CN117539168B - Hydraulic turbine cavitation diagnosis system and method based on semi-physical simulation - Google Patents
Hydraulic turbine cavitation diagnosis system and method based on semi-physical simulation Download PDFInfo
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Abstract
The invention belongs to the technical field of water turbine monitoring and discloses a water turbine cavitation diagnosis system and method based on semi-physical simulation, wherein the system comprises a sensor module, a three-dimensional scanning modeling module, a model correction module, a cavitation prediction module and the like, a three-dimensional model of a water turbine is established through the three-dimensional scanning modeling module, and a grid model of the water turbine is obtained through grid treatment; iterative calculation is carried out through a simulation calculation model, so that the pressure of a pressure monitoring point in a runner of the water turbine and the pressure of the lowest pressure point on the metal surface of the overcurrent component are obtained; carrying out iterative correction on the simulation calculation model through a model correction module; the cavitation condition of the water turbine under different water heads is simulated by changing the opening degree of guide vanes and blades of the three-dimensional model of the water turbine, the cavitation-prone water head interval is predicted by a multivariable single-target genetic algorithm, and the most serious cavitation water head point is positioned. The method can realize the rapid determination of the specific occurrence position of cavitation in the flow passage of the water turbine and the rapid prediction of the cavitation-prone water head interval of the water turbine.
Description
Technical Field
The invention belongs to the technical field of water turbine monitoring, and particularly relates to a water turbine cavitation diagnosis system and method based on semi-physical simulation.
Background
The hydraulic turbine is used as key mechanical equipment for the development of the hydraulic energy, and the working performance of the hydraulic turbine determines the utilization efficiency of the hydraulic energy. In the high-speed running process of the water turbine, cavitation phenomenon often occurs, damage is caused to the surface of a metal overflow part of the water turbine, abnormal vibration of the water turbine generator set is caused, and the safety operation of the water turbine generator set is threatened. The prior art mainly monitors cavitation of a water turbine through vibration and pressure pulsation signals of a water turbine generator set, has certain defects, and can judge the cavitation by combining experience of operators in a hydropower plant only when the cavitation is severe and causes unit vibration and noise to be large, the primary stage of the cavitation of the water turbine is difficult to find, and the occurrence position of the cavitation cannot be accurately positioned.
Disclosure of Invention
In order to solve the defects in the prior art, the invention provides a hydraulic turbine cavitation diagnosis system and method based on semi-physical simulation.
The aim of the invention can be achieved by the following technical scheme: a hydraulic turbine cavitation diagnosis system based on semi-physical simulation comprises a sensor module, a signal processing module, a three-dimensional scanning modeling module, a grid processing module, a simulation calculation module, a model correction module, a cavitation prediction module and a comprehensive display module;
the signal processing module is used for processing the real-time information acquired by the sensor module;
the three-dimensional scanning modeling module is used for building a three-dimensional model of the water turbine;
the grid processing module is used for carrying out gridding processing on the three-dimensional model of the water turbine established by the three-dimensional scanning modeling module according to the initial value of the size of the grid unit to obtain a grid model of the water turbine;
the simulation calculation module is used for importing the water turbine grid model into simulation calculation software, setting the simulation calculation model by taking the water temperature, the upstream water level and the downstream water level obtained by the sensor module as boundary conditions of the simulation calculation of the water turbine, and starting iterative calculation to obtain the pressure of the monitoring point in the water turbine runner, the pressure of the lowest pressure point on the metal surface of the overcurrent part, the water vapor volume fraction and the vapor-liquid two-phase distribution map;
the model correction module is used for carrying out iterative correction on the simulation calculation model through a multi-target genetic evolution algorithm (Nondominated Sorting Genetic Algorithm II, NSGA-II), inputting the corrected simulation calculation model into the simulation calculation module for carrying out iterative calculation again, and obtaining the simulation calculation model which is most consistent with the actual working condition;
the cavitation prediction module is used for simulating cavitation conditions of the water turbine under different water heads by changing opening degrees of guide vanes and blades of the three-dimensional model of the water turbine, predicting a cavitation-prone water head interval by a multivariable single-target genetic algorithm (Genetic Algorithm, GA) and positioning the most serious cavitation water head point;
the comprehensive display module is used for comprehensively displaying the gas-liquid two-phase distribution diagram obtained by the simulation calculation module and the vibration signal processed by the signal processing module, obtaining an accurate cavitation primary stage and a specific cavitation phenomenon occurrence position, positioning a serious cavitation position and displaying the vibration main frequency and amplitude information caused by cavitation.
Further preferably, the three-dimensional scanning modeling module acquires the geometric surface of each overcurrent component of the water turbine by adopting a laser three-dimensional scanning mode, and further rapidly establishes a three-dimensional model of the water turbine in three-dimensional software.
Further preferably, the sensor module comprises a pressure sensor, a temperature sensor, a vibration sensor and a water level sensor, wherein the pressure sensor is used for acquiring the pressure of water flow in a water turbine runner, the temperature sensor is used for acquiring the water temperature of the water flow in the water turbine runner, the vibration sensor is used for acquiring radial vibration signals of a water turbine runner, and the water level sensor is used for acquiring upstream water level and downstream water level; the water level sensor is respectively arranged at the upstream and downstream of the hydropower station and used for monitoring the water level at the upstream and downstream, the temperature sensor is arranged at the standing point position of the bulb body, the pressure sensor is arranged at the standing point position of the bulb body and the upstream pipe wall of the guide vane, and the vibration sensor is arranged at the guide bearing position of the water turbine and used for acquiring the radial vibration signal of the rotating wheel of the water turbine.
The invention also provides a hydraulic turbine cavitation diagnosis method based on semi-physical simulation, which comprises the following steps:
s1: the geometrical surfaces of all the overcurrent components of the water turbine are obtained by adopting a laser three-dimensional scanning mode, and then a three-dimensional model of the water turbine is quickly built in three-dimensional software;
s2: given initial grid cell size value d 0 Gridding the three-dimensional model of the water turbine established by the three-dimensional scanning modeling module to obtain a grid model of the water turbine:
s3: and a signal processing module processes: the opening information of the guide vane and the vane of the water turbine speed regulator is connected into a signal processing module, a sample data acquisition period is set, and an upstream water level mean value Z in a single period is calculated and acquired according to the pressure, the water temperature, the upstream water level and the downstream water level acquired by a sensor module 1 Downstream water level mean Z 2 The water temperature average value T and the bulb standing point position pressure average value P 1 Pressure average value P of monitoring point of guide vane upstream pipe wall 2 The method comprises the steps of carrying out a first treatment on the surface of the Monitoring radial vibration time domain signals of the turbine runner by adopting a vibration sensor, carrying out Fourier transformation on the radial vibration time domain signals of the turbine runner, obtaining a vibration signal spectrogram, and extracting vibration main frequency and amplitude information;
s4, setting a simulation calculation model and performing iterative calculation;
s5, adjusting and correcting the simulation calculation model: in the model correction module, the pressure simulation value of the standing point position of the bulb body is calculatedAnd pressure simulation value of monitoring point of upstream pipe wall of guide vaneMean value P of pressure at standing point position of bulb body actually measured by pressure sensor 1 Pressure average value P of monitoring point of guide vane upstream pipe wall 2 The sum of the standard deviation of (2) is minimum and the simulation calculation time is t shortest as the optimization objective function of the simulation calculation model, and the simulation value of the upstream water levelThe grid cell size d is used as an optimization variable of the simulation calculation model, the optimal solutions of a plurality of objective functions are searched by utilizing a multi-objective genetic evolution algorithm, and the simulation calculation model is corrected by utilizing the optimal solutions, so that the simulation calculation model which is most consistent with the actual working condition under the condition of minimum calculation time cost is obtained;
s6: predicting an easy cavitation water head interval: after the simulation calculation model is corrected, a cavitation prediction module adopts a multivariable single-target genetic algorithm to adjust the opening degree of a guide vane and the opening degree of a vane of a water turbine simulation calculation model, and calculates and obtains the minimum pressure point pressure of the metal surface of the overcurrent part and the average pressure P of the inlet section under the opening degree of each guide vane and the opening degree of the vane in And average pressure P of tail water outlet section out The method comprises the steps of carrying out a first treatment on the surface of the The working water head H of the bulb through-flow turbine has a calculation formula ofWherein ρ is the water flow density; g is gravity acceleration; acquiring cavitation characteristics of water turbines of different water heads under upstream and downstream water levels, realizing rapid prediction of an easy cavitation water head interval, and positioning the most serious cavitation water head;
s7: and comprehensively displaying cavitation diagnosis results of the water turbine.
Further preferably, the simulation calculation model setting and the iterative calculation include:
the grid model of the water turbine is imported into simulation calculation software, and the upstream water level average value Z 1 Downstream water level mean Z 2 And the water temperature mean value T is input into a simulation calculation model and used as a calculation boundary condition;
setting a fluid domain medium, wherein the first phase is water and the second phase is steam;
setting a simulation calculation model;
setting an initial upstream water level, an initial downstream water level and an iteration time step, carrying out iterative calculation until residual error converges, and obtaining simulation calculation time t spent from the iterative calculation to convergence;
processing the simulation calculation result to obtain the pressure simulation value of the bulb body resident point positionAnd pressure simulation value of monitoring point of upstream pipe wall of guide vane。
Further preferably, the simulation calculation model optimization variables include:
upstream water level simulation value，The upstream water level simulation values of the 1 st, 2 nd, 3 rd and … th iterations and the n th iterations are respectively,；
grid cell size d= (d 1 ,d 2 ,d 3 ,…,d n ), d∈[0.001,1]；
Simulation value of bulb standing point position pressure and standard deviation of bulb standing point position pressure mean valueThe following is shown:
；
in the method, in the process of the invention,the simulated value of the bulb body resident point position pressure of the jth iteration is obtained;
guide vane upstream pipe wall monitorStandard deviation of pressure simulation value of measuring point and pressure average value of monitoring point of upstream pipe wall of guide vaneThe following is shown:
；
in the method, in the process of the invention,the pressure simulation value of the monitoring point of the upstream pipe wall of the guide vane for the jth iteration is obtained;
the simulation calculation model optimizes the objective function as follows:
；
；
wherein,indicating that the pressure simulation error is minimal,represents that the simulation calculation time is shortest, the simulation calculation time t= (t) 1 ,t 2 ,t 3 ,…, t n )，t 1 ,t 2 ,t 3 ,…,t n Simulation calculation time for the 1 st, 2 nd, 3 rd, … th, n th iterations, respectively.
Further preferably, the simulation calculation model correction qualification criterion is:
if it isThe simulation calculation model has the accuracy meeting the requirement, the lowest pressure point pressure, the water vapor volume fraction and the vapor-liquid two-phase distribution diagram of the metal surface of the overcurrent part obtained through the calculation of the simulation calculation model are the final results, and the lowest metal surface of the overcurrent part is obtainedThe cavitation occurrence position can be accurately positioned by the pressure of the pressure point and the distribution diagram of the vapor phase and the liquid phase;
if it isAnd adjusting the optimization variable of the simulation calculation model by using the model correction module, and carrying out iterative correction on the simulation calculation model until the simulation calculation model meets the requirement.
Further preferably, the optimization variables of the cavitation prediction module are:
vane opening；The opening degree of the guide vane is 1 st, 2 nd, 3 rd, … th and n th iterations respectively;
vane opening，Vane opening for the 1 st, 2 nd, 3 rd, … th, n iterations;
constraint conditions of cavitation prediction module: p is less than or equal to P v ，P v Is the vaporization pressure of water, when the water flow pressure P in the geometric flow passage of the water turbine is lower than the vaporization pressure P of water v When the water is vaporized, cavitation is caused, and a polynomial relational expression between the vaporization pressure of the water and the water temperature mean value T is as follows:
；
the objective function of the cavitation prediction module is:
；
in the middle ofThe minimum pressure point pressure of the metal surface of the overcurrent part for the 1 st, 2 nd, 3 rd, … th, j, … th and n th iterations respectively, when P min,j ≤P v Cavitation occurs when; p (P) min The working condition with the minimum pressure in n iterations is the working condition point with the most serious cavitation.
Further preferably, the comprehensive display of the cavitation diagnosis result of the water turbine includes: and inputting the simulation calculation result, the vibration main frequency and amplitude information thereof and the cavitation-prone water head interval into a comprehensive display module, and displaying cavitation development and distribution conditions in the flow channel of the water turbine, the vibration main frequency and amplitude information caused by cavitation and the cavitation-prone water head prediction interval.
The invention has the beneficial effects that: according to the invention, the three-dimensional scanning modeling module can be used for realizing the rapid establishment of the three-dimensional model of the water turbine under the condition of lacking a water turbine structure drawing and a blade wood pattern drawing, so that the three-dimensional modeling time of the water turbine is greatly reduced, the geometric characteristics of the water turbine overflow component can be well captured, and the occurrence of the cavitation phenomenon in the geometric flow passage of the water turbine can be more accurately simulated.
According to the invention, the initial stage of cavitation phenomenon and the determination of cavitation position points in the water turbine are realized through the simulation calculation module, the iterative correction of the simulation calculation model is completed in a short time by utilizing the pressure parameters and the multi-target genetic algorithm acquired by the sensor module, the accuracy of the final simulation calculation model is ensured, the iterative correction time of the shortest simulation calculation model is realized, the correction rate of the simulation calculation model is accelerated, the quick determination of the specific cavitation position in the water turbine runner is realized, and the defect that the cavitation position cannot be accurately positioned in the prior art is overcome.
According to the invention, the working water head can be changed by adjusting the opening degree of the guide vane and the vane of the water turbine simulation calculation model, and the quick prediction of the cavitation-prone water head interval of the water turbine is realized by combining a multivariable single-target genetic algorithm, so that operators of the hydropower station are guided to reasonably avoid the cavitation-prone water head interval in operation, and the safe and stable operation of the hydroelectric generating set is ensured.
Drawings
FIG. 1 is a schematic diagram of a frame structure of a cavitation diagnosis system of a hydraulic turbine based on semi-physical simulation.
Fig. 2 is a schematic view showing the installation positions of the sensors in the sensor module of the bulb through-flow turbine as an example.
FIG. 3 is a flow chart of a hydraulic turbine cavitation diagnosis method based on semi-physical simulation.
Detailed Description
In order to make the objects, technical solutions and system principles of the present invention more clear, the technical principles of the present invention will be described in further detail with reference to the accompanying drawings and examples.
As shown in FIG. 1, the hydraulic turbine cavitation diagnosis system based on semi-physical simulation comprises a sensor module, a signal processing module, a three-dimensional scanning modeling module, a grid processing module, a simulation calculation module, a model correction module, a cavitation prediction module and a comprehensive display module;
the sensor module comprises a pressure sensor, a temperature sensor, a vibration sensor and a water level sensor, wherein the pressure sensor is used for acquiring the pressure of water flow in a water turbine runner, the temperature sensor is used for acquiring the water temperature of the water flow in the water turbine runner, the vibration sensor is used for acquiring radial vibration signals of the water turbine runner, and the water level sensor is used for acquiring the upstream water level and the downstream water level; as shown in fig. 2, water level sensors (an upstream water level sensor 100 and a downstream water level sensor 200) are installed at the upstream and downstream of the hydropower station for monitoring the upstream and downstream water levels, a temperature sensor 300 is installed at the bulb standing point position, a pressure sensor 400 is arranged at the bulb standing point position and on the upstream pipe wall of the guide vane, and a vibration sensor 500 is installed at the guide bearing position of the water turbine for acquiring the radial vibration signal of the turbine runner.
The signal processing module is used for processing the real-time information acquired by the sensor module;
the three-dimensional scanning modeling module is used for acquiring the geometric surface of each overcurrent component of the water turbine by adopting a laser three-dimensional scanning mode, and further rapidly establishing a three-dimensional model of the water turbine in three-dimensional software;
the grid processing module is used for carrying out gridding processing on the three-dimensional model of the water turbine established by the three-dimensional scanning modeling module according to the initial value of the size of the grid unit to obtain a grid model of the water turbine;
the simulation calculation module is used for importing the water turbine grid model into simulation calculation software, setting the simulation calculation model by taking the water temperature, the upstream water level and the downstream water level obtained by the sensor module as boundary conditions of the simulation calculation of the water turbine, and starting iterative calculation to obtain the pressure of the monitoring point in the water turbine runner, the pressure of the lowest pressure point on the metal surface of the overcurrent part, the water vapor volume fraction and the vapor-liquid two-phase distribution map;
the model correction module is used for carrying out iterative correction on the simulation calculation model through a multi-target genetic evolution algorithm (Nondominated Sorting Genetic Algorithm II, NSGA-II), inputting the corrected simulation calculation model into the simulation calculation module for carrying out iterative calculation again, and obtaining the simulation calculation model which is most consistent with the actual working condition;
the cavitation prediction module is used for simulating cavitation conditions of the water turbine under different water heads by changing opening degrees of guide vanes and blades of the three-dimensional model of the water turbine, predicting a cavitation-prone water head interval by a multivariable single-target genetic algorithm (Genetic Algorithm, GA) and positioning the most serious cavitation water head point;
the comprehensive display module is used for comprehensively displaying the gas-liquid two-phase distribution diagram obtained by the simulation calculation module and the vibration signal processed by the signal processing module, obtaining an accurate cavitation primary stage and a specific cavitation phenomenon occurrence position, positioning a serious cavitation position and displaying the vibration main frequency and amplitude information caused by cavitation.
Referring to fig. 3, the method for diagnosing cavitation of the water turbine based on semi-physical simulation comprises the following steps:
s1: and a laser three-dimensional scanning mode is adopted to obtain the geometric surface of each overcurrent component of the water turbine, and then a three-dimensional model of the water turbine is quickly built in three-dimensional software. Scanning the over-current components such as the guide vane, the rotating wheel body and the vane of the water turbine one by utilizing a handheld three-dimensional scanner, wherein the precision of the three-dimensional scanner can reach 0.02mm, the geometric curved surface modeling of the blade of the water turbine can be perfectly obtained, the modeling work of the most complex vane in the water turbine component is completed, the drawing information of the water inlet pipe and the draft tube is combined, a complete three-dimensional model of the over-current component of the water turbine is built in three-dimensional modeling software, and the fluid domain is filled in other parts, so that the modeling work of the fluid-solid two phases is completed; importing the established three-dimensional model into preprocessing software, performing geometric simplification processing on the three-dimensional model, eliminating unnecessary contour lines, reducing the later grid discretization quantity, and accelerating the iterative computation speed, thereby establishing a three-dimensional model of the water turbine;
s2: given initial grid cell size value d 0 Gridding the three-dimensional model of the water turbine established by the three-dimensional scanning modeling module to obtain a grid model of the water turbine:
s3: and a signal processing module processes: the opening information of the guide vane and the vane of the water turbine speed regulator is connected into a signal processing module, a sample data acquisition period is set, and an upstream water level mean value Z in a single period is calculated and acquired according to the pressure, the water temperature, the upstream water level and the downstream water level acquired by a sensor module 1 Downstream water level mean Z 2 The water temperature average value T and the bulb standing point position pressure average value P 1 Pressure average value P of monitoring point of guide vane upstream pipe wall 2 ；
And monitoring radial vibration time domain signals of the turbine runner by adopting a vibration sensor, carrying out Fourier transformation on the radial vibration time domain signals of the turbine runner, obtaining a vibration signal spectrogram, and extracting vibration main frequency and amplitude information.
S4, setting a simulation calculation model and performing iterative calculation:
introducing the water turbine grid model into simulation calculation software (such as Ansys Fluent, star-ccm+, ansys CFX, etc.), and averaging the upstream water level Z 1 Downstream water level mean Z 2 And inputting the water temperature mean value T into a simulation calculation model to be used as a calculation boundary condition.
A fluid domain medium is provided, the first phase being water and the second phase being water vapor.
Setting a simulation calculation model, such as: energy model, turbulence model, multiphase flow model, cavitation model, sliding grid model, etc.
Setting upThe initial upstream water level, the initial downstream water level and the iteration time step are iteratively calculated until residual error is converged, simulation calculation time t spent from the iterative calculation to the convergence is obtained, and the residual error convergence standard is set to be 1×e -4 。
Processing the simulation calculation result to obtain the pressure simulation value of the bulb body resident point positionAnd pressure simulation value of monitoring point of upstream pipe wall of guide vane。
S5, adjusting and correcting the simulation calculation model: because of the blockage of the trash rack and the friction between the fluid and the runner, a certain head loss exists, and therefore, in the simulation calculation, a model correction module is required to carry out certain adjustment correction on the simulation calculation model.
In the model correction module, the pressure simulation value of the standing point position of the bulb body is calculatedAnd pressure simulation value of monitoring point of upstream pipe wall of guide vaneMean value P of pressure at standing point position of bulb body actually measured by pressure sensor 1 Pressure average value P of monitoring point of guide vane upstream pipe wall 2 The sum of the standard deviation of (2) is minimum and the simulation calculation time is t shortest as the optimization objective function of the simulation calculation model, and the simulation value of the upstream water levelAnd the grid cell size d is used as an optimization variable of the simulation calculation model, the optimal solutions of a plurality of objective functions are searched by utilizing a multi-objective genetic evolution algorithm, and the simulation calculation model is corrected by utilizing the optimal solutions, so that the simulation calculation model which is most consistent with the actual working condition under the condition of minimum calculation time cost is obtained.
Simulation calculation model optimization variable:
(1) Upstream water level simulation value，The upstream water level simulation values of the 1 st, 2 nd, 3 rd and … th iterations and the n th iterations are respectively,units: rice;
(2) Grid cell size d= (d 1 ,d 2 ,d 3 ,…,d n ), d∈[0.001,1]Units: and (5) rice.
Simulation value of bulb standing point position pressure and standard deviation of bulb standing point position pressure mean valueThe following is shown:
；
in the method, in the process of the invention,the simulated value of the bulb body resident point position pressure of the jth iteration is obtained;
simulation value of pressure of monitoring point of upstream pipe wall of guide vane and standard deviation of pressure average value of monitoring point of upstream pipe wall of guide vaneThe following is shown:
；
in the method, in the process of the invention,and (5) simulating a value of the pressure of the monitoring point of the upstream pipe wall of the guide vane for the jth iteration.
The simulation calculation model optimizes the objective function as follows:
；
；
wherein,indicating that the pressure simulation error is minimal,represents that the simulation calculation time is shortest, the simulation calculation time t= (t) 1 ,t 2 ,t 3 ,…, t n ) Units: second, t 1 ,t 2 ,t 3 ,…,t n Simulation calculation time for the 1 st, 2 nd, 3 rd, … th, n th iterations, respectively.
Simulation calculation model correction qualification judgment standard:
if it isThe precision of the simulation calculation model meets the requirement, the lowest pressure point pressure, the water vapor volume fraction and the vapor-liquid two-phase distribution diagram of the metal surface of the overcurrent part are obtained through calculation of the simulation calculation model, namely the final result, and the cavitation occurrence position can be accurately positioned according to the lowest pressure point pressure and the vapor-liquid two-phase distribution diagram of the metal surface of the overcurrent part;
if it isAnd adjusting the optimization variable of the simulation calculation model by using the model correction module, and carrying out iterative correction on the simulation calculation model until the simulation calculation model meets the requirement.
S6: predicting an easy cavitation water head interval: after the simulation calculation model is corrected, a cavitation prediction module adopts a multivariable single-target genetic algorithm to adjust the opening degree of a guide vane and the opening degree of a vane of a water turbine simulation calculation model, and calculates and obtains the pressure of the lowest pressure point of the metal surface of the overcurrent part under each opening degree of the guide vane and the opening degree of the vane and the average pressure P of the inlet section in the graph 2 in Tail of the pipeAverage pressure of water outlet section P out 。
The following are the optimization variables, constraint conditions and objective functions of the cavitation prediction module:
optimization variables of cavitation prediction module:
(1) Vane opening；The opening degree of the guide vane is 1 st, 2 nd, 3 rd, … th and n th iterations respectively;
(2) Vane opening，The vane opening is 1,2,3, …, n iterations.
Constraint conditions of cavitation prediction module: p is less than or equal to P v ，P v Is the vaporization pressure of water, when the water flow pressure P in the geometric flow passage of the water turbine is lower than the vaporization pressure P of water v When the water is vaporized, cavitation is caused, and a polynomial relational expression between the vaporization pressure of the water and the water temperature mean value T is as follows:
；
the objective function of the cavitation prediction module is:
；
in the middle ofThe minimum pressure point pressure of the metal surface of the overcurrent part for the 1 st, 2 nd, 3 rd, … th, j, … th and n th iterations respectively, when P min,j ≤P v Cavitation occurs when; p (P) min The working condition with the minimum pressure in n iterations is the working condition with the most serious cavitationAnd (5) a dot.
Determining an easy cavitation working point and a cavitation worst working point by a multivariable single-target genetic algorithm, and calculating the obtained average pressure P of an inlet section under the working point by a simulation calculation model in Average pressure P of tail water outlet section out To calculate the working water head H of the bulb through-flow turbine, the calculation formula isWherein ρ is water flow density, 1000kg/m 3 The method comprises the steps of carrying out a first treatment on the surface of the g is gravity acceleration, 9.81m/s is taken 2 (taking the head calculation of a bulb through-flow turbine as an example, the position head and the speed head of the bulb through-flow turbine are negligible due to the structural characteristics of the runner). And further, the cavitation characteristics of the water turbines of different water heads under the upstream water level and the downstream water level are obtained, the quick prediction of the easily cavitation water head interval is realized, the most serious cavitation water head is positioned, the easily cavitation water head can be avoided in the daily operation of a hydropower plant, and the safe and stable operation of the water turbine generator set is ensured.
S7: comprehensive display is carried out on cavitation diagnosis results of the water turbine: the simulation calculation result, the vibration main frequency and amplitude information thereof and the section of the easy cavitation water head are input into the comprehensive display module, the cavitation development and distribution condition in the flow passage of the water turbine, the vibration main frequency and amplitude information caused by cavitation and the section of the easy cavitation water head prediction are displayed, and guiding suggestions and references are provided for avoiding damage to the surface of the flow passage part of the water turbine and vibration treatment of the water turbine caused by cavitation and cavitation erosion.
The foregoing description is only a preferred embodiment of the invention and is not intended to limit other embodiments of the invention, as those skilled in the art can modify the above disclosure into equivalent embodiments. However, any simple modification, substitution and simplification of the above-described embodiments shall fall within the scope of the technical solution of the present invention without departing from the content of the technical solution of the present invention.
Claims (9)
1. The hydraulic turbine cavitation diagnosis system based on semi-physical simulation is characterized by comprising a sensor module, a signal processing module, a three-dimensional scanning modeling module, a grid processing module, a simulation calculation module, a model correction module, a cavitation prediction module and a comprehensive display module;
the signal processing module is used for processing the real-time information acquired by the sensor module;
the three-dimensional scanning modeling module is used for building a three-dimensional model of the water turbine;
the grid processing module is used for carrying out gridding processing on the three-dimensional model of the water turbine established by the three-dimensional scanning modeling module according to the initial value of the size of the grid unit to obtain a grid model of the water turbine;
the simulation calculation module is used for importing the water turbine grid model into simulation calculation software, setting the simulation calculation model by taking the water temperature, the upstream water level and the downstream water level obtained by the sensor module as boundary conditions of the simulation calculation of the water turbine, and starting iterative calculation to obtain the pressure of the monitoring point in the water turbine runner, the pressure of the lowest pressure point on the metal surface of the overcurrent part, the water vapor volume fraction and the vapor-liquid two-phase distribution map;
the model correction module is used for carrying out iterative correction on the simulation calculation model through a multi-target genetic evolution algorithm, inputting the corrected simulation calculation model into the simulation calculation module for carrying out iterative calculation again, and obtaining the simulation calculation model which is most consistent with the actual working condition;
the cavitation prediction module is used for simulating cavitation conditions of the water turbine under different water heads by changing opening degrees of guide vanes and blades of the three-dimensional model of the water turbine, predicting a cavitation-prone water head interval by a multivariable single-target genetic algorithm and positioning the most serious cavitation water head point;
the comprehensive display module is used for comprehensively displaying the gas-liquid two-phase distribution diagram obtained by the simulation calculation module and the vibration signal processed by the signal processing module, obtaining an accurate cavitation primary stage and a specific cavitation phenomenon occurrence position, positioning a serious cavitation position and displaying the vibration main frequency and amplitude information caused by cavitation.
2. The semi-physical simulation-based water turbine cavitation diagnosis system according to claim 1, wherein the three-dimensional scanning modeling module adopts a laser three-dimensional scanning mode to obtain the geometric surface of each flow passage component of the water turbine, and further rapidly builds a three-dimensional model of the water turbine in three-dimensional software.
3. The semi-physical simulation-based water turbine cavitation diagnosis system according to claim 1, wherein the sensor module comprises a pressure sensor, a temperature sensor, a vibration sensor and a water level sensor, the pressure sensor is used for acquiring the pressure of water flow in a water turbine runner, the temperature sensor is used for acquiring the water temperature of the water flow in the water turbine runner, the vibration sensor is used for acquiring radial vibration signals of the water turbine runner, and the water level sensor is used for acquiring upstream water level and downstream water level; the water level sensor is respectively arranged at the upstream and downstream of the hydropower station and used for monitoring the water level at the upstream and downstream, the temperature sensor is arranged at the standing point position of the bulb body, the pressure sensor is arranged at the standing point position of the bulb body and the upstream pipe wall of the guide vane, and the vibration sensor is arranged at the guide bearing position of the water turbine and used for acquiring the radial vibration signal of the rotating wheel of the water turbine.
4. The hydraulic turbine cavitation diagnosis method based on semi-physical simulation is characterized by being realized by the hydraulic turbine cavitation diagnosis system according to any one of claims 1-3, and comprises the following steps:
s1: the geometrical surfaces of all the overcurrent components of the water turbine are obtained by adopting a laser three-dimensional scanning mode, and then a three-dimensional model of the water turbine is quickly built in three-dimensional software;
s2: given initial grid cell size value d 0 Gridding the three-dimensional model of the water turbine established by the three-dimensional scanning modeling module to obtain a grid model of the water turbine:
s3: and a signal processing module processes: the opening information of the guide vane and the vane of the water turbine speed regulator is connected into a signal processing module, a sample data acquisition period is set, and an upstream water level mean value Z in a single period is calculated and acquired according to the pressure, the water temperature, the upstream water level and the downstream water level acquired by a sensor module 1 Downstream waterBit mean Z 2 The water temperature average value T and the bulb standing point position pressure average value P 1 Pressure average value P of monitoring point of guide vane upstream pipe wall 2 The method comprises the steps of carrying out a first treatment on the surface of the Monitoring radial vibration time domain signals of the turbine runner by adopting a vibration sensor, carrying out Fourier transformation on the radial vibration time domain signals of the turbine runner, obtaining a vibration signal spectrogram, and extracting vibration main frequency and amplitude information;
s4, setting a simulation calculation model and performing iterative calculation;
s5, adjusting and correcting the simulation calculation model: in the model correction module, the pressure simulation value of the standing point position of the bulb body is calculatedAnd pressure simulation value ++of monitoring point of upstream pipe wall of guide vane>Mean value P of pressure at standing point position of bulb body actually measured by pressure sensor 1 Pressure average value P of monitoring point of guide vane upstream pipe wall 2 The sum of standard deviation of the (2) and the simulation calculation time tshort are used as a simulation calculation model optimization objective function, and the upstream water level simulation value +.>The grid cell size d is used as an optimization variable of the simulation calculation model, the optimal solutions of a plurality of objective functions are searched by utilizing a multi-objective genetic evolution algorithm, and the simulation calculation model is corrected by utilizing the optimal solutions, so that the simulation calculation model which is most consistent with the actual working condition under the condition of minimum calculation time cost is obtained;
s6: predicting an easy cavitation water head interval: after the simulation calculation model is corrected, a cavitation prediction module adopts a multivariable single-target genetic algorithm to adjust the opening degree of a guide vane and the opening degree of a vane of a water turbine simulation calculation model, and calculates and obtains the lowest pressure point pressure of the metal surface of the water turbine flow passage component and the average pressure P of an inlet section under the opening degree of each guide vane and the opening degree of the vane in And average pressure P of tail water outlet section out The method comprises the steps of carrying out a first treatment on the surface of the The working water head H of the bulb through-flow turbine has a calculation formula ofWherein ρ is the water flow density; g is gravity acceleration; acquiring cavitation characteristics of water turbines of different water heads under upstream and downstream water levels, realizing rapid prediction of an easy cavitation water head interval, and positioning the most serious cavitation water head;
s7: and comprehensively displaying cavitation diagnosis results of the water turbine.
5. The semi-physical simulation-based water turbine cavitation diagnosis method according to claim 4, wherein the simulation calculation model setting and iterative calculation comprises:
the grid model of the water turbine is imported into simulation calculation software, and the upstream water level average value Z 1 Downstream water level mean Z 2 And the water temperature mean value T is input into a simulation calculation model and used as a calculation boundary condition;
setting a fluid domain medium, wherein the first phase is water and the second phase is steam;
setting a simulation calculation model;
setting an initial upstream water level, an initial downstream water level and an iteration time step, carrying out iterative calculation until residual error converges, and obtaining simulation calculation time t spent from the iterative calculation to convergence;
processing the simulation calculation result to obtain the pressure simulation value of the bulb body resident point positionAnd pressure simulation value ++of monitoring point of upstream pipe wall of guide vane>。
6. The semi-physical simulation-based water turbine cavitation diagnosis method according to claim 5, wherein the simulation calculation model optimization variables comprise:
upstream water level simulation value，/>Upstream water level simulation values of 1 st, 2 nd, 3 rd, … th, n th iterations,/-th iteration, respectively>；
Grid cell size d e [0.001,1];
simulation value of bulb standing point position pressure and standard deviation of bulb standing point position pressure mean valueThe following is shown:
；
in the method, in the process of the invention,the simulated value of the bulb body resident point position pressure of the jth iteration is obtained;
simulation value of pressure of monitoring point of upstream pipe wall of guide vane and standard deviation of pressure average value of monitoring point of upstream pipe wall of guide vaneThe following is shown:
；
in the method, in the process of the invention,the pressure simulation value of the monitoring point of the upstream pipe wall of the guide vane for the jth iteration is obtained;
the simulation calculation model optimizes the objective function as follows:
；
；
wherein,representing that the pressure simulation error is minimal,/->Represents that the simulation calculation time is shortest, the simulation calculation time t= (t) 1 ,t 2 ,t 3 ,…, t n ),t 1 ,t 2 ,t 3 ,…,t n Simulation calculation time for the 1 st, 2 nd, 3 rd, … th, n th iterations, respectively.
7. The semi-physical simulation-based water turbine cavitation diagnosis method of claim 6, wherein the simulation calculation model correction qualification criterion is:
if it isThe precision of the simulation calculation model meets the requirement, the lowest pressure point pressure, the water vapor volume fraction and the vapor-liquid two-phase distribution diagram of the metal surface of the overcurrent part are obtained through calculation of the simulation calculation model, namely the final result, and the cavitation occurrence position can be accurately positioned according to the lowest pressure point pressure and the vapor-liquid two-phase distribution diagram of the metal surface of the overcurrent part;
if it isAnd adjusting the optimization variable of the simulation calculation model by using the model correction module, and carrying out iterative correction on the simulation calculation model until the simulation calculation model meets the requirement.
8. The semi-physical simulation-based water turbine cavitation diagnosis method of claim 7, wherein the optimization variables of the cavitation prediction module are:
vane opening；/>The opening degree of the guide vane is 1 st, 2 nd, 3 rd, … th and n th iterations respectively;
vane opening，/>Vane opening for the 1 st, 2 nd, 3 rd, … th, n iterations;
constraint conditions of cavitation prediction module: p is less than or equal to P v ，P v Is the vaporization pressure of water, when the water flow pressure P in the geometric flow passage of the water turbine is lower than the vaporization pressure P of water v When the water is vaporized, cavitation is caused, and a polynomial relational expression between the vaporization pressure of the water and the water temperature mean value T is as follows:；
the objective function of the cavitation prediction module is:
；
in the middle ofThe minimum pressure point pressure of the metal surface of the overcurrent part for the 1 st, 2 nd, 3 rd, … th, j, … th and n th iterations respectively, when P min,j ≤P v Cavitation occurs when; p (P) min The working condition with the minimum pressure in n iterations is the working condition point with the most serious cavitation.
9. The semi-physical simulation-based hydraulic turbine cavitation diagnosis method according to claim 8, wherein the comprehensively displaying the hydraulic turbine cavitation diagnosis result comprises: and inputting the simulation calculation result, the vibration main frequency and amplitude information thereof and the cavitation-prone water head interval into a comprehensive display module, and displaying cavitation development and distribution conditions in the flow channel of the water turbine, the vibration main frequency and amplitude information caused by cavitation and the cavitation-prone water head prediction interval.
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