Method for identifying synchronous pressure pulsation of draft tube of tubular water turbine

Abstract

The application discloses a kind of identification methods of synchronous pressure pulsation of tubular turbine draft tube, 3 pressure pulsation sensors are installed on the same section outside draft tube, and the synchronous pressure pulsation of tubular turbine draft tube pressure pulsation signal is identified by variational mode decomposition and time-frequency analysis;Through the application, the synchronous pressure pulsation in the tubular turbine draft tube pressure pulsation can be identified, which provides basis for avoiding the occurrence of synchronous pressure pulsation in the operation process during the design stage of water turbine.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of water turbines and relates to a method for identifying synchronous pressure pulsation of a draft tube of a tubular turbine. BACKGROUND

[0002] As a major renewable clean energy, hydropower needs to be developed on a large scale and through multiple channels to ensure the realization of the "double carbon" goal. In the future development of hydropower, low-head water energy resources have gradually become the focus of hydropower development. Tubular turbines are the main models for the development of low-head water energy resources, and their stable operation is of great significance to improving energy development efficiency. Pressure pulsation is a major indicator reflecting the stability of a water turbine, and synchronous pressure pulsation is an important factor causing water body resonance in the internal unit and thus leading to a stability catastrophe of the water turbine. At present, less attention is paid to synchronous pressure pulsation of the draft tube of a tubular turbine, and there is still a lack of a method that can quickly and accurately identify synchronous pressure pulsation in the draft tube to avoid water body resonance in the internal unit caused by synchronous pressure pulsation of the draft tube. SUMMARY

[0003] The application aims to provide a method for identifying synchronous pressure pulsation of a draft tube of a tubular turbine, which can identify synchronous pressure pulsation in pressure pulsation of the draft tube of the tubular turbine and provide a basis for avoiding the occurrence of synchronous pressure pulsation during operation in the design stage of the water turbine.

[0004] The technical solution adopted by the application is a method for identifying synchronous pressure pulsation of a draft tube of a tubular turbine, which specifically includes the following steps:

[0005] Step 1: Start a water turbine pressure pulsation signal acquisition test system.

[0006] Step 2: Obtain three groups of draft tube pressure pulsation time sequence signals on the same cross section.

[0007] Step 3: Filter and perform variational modal decomposition on the pressure pulsation signals obtained in Step 2 to obtain signal components.

[0008] Step 4: Calculate the energy of each signal component in Step 3 and the correlation coefficient of each signal component and the low-frequency signal before decomposition, and extract the signal component with the highest signal energy and correlation coefficient as the main component of the pressure pulsation signal.

[0009] Step 5: Perform time-frequency analysis on the main component of the pressure pulsation obtained in Step 4 through continuous wavelet transform, and extract the signal component with synchronous and same-frequency enhanced pressure pulsation.

[0010] Step 6, judging whether the waveform phases of the signal components extracted in step 5 are the same according to the correlation coefficient, and determining whether the signal components are synchronous pressure pulsations in the tail pipe pressure pulsation according to the judging result.

[0011] The application also has the characteristics that:

[0012] The specific process of step 2 is as follows:

[0013] Three tail pipe pressure pulsation signals on the same cross section are collected by a pressure pulsation sensor, and the pressure pulsation signals are sent to a control console by a data acquisition system to obtain three groups of tail pipe pressure pulsation time sequence signals X1(t), X2(t) and X3(t) on the same cross section.

[0014] The specific process of step 3 is as follows:

[0015] The three groups of pressure pulsation signals X1(t), X2(t) and X3(t) obtained in step 2 are input into a low-pass filter to obtain low-frequency signals x1(t), x2(t) and x3(t), and after Fourier transform, the signal frequency domain distribution is obtained, and then the three groups of signals are respectively decomposed by variational mode to obtain three groups of signal components IMF 1a (t), IMF 1b (t)…IMF 1k (t), IMF 2a (t), IMF 2b (t)…IMF 2k (t), IMF 3a (t), IMF 3b (t)…IMF 3k (t).

[0016] The specific process of step 4 is as follows:

[0017] Step 4.1, respectively, the three groups of signal components IMF 1a (t), IMF 1b (t)…IMF 1k (t), IMF 2a (t), IMF 2b (t)…IMF 2k (t), IMF 3a (t), IMF 3b (t)…IMF 3k (t) are substituted into the IMF(t) of formula (1) to calculate, and three groups of signal components IMF 1a (t), IMF 1b (t)…IMF 1k (t), IMF 2a (t), IMF 2b (t)…IMF 2k (t), IMF3a (t), IMF 3b (t)…IMF 3k (t) E 1a , E 1b …E 1k , E 2a , E 2b …E 2k , E 3a , E 3b …E 3k , the formula for calculating the signal energy is:

[0018] E =∑IMF 2 (t) (1);

[0019] In the formula, E is the calculated signal energy;

[0020] Step 4.2, three groups of signal components IMF 1a (t), IMF 1b (t)…IMF 1k (t), IMF 2a (t), IMF 2b (t)…IMF 2k (t), IMF 3a (t), IMF 3b (t)…IMF 3k (t) are calculated by substituting the pressure pulsation low-frequency signals x1(t), x2(t), x3(t) obtained in step 3 into IMF(t) and x(t) in formula (2), respectively, to obtain three groups of signal components IMF 1a (t), IMF 1b (t)…IMF 1k (t), IMF 2a (t), IMF 2b (t)…IMF 2k (t), IMF 3a (t), IMF 3b (t)…IMF 3k (t) and the correlation coefficient p 1a , p 1b …p 1k , p 2a , p 2b …p 2k , p 3a , p 3b …p 3k :

[0021]

[0022] wherein p is a correlation coefficient calculated, and respectively represent signal components IMF 1a (t), IMF 1b (t)…IMF 1k (t), IMF 2a (t), IMF 2b (t)…IMF 2k (t), IMF 3a (t), IMF 3b (t)…IMF 3k (t) and the average values of low-frequency signals x1(t), x2(t), x3(t).

[0023] Step 4.3, sort the energy E 1a , E 1b …E 1k , E 2a , E 2b …E 2k , E 3a , E 3b …E 3k of the 3 groups of signal components obtained in step 4.1 and the correlation coefficients p 1a , p 1b …p 1k , p 2a , p 2b …p 2k , p 3a , p 3b …p 3k obtained in step 4.2 from high to low, and extract the 3 groups of signal components with the highest signal energy and correlation coefficient, i.e., imf 1a (t), imf 1b (t), imf 1c (t), imf 2a (t), imf 2b (t), imf 2c (t), imf 3a (t), imf 3b (t), imf 3c (t) as the main components of the three groups of pressure pulsation signals.

[0024] The specific process of step 5 is as follows:

[0025] Step 5.1, select a Morlet complex wavelet function as the base function of continuous wavelet transform;

[0026] Step 5.2, take the three groups of pressure pulsation main components imf 1a (t), imf1b (t), imf 1c (t), imf 2a (t), imf 2b (t), imf 2c (t), imf 3a (t), imf 3b (t), imf 3c (t) into the imf(t) in formula (3) respectively, three groups of pressure pulsation main components imf 1a (t), imf 1b (t), imf 1c (t), imf 2a (t), imf 2b (t), imf 2c (t), imf 3a (t), imf 3b (t), imf 3c (t) are obtained by continuous wavelet transform function CWT 1a , CWT 1b , CWT 1c , CWT 2a , CWT 2b , CWT 2c , CWT 3a , CWT 3b , CWT 3c , and the time-frequency distribution cloud diagram is drawn according to the continuous wavelet transform function, from which the pressure pulsation synchronous and same frequency enhanced signal components IMF1(t), IMF2(t) and IMF3(t) are extracted:

[0027] CWT = [imf(t), ψ a,b (t)] (3).

[0028] In the formula, CWT is the continuous wavelet transform function calculated, a and b are the stretching factor and the translation factor respectively, and ψ a,b (t) is the wavelet base function.

[0029] The specific process of step 6 is as follows:

[0030] The signal components IMF1(t), IMF2(t), IMF1(t), IMF3(t) and IMF2(t), IMF3(t) obtained in step 5 are substituted into x(t) and y(t) in formula (4) respectively, and the correlation coefficients ρ 12 , ρ 13 , ρ 23 between the pressure pulsation synchronous and same frequency enhanced signal components IMF1(t), IMF2(t), IMF3(t) are calculated, and the calculation formula of the correlation coefficient is:

[0031]

[0032] In the formula, p is a correlation coefficient calculated, and are the average values of the two groups of signals, if the correlation coefficients p 12 , p 13 , p 23 of the signal components IMF1(t), IMF2(t), IMF3(t) all reach 0.99 or above, and the signal waveforms are highly consistent in phase, it is considered that the phases of the signal components IMF1(t), IMF2(t), IMF3(t) are the same, and the signal component is the synchronous pressure pulsation in the tail water pipe pressure pulsation.

[0033] The tail water pipe synchronous pressure pulsation recognition method of the tubular turbine according to the present application can quickly and accurately recognize the synchronous pressure pulsation component in the tail water pipe of the tubular turbine, provides a basis for avoiding the tail water pipe synchronous pressure pulsation from causing water body resonance in the turbine, and eliminates the risk of the synchronous pressure pulsation in the operation of the tubular turbine causing water body resonance in the turbine and further causing synchronous vibration of the unit. BRIEF DESCRIPTION OF DRAWINGS

[0034] Figure 1 is a tail water pipe pressure pulsation measuring point position schematic diagram in the tail water pipe synchronous pressure pulsation recognition method of the tubular turbine according to the present application;

[0035] Figure 2 is a pressure pulsation original signal collected in the tail water pipe synchronous pressure pulsation recognition method of the tubular turbine according to the present application;

[0036] Figure 3 is a tail water pipe pressure pulsation variational modal decomposition result in the tail water pipe synchronous pressure pulsation recognition method of the tubular turbine according to the present application;

[0037] Fig. 4(a)-(c) is a continuous wavelet transform result schematic diagram of different test points in the tail water pipe synchronous pressure pulsation recognition method of the tubular turbine according to the present application;

[0038] Figure 5 is a synchronous pressure pulsation phase schematic diagram in the tail water pipe synchronous pressure pulsation recognition method of the tubular turbine according to the present application. DETAILED DESCRIPTION

[0039] The present application will be described in detail below in combination with the drawings and specific embodiments.

[0040] The application discloses a method for identifying synchronous pressure pulsation of a draft tube of a tubular turbine, three pressure pulsation sensors (pressure test point 1, pressure test point 2 and pressure test point 3) are installed on the same cross section outside the draft tube of the tubular turbine, synchronous pressure pulsation of the draft tube of the tubular turbine is identified through variational modal decomposition and time-frequency analysis of pressure pulsation signals of the draft tube of the tubular turbine, and the method specifically comprises the following steps:

[0041] Step 1: start a pressure pulsation signal acquisition test system of the turbine, and water flows through a water inlet pipe, guide vanes, a runner and the draft tube in sequence;

[0042] Step 2: three draft tube pressure pulsation signals on the same cross section are acquired through the pressure pulsation sensors, the pressure pulsation sensors are arranged as shown in the figure, Figure 1 and the pressure pulsation signals are sent to a control console through a data acquisition system to obtain three groups of draft tube pressure pulsation time sequence signals X1(t), X2(t) and X3(t) on the same cross section, and the signal waveforms are as shown in the figure, Figure 2 .

[0043] Step 3: the three groups of pressure pulsation signals X1(t), X2(t) and X3(t) obtained in step 2 are input into a low-pass filter to obtain low-frequency signals x1(t), x2(t) and x3(t), and after the signal frequency domain distribution is obtained through fast Fourier transform, variational modal decomposition is respectively performed on the three groups of signals to obtain three groups of signal components IMF 1a (t), IMF 1b (t)…IMF 1k (t), IMF 2a (t), IMF 2b (t)…IMF 2k (t), IMF 3a (t), IMF 3b (t)…IMF 3k (t), as shown in the figure, Figure 3 and specifically as follows:

[0044] Step 3.1: the three groups of low-frequency pressure pulsation signals x1(t), x2(t) and x3(t) are respectively input, the number of modes K is preliminarily determined according to the number of wave peaks of the frequency domain signals, the number of modes K is generally 3-7, and a penalty factor a is set, the penalty factor a is generally 5000-10000;

[0045] Step 3.2: variational modal decomposition is respectively performed on the three groups of pressure pulsation signals according to the parameters set in step 3.1, and K signal components IMF 1a (t), IMF 1b (t)…IMF 1k (t), IMF 2a (t) with different center frequencies and non-overlapping frequency bands are obtained by increasing or decreasing the number of modes K and the penalty factor a.2b (t)…IMF 2k (t), IMF 3a (t), IMF 3b (t)…IMF 3k (t), whose waveform is as Figure 3 shown.

[0046] Step 4, the energy of each signal component in step 3 and the correlation coefficient of each signal component and the low frequency signal before decomposition are calculated respectively, and the three groups of signal components with the highest signal energy and correlation coefficient are extracted after the signal components are sorted according to the energy and correlation coefficient. 1a (t), IMF 1b (t), IMF 1c (t), IMF 2a (t), IMF 2b (t), IMF 2c (t), IMF 3a (t), IMF 3b (t), IMF 3c (t) are taken as the main components of the three groups of pressure pulsation signals. The specific method for calculating the signal energy and the correlation coefficient is:

[0047] Step 4.1, respectively, the three groups of signal components IMF 1a (t), IMF 1b (t)…IMF 1k (t), IMF 2a (t), IMF 2b (t)…IMF 2k (t), IMF 3a (t), IMF 3b (t)…IMF 3k (t) into IMF(t) in formula (1) to calculate, get three groups of signal components IMF 1a (t), IMF 1b (t)…IMF 1k (t), IMF 2a (t), IMF 2b (t)…IMF 2k (t), IMF 3a (t), IMF 3b (t)…IMF 3k (t) energy E 1a , E 1b ...E 1k , E 2a , E 2b ...E 2k , E 3a , E 3b ...E 3k, the formula for calculating the signal energy is:

[0048] E =∑IMF 2 (t) (1) ;

[0049] In the formula, E is the calculated signal energy;

[0050] Step 4.2, replace the three groups of signal components IMF 1a (t), IMF 1b (t) … IMF 1k (t), IMF 2a (t), IMF 2b (t) … IMF 2k (t), IMF 3a (t), IMF 3b (t) … IMF 3k (t) with the low-frequency pressure fluctuation signals x1(t), x2(t), x3(t) obtained in step 3 into IMF(t) and x(t) in formula (2) respectively to calculate three groups of signal components IMF 1a (t), IMF 1b (t) … IMF 1k (t), IMF 2a (t), IMF 2b (t) … IMF 2k (t), IMF 3a (t), IMF 3b (t) … IMF 3k (t) and the correlation coefficients ρ 1a , ρ 1b … ρ 1k , ρ 2a , ρ 2b … ρ 2k , ρ 3a , ρ 3b … ρ 3k of low-frequency signals x1(t), x2(t), x3(t) respectively, and the formula for calculating the correlation coefficient is:

[0051]

[0052] In the formula, ρ is the calculated correlation coefficient, and respectively represent the signal components IMF 1a (t), IMF 1b (t) … IMF 1k (t), IMF 2a (t), IMF 2b (t) … IMF 2k (t), IMF 3a(t), IMF 3b (t)…IMF 3k (t) and the average values of the low-frequency signals x1(t), x2(t), x3(t).

[0053] Step 4.3, respectively, the energy E 1a , E 1b ...E 1k , E 2a , E 2b ...E 2k , E 3a , E 3b ...E 3k and the correlation coefficient p 1a , p 1b ...p 1k , p 2a , p 2b ...p 2k , p 3a , p 3b ...p 3k of the three groups of signal components obtained in step 4.2 and the low-frequency signals are sorted from high to low, and the three groups of signal components with the highest signal energy and correlation coefficient IMF 1a (t), IMF 1b (t), IMF 1c (t), IMF 2a (t), IMF 2b (t), IMF 2c (t), IMF 3a (t), IMF 3b (t), IMF 3c (t) are extracted as the main components of the three groups of pressure pulsation signals.

[0054] Step 5, through continuous wavelet transform, the pressure pulsation main components IMF 1a (t), IMF 1b (t), IMF 1c (t), IMF 2a (t), IMF 2b (t), IMF 2c (t) and IMF 3a (t), IMF 3b (t), IMF 3c (t) obtained in step 4 are respectively subjected to time-frequency analysis, and a time-frequency distribution cloud chart is drawn, from which the pressure pulsation synchronous and same-frequency enhanced signal components IMF1(t), IMF2(t), IMF3(t) are extracted, wherein the specific steps of time-frequency analysis are as follows:

[0055] Step 5.1: To accurately display the local characteristics of the pressure pulsation signal in the time-frequency domain, the center frequency F is selected. c =3, Morlet complex wavelet function with bandwidth dB=3 is used as the basis function for continuous wavelet transform;

[0056] Step 5.2, convert the three sets of pressure pulsation main components (imf) obtained in Step 4 into... 1a (t), imf 1b (t), imf 1c (t), imf 2a (t), imf 2b (t), imf 2c (t), imf 3a (t), imf 3b (t), imf 3c Substituting (t) into imf(t) in formula (3) and performing continuous wavelet transform, we obtain the three sets of main pressure pulsation components imf. 1a (t), imf 1b (t), imf 1c (t), imf 2a (t), imf 2b (t), imf 2c (t), imf 3a (t), imf 3b (t), imf 3c CWT (t) continuous wavelet transform function 1a CWT 1b CWT 1c CWT 2a CWT 2b CWT 2c CWT 3a CWT 3b CWT 3c The time-frequency distribution cloud map was plotted based on the continuous wavelet transform function, and the signal components IMF1(t), IMF2(t), and IMF3(t) with synchronous and enhanced pressure pulsation frequency were extracted from it. Their time-frequency distribution cloud maps are shown in Figures 4(a), 4(b), and 4(c), respectively. The continuous wavelet transform calculation formula is as follows:

[0057] CWT=[imf(t),ψ a,b (t)] (3);

[0058] In the formula, CWT is the calculated continuous wavelet transform function, a and b are the scaling factor and translation factor, respectively, and ψ a,b (t) is the wavelet basis function.

[0059] Step 6: Substitute the signal components IMF1(t), IMF2(t), IMF1(t), IMF3(t) and IMF2(t), IMF3(t) obtained in Step 5 into x(t) and y(t) in Formula (4) respectively, and calculate the correlation coefficient ρ between each pair of the pressure pulsation synchronous and frequency-enhanced signal components IMF1(t), IMF2(t), and IMF3(t). 12 ρ 13 ρ 23 The formula for calculating the correlation coefficient is:

[0060]

[0061] In the formula, ρ is the calculated correlation coefficient. and These are the average values ​​of the two sets of signals, respectively. If the correlation coefficient ρ between the signal components IMF1(t), IMF2(t), and IMF3(t) is... 12 ρ 13 ρ 23 All reached above 0.99, and the signal waveform phase remained highly consistent, such as Figure 5 As shown, the signal components IMF1(t), IMF2(t), and IMF3(t) can be considered to have the same phase, and this signal component is the synchronous pressure pulsation in the tailrace pressure pulsation.

Claims

1. A method for identifying synchronous pressure pulsations in the tailrace of a once-through turbine, characterized in that: Specifically, the steps include the following: Step 1: Start the turbine pressure pulsation signal acquisition test system; Step 2: Obtain the timing signals of pressure pulsation in three sets of tailrace pipes on the same cross section; Step 3: Filter and perform variational mode decomposition on the pressure pulsation time-series signal obtained in Step 2 to obtain the signal components; Step 4: Calculate the energy of each signal component in Step 3 and the correlation coefficient between each signal component and the low-frequency signal before decomposition. Extract the signal component with the highest signal energy and correlation coefficient as the main component of the pressure pulsation signal. Step 5: Perform time-frequency analysis on the main components of the pressure pulsation obtained in Step 4 using continuous wavelet transform to extract the signal components that are synchronous with and enhance the pressure pulsation at the same frequency. Step 6: Determine whether the waveform phases of the signal components extracted in Step 5 are the same based on the correlation coefficient, and determine whether the signal component is the synchronous pressure pulsation in the tailrace pipe pressure pulsation based on the judgment result.

2. The method for identifying synchronous pressure pulsation in the tailrace of a axial-flow turbine according to claim 1, characterized in that: The specific process of step 2 is as follows: Pressure pulsation signals from three tailrace pipe locations on the same cross section were collected by a pressure pulsation sensor. The data acquisition system then sent the pressure pulsation signals to the control console, resulting in three sets of tailrace pipe pressure pulsation timing signals X1(t), X2(t), and X3(t) on the same cross section.

3. The method for identifying synchronous pressure pulsation in the tailrace of a once-through turbine according to claim 2, characterized in that: The specific process of step 3 is as follows: The three sets of pressure pulsation time-series signals X1(t), X2(t), and X3(t) obtained in step 2 are input into a low-pass filter to obtain low-frequency signals. x 1(t), x 2(t), x After Fourier transforming 3(t) to obtain the frequency domain distribution of the signal, variational mode decomposition is performed to obtain three sets of signal components IMF. 1a (t), IMF 1b (t)...IMF 1k (t), IMF 2a (t), IMF 2b (t)...IMF 2k (t), IMF 3a (t), IMF 3b (t)...IMF 3k (t).

4. The method for identifying synchronous pressure pulsation in the tailrace of a once-through turbine according to claim 3, characterized in that: The specific process of step 4 is as follows: Step 4.1, convert the three signal components into IMFs respectively. 1a (t), IMF 1b (t)...IMF 1k (t), IMF 2a (t), IMF 2b (t)...IMF 2k (t), IMF 3a (t), IMF 3b (t)...IMF 3k Substituting (t) into formula (1) The calculation yielded three sets of signal component IMFs. 1a (t), IMF 1b (t)...IMF 1k (t), IMF 2a (t), IMF 2b (t)...IMF 2k (t), IMF 3a (t), IMF 3b (t)...IMF 3k The energy E of (t) 1a E 1b ...E 1k E 2a E 2b …E 2k E 3a E 3b …E 3k The formula for calculating signal energy is: （1）； In the formula, E is the calculated signal energy; Step 4.2, convert the three signal components into IMFs. 1a (t), IMF 1b (t)...IMF 1k (t), IMF 2a (t), IMF 2b (t)...IMF 2k (t), IMF 3a (t), IMF 3b (t)...IMF 3k (t) and the low-frequency pressure pulsation signal obtained in step 3 x 1(t), x 2(t), x Substituting 3(t) into formula (2) respectively and Calculations were performed to obtain three sets of signal component IMFs. 1a (t), IMF 1b (t)...IMF 1k (t), IMF 2a (t), IMF 2b (t)...IMF 2k (t), IMF 3a (t), IMF 3b (t)...IMF 3k (t) and low-frequency signal x 1(t), x 2(t), x Correlation coefficient of 3(t) ρ 1a , ρ 1b … ρ 1k , ρ 2a , ρ 2b … ρ 2k , ρ 3a , ρ 3b … ρ 3k : （2）； In the formula, The obtained correlation coefficient, and They represent the signal components IMF respectively. 1a (t), IMF 1b (t)...IMF 1k (t), IMF 2a (t), IMF 2b (t)...IMF 2k (t), IMF 3a (t), IMF 3b (t)...IMF 3k (t) and low-frequency signal x 1(t), x 2(t), x The average value of 3(t); Step 4.3, calculate the energy E of the three signal components obtained in Step 4.1 respectively. 1a E 1b ...E 1k E 2a E 2b …E 2k E 3a E 3b …E 3k The correlation coefficients between the three sets of signal components obtained in step 4.2 and the low-frequency signal. ρ 1a , ρ 1b … ρ 1k , ρ 2a , ρ 2b … ρ 2k , ρ 3a , ρ 3b … ρ 3k Sort the signals from highest to lowest, and extract the three signal components with the highest signal energy and correlation coefficient (IMF). 1a (t), imf 1b (t), imf 1c (t), imf 2a (t), imf 2b (t), imf 2c (t), imf 3a (t), imf 3b (t), imf 3c (t) is the main component of the three pressure pulsation signals.

5. The method for identifying synchronous pressure pulsation in the tailrace of a axial-flow turbine according to claim 4, characterized in that: The specific process of step 5 is as follows: Step 5.1: Select the Morlet complex wavelet function as the basis function for the continuous wavelet transform; Step 5.2, convert the three sets of pressure pulsation main components (imf) obtained in Step 4 into... 1a (t), imf 1b (t), imf 1c (t), imf 2a (t), imf 2b (t), imf 2c (t), imf 3a (t), imf 3b (t), imf 3c Substitute (t) into formula (3) respectively Performing a continuous wavelet transform, we obtain three sets of main pressure pulsation components (IMF). 1a (t), imf 1b (t), imf 1c (t), imf 2a (t), imf 2b (t), imf 2c (t), imf 3a (t), imf 3b (t), imf 3c CWT (continuous wavelet transform function of t) 1a CWT 1b CWT 1c CWT 2a CWT 2b CWT 2c CWT 3a CWT 3b CWT 3c Based on the continuous wavelet transform function, a time-frequency distribution cloud map was plotted, from which the signal components IMF1(t), IMF2(t), and IMF3(t) with synchronous and frequency-enhanced pressure pulsation were extracted. （3）； In the formula, CWT is the calculated continuous wavelet transform function. a and b These are the scaling factor and the translation factor, respectively. These are wavelet basis functions.

6. The method for identifying synchronous pressure pulsation in the tailrace of a once-through turbine according to claim 5, characterized in that: The specific process of step 6 is as follows: Substitute the signal components IMF1(t), IMF2(t), IMF1(t), IMF3(t) and IMF2(t), IMF3(t) obtained in step 5 into formula (4). and Calculate the correlation coefficients between each pair of the pressure pulsation-synchronized and frequency-enhanced signal components IMF1(t), IMF2(t), and IMF3(t). ρ 12 , ρ 13 , ρ 23 The formula for calculating the correlation coefficient is: （4）； In the formula, The obtained correlation coefficient, and Let IMF1(t), IMF2(t), and IMF3(t) be the average values ​​of the two sets of signals, respectively. The correlation coefficients between the signal components IMF1(t), IMF2(t), and IMF3(t) are... ρ 12 , ρ 13 , ρ 23 If all three signals reach a value of 0.99 or higher and the signal waveforms maintain a high degree of consistency in phase, then the signal components IMF1(t), IMF2(t), and IMF3(t) are considered to have the same phase, and this signal component is the synchronous pressure pulsation in the tailrace pipe pressure pulsation.

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