Dynamic management system for water turbine

Abstract

The invention discloses a dynamic management system for a water turbine, and relates to the technical field of water turbine management, and the system comprises a data processing module which is used for collecting environment data and parameter data, calculating a water head and flow, and determining wave velocity data; and the dynamic calculation module constructs an elastic water hammer model to discretize and calculate the water head at the next moment, and dynamically calculates the output power of the water turbine. According to the method, dynamic change characteristics such as flow velocity, pressure and wave velocity are considered, the influence of sudden fluctuation on the system stability can be effectively inhibited, predictive control and intelligent optimization are supported through fusion of a dynamic model and real-time data, and an elastic water hammer model is combined with a continuity equation and a momentum equation, so that the stability of the system is improved. According to the method, complex factors such as water flow inertia and pressure wave propagation are considered, the dynamic characteristics of the water head and the flow in the pipeline can be accurately described, discretization calculation is performed by using a finite difference method, the water head and the flow can be dynamically updated based on time step iteration, and transient processes such as flow fluctuation, pressure change and load adjustment can be quickly responded.

Description

Technical Field

[0001] The invention relates to the technical field of water turbine management, in particular to a dynamic management system for water turbines. Background Art

[0002] With the rapid development of renewable energy technology, the operation efficiency and stability of turbines, as the core equipment of hydropower generation systems, are of great significance to improving the overall energy utilization rate and ensuring the reliability of power generation systems. In traditional turbine operation management, control strategies based on empirical rules and single monitoring parameters are often used. The existing dynamic management technology of hydraulic turbines has the problem of error accumulation when capturing the dynamic changes of pipeline head and flow. Especially under high-frequency fluctuations or complex flow conditions, it is difficult to reflect the transient characteristics of head and flow in real time, resulting in a delayed response of the management system to abnormal conditions. Secondly, the existing hydraulic turbine operation management system usually fails to fully integrate environmental data and hydraulic turbine parameter data, and cannot construct an accurate state equation model to describe the dynamic relationship between head, flow and speed, which makes the system less robust under abnormal conditions. Summary of the invention

[0003] In view of the above existing problems, the present invention is proposed.

[0004] Therefore, the present invention provides a dynamic management system for water turbines to solve the problem of error accumulation in existing water turbine dynamic management technologies when capturing dynamic changes in pipeline head and flow, especially under high-frequency fluctuations or complex flow conditions, it is difficult to reflect the transient characteristics of head and flow in real time, resulting in a delayed response of the management system to abnormal conditions. Secondly, existing water turbine operation management systems usually fail to fully integrate environmental data and water turbine parameter data, and cannot construct an accurate state equation model to describe the dynamic relationship between head, flow and speed, resulting in poor robustness of the system under abnormal conditions.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: In a first aspect, the present invention provides a dynamic management system for a water turbine, comprising: Data processing module, collects environmental data and parameter data, calculates water head and flow rate and determines wave velocity data; Dynamic calculation module, which constructs elastic water hammer model to discretize and calculate the water head at the next moment, and dynamically calculates the turbine output power; The error calculation module determines the state variables and measurable output variables of the turbine, constructs the dynamic state equation to calculate the rate of change, calculates the dynamic error of the state vector, and verifies the stability of the error; The simulation module simulates the environment and records the variable data, and determines the error based on the dynamically changing data; The report generation module outputs error reports and transmits data securely.

[0006] As a preferred solution of the dynamic management system for a water turbine according to the present invention, the collecting of environmental data and parameter data includes: Environmental data collection based on the turbine reservoir, including reservoir water level height and flow rate of reservoir water entering the turbine through the pipeline; Collect turbine parameter data, including turbine efficiency, guide vane opening and turbine speed; Preprocess the environmental data and parameter data and align their timestamps.

[0007] As a preferred solution of the dynamic management system for a water turbine of the present invention, the step of calculating the water head and flow rate and determining the wave velocity data includes: The turbine head and flow are calculated based on the reservoir water level and the head loss along the way, expressed as: ; ; in Indicates water head, Represents the water level of the reservoir. represents the pipeline friction coefficient, represents the length of the pipeline, v represents the flow velocity of the pipeline, represents the acceleration due to gravity, R represents the inner diameter of the pipe, It represents the cross-sectional flow rate through the pipe per unit time. Indicates the cross-sectional area of ​​the pipe; Due to the incompressible nature of water in the pipe, changes in flow will cause changes in water head. Therefore, the rate of change in water volume is equal to the rate of change in pipe volume caused by changes in water head. At the same time, the inertia and pressure effects in the water flow will cause dynamic changes in flow velocity, which can be expressed as: ; Where t represents time, and q represents the coordinate along the length of the pipeline; Considering that the compressibility of water and the elasticity of the pipe wall will affect the propagation speed of the pressure wave, the wave speed is determined based on the bulk elastic modulus of water, expressed as: ; Where a represents the wave velocity, K represents the bulk elastic modulus of water, represents the elastic modulus of the pipe material, p represents the density of water, It represents the thickness elastic modulus of the pipe wall.

[0008] As a preferred solution of the dynamic management system for a water turbine described in the present invention, the construction of an elastic water hammer model to discretize and calculate the water head at the next moment and dynamically calculate the output power of the water turbine includes: The continuity equation and momentum equation are combined to obtain the elastic water hammer model, and the finite difference method is used to discretize the elastic water hammer equation, which is expressed as: ; in and Respectively represent the pipeline position at the current moment and the next moment The water head, Indicates the time interval, and Respectively represent the pipeline position at the current time t and The flow rate at Indicates spatial interval; Iterate and calculate the water head of each spatial node based on the time step, update the flow of each spatial node, and output the dynamic change data of water head and flow at each time step; Based on the dynamic change data of the head and flow at each time step, combined with the parameter data of the turbine, the turbine output power is dynamically calculated, which is expressed as: ; in represents the turbine output power at time t, represents the turbine efficiency value, represents the guide vane opening value at time t, represents the hydraulic head data at time t, represents the turbine speed at time t.

[0009] As a preferred solution of the dynamic management system for a water turbine according to the present invention, the step of determining the state variables and measurable output variables of the water turbine and constructing a dynamic state equation to calculate the rate of change includes: Based on the engine state data, the linear dynamic relationship of the state variables is determined, which is expressed as: ; in It represents the state matrix, describing the linear dynamic relationship between the state variables, where each row represents the dynamic changes of water head, speed and flow, and each column represents the influence of water head, speed and flow on all state variables. represents the inertia time constant of water flow, D represents the damping coefficient, represents the turbine moment of inertia, represents the generator inertia constant; ; Where L represents the length of the pipeline, Indicates wave speed; Set the control variable matrix based on turbine efficiency and pipe length, expressed as: ; Where B represents the control variable matrix, and each row represents the influence of the input control on the head, speed and flow rate, among which only the speed of the turbine has a positive influence; Defining state variables and measurable output variables , expressed as: ; ; in represents the hydraulic head at time t, represents the speed at time t, represents the flow rate at time t, represents the output variable at time t, represents the state vector at time t; Define the output matrix C. Each row of the matrix C corresponds to an output variable, and each column of the matrix C corresponds to a state variable. If the output variable is directly equal to a state variable, the corresponding position is set to 1. If the output variable does not contain a state variable, the corresponding position is set to 0. The dynamic state equation is constructed based on the linear dynamic matrix, control variable matrix and output matrix, which is expressed as: ; ; in represents the rate of change of the state vector at time t, represents the guide vane opening at time t, The fault impact matrix represents the number of state variables n and the number of fault variables p, Represents the ratio of the state variable to the fault variable.

[0010] As a preferred solution of the dynamic management system for a water turbine according to the present invention, the dynamic error of the calculated state vector and the stability of the error are verified, including: The state vector is predicted at the next time step based on the rate of change of the state vector , based on the predicted state vector , determine the predicted output variables based on the output matrix C ; The error dynamic equation is defined based on the error between the real state vector and the predicted state vector, and the dynamic error is updated in real time, which is expressed as: ; ; ; ; in represents the dynamic error at time t, represents the state estimation error at time t, express The nonlinear dynamic error vector is represents the gain value at time t, represents the fault estimation error, represents the sensitivity coefficient; Verification error based on Lyapunov function The stability of is expressed as: ; in represents the error change value at time t, express The transpose calculation of express The transpose calculation of ; right Calculate the time derivative and get As The time derivative of If the time derivative Less than or equal to 0, then When it approaches 0, it is judged that the state of the turbine system is stable.

[0011] As a preferred solution of the dynamic management system for a water turbine according to the present invention, the step of performing simulation environment simulation and recording variable data includes: Use MATLAB simulation tools to build a simulation environment to simulate the dynamic model of the turbine, set the fault simulation scenario, and set the time step and total simulation time; The Euler method is used to perform numerical integration and solve the problem. The variable data including the state vector, the rate of change of the state vector, and the state estimation error are recorded, and the data are recorded in real time according to the dynamic changes over time.

[0012] As a preferred solution of the dynamic management system for a water turbine of the present invention, the error determination according to the dynamically changing data includes: Based on the dynamic change data of the variable data according to time, the root mean square value RMSE of the state estimation error is calculated, and the mean absolute error MAE of the root mean square value is calculated at the same time; The sum of the historical mean and standard deviation of the historical mean absolute error is used as the error threshold. If the historical mean absolute error is greater than or equal to the error threshold, the error is judged to be large.

[0013] As a preferred solution of the dynamic management system for a water turbine of the present invention, the output error report includes: Based on the judgment of error threshold, maintenance personnel are reminded through sound and light warning, and system maintenance and analysis are carried out based on the virtual environment for judgments with large errors; Through the report generation tool, error reports are generated based on the recorded dynamically changing variable data and error judgment information.

[0014] As a preferred solution of the dynamic management system for a water turbine of the present invention, the secure data transmission includes: The report information and maintenance personnel's analysis data are encrypted and transmitted based on the encrypted transmission protocol TLS, and the transmitted data is encrypted and protected by using the encryption algorithm AES. The asymmetric encryption RSA is used to actually implement key exchange, and the data is transmitted to the cloud and maintenance terminal respectively for double data backup.

[0015] The beneficial effects of the present invention are as follows: by considering the dynamic change characteristics of flow velocity, pressure, wave velocity, etc., the influence of sudden fluctuations on the stability of the system can be effectively suppressed; by fusion of dynamic models and real-time data, predictive control and intelligent optimization are supported; by combining the elastic water hammer model with the continuity equation and the momentum equation, complex factors such as water flow inertia and pressure wave propagation are considered, and the dynamic characteristics of the water head and flow in the pipeline can be accurately described; by using the finite difference method for discretization calculation, the water head and flow can be dynamically updated based on the time step iteration, and transient processes such as flow fluctuations, pressure changes, and load regulation can be quickly responded to; by combining the dynamic state equation with the elastic water hammer model, the changes in the water head and flow at each time step can be predicted in real time, providing high-precision input for control decisions; by the linkage verification of the Lyapunov function, the dynamic error convergence is ensured, and the instability of the system under complex working conditions such as load mutations and pressure fluctuations is reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0017] Figure 1 It is a structural schematic diagram of the dynamic management system for the water turbine in the present invention.

[0018] Figure 2 It is a schematic diagram of the flow of the dynamic management system for the water turbine in the present invention. DETAILED DESCRIPTION

[0019] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0020] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0021] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0022] Example, see Figure 1 and Figure 2 , this embodiment provides a dynamic management system for a water turbine, comprising the following steps: S1, collects environmental data and parameter data, calculates water head and flow rate and determines wave velocity data; Preferably, collecting environmental data and parameter data includes: Environmental data collection based on the turbine reservoir, including reservoir water level height and flow rate of reservoir water entering the turbine through the pipeline; Collect turbine parameter data, including turbine efficiency, guide vane opening and turbine speed; Preprocess the environmental data and parameter data and align their timestamps.

[0023] The collected data can provide an intuitive understanding of the turbine operation status. The water level height data is used to monitor the availability of water resources and promptly determine whether there is a water shortage or overflow risk. The water flow velocity data reflects the flow change and helps analyze the instantaneous impact of incoming water on turbine operation. The historical data and real-time water level and flow velocity data can be combined with the time series prediction model to determine the reservoir water supply capacity and turbine operation trend in advance, thereby enhancing the foresight of system regulation. Through denoising and normalization, the impact of sensor errors on system analysis can be reduced and the accuracy of analysis results can be improved. Through timestamp alignment, environmental data (such as water flow velocity) can be accurately matched with turbine parameters (such as speed and efficiency), supporting complex dynamic calculations and analysis.

[0024] Further, the head and flow are calculated and the wave velocity data is determined, including: The turbine head and flow are calculated based on the reservoir water level and the head loss along the way, expressed as: ; ; in Indicates water head, Represents the water level of the reservoir. represents the pipeline friction coefficient, represents the length of the pipeline, v represents the flow velocity of the pipeline, represents the acceleration due to gravity, R represents the inner diameter of the pipe, It represents the cross-sectional flow rate through the pipe per unit time. Indicates the cross-sectional area of ​​the pipe; Due to the incompressible nature of water in the pipe, changes in flow will cause changes in water head. Therefore, the rate of change in water volume is equal to the rate of change in pipe volume caused by changes in water head. At the same time, the inertia and pressure effects in the water flow will cause dynamic changes in flow velocity, which can be expressed as: ; Where t represents time, and q represents the coordinate along the length of the pipeline; Considering that the compressibility of water and the elasticity of the pipe wall will affect the propagation speed of the pressure wave, the wave speed is determined based on the bulk elastic modulus of water, expressed as: ; Where a represents the wave velocity, K represents the bulk elastic modulus of water, represents the elastic modulus of the pipe material, p represents the density of water, It represents the thickness elastic modulus of the pipe wall.

[0025] By accurately calculating the turbine head and flow, the rational allocation of water resources is ensured, and energy waste or equipment damage caused by overload or underload operation is avoided. Considering the head change caused by flow change, the dynamic behavior of the turbine during operation can be more realistically described, providing an accurate basis for the dynamic modeling of the system. Under the influence of water flow inertia and pressure changes, the dynamic change of flow velocity can be predicted, the operating parameters of the turbine can be optimized in advance, and the foresight of system regulation can be improved. The coupling of head and flow can reflect transient processes and support the suppression of turbine output power fluctuations that may be caused by rapid flow changes. Through wave velocity calculation, the propagation speed of head and flow changes in the pipeline is determined, providing key data for predicting and regulating pressure changes. After considering the compressibility of water bodies and pipeline elasticity, the pipeline wall thickness, material selection and length design can be optimized to reduce pressure loss and improve water transmission efficiency. By combining the characteristics of flow inertia and pressure dynamic changes, transient shocks and mechanical stresses caused by inertia effects can be predicted and alleviated, extending the life of equipment. By considering the dynamic changes in flow velocity, pressure, wave speed, etc., it is possible to effectively suppress the impact of sudden fluctuations on system stability and avoid equipment damage or unplanned downtime. Through the fusion of dynamic models and real-time data, it supports predictive control and intelligent optimization, provides support for the automated operation and maintenance of turbines, and reduces water energy losses and operating costs and improves system economy through pipeline design and water flow inertia analysis based on the propagation characteristics of pressure waves.

[0026] S2, construct an elastic water hammer model to discretize and calculate the water head at the next moment, and dynamically calculate the turbine output power; Preferably, an elastic water hammer model is constructed to discretize and calculate the water head at the next moment, and the output power of the turbine is dynamically calculated, including: The continuity equation and momentum equation are combined to obtain the elastic water hammer model, and the finite difference method is used to discretize the elastic water hammer equation, which is expressed as: ; in and Respectively represent the pipeline position at the current moment and the next moment The water head, Indicates the time interval, and Respectively represent the pipeline position at the current time t and The flow rate at Indicates spatial interval; Iterate and calculate the water head of each spatial node based on the time step, update the flow of each spatial node, and output the dynamic change data of water head and flow at each time step; Based on the dynamic change data of the head and flow at each time step, combined with the parameter data of the turbine, the turbine output power is dynamically calculated, which is expressed as: ; in represents the turbine output power at time t, represents the turbine efficiency value, represents the guide vane opening value at time t, represents the hydraulic head data at time t, represents the turbine speed at time t.

[0027] By combining the continuity equation and momentum equation with the elastic water hammer model, complex factors such as water flow inertia and pressure wave propagation are taken into account. The dynamic characteristics of the water head and flow in the pipeline can be accurately described. The finite difference method is used for discretization calculation. The water head and flow can be dynamically updated based on time step iteration, and transient processes such as flow fluctuations, pressure changes, and load regulation can be quickly responded to. The elastic water hammer model can be applied to a variety of operating conditions (such as rapid start and stop, load mutation), providing a reliable theoretical basis for the operation optimization of turbines in complex environments. By calculating the water head and flow at each spatial node, the hydraulic conditions at the turbine inlet can be dynamically grasped, supporting real-time monitoring and adjustment. The dynamically calculated elastic water hammer model and real-time head and flow data provide high-precision input for turbine load adjustment and energy optimization, ensuring that the system can operate at optimal efficiency under different operating conditions. The dynamic update of head and flow and the real-time calculation of output power enable the system to quickly respond to uncertain factors such as pressure fluctuations and load mutations, reduce oscillation and instability, and combine the dynamic calculation of the elastic water hammer model and its application in the turbine system to significantly improve the operating efficiency, stability and safety. At the same time, the combination of real-time head and flow data with power output calculation provides strong support for the intelligentization and long-term optimized operation of the turbine system.

[0028] Furthermore, the state variables and measurable output variables of the turbine are determined, and the dynamic state equation is constructed to calculate the rate of change, including: Based on the engine state data, the linear dynamic relationship of the state variables is determined, which is expressed as: ; in It represents the state matrix, describing the linear dynamic relationship between the state variables, where each row represents the dynamic changes of water head, speed and flow, and each column represents the influence of water head, speed and flow on all state variables. represents the inertia time constant of water flow, D represents the damping coefficient, represents the turbine moment of inertia, represents the generator inertia constant; ; Where L represents the length of the pipeline, Indicates wave speed; Set the control variable matrix based on turbine efficiency and pipe length, expressed as: ; Where B represents the control variable matrix, and each row represents the influence of the input control on the head, speed and flow rate, among which only the speed of the turbine has a positive influence; Defining state variables and measurable output variables , expressed as: ; ; in represents the hydraulic head at time t, represents the speed at time t, represents the flow rate at time t, represents the output variable at time t, represents the state vector at time t; Define the output matrix C. Each row of the matrix C corresponds to an output variable, including the output variable data of the water head and flow rate. Each column of the matrix C corresponds to a state variable, including the state variable data of the water head, speed and flow rate. If the output variable is directly equal to a state variable, the corresponding position is set to 1. If the output variable does not contain a state variable, the corresponding position is set to 0. The dynamic state equation is constructed based on the linear dynamic matrix, control variable matrix and output matrix, which is expressed as: ; ; in represents the rate of change of the state vector at time t, represents the guide vane opening at time t, The fault impact matrix represents the number of state variables n (such as water head and speed variable values ​​at the time of fault) and the number of fault variables p (such as actuator fault, sensor fault, etc.). Represents the ratio of the state variable to the fault variable.

[0029] Through the linear dynamic matrix A combined with the dynamic relationship of state variables (water head, speed, flow), the contribution of each state variable to the dynamic change of the system is clarified. The linear dynamic matrix simplifies the complex nonlinear equations through the matrixed dynamic relationship, which significantly improves the modeling efficiency and calculation accuracy. The optimal control relationship between the guide vane opening and the speed can be found through the control variable matrix, which maximizes the utilization efficiency of water energy, improves the power generation, realizes the dynamic adjustment of the turbine operation, and reduces unstable factors. The key state variables (water head, speed, flow) are mapped to the output variables through the output matrix, so that the control system can monitor the hydraulic conditions in real time through the measured output. The relationship between the output variables and the state variables is clarified, so that the measured data can be directly used for control and analysis, reducing unnecessary calculation links. The dynamic state equation introduces the fault influence matrix, which can quantify the impact of actuator faults and sensor faults on the system state. Through the fault influence matrix and real-time monitoring, abnormal conditions can be quickly identified, protective measures can be initiated, and equipment damage can be avoided. The dynamic equation provides theoretical support for the construction of intelligent control systems; By combining the dynamic state equation with the elastic water hammer model, the changes in head and flow at each time step can be predicted in real time, providing high-precision input for control decisions. Based on dynamic power calculation, the loads of different turbine units can be reasonably allocated to improve the power generation capacity of the overall system. The fusion analysis of head, flow and power data supports the coordinated optimization of turbine units and improves the overall power generation efficiency of the system. Combining historical and real-time data, unmanned or semi-automatic operation can be achieved through trend prediction and intelligent scheduling.

[0030] S3, determine the state variables and measurable output variables of the turbine, construct the dynamic state equation to calculate the rate of change, calculate the dynamic error of the state vector, and verify the stability of the error; Preferably, calculating the dynamic error of the state vector and verifying the stability of the error include: The state vector is predicted at the next time step based on the rate of change of the state vector , based on the predicted state vector , determine the predicted output variables based on the output matrix C ; The error dynamic equation is defined based on the error between the real state vector and the predicted state vector, and the dynamic error is updated in real time, which is expressed as: ; ; ; ; in represents the dynamic error at time t, represents the state estimation error at time t, express The nonlinear dynamic error vector is represents the gain value at time t, represents the fault estimation error, which is and The difference in the estimated error of represents the sensitivity coefficient, determined based on historical data, and The T in it represents transpose calculation; Verification error based on Lyapunov function The stability of is expressed as: ; in represents the error change value at time t, express The transpose calculation of express The transpose calculation of ; right Calculate the time derivative and get As The time derivative of is expressed as: ; ; ; in express The time derivative of express The time derivative of If the time derivative Less than or equal to 0, then When it approaches 0, it is judged that the state of the turbine system is stable.

[0031] By timely calculating the output variables according to the state prediction, it supports the real-time monitoring of the turbine operation state, quickly discovers abnormalities, provides a reference value of the future state for the predicted state vector, supports the early adjustment of the guide vane opening or flow rate, and avoids the efficiency loss caused by the lagging regulation. The predicted value calculation is quickly completed through matrix operation, reducing the dependence on complex computing resources, and providing support for embedded systems. The long-term cumulative deviation of the observation data is reduced through the real-time update of the dynamic error, ensuring the accuracy of the state prediction. Even if the system is disturbed or affected by environmental changes (such as flow velocity fluctuations, load changes), the real-time error update mechanism can quickly converge to a steady state and maintain the stability of the system operation. Based on the error stability verification of the Lyapunov function, it can quickly determine whether the turbine system is running stably, reduce the burden of manual monitoring, determine the stability of the system state in real time, and effectively avoid the destructive impact of unstable states on the operation of the turbine. The high-precision discretization calculation of the elastic water hammer model provides accurate initial data for the observer. The dynamic error update mechanism further optimizes the predicted value, reduces the deviation, and supports dynamic regulation under complex hydraulic conditions. When pressure fluctuations or sudden changes in flow velocity occur in the pipeline, the dynamic error update promptly adjusts the estimated values ​​of head and flow to ensure that the control system can respond quickly. The dynamic error update combines the elastic water hammer model and the state equation to optimize system modeling and control, reducing the energy loss caused by prediction deviation and measurement error. The elastic water hammer model and the dynamic state equation provide the Lyapunov function with accurate dynamic change data of head, flow and speed, verifying whether the error update process converges, and providing theoretical guarantee for dynamic regulation. The linkage verification of the Lyapunov function ensures the convergence of dynamic errors and reduces the instability of the system under complex working conditions such as load mutations and pressure fluctuations.

[0032] S4, performing simulation in a simulation environment to record variable data, and judging errors based on dynamically changing data; Preferably, performing simulation environment simulation and recording variable data includes: Use MATLAB simulation tools to build a simulation environment to simulate the dynamic model of the turbine, set the fault simulation scenario, and set the time step and total simulation time; The Euler method is used to perform numerical integration and solve the problem. The variable data including the state vector, the rate of change of the state vector, and the state estimation error are recorded, and the data are recorded in real time according to the dynamic changes over time.

[0033] Through fault simulation scenarios, the dynamic response of the system under abnormal conditions, such as head fluctuations and speed changes, is observed to help design more robust control strategies. MATLAB simulation provides a precise computing environment that can verify the accuracy of the elastic water hammer model and dynamic state equation under different working conditions, providing a reliable basis for practical applications. The Euler method combined with real-time recording can capture the transient behavior of the turbine in a dynamic environment (such as head fluctuations and flow changes), providing high-resolution data for control system design. The recorded state vector and change rate data provide real-time input for dynamic control strategies, supporting rapid adjustment of key parameters such as guide vane opening and speed.

[0034] Furthermore, the error is determined based on the dynamically changing data, including: Based on the dynamic change data of the variable data according to time, the root mean square value RMSE of the state estimation error is calculated, and the mean absolute error MAE of the root mean square value is calculated at the same time; The sum of the historical mean and standard deviation of the historical mean absolute error is used as the error threshold. If the historical mean absolute error is greater than or equal to the error threshold, the error is judged to be large.

[0035] RMSE provides an overall measure of the state estimation error, clarifies the deviation between the model prediction and the actual state, and MAE describes the volatility of the error. It analyzes the distribution characteristics of the error over time and provides a basis for model tuning. By adjusting the changing trends of the RMSE and MAE indicators, the observer gain value or sensitivity coefficient is adjusted to gradually reduce the estimation error. Combined with the trend analysis of RMSE and MAE, the control parameters (such as guide vane opening and speed) are dynamically adjusted to improve the short-term regulation performance.

[0036] S5, output error report and perform data security transmission; Preferably, the output error report includes: Based on the judgment of error threshold, maintenance personnel are reminded through sound and light warning, and system maintenance and analysis are carried out based on the virtual environment for judgments with large errors; Through the report generation tool, error reports are generated based on the recorded dynamically changing variable data and error judgment information.

[0037] The sound and light early warning mechanism can issue a warning the moment the error exceeds the threshold, reducing the response delay of abnormal conditions. Analyzing problems in a virtual environment avoids the need for frequent shutdowns of actual equipment for troubleshooting, reducing maintenance costs and time loss. The dynamic variable trend charts, error information, fault markers and other content in the report provide a clear data basis for maintenance and optimization decisions. By summarizing reports for multiple time periods, the long-term trends of errors and dynamic changes are analyzed to identify potential problems and optimization directions.

[0038] Furthermore, data security transmission is performed, including: The report information and maintenance personnel's analysis data are encrypted and transmitted based on the encrypted transmission protocol TLS, and the transmitted data is encrypted and protected by using the encryption algorithm AES. The asymmetric encryption RSA is used to actually implement key exchange, and the data is transmitted to the cloud and maintenance terminal respectively for double data backup.

[0039] The encrypted channel of the TLS protocol protects report information and analytical data from network attacks during transmission. The data is efficiently encrypted and protected using a symmetric encryption algorithm to ensure the confidentiality of report information and maintenance data. The public key is used to encrypt the session key and the private key is used to decrypt the session key to achieve secure key exchange. The dual backup of the cloud and maintenance terminal enhances the data's disaster recovery capability and improves the flexibility of data management.

[0040] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A dynamic management system for a water turbine, characterized in that: include: Data processing module, collects environmental data and parameter data, calculates water head and flow rate and determines wave velocity data; Dynamic calculation module, which constructs elastic water hammer model to discretize and calculate the water head at the next moment, and dynamically calculates the turbine output power; The error calculation module determines the state variables and measurable output variables of the turbine, constructs the dynamic state equation to calculate the rate of change, calculates the dynamic error of the state vector, and verifies the stability of the error; The simulation module simulates the environment and records the variable data, and determines the error based on the dynamically changing data; The report generation module outputs error reports and transmits data securely.

2. The dynamic management system for a water turbine according to claim 1, characterized in that: The collecting of environmental data and parameter data includes: Environmental data collection based on the turbine reservoir, including reservoir water level height and flow rate of reservoir water entering the turbine through the pipeline; Collect turbine parameter data, including turbine efficiency, guide vane opening and turbine speed; Preprocess the environmental data and parameter data and align their timestamps.

3. The dynamic management system for a water turbine according to claim 2, characterized in that: The method of calculating the water head and flow rate and determining the wave velocity data includes: The turbine head and flow are calculated based on the reservoir water level and the head loss along the way, expressed as: ； ； in Indicates water head, Represents the water level of the reservoir. represents the pipeline friction coefficient, represents the length of the pipeline, v represents the flow velocity of the pipeline, represents the acceleration due to gravity, R represents the inner diameter of the pipe, It represents the cross-sectional flow rate through the pipe per unit time. Indicates the cross-sectional area of ​​the pipe; Due to the incompressible nature of water in the pipe, changes in flow will cause changes in water head. Therefore, the rate of change in water volume is equal to the rate of change in pipe volume caused by changes in water head. At the same time, the inertia and pressure effects in the water flow will cause dynamic changes in flow velocity, which can be expressed as: ； Where t represents time, and q represents the coordinate position along the length of the pipeline; Considering that the compressibility of water and the elasticity of the pipe wall will affect the propagation speed of the pressure wave, the wave speed is determined based on the bulk elastic modulus of water, expressed as: ； Where a represents the wave velocity, K represents the bulk elastic modulus of water, represents the elastic modulus of the pipe material, p represents the density of water, It represents the thickness elastic modulus of the pipe wall.

4. The dynamic management system for a water turbine according to claim 3, characterized in that: The elastic water hammer model is constructed to discretize and calculate the water head at the next moment, and the output power of the turbine is dynamically calculated, including: The continuity equation and momentum equation are combined to obtain the elastic water hammer model, and the finite difference method is used to discretize the elastic water hammer equation, which is expressed as: ； in and Respectively represent the pipeline position at the current moment and the next moment The water head, Indicates the time interval, and Respectively represent the pipeline position at the current time t and The flow rate at Indicates spatial interval; Iterate and calculate the water head of each spatial node based on the time step, update the flow of each spatial node, and output the dynamic change data of water head and flow at each time step; Based on the dynamic change data of the head and flow at each time step, combined with the parameter data of the turbine, the turbine output power is dynamically calculated, which is expressed as: ； in represents the turbine output power at time t, represents the turbine efficiency value, represents the guide vane opening value at time t, represents the hydraulic head data at time t, represents the turbine speed at time t.

5. The dynamic management system for a water turbine according to claim 4, characterized in that: The step of determining the state variables and measurable output variables of the turbine and constructing a dynamic state equation to calculate the rate of change includes: Based on the engine state data, the linear dynamic relationship of the state variables is determined, which is expressed as: ； in It represents the state matrix, describing the linear dynamic relationship between the state variables, where each row represents the dynamic changes of water head, speed and flow, and each column represents the influence of water head, speed and flow on all state variables. represents the inertia time constant of water flow, D represents the damping coefficient, represents the turbine moment of inertia, represents the generator inertia constant; ； Where L represents the length of the pipeline, Indicates wave speed; Set the control variable matrix based on turbine efficiency and pipe length, expressed as: ； Where B represents the control variable matrix, and each row represents the influence of the input control on the head, speed and flow rate, among which only the speed of the turbine has a positive influence; Defining state variables and measurable output variables , expressed as: ； ； in represents the hydraulic head at time t, represents the speed at time t, represents the flow rate at time t, represents the output variable at time t, represents the state vector at time t; Define the output matrix C. Each row of the matrix C corresponds to an output variable, and each column of the matrix C corresponds to a state variable. If the output variable is directly equal to a state variable, the corresponding position is set to 1. If the output variable does not contain a state variable, the corresponding position is set to 0. The dynamic state equation is constructed based on the linear dynamic matrix, control variable matrix and output matrix, which is expressed as: ； ； in represents the rate of change of the state vector at time t, represents the guide vane opening at time t, The fault impact matrix represents the number of state variables n and the number of fault variables p, Represents the ratio of the state variable to the fault variable.

6. The dynamic management system for a water turbine according to claim 5, characterized in that: The step of calculating the dynamic error of the state vector and verifying the stability of the error includes: The state vector is predicted at the next time step based on the rate of change of the state vector , based on the predicted state vector , determine the predicted output variables based on the output matrix C ; The error dynamic equation is defined based on the error between the real state vector and the predicted state vector, and the dynamic error is updated in real time, which is expressed as: ； ； ； ； in represents the dynamic error at time t, represents the state estimation error at time t, express The nonlinear dynamic error vector is represents the gain value at time t, represents the fault estimation error, represents the sensitivity coefficient; Verification error based on Lyapunov function The stability of is expressed as: ； in represents the error change value at time t, express The transpose calculation of express The transpose calculation of ; right Calculate the time derivative and get As The time derivative of If the time derivative Less than or equal to 0, then When it approaches 0, it is judged that the state of the turbine system is stable.

7. The dynamic management system for a water turbine according to claim 6, characterized in that: The step of performing simulation environment simulation and recording variable data includes: Use MATLAB simulation tools to build a simulation environment to simulate the dynamic model of the turbine, set the fault simulation scenario, and set the time step and total simulation time; The Euler method is used to perform numerical integration and solve the problem. The variable data including the state vector, the rate of change of the state vector, and the state estimation error are recorded, and the data are recorded in real time according to the dynamic changes over time.

8. The dynamic management system for a water turbine according to claim 7, characterized in that: The step of judging the error according to the dynamically changing data includes: Based on the dynamic change data of the variable data according to time, the root mean square value RMSE of the state estimation error is calculated, and the mean absolute error MAE of the root mean square value is calculated at the same time; The sum of the historical mean and standard deviation of the historical mean absolute error is used as the error threshold. If the historical mean absolute error is greater than or equal to the error threshold, the error is judged to be large.

9. The dynamic management system for a water turbine according to claim 8, characterized in that: The output error report includes: Based on the judgment of error threshold, maintenance personnel are reminded through sound and light warning, and system maintenance and analysis are carried out based on the virtual environment for judgments with large errors; Through the report generation tool, error reports are generated based on the recorded dynamically changing variable data and error judgment information.

10. The dynamic management system for a water turbine according to claim 9, characterized in that: The secure data transmission includes: The report information and maintenance personnel's analysis data are encrypted and transmitted based on the encrypted transmission protocol TLS, and the transmitted data is encrypted and protected by using the encryption algorithm AES. The asymmetric encryption RSA is used to actually implement key exchange, and the data is transmitted to the cloud and maintenance terminal respectively for double data backup.

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