Anti-noise power system high-frequency oscillation detection method based on frequency band self-adaption

Abstract

The invention provides an anti-noise power system high-frequency oscillation detection method based on frequency band self-adaption, and the method comprises the steps: installing a three-phase high-frequency voltage transformer at a resonance sensitive point, selecting the width of a data collection window, collecting three-phase voltage data, and constructing a three-phase voltage tensor mathematical model; performing Tucker decomposition on the voltage tensor in the three-phase voltage tensor mathematical model to realize voltage noise reduction; converting the denoised voltage tensor into a three-phase voltage, and carrying out wave crest detection by using dynamic measurement; performing wavelet packet decomposition on the three-phase voltage according to the detected high-frequency oscillation signal and the frequency range thereof, so that the high-frequency oscillation signal, a low-frequency fundamental wave and related harmonic components are separated to wavelet coefficients of all frequency bands; performing oscillation signal reduction on the wavelet coefficient containing the oscillation signal; and performing exponential decomposition on the reduced oscillation signal by adopting a Prony method to obtain a high-frequency oscillation characteristic. The high-frequency oscillation detection precision can be improved.

Description

Technical Field

[0001] The invention relates to the technical field of protecting the safety of a new type of electric power system, and in particular to a method for detecting high-frequency oscillation of an electric power system based on frequency band self-adaptation and noise resistance. Background Art

[0002] The rapid rise of various new energy sources, the massive access of distributed photovoltaics, microgrid clusters and energy storage, and the high penetration of power electronic devices have not only greatly increased the noise level of the power grid, but also caused a large number of high-frequency oscillations due to various impedance mismatches, which has brought huge challenges to the safe operation of the power grid. Accurate detection of oscillation frequency and amplitude is the basis for effective oscillation suppression, but existing methods are difficult to accurately detect high-frequency oscillations in low signal-to-noise ratio environments. Summary of the invention

[0003] The present invention provides a method for detecting high-frequency oscillation in an electric power system based on frequency band adaptive anti-noise. The method constructs a third-order tensor of the three-phase voltage and uses the data structure to achieve data adaptive noise reduction to obtain a high signal-to-noise ratio oscillation signal. The oscillation amplitude and frequency are then accurately obtained through frequency band adaptive analysis to improve the detection accuracy of high-frequency oscillation.

[0004] A method for detecting high-frequency oscillation in a power system based on frequency band adaptive anti-noise comprises the following steps:

[0005] Step 1: Install a three-phase high-frequency voltage transformer at a resonance sensitive point, select a data acquisition window width, collect three-phase voltage data, and construct a three-phase voltage tensor mathematical model based on the collected three-phase voltage data;

[0006] Step 2: Perform Tucker decomposition on the voltage tensor in the three-phase voltage tensor mathematical model to achieve voltage noise reduction;

[0007] Step 3: convert the denoised voltage tensor into a three-phase voltage, and use dynamic measurement to perform peak detection on the three-phase voltage, detect the high-frequency oscillation signal and estimate the frequency range of the high-frequency oscillation signal;

[0008] Step 4: Decompose the three-phase voltage by wavelet packet according to the high-frequency oscillation signal and its frequency range obtained by dynamic measurement in step 3, so that the high-frequency oscillation signal and the low-frequency fundamental wave and related harmonic components are separated into wavelet coefficients of each frequency band;

[0009] Step 5, restoring the oscillation signal to the wavelet coefficients containing the oscillation signal;

[0010] Step 6: Use the Prony method to perform exponential decomposition on the oscillation signal restored in step 5 to obtain high-frequency oscillation characteristics.

[0011] Furthermore, in step 1, the duration T of the data acquisition window width is selected according to the calculation requirements. W , taking the integer multiple M of the period as the sampling rate, assuming that there are N points per cycle, the three-phase sampled voltage is constructed into a three-phase voltage tensor mathematical model U(M,N,3).

[0012] Furthermore, the step 2 specifically includes:

[0013] 2.1) Perform Tucker decomposition on the three-phase voltage tensor mathematical model U(M,N,3) to obtain

[0014] U≈G× 1 A× 2 B× 3 C(2-1)

[0015] in is the core tensor, is the projection matrix of tensor decomposition, λ 1 ,λ 2 ,λ 3 is the rank of Tucker decomposition and is less than M, N, 3 respectively;

[0016] 2.2) Select a suitable rank so that the core tensor G contains more than 99% of the energy of the third-order tensor U, and as the rank increases, the energy share of G increases by a gradient less than 1%.

[0017] Furthermore, the step 3 specifically includes:

[0018] 3.1) Determine the dynamic measurement threshold Th D , when the amplitude difference between two adjacent voltage extreme points detected by the peak is greater than Th D When , these two extreme points are marked using dynamic measurement;

[0019] 3.2) Make a set of extreme points marked by dynamic measurement, and calculate the average duration of each two adjacent extreme points, recorded as t 1 , the average duration of each adjacent f points is recorded as t f , find the inverse of the average duration to get the corresponding frequency, and record these frequencies as an array The frequency bands are divided with each element in the array as the center frequency, and the frequency bands are aligned, that is, [F 1 ,F 2 ,...,F f ].

[0020] Furthermore, the step 4 specifically includes:

[0021] 4.1) Select the high-order vanishing moment function as the mother wavelet;

[0022] 4.2) Perform wavelet packet decomposition on the three-phase voltage signal according to the obtained frequency band and mother wavelet, so that the frequency band of the last layer of detail wavelet is consistent with the frequency band in step 3.2):

[0023]

[0024] in and are the even and odd wavelet coefficients of wavelet decomposition respectively, h and g are orthogonal low and high filter bank functions determined by the mother wavelet.

[0025] Furthermore, the step 5 specifically includes:

[0026] 5.1) Set the wavelet threshold according to each layer band:

[0027] Assume that the wavelet coefficient d of the lth layer l , then the wavelet threshold is as follows:

[0028]

[0029] Where median is to find the middle value of the sequence;

[0030] 5.2) Perform inverse transform on each layer of wavelet coefficients layer by layer to restore the time domain form of the signal components in the frequency band. D If the signal duration is less than the window T W , it is determined to be a high-frequency oscillation, and the maximum extreme value P of the signal is taken as the oscillation amplitude.

[0031] Furthermore, the step 6 specifically includes:

[0032] 6.1) The high frequency signal x obtained in 5.2) is used Perform the fit:

[0033]

[0034] Where p is the number of signal components and N is the number of signal sampling points;

[0035] 6.2) Use least squares to find Then the amplitude A of the i-th signal component is i , frequency f i , attenuation factor α i Obtained by the following formula:

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] 1) Through the construction of three-phase voltage tensor and Tucker decomposition, the noise can be reduced and the high-frequency oscillation signal can be completely preserved in the case of low signal-to-noise ratio in a data-adaptive manner;

[0038] 2) Through dynamic measurement, the high-frequency oscillation frequency is preliminarily estimated to obtain the frequency range of the high-frequency oscillation, and then the oscillation characteristics can be obtained in a targeted manner without being interfered by signals in other frequency bands;

[0039] 3) The oscillation signal is extracted through frequency-band adaptive wavelet packet decomposition, and the high-frequency oscillation characteristics are accurately obtained through the Prony algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a schematic diagram of a three-phase voltage signal containing oscillations according to an embodiment of the present invention, with the unit being per unit value pu and the signal-to-noise ratio being 30 dB.

[0041] Figure 2 It is a schematic diagram of a voltage signal after Tucker decomposition and noise reduction according to an embodiment of the present invention.

[0042] Figure 3 It is a schematic diagram of the three-phase oscillation signal components after dynamic measurement and frequency band adaptive wavelet decomposition and restoration according to an embodiment of the present invention.

[0043] Figure 4 The present invention is a flowchart of a method for detecting high-frequency oscillation in a power system based on frequency band adaptive anti-noise. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] See also Figure 4 The embodiment of the present invention provides a method for detecting high-frequency oscillation of an electric power system based on frequency band adaptive anti-noise, comprising the following steps:

[0046] Step 1: Install a three-phase high-frequency voltage transformer at the resonance sensitive point, select the data acquisition window width, and collect three-phase voltage data (such as Figure 1 As shown), and construct a three-phase voltage tensor mathematical model based on the collected three-phase voltage data;

[0047] The specific process of step 1 is as follows:

[0048] 1.1) Select the duration T of the data acquisition window width according to the calculation requirements W , measured in integer multiples of the period M;

[0049] 1.2) Assume that the sampling rate is N points per cycle, and construct the three-phase sampled voltage into a three-phase voltage tensor mathematical model U(M,N,3);

[0050] Step 2: Perform Tucker decomposition on the voltage tensor in the three-phase voltage tensor mathematical model to achieve voltage noise reduction. The noise reduction effect is as follows: Figure 2 ;

[0051] The specific process of step 2 is as follows:

[0052] 2.1) Perform Tucker decomposition on the three-phase voltage tensor mathematical model U(M,N,3) to obtain

[0053] U≈G× 1 A× 2 B× 3 C(2-1) is the core tensor, is the projection matrix of tensor decomposition, λ 1 ,λ 2 ,λ 3 is the rank of Tucker decomposition and is less than M, N, and 3 respectively.

[0054] 2.2) Select a suitable rank so that the core tensor G contains more than 99% of the energy of the third-order tensor U, and as the rank increases, the energy share of G increases by less than 1%. At this time, G contains almost no noise in U, and the three-phase voltage after noise reduction can be obtained by reconstructing with G.

[0055] Step 3: Convert the denoised voltage tensor into a three-phase voltage, and use dynamic measurement to detect the peak of the three-phase voltage. When dense peaks are measured in a short period of time, the dynamic measurement will increase sharply. Based on this, it can be determined whether there is a high-frequency oscillation signal and the frequency range of the high-frequency oscillation signal can be estimated.

[0056] The specific process of step 3 is as follows:

[0057] 3.1) Determine the dynamic measurement threshold Th D , when the amplitude difference between two adjacent voltage extreme points detected by the peak is greater than Th D , these two extreme points will be marked by dynamic measurement.

[0058] 3.2) Make a set of extreme points marked by dynamic measurement, and calculate the average duration of each two adjacent extreme points, recorded as t 1 , the average duration of each adjacent f points is recorded as t f , we can get the corresponding frequency by taking the inverse of this duration. These frequencies are recorded as an array The frequency bands are divided with each element in the array as the center frequency, and the frequency bands are aligned, that is, [F 1 ,F2 ,...,F f ].

[0059] Step 4: Decompose the three-phase voltage by wavelet packet according to the high-frequency oscillation signal and its frequency range obtained by dynamic measurement in step 3, so that the high-frequency oscillation signal and the low-frequency fundamental wave and related harmonic components are separated into wavelet coefficients of each frequency band;

[0060] The specific process of step 4 is as follows:

[0061] 4.1) Select a high-order vanishing moment function as the mother wavelet, such as db40. The purpose of this is to reduce the energy leakage of each layer of the wavelet, which is helpful for the separation of oscillation signals;

[0062] 4.2) Perform wavelet packet decomposition on the three-phase voltage signal according to the obtained frequency band and the mother wavelet, so that the frequency band of the last layer of detail wavelet is consistent with the frequency band in step 3.2).

[0063]

[0064] in and are the even and odd wavelet coefficients of wavelet decomposition respectively, h and g are orthogonal low and high filter bank functions determined by the mother wavelet.

[0065] Step 5, restoring the oscillation signal to the wavelet coefficients containing the oscillation signal;

[0066] The specific process of step 5 is as follows:

[0067] 5.1) Set the wavelet threshold according to each layer band:

[0068] Assume that the wavelet coefficient d of the lth layer l , then the wavelet threshold is as follows

[0069]

[0070] Where median is the middle value of the sequence. This wavelet threshold is mainly used to remove the interference of energy leakage of other wavelet layer coefficients on the wavelet coefficients of this layer.

[0071] 5.2) Perform inverse transform on each layer of wavelet coefficients layer by layer to restore the time domain form of the signal components in the frequency band. D If the signal duration is less than the window T W , then it is a high frequency oscillation. At this time, the maximum extreme value P of the signal is taken as the oscillation amplitude, such as Figure 3 .

[0072] Step 6: Use the Prony method to perform exponential decomposition on the oscillation signal restored in step 5 to obtain high-frequency oscillation characteristics.

[0073] The specific process of step 6 is as follows:

[0074] 6.1) The high frequency signal x obtained in 5.2) is used Perform the fit:

[0075]

[0076] Where p is the number of signal components and N is the number of signal sampling points.

[0077] 6.2) Use least squares to find Then the amplitude A of the i-th signal component is i , frequency f i , attenuation factor α i Obtained by the following formula:

[0078]

[0079] Where Δt is the sampling interval, Im is the imaginary part, and Re is the real part.

[0080] In a 30 dB noise environment, the real characteristics of the oscillation signal are compared with the oscillation characteristics obtained by the method of the present invention, as shown in Table 1:

[0081] Table 1 Comparison of the real characteristics of the oscillation signal in a 30 dB noise environment and the oscillation characteristics obtained by the method of the present invention

[0082]

[0083] From the results in Table 1, it can be seen that the present invention can reduce noise and completely preserve high-frequency oscillation signals in a data-adaptive manner under low signal-to-noise ratio conditions by constructing a three-phase voltage tensor and performing Tucker decomposition; a preliminary estimate of the high-frequency oscillation frequency is made through dynamic measurement to obtain the frequency range of the high-frequency oscillation, and then the oscillation characteristics can be obtained in a targeted manner without being interfered by signals in other frequency bands; the oscillation signal is extracted through frequency-band adaptive wavelet packet decomposition, and the amplitude, frequency and attenuation of the oscillation are accurately obtained through the Prony algorithm.

[0084] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for detecting high-frequency oscillation in a power system based on frequency band adaptive anti-noise, characterized in that: The steps include: Step 1: Install a three-phase high-frequency voltage transformer at a resonance sensitive point, select a data acquisition window width, collect three-phase voltage data, and construct a three-phase voltage tensor mathematical model based on the collected three-phase voltage data; Step 2: Perform Tucker decomposition on the voltage tensor in the three-phase voltage tensor mathematical model to achieve voltage noise reduction; Step 3: convert the denoised voltage tensor into a three-phase voltage, and use dynamic measurement to perform peak detection on the three-phase voltage, detect the high-frequency oscillation signal and estimate the frequency range of the high-frequency oscillation signal; Step 4: Decompose the three-phase voltage by wavelet packet according to the high-frequency oscillation signal and its frequency range obtained by dynamic measurement in step 3, so that the high-frequency oscillation signal and the low-frequency fundamental wave and related harmonic components are separated into wavelet coefficients of each frequency band; Step 5, restoring the oscillation signal to the wavelet coefficients containing the oscillation signal; Step 6: Use the Prony method to perform exponential decomposition on the oscillation signal restored in step 5 to obtain high-frequency oscillation characteristics.

2. The method for detecting high-frequency oscillation in a power system based on frequency band adaptive anti-noise as claimed in claim 1, characterized in that: In step 1, the duration T of the data acquisition window width is selected according to the calculation requirements. W , taking the integer multiple M of the period as the sampling rate, assuming that there are N points per cycle, the three-phase sampled voltage is constructed into a three-phase voltage tensor mathematical model U(M,N,3).

3. The method for detecting high-frequency oscillation in a power system based on frequency band adaptive anti-noise as claimed in claim 2, characterized in that: The step 2 specifically includes: 2.1) Perform Tucker decomposition on the three-phase voltage tensor mathematical model U(M,N,3) to obtain U≈G×1A×2B×3C(2-1) in is the core tensor, is the projection matrix of tensor decomposition, λ1, λ2, λ3 are the ranks of Tucker decomposition and are less than M, N, 3 respectively; 2.2) Select a suitable rank so that the core tensor G contains more than 99% of the energy of the third-order tensor U, and as the rank increases, the energy share of G increases by a gradient less than 1%.

4. The method for detecting high-frequency oscillation in a power system based on frequency band adaptive anti-noise as claimed in claim 1, characterized in that: The step 3 specifically includes: 3.1) Determine the dynamic measurement threshold Th D , when the amplitude difference between two adjacent voltage extreme points detected by the peak is greater than Th D When , these two extreme points are marked using dynamic measurement; 3.2) The extreme value points marked by the dynamic measurement are grouped together, and the average duration of each adjacent extreme value point is calculated, recorded as t1, and the average duration of each adjacent f points is recorded as t f , find the inverse of the average duration to get the corresponding frequency, and record these frequencies as an array The frequency bands are divided with each element in the array as the center frequency, and the frequency bands are aligned, that is, [F1, F2, ..., F f ].

5. The method for detecting high-frequency oscillation in a power system based on frequency band adaptive anti-noise as claimed in claim 4, characterized in that: The step 4 specifically includes: 4.1) Select the high-order vanishing moment function as the mother wavelet; 4.2) Perform wavelet packet decomposition on the three-phase voltage signal according to the obtained frequency band and mother wavelet, so that the frequency band of the last layer of detail wavelet is consistent with the frequency band in step 3.2): in and are the even and odd wavelet coefficients of wavelet decomposition respectively, h and g are orthogonal low and high filter bank functions determined by the mother wavelet.

6. The method for detecting high-frequency oscillation in a power system based on frequency band adaptive anti-noise as claimed in claim 1, characterized in that: The step 5 specifically includes: 5.1) Set the wavelet threshold according to each layer band: Assume that the wavelet coefficient d of the lth layer l , then the wavelet threshold is as follows: Where median is to find the middle value of the sequence; 5.2) Perform inverse transform on each layer of wavelet coefficients layer by layer to restore the time domain form of the signal components in the frequency band. D If the signal duration is less than the window T W , it is determined to be a high-frequency oscillation, and the maximum extreme value P of the signal is taken as the oscillation amplitude.

7. The method for detecting high-frequency oscillation in a power system based on frequency band adaptive anti-noise as claimed in claim 1, characterized in that: The step 6 specifically includes: 6.1) The high frequency signal x obtained in 5.2) is used Perform the fit: Where p is the number of signal components and N is the number of signal sampling points; 6.2) Use least squares to find Then the amplitude A of the i-th signal component i , frequency f i , attenuation factor α i Obtained by the following formula: Where Δt is the sampling interval, Im is the imaginary part, and Re is the real part.

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