Water turbine main shaft sealing temperature online monitoring method and system and electronic equipment

Abstract

The invention provides a water turbine main shaft sealing temperature on-line monitoring method and system and electronic equipment, and relates to the technical field of water turbine on-line monitoring. The method includes: performing calibration experiment on the optical fiber temperature sensor, and establishing a corresponding relation curve; mounting and wire outlet holes are formed in a floating ring and a sealing block of the water turbine, a sensor array with a guide pipe is arranged, the guide pipe penetrates into the mounting and wire outlet holes and is fixed to the floating ring, and then the wavelength value, collected by the sensor, of the fiber bragg grating is demodulated in real time; converting the wavelength value into a temperature value according to the corresponding relation curve; the overall temperature distribution of the sealing block is calculated through temperature data of the sensor array, and online monitoring of the temperature of the sealing block is achieved; errors caused by parameter differences of the sensor are greatly reduced and eliminated, the measurement precision is improved, the system reliability is enhanced, the temperature of the main shaft sealing block is obtained through direct measurement, and the operation condition of main shaft sealing in the current state can be better judged.

Description

Technical Field

[0001] The present invention relates to the technical field of water turbine online monitoring, and in particular to a method, system and electronic equipment for online monitoring of the main shaft seal temperature of a water turbine. Background Art

[0002] The main shaft seal is a key component of a hydroturbine. As the water seal between the turbine shaft and the top cover, it prevents water from overflowing from the runner chamber through the gap between the main shaft and the top cover during power generation and shutdown, thereby preventing flooding of the water-guided bearing and the top cover. The temperature and temperature fluctuations of the main shaft seal block in the end-face seal structure are important indicators of the unit's operating status, as temperature changes directly reflect the state of seal friction.

[0003] In the existing technology, there are: 1. Contact RTD temperature measurement: a copper thermometer probe is used as a temperature transmission medium to indirectly obtain the temperature of the sealing block; the results obtained by this method are often difficult to reflect the actual temperature of the sealing block. 2. Indirect temperature measurement methods, such as the leakage water temperature difference method, by installing a first and a second temperature sensor, respectively measure the temperature of the leakage water between the sealing ring and the anti-wear ring, and the temperature of the clean pressure water, and the temperature is calculated in real time by the data processing unit. The difference in temperature measured by the first temperature sensor and the second temperature sensor is calculated, and the temperature is inferred; but it is significantly affected by the water flow state, and the error is large under variable working conditions; infrared non-contact temperature measurement is affected by water mist obstruction and surface emissivity, resulting in low reliability of detection results in industrial sites. 3. The existing technology also has the following deficiencies: during the operation of the unit, the flow state of water inside the main shaft seal is very complex, and considering factors such as the flow rate, flow velocity and thermal conductivity of the leakage water, the measured temperature difference cannot effectively reflect the actual temperature of the sealing block, and it is very inconvenient when the sensor needs to be replaced. Therefore, an effective temperature detection method is urgently needed to solve the above technical problems. Summary of the Invention

[0004] The main purpose of the present invention is to provide a method, system and electronic equipment for online monitoring of the main shaft seal temperature of a turbine, so as to solve the technical problems in the prior art of online monitoring of the main shaft seal temperature, such as low accuracy, insufficient reliability, high maintenance difficulty and long dynamic response delay.

[0005] To solve the above technical problems, the technical solution adopted by the present invention is: a method for online monitoring of the main shaft seal temperature of a turbine, comprising the following steps: Conduct calibration experiments on optical fiber temperature sensors and establish a corresponding relationship curve between temperature and wavelength values; Installation and outlet holes are opened on the turbine floating ring and sealing block, and a sensor array with a guide tube is arranged. The guide tube is inserted into the installation and outlet holes and fixed on the floating ring, and the optical fiber temperature sensor is installed and fixed in the guide tube. Connect the sensor cable to the fiber optic interrogator to demodulate the fiber grating wavelength value collected by the sensor in real time; Converting the wavelength value into a temperature value according to the corresponding relationship curve; The overall temperature distribution of the sealing block is calculated using the temperature data from the sensor array.

[0006] In a preferred embodiment, the calibration experiment of the optical fiber temperature sensor includes: Several temperature sensors are placed in a constant temperature bath, arranged in a three-dimensional spiral with a preset axial layer spacing; The angle between adjacent sensors in the circumferential direction is ≤120°, and at least three groups of sensors are arranged to form a sensor array; Set several equidistant temperature adjustment points and adjust the temperature; When the temperature adjustment point is reached, the output value of the current temperature sensor is saved, and the temperature sensor is illuminated with a light source, and the wavelength value of the current temperature adjustment point is measured, and the wavelength value corresponding to each temperature adjustment point is saved; The saved wavelength and temperature values ​​are processed to obtain a relationship curve between the temperature and wavelength values.

[0007] In the preferred solution, a number of equidistant temperature adjustment points are set, and the calculation formula is: ; ； Where, For the The temperature value of the temperature adjustment point, For the The temperature value of the temperature adjustment point, Two change values ​​of adjacent temperature adjustment points, is the temperature sensor response time value, For the Adjust the temperature control point to The time value of each temperature adjustment point, is the error rate of the temperature sensor, is the total number of temperature adjustment points, is the maximum value that the temperature sensor can withstand, This is the minimum value that the temperature sensor can withstand.

[0008] In a preferred embodiment, the process of processing the stored wavelength value and temperature value to obtain a relationship curve between the temperature value and the wavelength value includes: Save the temperature value and the corresponding wavelength value to the temperature-wavelength data table; Then perform data preprocessing, including filtering abnormal data and performing temperature lag compensation; After data preprocessing, the data analysis model is input and the relationship curve between temperature and wavelength is obtained using scatter plot or linear regression method.

[0009] In a preferred embodiment, the real-time demodulation of the fiber Bragg grating wavelength value collected by the sensor includes: Determine the relationship model between temperature and wavelength: ; Where, is the temperature value, is the real-time wavelength of the temperature sensor, is the wavelength value of the temperature sensor at 0 degrees Celsius, is the first relationship coefficient, is the second relationship coefficient.

[0010] In a preferred embodiment, the first relationship coefficient is: ; The second relationship coefficient is: .

[0011] In a preferred embodiment, obtaining the overall temperature of the sealing block by using the temperature data of the sensor array includes: ; Where, is the overall temperature of the sealing block, is the total number of sealing blocks, For the The calculated weight value of each temperature sensor, For the The temperature value of a temperature sensor.

[0012] The preferred solution also includes taking into account the sensor heat source distance and thermal conductivity coefficient, and using a multi-dimensional weight fusion algorithm to calculate the weight value of the temperature sensor. The formula is: ; Where, is the distance between the i-th sensor and the heat source, is the i-th local thermal conductivity, is the effective contact area between the i-th sensor and the sealing block, and is the real-time flow rate and spindle speed; 、 、 is the adjustment factor of distance, heat conduction and working condition weight; The weight is then adjusted based on the residual feedback, and the heat conduction weight component is corrected in combination with the CFD simulation data.

[0013] A turbine main shaft seal temperature online monitoring system, comprising: Calibration module, used to perform calibration experiments on optical fiber temperature sensors and establish a corresponding relationship curve between temperature value and wavelength value; The installation module is used to open installation and outlet holes on the turbine floating ring and sealing block, and arrange the sensor array with guide tubes: the guide tubes are inserted into the installation and outlet holes and fixed on the floating ring, and the optical fiber temperature sensor is installed and fixed in the guide tubes; The acquisition module is used to connect the sensor cable to the fiber demodulator and demodulate the fiber Bragg grating wavelength value collected by the sensor in real time; An output module, configured to convert the wavelength value into a temperature value according to the corresponding relationship curve, and calculate the overall temperature distribution of the sealing block through the temperature data of the sensor array; The monitoring and analysis module is used to connect the monitoring and analysis module with the calibration module, the installation module, the acquisition module and the output module, and is used to execute the online monitoring method for the main shaft seal temperature of the turbine.

[0014] An electronic device comprising a memory and a processor; The memory is used to store computer programs; The processor is used to implement the method for online monitoring of the main shaft seal temperature of a turbine when executing the computer program.

[0015] The present invention provides an online monitoring method for the seal temperature of a turbine main shaft. The method comprises the following steps: performing a calibration experiment on an optical fiber temperature sensor and establishing a corresponding relationship curve between the two; providing installation and outlet holes on a floating ring and a sealing block of the turbine, and arranging a sensor array with guide tubes; inserting the guide tubes into the installation and outlet holes and fixing them on the floating ring; installing the sensors, connecting them to an optical fiber demodulator, and demodulating the optical fiber Bragg grating wavelength values ​​collected by the sensors in real time; converting the wavelength values ​​into temperature values ​​according to the corresponding relationship curve; and calculating the overall temperature distribution of the sealing block using the temperature data from the sensor array.

[0016] The technical solutions of the embodiments of the present invention have at least the following advantages and beneficial effects: 1. Suitable for online monitoring of the seal block temperature of the end-seal main shaft seal of vertical turbines. The fiber optic temperature sensor is installed directly inside the seal block without any direct contact with the water, so the measured temperature is the actual seal block temperature. Proper sensor installation provides an overview of the overall seal block temperature distribution. By adding a fiber optic temperature sensor inside the seal block, online monitoring of the seal block temperature is achieved, enabling better assessment of the current operating status of the main shaft seal.

[0017] 2. The temperature-fiber Bragg grating wavelength relationship curve obtained through temperature calibration test improves the accuracy of measured temperature values ​​and reduces the error caused by the difference in sensor parameters.

[0018] 3. Applying optical fiber temperature sensors to the temperature monitoring of the spindle seal has higher accuracy and better stability than conventional sensors.

[0019] 4. Install the guide pipe, which is easy to install and disassemble. When replacing, you only need to remove the water tank cover, which is convenient for users to maintain later. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The present invention will be further described below with reference to the accompanying drawings and examples: Figure 1 It is a schematic diagram of the process of the present invention; Figure 2 is a schematic diagram of the system of the present invention; Figure 3 A schematic diagram of the device of the present invention; Figure 4 This is a reference graph of temperature and wavelength curves for different optical fiber temperature sensors of the present invention.

[0021] Icon: 1- floating ring, 2- sealing block, 3- rotating ring, 4- sensor cable, 5- guide tube, 6- installation and outlet hole, 7- sensor probe. DETAILED DESCRIPTION

[0022] Example 1 like Figure 1-4 As shown, a method for online monitoring of the main shaft seal temperature of a turbine comprises the following steps: S1: Conduct a calibration experiment on the optical fiber temperature sensor to establish a corresponding relationship curve between temperature value and wavelength value.

[0023] S2: Open installation and wire outlet holes on the turbine floating ring 1 and sealing block 2.

[0024] Drill holes in the floating ring 1 according to the number and position of the installed sensors. The diameter of the holes should be slightly larger than the outer diameter of the guide tube. Put the sealing block 2 and the floating ring 1 together, calibrate the position on the sealing block 2 according to the position of the drilled holes, and drill the sensor installation holes according to the marks. The bottom of the sensor installation hole should not exceed the wearable area of ​​the sealing block 2, and the turning angle between the two holes should not be less than 120°.

[0025] The guide tube adopts a flexible metal armored structure, and the end portion is fixed to the non-wear area of ​​the sealing block.

[0026] S3: Arranging the sensor array with the guide tube 5: inserting the guide tube 5 into the installation and wire outlet hole 6 and fixing it on the floating ring 1.

[0027] Remove the sealing block 2 from the floating ring 1, insert the end of the guide tube 5 into the installation and outlet hole 6 until the bottom of the hole and securely fix it, then pull the guide tube 5 along the installation and outlet hole 6 opened in the floating ring 1 to the outside of the floating ring 1, trim off the excess guide tube 5 and fix it to the floating ring 1 at the outlet.

[0028] S4: Install the fiber optic sensor. Fix the fiber optic temperature sensor in the guide tube and connect the sensor cable to the fiber optic demodulator to demodulate the fiber optic Bragg grating wavelength value collected by the sensor in real time.

[0029] Insert the sensor probe 7 and the sensor cable 4 into the guide tube 5, push the sensor cable 4 until the sensor probe 7 reaches the bottom of the installation and outlet hole 6, connect each sensor cable 4 to the fiber optic demodulator, and connect the fiber optic demodulator to the host computer system.

[0030] S5: Substitute the wavelength values ​​of all sensors into the relationship curve between temperature and wavelength to obtain the temperature values ​​corresponding to all sensors; and obtain the overall temperature of the sealing block through the temperature values ​​of all sensors.

[0031] In this embodiment, when the unit is running, the sensor probe 7 uploads the collected data to the fiber optic demodulator, and the fiber optic demodulator processes and analyzes the data to obtain the wavelength value of the grating in the sensor and uploads it to the host computer system. The host computer system obtains the temperature information of the sealing block according to the calibrated "temperature value-wavelength value" correspondence, and at the same time obtains the water inlet temperature, flow rate and other information of the unit from the computer monitoring system and the status monitoring system to analyze the overall temperature of the sealing block at this moment under the working condition; it greatly reduces the error caused by eliminating the difference in the sensor's own parameters, improves the measurement accuracy, and enhances the system reliability. By directly measuring the temperature of the main shaft sealing block, it can better judge the operating status of the main shaft seal in the current state.

[0032] like Figure 3 , which is a schematic diagram of the device of this embodiment.

[0033] In the preferred embodiment, Figure 4 As shown in the figure, the calibration experiment of the optical fiber temperature sensor is carried out, including: Several temperature sensors are placed in a constant temperature bath, arranged in a three-dimensional spiral with a preset axial layer spacing; The angle between adjacent sensors in the circumferential direction is ≤120°, and at least three groups of sensors are arranged to form a sensor array; Set several equidistant temperature adjustment points and adjust the temperature; When the temperature adjustment point is reached, the output value of the current temperature sensor is saved, and the temperature sensor is illuminated with a light source, and the wavelength value of the current temperature adjustment point is measured, and the wavelength value corresponding to each temperature adjustment point is saved; The saved wavelength and temperature values ​​are processed to obtain a relationship curve between the temperature and wavelength values.

[0034] In the preferred solution, several equidistant temperature adjustment points are set, and the calculation formula is: ; ； Where, For the The temperature value of the temperature adjustment point, For the The temperature value of the temperature adjustment point, Two change values ​​of adjacent temperature adjustment points, is the temperature sensor response time value, For the Adjust the temperature control point to The time value of each temperature adjustment point, is the error rate of the temperature sensor, is the total number of temperature adjustment points, is the maximum value that the temperature sensor can withstand, This is the minimum value that the temperature sensor can withstand.

[0035] In this embodiment, the error rate is defined as follows: first, the difference between the output value of the temperature sensor and the actual value is calculated, and then the ratio between the difference and the actual value is calculated as the error rate.

[0036] In this embodiment, the time data and temperature value error data of the temperature sensor are combined to further optimize the change value of two adjacent temperature adjustment points to achieve a more accurate calibration.

[0037] In a preferred embodiment, the stored wavelength and temperature values ​​are processed to obtain a relationship curve between the temperature and wavelength values, including: Save the temperature value and the corresponding wavelength value to the temperature-wavelength data table; Then perform data preprocessing, including filtering abnormal data and performing temperature lag compensation; After data preprocessing, the data analysis model is input and the relationship curve between temperature and wavelength is obtained using scatter plot or linear regression method.

[0038] In this embodiment, the sensor system error is eliminated by calibrating the temperature value to ensure that the relationship curve reflects the actual physical properties. The linear or nonlinear fitting method is flexibly selected according to the scatter point distribution, which improves the accuracy and practicality of the relationship curve and reduces the cost of manual calculation.

[0039] Furthermore, quantitative indicators such as RMSE can be introduced to evaluate the reliability of the model to meet the accuracy requirements of industrial-grade online monitoring.

[0040] In the preferred embodiment, real-time demodulation of the fiber Bragg grating wavelength value collected by the sensor includes: Determine the relationship model between temperature and wavelength: ; Where, is the temperature value, is the real-time wavelength of the temperature sensor, is the wavelength value of the temperature sensor at 0 degrees Celsius, is the first relationship coefficient, is the second relationship coefficient.

[0041] In the preferred embodiment, the first relationship coefficient is: ; The second relationship coefficient is: .

[0042] In a preferred embodiment, obtaining the overall temperature of the sealing block by using the temperature data of the sensor array includes: ; Where, is the overall temperature of the sealing block, is the total number of sealing blocks, For the The calculated weight value of each temperature sensor, For the The temperature value of a temperature sensor.

[0043] In this embodiment, the temperature sensor array is The sum of the calculated weight values ​​of the temperature sensors is 1. The specific analysis is as follows: The calculation weight value is set according to the distance between the sensor and the sealing block. For example, there are three temperature sensors, including sensor 1, sensor 2 and sensor 3. The distance between the three temperature sensors and the sealing block is arranged from near to far as sensor 2, sensor 3 and sensor 1. The corresponding weights can be set respectively. The calculation weight value of sensor 2 is , the calculated weight value of sensor 3 is , the calculated weight value of sensor No. 1 is .

[0044] The preferred solution also includes taking into account the sensor heat source distance and thermal conductivity coefficient, and using a multi-dimensional weight fusion algorithm to calculate the weight value of the temperature sensor. The formula is: ; Where, is the distance between the i-th sensor and the heat source, is the i-th local thermal conductivity, is the effective contact area between the i-th sensor and the sealing block, and is the real-time flow rate and spindle speed; 、 and is the adjustment factor for distance, heat conduction and working condition weight.

[0045] The weight is then adjusted based on the residual feedback, and the heat conduction weight component is corrected in combination with the CFD simulation data.

[0046] In this embodiment, the residual feedback adjustment can adopt weight optimization based on the least squares method; the three-dimensional spiral point distribution is adopted and integrated with dynamic weight distribution, which further reduces the temperature field reconstruction error and improves the accuracy of the calculation results.

[0047] Example 2 Further illustrate with reference to Example 1, Figure 2 The figure shows a schematic diagram of the system, which provides an online monitoring system for the main shaft seal temperature of a turbine, including: Calibration module, used to perform calibration experiments on optical fiber temperature sensors and establish a corresponding relationship curve between temperature value and wavelength value; The installation module is used to open installation and outlet holes on the turbine floating ring and sealing block, and arrange the sensor array with guide tubes: the guide tubes are inserted into the installation and outlet holes and fixed on the floating ring, and the optical fiber temperature sensor is installed and fixed in the guide tubes; The acquisition module is used to connect the sensor cable to the fiber demodulator and demodulate the fiber Bragg grating wavelength value collected by the sensor in real time; An output module, configured to convert the wavelength value into a temperature value according to the corresponding relationship curve, and calculate the overall temperature distribution of the sealing block through the temperature data of the sensor array; The monitoring and analysis module is used to connect the monitoring and analysis module with the calibration module, the installation module, the acquisition module and the output module, and is used to execute an online monitoring method for the main shaft seal temperature of a turbine in Example 1.

[0048] An electronic device comprising a memory and a processor; Memory for storing computer programs; The processor is configured to implement a method for online monitoring of the main shaft seal temperature of a turbine as described in Example 1 when executing the computer program.

[0049] The above embodiments are merely preferred technical solutions of the present invention and should not be construed as limiting the present invention. The scope of protection of the present invention shall be the technical solutions set forth in the claims, including equivalent alternatives to the technical features of the technical solutions set forth in the claims. In other words, equivalent alternatives and improvements within this scope are also within the scope of protection of the present invention.

Claims

1. A method for online monitoring of turbine main shaft seal temperature, characterized in that: The following steps are involved: Conduct calibration experiments on optical fiber temperature sensors and establish a corresponding relationship curve between temperature and wavelength values; Installation and outlet holes are opened on the turbine floating ring and sealing block, and a sensor array with a guide tube is arranged. The guide tube is inserted into the installation and outlet holes and fixed on the floating ring, and the optical fiber temperature sensor is installed and fixed in the guide tube. Connect the sensor cable to the fiber optic interrogator to demodulate the fiber grating wavelength value collected by the sensor in real time; Converting the wavelength value into a temperature value according to the corresponding relationship curve; The overall temperature distribution of the sealing block is calculated using the temperature data from the sensor array.

2. The method for online monitoring of the turbine main shaft seal temperature according to claim 1, characterized in that: The calibration experiment for the optical fiber temperature sensor includes: Several temperature sensors are placed in a constant temperature bath, arranged in a three-dimensional spiral with a preset axial layer spacing; The angle between adjacent sensors in the circumferential direction is ≤120°, and at least three groups of sensors are arranged to form a sensor array; Set several equidistant temperature adjustment points and adjust the temperature; When the temperature adjustment point is reached, the output value of the current temperature sensor is saved, and the temperature sensor is illuminated with a light source, and the wavelength value of the current temperature adjustment point is measured, and the wavelength value corresponding to each temperature adjustment point is saved; The saved wavelength and temperature values ​​are processed to obtain a relationship curve between the temperature and wavelength values.

3. The method for online monitoring of the turbine main shaft seal temperature according to claim 2, characterized in that: The calculation formula for setting a number of equidistant temperature adjustment points is: ； ； Where, For the The temperature value of the temperature adjustment point, For the The temperature value of the temperature adjustment point, Two change values ​​of adjacent temperature adjustment points, is the temperature sensor response time value, For the Adjust the temperature control point to The time value of each temperature adjustment point, is the error rate of the temperature sensor, is the total number of temperature adjustment points, is the maximum value that the temperature sensor can withstand, This is the minimum value that the temperature sensor can withstand.

4. The method for online monitoring of the turbine main shaft seal temperature according to claim 3, characterized in that: The stored wavelength value and temperature value are processed to obtain a relationship curve between the temperature value and the wavelength value, including: Save the temperature value and the corresponding wavelength value to the temperature-wavelength data table; Then perform data preprocessing, including filtering abnormal data and performing temperature lag compensation; After data preprocessing, the data analysis model is input and the relationship curve between temperature and wavelength is obtained using scatter plot or linear regression method.

5. The method for online monitoring of the turbine main shaft seal temperature according to claim 1, characterized in that: The fiber Bragg grating wavelength value collected by the real-time demodulation sensor includes: Determine the relationship model between temperature and wavelength: ； Where, is the temperature value, is the real-time wavelength of the temperature sensor, is the wavelength value of the temperature sensor at 0 degrees Celsius, is the first relationship coefficient, is the second relationship coefficient.

6. The method for online monitoring of the turbine main shaft seal temperature according to claim 5, characterized in that: The first relationship coefficient is: ； The second relationship coefficient is: 。 7. The method for online monitoring of the turbine main shaft seal temperature according to claim 1, characterized in that: Obtaining the overall temperature of the sealing block through the temperature data of the sensor array includes: ； Where, is the overall temperature of the sealing block, is the total number of sealing blocks, For the The calculated weight value of each temperature sensor, For the The temperature value of a temperature sensor.

8. The method for online monitoring of the turbine main shaft seal temperature according to claim 7, characterized in that: It also includes considering the sensor heat source distance and thermal conductivity, using a multi-dimensional weight fusion algorithm to calculate the weight value of the temperature sensor. The formula is: ； Where, is the distance between the i-th sensor and the heat source, is the i-th local thermal conductivity, is the effective contact area between the i-th sensor and the sealing block, and is the real-time flow rate and spindle speed; 、 、 is the adjustment factor of distance, heat conduction and working condition weight; The weight is then adjusted based on the residual feedback, and the heat conduction weight component is corrected in combination with the CFD simulation data.

9. A turbine main shaft seal temperature online monitoring system, characterized in that: include: Calibration module, used to perform calibration experiments on optical fiber temperature sensors and establish a corresponding relationship curve between temperature value and wavelength value; The installation module is used to open installation and outlet holes on the turbine floating ring and sealing block, and arrange the sensor array with guide tubes: the guide tubes are inserted into the installation and outlet holes and fixed on the floating ring, and the optical fiber temperature sensor is installed and fixed in the guide tubes; The acquisition module is used to connect the sensor cable to the fiber demodulator and demodulate the fiber Bragg grating wavelength value collected by the sensor in real time; An output module, configured to convert the wavelength value into a temperature value according to the corresponding relationship curve, and calculate the overall temperature distribution of the sealing block through the temperature data of the sensor array; A monitoring and analysis module is used to connect the monitoring and analysis module with the calibration module, installation module, acquisition module and output module, and is used to execute the online monitoring method for the main shaft seal temperature of a turbine according to any one of claims 1 to 8.

10. An electronic device, characterized in that: including memory and processor; The memory is used to store computer programs; The processor is configured to implement the method for online monitoring of the main shaft seal temperature of a turbine according to any one of claims 1 to 8 when executing the computer program.

Images