Hydropower station workshop vibration prediction method

Abstract

The invention belongs to the technical field of vibration prediction, and particularly discloses a hydropower station workshop vibration prediction method, which comprises the following steps: constructing a three-dimensional cellular network of a hydropower station workshop, and determining a plurality of vibration source cells from the cells of the three-dimensional cellular network; determining the predicted acceleration of each vibration source cell at the # imgabs 1 # moment according to the vibration force and the damping force of each vibration source cell at the # imgabs 0 # moment and the elastic force generated by the displacement difference between each vibration source cell and the cells in the neighborhood of the vibration source cell; determining the predicted acceleration of each cell in the three-dimensional cellular network at the # imgabs3 # moment according to the predicted acceleration of each vibration source cell at the # imgabs2 # moment; and determining the cell where the accelerometer for monitoring the hydropower station plant is located, and correcting the predicted acceleration of each cell according to the deviation between the monitored acceleration and the predicted acceleration of the cell where the accelerometer is located. The method can give consideration to physical mechanisms and data driving, can rapidly and accurately predict the vibration of the hydropower plant, and supports the construction of an intelligent hydraulic power plant.

Description

Technical Field

[0001] The present invention belongs to the technical field of vibration prediction, and discloses a vibration prediction method for a hydropower station powerhouse. Background Technique

[0002] Vibration is an important indicator reflecting the operation technical condition of a structure. Especially for the structure of a hydropower station powerhouse, the vibration problem is particularly serious. With the increase in the scale of the hydropower station powerhouse structure and the increase in unit capacity, the instances of vibration problems during the operation of the unit are also increasing. Excessive vibration of the load during operation will cause abnormal vibration of the hydropower station powerhouse structure. Being in a vibrating state for a long time is likely to cause fatigue damage to the structure, and seriously will lead to cracks in the structure, affecting the structural safety.

[0003] Currently, the vibration analysis of a hydropower station powerhouse often relies on finite element numerical simulation. However, the vibration of a hydropower station powerhouse is affected by the coupling of multiple factors such as machinery, hydraulics, and electricity, and has significant non-linear characteristics. When using finite element numerical simulation, complex parameters need to be set, and it depends on a static model. It is difficult to update the water pressure load in the flow channel and the unit operation load in real time dynamically. The calculation is time-consuming and cannot meet the requirements of online early warning. The existing method of simply relying on sensors to monitor the vibration of a hydropower station powerhouse can only reflect the local vibration state, lacks the quantitative analysis of the global risk propagation path, and cannot predict the crack propagation. Moreover, it only knows after it has occurred, which belongs to "passive monitoring" and lacks foresight, and is not conducive to preventing the occurrence of dangers. In addition, if simply using the currently popular machine learning method, there is a black box problem. Although the neural network model can achieve certain prediction, it lacks physical interpretability and is not conducive to tracing the source and proposing treatment measures.

[0004] Therefore, there is an urgent need for an efficient prediction tool that combines physical mechanism and data-driven to realize the upgrade from "passive monitoring" to "active prevention and control", and at the same time can quickly, real-time, efficiently and accurately predict the vibration of a hydropower station powerhouse in various situations to support the construction of an intelligent hydropower plant. Summary of the Invention

[0005] The purpose of the present invention is to provide a vibration prediction method for a hydropower station powerhouse to solve the technical problems that the existing methods are time-consuming in calculation, cannot meet the requirements of online early warning, and lack physical interpretability.

[0006] The present invention provides a vibration prediction method for a hydropower station powerhouse, including:

[0007] Step 1: Construct a three-dimensional cellular network of a hydropower station powerhouse, and determine a plurality of vibration source cells from the cells of the three-dimensional cellular network.

[0008] Step 2: According to each vibration source cell in The vibration force, damping force at a moment, and the elastic force generated by the displacement difference between each vibration source cell and the cells in its neighborhood determine the predicted acceleration of each vibration source cell at the moment.

[0009] Step 3: Determine the predicted acceleration of each cell in the three-dimensional cell network at the moment according to the predicted acceleration of each vibration source cell at the moment.

[0010] Step 4: Determine the cell where the accelerometer for monitoring the hydropower station powerhouse is located, and correct the predicted acceleration of each cell according to the deviation between the monitored acceleration and the predicted acceleration of the cell where the accelerometer is located.

[0011] Preferably, step 2 is specifically:

[0012] Determine the equivalent stiffness of each vibration source cell along the direction of its neighborhood.

[0013] Determine the elastic force according to the equivalent stiffness and the displacement difference between each vibration source cell and the cells in its neighborhood.

[0014] According to the elastic force and the vibration force, damping force of each vibration source cell at the moment, determine the predicted acceleration of each vibration source cell at the moment.

[0015] Preferably, step 3 is specifically:

[0016] Determine the distribution weight of the cells in the neighborhood according to the length between each vibration source cell and the cells in its neighborhood.

[0017] Determine the transitional acceleration of the cells in the neighborhood of the vibration source cell according to the distribution weight and the predicted acceleration of the vibration source cell at the moment.

[0018] Determine the predicted acceleration of each cell at the moment according to the multiple transitional accelerations corresponding to each cell.

[0019] Preferably, step 4 is specifically:

[0020] Determine the cell where the accelerometer for monitoring the hydropower station powerhouse is located, and determine the deviation between the monitored acceleration and the predicted acceleration of the cell where the accelerometer is located.

[0021] Determine the correction value of each cell according to the deviation and the distribution weight, and use the correction value to correct the predicted acceleration to obtain the corrected acceleration.

[0022] Determine a correction ratio based on the predicted acceleration and the corrected acceleration, and use the correction ratio to correct the assigned weight until the deviation is within a preset range, so as to obtain the final predicted acceleration of each cell.

[0023] Preferably, determining a correction ratio based on the predicted acceleration and the corrected acceleration, and using the correction ratio to correct the assigned weight, specifically:

[0024] Determine the correction ratio according to the quotient of the corrected acceleration and the predicted acceleration.

[0025] Multiply the correction ratio by the assigned weight to correct the assigned weight.

[0026] Preferably, after step 4, it further includes:

[0027] Step 5: Determine the predicted velocity of each cell at time according to the predicted velocity and predicted acceleration of each cell at time.

[0028] Preferably, after step 5, it further includes:

[0029] Step 6: Determine the predicted displacement of each cell at time according to the predicted displacement and predicted velocity of each cell at time.

[0030] Preferably, after step 6, it further includes:

[0031] Step 7: Determine the predicted stress of each cell at time according to the elastic modulus of each cell, the predicted displacement of each cell at time, and predicted displacement at

[0032] Preferably, step 1 is specifically:

[0033] Construct a three-dimensional cell network of the hydropower station powerhouse.

[0034] Apply the excitation load of the hydropower station powerhouse to the corresponding cells in the three-dimensional cell network to obtain a plurality of vibration source cells.

[0035] Preferably, the excitation load includes the water pressure load in the flow channel and the unit operation load.

[0036] The vibration prediction method of the hydropower station powerhouse of the present invention has the following beneficial effects compared with the prior art:

[0037] The vibration prediction method for a hydropower station powerhouse of the present invention can take into account both physical mechanisms and data-driven approaches, achieve the upgrade from "passive monitoring" to "active prevention and control", and simultaneously predict the vibrations of the hydropower station powerhouse in various situations in real time, efficiently, and accurately, supporting the construction of an intelligent hydropower plant. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a schematic flowchart of the vibration prediction method for a hydropower station powerhouse in an embodiment of the present invention.

[0039] Figure 2 It is a schematic diagram of the partition of the spiral case and draft tube flow passage in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0040] In the following description, specific details such as specific system structures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present invention. However, those skilled in the art should clearly understand that the present invention can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present invention.

[0041] An embodiment of the present invention provides a vibration prediction method for a hydropower station powerhouse, as Figure 1 shown, including:

[0042] Step 1: Construct a three-dimensional cellular network of the hydropower station powerhouse, and determine a plurality of vibration source cells from the cells of the three-dimensional cellular network. Specifically:

[0043] Step 1.1: Construct a three-dimensional cellular network of the hydropower station powerhouse.

[0044] In an embodiment of the present invention, the water turbine layer is used as the demarcation line to construct a three-dimensional cellular network with different cell sizes. Specifically, large-volume concrete below the water turbine layer uses coarse-sized cells, and the plate beam structure above the water turbine layer uses fine-sized cells. The combination of coarse and fine sizes balances the calculation efficiency and accuracy. Exemplarily, the cell size of the large-volume concrete below the water turbine layer is 2.5 m × 2.5 m × 2.5 m; the cell size of the plate beam structure above the water turbine layer is 0.5 m × 0.5 m × 0.5 m.

[0045] Before predicting the vibration of the hydropower station powerhouse in an embodiment of the present invention, it is necessary to define the state of each cell in the three-dimensional cellular network. Specifically, the state of each cell includes state variables and dynamic risk parameters. Among them, the state variables include mechanical parameters and material properties. The mechanical parameters are velocity, acceleration, displacement, and stress; the material properties are density, elastic modulus, and damping coefficient. The dynamic risk parameters are the width of concrete cracks and the excitation loads composed of unit operation loads and flow passage water pressure loads.

[0046] The initial state of all cells in the embodiments of the present invention is as follows: the velocity, displacement, acceleration, and concrete crack width are all 0, and the stress is the self-weight stress of the structure, that is . Among them The th cell is the stress at the position where the th cell is located; is the unit weight of all structures above the th cell. The unit weight of concrete is taken as 25 kN / m³;

[0047] Step 1.2: Apply the excitation load of the hydropower station powerhouse to the corresponding cells in the three-dimensional cell network to obtain multiple vibration source cells.

[0048] The excitation loads in the embodiments of the present invention include the unit operation load and the water pressure load in the flow channel.

[0049] The sources of vibration of the hydropower station powerhouse are mainly unit operation and water flow pulsation. Therefore, in the embodiments of the present invention, when constructing the three-dimensional cell network, the unit operation load and the water pressure load in the flow channel are applied to the corresponding cells, so as to effectively simulate nonlinear dynamic behaviors such as unit operation and water flow pulsation.

[0050] When determining the excitation load in the embodiments of the present invention, it is assumed that each excitation load is a harmonic load and the phases of each excitation load are the same, that is, each excitation load reaches the vibration amplitude simultaneously, which actually considers the most unfavorable load combination.

[0051] The water pressure load in the flow channel specifically includes the internal water pressure in the spiral case and the draft tube flow channel, which is simplified into a periodic load. The zoning in the embodiments of the present invention can be divided according to the flow channel pulsation pressure test or experience. Multiple zones can be divided in both the spiral case and the draft tube flow channel, or the spiral case can be taken as one zone and the draft tube flow channel can be taken as one zone. Generally, according to experience, the spiral case is divided into one zone every 90 degrees along the water flow direction, that is, the spiral case is divided into 4 zones, and the draft tube is divided into 2 - 3 zones.

[0052] The water pressure load in the flow channel in the embodiments of the present invention is characterized by the vibration force. If the loads in the X, Y, and Z directions are the same, the vibration forces are also the same, ; is the maximum vibration force in zone ; respectively represent different zones in the spiral case and the draft tube flow channel, as shown in Figure 2 ; is the corresponding frequency in zone is the time; is The maximum vibration force in the X direction of area X; is The maximum vibration force in the Y direction of area Y; is The maximum vibration force in the Z direction of area Z.

[0053] The operating load of the unit in the embodiment of the present invention includes the vibration load of the upper frame, the vibration load of the lower frame, and the vibration load of the stator foundation, all of which include three directions in the X, Y, and Z spaces.

[0054] The vibration loads in the X, Y, and Z directions of the upper frame are characterized by vibration forces, which are respectively: , , ; where , , are respectively the vibration forces of the upper frame in the X, Y, and Z directions; , , are respectively the unit loads acting on the upper frame in the X, Y, and Z directions; is the frequency corresponding to the generator set; is the time.

[0055] The vibration loads in the X, Y, and Z directions of the lower frame are characterized by vibration forces, which are respectively: , , ; where , , are respectively the vibration forces of the lower frame in the X, Y, and Z directions; , , are respectively the unit loads acting on the lower frame in the X, Y, and Z directions; is the frequency corresponding to the generator set; is the time.

[0056] The vibration loads in the X, Y, and Z directions of the stator foundation are characterized by vibration forces, which are respectively: , , ; where , , are respectively the vibration forces of the stator foundation in the X, Y, and Z directions; , , are respectively the unit loads acting on the stator foundation in the X, Y, and Z directions; is the frequency corresponding to the generator set; is the time.

[0057] Step 2. Determine the predicted acceleration of each vibration source cell at time according to the vibration force, damping force of each vibration source cell at time and the elastic force generated by the displacement difference between each vibration source cell and the cells in its neighborhood. Specifically:

[0058] Step 2.1. Determine the equivalent stiffness of each vibration source cell along the direction of its neighborhood.

[0059] The neighborhood rule adopted in the embodiment of the present invention is: The Moore - type three - dimensional neighborhood (that is, each cell directly interacts with 26 surrounding cells) is used to define the inter - connection between cells to ensure the reliable propagation of vibrations in three - dimensional space.

[0060] The embodiment of the present invention uses to represent the equivalent stiffness, that is, is the equivalent stiffness formed by the th vibration source cell along the th neighborhood direction.

[0061] Step 2.2. Determine the elastic force according to the equivalent stiffness and the displacement difference between each vibration source cell and the cells in its neighborhood.

[0062] Step 2.3. Determine the predicted acceleration of each vibration source cell at time according to the elastic force, vibration force, and damping force of each vibration source cell at time.

[0063] Exemplarily, the predicted acceleration of the th vibration source cell at time is , which includes three directions in the X, Y, Z space, respectively denoted as: , , .

[0064] (1)

[0065] (2)

[0066] (3)

[0067] In the formula, is the mass of the th vibration source cell, , is the density, the th vibration source cell forms a volume in the three - dimensional grid; , , are respectively the sum of elastic forces generated by the displacement differences in the X, Y, and Z directions between the th vibration source cell and the cells within its neighborhood ( is the total number of neighborhood cells of the th vibration source cell). Among them, , , are respectively the displacements in the X, Y, and Z directions of the th vibration source cell at moment; , , are respectively the displacements in the X, Y, and Z directions of the th vibration source cell corresponding to the th neighborhood cell at moment; is the equivalent stiffness formed by the th vibration source cell along the th neighborhood direction, ; is the equivalent moment of inertia along the th neighborhood direction of the cross-section perpendicular to the th neighborhood direction of the th vibration source cell. is the elastic modulus of the th vibration source cell, , is the elastic modulus of concrete; is the maximum allowable operating crack width (empirically taken as 1 mm); is the crack width of the th vibration source cell at moment; is the stiffness degradation coefficient of the th vibration source cell at moment, , is the stress of the th vibration source cell at moment, , is the allowable value of the material's tensile strength. For the part with steel bars, it takes 0.4 - 0.6 times the allowable value of the steel bar strength, and for the part without steel bars, it takes the allowable value of the tensile strength of the concrete material itself; , , are respectively the stresses in the X, Y, and Z directions of the th vibration source cell at moment. When , , When it is compressive stress, the stress in this direction is not considered; when , ; when , , is the stress of the th vibration source cell at time; when , . is the damping force of the th vibration source cell, where is the damping coefficient of the th vibration source cell (the damping is set according to the material properties, and 0.07 is taken according to the standard workshop), , , are the velocities in the X, Y, and Z directions of the th vibration source cell at time respectively.

[0068] , , are the vibration forces of the exciting load in the X, Y, and Z directions of the th vibration source cell at time respectively. The vibration forces of the vibration source cells in the flow channel range are ; the vibration forces of the vibration source cells in the upper frame part are , , ; the vibration forces of the vibration source cells in the lower frame part are , , ; the vibration forces of the vibration source cells in the stator base part are , , .

[0069] Step 3: Determine the predicted acceleration of each cell in the three-dimensional cell network at time according to the predicted acceleration of each vibration source cell at time.

[0070] In the embodiment of the present invention, the transfer rule of the cell acceleration is: the weights are distributed according to the neighboring cells and attenuate sequentially to the surrounding. The acceleration of each cell is the superposition effect of different exciting loads transmitted here.

[0071] Then step 3 is specifically:

[0072] Step 3.1: Determine the allocation weight of the cells in the neighborhood according to the length between each vibration source cell and the cells in its neighborhood, as shown in formula (4).

[0073] (4)

[0074] In the formula, is the allocation weight corresponding to the th vibration source cell for the th neighborhood, is the length of the th vibration source cell in the direction of the cells in the th neighborhood, is the attenuation coefficient, which can be obtained through experiments or empirical formulas. Exemplarily, , A is a material characteristic constant, such as 0.12 for concrete, is the elastic modulus of concrete, is the material density, is the vibration source frequency, is the material damping coefficient.

[0075] Step 3.2: Determine the transitional acceleration of the cells in the neighborhood of the vibration source cell according to the allocation weight and the predicted acceleration of the vibration source cell at time.

[0076] Exemplarily, determine the transitional acceleration of each cell in the neighborhood according to the product of the allocation weight and the predicted acceleration of the vibration source cell at time.

[0077] Step 3.3: Determine the predicted acceleration of each cell at time according to the multiple transitional accelerations corresponding to each cell.

[0078] Since each cell is affected by multiple vibration source cells, therefore, the predicted acceleration of each cell at time is the superposition of the transitional accelerations applied to the cell by multiple vibration source cells.

[0079] In the embodiment of the present invention, to reduce the prediction error, the monitored acceleration is introduced to modify the predicted acceleration, as described in the following step 4.

[0080] Step 4: Determine the cell where the accelerometer for monitoring the hydropower station powerhouse is located, and correct the predicted acceleration of each cell according to the deviation between the monitored acceleration and the predicted acceleration of the cell where the accelerometer is located.

[0081] The monitoring device in the embodiment of the present invention includes, in addition to the accelerometer, a stress gauge, a displacement sensor, etc.

[0082] The above step 4 is specifically as follows:

[0083] Step 4.1: Determine the cell where the accelerometer for monitoring the hydropower station powerhouse is located, and determine the deviation between the monitored acceleration and the predicted acceleration of the cell where the accelerometer is located.

[0084] In the embodiment of the present invention, when determining the predicted acceleration, in addition to using the above physical driving rules, a data-driven rule is also used to correct the predicted acceleration based on the monitored acceleration, reducing the prediction error.

[0085] In the embodiment of the present invention, first, 80% of the existing monitored acceleration is used to correct the predicted acceleration, and the remaining 20% is used for verification after correction to ensure the reliability of the prediction model. Then, the time interval of the monitored acceleration is aligned with the time step of the three-dimensional cell network, which can be specifically aligned by interpolation. Finally, the existing monitored acceleration is extended to all cells of the three-dimensional cell network to construct a spatial correlation matrix.

[0086] The process of determining the deviation in the embodiment of the present invention is specifically as follows: approximately map the position of the accelerometer for monitoring the hydropower station powerhouse to the corresponding cell in the three-dimensional cell network, extract its acceleration value, denoted as the monitored acceleration, and establish a monitoring set , extract the predicted acceleration of the cell at the corresponding position, and establish a prediction set . Among them is the number of monitoring positions, . In the embodiment of the present invention, to clearly show the deviation calculation process, a simplified is used to represent the monitored acceleration, and a simplified Y is used to represent the predicted acceleration. Then, the deviation between the monitored acceleration and the predicted acceleration of the corresponding cell is shown in formula (5).

[0087] (5)

[0088] In the formula is the deviation set, is the deviation at the th monitoring position.

[0089] Step 4.2: Determine the correction value of each cell according to the deviation and the assigned weight, and use the correction value to correct the predicted acceleration to obtain the corrected acceleration.

[0090] In the embodiment of the present invention, the correction value of each cell is corrected according to the deviation diffusion principle according to the following steps.

[0091] 1) Define the initial correction matrix: Assume that the initial correction value of the cell corresponding to the monitoring position is , and the initial correction amount of the remaining cells is .

[0092] 2) Three-dimensional neighborhood diffusion: For each time step, the deviation at each monitoring position is sequentially weighted according to the neighborhood and diffused to all surrounding cells, enabling the deviation to spread among the cells. The deviation at each monitoring position is the superposition effect of the deviations from different monitoring positions transmitted here (for each cell, the deviations from as many monitoring positions as there are are superimposed), that is: ( is the deviation of the monitoring position deviation diffused to the th cell), and the predicted acceleration after correction (corrected acceleration) is the original predicted acceleration + deviation, that is:

[0093] .

[0094] Step 4.3: Determine the correction ratio based on the predicted acceleration and the corrected acceleration, and use the correction ratio to correct the allocation weight until the deviation is within the preset range, obtaining the final predicted acceleration for each cell.

[0095] Specifically, determining the correction ratio based on the predicted acceleration and the corrected acceleration, and using the correction ratio to correct the allocation weight means: determining the correction ratio according to the quotient of the corrected acceleration and the predicted acceleration; multiplying the correction ratio by the allocation weight to correct the allocation weight.

[0096] Among them, the correction ratio is specifically as shown in formula (6):

[0097] (6)

[0098] Specifically, the above-mentioned correction of the allocation weight is as shown in formula (7):

[0099] (7)

[0100] In the formula, is the corrected allocation weight.

[0101] Repeat steps 4.1 to 4.3 several times (usually 2 - 3 times is sufficient) so that the deviation at the monitoring position is controlled within 5% of the monitored acceleration.

[0102] In the embodiment of the present invention, a new predicted acceleration is recalculated based on the corrected , and then the remaining 20% of the monitored acceleration is compared with the final predicted acceleration. The root mean square error of the obtained result is less than 5%, indicating the reliability of the final predicted acceleration.

[0103] In order to improve the comprehensiveness of vibration prediction, after step 4, the embodiment of the present invention further includes:

[0104] Step 5: Determine the predicted velocity of each cell at moment based on the predicted velocity and predicted acceleration of each cell at moment. Specifically: , , . Wherein , , are the predicted velocities in the X, Y, and Z directions of the -th cell at moment respectively; , , are the predicted velocities in the X, Y, and Z directions of the -th cell at moment respectively; , , are the predicted accelerations in the X, Y, and Z directions of the -th cell at moment respectively; is the time step.

[0105] To further improve the comprehensiveness of vibration prediction, after Step 5, the embodiments of the present invention further include:

[0106] Step 6: Determine the predicted displacement of each cell at moment based on the predicted displacement and predicted velocity of each cell at moment. Specifically: , , . Wherein , , are the predicted displacements in the X, Y, and Z directions of the -th cell at moment respectively; , , are the predicted displacements in the X, Y, and Z directions of the -th cell at moment respectively; , , are the predicted velocities in the X, Y, and Z directions of the -th cell at moment respectively; is the time step.

[0107] To further improve the comprehensiveness of vibration prediction, after Step 6, the embodiments of the present invention further include:

[0108] Step 7. Determine the predicted stress of each cell at moment according to the elastic modulus of each cell, the predicted displacement of each cell at moment, and the predicted displacement of each cell at moment. Specifically, , , . Among them, , , are the predicted stresses in the X, Y, and Z directions of the th cell at moment respectively; , , are the predicted displacements in the X, Y, and Z directions of the th cell at moment respectively; , , are the predicted displacements in the X, Y, and Z directions of the th cell at moment respectively; is the elastic modulus of the th cell, ; , , are the lengths of the th cell along the X, Y, and Z directions respectively.

[0109] By monitoring the acceleration, the present invention dynamically updates the cell state, predicts the data conditions of various indicators (stress, displacement, velocity, acceleration) caused by vibration in future time steps, generates corresponding prediction results, and combines the structural safety standards to set standard values, so as to quickly capture dangerous positions and assist in locating high-risk areas. The present invention can also be further combined with digital graphics processing methods to generate a map for display.

[0110] The present invention proposes a prediction method combining dynamic risk propagation and cellular automata to predict the vibration conditions of a hydropower station powerhouse. Through the method of the present invention, the vibration characteristics of the hydropower station powerhouse can be quickly evaluated, and the anti-vibration risk of the structure can be reduced. Specifically, by quantifying the potential hazards of vibration propagation (such as crack propagation, resonance probability, stress concentration, etc.) to form spatial distribution characteristics, the predicted values of each cell are dynamically updated to reflect the real-time risk propagation path, and the relationship between local interactions and global dynamic evolution in the cells can be quickly displayed. Through hierarchical modeling, the present invention can not only macroscopically reflect the overall vibration mode, but also refine to the microscopic response of local components. Combining visualization tools on platforms such as Matlab, the diffusion process of vibration energy in the powerhouse can be intuitively displayed to assist engineers in quickly locating weak links.

[0111] Through the discretization of space and local evolution rules, the present invention can effectively simulate nonlinear dynamic behaviors such as unit vibration and water flow pulsation, avoiding the computational burden of the traditional finite element method when solving high-dimensional differential equations. The present invention can dynamically update the cell state according to real-time monitoring data to capture the instantaneous changes of vibration. Compared with the limitation of the finite element method that the load needs to be pre-selected, the model autonomously evolves the vibration propagation path through the neighborhood interaction rule, can autonomously change the boundary and load conditions, adapt to the emergencies under different working conditions, and update the vibration situation caused by the power generation process in real time, and efficiently and quickly achieve the vibration response.

[0112] The present invention is a comprehensive vibration analysis method combining a physical mechanism model and real-time monitoring data, which significantly improves the efficiency and global nature of prediction and reduces engineering risks.

[0113] The above are only several embodiments of the present invention, and do not impose any form of limitation on the present invention. Although the present invention is disclosed as above with preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art, without departing from the scope of the technical solution of the present invention, makes some changes or modifications using the technical content disclosed above, which are equivalent to equivalent implementation cases and all fall within the scope of the technical solution.

Claims

1. A vibration prediction method for a hydropower station powerhouse, characterized in that, Including: Step 1: Construct a three-dimensional cellular network of the hydropower station powerhouse, and determine multiple vibration source cells from the cells of the three-dimensional cellular network; Step 2. Determine the predicted acceleration of each vibration source cell at moment according to the vibration force, damping force of each vibration source cell and the elastic force generated by the displacement difference between each vibration source cell and the cells in its neighborhood at moment. Step 3: Determine the predicted acceleration of each cell in the three-dimensional cellular network at moment according to the predicted acceleration of each vibration source cell at moment; Step 4: Determine the cell where the accelerometer for monitoring the hydropower station powerhouse is located, and correct the predicted acceleration of each cell according to the deviation between the monitored acceleration and the predicted acceleration of the cell where the accelerometer is located.

2. The vibration prediction method for a hydropower station powerhouse according to claim 1, wherein Step 2 specifically is: Determine the equivalent stiffness of each vibration source cell along the direction of its neighborhood; Determine the elastic force according to the equivalent stiffness and the displacement difference between each vibration source cell and the cells within its neighborhood; According to the elastic force and the vibration force and damping force of each vibration source cell at moment, determine the predicted acceleration of each vibration source cell at moment.

3. The method for predicting the vibration of a hydropower station powerhouse according to claim 2, wherein, Step 3 specifically is: Determine the distribution weight of the cells within the neighborhood according to the length between each vibration source cell and the cells within its neighborhood; Determine the transitional acceleration of the cells in the neighborhood of the vibration source cell according to the assigned weight and the predicted acceleration of the vibration source cell at the moment; Determine the predicted acceleration of each cell at time according to the multiple transition accelerations corresponding to each cell.

4. The method for predicting the vibration of a hydropower station powerhouse according to claim 3, wherein Step 4 specifically is: Determine the cell where the accelerometer for monitoring the hydropower station powerhouse is located, and determine the deviation between the monitored acceleration and the predicted acceleration of the cell where the accelerometer is located; Determine the correction value of each cell according to the deviation and the distribution weight, and use the correction value to correct the predicted acceleration to obtain the corrected acceleration; Determine the correction ratio according to the predicted acceleration and the corrected acceleration, and use the correction ratio to correct the distribution weight until the deviation is within the preset range to obtain the final predicted acceleration of each cell.

5. The vibration prediction method for a hydropower station powerhouse according to claim 4, characterized in that Determine the correction ratio according to the predicted acceleration and the corrected acceleration, and use the correction ratio to correct the distribution weight, specifically: Determine the correction ratio according to the quotient of the corrected acceleration and the predicted acceleration; Multiply the correction ratio by the distribution weight to correct the distribution weight.

6. The method for predicting the vibration of a hydropower station powerhouse according to any one of claims 1-5, characterized in that, After Step 4, it further includes: Step 5. Determine the predicted velocity of each cell at according to the predicted velocity and predicted acceleration of each cell at moment.

7. The method for predicting the vibration of a hydropower station powerhouse according to claim 6, wherein, After Step 5, it further includes: Step 6. Determine the predicted displacement of each cell at time according to the predicted displacement of each cell at time and the predicted velocity at time.

8. The method for predicting the vibration of a hydropower station powerhouse according to claim 7, wherein, After Step 6, it further includes: Step 7. Determine the predicted stress of each cell at time according to the elastic modulus of each cell, the predicted displacement of each cell at time, and the predicted displacement of each cell at time.

9. The method for predicting the vibration of a hydropower plant powerhouse according to claim 1, characterized in that Step 1 specifically is: Construct a three-dimensional cellular network of the hydropower station powerhouse; Apply the excitation load of the hydropower station powerhouse to the corresponding cells in the three-dimensional cellular network to obtain multiple vibration source cells.

10. The method for predicting the vibration of a hydropower plant powerhouse according to claim 9, characterized in that, The excitation load includes the flow channel water pressure load and the unit operation load.

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