Thermal power generating unit speed regulating system low-frequency oscillation multi-physics field cooperative monitoring method

Abstract

The invention discloses a thermal power generating unit speed regulation system low-frequency oscillation multi-physics field cooperative monitoring method. The method comprises the steps of S1, multi-physics field signal cooperative acquisition; s2, analyzing the change trend of the multi-physical field signal; s3, inputting real-time received multi-physical field signals into the constructed and trained mechanical-thermal-electrical combined monitoring model, and performing vibration reason analysis; and S4, according to a preset vibration intensity range, judging whether an early warning signal is sent out or not. The problems that an existing thermal power generating unit speed regulating system low-frequency oscillation monitoring method is prone to false alarm and the time-varying characteristic of the oscillation mode is difficult to capture are solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of operation monitoring of thermal power units in power grids. Specifically, it relates to a multi-physical-field collaborative monitoring method for low-frequency oscillation of the speed control system of thermal power units. Background Technique

[0002] With the increasing demand for deep peak shaving of thermal power units, the problem of low-frequency oscillation in the 0.1 - 2 Hz frequency band of the speed control system has become increasingly prominent. Traditional monitoring methods rely on single-point signals of rotational speed or power, and there are two major defects:

[0003] It is impossible to distinguish mechanical torsional vibration (shafting dynamic stress) from grid coupling oscillation (power angle instability), resulting in more than 30% false alarms;

[0004] The frequency spectrum analysis based on FFT is difficult to capture the time-varying characteristics of oscillation modes. Summary of the Invention

[0005] Aiming at the problems that the existing low-frequency oscillation monitoring method for the speed control system of thermal power units is prone to false alarms and the time-varying characteristics of oscillation modes are difficult to capture, the present invention provides a multi-physical-field collaborative monitoring method for low-frequency oscillation of the speed control system of thermal power units.

[0006] To achieve the above technical objectives, the technical solution adopted by the present invention is as follows:

[0007] A multi-physical-field collaborative monitoring method for low-frequency oscillation of the speed control system of thermal power units, including the steps of:

[0008] S1. Collaborative acquisition of multi-physical-field signals;

[0009] S2. Analyze the change trend of multi-physical-field signals;

[0010] S3. Input the multi-physical-field signals received in real time into the constructed and trained mechanical-thermal-electrical joint monitoring model for vibration cause analysis;

[0011] S4. Determine whether to issue a warning signal according to the preset vibration intensity range.

[0012] Further, the multi-physical-field signals include four-dimensional signals of governor oil pressure pulsation, shafting torsional vibration angular displacement, boiler heat storage coefficient, and grid power angle.

[0013] Further, the sensors for collaborative acquisition of multi-physical-field signals include high-frequency pressure sensors, laser Doppler vibrometers, grid dynamic coupling signal acquisition devices, steam drum pressure sensors, and steam flow sensors.

[0014] Further, the high-frequency pressure sensors are arranged at the inlet and outlet of the oil actuator, with a collection range of 0 - 30 MPa and a sampling rate ≥ 2 kHz;

[0015] Four sets of laser Doppler vibrometers are arranged along the high and intermediate pressure rotors of the steam turbine to measure the circumferential torsional vibration displacement Δθ (accuracy ±0.01°).

[0016] The power grid dynamic coupling signal acquisition device acquires and accesses the PMU to synchronously measure the grid connection point frequency f_grid and power angle δ of the unit.

[0017] The steam drum pressure sensor and the main steam flow sensor respectively collect the steam drum pressure and the main steam flow, and the heat storage coefficient is calculated.

[0018] Furthermore, before analyzing the change trend of the multi-physical field signals, it is necessary to preprocess the collected multi-physical field signals. The preprocessing is to remove noise, and the "intelligent filter" (adaptive Kalman filter) is used to eliminate the noise of the sensor signals and retain the key fluctuation characteristics.

[0019] Furthermore, the steps for analyzing the change trend of the multi-physical field signals include:

[0020] Decompose the oil pressure signal into fluctuations of different frequencies (similar to decomposing a piece of music into high, medium, and low tones).

[0021] Extract the fluctuation components in the range of 0.1 - 2 Hz, and mark their intensities and change trends (similar to locking the "low-frequency drum beats").

[0022] Furthermore, the reasons for analyzing the vibration include:

[0023] If the oil pressure fluctuation is highly synchronous with the rotor torsion (correlation coefficient > 0.8), it is determined as a mechanical problem of the machine's own vibration.

[0024] If the vibration is consistent with the power grid frequency fluctuation (phase difference < 5°), it is determined as a power grid problem caused by power grid disturbances.

[0025] If the boiler heat storage change occurs simultaneously with the vibration, it is determined as a composite problem of the "thermal - power grid" combined action.

[0026] Furthermore, when the vibration intensity range is that the vibration intensity increases by more than 5% per second, or the frequency suddenly drifts by more than 0.1 Hz, a yellow warning is immediately issued (reminding the operator 10 - 15 seconds in advance).

[0027] Furthermore, the mechanical - thermal - electrical combined monitoring model identifies the key fluctuation characteristics, and the intensity of the fluctuation characteristics is obtained by the signal change trend through the wavelet packet - EMD hybrid time - frequency decomposition.

[0028] The cascade analysis method using the improved Prony algorithm is used to analyze the amplitude, phase, damping factor, and frequency - related data of the signal.

[0029] Finally, according to the preset warning range, determine whether the changing trends of the amplitude, phase, damping factor, and frequency-related data of the signal fall within the preset warning range after 10 - 15 seconds. If so, output the cause of the vibration and the warning signal.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] By collecting multi-physical field signals, analyzing the changing rules of multi-physical field signals, and using a mechanical-thermal-electrical combined monitoring model to analyze the cause of vibration, the multi-physical field collaborative monitoring of low-frequency oscillation in the speed control system of thermal power units is realized. At the same time, the time-varying characteristics of oscillation modes are captured through the collaborative acquisition of multi-physical field signals, and the false alarm rate is reduced through the mechanical-thermal-electrical combined monitoring model.

[0032] For the first time, four-dimensional signals of governor oil pressure pulsation, shafting torsional vibration angular displacement, boiler heat storage coefficient, and power grid power angle are fused to construct a mechanical-thermal-electrical combined monitoring model. A cascaded analysis method of wavelet packet-EMD hybrid time-frequency decomposition and improved Prony algorithm is proposed to extract key low-frequency fluctuations from complex noise, and tiny vibrations of 0.05 Hz can be detected. When abnormal growth or frequency mutation of vibration is found, a 10-second early warning is given to gain time for manual intervention and avoid the expansion of accidents. Description of the Drawings

[0033] Figure 1 It is the overall flowchart of a multi-physical field collaborative monitoring method for low-frequency oscillation in the speed control system of a thermal power unit in an embodiment of the present invention. Detailed Embodiments

[0034] For the convenience of understanding by those skilled in the art, the present invention will be further described below in conjunction with embodiments and the drawings. The content mentioned in the embodiments does not limit the present invention.

[0035] As Figure 1 shown, this embodiment provides a multi-physical field collaborative monitoring method for low-frequency oscillation in the speed control system of a thermal power unit, including the steps of:

[0036] S1. Collaborative acquisition of multi-physical field signals;

[0037] S2. Analyze the changing trends of multi-physical field signals;

[0038] S3. Input the multi-physical field signals received in real time into the constructed and trained mechanical-thermal-electrical combined monitoring model for vibration cause analysis;

[0039] S4. Determine whether to issue a warning signal according to the preset vibration intensity range.

[0040] The multi-physical field signals include four-dimensional signals of governor oil pressure pulsation, shafting torsional vibration angular displacement, boiler heat storage coefficient, and power grid power angle.

[0041] The sensors for collaborative acquisition of multi - physical - field signals include high - frequency pressure sensors, laser Doppler vibrometers, power grid dynamic coupling signal acquisition devices, drum pressure sensors, and steam flow sensors.

[0042] The high - frequency pressure sensors are arranged at the inlet and outlet of the oil servo motor, with a collection range of 0 - 30 MPa and a sampling rate ≥ 2 kHz;

[0043] Four groups of laser Doppler vibrometers are arranged along the high - and medium - pressure rotors of the steam turbine to measure the circumferential torsional vibration displacement Δθ (accuracy ±0.01°);

[0044] The power grid dynamic coupling signal acquisition device acquires the grid connection point frequency f_grid and power angle δ of the synchronous measurement unit connected to the PMU;

[0045] The drum pressure sensor and the steam flow sensor respectively collect the drum pressure and the main steam flow, and calculate the heat storage coefficient.

[0046] Before analyzing the change trend of multi - physical - field signals, it is necessary to pre - process the collected multi - physical - field signals. The pre - processing is to remove noise. The "intelligent filter" (adaptive Kalman filter) is used to eliminate the noise of the sensor signals and retain the key fluctuation characteristics.

[0047] The steps for analyzing the change trend of multi - physical - field signals include:

[0048] Decompose the oil pressure signal into fluctuations of different frequencies (similar to decomposing a piece of music into high, medium, and low frequencies);

[0049] Extract the fluctuation components in the range of 0.1 - 2 Hz, and mark their intensity and change trend (similar to locking the "low - frequency drumbeat").

[0050] The reasons for analyzing vibration include:

[0051] If the oil pressure fluctuation is highly synchronous with the rotor torsion (correlation coefficient > 0.8), it is determined as a mechanical problem of machine self - vibration;

[0052] If the vibration is consistent with the power grid frequency fluctuation (phase difference < 5°), it is determined as a power grid problem caused by power grid disturbance;

[0053] If the boiler heat storage change occurs simultaneously with the vibration, it is determined as a composite problem of the combined action of "thermal - power grid".

[0054] The vibration intensity range is that the vibration intensity increases by more than 5% per second, or the frequency suddenly drifts by more than 0.1 Hz, and a yellow warning is immediately issued (reminding the operator 10 - 15 seconds in advance).

[0055] The mechanical-thermal-electrical joint monitoring model identifies key fluctuation characteristics, and the intensity of fluctuation characteristics is decomposed by wavelet packet-EMD hybrid time-frequency decomposition to obtain the signal change trend;

[0056] The cascade analysis method of the improved Prony algorithm is used to analyze the amplitude, phase, damping factor and frequency-related data of the signal;

[0057] Finally, according to the preset warning range, determine whether the change trend of the signal's amplitude, phase, damping factor and frequency-related data falls into the preset warning range after 10-15 seconds. If so, output the cause of the vibration and the warning signal.

[0058] Compared with the prior art, the present invention has the following beneficial effects:

[0059] By collecting multi-physical field signals, analyzing the changing rules of multi-physical field signals, and using the mechanical-thermal-electrical joint monitoring model to analyze the vibration causes, the multi-physical field collaborative monitoring of low-frequency oscillations in the speed control system of thermal power units is realized. At the same time, the time-varying characteristics of the oscillation mode are captured through the collaborative acquisition of multi-physical field signals, and the false alarm rate is reduced through the mechanical-thermal-electrical joint monitoring model.

[0060] For the first time, the four-dimensional signals of governor oil pressure pulsation, shaft torsional angular displacement, boiler heat storage coefficient and power grid power angle are integrated to build a mechanical-thermal-electrical joint monitoring model. A cascade analysis method of wavelet packet-EMD hybrid time-frequency decomposition and improved Prony algorithm is proposed to extract key low-frequency fluctuations from complex noise and detect tiny vibrations of 0.05Hz. When abnormal vibration growth or frequency mutation is found, an early warning of 10 seconds is issued to buy time for manual intervention and avoid the expansion of accidents.

[0061] The above is a detailed introduction to a method for collaboratively monitoring low-frequency oscillations and multi-physical fields of a thermal power unit speed control system provided by the present application. The description of the specific embodiments is only used to help understand the method and its core idea of the present application. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of the present application, several improvements and modifications can be made to the present application, and these improvements and modifications also fall within the scope of protection of the claims of the present application.

Claims

1. A multi-physical field collaborative monitoring method for low-frequency oscillation of a thermal power unit speed regulation system, characterized in that Including the steps: S1. Cooperative acquisition of multi-physical field signals; S2. Analyze the changing trends of multi-physical field signals; S3. Input the real-time received multi-physical field signals into the constructed and trained mechanical-thermal-electrical joint monitoring model for vibration cause analysis; S4. Determine whether to issue a warning signal according to the preset vibration intensity range.

2. The multi-physical field collaborative monitoring method for low-frequency oscillation of a thermal power unit speed regulation system according to claim 1, wherein The multi-physical field signals include four-dimensional signals of governor oil pressure pulsation, shafting torsional vibration angular displacement, boiler heat storage coefficient, and power angle of the power grid.

3. A multi-physical field collaborative monitoring method for low-frequency oscillation of a speed regulation system of a thermal power unit according to claim 2, characterized in that The sensors for cooperative acquisition of multi-physical field signals include high-frequency pressure sensors, laser Doppler vibrometers, power grid dynamic coupling signal acquisition devices, steam drum pressure sensors, and steam flow sensors.

4. A multi-physical field collaborative monitoring method for low-frequency oscillation of a thermal power unit speed regulation system according to claim 3, characterized in that The high-frequency pressure sensors are arranged at the inlet and outlet of the servomotor, with a sampling range of 0 - 30 MPa and a sampling rate ≥ 2 kHz; Four groups of laser Doppler vibrometers are arranged along the high-pressure and intermediate-pressure rotors of the steam turbine to measure the circumferential torsional vibration displacement; The power grid dynamic coupling signal acquisition device acquires the grid connection point frequency f_grid and power angle δ of the PMU synchronous measurement unit; The steam drum pressure sensor and the steam flow sensor respectively acquire the steam drum pressure and the main steam flow rate, and calculate the heat storage coefficient.

5. A multi-physical field collaborative monitoring method for low-frequency oscillation of a thermal power unit speed regulation system according to claim 4, characterized in that Before analyzing the changing trends of multi-physical field signals, it is necessary to preprocess the acquired multi-physical field signals. The preprocessing is to remove noise, and the adaptive Kalman filter is used to eliminate the noise of the sensor signals and retain the key fluctuation characteristics.

6. The multi-physical field collaborative monitoring method for low-frequency oscillation of a thermal power unit speed regulation system according to claim 5, wherein The steps for analyzing the changing trends of multi-physical field signals include: Decompose the oil pressure signal into fluctuations of different frequencies; Extract the fluctuation components in the range of 0.1 - 2 Hz, and mark their intensities and changing trends.

7. A method for collaborative monitoring of low-frequency oscillations in multiple physical fields of a speed regulation system of a thermal power unit according to claim 6, characterized in that The analysis of vibration causes includes: If the oil pressure fluctuation is highly synchronous with the rotor torsion, it is determined as a mechanical problem of machine self-vibration; If the vibration is consistent with the power grid frequency fluctuation, it is determined as a power grid problem caused by power grid disturbance; If the boiler heat storage change occurs simultaneously with the vibration, it is determined as a composite problem of the combined action of "thermal - power grid".

8. A multi-physical field collaborative monitoring method for low-frequency oscillation of a speed governing system of a thermal power unit according to claim 7, characterized in that The mechanical-thermal-electrical joint monitoring model identifies the key fluctuation characteristics, and the intensity of the fluctuation characteristics is obtained through the signal changing trend by wavelet packet-EMD hybrid time-frequency decomposition; The cascade analysis method using the improved Prony algorithm is used to analyze the amplitude, phase, damping factor, and frequency-related data of the signal; Finally, according to the preset warning range, it is judged whether the changing trends of the amplitude, phase, damping factor, and frequency-related data of the signal fall within the preset warning range after 10 - 15 seconds. If so, the cause of the vibration and the warning signal are output.

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