Control simulation method and system for hydroelectric generation

Abstract

The invention discloses a control simulation method and system for hydroelectric generation. The method comprises the following steps: collecting operation parameters of hydropower station equipment in real time, wherein the operation parameters comprise guide vane opening, a blade wear value, a water head, flow, generator stator temperature, bearing vibration amplitude and insulation resistance; calculating a water turbine blade wear factor, a generator insulation aging factor and a bearing wear factor based on the operation data, and constructing a hydropower station control simulation model considering equipment aging; real-time optimization calculation of control parameters is carried out according to the control simulation model, and simulation control parameters obtained through optimization are sent to an actual hydroelectric generation control system with the purpose of minimizing performance deviation and equipment damage; the device aging state can be dynamically responded, self-adaptive optimization of control parameters is achieved, the service life of the device is prolonged while the power generation efficiency is guaranteed, and the intelligent operation level of the hydropower station is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of hydropower generation control, and particularly relates to a control simulation method and system for hydropower generation. Background Art

[0002] With the increase in the operation years of hydropower stations, the problem of equipment aging has become increasingly prominent, seriously affecting the safe and stable operation and power generation efficiency of hydropower stations; traditional hydropower station control systems often adopt fixed control parameters, failing to fully consider the impact of equipment aging on system performance, resulting in problems such as low equipment utilization rate, high maintenance cost, and rising failure rate.

[0003] In the prior art, the hydropower station control system mainly has the following deficiencies: First, the control parameters are static, lacking the dynamic response ability to the real-time state of equipment; second, the equipment aging assessment method is single, unable to accurately reflect the comprehensive health state of equipment; third, there is a lack of an effective predictive maintenance mechanism, and often an after-fact maintenance strategy is adopted, increasing the operation and maintenance cost and safety risk; fourth, the control strategy optimization is insufficient, failing to minimize equipment damage while ensuring power generation efficiency.

[0004] Therefore, there is an urgent need to develop an intelligent hydropower station control simulation method that can real-time evaluate the equipment aging state and dynamically optimize control parameters, so as to improve the operation efficiency of hydropower stations, extend the service life of equipment, and reduce maintenance costs. Summary of the Invention

[0005] The purpose of the present invention is to solve the technical problems in the prior art that the hydropower station control system cannot dynamically respond to the equipment aging state and the control parameter optimization is insufficient, and to provide a hydropower generation control simulation method and system based on equipment aging assessment. By real-time monitoring the equipment operation parameters, calculating the equipment aging factor, and constructing a control simulation model considering equipment aging, the dynamic optimization of control parameters is realized, minimizing equipment damage while ensuring power generation efficiency, and improving the intelligent operation level of hydropower stations.

[0006] In the first aspect, the present invention provides a control simulation method for hydropower generation, dynamically correcting the simulation control parameters based on the obtained equipment aging parameters, and sending the corrected simulation control parameters to the actual hydropower generation control system; the control simulation method includes the following steps: Step S1, real-time collecting the operation parameters of hydropower station equipment, where the operation parameters include guide vane opening, blade wear value, water head, flow rate, generator stator temperature, bearing vibration amplitude, and insulation resistance.

[0007] Step S2, calculating the equipment aging factor based on the operation data, and constructing a hydropower station control simulation model considering equipment aging.

[0008] Step S3, perform real-time optimization calculation of control parameters according to the hydropower station control simulation model. With the goal of minimizing performance deviation and equipment damage, send the optimized simulated control parameters to the actual hydropower control system to replace the current operating parameters.

[0009] Further, the calculation of the equipment aging factor considers the water turbine blade wear factor , the generator insulation aging factor and the bearing wear factor in three categories; The formula for calculating the blade wear factor is: ; where , is the wear coefficient, and are the rated flow and head, and are the real-time flow and head at time; , is the cavitation damage function related to the cavitation number ; The formula for calculating the generator insulation aging factor is: ; Where , is the thermal life function based on the Arrhenius law; , is the reference life, is the activation energy constant; , is the electrical life function related to the electric field strength, , , is the voltage aging index, and the electric field strength is calculated through the collected stator temperature and insulation resistance : ; where , is the electric field strength correction coefficient, , is the rated voltage, is the insulation thickness, , is the reference insulation resistance, , is the temperature correction coefficient, with the unit , is the reference temperature; The bearing wear factor is calculated based on the modified L10 life theory: ; Where , is the modified bearing life function, , is the basic dynamic load rating, , is the equivalent dynamic load, and They are the radial force and the axial force respectively, , which is the life index of the ball bearing, , which is the ISO correction coefficient.

[0010] Furthermore, the hydropower station control simulation model is established based on the multiple linear weighting method: ; Among them, is the comprehensive equipment aging degree at time t, and its value range is 0 - 1.

[0011] Furthermore, the equipment aging factor is input into the equipment aging degree evaluation model, and the operating state of the hydropower station equipment is judged according to the comprehensive equipment aging degree ; When , it indicates that the equipment is in good operating state; When , it indicates that the equipment is in the aging state; When , it indicates that the equipment needs maintenance.

[0012] Furthermore, when , the virtual system parameters are updated in a real - time data - driven manner. Taking the minimization of performance deviation and equipment damage as the objective function, and cooperating with the constraint conditions to obtain the optimal simulation power and frequency at the current moment, and accordingly adjusting the turbine speed and guide vane opening of the actual hydropower control system.

[0013] Furthermore, the objective function is: .

[0014] Among them, is the prediction time window length, and are the reference power and frequency respectively, and are the simulation power and frequency, is the increment of the comprehensive equipment aging degree at time t, and its calculation formula is: ; The constraint conditions include the guide vane opening constraint: , the power constraint: and the frequency constraint: .

[0015] Furthermore, during the optimization process of the control parameters, a vibration monitoring and early warning mechanism is established. When the bearing vibration amplitude exceeds the preset threshold , the response speed of the speed regulation system is reduced to 50% - 70% of the current value.

[0016] Further, the preset threshold is dynamically set according to the bearing model and operating conditions, and the value range is 2.5 - 5.0 mm / s.

[0017] Further, after receiving the analog control parameters, the actual hydroelectric power control system updates the control parameters through parameter validity verification and smooth switching mechanism; parameter validity verification includes checking whether the guide vane opening, power, and speed are within the safe range, and verifying whether the parameter change rate exceeds the maximum change rate allowed by the equipment; the smooth switching mechanism adopts a progressive parameter transition method, and the switching time window is set to 5 - 10 seconds.

[0018] In a second aspect, based on the same inventive concept, the present invention provides a control simulation system for hydroelectric power generation, the system comprising: a data acquisition module, an aging factor calculation module, an optimization control module, and a parameter transmission module.

[0019] The data acquisition module is used to collect the operation parameters of the hydroelectric power station equipment in real time; the aging factor calculation module is used to calculate the equipment aging factor and construct a control simulation model of the hydroelectric power station considering equipment aging; the optimization control module is used to perform real-time optimization calculation of the control parameters according to the control simulation model; the parameter transmission module is used to send the optimized analog control parameters to the actual hydroelectric power control system.

[0020] Compared with the prior art, the beneficial effects of the present invention are: By real-time monitoring of the equipment operation parameters and calculating the aging factor, the present invention can dynamically reflect the equipment health status, realize the adaptive adjustment of the control parameters, and overcome the defect of parameter solidification in the traditional control system; a multi-dimensional aging evaluation model covering turbine blade wear, generator insulation aging, and bearing wear is established, which is more comprehensive and accurate than the single-index evaluation, provides a reliable basis for control decision-making, and can adjust the parameters according to the equipment types and operating conditions of different hydroelectric power stations, having a wide application prospect. Brief Description of the Drawings

[0021] Figure 1 is a flowchart of a control simulation method for hydroelectric power generation according to the present invention; Figure 2 is a schematic diagram of the composition of a control simulation system for hydroelectric power generation according to the present invention. Detailed Embodiments

[0022] To make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions in the present invention will be described clearly and completely below. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present invention without creative efforts belong to the scope of protection of the present invention.

[0023] Embodiment 1 As Figure 1 shown, it is a flowchart of a control simulation method for hydropower generation according to the present invention. This method dynamically corrects simulation control parameters based on the obtained equipment aging parameters and sends the corrected simulation control parameters to the actual hydropower generation control system, including the following steps: Step S1, collect the operation parameters of the hydropower station equipment in real time. The operation parameters include guide vane opening, blade wear value, water head, flow rate, generator stator temperature, bearing vibration amplitude, and insulation resistance.

[0024] The data acquisition system realizes real-time monitoring of key operation parameters through a distributed sensor network. For example, the guide vane opening is measured by a high-precision angle sensor with a measurement accuracy of ±0.1°; the blade wear value is obtained by an ultrasonic thickness gauge or eddy current detection equipment, and wear changes at the 0.1 mm level can be detected; the water head is measured by a pressure sensor with a measurement range of 0 - 200 m and an accuracy of ±0.1%; the flow rate is measured by an electromagnetic flowmeter or ultrasonic flowmeter, and the upper limit of the flow measurement range is 3000 thousand cubic meters per second.

[0025] The generator stator temperature is monitored by a distributed temperature sensing system. Multiple temperature sensors, such as PT100 platinum resistance temperature sensors, are arranged at different positions of the stator winding, and the measurement accuracy can reach ±0.1°C. The bearing vibration amplitude is measured by an acceleration sensor or velocity sensor with a measurement frequency range of 1 Hz - 10 kHz, and vibration changes at 0.1 mm / s can be detected. The insulation resistance is measured regularly by an insulation resistance tester with a measurement voltage of 500 V - 5000 V and a measurement range of 1 MΩ - 100 GΩ.

[0026] The data acquisition frequency is set according to the parameter characteristics: for fast-changing parameters such as guide vane opening and flow rate, the acquisition frequency is 1 Hz - 10 Hz; for slow-changing parameters such as blade wear value and insulation resistance, the acquisition frequency is 0.1 Hz - 1 Hz. All the acquired data is transmitted to the central control system through industrial Ethernet or fieldbus to ensure the real-time and reliability of data transmission.

[0027] Step S2, calculate the equipment aging factor based on the operation data and construct a hydropower station control simulation model considering equipment aging.

[0028] The calculation of the equipment aging factor takes into account the turbine blade wear factor , the generator insulation aging factor and the bearing wear factor in three categories; The formula for calculating the blade wear factor is: ; where , is the wear coefficient, which is applicable to the turbine blades made of stainless steel or alloy steel. and are the rated flow rate and head, and are the real-time flow rate and head at time[[ID=?]] , is the cavitation damage function related to the cavitation number ; the formula for calculating the cavitation number is , where is the atmospheric pressure head (about 10.33m), is the suction height, is the saturated vapor pressure head (related to the water temperature), is the net head.

[0029] When the cavitation number is below 0.8, the cavitation phenomenon intensifies, resulting in accelerated blade wear. For example, when , , indicating that the wear rate increases by 40%.

[0030] The formula for calculating the generator insulation aging factor is: ; where , is the thermal life function based on the Arrhenius law; , is the reference life, is the activation energy constant, applicable to common polyesterimide insulation materials; taking actual operation as an example, when the stator temperature , ; when the temperature rises to 100°C, , the life is significantly shortened.

[0031] , is the electrical life function related to the electric field strength, , , is the voltage aging index, and the electric field strength is calculated through the collected stator temperature and insulation resistance : ]>; where , is the electric field strength correction coefficient, , is the rated voltage, It should be noted that there is a missing value in the original text for the variable related to the time in the blade wear factor formula ( ), which is marked as "?". This may affect the accuracy of the translation if the specific value is crucial for understanding the formula. is the insulation thickness, is the reference insulation resistance, is the temperature correction coefficient, with the unit , is the reference temperature; when the insulation resistance decreases from 1000 MΩ to 500 MΩ, the electric field strength increases to times the original value, and the electrical life is shortened to 0.5 times the original value.

[0032] The bearing wear factor is calculated based on the modified L10 life theory: ; wherein, is the modified bearing life function, is the basic dynamic load rating, and this value is applicable to large deep groove ball bearings; the rated dynamic loads of different types of bearings vary greatly. For example, the rated dynamic load of a tapered roller bearing may reach more than 300 kN.

[0033] is the equivalent dynamic load, and are the radial force and axial force respectively, is the ball bearing life index, is the ISO correction coefficient. During the operation of a hydropower station, the radial force mainly comes from the rotor weight and electromagnetic force, and the axial force mainly comes from the axial thrust of the water turbine. For example, for a 100 MW hydro-generator unit, the radial force may be 500 - 800 kN, and the axial force may be 200 - 400 kN.

[0034] The control simulation model of the hydropower station is established based on the multiple linear weighting method: ; The selection of the weight coefficient is based on the influence degree of each component on the overall performance of the machine and the severity of the failure consequences. As the core component of the hydro-generator unit, the wear of the water turbine blade directly affects the power generation efficiency and operation safety, so it has the largest weight (0.4); the generator insulation and bearing system are equally important, each accounting for a weight of 0.3.

[0035] wherein, is the comprehensive aging degree of the equipment at time, and the value range is 0 - 1.

[0036] Input the equipment aging factor into the equipment aging degree evaluation model, and judge the operation state of the hydropower station equipment according to the comprehensive aging degree of the equipment; When , it indicates that the equipment is in good operation state; at this time, all performance indicators of the equipment are normal, and it can operate according to the rated parameters without special maintenance measures.

[0037] When When it indicates that the device is in an aging state; at this time, the monitoring frequency should be increased, the operating parameters should be adjusted appropriately to slow down the aging process, and a maintenance plan should be formulated; for example, the load operation can be appropriately reduced, the number of starts and stops can be reduced, and the service life of the device can be extended.

[0038] When it indicates that the device needs repair. At this time, the shutdown for maintenance should be arranged immediately, and the severely aged components should be replaced to ensure the safe operation of the device.

[0039] Step S3, perform real-time optimization calculation of the control parameters according to the hydropower station control simulation model, aiming to minimize the performance deviation and equipment damage, and send the optimized simulated control parameters to the actual hydropower control system to replace the current operating parameters.

[0040] When it is the case, update the virtual system parameters in a real-time data-driven manner, with the objective function of minimizing the performance deviation and equipment damage, and obtain the optimal simulated power and frequency at the current moment in cooperation with the constraint conditions, and accordingly adjust the turbine speed and guide vane opening of the actual hydropower control system.

[0041] The optimization strategy adopts the Model Predictive Control (MPC) method to solve an optimization problem with a finite time domain in each control period. The optimization period is usually set to 1 - 5 seconds, and the prediction time domain is set to 10 - 30 seconds; the objective function is: .

[0042] Among them, is the length of the prediction time window, corresponding to 10 control steps. and are the reference power and frequency respectively, and are the simulated power and frequency, is the increment of the comprehensive aging degree of the device at the moment, and the calculation formula is: ; The constraint conditions include the guide vane opening constraint: , the minimum opening limit (0.05) avoids the unstable phenomenon generated when the turbine operates at a very small opening; the maximum opening limit (1.0) corresponds to the fully open state of the guide vane; the power constraint: Among them, is 10% - 20% of the rated power, is 105% - 110% of the rated power, allowing short-term overload operation; the frequency constraint: .

[0043] During the optimization process of the control parameters, a vibration monitoring and early warning mechanism is established. When the vibration amplitude of the bearing exceeds the preset threshold , the response speed of the speed regulation system is reduced to 50%-70% of the current value. Vibration monitoring adopts a multi-point measurement method, and vibration sensors are installed at the upper guide bearing, lower guide bearing, and thrust bearing respectively. After the vibration signal is filtered and amplified, frequency domain analysis is carried out to extract the characteristic frequency components. Common fault characteristic frequencies include: rotation frequency (1×), double frequency (2×), blade passing frequency, etc.

[0044] The preset threshold is dynamically set according to the bearing model and operating conditions, and the value range is 2.5-5.0mm / s; the vibration threshold of the sliding bearing is usually 2.5-3.5mm / s, and that of the rolling bearing is 3.5-5.0mm / s; when vibration exceeding the limit is detected, the system automatically reduces the response speed of the speed regulation system to avoid further aggravating the vibration due to rapid guide vane movement.

[0045] After receiving the analog control parameters, the actual hydroelectric power control system realizes the update of the control parameters through parameter validity verification and smooth switching mechanism; parameter validity verification includes checking whether the guide vane opening, power, and speed are within the safe range, and verifying whether the parameter change rate exceeds the maximum change rate allowed by the equipment; the smooth switching mechanism adopts a progressive parameter transition method, and the switching time window is set to 5-10 seconds.

[0046] Parameter validity verification includes verifying whether the guide vane opening, power, and speed are within the safe range. For example, the guide vane opening should be between 0.05-1.0, and the power should be between 10%-110% of the rated power; verifying whether the parameter change rate exceeds the maximum change rate allowed by the equipment. For example, the change rate of the guide vane opening should not exceed 0.1 / s, and the change rate of the power should not exceed 5% / s of the rated power. Verifying whether the logical relationship between the parameters is reasonable. For example, under the same head condition, the power should increase correspondingly when the guide vane opening increases.

[0047] The smooth switching mechanism adopts a progressive parameter transition method, and the switching time window is set to 5-10 seconds; the specific implementation method includes: within the switching time window, the control parameters transition from the current value to the target value according to a linear law; a first-order inertia link is used to achieve the smooth transition of the parameters, and the time constant is set to 2-3 seconds. For large parameter changes, a segmented switching method is adopted, and the total change amount is decomposed into multiple small change steps.

[0048] Embodiment 2 As Figure 2 shown, it is a schematic diagram of the composition of a control simulation system for hydroelectric power generation according to the present invention. The system includes: a data acquisition module, an aging factor calculation module, an optimization control module, and a parameter transmission module.

[0049] The data acquisition module is used to collect the operation parameters of hydropower station equipment in real time. This module adopts a distributed architecture and includes multiple subsystems: a sensor subsystem, a data acquisition station, a communication network, and a data preprocessing unit.

[0050] The sensor subsystem is deployed at various key positions in the hydropower station, including parts such as turbines, generators, main shafts, and bearings. Sensor types include temperature sensors (such as PT100, thermocouples), pressure sensors (such as diffused silicon pressure sensors), flow sensors (such as electromagnetic flowmeters), vibration sensors (such as acceleration sensors, velocity sensors), displacement sensors (such as eddy current sensors), etc. Each sensor features high precision and high reliability and can work stably for a long time in a harsh industrial environment.

[0051] The data acquisition station is responsible for the acquisition, conversion, and preprocessing of sensor signals. It uses an industrial-grade data acquisition card, which features multiple channels, a high sampling rate, and low noise. The data acquisition station is usually deployed in the on-site control room, connected to the sensors through shielded cables, and adopts a differential input method to improve the anti-interference ability. The data acquisition frequency is set according to the signal characteristics: fast-changing signals such as flow and pressure adopt a sampling frequency of 100Hz - 1kHz; slow-changing signals such as temperature and insulation resistance adopt a sampling frequency of 1Hz - 10Hz.

[0052] The communication network uses industrial Ethernet or fieldbus technology to achieve data transmission. The network topology is star or ring-shaped, with redundant configuration to ensure the reliability of communication. The data transmission protocol adopts industrial standard protocols such as Modbus TCP / IP, Profinet, or EtherCAT to ensure compatibility with equipment from different manufacturers.

[0053] The data preprocessing unit performs preprocessing on the collected raw data, such as filtering, calibration, and format conversion. Filtering algorithms include low-pass filtering, band-pass filtering, median filtering, etc., effectively removing noise and interference. The calibration function performs linear or nonlinear calibration according to the sensor characteristics to improve the measurement accuracy. The data format conversion unifies the data of different sensors into a standard format for subsequent processing.

[0054] The aging factor calculation module is used to calculate the equipment aging factor and construct a hydropower station control simulation model considering equipment aging; and realize the real-time calculation of three aging factors. The calculation of the water turbine blade wear factor uses numerical integration methods such as the trapezoidal rule or Simpson's rule to discretize and solve the integral equation. The calculation of the generator insulation aging factor involves complex physical models and needs to consider the comprehensive influence of multiple factors such as temperature, electric field strength, and humidity. The calculation of the bearing wear factor is based on the fatigue life theory and considers the influence of factors such as load change, lubrication condition, and temperature. The comprehensive aging degree of the equipment is calculated based on the multiple linear weighting method. The weight coefficients can be adjusted according to actual operation experience and expert knowledge. The evaluation results are displayed on a graphical interface for the convenience of operators to intuitively understand the equipment status.

[0055] The optimization control module is used to perform real-time optimization calculation of control parameters according to the control simulation model; establish a dynamic prediction model of the hydropower unit to predict the system state at future moments. The prediction model is described by the state space method or the transfer function method, considering the coupling characteristics of the hydraulic system, mechanical system, and electrical system. The model structure includes the characteristics of the water turbine, generator, speed regulation system, etc. The model parameters are obtained through system identification methods and are corrected online according to operation data.

[0056] The parameter transmission module is used to send the optimized simulated control parameters to the actual hydropower control system.

[0057] The specific embodiments described above further elaborate on the purpose, technical solutions, and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A control simulation method for hydropower generation, which dynamically corrects simulation control parameters based on the obtained equipment aging parameters and sends the corrected simulation control parameters to the actual hydropower generation control system; characterized in that, The described control simulation method includes the following steps: Step S1, collect the operation parameters of the hydropower station equipment in real time. The operation parameters include the guide vane opening, blade wear value, water head, flow rate, generator stator temperature, bearing vibration amplitude, and insulation resistance; Step S2, calculate the equipment aging factor based on the operation data, and construct a hydropower station control simulation model considering equipment aging; Step S3, perform real-time optimization calculation of the control parameters according to the hydropower station control simulation model. With the goal of minimizing performance deviation and equipment damage, send the optimized simulated control parameters to the actual hydropower control system to replace the current operation parameters.

2. The method according to claim 1, wherein The calculation of the device aging factor takes into account the wear factor of the turbine blade , the insulation aging factor of the generator and the bearing wear factor These are three categories; The calculation formula for the blade wear factor is as follows: ; where , is the wear coefficient and are the rated flow rate and head and are the real-time flow rate and head at time , is the cavitation damage function related to the cavitation number ; The calculation formula for the generator insulation aging factor is: ; Among them, is the thermal life function based on the Arrhenius law; , is the reference life, is the activation energy constant; , is the electrical life function related to the electric field strength, , , is the voltage aging index, the electric field strength is obtained by collecting the stator temperature and the insulation resistance is calculated as: ; among them, , is the electric field strength correction coefficient, , is the rated voltage, is the insulation thickness, , is the reference insulation resistance, , is the temperature correction coefficient, unit , is the reference temperature; The bearing wear factor is calculated based on the modified L10 life theory: ; Among them, , is the corrected bearing life function, , is the basic dynamic load rating, , is the equivalent dynamic load, and are the radial force and axial force respectively, , is the ball bearing life index, , is the ISO correction factor.

3. The method according to claim 2, characterized in that, The hydropower station control simulation model is established based on the multivariate linear weighting method: ； Among them, is the comprehensive aging degree of the device at a certain moment, and the value range is 0-1.

4. The method according to claim 3, characterized in that, Input the equipment aging factor into the equipment aging degree evaluation model, and judge the operation status of the hydropower station equipment according to the comprehensive equipment aging degree ​ When it indicates that the device is in good operating condition; When it indicates that the device is in an aging state; When it indicates that the device needs to be repaired.

5. The method according to claim 4, characterized in that, When occurs, the virtual system parameters are updated in a real-time data-driven manner. With the objective of minimizing performance deviation and equipment damage, the optimal simulated power and frequency at the current moment are obtained in combination with the constraint conditions, and based on this, the turbine speed and guide vane opening of the actual hydroelectric power control system are adjusted.

6. The method according to claim 5, characterized in that, The objective function is: ； Among them, is the prediction time window length, and are the reference power and frequency respectively, and are the simulated power and frequency, is the increment of the comprehensive aging degree of the device at a moment, and the calculation formula is: ； The constraint conditions include the guide vane opening Constraints: , power constraint: and frequency constraint: .

7. The method according to claim 6, characterized in that, During the optimization process of the control parameters, a vibration monitoring and early warning mechanism is established. When the vibration amplitude of the bearing exceeds the preset threshold , the response speed of the speed control system is reduced to 50%-70% of the current value.

8. The method according to claim 7, wherein The preset threshold value is dynamically set according to the bearing model and operating conditions, and the value range is 2.5 - 5.0 mm / s.

9. The method according to claim 8, characterized in that, After receiving the simulated control parameters, the actual hydropower control system realizes the update of the control parameters through the parameter validity verification and smooth switching mechanism; the parameter validity verification includes checking whether the guide vane opening, power, and speed are within the safe range, and verifying whether the parameter change rate exceeds the maximum change rate allowed by the equipment; the smooth switching mechanism adopts the progressive parameter transition method, and the switching time window is set to 5 - 10 seconds.

10. A control simulation system for hydroelectric power generation, which is used to implement the method according to any one of claims 1-9, characterized in that The system includes: a data acquisition module, an aging factor calculation module, an optimization control module, and a parameter transmission module; The data acquisition module is used to collect the operation parameters of the hydropower station equipment in real time; The aging factor calculation module is used to calculate the equipment aging factor and construct a hydropower station control simulation model considering equipment aging; The optimization control module is used to perform real-time optimization calculation of the control parameters according to the control simulation model; The parameter transmission module is used to send the optimized simulated control parameters to the actual hydropower control system.

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