A control simulation method and system for hydropower generation

Abstract

The present invention discloses a control simulation method and system for hydropower generation. The method comprises: real-time collection of hydropower station equipment operating parameters, including guide vane opening, blade wear value, water head, flow rate, generator stator temperature, bearing vibration amplitude, and insulation resistance; calculation of turbine blade wear factor, generator insulation aging factor, and bearing wear factor based on the operating data, and construction of a hydropower station control simulation model that takes equipment aging into account; real-time optimization calculation of control parameters based on the control simulation model, with the goal of minimizing performance deviation and equipment damage, and sending the optimized simulated control parameters to the actual hydropower generation control system. The method can dynamically respond to equipment aging status and achieve adaptive optimization of control parameters, thereby extending equipment service life while ensuring power generation efficiency and improving the intelligent operation level of the hydropower station.

Description

Technical Field

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

[0002] As hydropower stations age, the problem of equipment aging becomes increasingly prominent, seriously impacting their safe and stable operation and power generation efficiency. Traditional hydropower station control systems often use fixed control parameters and fail to fully consider the impact of equipment aging on system performance, leading to problems such as low equipment utilization, high maintenance costs, and increased failure rates.

[0003] In the existing technology, the control system of hydropower stations has the following major deficiencies: first, the control parameters are static and lack the ability to dynamically respond to the real-time status of the equipment; second, the equipment aging assessment method is single and cannot accurately reflect the comprehensive health status of the equipment; third, there is a lack of effective predictive maintenance mechanism, and post-maintenance strategies are often adopted, which increases operation and maintenance costs and safety risks; fourth, the control strategy is not sufficiently optimized, 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 evaluate the aging status of equipment in real time and dynamically optimize control parameters, so as to improve the operating efficiency of the hydropower station, 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 control system of a hydropower station cannot dynamically respond to the aging status of equipment and the control parameters are insufficiently optimized. A hydropower generation control simulation method and system based on equipment aging assessment is provided. By real-time monitoring of equipment operating parameters, calculation of equipment aging factors, and construction of a control simulation model that takes equipment aging into account, dynamic optimization of control parameters is achieved, minimizing equipment damage while ensuring power generation efficiency, and improving the intelligent operation level of the hydropower station.

[0006] In a first aspect, the present invention provides a control simulation method for hydropower generation, which dynamically modifies simulated control parameters based on acquired equipment aging parameters and transmits the modified simulated control parameters to an actual hydropower generation control system. The control simulation method comprises the following steps:

[0007] Step S1 , real-time collection of operating parameters of hydropower station equipment, including guide vane opening, blade wear value, water head, flow rate, generator stator temperature, bearing vibration amplitude and insulation resistance.

[0008] Step S2: Calculate the equipment aging factor based on the operating data and construct a hydropower station control simulation model that takes equipment aging into consideration.

[0009] Step S3: performing real-time optimization calculation of control parameters based on the hydropower station control simulation model, with the goal of minimizing performance deviation and equipment damage, and sending the optimized simulation control parameters to the actual hydropower generation control system to replace the current operating parameters.

[0010] Furthermore, the calculation of the equipment aging factor takes into account the turbine blade wear factor , generator insulation aging factor and bearing wear factor Three categories;

[0011] The calculation formula of blade wear factor is: ;in, , is the wear coefficient, and is the rated flow and head, and for Real-time flow and head at all times;

[0012] , is the cavitation number Related cavitation damage function;

[0013] The calculation formula for the generator insulation aging factor is: ;

[0014] in, , is the thermal life function based on the Arrhenius law;

[0015] , is the reference life, is the activation energy constant; , is the electric lifetime function related to the electric field strength, , , is the voltage aging index, the electric field strength The stator temperature is collected and insulation resistance The calculation shows that:

[0016] ;in, , 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;

[0017] The bearing wear factor is calculated based on the modified L10 life theory: ;

[0018] in, , is the corrected bearing life function, , is the basic dynamic load rating, , is the equivalent dynamic load, and are radial force and axial force respectively, , is the ball bearing life index, , is the ISO correction factor.

[0019] Furthermore, the hydropower station control simulation model is established based on the multivariate linear weighted method:

[0020] ;

[0021] in, for The comprehensive aging degree of the equipment at any moment, ranging from 0 to 1.

[0022] Furthermore, the equipment aging factor is input into the equipment aging assessment model, and the equipment comprehensive aging degree is evaluated. Determine the operating status of hydropower station equipment;

[0023] when When , it indicates that the equipment is in good operating condition;

[0024] when When , it indicates that the device is in the aging state;

[0025] when , it indicates that the device needs maintenance.

[0026] Furthermore, when When the virtual system parameters are updated in a real-time data-driven manner, the objective function is to minimize performance deviation and equipment damage. The optimal simulated power and frequency at the current moment are obtained in accordance with the constraints, and the turbine speed and guide vane opening of the actual hydropower control system are adjusted accordingly.

[0027] Furthermore, the objective function is:

[0028] .

[0029] in, is the length of the prediction time window, and are the reference power and frequency respectively, and To simulate power and frequency, is the increment of comprehensive aging degree of equipment at the moment, and the calculation formula is:

[0030] ;

[0031] Constraints include guide vane opening constraint: , Power Constraints: and frequency constraints: .

[0032] Furthermore, a vibration monitoring and early warning mechanism is established during the control parameter optimization process. Exceeding the preset threshold When the speed control system is running, the response speed will be reduced to 50%-70% of the current value.

[0033] Furthermore, the preset threshold Dynamic setting is based on the bearing model and operating conditions, with a value range of 2.5-5.0mm / s.

[0034] Furthermore, after the actual hydropower control system receives the simulated control parameters, it updates the control parameters through parameter validity verification and a smooth switching mechanism; parameter validity verification includes checking whether the guide vane opening, power and speed are within a 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.

[0035] In a second aspect, based on the same inventive concept, the present invention provides a control simulation system for hydropower generation, the system comprising: a data acquisition module, an aging factor calculation module, an optimization control module and a parameter transmission module.

[0036] The data acquisition module is used to collect the operating 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 taking into account equipment aging; the optimization control module is used to perform real-time optimization calculation of the control parameters based on the control simulation model; and the parameter transmission module is used to send the optimized simulation control parameters to the actual hydropower generation control system.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] The present invention can dynamically reflect the health status of the equipment and realize adaptive adjustment of the control parameters by real-time monitoring of the equipment operating parameters and calculating the aging factor, thus overcoming the defect of fixed parameters in traditional control systems. It establishes a multi-dimensional aging assessment model covering turbine blade wear, generator insulation aging and bearing wear. Compared with single indicator assessment, it is more comprehensive and accurate, providing a reliable basis for control decision-making. It can adjust parameters according to the equipment type and operating conditions of different hydropower stations, and has broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is a flow chart of a control simulation method for hydropower generation according to the present invention;

[0040] Figure 2 The figure is a schematic diagram of the composition of a control simulation system for hydropower generation according to the present invention. DETAILED DESCRIPTION

[0041] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention are described clearly and completely below. Obviously, the embodiments described are only part of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0042] Example 1

[0043] like Figure 1 FIG. 1 is a flow chart of a control simulation method for hydropower generation according to the present invention. The 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:

[0044] Step S1 , real-time collection of operating parameters of hydropower station equipment, including guide vane opening, blade wear value, water head, flow rate, generator stator temperature, bearing vibration amplitude and insulation resistance.

[0045] The data acquisition system uses a distributed sensor network to monitor key operating parameters in real time. For example, guide vane opening is measured using a high-precision angle sensor with an accuracy of ±0.1°. Blade wear is measured using an ultrasonic thickness gauge or eddy current testing equipment, capable of detecting wear changes as small as 0.1mm. Water head is measured using a pressure sensor with a range of 0-200m and an accuracy of ±0.1%. Flow rate is measured using an electromagnetic flowmeter or ultrasonic flowmeter, with an upper limit of 3,000,000 cubic meters per second.

[0046] Generator stator temperature is monitored using a distributed temperature sensing system. Multiple temperature sensors, such as PT100 platinum resistance temperature sensors, are placed at various locations within the stator windings, achieving a measurement accuracy of ±0.1°C. Bearing vibration amplitude is measured using accelerometers or velocity sensors with a frequency range of 1Hz-10kHz, capable of detecting vibration changes as small as 0.1mm / s. Insulation resistance is regularly measured using an insulation resistance tester with a voltage of 500V-5000V and a range of 1MΩ-100GΩ.

[0047] The data collection frequency is set based on parameter characteristics: fast-changing parameters such as guide vane opening and flow rate are collected at a frequency of 1Hz-10Hz; slowly changing parameters such as blade wear and insulation resistance are collected at a frequency of 0.1Hz-1Hz. All collected data is transmitted to the central control system via industrial Ethernet or fieldbus, ensuring real-time and reliable data transmission.

[0048] Step S2: Calculate the equipment aging factor based on the operating data and construct a hydropower station control simulation model that takes equipment aging into consideration.

[0049] The calculation of the equipment aging factor takes into account the turbine blade wear factor , generator insulation aging factor and bearing wear factor Three categories;

[0050] The calculation formula of blade wear factor is: ;in, , is the wear coefficient, which is applicable to turbine blades made of stainless steel or alloy steel. and is the rated flow and head, and for Real-time flow and head at all times;

[0051] , is the cavitation number The relevant cavitation damage function; the calculation formula of the cavitation number is ,in, is the atmospheric pressure head (about 10.33m), is the suction height, is the saturated steam pressure head (related to water temperature), For clean water head.

[0052] When the cavitation number is lower than 0.8, the cavitation phenomenon intensifies, causing the blade wear to accelerate. hour, , indicating a 40% increase in wear rate.

[0053] The calculation formula for the generator insulation aging factor is: ;

[0054] in, , is the thermal life function based on the Arrhenius law;

[0055] , is the reference life, is the activation energy constant, which is applicable to common polyesterimide insulation materials; taking actual operation as an example, when the stator temperature hour, ; When the temperature rises to 100°C, , life expectancy is significantly shortened.

[0056] , is the electric lifetime function related to the electric field strength, , , is the voltage aging index, the electric field strength The stator temperature is collected and insulation resistance The calculation shows that:

[0057] ;in, , 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; when the insulation resistance decreases from 1000MΩ to 500MΩ, the electric field strength increases to the original times, and the electrical life is shortened to 0.5 times the original.

[0058] The bearing wear factor is calculated based on the modified L10 life theory: ;

[0059] in, , is the corrected bearing life function, , is the basic dynamic load rating, which is applicable to large deep groove ball bearings; the dynamic load ratings of different types of bearings vary greatly. For example, the dynamic load rating of tapered roller bearings may reach more than 300kN.

[0060] , is the equivalent dynamic load, and are radial force and axial force respectively, , is the ball bearing life index, , is the ISO correction factor. During hydropower station operation, radial forces primarily come from rotor weight and electromagnetic forces, while axial forces primarily come from the turbine's axial thrust. For example, for a 100MW hydro-generator set, the radial force may be 500-800kN, and the axial force may be 200-400kN.

[0061] The hydropower station control simulation model is established based on the multivariate linear weighted method:

[0062] Weighting factors are selected based on the impact of each component on overall unit performance and the severity of the consequences of a failure. Turbine blades, as core components of hydropower units, have the highest weight (0.4), as their wear directly impacts power generation efficiency and operational safety. Generator insulation and bearing systems are equally important, each receiving a weight of 0.3.

[0063] in, for The comprehensive aging degree of the equipment at any moment, ranging from 0 to 1.

[0064] Input the equipment aging factor into the equipment aging assessment model, and then calculate the equipment aging degree according to the comprehensive aging degree of the equipment. Determine the operating status of hydropower station equipment;

[0065] when , it indicates that the equipment is in good operating condition; at this time, all performance indicators of the equipment are normal and can operate according to rated parameters without special maintenance measures.

[0066] when When the device is aging, 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 can be appropriately reduced, the number of starts and stops can be reduced, and the life of the equipment can be extended.

[0067] when When the device is in need of maintenance, it should be shut down for inspection and overhaul immediately to replace seriously aged parts to ensure safe operation of the equipment.

[0068] Step S3: performing real-time optimization calculation of control parameters based on the hydropower station control simulation model, with the goal of minimizing performance deviation and equipment damage, and sending the optimized simulation control parameters to the actual hydropower generation control system to replace the current operating parameters.

[0069] when When the virtual system parameters are updated in a real-time data-driven manner, the objective function is to minimize performance deviation and equipment damage. The optimal simulated power and frequency at the current moment are obtained in accordance with the constraints, and the turbine speed and guide vane opening of the actual hydropower control system are adjusted accordingly.

[0070] The optimization strategy uses the Model Predictive Control (MPC) method to solve a finite-horizon optimization problem within each control cycle. The optimization cycle is usually set to 1-5 seconds, and the prediction horizon is set to 10-30 seconds. The objective function is:

[0071] .

[0072] in, is the length of the prediction time window, corresponding to 10 control steps. and are the reference power and frequency respectively, and To simulate power and frequency, is the increment of comprehensive aging degree of equipment at the moment, and the calculation formula is:

[0073] ;

[0074] Constraints include guide vane opening constraint: The minimum opening limit (0.05) avoids instability when the turbine is running at a very small opening; the maximum opening limit (1.0) corresponds to the fully open state of the guide vanes; power constraints: ,in, 10%-20% of the rated power, 105%-110% of rated power, allowing short-term overload operation; frequency constraints: .

[0075] During the control parameter optimization process, a vibration monitoring and early warning mechanism is established. Exceeding the preset threshold When the speed control system is in a state of high tension, the response speed is reduced to 50%-70% of the current value. Vibration monitoring uses a multi-point measurement method, with vibration sensors installed on the upper guide bearing, lower guide bearing, and thrust bearing. The vibration signal is filtered and amplified before frequency domain analysis to extract characteristic frequency components. Common fault characteristic frequencies include the rotation frequency (1×), the double frequency (2×), and the blade pass frequency.

[0076] The preset threshold Dynamically set according to the bearing model and operating conditions, the value range is 2.5-5.0mm / s; the vibration threshold of sliding bearings is usually 2.5-3.5mm / s, and that of rolling bearings is 3.5-5.0mm / s; when vibration exceeding the limit is detected, the system automatically reduces the response speed of the speed control system to avoid further aggravation of vibration by rapid guide vane movement.

[0077] After receiving the simulated control parameters, the actual hydropower control system updates the control parameters through parameter validity verification and a smooth switching mechanism. The parameter validity verification includes checking whether the guide vane opening, power and speed are within a safe range, and verifying whether the parameter change rate exceeds the maximum change rate allowed by the equipment. The smooth switching mechanism adopts a gradual parameter transition method, and the switching time window is set to 5-10 seconds.

[0078] Parameter validity verification includes verifying that the guide vane opening, power, and speed are within safe ranges. For example, the guide vane opening should be between 0.05 and 1.0, and the power should be between 10% and 110% of the rated power. Verifying that the parameter change rate exceeds the maximum allowable rate of change for the equipment is also important. For example, the guide vane opening change rate should not exceed 0.1 / s, and the power change rate should not exceed 5% / s of the rated power. Verifying that the logical relationship between parameters is reasonable is also important. For example, under the same head conditions, increasing the guide vane opening should increase the power accordingly.

[0079] The smooth switching mechanism uses a gradual parameter transition method with a switching window set to 5-10 seconds. Within the switching window, the control parameters transition linearly from their current values ​​to their target values. A first-order inertia link is used to achieve smooth parameter transition, with a time constant set to 2-3 seconds. For large parameter changes, a segmented switching method is used to break down the total change into multiple smaller steps.

[0080] Example 2

[0081] like Figure 2 FIG. 1 is a schematic diagram of the composition of a control simulation system for hydropower 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.

[0082] The data acquisition module is used to collect the operating parameters of the hydropower station equipment in real time; the module adopts a distributed architecture and includes multiple subsystems: a sensor subsystem, a data acquisition station, a communication network and a data preprocessing unit.

[0083] Sensor subsystems are deployed at key locations throughout the hydropower station, including turbines, generators, main shafts, and bearings. Sensor types include temperature sensors (such as PT100s and thermocouples), pressure sensors (such as diffused silicon pressure sensors), flow sensors (such as electromagnetic flowmeters), vibration sensors (such as accelerometers and velocity sensors), and displacement sensors (such as eddy current sensors). Each sensor offers high precision and reliability, ensuring long-term stable operation in harsh industrial environments.

[0084] The data acquisition station is responsible for collecting, converting, and preprocessing sensor signals. It utilizes industrial-grade data acquisition cards featuring multiple channels, high sampling rates, and low noise. The data acquisition station is typically deployed in the field control room and connected to the sensors via shielded cables. It uses differential inputs to enhance interference immunity. The data acquisition frequency is set based on the signal characteristics: fast-changing signals such as flow and pressure use a sampling frequency of 100Hz-1kHz; slow-changing signals such as temperature and insulation resistance use a sampling frequency of 1Hz-10Hz.

[0085] The communication network utilizes industrial Ethernet or fieldbus technology for data transmission. The network topology is either star or ring, with redundant configurations to ensure communication reliability. Data transmission utilizes industry-standard protocols such as Modbus TCP / IP, Profinet, or EtherCAT, ensuring compatibility with equipment from different manufacturers.

[0086] The data preprocessing unit performs preprocessing on the collected raw data, including filtering, calibration, and format conversion. Filtering algorithms, including low-pass filtering, band-pass filtering, and median filtering, effectively remove noise and interference. The calibration function performs linear or nonlinear calibration based on sensor characteristics to improve measurement accuracy. Data format conversion standardizes data from different sensors into a standard format for easier processing.

[0087] The aging factor calculation module is used to calculate the equipment aging factor and construct a hydropower station control simulation model that takes into account the aging of the equipment; and realize the real-time calculation of the three aging factors. The calculation of the turbine blade wear factor adopts a numerical integration method, such as the trapezoidal rule or the Simpson rule, to discretize and solve the integral equation. The calculation of the generator insulation aging factor involves a complex physical model and needs to consider the combined influence of multiple factors such as temperature, electric field strength, and humidity. The calculation of the bearing wear factor is based on fatigue life theory and takes into account the influence of factors such as load changes, lubrication conditions, and temperature. The comprehensive aging degree of the equipment is calculated based on the multivariate linear weighted method. The weight coefficient can be adjusted according to actual operating experience and expert knowledge. The evaluation results are displayed in a graphical interface to facilitate the operator to intuitively understand the equipment status.

[0088] The optimization control module is used to perform real-time optimization calculations of control parameters based on the control simulation model. It also establishes a dynamic prediction model for the hydropower unit to predict future system states. This prediction model utilizes state-space or transfer function methods, taking into account the coupled characteristics of the hydraulic, mechanical, and electrical systems. The model structure includes turbine characteristics, generator characteristics, and speed control system characteristics. Model parameters are obtained through system identification methods and are adjusted online based on operational data.

[0089] The parameter transmission module is used to send the optimized simulation control parameters to the actual hydropower generation control system.

[0090] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A control simulation method for hydropower generation, which dynamically modifies simulation control parameters based on acquired equipment aging parameters and sends the modified simulation control parameters to an actual hydropower generation control system; characterized in that: The control simulation method comprises the following steps: Step S1, real-time collection of operating parameters of hydropower station equipment, including guide vane opening, blade wear value, water head, flow rate, generator stator temperature, bearing vibration amplitude and insulation resistance; Step S2, calculating the equipment aging factor based on the operating data, and constructing a hydropower station control simulation model taking into account the equipment aging; the calculation of the equipment aging factor takes into account the turbine blade wear factor , generator insulation aging factor and bearing wear factor Three categories; The calculation formula of blade wear factor is: ;in, , is the wear coefficient, and is the rated flow and head, and for Real-time flow and head at all times; , is the cavitation number Related cavitation damage function; The calculation formula for the generator insulation aging factor is: ; in, , is the thermal life function based on the Arrhenius law; , is the reference life, is the activation energy constant; , is the electric lifetime function related to the electric field strength, , , is the voltage aging index, the electric field strength The stator temperature is collected and insulation resistance The calculation shows that: ;in, , 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: ; in, , is the corrected bearing life function, , is the basic dynamic load rating, , is the equivalent dynamic load, and are radial force and axial force respectively, , is the ball bearing life index, , is the ISO correction factor; Step S3: performing real-time optimization calculation of control parameters based on the hydropower station control simulation model, with the goal of minimizing performance deviation and equipment damage, and sending the optimized simulated control parameters to the actual hydropower generation control system to replace the current operating parameters; The hydropower station control simulation model is established based on the multivariate linear weighted method: ;in, for The comprehensive aging degree of the equipment at the moment, the value range is 0-1; According to the comprehensive aging degree of the equipment Determine the operating status of hydropower station equipment; when When , it indicates that the equipment is in good operating condition; when When , it indicates that the device is in the aging state; when When When the virtual system parameters are updated in a real-time data-driven manner, the objective function is to minimize performance deviation and equipment damage. The optimal simulated power and frequency at the current moment are obtained in accordance with the constraints. The turbine speed and guide vane opening of the actual hydropower control system are adjusted accordingly. The objective function is: ; in, is the length of the prediction time window, and are the reference power and frequency respectively, and To simulate power and frequency, is the increment of comprehensive aging degree of equipment at the moment, and the calculation formula is: ； Constraints include guide vane opening constraint: , Power Constraints: and frequency constraints: .

2. The method according to claim 1, characterized in that During the control parameter optimization process, a vibration monitoring and early warning mechanism is established. Exceeding the preset threshold When the speed control system is running, the response speed will be reduced to 50%-70% of the current value.

3. The method according to claim 2, characterized in that The preset threshold Dynamic setting is based on the bearing model and operating conditions, with a value range of 2.5-5.0mm / s.

4. The method according to claim 3, characterized in that After receiving the simulated control parameters, the actual hydropower control system updates the control parameters through parameter validity verification and a smooth switching mechanism. The parameter validity verification includes checking whether the guide vane opening, power and speed are within a safe range, and verifying whether the parameter change rate exceeds the maximum change rate allowed by the equipment. The smooth switching mechanism adopts a gradual parameter transition method, and the switching time window is set to 5-10 seconds.

5. A control simulation system for hydropower generation, used to implement the method according to any one of claims 1 to 4, 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 operating 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 taking equipment aging into account; The optimization control module is used to perform real-time optimization calculation of control parameters according to the control simulation model; The parameter transmission module is used to send the optimized simulation control parameters to the actual hydropower generation control system.

Images