Water turbine transition process runner cavitation early warning emergency treatment method and system

Abstract

The invention discloses a water turbine transition process runner cavitation early warning emergency processing method and system, and belongs to the technical field of hydraulic machinery safe operation, and the method comprises the steps: building a full-working-condition high-fidelity transient cavitation CFD simulation model library covering a transition process according to a target pump turbine; collecting unit operation data at high frequency through a sensor network, and performing synchronous mapping on a real-time data sequence and an input boundary condition of the CFD model library; based on the current and predicted moving tracks, calling a matched CFD prediction result, calculating real-time and future cavitation risk indexes, and performing early warning judgment according to a preset multi-level threshold value; and according to the early warning signal type and the risk characteristics, automatically matching an emergency strategy library, generating an optimization control instruction sequence, and issuing the optimization control instruction sequence to a unit control system to execute closed-loop control. The operation safety and stability of the pump turbine in the transition process are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of hydraulic machinery safety operation technology, specifically to a system and method for real-time early warning and proactive emergency handling of cavitation phenomena in the flow channel of a water pump turbine (especially a reversible unit) during start-up, shutdown, and operating condition transitions. It is particularly suitable for scenarios such as pumped storage power stations that require frequent operating condition transitions. Background Technology

[0002] As the core equipment of pumped storage power stations, pump-turbines frequently undergo complex transition processes such as switching between pump and turbine operating conditions and rapid load changes. During these transient processes, the water flow state within the flow channel deteriorates drastically, making cavitation highly likely. Cavitation not only leads to decreased unit efficiency, vibration, and noise, but more seriously, it can cause cavitation erosion damage to the flow channel walls (such as turbine blades and guide vanes), threatening the safe, stable operation and lifespan of the unit.

[0003] Currently, cavitation monitoring mainly relies on fixed empirical threshold alarms (such as excessive vibration and noise) and periodic maintenance, which has a significant lag. Although CFD (Computational Fluid Dynamics) numerical simulation can predict the cavitation characteristics of the flow field in detail, it is usually used for offline design and analysis and cannot be linked with the real-time operating status of the unit. Therefore, existing technologies lack an active protection system that can predict cavitation risks in advance during transient processes and automatically trigger emergency responses, resulting in the unit often operating in a passive mode of "cavitation occurrence - passive alarm - manual intervention," with limited protective effectiveness.

[0004] With the development of digital twin and real-time simulation technologies, it has become possible to deeply integrate high-precision CFD models with real-time unit operation data to build online predictive and active control systems. Summary of the Invention

[0005] The purpose of this invention is to overcome the lag and passivity of existing technologies. This invention provides an early warning and emergency handling method for cavitation in the flow channel during the transient process of a water turbine. By constructing a high-fidelity CFD simulation model library and deeply integrating it with real-time operating data, it can achieve online and advanced prediction of cavitation risks and automatically execute optimized control strategies, thereby transforming passive alarms into active protection.

[0006] Technical solution: To achieve the above objectives, the technical solution adopted by this invention is as follows: A method for early warning and emergency response to cavitation in the flow channel during the transient process of a water turbine includes the following steps: Step S1: Establish a high-fidelity transient cavitation CFD simulation model library covering the entire operating condition of the target pump turbine, and calibrate the model parameters using experimental or historical data. Step S2: Collect unit operation data at high frequency through sensor network, and synchronously map the real-time data sequence with the input boundary conditions of CFD model library; Step S3: Based on the current and predicted operating trajectories, retrieve the matching CFD prediction results and calculate the real-time and future cavitation risk indicators. And make early warning judgments based on preset multi-level thresholds; Step S4: Based on the type of early warning signal and risk characteristics, automatically match the emergency strategy library and generate an optimized control command sequence, which is then sent to the unit control system to execute closed-loop control.

[0007] Preferably, in step S3, the cavitation risk index The formula is:

[0008] in, To predict the minimum local pressure within the flow channel, Current water temperature Lower vaporization pressure, The time delay constant is The rate of change of air bubble volume fraction. The local vorticity intensity of the flow field. For reference vorticity value, When the volume fraction of air bubbles exceeds the critical threshold The volume of the region, The total volume of the flow channel. , , , These are weighting coefficients assigned based on the degree of influence.

[0009] Preferably, in step S3, the method for making a warning judgment based on preset multi-level thresholds includes: preset three-level warning thresholds for attention, warning, and danger; when predicting a certain future moment... satisfy This generates a primary early warning signal. When predicting a certain future moment... satisfy When a warning trigger signal is generated, it is used to predict a future time. satisfy At that time, an advanced emergency trigger signal is generated, in which, Indicates a certain moment Cavitation risk indicators Indicates the attention threshold. Indicates the warning threshold. This indicates the danger threshold.

[0010] Preferably, the CFD simulation model library mentioned in step S1 adopts... SST k-ω Turbulence models and The cavitation model performs large-scale pre-calculations for all typical transient processes, generating time-series data including pressure field, velocity field, and cavitation volume fraction field, which are then stored and indexed in a database.

[0011] Preferred method: Use experimental or historical data to calibrate the model parameters of the CFD simulation model library.

[0012] Preferably, in step S2, the real-time data collected includes the working head H, the guide vane opening α, and its rate of change. The unit's rotational speed n, active power P, pressure in the volute / top cover / tailrace pipe, and vibration acceleration of key components. .

[0013] Preferably, in step S4, the emergency strategy library contains optimized control strategies for different cavitation types and risk levels, including fine-tuning the guide vane closing / opening rate, slightly adjusting the target load setpoint, and linking the activation of the central shaft or tailrace pipe air supply system; after the control command is issued, the unit's vibration, pressure pulsation, and other responses are monitored in real time, and compared with the expected improvement effect for verification.

[0014] Preferred methods also include recording prediction, control, and feedback data throughout the entire process, comparing the correlation between CFD predictions and actual responses using machine learning algorithms, and iteratively correcting model parameters or optimizing emergency strategies.

[0015] Preferred approach: Employing reinforcement learning algorithms to adaptively adjust empirical coefficients in the CFD model or optimize specific parameters of the emergency strategy based on the difference between the actual and expected changes in key parameters after control execution.

[0016] Another objective of this invention is to provide a cavitation early warning and emergency response system for the flow channel during the transient process of a water turbine, comprising a multi-condition CFD simulation model library module, a real-time data acquisition and synchronization module, an online cavitation risk assessment and early warning module, and an intelligent emergency response control module, wherein: The multi-condition CFD simulation model library module is used to store and run a full-condition high-fidelity transient cavitation CFD simulation model library based on the target pump turbine, covering start-up, shutdown, condition transition and load change. The real-time data acquisition and synchronization module is used to acquire unit operation data at high frequency through a sensor network and to synchronously map the real-time data sequence with the input boundary conditions of the CFD model library. The online cavitation risk assessment and early warning module is used to retrieve matching CFD prediction results based on the current and predicted operating trajectories, and calculate real-time and future cavitation risk indicators. And make early warning judgments based on preset multi-level thresholds; The intelligent emergency response control module is used to automatically match the emergency strategy library and generate an optimized control command sequence based on the type of early warning signal and risk characteristics, and then send it to the unit control system to execute closed-loop control.

[0017] Compared with the prior art, the present invention has the following advantages: 1. Early warning: Utilizing the powerful predictive capabilities of CFD simulation for flow details, risks can be identified before cavitation actually occurs or causes serious physical effects (such as a surge in vibration), providing valuable reaction time.

[0018] 2. Precise positioning: It can not only determine whether cavitation has occurred, but also locate the specific flow channel areas where cavitation begins and develops (such as the upper crown of the runner, the lower ring, the back of the blade, etc.) through CFD cloud maps, providing targeted basis for treatment strategies.

[0019] 3. Active protection: The system automatically executes optimized control strategies verified by CFD, actively intervenes in the transition process trajectory, suppresses the development of cavitation from the source, and eliminates possible accidents in the bud.

[0020] 4. Adaptive optimization: Through an online learning mechanism, the CFD model and emergency strategies continuously align with the actual characteristics of the generating units, improving the system's predictive accuracy and control effectiveness, and becoming increasingly intelligent with use.

[0021] 5. Extend equipment life: Significantly reduce cavitation damage during the transition process, lower maintenance costs, and improve the availability and service life of pumps and turbines. Attached Figure Description

[0022] Figure 1 This is a flowchart of an emergency response method for early warning of cavitation in the flow channel during the transient process of a water turbine, according to the present invention. Figure 2 This is a pressure cloud map predicted by CFD in an embodiment of the present invention.

[0023] Figure 3 This is a schematic diagram of the flow field predicted by CFD in an embodiment of the present invention; Figure 4 A detailed logic diagram of the cavitation risk online assessment and early warning module provided in this embodiment of the invention. Detailed Implementation

[0024] The present invention will be further illustrated below with reference to the accompanying drawings and specific embodiments. It should be understood that these examples are for illustrative purposes only and are not intended to limit the scope of the invention. After reading this invention, any modifications of the invention in various equivalent forms by those skilled in the art will fall within the scope defined by the appended claims.

[0025] Example 1 This embodiment provides an emergency response method for early warning of cavitation in the flow channel during the transient process of a water turbine, such as... Figure 1 As shown, it includes the following steps: Step S1, Construction and Calibration of a Full-Condition CFD Digital Twin Model Library: Based on the target pump-turbine, a high-fidelity transient cavitation CFD simulation model library covering typical transient processes (start-up, shutdown, power generation / pumping conversion, load changes) is established. Key parameters of the model (such as turbulence model coefficients and cavitation model coefficients) are calibrated using model test data or historical operating data to ensure predictive accuracy. The model library is stored in a database format, with key outputs including pressure distribution cloud maps, cavitation volume fraction cloud maps, and spatiotemporal evolution data of the critical cavitation coefficient under each operating condition. The CFD simulation model library adopts... SST k-ω Turbulence models and The cavitation model performs large-scale pre-calculations for all typical transient processes, generating time-series data including pressure field, velocity field, and cavitation volume fraction field, which are then stored and indexed in a database.

[0026] Step S2, Real-time Data Acquisition and Operating Condition Synchronization Matching: High-frequency acquisition of unit operating data is achieved through a sensor network, and the real-time data sequence is synchronously mapped with the input boundary conditions of the CFD model library. The acquired real-time data includes the operating head H, guide vane opening α, and its rate of change. The unit's rotational speed n, active power P, pressure in the volute / top cover / tailrace pipe, and vibration acceleration of key components. Through sensor networks at high frequencies (≥100) Hz The system collects real-time unit operating data, including operating head H, guide vane opening α, unit speed n, active power P, pressure in the volute / top cover / tailrace pipe, and vibration acceleration a and noise signals in key components. The real-time data sequence (H(t), α(t), n(t), ...) is synchronously mapped to the input boundary conditions of the CFD model library.

[0027] Step S3, Online Assessment and Early Warning of Cavitation Risk: Based on the current and predicted operating trajectory, retrieve the matching CFD prediction results and calculate real-time and future cavitation risk indicators. And make early warning judgments based on preset multi-level thresholds; Step S31, Operating condition matching and trajectory prediction: Based on the current data points (H(t0), α(t0), n(t0)) and the changing trends (such as guide vane closing rate) In the CFD model library, interpolation or nearest neighbor algorithms are used to quickly match the current running point and predict the future time period. The transition process trajectory within 5-30 seconds.

[0028] Step S32, Risk Indicator Calculation: Retrieve the CFD prediction results corresponding to the matching working condition, and calculate the real-time and future time points. t cavitation risk indicators This indicator can be considered as the pressure safety factor. That is, the lowest local pressure in the flow channel Vaporization pressure at current water temperature ratio Or, the volume fraction of air bubbles. Exceeding the critical threshold Regional volume percentage .

[0029] The cavitation risk indicators The formula is:

[0030] in, To predict the minimum local pressure within the flow channel, Current water temperature Lower vaporization pressure, The time delay constant is The rate of change of air bubble volume fraction. The local vorticity intensity of the flow field. For reference vorticity value, When the volume fraction of air bubbles exceeds the critical threshold The volume of the region, The total volume of the flow channel. , , , These are weighting coefficients assigned based on the degree of influence.

[0031] Step S33, Multi-level Early Warning Judgment: Preset three-level early warning thresholds: Attention, Warning, and Danger. When predicting a certain future moment... satisfy This generates a primary early warning signal. When predicting a certain future moment... satisfy When a warning trigger signal is generated, it is used to predict a future time. satisfy At that time, an advanced emergency trigger signal is generated, in which, Indicates a certain moment Cavitation risk indicators Indicates the attention threshold. Indicates the warning threshold. This indicates the danger threshold.

[0032] Step S4, Intelligent Emergency Handling and Control Execution: Based on the type of early warning signal and risk characteristics, the system automatically matches the emergency strategy library and generates an optimized control command sequence, which is then sent to the unit control system for closed-loop control. The emergency strategy library contains optimized control strategies for different cavitation types and risk levels, including fine-tuning the guide vane closing / opening rate, slightly adjusting the target load setpoint, and activating the central shaft or tailrace gas injection system. After the control commands are issued, the system monitors unit vibration, pressure pulsation, and other responses in real time, and compares and verifies these responses with the expected improvement effects.

[0033] Step S41, Strategy Matching and Generation: A built-in emergency strategy library is provided for different cavitation types (blade inlet cavitation, flow separation cavitation, and tailrace vortex cavitation) and risk levels. Based on the warning signal type and risk characteristics (such as the cavitation occurrence area), optimized control sequences are automatically matched and generated. Typical strategies include: fine-tuning the guide vane motion (e.g., changing the closing / opening rate), slightly adjusting the target load setpoint, and activating the supplementary air system (central shaft supplementary air, tailrace supplementary air).

[0034] Step S42, Closed-loop control execution: Execute the generated control commands (such as a new guide vane opening setting sequence). The data is sent to the unit's speed control system, excitation system, and auxiliary equipment control system in milliseconds. After execution, the unit's response (such as vibration amplitude) is monitored in real time. Tailwater pipe pressure pulsation And compare the improvement effect with that predicted by CFD.

[0035] Emergency response strategy execution is state feedback driven, comparing changes in key parameters actually monitored after control execution. (e.g., vibration decrease rate) and expected change Difference value .when Δ Exceeding the preset tolerance threshold ε When this occurs, a mechanism for evaluating the effectiveness of the strategy and switching to a backup strategy is triggered.

[0036] Step S5, Learning Optimization and Model Update: Record the prediction, control, and feedback data throughout the process. Use machine learning algorithms to compare the correlation between CFD predictions and actual responses, and iteratively correct model parameters or optimize emergency strategies. Employ reinforcement learning algorithms to adaptively adjust empirical coefficients (such as turbulence model constants) in the CFD model or optimize specific parameters of the emergency strategy based on the difference between the actual monitored changes in key parameters after control execution and the expected changes.

[0037] The system records data packets for each "prediction-early warning-control-feedback" process. Through machine learning algorithms (such as reinforcement learning), it compares the correlation between the pressure / cavitation distribution changes predicted by CFD and the actual vibration / noise changes monitored, iteratively corrects the empirical coefficients (such as turbulence model constants) in the CFD model or optimizes the parameters of the emergency strategy, forming a self-learning and self-optimizing closed-loop system.

[0038] Example 2 This embodiment provides an emergency response method for early warning of cavitation in the flow channel during the transient process of a water turbine. For example... Figure 1-3 As shown, it includes the following steps: Step S1: Construction and calibration of the full-condition CFD digital twin model library. For a 300MW reversible pump-turbine in a pumped storage power station, a three-dimensional unsteady cavitation flow numerical model of the entire flow path is established using commercial CFD software. The model adopts... SST k-ω Turbulence model combined Cavitation model. The model was calibrated using the "head-opening-efficiency" characteristic curves and cavitation initiation curves obtained from model tests, ensuring that the prediction errors for efficiency and cavitation characteristics were within 3%. Subsequently, all possible transient processes of the unit (including pump start-up, turbine start-up, normal shutdown, emergency shutdown, power generation to pumping, pumping to power generation, and...) were investigated. The load variation is pre-computed on a large scale at the supercomputing center to generate a model library containing more than 100,000 pre-computed operating points. Each operating point data includes time series pressure field, velocity field, and cavitation volume fraction field.

[0039] Step S2: Real-time data acquisition and synchronization with operating conditions. Vibration sensors (upper frame, lower frame, top cover), water pressure sensors (volute inlet, after the movable guide vane, runner outlet, tailrace inlet), and noise sensors are deployed at the unit site. The data acquisition frequency is set to 200 Hz. Hz The real-time data acquisition module filters and aligns the acquired data stream, then extracts the current head (H=250m), guide vane opening (α=35%), and guide vane opening change rate in real time. =-1% / s Rotational speed n=500 rpm Key parameters, etc.

[0040] Step S3: Online cavitation risk assessment and early warning. The online risk assessment module is activated once per second. First, with H=250... m α=35%, n=500 rpm Based on ) and combined =-1% / sThe trend was matched with a predicted trajectory group for the "guide vane closure process under 250m head" in the CFD model library. Subsequently, CFD prediction data for the next 10 seconds of this trajectory were retrieved at 0.1-second intervals. Calculations revealed that at the predicted 6.2-second mark, the cavitation volume fraction in the region near the lower ring of the runner was... More than 5% of the area volume The risk level will reach 18%, exceeding the preset danger threshold of 15%. Therefore, the module immediately generates an advanced emergency trigger signal and locates the risk area as "local cavitation in the lower ring of the rotor".

[0041] Step S4, Intelligent Emergency Handling and Control Execution. Upon receiving the signal, the emergency handling control module matches a combined strategy from the strategy library based on the risk type and level: "reduce the guide vane closing rate and activate central shaft air supply." The specific instruction is: reduce the guide vane closing rate from -1%... / s Adjusted to -0.5% / s Simultaneously, the central shaft air supply valve is opened, and the air supply flow rate is set to 0.8% of the rated flow rate. This control command is in 50... ms The data is then sent to the speed controller and auxiliary equipment control system.

[0042] Step S5: Learning Optimization and Model Update. After the strategy was executed, the system monitored that the actual pressure pulsation amplitude in the unit's tailrace pipe decreased by approximately 35% within 5 seconds, without causing excessive fluctuations in active power. The "Predicted Risk Index Curve," "Control Command," and "Actual Vibration / Pressure Feedback Curve" for this event were packaged and stored. The learning optimization module analyzed the data in the background and found that the CFD model's prediction of the pressure recovery after gas injection was slightly faster than the actual result. Based on this, the empirical coefficients related to gas dissolution in the model were fine-tuned to make the model more closely reflect reality.

[0043] Example 3 This embodiment details the internal logic of the online cavitation risk assessment module, such as... Figure 4 As shown. The core of this module is a dual-channel evaluation mechanism: a pressure-based evaluation channel and a cavitation distribution-based evaluation channel.

[0044] In the pressure-based evaluation channel, the system extracts the global minimum pressure point within the flow channel from the matched CFD data. The time series was analyzed, and real-time water temperature was read. Calculate the corresponding vaporization pressure Calculate the pressure safety factor. ,in For turbulent pressure correction term, This is a dynamic pressure correction term. When... When <1.2, the risk of cavitation initiation is considered high; when When the value is less than 1.0, cavitation is considered to have occurred.

[0045] In the evaluation channel based on cavitation distribution, the system reads the cavitation volume fraction field predicted by CFD. Set a critical value for significant cavitation occurrence. =5%. The system calculates in real time using image processing algorithms. The region's volume percentage in the entire flow channel computational domain At the same time, high-risk areas are also calculated ( The centroid location (>10%) is used for cavitation type identification (such as blade inlet, blade back surface, tailpipe).

[0046] Comprehensive risk indicators The above two channels are weighted and merged: ,in For the pressure field, it is the Laplace operator, reflecting the intensity of the pressure gradient; It is vorticity; The reference pressure is used. The preset three threshold levels are: Attention = 0.3, Warning = 0.6, Danger = 0.8. The early warning judgment unit continuously compares... With the threshold, and calculate its derivative. To determine the trend of risk changes. When >Note and When the value is >0, a warning is issued; when the prediction is made within the next 5 seconds... When the danger level is exceeded, an advanced emergency trigger signal will be issued, along with a screenshot of the predicted risk area.

[0047] Example 4 This embodiment provides an early warning and emergency response system for cavitation in the flow channel during the transient process of a hydroelectric turbine. The system hardware is deployed on a dedicated server in the power plant control layer, and the software adopts a modular design, including: 1. The multi-condition CFD simulation model library module stores and runs a library of high-fidelity transient cavitation CFD simulation models covering start-up, shutdown, condition transitions, and load changes for the target pump-turbine. This module stores and manages pre-calculated and calibrated high-fidelity digital twin datasets of the entire transient process flow field. Deployed on a high-performance computing and storage appliance, it indexes, stores, and rapidly retrieves massive amounts of pre-calculated flow field data through database management software. An API is provided for the risk assessment module to call.

[0048] 2. The real-time data acquisition and synchronization module is used to acquire unit operating data at high frequency through a sensor network and synchronize the real-time data sequence with the input boundary conditions of the CFD model library. It consists of an industrial data acquisition card, a communication gateway, and preprocessing software. It is responsible for reading data from the power plant monitoring system (such as SIS) or direct sensors, performing signal conditioning, timestamp synchronization, and anomaly data removal, and encapsulating it into a real-time data stream according to the format required by the CFD model.

[0049] 3. The online cavitation risk assessment and early warning module is used to retrieve matching CFD prediction results based on the current and predicted operating trajectories, and calculate real-time and future cavitation risk indicators. The system performs online cavitation risk assessment and early warning based on preset multi-level thresholds, and generates early warning or emergency trigger signals. This module is the core computing engine of the system, running on a dedicated server's CPU / GPU. It includes an operating condition matching algorithm, a flow field data interpolation program, a risk indicator calculation unit, and an early warning logic judgment unit. This module operates at a speed of no less than 1... Hz It runs in a cyclical pattern at a certain frequency.

[0050] 4. The intelligent emergency response control module automatically matches the emergency strategy library and generates an optimized control command sequence based on the type and risk characteristics of the early warning signal, then sends it to the unit control system for closed-loop control. It includes a rule engine and a strategy library. The rule engine parses the early warning signal and matches it with the "IF-THEN" rule in the strategy library. Each strategy in the strategy library includes applicable conditions, a control command sequence (such as the guide vane opening adjustment curve), and expected effect parameters. This module communicates with the field control system via OPCUA or IEC 61850 protocol. The intelligent emergency response control module also communicates with the unit speed control system, excitation system, and auxiliary equipment control system via the OPCUA or IEC 61850 standard protocol.

[0051] 5. The Human-Computer Interaction and Learning Optimization module records prediction, control, and feedback data throughout the entire process. It compares the correlation between CFD predictions and actual responses using machine learning algorithms, iteratively corrects model parameters or optimizes emergency strategies, and provides a visual interface for system status, flow field cloud maps, and risk curves. It also drives adaptive optimization of models and strategies based on historical data. A B / S architecture web graphical interface is provided for operators to monitor CFD prediction cloud maps, risk curves, early warning logs, and control actions. Background data mining and machine learning services regularly analyze accumulated historical cases, generating model parameter optimization suggestion reports or strategy optimization schemes, which are then updated to the model and strategy libraries after engineer confirmation.

[0052] It also includes an interface for linkage with the power plant's centralized control system (such as AGC / AVC). When emergency handling involves load adjustment, the adjustment amount and constraints are automatically sent to the AGC module for plant-wide power redistribution coordination, ensuring that grid-side demand is not affected and achieving coordinated control of unit safety and grid stability.

[0053] Through the collaborative work of its various modules, the system achieves online, proactive, and closed-loop management of cavitation risks during the transition process of the pump-turbine, forming a complete technology chain of "digital twin prediction, real-time data-driven, intelligent decision control, and feedback learning optimization," providing innovative proactive protection measures for the safe and stable operation of the unit.

[0054] The system constructs a high-fidelity transient cavitation CFD simulation model library covering all typical transient processes and deeply integrates it with real-time high-frequency operating data of the unit to map and predict operating trajectories online. The system calculates and dynamically evaluates cavitation risk indicators based on minimum local pressure and cavitation volume fraction in real time, achieving three levels of proactive early warning: "attention, warning, and danger." When a high risk is predicted, the system intelligently matches the emergency strategy library, automatically generates and issues optimized control commands (such as adjusting guide vane motion patterns and linking the air supply system), and executes closed-loop control. Simultaneously, the system possesses self-learning optimization capabilities, iteratively correcting the model and strategies by comparing predicted and feedback data. This invention transforms traditional passive delayed alarms into proactive closed-loop protection of "prediction-early warning-control," significantly improving the operational safety and stability of the pump-turbine during transient processes. Through the collaboration of various modules, this system achieves online, proactive, and closed-loop management of cavitation risks during transient processes, forming a complete technology chain and providing innovative proactive protection methods for the safe and stable operation of the unit. By deeply integrating CFD digital twins with real-time data, a proactive closed-loop protection mechanism for cavitation risks during the transition process of water pumps and turbines was achieved, encompassing prediction, early warning, and control. This significantly improved the safety and economy of unit operation.

[0055] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for early warning and emergency handling of cavitation in the flow channel during the transient process of a water turbine, characterized in that, Includes the following steps: Step S1: Establish a high-fidelity transient cavitation CFD simulation model library covering the entire operating condition of the target water pump turbine. Step S2: Collect unit operation data at high frequency through sensor network, and synchronously map the real-time data sequence with the input boundary conditions of CFD model library; Step S3: Based on the current and predicted operating trajectories, retrieve the matching CFD prediction results and calculate the real-time and future cavitation risk indicators. And make early warning judgments based on preset multi-level thresholds; Step S4: Based on the type of early warning signal and risk characteristics, automatically match the emergency strategy library and generate an optimized control command sequence, which is then sent to the unit control system to execute closed-loop control.

2. The method for early warning and emergency handling of cavitation in the flow channel during the turbine transition process according to claim 1, characterized in that: In step S3, the cavitation risk index The formula is: in, To predict the lowest local pressure within the flow channel, Current water temperature Lower vaporization pressure, The time delay constant is The rate of change of air bubble volume fraction. The local vorticity intensity of the flow field. For reference vorticity value, When the volume fraction of air bubbles exceeds the critical threshold The volume of the region, The total volume of the flow channel. , , , These are weighting coefficients assigned based on the degree of influence.

3. The method for early warning and emergency handling of cavitation in the flow channel during the turbine transition process according to claim 2, characterized in that: A method for early warning judgment based on preset multi-level thresholds: Preset three levels of early warning thresholds: attention, warning, and danger. When predicting a certain future moment... satisfy Generate a primary early warning signal; when predicting a certain future moment. satisfy When a warning trigger signal is generated; when a future moment is predicted. satisfy At that time, an advanced emergency trigger signal is generated, in which, Indicates a certain moment Cavitation risk indicators Indicates the attention threshold. Indicates the warning threshold. This indicates the danger threshold.

4. The method for early warning and emergency handling of cavitation in the flow channel during the turbine transition process according to claim 3, characterized in that: The CFD simulation model library mentioned in step S1 adopts SST k-ω Turbulence models and The cavitation model performs large-scale pre-calculations for all typical transient processes, generating time-series data including pressure field, velocity field, and cavitation volume fraction field, which are then stored and indexed in a database.

5. The method for early warning and emergency handling of cavitation in the flow channel during the turbine transition process according to claim 4, characterized in that: Use experimental or historical data to calibrate the model parameters of the CFD simulation model library.

6. The method for early warning and emergency handling of cavitation in the flow channel during the turbine transition process according to claim 5, characterized in that: In step S2, the real-time data collected includes the working head H, the guide vane opening α, and its rate of change. The unit's rotational speed n, active power P, pressure in the volute / top cover / tailrace pipe, and vibration acceleration of key components. .

7. The method for early warning and emergency handling of cavitation in the flow channel during the turbine transition process according to claim 6, characterized in that: In S4, the emergency strategy library has built-in optimized control strategies for different cavitation types and risk levels, including fine-tuning the guide vane closing / opening rate, slightly adjusting the target load setpoint, and linking the central shaft or tailrace pipe air supply system; after the control command is issued, the unit vibration, pressure pulsation and other responses are monitored in real time and compared with the expected improvement effect for verification.

8. The method for early warning and emergency handling of cavitation in the flow channel during the turbine transition process according to claim 7, characterized in that: It also includes recording prediction, control, and feedback data throughout the entire process, comparing the correlation between CFD predictions and actual responses using machine learning algorithms, and iteratively correcting model parameters or optimizing emergency strategies.

9. The method for early warning and emergency handling of cavitation in the flow channel during the turbine transition process according to claim 8, characterized in that: By employing reinforcement learning algorithms, the empirical coefficients in the CFD model or the specific parameters of the emergency strategy are adaptively adjusted based on the difference between the actual and expected changes in key parameters after control execution.

10. A system for emergency response to cavitation early warning in the flow channel of a water turbine during the transient process, as described in claim 1, characterized in that, It includes a multi-condition CFD simulation model library module, a real-time data acquisition and synchronization module, an online cavitation risk assessment and early warning module, and an intelligent emergency response control module, among which: The multi-condition CFD simulation model library module is used to store and run a full-condition high-fidelity transient cavitation CFD simulation model library based on the target pump turbine, covering start-up, shutdown, condition transition and load change. The real-time data acquisition and synchronization module is used to acquire unit operation data at high frequency through a sensor network and to synchronously map the real-time data sequence with the input boundary conditions of the CFD model library. The online cavitation risk assessment and early warning module is used to retrieve matching CFD prediction results based on the current and predicted operating trajectories, and calculate real-time and future cavitation risk indicators. And make early warning judgments based on preset multi-level thresholds; The intelligent emergency response control module is used to automatically match the emergency strategy library and generate an optimized control command sequence based on the type of early warning signal and risk characteristics, and then send it to the unit control system to execute closed-loop control.

Images