error of voltage dip duration contrast under the different noise of table 2
As shown in Table 1, at the same time under the input condition containing voltage magnitude decline and phase hit, the time-frequency aggregation properties criterion S transform method that the present invention proposes can carry out the detection of voltage dip very effectively, and the temporary drawdown degree of voltage magnitude, the detection error of falling temporarily the one of transient characteristic quantity such as duration, phase hit are all less than other two kinds of detection methods; Improve as seen from Table 2 S conversion affected by noise not remarkable compared with other two kinds of detection methods, ensured higher voltage dip input analysis accuracy and practicality.
The present invention, basic S conversion and improvement S are converted on the basis of Gauss's window Functional Analysis research, is incorporated into Gauss's window function by characterization signal time-frequency aggregation properties criterion and regulates in choosing of parameter.Improve value that S converts the time-frequency aggregation properties expression formula in analytic signal process and reach maximum this constraint condition optimization and choose the conditional parameter of Gauss's window function by utilization, avoid utilizing empirical value to regulate choosing of parameter, and the S conversion after improving is used for to extraction, the detection to voltage dip characteristic quantity and analyzes, example shows that the method has higher accuracy of detection and signal denoising ability aspect transient power quality signal.
The foregoing is only preferred embodiment of the present invention, not in order to limit the present invention.All any amendments of doing within the spirit and principles in the present invention, be equal to and replace and improvement etc., within all should being included in protection scope of the present invention.
Claims (6)
1. the electric voltage temporary drop detecting method based on time-frequency aggregation properties criterion S conversion, comprises the following steps:
Step 1: obtain the voltage Sag Disturbance measured signal occurring in power distribution network;
Step 2: the voltage Sag Disturbance signal model obtaining in step 1 is carried out to analog to digital conversion, comprise sampling, the discretize of signal, simulating signal is converted to digital signal;
Step 3: utilize time-frequency aggregation properties criterion that the Gauss's time frequency window width adjusting parameter in improvement S transform method is optimized and is chosen, replace Gauss's time frequency window width adjusting parameter of utilizing empirical value to choose, and utilize the digital signal to be measured in the S transfer pair step 2 after improving to carry out extraction, detection, the analysis of characteristic quantity, determine the characteristic quantity of voltage Sag Disturbance signal, comprise start-stop moment, duration, the saltus step of range of decrease value, harmonic wave and phase place temporarily of falling temporarily.
2. the electric voltage temporary drop detecting method based on time-frequency aggregation properties criterion S conversion according to claim 1, is characterized in that: in step 3, described improvement S transform method comprises the steps:
A, the discrete expression converting according to improvement S, for
each in scope
be worth, calculate respectively the time-frequency distributions of signal to be analyzed
;
B, selected a certain original frequency arbitrarily
, for each
value, brings respectively time-frequency aggregation properties measurement criterion expression formula into
middle calculating; In steps A
to regulate parameter
value correspondence
time-frequency distributions is in selected frequency
the cross-sectional distribution at place, is designated as
;
C, for any selected frequency
, each that comparison step B obtains
value, its maximal value is corresponding
value is exactly selected frequency
corresponding optimal adjustment parameter
, also:
；
D, repeating step B and C, until calculate adjusting parameter optimization value corresponding to all frequencies
;
After E, use optimization
as regulating parameter to be brought in improved discrete S conversion, replace the adjusting parameter that empirical value is chosen
.
3. the electric voltage temporary drop detecting method based on time-frequency aggregation properties criterion S conversion according to claim 2, is characterized in that: the time-frequency distributions of described signal to be analyzed
expression formula is as follows:
，
Order
,
,
, the discrete expression of the S that is improved conversion is as follows:
，
Wherein,
for the Gauss's window function width adjusting parameter adding;
with
for discrete-time variable;
for discrete frequency variable;
for sampling number;
for the sampling period.
4. according to the electric voltage temporary drop detecting method based on time-frequency aggregation properties criterion S conversion described in claim 2 or 3, it is characterized in that: aspect parameter arranges, the adjusting parameter in steps A in Gauss's window function
value be not more than 1; In step B, for time-frequency aggregation properties measurement criterion expression formula
in parameter
,
for constant, and
,
span be set to respectively
,
.
5. the electric voltage temporary drop detecting method based on time-frequency aggregation properties criterion S conversion according to claim 1, is characterized in that:
In step 3, the step of voltage dip Characteristic Extraction, detection and the analytical approach of described improvement S mapping algorithm is:
A, voltage signal to be measured is improved to S transform operation, obtain the multiple time-frequency matrix after conversion, multiple time-frequency matrix is processed, separate depanning value matrix
and phasing matrix
;
B, by mould value matrix
obtain fundamental voltage amplitude curve map, thereby can calculate the temporary drawdown degree of voltage magnitude
;
C, by mould value matrix
obtain high frequency spectrum value and curve map, thereby can calculate voltage dip start-stop moment and duration;
D, by phasing matrix
can calculate voltage dip phase hit value
thereby, can determine the SPA sudden phase anomalies point of voltage dip signal.
6. the electric voltage temporary drop detecting method based on time-frequency aggregation properties criterion S conversion according to claim 5, it is characterized in that: the characteristic quantity of described voltage dip comprises voltage dip amplitude, voltage dip start-stop moment and voltage dip duration, voltage dip phase hit, and the analytical procedure of each characteristic quantity is:
A. the detection of voltage dip amplitude:
Voltage dip amplitude, there is the size of the voltage magnitude after decline suddenly in voltage, often with the temporary drawdown degree of voltage magnitude
represent, wherein,
;
for falling temporarily the effective value of front voltage,
the effective value of voltage when falling temporarily; By the modular matrix that improves S conversion
column vector corresponding to fundamental frequency obtains the time dependent fundamental voltage amplitude curve of reflected signal fundamental voltage amplitude
; The temporary drawdown degree of voltage magnitude
by
calculate:
；
B. the detection of voltage dip start-stop moment and voltage dip duration:
By modular matrix
in be more than or equal to ten times of fundamental frequencies column vector corresponding to all high fdrequency components can obtain each high frequency amplitude characteristic of signal, these vectors with the high frequency spectrum and the matrix that have formed signal
, that is:
，
The curve with forming of high frequency amplitude matrix reflects the high fdrequency component rule over time of signal; In the sudden change moment of the start-stop moment respective signal of voltage dip, the sudden change moment of voltage signal causes the appearance of high fdrequency component; There is the moment and all have spike to exist the finish time through improving high frequency spectrum amplitude after the conversion of S mapping algorithm and curve at voltage dip in voltage dip signal, can accurately detect voltage dip start-stop moment, duration by these two spike points generation moment;
C. the detection of voltage dip phase hit:
By phasing matrix
in extract fundamental frequency row phase vectors
, and then adjacent 2 phase increments of point-by-point comparison
, i.e. phase hit value:
，
Wherein,
for length is
column vector,
, and
.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410212549.7A CN103995178A (en) | 2014-05-20 | 2014-05-20 | Voltage sag detection method for S-transformation on basis of time-frequency gathering characteristic criteria |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410212549.7A CN103995178A (en) | 2014-05-20 | 2014-05-20 | Voltage sag detection method for S-transformation on basis of time-frequency gathering characteristic criteria |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN103995178A true CN103995178A (en) | 2014-08-20 |
Family
ID=51309397
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410212549.7A Pending CN103995178A (en) | 2014-05-20 | 2014-05-20 | Voltage sag detection method for S-transformation on basis of time-frequency gathering characteristic criteria |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN103995178A (en) |
Cited By (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104267310A (en) * | 2014-09-09 | 2015-01-07 | 中国矿业大学 | Voltage dip source positioning method based on disturbance power direction |
| CN104391207A (en) * | 2014-12-09 | 2015-03-04 | 湖南工业大学 | Voltage sag detection method adopting fundamental frequency single vector S transformation |
| CN104459397A (en) * | 2014-12-08 | 2015-03-25 | 东北电力大学 | Power quality disturbance recognizing method with self-adaptation multi-resolution generalized S conversion adopted |
| CN104730384A (en) * | 2015-03-16 | 2015-06-24 | 华南理工大学 | Power disturbance identification and localization method based on incomplete S transformation |
| CN104749432A (en) * | 2015-03-12 | 2015-07-01 | 西安电子科技大学 | Estimation method of multi-component non-stationary signal instantaneous frequency based on focusing S-transform |
| CN104833844A (en) * | 2015-05-11 | 2015-08-12 | 上海市计量测试技术研究院 | Alternating-current effective value sampling measurement method |
| CN104993711A (en) * | 2015-05-22 | 2015-10-21 | 国网河南省电力公司电力科学研究院 | Voltage sag transition process simulation device and method |
| CN105137164A (en) * | 2015-08-06 | 2015-12-09 | 江苏省电力公司苏州供电公司 | Voltage sag on-line monitoring device applied in power system |
| WO2016197484A1 (en) * | 2015-06-09 | 2016-12-15 | 国网四川省电力公司经济技术研究院 | Optimal configuration method for voltage sag monitoring node |
| CN106548013A (en) * | 2016-10-19 | 2017-03-29 | 西安工程大学 | Using the voltage sag source identification method for improving incomplete S-transformation |
| CN108983046A (en) * | 2018-08-16 | 2018-12-11 | 国网山东省电力公司泰安供电公司 | A kind of voltage dip situation estimation method and system based on singular value decomposition method |
| CN109635430A (en) * | 2018-12-12 | 2019-04-16 | 中国科学院深圳先进技术研究院 | Grid power transmission route transient signal monitoring method and system |
| CN110444005A (en) * | 2019-08-08 | 2019-11-12 | 国网新疆电力有限公司电力科学研究院 | Low Voltage Power Line Carrier Record System interference free performance test method and system |
| CN110954779A (en) * | 2019-11-29 | 2020-04-03 | 国网上海市电力公司 | Voltage sag source feature identification method based on S transformation and multidimensional fractal |
| CN111122941A (en) * | 2019-12-04 | 2020-05-08 | 国网湖南综合能源服务有限公司 | Kaiser window function-based voltage sag characteristic quantity detection method, system and medium for improving S conversion |
| CN111257619A (en) * | 2020-02-17 | 2020-06-09 | 南京工程学院 | Voltage sag detection method based on multi-attribute decision and improved S transformation method |
| CN111398721A (en) * | 2020-04-14 | 2020-07-10 | 南京工程学院 | Power distribution network voltage sag source classification and identification method introducing adjustment factors |
| CN112034232A (en) * | 2020-08-21 | 2020-12-04 | 上海电机学院 | Power supply system voltage sag detection method |
| CN112540220A (en) * | 2020-11-05 | 2021-03-23 | 广东电网有限责任公司广州供电局 | Voltage sag detection circuit and device |
| CN113627313A (en) * | 2021-08-02 | 2021-11-09 | 国网江苏省电力有限公司镇江供电分公司 | Electric energy meter metering method based on S transformation under non-ideal condition |
| CN113946959A (en) * | 2021-10-18 | 2022-01-18 | 国网河南省电力公司电力科学研究院 | Voltage sag data segment extraction method |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101339208A (en) * | 2008-08-12 | 2009-01-07 | 中国矿业大学 | Voltage quality monitoring and perturb automatic classification method based on analysis in time-domain |
| CN101738551A (en) * | 2009-12-15 | 2010-06-16 | 西南交通大学 | Method for intelligent analysis of transient power quality disturbance based on networking |
| CN102072983A (en) * | 2010-11-22 | 2011-05-25 | 华北电力大学（保定） | Method for judging voltage sag reason |
| CN102831433A (en) * | 2012-06-06 | 2012-12-19 | 西南交通大学 | Method for classifying electric energy quality mixing disturbances based on multi-feature quantity of time-frequency domain |
| CN103245832A (en) * | 2013-05-16 | 2013-08-14 | 湖南大学 | Harmonic time frequency characteristic parameter estimating method based on fast S conversion and analysis meter |
| CN103308804A (en) * | 2013-06-17 | 2013-09-18 | 湖南大学 | Method for extracting time-frequency parameters of power quality disturbance signals on basis of fast K-S (Kaiser-S) transformation |
| CN103424600A (en) * | 2013-08-20 | 2013-12-04 | 昆明理工大学 | Voltage sag source identification method based on Hilbert-Huang transformation and wavelet packet energy spectra |
| CN103487682A (en) * | 2013-09-13 | 2014-01-01 | 深圳供电局有限公司 | Early warning method for sensitive customer electric energy experience quality under voltage sag disturbance |
| KR101352204B1 (en) * | 2012-07-05 | 2014-01-16 | 성균관대학교산학협력단 | Apparatus and method for classification of power quality disturbances at power grids |
-
2014
- 2014-05-20 CN CN201410212549.7A patent/CN103995178A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101339208A (en) * | 2008-08-12 | 2009-01-07 | 中国矿业大学 | Voltage quality monitoring and perturb automatic classification method based on analysis in time-domain |
| CN101738551A (en) * | 2009-12-15 | 2010-06-16 | 西南交通大学 | Method for intelligent analysis of transient power quality disturbance based on networking |
| CN102072983A (en) * | 2010-11-22 | 2011-05-25 | 华北电力大学（保定） | Method for judging voltage sag reason |
| CN102831433A (en) * | 2012-06-06 | 2012-12-19 | 西南交通大学 | Method for classifying electric energy quality mixing disturbances based on multi-feature quantity of time-frequency domain |
| KR101352204B1 (en) * | 2012-07-05 | 2014-01-16 | 성균관대학교산학협력단 | Apparatus and method for classification of power quality disturbances at power grids |
| CN103245832A (en) * | 2013-05-16 | 2013-08-14 | 湖南大学 | Harmonic time frequency characteristic parameter estimating method based on fast S conversion and analysis meter |
| CN103308804A (en) * | 2013-06-17 | 2013-09-18 | 湖南大学 | Method for extracting time-frequency parameters of power quality disturbance signals on basis of fast K-S (Kaiser-S) transformation |
| CN103424600A (en) * | 2013-08-20 | 2013-12-04 | 昆明理工大学 | Voltage sag source identification method based on Hilbert-Huang transformation and wavelet packet energy spectra |
| CN103487682A (en) * | 2013-09-13 | 2014-01-01 | 深圳供电局有限公司 | Early warning method for sensitive customer electric energy experience quality under voltage sag disturbance |
Non-Patent Citations (4)
| Title |
|---|
| 仲飞等: "基于 S 变换的三相短路故障电压暂降检测研究", 《煤炭工程》 * |
| 史燕红等: "最优化广义S变换及其在油气检测中的应用", 《石油与天然气地质》 * |
| 程浩忠等: "《电能质量监测与分析》", 30 June 2012, 科学出版社 * |
| 迟华山等: "基于广义 S 变换的时相调制时频聚集性能优化", 《北京邮电大学学报》 * |
Cited By (31)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104267310A (en) * | 2014-09-09 | 2015-01-07 | 中国矿业大学 | Voltage dip source positioning method based on disturbance power direction |
| CN104267310B (en) * | 2014-09-09 | 2017-03-08 | 中国矿业大学 | A kind of voltage sag source localization method based on power of disturbance direction |
| CN104459397A (en) * | 2014-12-08 | 2015-03-25 | 东北电力大学 | Power quality disturbance recognizing method with self-adaptation multi-resolution generalized S conversion adopted |
| CN104459397B (en) * | 2014-12-08 | 2017-05-17 | 东北电力大学 | Power quality disturbance recognizing method with self-adaptation multi-resolution generalized S conversion adopted |
| CN104391207A (en) * | 2014-12-09 | 2015-03-04 | 湖南工业大学 | Voltage sag detection method adopting fundamental frequency single vector S transformation |
| CN104749432B (en) * | 2015-03-12 | 2017-06-16 | 西安电子科技大学 | Based on the multi -components non-stationary signal instantaneous Frequency Estimation method for focusing on S-transformation |
| CN104749432A (en) * | 2015-03-12 | 2015-07-01 | 西安电子科技大学 | Estimation method of multi-component non-stationary signal instantaneous frequency based on focusing S-transform |
| CN104730384A (en) * | 2015-03-16 | 2015-06-24 | 华南理工大学 | Power disturbance identification and localization method based on incomplete S transformation |
| CN104833844A (en) * | 2015-05-11 | 2015-08-12 | 上海市计量测试技术研究院 | Alternating-current effective value sampling measurement method |
| CN104833844B (en) * | 2015-05-11 | 2017-09-05 | 上海市计量测试技术研究院 | A kind of method of sampled measurements AC value |
| CN104993711A (en) * | 2015-05-22 | 2015-10-21 | 国网河南省电力公司电力科学研究院 | Voltage sag transition process simulation device and method |
| CN104993711B (en) * | 2015-05-22 | 2018-01-30 | 国网河南省电力公司电力科学研究院 | A kind of voltage dip transient process analogue means and method |
| WO2016197484A1 (en) * | 2015-06-09 | 2016-12-15 | 国网四川省电力公司经济技术研究院 | Optimal configuration method for voltage sag monitoring node |
| CN105137164A (en) * | 2015-08-06 | 2015-12-09 | 江苏省电力公司苏州供电公司 | Voltage sag on-line monitoring device applied in power system |
| CN106548013B (en) * | 2016-10-19 | 2019-05-17 | 西安工程大学 | Utilize the voltage sag source identification method for improving incomplete S-transformation |
| CN106548013A (en) * | 2016-10-19 | 2017-03-29 | 西安工程大学 | Using the voltage sag source identification method for improving incomplete S-transformation |
| CN108983046A (en) * | 2018-08-16 | 2018-12-11 | 国网山东省电力公司泰安供电公司 | A kind of voltage dip situation estimation method and system based on singular value decomposition method |
| CN109635430A (en) * | 2018-12-12 | 2019-04-16 | 中国科学院深圳先进技术研究院 | Grid power transmission route transient signal monitoring method and system |
| CN109635430B (en) * | 2018-12-12 | 2023-10-03 | 中国科学院深圳先进技术研究院 | Power grid transmission line transient signal monitoring method and system |
| CN110444005A (en) * | 2019-08-08 | 2019-11-12 | 国网新疆电力有限公司电力科学研究院 | Low Voltage Power Line Carrier Record System interference free performance test method and system |
| CN110954779A (en) * | 2019-11-29 | 2020-04-03 | 国网上海市电力公司 | Voltage sag source feature identification method based on S transformation and multidimensional fractal |
| CN111122941A (en) * | 2019-12-04 | 2020-05-08 | 国网湖南综合能源服务有限公司 | Kaiser window function-based voltage sag characteristic quantity detection method, system and medium for improving S conversion |
| CN111257619B (en) * | 2020-02-17 | 2022-06-14 | 南京工程学院 | Voltage sag detection method based on multi-attribute decision and improved S transformation method |
| CN111257619A (en) * | 2020-02-17 | 2020-06-09 | 南京工程学院 | Voltage sag detection method based on multi-attribute decision and improved S transformation method |
| CN111398721A (en) * | 2020-04-14 | 2020-07-10 | 南京工程学院 | Power distribution network voltage sag source classification and identification method introducing adjustment factors |
| CN112034232A (en) * | 2020-08-21 | 2020-12-04 | 上海电机学院 | Power supply system voltage sag detection method |
| CN112540220A (en) * | 2020-11-05 | 2021-03-23 | 广东电网有限责任公司广州供电局 | Voltage sag detection circuit and device |
| CN113627313A (en) * | 2021-08-02 | 2021-11-09 | 国网江苏省电力有限公司镇江供电分公司 | Electric energy meter metering method based on S transformation under non-ideal condition |
| CN113627313B (en) * | 2021-08-02 | 2022-07-29 | 国网江苏省电力有限公司镇江供电分公司 | Electric energy meter metering method based on S transformation under non-ideal condition |
| CN113946959A (en) * | 2021-10-18 | 2022-01-18 | 国网河南省电力公司电力科学研究院 | Voltage sag data segment extraction method |
| CN113946959B (en) * | 2021-10-18 | 2024-06-25 | 国网河南省电力公司电力科学研究院 | Voltage sag data segment extraction method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103995178A (en) | Voltage sag detection method for S-transformation on basis of time-frequency gathering characteristic criteria | |
| Zhan et al. | A Clarke transformation-based DFT phasor and frequency algorithm for wide frequency range | |
| CN103454497B (en) | Based on the method for measuring phase difference improving windowed DFT | |
| Yao et al. | Measurement of power system harmonic based on adaptive Kaiser self‐convolution window | |
| Turunen | A wavelet-based method for estimating damping in power systems | |
| CN102222911A (en) | Power system interharmonic estimation method based on auto-regression (AR) model and Kalman filtering | |
| CN106154037B (en) | A kind of synchronized phasor self-adaptive computing method based on verification | |
| CN203287435U (en) | A micro electrical network harmonic wave and inter-harmonic wave test apparatus based on an STM32F107VCT6 | |
| CN102955068A (en) | Harmonic detection method based on compressive sampling orthogonal matching pursuit | |
| CN102944773B (en) | Method for detecting and classifying power disturbances based on space conversion | |
| CN104217112A (en) | Multi-type signal-based power system low-frequency oscillation analysis method | |
| CN106845334A (en) | A kind of innovative noise extracting method based on mathematical morphology | |
| CN103983849A (en) | Real-time high-accuracy power harmonic analysis method | |
| CN104777356A (en) | Neural-network-based real-time high-accuracy harmonic detection method | |
| Giarnetti et al. | Non recursive multi-harmonic least squares fitting for grid frequency estimation | |
| Yang et al. | Oscillation mode analysis for power grids using adaptive local iterative filter decomposition | |
| Ma et al. | Harmonic and interharmonic analysis of mixed dense frequency signals | |
| Tashman et al. | Modal energy trending for ringdown analysis in power systems using synchrophasors | |
| CN104535082A (en) | Method for determining inertial navigation element performances based on flight test and theoretical calculation | |
| Cho et al. | Oscillation recognition using a geometric feature extraction process based on periodic time-series approximation | |
| Xiaomeng et al. | A sensor fault diagnosis method research based on wavelet transform and hilbert-huang transform | |
| Chen et al. | Coherent clustering method based on weighted clustering of multi-indicator panel data | |
| CN105319479B (en) | Two ends of electric transmission line fault localization system | |
| CN107064634B (en) | The detection method of Harmonious Waves in Power Systems | |
| CN113627313B (en) | Electric energy meter metering method based on S transformation under non-ideal condition |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20140820 |
|
| RJ01 | Rejection of invention patent application after publication |