CN115792336A - Voltage quality disturbance detection method and system based on improved Hilbert-Huang transform - Google Patents
Voltage quality disturbance detection method and system based on improved Hilbert-Huang transform Download PDFInfo
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Abstract
The invention relates to a voltage quality disturbance detection method and a system based on improved Hilbert-Huang transform, which preprocesses a power grid power supply voltage signal, and comprises the steps of judging whether the voltage signal is a dense modal signal or not and removing discontinuous signal components in the voltage signal to obtain a preprocessed voltage signal; if the signal is a dense modal signal, performing improved Hilbert-Huang transform on the preprocessed voltage signal to obtain a fundamental voltage signal of the preprocessed voltage signal; if not, performing original Hilbert-Huang transform on the preprocessed voltage signal to obtain a fundamental voltage signal of the preprocessed voltage signal; a voltage brown-out fault is identified based on the fundamental voltage signal. The technical scheme of the invention can effectively solve the problem of mode aliasing existing in the traditional except Hilbert-Huang transform, and can obviously improve the accuracy of the Hilbert-Huang transform on the detection of voltage quality disturbance.
Description
Technical Field
The invention belongs to the technical field of power system relay protection, and particularly relates to a voltage quality disturbance detection method and system based on improved Hilbert-Huang transformation.
Background
Electric power is a leading industry. High-end manufacturing industries such as high-end traffic equipment, energy and environment-friendly equipment, integrated electronic circuits and the like in China develop rapidly, and power grid voltage sensitive users mainly in the high-end manufacturing industries are increased continuously, but the high-end manufacturing industries have high requirements on the reliability and the power supply continuity of a power supply and distribution system, namely the quality of electric energy due to tight flow connection and high power load sensitivity. However, the existing power supply and distribution system has a complex network, which is very easy to cause the short-time voltage fluctuation or short-time power failure phenomenon of the power grid, that is, the phenomenon known as "power shaking". Therefore, necessary interference control measures need to be taken at the electricity utilization side, the electricity load is maintained not to stop working due to short-time voltage fluctuation of the power supply, the normal work of the electricity load is continuously maintained after the voltage of the power supply is recovered to be normal, the problems of electric energy quality such as incapability of starting operation and accidental shutdown of sensitive load users in high-tech industries, high-end manufacturing industries and the like caused by interference are solved, and other electricity accidents due to unqualified electric energy quality, such as contactor release, non-fault branch circuit cutting of protection equipment actions, motor stopping operation and the like caused by interference are avoided. How to realize the quick identification of the medium and low voltage distribution network voltage interference fault becomes the primary problem to be solved.
Hilbert-Huang Transform (HHT) is a disturbance detection technology which is used more at present, and compared with S Transform, the Hilbert-Huang Transform (HHT) has higher calculation speed and is more suitable for detection in the aspect of composite disturbance; meanwhile, compared with analysis methods such as Fourier transform and wavelet transform which need prior function bases, the method is more suitable for processing detection of non-stationary signals. The HHT method comprises Empirical Mode Decomposition (EMD) and Hilbert transformation, and specifically comprises the steps of firstly converting initial signals into Mode function sets with different scales by using the EMD, and then obtaining instantaneous amplitude-frequency characteristics corresponding to each Mode function through the Hilbert transformation so as to obtain specific change conditions of the signals.
The traditional HHT can encounter the problem of modal aliasing in the disturbance detection of the power quality, and the specific expression form is that after the original signal is subjected to EMD decomposition, different Intrinsic Mode Functions (IMFs) are specifically distributed under the same time scale, so that the IMFs cannot accurately obtain the time-frequency characteristics of the signal, and difficulty is brought to the accurate detection of the power quality disturbance.
Disclosure of Invention
In view of the foregoing analysis, the present invention aims to provide a method and a system for detecting voltage quality disturbance by using an improved hilbert-yellow transform, so as to effectively remove the problem of modal aliasing existing in the conventional HHT transform, and to significantly improve the accuracy of HHT on detecting voltage quality disturbance.
On one hand, the invention provides a voltage quality disturbance detection method based on improved Hilbert-Huang transform, which specifically comprises the following steps:
preprocessing a power supply voltage signal of a power grid, wherein the preprocessing comprises judging whether the voltage signal is an intensive modal signal or not and removing discontinuous signal components and noise signals in the voltage signal to obtain a preprocessed voltage signal;
if the signal is the dense modal signal, performing improved Hilbert-Huang transform on the preprocessed voltage signal to obtain a fundamental voltage signal; if not, performing original Hilbert-Huang transform on the preprocessed voltage signal to obtain a fundamental voltage signal of the preprocessed voltage signal;
a voltage brown-out fault is identified based on the fundamental voltage signal.
Further, the step of performing modified hilbert-yellow transform on the preprocessed voltage signal to obtain a fundamental voltage signal thereof includes:
performing Hilbert transform on the preprocessed voltage signal to obtain an analytic signal;
carrying out frequency modulation transformation on the analytic signal based on the frequency modulation frequency to obtain a frequency-modulated analytic signal;
performing EMD on the analyzed signals after frequency modulation to obtain IMF components;
performing Fourier transform on the IMF component to obtain a component analysis signal;
carrying out inverse frequency modulation on the basis of the component analysis signal and the frequency modulation frequency to obtain an inverse frequency modulated fundamental voltage signal;
and performing Hilbert spectrum analysis on the inverse frequency modulated fundamental wave voltage signal to obtain amplitude and frequency information of a fundamental wave signal of the preprocessed voltage signal.
Further, the determining whether the voltage signal is a dense modal signal and removing an intermittent signal component and a noise signal in the voltage signal to obtain a preprocessed voltage signal includes:
performing Fourier transform on the voltage signal to obtain fundamental wave signal information and harmonic wave signal information;
judging whether the voltage signal is a dense mode signal or not based on the fundamental wave signal information and the harmonic wave signal information; and
and removing discontinuous signal components and noise signals in the voltage signals to obtain preprocessed voltage signals.
Further, the determining whether the voltage signal is a dense modal signal includes:
and judging whether the fundamental frequency and the harmonic frequency meet the following formula, otherwise, judging that the signals are dense modal signals:
wherein, f 1 For one of fundamental or harmonic signal frequencies, f 2 Is the other of the fundamental signal frequency or the harmonic signal frequency; a is a 1 And a 2 Is the amplitude of the corresponding fundamental or harmonic signal.
Further, the removing the discontinuous signal component and the noise signal in the voltage signal to obtain the preprocessed voltage signal includes:
constructing a hankel matrix based on the supply voltage signal;
obtaining a singular value matrix based on the Hankel matrix;
reconstructing the singular value matrix to obtain a reconstructed singular value matrix;
obtaining an updated Hankel matrix based on the reconstructed singular value matrix;
and obtaining a preprocessed voltage signal based on the updated Hankel matrix.
Further, the reconstructing the singular value matrix to obtain a reconstructed singular value matrix includes:
reserving the first 2n singular values in the singular value matrix, and setting the singular values which do not accord with the set conditions to be 0; wherein n is a main frequency number, and the main frequency number refers to the main frequency number of the power supply voltage signal; the main frequency refers to the frequency of fundamental waves and dominant harmonics;
and screening the reserved singular values according to the sequence of singular values, and setting the singular value and the subsequent singular value to be 0 when the size of the subsequent singular value of a certain singular value is less than 1/5 of the singular value.
Further, the identifying a voltage brown-out fault based on the fundamental voltage signal comprises identifying a voltage brown-out fault based on amplitude and frequency fluctuations of the fundamental voltage signal: identifying a power-on-interference fault nature and a numerical value based on the amplitude, and identifying a time of occurrence of the power-on-interference fault based on the amplitude and the frequency fluctuation; wherein the nature of the power-on-interference fault includes dip, swell and break.
Further, the fundamental wave signal and the harmonic wave signal are obtained by fourier transform:
wherein X (t) is the power supply voltage signal, X (ω) is a frequency spectral density function, | X (ω) | is an amplitude frequency spectral density function; ω =2 π f; f is the signal frequency;
wherein, the signal with the frequency within the range of 50Hz +/-0.5 Hz is a fundamental wave signal, and other frequency signals are harmonic wave signals.
On the other hand, the invention also provides a voltage quality disturbance detection system based on the improved Hilbert-Huang transform, which comprises a voltage acquisition module, a preprocessing module, an improved Hilbert-Huang transform module and a fault identification module; wherein,
the voltage acquisition module is used for acquiring a power supply voltage signal of a power grid;
the preprocessing module is used for judging whether the voltage signal is a dense modal signal or not and removing discontinuous signal components in the voltage signal to obtain a preprocessed voltage signal;
the improved Hilbert-Huang transform module is used for obtaining a fundamental voltage signal of the improved Hilbert-Huang transform module based on the judgment result of the preprocessing module and the preprocessed voltage signal;
the fault identification module is used for identifying voltage brown-out faults based on the fundamental voltage signals.
Further, the fundamental voltage signal is obtained based on the judgment result of the preprocessing module and the preprocessed voltage signal:
if the signal is a dense modal signal, performing improved Hilbert-Huang transform on the preprocessed voltage signal to obtain a fundamental voltage signal of the voltage signal; if not, performing original Hilbert-Huang transform on the preprocessed voltage signal to obtain a fundamental voltage signal.
The invention can realize at least one of the following beneficial effects:
the voltage signal is converted into the Hankel matrix to obtain the singular value matrix, the singular value matrix is reconstructed to obtain the updated Hankel matrix, the updated voltage signal is obtained based on the updated Hankel matrix, the influence of the discontinuous signal in the voltage signal is removed, the modal aliasing phenomenon caused by the discontinuous signal in the voltage signal is avoided, and the accuracy of voltage quality disturbance detection is improved.
The voltage signal which is judged to be the intensive modal signal is subjected to frequency modulation processing to obtain an analytic signal after frequency modulation, EMD decomposition is carried out based on the analytic signal, and then the fundamental wave voltage signal in the voltage signal is reduced based on the IMF component obtained after decomposition, so that the modal aliasing phenomenon caused when the voltage signal is the intensive modal signal is effectively avoided, and the accuracy of voltage quality disturbance detection is further improved.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
The drawings are only for purposes of illustrating particular embodiments and are not to be construed as limiting the invention, wherein like reference numerals are used to designate like parts throughout.
FIG. 1 is a flow chart of a detection method according to embodiment 1 of the present invention;
FIG. 2 is a schematic diagram illustrating an exploded voltage signal including an interrupt signal in embodiment 1 of the present invention;
FIG. 3 is a spectrum diagram of a composite voltage signal after Fourier decomposition according to embodiment 2 of the present invention;
FIG. 4 is a diagram illustrating singular values in embodiment 2 of the present invention;
FIG. 5 is an exploded view of the modified Hilbert-Huang transform EMD of embodiment 2 of the present invention;
FIG. 6 is a diagram showing the result of modified Hilbert-Huang transform in example 2 of the present invention;
FIG. 7 is a time domain diagram of a composite voltage signal according to embodiment 3 of the present invention;
FIG. 8 is a graph of a composite voltage signal after Fourier decomposition according to embodiment 3 of the present invention;
FIG. 9 is a diagram illustrating singular values in embodiment 3 of the present invention;
FIG. 10 is an exploded view of the modified Hilbert-Huang transform EMD of embodiment 3 of the present invention;
fig. 11 is an exploded view of the EMD of the original hubert-yellow transform in accordance with embodiment 3 of the present invention.
Detailed Description
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate preferred embodiments of the invention and together with the description, serve to explain the principles of the invention and not to limit the scope of the invention.
Method embodiment
Example 1
The invention discloses a specific embodiment of a voltage quality disturbance detection method for improving Hilbert-Huang transform, which specifically comprises the following steps of:
the method comprises the following steps of S1, preprocessing a power grid power supply voltage signal, wherein the preprocessing comprises the steps of judging whether the voltage signal is an intensive modal signal or not, and removing discontinuous signal components and noise signals in the voltage signal to obtain a preprocessed voltage signal.
Specifically, the pretreatment process comprises the following steps:
s11, collecting power supply voltage signals of a power grid, and performing Fourier transform to obtain fundamental wave signals and harmonic signals.
Specifically, the fourier transform is represented by the following equation:
wherein X (t) is the power supply voltage signal, X (ω) is a frequency spectral density function, | X (ω) | is an amplitude frequency spectral density function; ω =2 π f; f is the signal frequency;
specifically, according to the frequency-attached spectrum density function, a signal with a larger amplitude and a frequency within the range of 50Hz +/-0.5 Hz is a fundamental wave signal; the signals with larger amplitude and the frequency out of the range of 50Hz +/-0.5 Hz are harmonic signals.
And S12, judging whether the voltage signal is a dense mode signal.
Specifically, when the parameters of the fundamental wave signal and the harmonic wave signal satisfy the following formula, it is determined that the supply voltage signal is not a dense mode signal, and it is determined that the supply voltage signal is a dense mode signal:
wherein, f 1 For one of fundamental or harmonic signal frequencies, f 2 Is the other of the fundamental or harmonic signal frequency, a 1 And a 2 Is the amplitude of the corresponding fundamental or harmonic signal.
In particular, f 1 Possibly as fundamental and possibly as harmonic signal frequencies, when f 1 At the frequency of the fundamental wave signal, f 2 Is the harmonic signal frequency; when f is 1 At harmonic signal frequency, f 2 Is the fundamental signal frequency.
And S13, removing discontinuous signal components and noise signals in the voltage signals to obtain preprocessed voltage signals.
Specifically, the voltage signal is a composite signal and is composed of a fundamental wave signal, a harmonic signal, an intermittent signal component and a noise signal. The discontinuous signals refer to discontinuous signal components with small amplitude values, and the discontinuous signals cause modal aliasing when HHT is carried out on the voltage signals; modal aliasing caused by the discontinuity signal is avoided by removing the discontinuity signal component. The noise signal is gaussian white noise, which represents an error between the voltage signal obtained by the voltage acquisition module and the actual voltage signal, and has a small and discontinuous amplitude.
Specifically, the step of removing the discontinuous signal component and the noise signal includes the steps of:
s13-1, constructing a Hankel matrix based on the power grid supply voltage signal.
Specifically, the voltage signals x (i) (i =1,2, \8230;, N) corresponding to N sampling points are constructed as follows:
where H denotes a hankel matrix, the number of matrix rows m = L, and the number of matrix columns N = N-L +1.
And S13-2, obtaining a singular value matrix based on the Hankel matrix.
Specifically, performing singular value decomposition on the matrix includes:
H＝UDV T ；
wherein, U and V are m × m and n × n orthogonal matrices, respectively, and D is a singular value matrix, which can be expressed as:
where Σ is a diagonal matrix, Σ = diag (σ) 1 ,σ 2 ,…,σ r ),σ 1 ＞σ 2 ＞…σ r The > 0,diag function represents the construction of a diagonal matrix whose rank R (H) = R, ∑ diagonal elements σ r I.e. singular values.
S13-3, reconstructing the singular value matrix to obtain a reconstructed singular value matrix D';
specifically, the first 2n singular values in the singular value matrix are reserved, and the singular values which do not meet the set conditions are set to be 0; wherein n is a main frequency number, and the main frequency number refers to the main frequency number of the voltage signal; the main frequency refers to the frequency of fundamental waves and dominant harmonics;
specifically, setting the singular value that does not meet the set condition to 0 includes: and screening the singular values in the reserved singular values according to the sequence of singular value arrangement, and setting the singular value and the subsequent singular value to be 0 when the size of the subsequent singular value of a certain singular value is less than 1/5 of the singular value.
Specifically, because the singular value corresponding to the noise signal is also small, the noise signal is removed while the discontinuous signal component is removed in the singular value matrix reconstruction process.
And S13-4, obtaining an updated Hankel matrix H' based on the reconstructed singular value matrix.
Specifically, the hankel matrix is constructed under the condition that the number of rows of the matrix is half of the number of signal samples, i.e., m = L = N/2.
And S13-5, obtaining a preprocessed voltage signal based on the updated Hankel matrix.
Specifically, the preprocessed voltage signal x '(t) is obtained based on the first row and the last column of the updated hankerr matrix H'.
The principle of step S13 is: the actually acquired power grid supply voltage signal is usually a composite signal and consists of a fundamental wave signal, a harmonic wave signal and an intermittent interference signal. When the discontinuous signals exist in the voltage signals, the discontinuous signals are discontinuous on a time domain scale, so that the energy concentration situation of the discontinuous signals is small, and meanwhile, the singular values in the singular value matrix are arranged in a descending order and reflect the specific energy concentration situation of the signals, so that the signals can be decomposed according to the specific singular value, and the small singular values are set to be 0, so that the discontinuous signals and the noise signals of the interference signals in the signals can be removed.
It should be noted that the execution order of steps S12 and S13 is not limited, and the execution order may be exchanged or performed simultaneously.
S2, if the signal is judged to be a dense modal signal, performing improved Hilbert-Huang transform on the preprocessed voltage signal to obtain a fundamental voltage signal of the preprocessed voltage signal; if not, performing original Hilbert-Huang transform on the preprocessed voltage signal to obtain a fundamental voltage signal of the preprocessed voltage signal.
Dense mode signals refer to signals that are frequency dense; if the voltage signal is a dense modal signal, modal aliasing is likely to occur when HHT is performed.
Specifically, if the signal is a dense modal signal, the fundamental voltage signal of the preprocessed voltage signal is obtained by performing improved hilbert-yellow transform, and the method comprises the following steps:
s21-1, performing Hilbert transform on the preprocessed voltage signal to obtain an analytic signal.
Specifically, the hilbert transform is represented by the following formula:
wherein x' (t) is a voltage signal from which the discontinuous signal component is removed; h [ x' (t)]Represents the hilbert transform of x' (t); j is an imaginary unit; omega 1 ＝2πf 1 ,ω 2 ＝2πf 2 。
S21-2, carrying out frequency modulation conversion on the analytic signal based on the frequency modulation frequency to obtain the analytic signal after frequency modulation.
In particular, a suitable frequency-modulation frequency ω is selected 0 FM conversion, i.e. multiplication, of X' (t)
Obtaining an analytic signal Z' (t) after frequency modulation;
wherein, Z' r (t) is the transformed real part signal; jZ' j (t) is the transformed imaginary signal, exp is a logarithmic function based on e.
In particular, the frequency of modulation omega 0 Is selected to satisfy, omega 0 ＝2πf 0 ，f 0 ＞2f 2 -f 1 ＞0。
And S21-3, performing EMD on the analyzed signals after frequency modulation to obtain IMF components.
Specifically, EMD decomposition is performed on the real part and the imaginary part of the frequency-modulated analytic signal Z' (t), and is represented by the following formula:
wherein, C rk (t) and C jk (t) is each independently of Z' r (t) and Z' j (t) the decomposed IMF component; r is a radical of hydrogen nr And r nj The residuals are decomposed for the corresponding EMD.
And S21-4, carrying out Fourier transform on the IMF component to obtain a component analysis signal.
Specifically, fourier transform is performed on each modal component to find out the frequency f of each modal component 1 -f 0 The specific modal component (i.e. the modal component corresponding to the fundamental wave) constitutes a specific analytic signal:
Z 0 (t)＝C rk0 (t)+jC jk0 (t)；
wherein, Z 0 (t) a specific resolution signal corresponding to a modal component corresponding to the fundamental wave, C rk0 (t) and jC jk0 (t) is its real and imaginary parts, respectively.
And S21-5, carrying out inverse frequency modulation on the basis of the component analysis signal and the frequency modulation frequency to obtain an inverse frequency modulated fundamental voltage signal.
Specifically, the fundamental wave signal x after the inverse frequency modulation is obtained by the following formula 0 (t)：
x 0 (t)＝Re[X 0 (t)]。
S21-6, performing Hilbert spectrum analysis on the fundamental wave voltage signal after inverse frequency modulation to obtain a fundamental wave signal of the preprocessed voltage signal.
By the formula:
X 0 (t)＝＝Z 0 (t)×exp(jω 0 t)
＝(C rk0 (t)+jC jk0 (t)exp(jω 0 t)
＝C rkx0 (t)+jC jkx0 (t)＝A 0 (t)e jφ(t)
obtaining the amplitude A (t) = | X 0 (t) |, phase
The frequency is found based on the phase:
by the improved Hilbert-Huang transform, the problem that original Hilbert-Huang transform cannot be correctly separated due to too close modal frequencies of all orders in dense modal signals is solved, and therefore aliasing possibly generated by the original Hilbert-Huang transform is effectively avoided.
Specifically, if the signal is not the dense mode signal, the step of performing the original hilbert-yellow transform on the preprocessed voltage signal to obtain the fundamental voltage signal includes the following steps:
s22-1, performing EMD on the preprocessed voltage signal to obtain an IMF component.
Specifically, the EMD decomposition process is represented as:
fourier transform is carried out on each IMF component to find the IMF component with the corresponding frequency within the range of 50Hz +/-0.5 Hz, namely the corresponding fundamental voltage signal C 0 (t)。
S22-2, performing Hilbert transform on the fundamental voltage signal and performing spectrum analysis to obtain amplitude and frequency information of the fundamental voltage signal.
Specifically, it is represented by the following formula:
X 0 (t)＝C 0 (t)+H[C 0 (t)]＝A 0 (t)e jφ(t) ；
obtaining the amplitude A (t) = | X 0 (t) |, phase
The frequency is found based on the phase:
and S3, identifying voltage interference faults based on the fundamental wave voltage signals.
Identifying a voltage brown-out fault based on the fundamental voltage signal obtained at S21-6 or S22, including identifying a voltage brown-out fault based on amplitude and frequency fluctuations of the fundamental voltage signal: identifying a power-on-interference fault nature and a numerical value based on the amplitude, and identifying a time of occurrence of the power-on-interference fault based on the amplitude and the frequency fluctuation; wherein the nature of the power-on-interference fault includes dip, swell and break.
The following explains the principle of the technical solution in this embodiment:
the invention aims to solve the problem of mode aliasing existing in the aspect of computer quality disturbance detection of the conventional HHT. The modal aliasing phenomenon is characterized in that components are distributed in different IMFs at the same time scale, so that the IMFs cannot accurately reflect the time-frequency characteristics of signals. Generally, when high-frequency discontinuous signals exist in a power grid voltage signal and when the decomposed signals are dense modal signals, modal aliasing is easy to generate when HHT conversion is applied.
1. Regarding the case of a high frequency discontinuity signal in the grid voltage signal:
in the time-frequency diagram shown in fig. 2, the horizontal axis represents time, the vertical axis represents frequency, and the signal iii is a discontinuous signal, and in an expected ideal situation, it is desirable to divide the decomposed signal into ABCD, iii, and iiiivv 3 IMF components by HHT conversion. The actual decomposition results are ABCD, iiii, iiiv 3 IMF components, which apparently causes modal aliasing. Since it is difficult for the spectrum analysis to determine whether or not there is a discontinuity in the decomposed signal, in the present invention, it is assumed that there is a discontinuity in the decomposed signal.
2. Regarding the case where the grid voltage signal is a dense modal signal:
in practical situations, the grid voltage signal that is actually detected is typically a composite signal.
When the decomposed signal is a dense modal signal, the modal frequencies of the orders in the decomposed signal are too close to each other to separate the HHT transformation correctly. Consider the following signal model:
wherein x (t) represents a grid voltage signal, a 1 ,a 2 To the amplitude of the corresponding signal, f 1 ,f 2 In order to correspond to the frequency of the signal,
for the initial phase angle of the corresponding signal, assume a for simplicity of analysis 1 ＝a 2 ＝1，
Then the following holds:
x(t)＝2cosπ(f 1 -f 2 )tcosπ(f 1 +f 2 )t；
when the signal frequencies are close, the signals can be regarded as certain special amplitude modulation signals, the mean value of the extreme value envelope is zero, and the number of the zero-crossing points is the same as or different from the number of the extreme values by one, so that modal aliasing can be generated when HHT decomposition is adopted.
Therefore, it is necessary to determine whether modal aliasing is likely to occur in the HHT transformation of the decomposed voltage signal by spectrum analysis, and the corresponding determination criteria are: when the composite signal satisfies the following formula, performing ordinary HHT decomposition on the composite signal does not generate modal aliasing:
in this embodiment, a voltage quality disturbance detection method for improving hilbert-yellow transform is disclosed, in which a singular value matrix is obtained by converting a voltage signal into a hankerr matrix, an updated hankerr matrix is obtained by reconstructing the singular value matrix, and an updated voltage signal is obtained based on the updated hankerr matrix, so that an influence of an intermittent signal in the voltage signal is removed, a mode aliasing phenomenon caused by the presence of the intermittent signal in the voltage signal is avoided, and accuracy of voltage quality disturbance detection is improved.
The voltage signal is subjected to frequency modulation processing to obtain an analytic signal after frequency modulation, EMD decomposition is carried out on the basis of the analytic signal, and then the fundamental voltage signal in the voltage signal is reduced on the basis of the IMF component obtained after decomposition, so that the modal aliasing phenomenon caused when the voltage signal is an intensive modal signal is effectively avoided, and the accuracy of voltage quality disturbance detection is further improved.
Example 2
In another embodiment of the present invention, a method for detecting voltage quality disturbance by using simulation data to verify an improved hilbert-yellow transform specifically includes the following steps:
step S201, constructing a composite voltage signal and a voltage swing fault, and simulating the voltage swing fault of a power supply voltage signal in an actual power grid.
Specifically, the composite voltage signal Z (t) comprises a fundamental wave Z of 220V,50Hz 1 (t), the fundamental wave generates voltage dip in 0.4-0.6s, the amplitude drops by 60V, namely the amplitude becomes 160V; and also includes a harmonic Z of 70Hz 2 (t) the amplitude and phase of which remain unchanged during the detection time; also included is intermittent white Gaussian noise Z of 10dB 3 (t) of (d). Expressed as:
step S202, preprocessing the composite voltage signal, judging whether the composite voltage signal is an intensive modal signal or not, and removing discontinuous signal components and noise signals in the composite voltage signal to obtain a preprocessed voltage signal.
And S2021, performing Fourier transform on the composite voltage signal to obtain a fundamental wave signal and a harmonic signal. As shown in fig. 3, the frequency spectrum obtained after fourier transform has a fundamental signal frequency of 50Hz, a harmonic frequency of 70Hz, and a main frequency of 2.
S2022, judging the voltage signal to be a dense mode signal.
S2023, removing discontinuous signal components and noise signals in the voltage signal to obtain a preprocessed voltage signal, and specifically, when reconstructing the singular value matrix, according to fig. 3 and 4, obtaining an updated singular value matrix and retaining 4 singular values.
And S203, because the voltage signal is judged to be the dense mode signal, performing improved Hilbert-Huang transform on the preprocessed voltage signal to obtain a fundamental voltage signal.
Specifically, the frequency of the frequency modulation signal is 40Hz, and the frequency modulation processing is carried out on the signal. FIG. 5 shows the result of EMD decomposition.
The improved hilbert-yellow transform results are shown in fig. 6.
And step S204, identifying the voltage sag fault based on the fundamental wave voltage signal.
The identified voltage brown-out fault verification based on the amplitude and frequency conversion of the fundamental voltage signal shown in fig. 6 is shown in the following table.
In the embodiment, the voltage quality disturbance detection method for improving the Hilbert-Huang transform is verified to be capable of accurately and quickly acquiring the frequency components, the amplitudes and the mutation time of the fundamental wave signals and the harmonic wave signals, analyzing the amplitudes and the start-stop time of the voltage sag and short interruption signals, and being suitable for detecting the composite voltage signals.
Example 3
In another embodiment of the present invention, a method for detecting voltage quality disturbance by using simulation data to verify an improved hilbert-yellow transform specifically includes the following steps:
step S301, constructing a composite voltage signal and a voltage swing fault to simulate the voltage swing fault of a power supply voltage signal in an actual power grid.
Specifically, the composite voltage signal Z (t) includes the following components:
dense modal signal Z 1 (t) containing a fundamental voltage of 220v,50hz and a harmonic having an amplitude of 100V and a frequency of 70 Hz;
3 th harmonic Z 2 (t), amplitude of 100V;
high frequency intermittent harmonic wave Z 3 (t), the amplitude is 80V, the frequency is 450Hz, and the amplitude and the phase are kept unchanged in the detection time;
white gaussian noise Z 4 (t), intermittent white Gaussian noise of 10 dB.
Z (t) is represented by:
the time domain diagram of the signal is shown in fig. 7.
Step S302, preprocessing the composite voltage signal, judging whether the composite voltage signal is an intensive modal signal or not, and removing discontinuous signal components and noise signals in the composite voltage signal to obtain a preprocessed voltage signal.
And S3021, carrying out Fourier transform on the composite voltage signal to obtain a fundamental wave signal and a harmonic signal. As shown in fig. 8, the spectrogram obtained after fourier transform has a dominant frequency of 4.
And S3022, judging that the voltage signal is a dense mode signal.
S3023, removing discontinuous signal components and noise signals from the voltage signal to obtain a preprocessed voltage signal, and specifically, when reconstructing the singular value matrix, according to fig. 9, obtaining an updated singular value matrix and retaining 6 singular values (where a difference between a 7 th singular value and a 6 th singular value is too large, and a condition is not satisfied).
And step S303, because the voltage signal is judged to be a dense modal signal, performing improved Hilbert-Huang transformation on the preprocessed voltage signal to obtain a fundamental voltage signal of the preprocessed voltage signal.
Specifically, the frequency of the frequency modulated signal is 40Hz, and the frequency modulated signal is subjected to frequency modulation, and fig. 10 shows the EMD decomposition result. Fig. 11 shows the results of EMD decomposition using the original hilbert-yellow transform. As can be seen from a comparison between fig. 11 and fig. 10, EMD decomposition using the original hilbert-yellow transform has significant modal aliasing.
And step S304, identifying voltage swing faults based on the fundamental wave voltage signals.
In the embodiment, the improved hilbert-yellow transform is verified to perform the EMD decomposition on the voltage signal, and compared with the original hilbert-yellow transform, the EMD decomposition is performed, so that the modal aliasing phenomenon is remarkably avoided.
System embodiment
A voltage quality disturbance detection system based on improved Hilbert-Huang transform comprises a voltage acquisition module, a preprocessing module, an improved Hilbert-Huang transform module and a fault identification module.
The voltage acquisition module is used for acquiring a power supply voltage signal of a power grid.
The preprocessing module is used for judging whether the voltage signal is a dense modal signal or not and removing discontinuous signal components and noise signals in the voltage signal to obtain a preprocessed voltage signal: if the signal is a dense modal signal, performing improved Hilbert-Huang transform on the preprocessed voltage signal to obtain a fundamental voltage signal of the voltage signal; if not, performing original Hilbert-Huang transform on the preprocessed voltage signal to obtain a fundamental voltage signal.
The improved Hilbert-Huang transform module is used for obtaining a fundamental voltage signal of the voltage signal based on the judgment result of the preprocessing module and the preprocessed voltage signal.
The fault identification module is used for identifying voltage brown-out faults based on the fundamental voltage signals.
Compared with the prior art, the voltage quality disturbance detection system based on the improved hilbert-yellow transform provided by the embodiment and the method embodiments are based on the same concept, and the description is not repeated, so that the system and the method embodiments can be used for reference.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.
Claims (10)
1. A voltage quality disturbance detection method based on improved Hilbert-Huang transform is characterized by comprising the following steps:
preprocessing a power supply voltage signal of a power grid, wherein the preprocessing comprises judging whether the voltage signal is an intensive modal signal or not and removing discontinuous signal components and noise signals in the voltage signal to obtain a preprocessed voltage signal;
if the signal is judged to be the dense modal signal, performing improved Hilbert-Huang transform on the preprocessed voltage signal to obtain a fundamental voltage signal of the preprocessed voltage signal; if not, performing original Hilbert-Huang transform on the preprocessed voltage signal to obtain a fundamental voltage signal of the preprocessed voltage signal;
a voltage sag fault is identified based on the fundamental voltage signal.
2. The detection method according to claim 1, wherein the performing the modified hilbert-yellow transform on the preprocessed voltage signal to obtain the fundamental voltage signal thereof comprises:
performing Hilbert transform on the preprocessed voltage signal to obtain an analytic signal;
carrying out frequency modulation transformation on the analytic signal based on the frequency modulation frequency to obtain a frequency-modulated analytic signal;
performing EMD on the analyzed signals after frequency modulation to obtain IMF components;
performing Fourier transform on the IMF component to obtain a component analysis signal;
carrying out inverse frequency modulation on the basis of the component analysis signal and the frequency modulation frequency to obtain an inverse frequency modulated fundamental voltage signal;
and performing Hilbert spectrum analysis on the inverse frequency modulated fundamental wave voltage signal to obtain amplitude and frequency information of a fundamental wave signal of the preprocessed voltage signal.
3. The detection method according to claim 1, wherein the determining whether the voltage signal is a dense mode signal and removing discontinuous signal components and noise signals in the voltage signal to obtain a preprocessed voltage signal comprises:
performing Fourier transform on the voltage signal to obtain fundamental wave signal information and harmonic wave signal information;
judging whether the voltage signal is a dense mode signal or not based on the fundamental wave signal information and the harmonic wave signal information; and
and removing discontinuous signal components and noise signals in the voltage signals to obtain preprocessed voltage signals.
4. The detection method according to claim 3, wherein the determining whether the voltage signal is a dense mode signal comprises:
and judging whether the fundamental frequency and the harmonic frequency meet the following formula, otherwise, judging that the signals are dense modal signals:
wherein, f 1 At one of fundamental or harmonic signal frequencies, f 2 Is the other of the fundamental signal frequency or the harmonic signal frequency; a is a 1 And a 2 Is the amplitude of the corresponding fundamental wave signal or harmonic wave signal.
5. The detection method according to claim 3 or 4, wherein the removing of the discontinuous signal component and the noise signal in the voltage signal to obtain a pre-processed voltage signal comprises:
constructing a hankel matrix based on the supply voltage signal;
obtaining a singular value matrix based on the Hankel matrix;
reconstructing the singular value matrix to obtain a reconstructed singular value matrix;
obtaining an updated Hankel matrix based on the reconstructed singular value matrix;
and obtaining a preprocessed voltage signal based on the updated Hankel matrix.
6. The detection method according to claim 5, wherein the reconstructing the singular value matrix to obtain a reconstructed singular value matrix comprises:
reserving the first 2n singular values in the singular value matrix, and setting the singular values which do not accord with the set conditions to be 0; wherein n is a main frequency number, and the main frequency number refers to the main frequency number of the power supply voltage signal; the main frequency refers to the frequency of a fundamental wave and a dominant harmonic wave;
and screening the reserved singular values according to the sequence of singular values, and setting the singular value and the subsequent singular value to be 0 when the size of the subsequent singular value of a certain singular value is less than 1/5 of the singular value.
7. The detection method of claim 6, wherein identifying a voltage brown-out fault based on the fundamental voltage signal comprises identifying a voltage brown-out fault based on amplitude and frequency fluctuations of the fundamental voltage signal: identifying a power-on fault property and a value based on the amplitude, and identifying the occurrence time of the power-on fault based on the amplitude and the frequency fluctuation; wherein the nature of the power-on-interference fault includes dip, swell and break.
8. The detection method according to claim 3 or 4, wherein the fundamental signal and the harmonic signal are obtained by Fourier transform:
wherein X (t) is the power supply voltage signal, X (ω) is a frequency spectrum density function, | X (ω) | is an amplitude frequency spectrum density function; ω =2 π f; f is the signal frequency;
wherein, the signal with the frequency within the range of 50Hz +/-0.5 Hz is a fundamental wave signal, and other frequency signals are harmonic wave signals.
9. A voltage quality disturbance detection system based on improved Hilbert-Huang transform is characterized by comprising a voltage acquisition module, a preprocessing module, an improved Hilbert-Huang transform module and a fault identification module; wherein,
the voltage acquisition module is used for acquiring a power supply voltage signal of a power grid;
the preprocessing module is used for judging whether the voltage signal is a dense modal signal or not and removing discontinuous signal components in the voltage signal to obtain a preprocessed voltage signal;
the improved Hilbert-Huang transform module is used for obtaining a fundamental voltage signal of the improved Hilbert-Huang transform module based on the judgment result of the preprocessing module and the preprocessed voltage signal;
the fault identification module is used for identifying voltage swing faults based on the fundamental wave voltage signals.
10. The voltage quality disturbance detection system according to claim 9, wherein the fundamental voltage signal is obtained based on the pre-processing module's determination and the pre-processed voltage signal:
if the signal is a dense modal signal, performing improved Hilbert-Huang transform on the preprocessed voltage signal to obtain a fundamental voltage signal of the voltage signal; if not, performing original Hilbert-Huang transform on the preprocessed voltage signal to obtain a fundamental voltage signal.
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| CN117200242A (en) * | 2023-11-08 | 2023-12-08 | 西安米特电子科技有限公司 | Monitoring data processing method and system for intelligent voltage regulating cabinet |
| WO2024109031A1 (en) * | 2022-11-25 | 2024-05-30 | 国网浙江省电力有限公司嘉善县供电公司 | Hht-based voltage quality disturbance detection method |
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| WO2024109031A1 (en) * | 2022-11-25 | 2024-05-30 | 国网浙江省电力有限公司嘉善县供电公司 | Hht-based voltage quality disturbance detection method |
| CN117200242A (en) * | 2023-11-08 | 2023-12-08 | 西安米特电子科技有限公司 | Monitoring data processing method and system for intelligent voltage regulating cabinet |
| CN117200242B (en) * | 2023-11-08 | 2024-02-02 | 西安米特电子科技有限公司 | Monitoring data processing method and system for intelligent voltage regulating cabinet |
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