Pump turbine transition process dynamic simulation method and device and electronic equipment

Abstract

The invention provides a dynamic simulation method and device for the transition process of a pump turbine and electronic equipment, and the method comprises the steps: obtaining a full-flow-channel model of the pump turbine, and dividing the model into fluid domain grids; operation characteristic signals of the pump turbine in the transition process are obtained, wherein the operation characteristic signals comprise a pressure pulsation signal, the rotating speed and the guide vane opening degree; carrying out variational mode decomposition on the operation characteristic signal by utilizing a decomposition layer number and a penalty factor which are dynamically set according to a working condition type to obtain a plurality of intrinsic mode IMF components; obtaining a reconstruction signal according to the IMF component after modal screening, and obtaining a dynamic boundary condition according to the reconstruction signal; and simulating the operation characteristic signal based on the dynamic boundary condition by using the full-flow-channel model, thereby solving the problems of modal aliasing and insufficient noise interference in the traditional method, and improving the simulation precision of the transition process.

Description

Technical Field

[0001] The present application relates to the technical field of operation and maintenance of hydropower equipment, and in particular to a method, device and electronic equipment for dynamic simulation of a transient process of a pump turbine. Background Art

[0002] Pump-turbine systems are crucial components of the hydropower industry, and the accuracy of their process calculations is crucial to the operational efficiency, safety, and stability of the equipment. In particular, pump-turbines generate complex, unsteady pressure pulsations during transient processes (such as load rejection and startup and shutdown), which can lead to increased vibration and even structural damage. Therefore, dynamic simulation of pump-turbine flow processes is particularly critical.

[0003] Traditional numerical simulation methods often have inaccurate boundary conditions, which lead to deviations between simulation results and actual operation conditions. In order to improve the computational accuracy of numerical simulation of the transient process of pump-turbine, it is urgent to improve the simulation method. Summary of the Invention

[0004] In view of this, the present application provides a method, device and electronic equipment for dynamic simulation of the transient process of a pump-turbine to improve the simulation accuracy of the transient process.

[0005] To achieve the above objectives, one or more embodiments of this specification provide the following technical solutions:

[0006] According to a first aspect of one or more embodiments of this specification, a method for dynamic simulation of a transient process of a pump-turbine is proposed, comprising:

[0007] Obtaining a full flow channel model of a pump-turbine, wherein the model is divided into a fluid domain grid;

[0008] Acquiring an operating characteristic signal of the pump-turbine during a transition process, wherein the operating characteristic signal includes a pressure pulsation signal, a rotation speed, and a guide vane opening;

[0009] Using the number of decomposition layers and penalty factors dynamically set according to the operating condition type, variational modal decomposition is performed on the operating characteristic signal to obtain multiple intrinsic mode IMF components;

[0010] Obtaining a reconstructed signal according to the IMF components after modal screening, and obtaining a dynamic boundary condition according to the reconstructed signal;

[0011] The operation characteristic signal is simulated using the full flow channel model based on the dynamic boundary conditions.

[0012] In some embodiments, obtaining the operational characteristic signal of the pump-turbine includes:

[0013] The signals output by the pressure sensor, the speed encoder and the guide vane opening sensor installed in the set area are synchronously collected, wherein the set area includes the runner inlet section and the tailwater pipe outlet section.

[0014] In some embodiments, the method further comprises:

[0015] The corresponding decomposition layer values are set according to the technical objectives corresponding to different working conditions, wherein the startup working condition corresponds to the decomposition layer number K1 of the first predetermined value range, the shutdown working condition corresponds to the decomposition layer number K2 of the second predetermined value range, and the load rejection working condition corresponds to the decomposition layer number K3 of the third predetermined value range, and K2 <K3≤K1。

[0016] In some embodiments, the method further comprises:

[0017] According to the numerical range of the signal-to-noise ratio of the pressure pulsation signal under different working conditions, and the numerical relationship between the penalty factor and the pressure pulsation signal, the value of the penalty factor corresponding to the different working conditions is determined.

[0018] The numerical relationship is shown in formula (1):

[0019]

[0020] Among them, α is the penalty factor, SNR is the signal-to-noise ratio of the pressure pulsation signal, c1 is the reference coefficient, and c2 is the sensitivity coefficient.

[0021] In some embodiments, the method further comprises at least one of the following:

[0022] Calculate sample entropy for each IMF component and filter out IMF components whose sample entropy is greater than a first set threshold;

[0023] The correlation between the frequency of each IMF component and the rotational frequency and the blade passing frequency is calculated respectively, and the IMF components whose correlation with the rotational frequency or the blade passing frequency is less than a second set threshold are filtered out.

[0024] In some embodiments, the method further comprises:

[0025] The reconstructed signal is loaded into the pressure inlet / outlet boundary of the computational fluid dynamics (CFD) model in real time through a user-defined programming (UDF) interface.

[0026] In some embodiments, the method further includes: under each operating condition, iteratively adjusting the number of decomposition layers and the penalty factor until a root mean square error between the experimental pressure pulsation and the simulated pressure pulsation is less than a third threshold.

[0027] According to a second aspect of one or more embodiments of this specification, a pump-turbine transient process dynamic simulation device is proposed, comprising:

[0028] A first acquisition unit is used to acquire a full flow channel model of the pump-turbine, wherein the model is divided into a fluid domain grid;

[0029] a second acquiring unit, configured to acquire an operating characteristic signal of the pump-turbine, wherein the operating characteristic signal includes a pressure pulsation signal, a rotational speed, and a guide vane opening;

[0030] a decomposition unit, configured to perform variational mode decomposition on the operating characteristic signal using a number of decomposition layers and a penalty factor dynamically set according to the operating condition type to obtain an IMF component;

[0031] A reconstruction unit, configured to obtain a reconstructed signal based on the IMF after modal screening, and obtain a dynamic boundary condition based on the reconstructed signal;

[0032] A simulation unit is used to simulate the operation characteristic signal based on the dynamic boundary condition using the full flow channel model.

[0033] In some embodiments, the second acquiring unit is configured to:

[0034] The signals output by the pressure sensor, the speed encoder and the guide vane opening sensor installed in the set area are synchronously collected, wherein the set area includes the runner inlet section and the tailwater pipe outlet section.

[0035] In some embodiments, the apparatus further comprises a first setting unit configured to:

[0036] The corresponding decomposition layer values are set according to the technical objectives corresponding to different working conditions, wherein the startup working condition corresponds to the decomposition layer number K1 of the first predetermined value range, the shutdown working condition corresponds to the decomposition layer number K2 of the second predetermined value range, and the load rejection working condition corresponds to the decomposition layer number K3 of the third predetermined value range, and K2 <K3≤K1。

[0037] In some embodiments, the apparatus further comprises a second setting unit, configured to:

[0038] According to the numerical range of the signal-to-noise ratio of the pressure pulsation signal under different working conditions, and the numerical relationship between the penalty factor and the pressure pulsation signal, the value of the penalty factor corresponding to the different working conditions is determined.

[0039] The numerical relationship is shown in formula (1):

[0040]

[0041] Among them, α is the penalty factor, SNR is the signal-to-noise ratio of the pressure pulsation signal, c1 is the reference coefficient, and c2 is the sensitivity coefficient.

[0042] In some embodiments, the device is further used for at least one of the following:

[0043] Calculate sample entropy for each IMF component and filter out IMF components whose sample entropy is greater than a first set threshold;

[0044] The correlation between the frequency of each IMF component and the rotational frequency and the blade passing frequency is calculated respectively, and the IMF components whose correlation with the rotational frequency or the blade passing frequency is less than a second set threshold are filtered out.

[0045] In some embodiments, the device further comprises a loading unit for:

[0046] The reconstructed signal is loaded into the pressure inlet / outlet boundary of the computational fluid dynamics (CFD) model in real time through a user-defined programming (UDF) interface.

[0047] In some embodiments, the apparatus further comprises an iteration unit configured to:

[0048] Under each working condition, the number of decomposition layers and the penalty factor are iteratively adjusted until the root mean square error between the experimental pressure pulsation and the simulated pressure pulsation is less than a third threshold.

[0049] According to a third aspect of one or more embodiments of this specification, an electronic device is provided, including:

[0050] processor;

[0051] a memory for storing processor-executable instructions;

[0052] The processor implements the steps of the method proposed in the above embodiment by running the executable instructions.

[0053] According to a fourth aspect of one or more embodiments of this specification, a computer-readable storage medium is provided, on which computer instructions are stored. When the instructions are executed by a processor, the steps of the method provided in the above embodiment are implemented.

[0054] According to a fifth aspect of one or more embodiments of this specification, a computer program product is proposed, comprising a computer program / instruction, which implements the steps of the method proposed in the above embodiment when executed by a processor.

[0055] The dynamic simulation method for the transient process of a pump-turbine, proposed in the embodiments of this specification, uses a number of decomposition levels and a penalty factor dynamically set according to the operating condition to perform variational modal decomposition on the pump-turbine's operating characteristic signals, obtaining IMF components. A reconstructed signal is then generated based on the modally filtered IMFs, and dynamic boundary conditions are derived from the reconstructed signal. Using a full flow path model of the pump-turbine and based on these dynamic boundary conditions, the operating characteristic signals are simulated, addressing the problems of modal aliasing and insufficient noise interference found in traditional methods and improving the accuracy of transient process simulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a flowchart of a method for dynamic simulation of a transient process of a pump-turbine provided by an exemplary embodiment.

[0057] Figure 2 It is a block diagram of a dynamic simulation device for a transient process of a pump-turbine provided by an exemplary embodiment.

[0058] Figure 3 It is a block diagram of another device for dynamic simulation of transient process of a pump-turbine provided by an exemplary embodiment.

[0059] Figure 4 It is a block diagram of another device for dynamic simulation of transient process of a pump-turbine provided by an exemplary embodiment.

[0060] Figure 5 It is a structural diagram of a device provided by an exemplary embodiment. DETAILED DESCRIPTION

[0061] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The implementations described in the following exemplary embodiments are not intended to represent all implementations consistent with one or more embodiments of this specification. Rather, they are merely examples of apparatuses and methods consistent with certain aspects of one or more embodiments of this specification, as detailed in the appended claims.

[0062] It should be noted that in other embodiments, the steps of the corresponding method are not necessarily performed in the order shown and described in this specification. In some other embodiments, the method may include more or fewer steps than those described in this specification. In addition, a single step described in this specification may be broken down into multiple steps for description in other embodiments, and multiple steps described in this specification may be combined into a single step for description in other embodiments.

[0063] The terms used in this application are for the purpose of describing particular embodiments only and are not intended to limit this application. As used in this application and the appended claims, the singular forms "a," "an," "the," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.

[0064] In order to enable those skilled in the art to better understand the technical solutions provided by the embodiments of the present application, and to make the above-mentioned purposes, features and advantages of the embodiments of the present application more obvious and easy to understand, the technical solutions in the embodiments of the present application are further described in detail below with reference to the accompanying drawings.

[0065] First, some technical abbreviations involved in this application are explained:

[0066] Variational Mode Decomposition (VMD) is a signal decomposition and estimation method used to extract modal components with strong physical significance and isolated center frequencies from complex signals. This method iteratively searches for the optimal solution of a variational model to determine the frequency center and bandwidth of each component, thus adaptively partitioning the signal in the frequency domain and effectively separating its components.

[0067] CFD simulation: Computational Fluid Dynamics (CFD) simulation is equivalent to conducting "virtual" experiments on a computer to simulate actual fluid flow conditions. Its basic principle is to numerically solve the differential equations governing fluid flow, deriving the discrete distribution of the fluid flow field over a continuous region, thereby approximately simulating fluid flow conditions.

[0068] See also Figure 1 , is a flow chart of a method for dynamic simulation of a pump-turbine transient process provided by an embodiment of the present application. The simulation method may include the following steps:

[0069] S101. Obtain a full flow channel model of a pump-turbine, wherein the model is divided into a fluid domain grid.

[0070] A pump-turbine system includes components such as a volute, balancing duct, fixed guide vanes, movable guide vanes, runner, and draft tube. A full flow path model of a pump-turbine system can be obtained from public databases or created using 3D scanning or field measurement data.

[0071] By removing solid parts (such as the runner hub) from the geometry, a continuous fluid domain without internal voids in the model was obtained and meshed.

[0072] In some embodiments, the fluid domain may be divided into multiple subdomains, such as a volute, a guide vane area, a runner area, a draft tube, etc., and these subdomains are connected through interfaces after being meshed.

[0073] S102: Acquire an operating characteristic signal of the pump-turbine during a flow process, wherein the operating characteristic signal includes a pressure pulsation signal, a rotation speed, and a guide vane opening.

[0074] The above-mentioned operating characteristic signals can be obtained based on experimental test data during the transient process of the pump-turbine, such as the pressure pulsation time domain curves at the inlet and outlet of the pump-turbine unit, the speed curve of the unit's runner, the opening curve of the unit's movable guide vanes, etc.

[0075] In some embodiments, a pressure sensor can be installed in a designated area, a speed encoder can be installed at the end of the runner shaft, and a guide vane opening sensor can be installed at the end of the guide vane shaft. The signals output by the pressure sensor, the speed encoder, and the guide vane opening sensor can be collected simultaneously. The designated area can be, for example, the runner inlet section and the draft tube outlet section. Specifically, a pressure sensor can be installed at measuring point P1 in the runner inlet section, and a pressure sensor can be installed at measuring point P2 in the draft tube outlet section. These pressure sensors can be high-frequency pressure sensors with a sampling rate greater than 1 kHz.

[0076] In one example, multi-channel signal synchronous acquisition can be implemented through the LabVIEW platform to ensure that the time domain data timestamp alignment error meets the requirements, for example, the alignment error is less than 0.1ms, thereby ensuring data consistency and accuracy.

[0077] S103 , using the number of decomposition layers and penalty factors dynamically set according to the operating condition type, performing variational mode decomposition on the operating characteristic signal to obtain a plurality of intrinsic mode IMF components.

[0078] The variational mode decomposition is performed on the operating characteristic signal. The goal is to decompose the operating characteristic signal into K (number of decomposition layers) bandwidth-limited IMF components u k (t), and at the same time minimize the sum of the estimated bandwidths of each component. Finally, we get K IMF components {u1(t),u2(t),...,u K (t)} and its corresponding center frequencies {ω1,ω2,...,ω K}.

[0079] In the embodiment of the present disclosure, the corresponding decomposition layer values are set according to the technical objectives corresponding to different working conditions, wherein the startup working condition corresponds to the decomposition layer number K1 of the first predetermined value range, the shutdown working condition corresponds to the decomposition layer number K2 of the second predetermined value range, and the load rejection working condition corresponds to the decomposition layer number K3 of the third predetermined value range, and K2 <K3≤K1。

[0080] Under the startup condition, the technical goal is to cover the first frequency band and meet the multi-frequency feature extraction of the multi-frequency coupled transient process. Based on this technical goal, the startup condition can be mapped to the decomposition level K1 of the first predetermined numerical range. For example, when the first frequency band is 0-800 Hz, the first predetermined numerical range can be 5-8, and K1 can be 6, for example;

[0081] For shutdown conditions, since low-frequency components account for over 80%, high-frequency noise modes will be introduced when the same decomposition level K as for startup conditions is used. Therefore, under shutdown conditions, the technical goal is to cover the second frequency band and suppress high-frequency noise interference. Based on this technical goal, the startup condition can be assigned a decomposition level K2 within a second predetermined numerical range. For example, when the second frequency band is 0-200Hz, the second predetermined numerical range can be 5-8, and K2 can be 4, for example. By setting a lower decomposition level K for the shutdown condition, the problem of redundant components in the shutdown condition can be avoided.

[0082] Under load shedding conditions, the technical goal is to cover the third frequency band and balance the resolution of key frequency bands within this range. Based on this technical goal, the load shedding condition can be assigned a decomposition level K3 within a third predetermined numerical range. For example, if the third frequency band is 0-500 Hz, the third predetermined numerical range can be 4-6, and K3 can be 5. By properly setting the decomposition level K under load shedding conditions, the loss of key frequency band characteristics can be avoided.

[0083] The first frequency band, the second frequency band, and the third frequency band generally have the following numerical relationship: the second frequency band < the third frequency band < the first frequency band.

[0084] The size of the penalty factor α directly affects the bandwidth of each IMF component. When the bandwidth is too large, some IMF components may contain signals from other modal components, resulting in modal aliasing. In the disclosed embodiments, the corresponding penalty factor values for different operating conditions can be determined based on the numerical range of the pressure pulsation signal-to-noise ratio under different operating conditions and the numerical relationship between the penalty factor α and the pressure pulsation signal.

[0085] The numerical relationship is shown in formula (1):

[0086]

[0087] In formula (1), α is the penalty factor, SNR is the signal-to-noise ratio of the pressure pulsation signal, c1 is the reference coefficient, and c2 is the sensitivity coefficient. In one example, c1 = 1000 and c2 = 20.

[0088] Under the shutdown condition, the general value range of SNR is SNR < 15 dB. According to formula (1), the value range of α is: 3000 < α < 10000. α can be set to 3200, for example.

[0089] Under the load rejection condition, SNR is higher than that under the shutdown condition. Generally, the value range is: 20 dB < SNR < 25 dB. According to formula (1), the value range of α is 800 < α < 2000. α can be set to 1800, for example.

[0090] Under the startup condition, the general value range of SNR is 15 dB < SNR < 20 dB. The value range of α is 1500 < α < 3000. α can be set to 2550, for example.

[0091] By setting the penalty factor α according to different working condition types, modal aliasing can be reduced, thereby improving the phase distortion of the pressure pulsation when loading the boundary conditions.

[0092] S104. Obtain a reconstructed signal based on the IMF components after modal screening, and obtain dynamic boundary conditions based on the reconstructed signal.

[0093] Perform modal screening on the IMF components obtained in step S103 to obtain effective IMF components that meet the set requirements, and reconstruct based on these effective IMF components to obtain a reconstructed signal for loading boundary conditions.

[0094] In some embodiments, effective IMF components can be screened by entropy value analysis.

[0095] Specifically, sample entropy calculation can be performed for each IMF component, and the IMF components with sample entropy greater than the first set threshold b1 can be filtered out.

[0096] Since sample entropy is a mathematical measure of the uncertainty of the IMF component time series, the higher the entropy value, the stronger the randomness of the sequence. By filtering out the IMF components with sample entropy greater than the first set threshold, ineffective modes with noise interference or high chaos can be removed, and effective IMF components can be retained.

[0097] In one example, the first set threshold b1 can be set to 0.8, that is, the IMF components with sample entropy values less than or equal to 0.8 are retained as effective IMF components.

[0098] In some embodiments, effective IMF components may also be screened by frequency band matching.

[0099] Specifically, the frequency and rotation frequency f of each IMF component are calculated separately. r and blade passing frequency f b and filters out the IMF components whose rotational frequency correlation or blade passing frequency correlation is less than the second set threshold b2.

[0100] For the frequency f r The correlation of , the screening conditions are shown in formula (2):

[0101]

[0102] For the frequency f b The correlation of , the screening conditions are shown in formula (3):

[0103]

[0104] Among them, f IMF is the frequency of the IMF component, f r is the frequency conversion, f b is the blade passing frequency, b2 is a second set threshold, for example, it can be 15%.

[0105] By filtering out the IMF components that have a low correlation with the rotation frequency or the blade passing frequency, noise or irrelevant harmonics can be filtered out and the reconstructed signal can be guaranteed to conform to the real dynamics of the machine.

[0106] S105 , simulating the operation characteristic signal using the full flow channel model based on the dynamic boundary conditions.

[0107] Based on the full flow channel model established in step S101 and the dynamic boundary conditions obtained in step S104, various operating characteristic signals (including pressure pulsation signals, speed, and guide vane opening) under startup conditions, shutdown conditions, and load rejection conditions are simulated to obtain the transient flow field parameters of the pump-turbine during the transition process.

[0108] The dynamic simulation method for the transient process of a pump-turbine, proposed in the embodiments of this specification, uses a number of decomposition levels and a penalty factor dynamically set according to the operating condition to perform variational modal decomposition on the pump-turbine's operating characteristic signal, obtaining IMF components. A reconstructed signal is then obtained based on the modally filtered IMFs, and dynamic boundary conditions are derived from the reconstructed signal. By simulating the operating characteristic signal using a full flow channel model of the pump-turbine and based on these dynamic boundary conditions, the method overcomes the problems of modal aliasing, noise interference, and insufficient temporal resolution found in traditional methods, thereby improving the accuracy of transient process simulation.

[0109] In some embodiments, the loading of boundary conditions can be achieved by loading the reconstructed signal to the pressure inlet / outlet boundary of the CFD model in real time via a UDF interface.

[0110] Specifically, the UDF interface of the CFD solver can be used to read the latest data in the interface at each time step, and update the boundary conditions to achieve dynamic loading of boundary conditions.

[0111] In some embodiments, the decomposition level number K and the penalty factor may be adjusted iteratively until a root mean square error between the experimental pressure pulsation and the simulated pressure pulsation is less than a third threshold.

[0112] In one example, the particle swarm algorithm can be used to iteratively adjust K and α, and the convergence condition is shown in formula (4):

[0113]

[0114] Wherein, E is the root mean square error between the experimental pressure pulsation and the simulated pressure pulsation, n is the number of iterations, and b3 is the third threshold. In one example, b3 may be 0.5%, for example.

[0115] In this way, the number of decomposition layers and the value of penalty factors under different working conditions can be further optimized, further alleviating the problems of modal aliasing and noise interference in boundary components.

[0116] In some embodiments, the CFD model may be adjusted accordingly based on the error analysis results, including modifying the grid size, selecting more suitable initial conditions, etc.

[0117] After the simulation is complete, the results can be post-processed and analyzed, including flow field visualization and analysis of changes in parameters such as pressure and velocity. The accuracy of the simulation can also be verified by comparing the simulation results with experimental data.

[0118] In some embodiments, the time step can be set according to the frequency variation characteristics of the pressure pulsation data.

[0119] The time step must satisfy Δt ≤ 1 / (2f_max), where f_max is the highest significant frequency component in the pressure pulsation signal. For example, if the pressure pulsation frequency is 100 Hz, the time step must be ≤ 5 ms to ensure signal integrity. This solves the problem of using a fixed time step, which makes it difficult to capture high-frequency pulsation components in load shedding conditions, and improves simulation accuracy.

[0120] Figure 2 FIG. 1 is a block diagram of a search enhancement generation device provided by an exemplary embodiment. Figure 2 As shown, the device includes:

[0121] A first acquisition unit 201 is used to acquire a full flow channel model of a pump-turbine, wherein the model is divided into a fluid domain grid;

[0122] A second acquiring unit 202 is configured to acquire an operating characteristic signal of the pump-turbine, wherein the operating characteristic signal includes a pressure pulsation signal, a rotation speed, and a guide vane opening;

[0123] A decomposition unit 203 is configured to perform variational mode decomposition on the operating characteristic signal using a number of decomposition layers and a penalty factor dynamically set according to the operating condition type to obtain an IMF component;

[0124] The reconstruction unit 204 is configured to obtain a reconstructed signal based on the IMF after modal screening, and obtain a dynamic boundary condition based on the reconstructed signal;

[0125] The simulation unit 205 is configured to simulate the operation characteristic signal using the full flow channel model and based on the dynamic boundary conditions.

[0126] In some embodiments, the second acquiring unit is configured to:

[0127] The signals output by the pressure sensor, the speed encoder and the guide vane opening sensor installed in the set area are synchronously collected, wherein the set area includes the runner inlet section and the tailwater pipe outlet section.

[0128] like Figure 3 As shown, in some embodiments, the apparatus further includes a first setting unit 2031, configured to:

[0129] The corresponding decomposition layer values are set according to the technical objectives corresponding to different working conditions, wherein the startup working condition corresponds to the decomposition layer number K1 of the first predetermined value range, the shutdown working condition corresponds to the decomposition layer number K2 of the second predetermined value range, and the load rejection working condition corresponds to the decomposition layer number K3 of the third predetermined value range, and K2 <K3≤K1。

[0130] Still Figure 3 As shown, in some embodiments, the apparatus further includes a second setting unit 2032, configured to:

[0131] According to the numerical range of the signal-to-noise ratio of the pressure pulsation signal under different working conditions, and the numerical relationship between the penalty factor and the pressure pulsation signal, the value of the penalty factor corresponding to the different working conditions is determined.

[0132] The numerical relationship is shown in formula (1):

[0133]

[0134] Among them, α is the penalty factor, SNR is the signal-to-noise ratio of the pressure pulsation signal, c1 is the reference coefficient, and c2 is the sensitivity coefficient.

[0135] In some embodiments, the device is further used for at least one of the following:

[0136] Calculate sample entropy for each IMF component and filter out IMF components whose sample entropy is greater than a first set threshold;

[0137] The correlation between the frequency of each IMF component and the rotational frequency and the blade passing frequency is calculated respectively, and the IMF components whose correlation with the rotational frequency or the blade passing frequency is less than a second set threshold are filtered out.

[0138] In some embodiments, the device further comprises a loading unit for:

[0139] The reconstructed signal is loaded into the pressure inlet / outlet boundary of the computational fluid dynamics (CFD) model in real time through a user-defined programming (UDF) interface.

[0140] like Figure 4 As shown, in some embodiments, the apparatus further includes an iteration unit 206, configured to:

[0141] Under each working condition, the number of decomposition layers and the penalty factor are iteratively adjusted until the root mean square error between the experimental pressure pulsation and the simulated pressure pulsation is less than a third threshold.

[0142] Figure 5 This is a schematic structural diagram of a device provided by an exemplary embodiment. Figure 5 At the hardware level, the device includes a processor 502, an internal bus 504, a network interface 506, a memory 508, and a non-volatile memory 510. Of course, it may also include hardware required for other services. One or more embodiments of this specification can be implemented based on software, such as the processor 502 reading the corresponding computer program from the non-volatile memory 510 into the memory 508 and then running it. Of course, in addition to software implementation, one or more embodiments of this specification do not exclude other implementation methods, such as logic devices or a combination of software and hardware, etc., that is, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0143] The systems, devices, modules, or units described in the above embodiments may be implemented by computer chips or entities, or by products having certain functions. A typical implementation device is a computer, which may be in the form of a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email transceiver, game console, tablet computer, wearable device, or any combination of these devices.

[0144] In a typical configuration, a computer includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0145] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0146] Computer-readable media include permanent and non-permanent, removable and non-removable media that can be used to store information using any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, disk storage, quantum memory, graphene-based storage media or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory media such as modulated data signals and carrier waves.

[0147] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0148] The foregoing description of this specification describes specific embodiments. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or the sequential order to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0149] The terms used in one or more embodiments of this specification are for the purpose of describing specific embodiments only and are not intended to limit one or more embodiments of this specification. The singular forms "a," "an," "the," and "the" used in one or more embodiments of this specification and the appended claims are also intended to include plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0150] It should be understood that although the terms first, second, third, etc. may be used to describe various information in one or more embodiments of this specification, such information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of one or more embodiments of this specification, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when..." or "when..." or "in response to determining."

[0151] The above description is merely a preferred embodiment of one or more embodiments of this specification and is not intended to limit one or more embodiments of this specification. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of one or more embodiments of this specification shall be included in the scope of protection of one or more embodiments of this specification.

Claims

1. A method for dynamic simulation of transient process of a pump-turbine, characterized in that: include: Obtaining a full flow channel model of a pump-turbine, wherein the model is divided into a fluid domain grid; Acquiring an operating characteristic signal of the pump-turbine during a transition process, wherein the operating characteristic signal includes a pressure pulsation signal, a rotation speed, and a guide vane opening; Using the number of decomposition layers and penalty factors dynamically set according to the operating condition type, variational modal decomposition is performed on the operating characteristic signal to obtain multiple intrinsic mode IMF components; Obtaining a reconstructed signal according to the IMF components after modal screening, and obtaining a dynamic boundary condition according to the reconstructed signal; The operation characteristic signal is simulated using the full flow channel model based on the dynamic boundary conditions.

2. The method according to claim 1, characterized in that The obtaining of the operational characteristic signal of the pump-turbine comprises: The signals output by the pressure sensor, the speed encoder and the guide vane opening sensor installed in the set area are synchronously collected, wherein the set area includes the runner inlet section and the tailwater pipe outlet section.

3. The method according to claim 1, characterized in that The method further comprises: The corresponding decomposition layer values are set according to the technical objectives corresponding to different working conditions, wherein the startup working condition corresponds to the decomposition layer number K1 of the first predetermined value range, the shutdown working condition corresponds to the decomposition layer number K2 of the second predetermined value range, and the load rejection working condition corresponds to the decomposition layer number K3 of the third predetermined value range, and K2 <K3≤K1。 4. The method according to claim 1, wherein The method further comprises: According to the numerical range of the signal-to-noise ratio of the pressure pulsation signal under different working conditions, and the numerical relationship between the penalty factor and the pressure pulsation signal, the value of the penalty factor corresponding to the different working conditions is determined. The numerical relationship is shown in formula (1): Among them, α is the penalty factor, SNR is the signal-to-noise ratio of the pressure pulsation signal, c1 is the reference coefficient, and c2 is the sensitivity coefficient.

5. The method according to claim 1, wherein The method further comprises at least one of the following: Calculate sample entropy for each IMF component and filter out IMF components whose sample entropy is greater than a first set threshold; The correlation between the frequency of each IMF component and the rotational frequency and the blade passing frequency is calculated respectively, and the IMF components whose correlation with the rotational frequency or the blade passing frequency is less than a second set threshold are filtered out.

6. The method according to claim 1, characterized in that The method further comprises: The reconstructed signal is loaded into the pressure inlet / outlet boundary of the computational fluid dynamics (CFD) model in real time through a user-defined programming (UDF) interface.

7. The method according to claim 1, characterized in that The method further comprises: Under each working condition, the number of decomposition layers and the penalty factor are iteratively adjusted until the root mean square error between the experimental pressure pulsation and the simulated pressure pulsation is less than a third threshold.

8. A dynamic simulation device for transient process of a pump-turbine, characterized in that: include: A first acquisition unit is used to acquire a full flow channel model of the pump-turbine, wherein the model is divided into a fluid domain grid; a second acquiring unit, configured to acquire an operating characteristic signal of the pump-turbine, wherein the operating characteristic signal includes a pressure pulsation signal, a rotational speed, and a guide vane opening; a decomposition unit, configured to perform variational mode decomposition on the operating characteristic signal using a number of decomposition layers and a penalty factor dynamically set according to the operating condition type to obtain an IMF component; A reconstruction unit, configured to obtain a reconstructed signal based on the IMF after modal screening, and obtain a dynamic boundary condition based on the reconstructed signal; A simulation unit is used to simulate the operation characteristic signal based on the dynamic boundary condition using the full flow channel model.

9. An electronic device, characterized in that: include: A processor and a memory, wherein the memory stores machine-executable instructions that can be executed by the processor, and the processor is configured to execute the machine-executable instructions to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: The computer program implements the method according to any one of claims 1 to 7 when executed by a processor.

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