Axial-flow movable paddle turbine speed regulation optimization method based on guide vane and paddle cooperative control
By adding a signal distribution device and particle swarm algorithm optimization coefficient K to the axial flow rotation paddle turbine speed regulation system, the problem of the blade angle change lag behind the blade opening change is solved, and faster speed regulation response and higher system stability are achieved.
Patent Information
- Application Number
- CN202411771989.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-04
- Publication Date
- 2025-03-11
AI Technical Summary
In the speed regulation system of traditional axial flow paddle turbines, the change in the rotation angle of the blade lags behind the change in the opening degree of the guide blade, resulting in insensitive speed regulation and slow response speed, and may cause mechanical impact on key components of the turbine unit, increasing the risk of failure.
The speed regulation optimization method based on the coordinated control of the guide blade and the blade is adopted. By adding a signal distribution device between the PID controller and the actuator, the output of the PID controller is decomposed into the guide blade opening signal and the blade rotation angle signal, and the optimization coefficient K is optimized using the particle swarm algorithm to speed up the response speed of the speed regulation system.
It effectively overcomes the problem that the blade angle changes lag behind the blade opening changes, accelerates the adjustment time of the dynamic process, improves the response speed of the speed regulation system, reduces mechanical impact, and enhances the stability and reliability of the turbine unit.
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Figure CN119597033A_ABST
Abstract
Description
Technical Field
[0001] The invention relates to the technical field of axial flow propeller type water turbine speed regulation, in particular to an axial flow propeller type water turbine speed regulation optimization method based on coordinated control of guide vanes and blades. Background Art
[0002] With the development of new energy technologies, emerging power generation methods such as wind power generation, hydropower generation, and photovoltaic power generation account for an increasing proportion of power generation in the power grid, especially hydropower generation, which not only produces electricity, but also has other important functions, such as peak load regulation and dispatching. Since turbines have the functions of peak load regulation and dispatching in the power grid, they need to be regulated in order to maintain frequency stability.
[0003] The traditional axial flow propeller turbine speed control system is mainly composed of PID controller, actuator, turbine-pipeline and generator-load. Each part of the speed control system is indispensable to the turbine. They influence and cooperate with each other to complete the tasks of power generation and peak load regulation of the turbine.
[0004] Different from the mixed flow turbine, the actuators of the axial flow propeller turbine include two types: guide vanes and blades. In order to make it operate in the best state, during the operation of the turbine, the water head, blade angle and guide vane opening need to meet a specific relationship, which is called a coordinated relationship. The coordinated relationship of the three is controlled by a coordinated device. In the traditional axial flow propeller turbine speed control system, in order to simplify the control system, the guide vane opening size is only controlled by the governor, and the blade angle changes according to a specific coordinated curve, following the change of the guide vane. Such a control method can often achieve higher operating efficiency when operating in the guide vane-blade coordinated area. However, although this simplifies the structure of the control system and the complexity of the control, under other working conditions, if the turbine does not operate in the guide vane-blade coordinated area, the efficiency of the axial flow propeller turbine unit during the speed regulation process will be reduced, resulting in the turbine adjustment response being insensitive and slow, and it will also cause a large mechanical impact on the key components of the turbine unit such as the blades and flanges, which is easy to cause the turbine unit to fail. The specific construction of the traditional axial flow propeller turbine speed control system is as follows:
[0005] The speed regulator uses the PID controller commonly used in turbines. There are three important parameters in the PID controller: p , K i and K d , where K p Represents the proportional gain, which will affect the response speed of the speed control system and the accuracy of the system after it is in a steady state. K i Indicates the integral gain, which is generally used to eliminate steady-state errors and make the output value consistent with the given value. K dRepresents the differential gain, which can alleviate the hysteresis phenomenon of the controlled object and reduce the overshoot and oscillation of the speed control system. The transfer function of the PID controller linear model is:
[0006]
[0007] Where x is the grid frequency, c is the given frequency, σ is the controller output signal, T 1v is the differential time constant, b p is the permanent slip coefficient, and Δ is the relative deviation.
[0008] The actuator has two major functions: one is to convert the received electrical signal into a mechanical signal, that is, to generate the corresponding mechanical displacement; the other is to amplify the electrical signal so that the signal can be recognized by the system more effectively. Common actuators are generally simplified to first-order systems. The linear model transfer function of the axial flow propeller turbine actuator is:
[0009]
[0010] Among them, y is the guide vane opening, y pid is the PID controller output, T y is the inertia time constant. In actual situations, the actuator usually contains dead zone and saturation nonlinear links, which are usually caused by mechanical dead zone, guide vane opening limit and rate limit caused by mechanical links.
[0011] The task of the water diversion system is to guide the water from the upstream reservoir to the turbine, and drive the turbine to rotate by the water flow impacting the blades, and finally flow to the downstream reservoir. The water diversion system includes water diversion pipelines, surge tanks, pressure pipelines and tailwater pipes. The research on the pressure water diversion system model is usually divided into two categories: rigid water hammer model and elastic water hammer model. In small hydropower stations with small water flow fluctuations in the water diversion pipeline and a pipeline length of less than 800 meters, the water flow inertia can be ignored and the model can be regarded as a rigid water hammer model; when the length of the water diversion pipeline exceeds 800 meters, the water flow inertia is large, and the elasticity between the water and the pipeline wall needs to be considered, and the elastic water hammer model is often used.
[0012] The transfer function of the pipeline rigid water hammer model is:
[0013] F1(s)=-T w s;
[0014] Among them, T w is the water time constant. The transfer function of the pipeline elastic water hammer model is:
[0015]
[0016] Among them, T e Allow time for water to spread.
[0017] The water turbine is the most important equipment in a hydroelectric power station and the core of a hydroelectric power generation system. The basic working principle of a water turbine is to use the kinetic energy and potential energy of the water flow to drive the impeller to rotate, thereby driving the generator to generate electricity. Water flows from high to low, and this height difference provides potential energy, while the speed of the water flow provides kinetic energy. The expression of the axial flow propeller turbine model is:
[0018]
[0019] Among them, m t is the turbine torque, q is the flow rate, y is the guide vane opening, z is the blade angle, h is the water head, x is the speed, e is the x 、e y 、e h 、e qx 、e qy and e qh represents the transfer coefficient of the turbine, e z is the transmission coefficient of turbine torque to blade angle, e qz is the transfer coefficient of turbine flow to blade angle.
[0020] The generator is the core component that converts mechanical energy into electrical energy. It plays a key role in the production of electrical energy. Its design and operation need to take into account the performance of the turbine, the requirements of the power grid, and the overall reliability of the system. The first-order model of the generator-load is commonly used, and the transfer function is
[0021]
[0022] Among them, m g Indicates load torque, T a represents the total constant taking into account the inertia time constant of the generator and the inertia time constant of the load, e g Indicates the regulation factor of the generator.
[0023] In this way, a complete axial-flow propeller turbine speed control system model is established. Therefore, it can be seen that the traditional axial-flow propeller turbine usually controls the amount of water entering the runner chamber by adjusting the opening of the guide vane, and the movement of the blade corresponds to the opening of the guide vane. When the grid load changes, the unit frequency will also change accordingly. At this time, the controller will send a regulation signal to control the increase or decrease of the guide vane opening, and the blade angle will be adjusted with the change of the guide vane opening. When the unit is running stably, the guide vane opening and the blade angle will move in a specific coordination area. However, due to the large inertia of the water flow in the water turbine generator water diversion system, the adjustment of the water torque cannot respond immediately when the load torque changes, which reduces the efficiency of the axial-flow propeller turbine unit in the speed control process, resulting in the insensitive response of the turbine regulation and slow response speed, and will also cause great mechanical impact on the key components of the turbine unit such as the blades and flanges, which is easy to cause the turbine unit to fail. Summary of the invention
[0024] The purpose of the present invention is to overcome the above-mentioned shortcomings and provide an axial flow propeller turbine speed regulation optimization method based on coordinated control of guide vanes and blades, so as to overcome the problem that the blade angle change of the traditional axial flow propeller turbine lags behind the guide vane opening change, speed up the adjustment time of the dynamic process, make the blade no longer lag behind the guide vane, speed up the action of the actuator, and thus speed up the response speed of the speed regulation system.
[0025] In order to solve the above technical problems, the technical solution adopted by the present invention is: a speed regulation optimization method of an axial flow propeller turbine based on coordinated control of guide vanes and blades, comprising the following steps:
[0026] Step 1: Establish models of various parts of the axial flow propeller turbine speed control system, including PID controller, actuator, water diversion system, turbine and generator-load;
[0027] Step 2: Add a signal distribution device between the PID controller and the actuator of the model, and the PID controller outputs the control signal y pid The actuator receives the signal and converts it into the guide vane opening signal y, and then calculates the blade angle signal z through the association curve; in the model, the signal distribution device first receives the control signal y pid , the signal distribution device decomposes it into a guide vane opening signal y and a blade angle signal z, and then transmits the guide vane opening signal and the blade opening angle to the actuator respectively, so as to speed up the actuator action time and reduce the system adjustment time;
[0028] Step 3: Use the particle swarm algorithm to optimize the coefficient K. Take different optimization coefficients K under different working conditions to optimize the axial flow propeller turbine under different working conditions. Input the coefficient K corresponding to the working condition into the signal distribution device to enable the signal distribution device to exert its optimal performance.
[0029] Preferably, in step 1, the PID controller model building process is as follows:
[0030] There are three important parameters K in the PID controller: p , K i and K d , where K p Represents the proportional gain, which affects the response speed of the speed control system and the accuracy of the system after it is in a steady state. K i Indicates the integral gain, which is used to eliminate the steady-state error and make the output value consistent with the given value. d Represents the differential gain, which alleviates the hysteresis of the controlled object and reduces the overshoot and oscillation of the speed control system; the transfer function of the PID controller linear model is:
[0031]
[0032] Where x is the grid frequency, c is the given frequency, σ is the controller output signal, T 1v is the differential time constant, b p is the permanent slip coefficient, Δ is the relative deviation, and s is a complex variable, which represents the frequency response when the real part is zero and the imaginary part is the angular frequency.
[0033] Preferably, in step 1, the actuator model building process is as follows:
[0034] The actuator has two major functions: one is to convert the received electrical signal into a mechanical signal, that is, to generate the corresponding mechanical displacement; the other is to amplify the electrical signal so that the signal can be recognized by the system more effectively. The actuator is simplified to a first-order system, and the linear model transfer function of the axial-flow propeller turbine actuator is:
[0035]
[0036] Among them, y is the guide vane opening, y pid is the PID controller output, T y is the inertia time constant; in actual situations, the actuator contains dead zone and saturated nonlinear links, which are caused by the mechanical dead zone, guide vane opening limit and rate limit caused by the mechanical link.
[0037] Preferably, in step 1, the water diversion system model building process is as follows:
[0038] The task of the water diversion system is to guide the water from the upstream reservoir to the turbine, and drive the turbine to rotate through the water flow impacting the blades, and finally flow to the downstream reservoir; the water diversion system includes the water diversion pipeline, the pressure surge chamber, the pressure pipeline and the tailwater pipe; the pressure water diversion system model is divided into two categories: rigid water hammer model and elastic water hammer model; in small hydropower stations with small water flow fluctuations in the water diversion pipeline and a pipeline length of less than 800 meters, the water flow inertia can be ignored and the model can be regarded as a rigid water hammer model; when the water diversion pipeline is more than 800 meters long, the water flow inertia is large, and the elasticity between the water and the pipeline wall needs to be considered, and the elastic water hammer model is adopted;
[0039] The transfer function of the pipeline rigid water hammer model is:
[0040] F1(s)=-T w s;
[0041] Among them, T w is the water time constant;
[0042] The transfer function of the pipeline elastic water hammer model is:
[0043]
[0044] Among them, T e Allow time for water to spread.
[0045] Preferably, in step 1, the turbine model building process is as follows:
[0046] The turbine uses the kinetic energy and potential energy of water flow to drive the impeller to rotate, thereby driving the generator to generate electricity; water flows from high to low, the height difference provides potential energy, and the speed of the water flow provides kinetic energy; the expression of the axial flow propeller turbine model is:
[0047]
[0048] Among them, m t is the turbine torque, q is the flow rate, y is the guide vane opening, z is the blade angle, h is the water head, x is the speed, e is the x 、e y 、e h 、e qx 、e qy and e qh represents the transfer coefficient of the turbine, e z is the transmission coefficient of turbine torque to blade angle, e qz is the transfer coefficient of turbine flow to blade angle.
[0049] Preferably, in step 1, the generator-load model building process is as follows:
[0050] The generator is the core component that converts mechanical energy into electrical energy. It plays a key role in the production of electrical energy. Its design and operation need to take into account the performance of the turbine, the requirements of the power grid, and the overall reliability of the system. The generator-load is its first-order model, and the transfer function is:
[0051]
[0052] Among them, m g Indicates load torque, T a represents the total constant taking into account the inertia time constant of the generator and the inertia time constant of the load, e g Indicates the regulation factor of the generator.
[0053] Preferably, the specific process of step 2 is:
[0054] After receiving the control signal output by the PID controller, pid Finally, the original guide vane opening y′ is calculated according to the original actuator algorithm, the original guide vane opening y′ is input into the original symmetric curve, the original blade angle z′ is calculated, the original blade angle z′ is multiplied by an optimization coefficient K, the range of K is between 0 and 1, and the blade angle z is obtained. The original blade angle z′ is subtracted from the current blade angle z to obtain the change in blade angle Δz, and this part of the blade angle change is converted into the guide vane opening according to a certain ratio, so as to obtain the change in guide vane opening Δy, and the change in guide vane opening Δy plus the original guide vane opening y′ can obtain the current guide vane opening y, and finally the guide vane opening y and the blade angle z are input into the guide vane actuator and the blade actuator respectively.
[0055] Preferably, the specific process of optimizing the coefficient K by using the particle swarm algorithm in step 3 is as follows:
[0056] The specific steps of the particle swarm algorithm include initializing the position and speed of the particle swarm, evaluating the fitness of each particle according to the objective function, updating the speed and position of the particle, and determining whether to terminate the algorithm according to certain stopping conditions; assuming that in a D-dimensional search space, there are N particles forming a colony, then the speed and position update formula of the i-th particle is:
[0057]
[0058] Among them, k is the number of iterations, d is the particle dimension number, is the velocity vector of particle i in the kth iteration, is the position vector of particle i in the dth dimension in the kth iteration, is the historical optimal position of particle i in the dth dimension in the kth iteration, that is, the optimal solution searched by the i-th particle after the kth iteration, is the historical optimal position of the swarm in the dth dimension in the kth iteration, that is, the optimal solution in the entire particle swarm after the kth iteration, ω is the inertia weight, c1 is the individual learning factor, c2 is the group learning factor, r1 and r2 are random numbers in the interval [0,1];
[0059] In the optimization of coefficient K, the objective function should be to minimize the adjustment time of the system. The constraint condition is that the guide vane and blade openings are within the acceptable range of the speed control system and cannot exceed the safety value specified by the system. The evaluation index is the percentage of the adjustment time t of the optimized system compared to the original adjustment time t0:
[0060]
[0061] When the operating conditions of the axial-flow propeller turbine change, the original optimization coefficient K can no longer guarantee the rationality of the signal distribution at this time, because the K value at this time is optimized for the previous operating conditions, so the coefficient K needs to be adjusted according to the changes in the operating conditions, and the value of Δt% can be adjusted according to actual needs; when Δt% is less than the set value, the signal distribution device will recalculate the optimization coefficient K so that the guide vane opening and blade angle allocated by the device can meet the operating conditions at this time; therefore, the evaluation index Δt% can ensure that the signal distribution device can perform optimally under different conditions.
[0062] Beneficial effects of the present invention:
[0063] 1. Compared with the traditional axial flow propeller turbine speed control system, the present invention has a faster adjustment speed while ensuring the adjustment accuracy, and is suitable for different working conditions. The added signal distribution device has high reliability, convenient control and easy implementation.
[0064] 2. Since the current axial-flow propeller-type water turbine units have a long idle time and a large moment of inertia, and the blades will lag behind when following the changes in the guide vanes, causing the speed control system to respond untimely, it is difficult to meet the frequency regulation requirements of the power grid in terms of speed regulation performance. The present invention overcomes the problem that the blade angle change of the traditional axial-flow propeller-type water turbine lags behind the guide vane opening change, and speeds up the adjustment time of the dynamic process. It uses a signal distribution device to reasonably distribute the guide vane opening and the blade angle, and simultaneously outputs control signals of the guide vanes and the blades, so that the blades no longer lag behind the guide vanes, speeding up the action of the actuator, thereby speeding up the response speed of the speed control system. BRIEF DESCRIPTION OF THE DRAWINGS
[0065] Figure 1 It is the transfer function block diagram of the speed control system of the axial flow propeller turbine;
[0066] Figure 2 The schematic diagram of the structure of the improved axial flow propeller type water turbine speed regulating system of the present invention;
[0067] Figure 3 It is a schematic diagram of the specific structure of the signal distribution device;
[0068] Figure 4 Flowchart for optimizing coefficient K using particle swarm algorithm. DETAILED DESCRIPTION
[0069] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments.
[0070] Embodiment 1: A speed regulation optimization method for an axial flow propeller turbine based on coordinated control of guide vanes and blades, comprising the following steps:
[0071] Step 1: Establish models of various parts of the axial flow propeller turbine speed control system, including PID controller, actuator, water diversion system, turbine and generator-load; Figure 1 The figure is a transfer function block diagram of the axial flow propeller turbine speed control system. The traditional axial flow propeller turbine speed control system is mainly composed of a PID controller, an actuator, a turbine-pipeline, and a generator-load. Each part of the speed control system is indispensable to the turbine. They influence and cooperate with each other to complete the turbine's power generation, peak load and other tasks.
[0072] The PID controller model construction process is as follows:
[0073] There are three important parameters K in the PID controller: p , K i and K d , where K p Represents the proportional gain, which affects the response speed of the speed control system and the accuracy of the system after it is in a steady state. K i Indicates the integral gain, which is used to eliminate the steady-state error and make the output value consistent with the given value. d Represents the differential gain, which alleviates the hysteresis of the controlled object and reduces the overshoot and oscillation of the speed control system; the transfer function of the PID controller linear model is:
[0074]
[0075] Where x is the grid frequency, c is the given frequency, σ is the controller output signal, T 1v is the differential time constant, b p is the permanent slip coefficient, Δ is the relative deviation, and s is a complex variable, which represents the frequency response when the real part is zero and the imaginary part is the angular frequency.
[0076] The process of building the actuator model is as follows:
[0077] The actuator has two major functions: one is to convert the received electrical signal into a mechanical signal, that is, to generate the corresponding mechanical displacement; the other is to amplify the electrical signal so that the signal can be recognized by the system more effectively. The actuator is simplified to a first-order system, and the linear model transfer function of the axial-flow propeller turbine actuator is:
[0078]
[0079] Among them, y is the guide vane opening, y pid is the PID controller output, T y is the inertia time constant; in actual situations, the actuator contains dead zone and saturated nonlinear links, which are caused by the mechanical dead zone, guide vane opening limit and rate limit caused by the mechanical link.
[0080] The process of building the water diversion system model is as follows:
[0081] The task of the water diversion system is to guide the water from the upstream reservoir to the turbine, and drive the turbine to rotate through the water flow impacting the blades, and finally flow to the downstream reservoir; the water diversion system includes the water diversion pipeline, the pressure surge chamber, the pressure pipeline and the tailwater pipe; the pressure water diversion system model is divided into two categories: rigid water hammer model and elastic water hammer model; in small hydropower stations with small water flow fluctuations in the water diversion pipeline and a pipeline length of less than 800 meters, the water flow inertia can be ignored and the model can be regarded as a rigid water hammer model; when the water diversion pipeline is more than 800 meters long, the water flow inertia is large, and the elasticity between the water and the pipeline wall needs to be considered, and the elastic water hammer model is adopted;
[0082] The transfer function of the pipeline rigid water hammer model is:
[0083] F1(s)=-T w s;
[0084] Among them, T w is the water time constant;
[0085] The transfer function of the pipeline elastic water hammer model is:
[0086]
[0087] Among them, T e Allow time for water to spread.
[0088] The turbine model construction process is as follows:
[0089] The turbine is the most important equipment in a hydroelectric power station and the core of a hydroelectric power generation system. The turbine uses the kinetic energy and potential energy of the water flow to drive the impeller to rotate, thereby driving the generator to generate electricity; water flows from high to low, and this height difference provides potential energy, while the speed of the water flow provides kinetic energy; the expression of the axial flow propeller turbine model is:
[0090]
[0091] Among them, m t is the turbine torque, q is the flow rate, y is the guide vane opening, z is the blade angle, h is the water head, x is the speed, e is the x 、e y 、e h 、e qx 、e qy and e qh represents the transfer coefficient of the turbine, e z is the transmission coefficient of turbine torque to blade angle, e qz is the transfer coefficient of turbine flow to blade angle.
[0092] The generator-load model construction process is as follows:
[0093] The generator is the core component that converts mechanical energy into electrical energy. It plays a key role in the production of electrical energy. Its design and operation need to take into account the performance of the turbine, the requirements of the power grid, and the overall reliability of the system. The generator-load is its first-order model, and the transfer function is:
[0094]
[0095] Among them, m g Indicates load torque, T a represents the total constant taking into account the inertia time constant of the generator and the inertia time constant of the load, e g Indicates the regulation factor of the generator.
[0096] Step 2: Add a signal distribution device between the PID controller and the actuator of the model, and the PID controller outputs the control signal y pid The actuator receives the signal and converts it into the guide vane opening signal y, and then calculates the blade angle signal z through the association curve; in the model, the signal distribution device first receives the control signal y pid , the signal distribution device decomposes it into a guide vane opening signal y and a blade angle signal z, and then transmits the guide vane opening signal and the blade opening angle to the actuator respectively, so as to speed up the actuator action time and reduce the system adjustment time; Figure 2 The diagram is a structural diagram of the improved axial flow propeller turbine speed control system. The original system structure is that the PID output control signal is directly sent to the actuator. The present invention adds a signal distribution device between the PID controller and the actuator. After receiving the control signal of the PID controller, the signal distribution device is used as follows: Figure 3The method shown is decomposed into a guide vane control signal y and a blade control signal z. When allocating signals, more adjustment tasks are allocated to the guide vanes within a reasonable range, and the adjustment tasks of the blades are reduced to speed up the response speed of the system.
[0097] The specific process is:
[0098] Figure 3 The specific structure of the signal distribution device. After receiving the control signal output by the PID controller pid Finally, the original guide vane opening y′ is calculated according to the original actuator algorithm, the original guide vane opening y′ is input into the original symmetric curve, the original blade angle z′ is calculated, the original blade angle z′ is multiplied by an optimization coefficient K, the range of K is between 0 and 1, and the blade angle z is obtained. The original blade angle z′ is subtracted from the current blade angle z to obtain the change in blade angle Δz, and this part of the blade angle change is converted into the guide vane opening according to a certain ratio, so as to obtain the change in guide vane opening Δy, and the change in guide vane opening Δy plus the original guide vane opening y′ can obtain the current guide vane opening y, and finally the guide vane opening y and the blade angle z are input into the guide vane actuator and the blade actuator respectively.
[0099] Step 3: Use the particle swarm algorithm to calculate the coefficient K (i.e. the previous parameter K p , K i and K d A general term for the signal distribution device) is used for optimization, and different optimization coefficients K are adopted under different working conditions, so as to optimize the axial-flow propeller turbine under different working conditions, and the coefficient K corresponding to the working condition is input into the signal distribution device, so that the signal distribution device can exert its optimal performance.
[0100] The parameter that has the greatest impact on the performance of the signal distribution device is the optimization coefficient K, which directly determines the feasibility and optimization performance of the signal distribution device. If the value of K is too small, it will cause too many adjustment tasks to be assigned to the guide vanes, and the adjustment capacity of the guide vanes is limited and cannot be increased indefinitely, so a value of K that is too small will make the device infeasible. If the value of K is too large, the adjustment tasks assigned to the guide vanes will not be much different from the original ones, and the optimization effect will not be obvious. Under different working conditions, the optimal coordination relationship between the guide vane opening and the blade angle is different, so different K values should be adopted under different working conditions. Therefore, the value of the optimization coefficient K is very critical, and the particle swarm algorithm is used here to optimize the coefficient K.
[0101] Figure 4The flowchart of using particle swarm algorithm to optimize coefficient K. At the beginning, the particle swarm algorithm calculates a coefficient K that meets the current working conditions. When the turbine working conditions change and the K value no longer meets the requirements of the current working conditions, the particle swarm algorithm will recalculate the K that meets the current working conditions and output it to the signal distribution device. The specific process of using particle swarm algorithm to optimize coefficient K is as follows:
[0102] The specific steps of the particle swarm algorithm include initializing the position and speed of the particle swarm, evaluating the fitness of each particle according to the objective function, updating the speed and position of the particle, and determining whether to terminate the algorithm according to certain stopping conditions; assuming that in a D-dimensional search space, there are N particles forming a colony, then the speed and position update formula of the i-th particle is:
[0103]
[0104] Among them, k is the number of iterations, d is the particle dimension number, is the velocity vector of particle i in the kth iteration, is the position vector of particle i in the dth dimension in the kth iteration, is the historical optimal position of particle i in the dth dimension in the kth iteration, that is, the optimal solution searched by the i-th particle after the kth iteration, is the historical optimal position of the swarm in the dth dimension in the kth iteration, that is, the optimal solution in the entire particle swarm after the kth iteration, ω is the inertia weight, c1 is the individual learning factor, c2 is the group learning factor, r1 and r2 are random numbers in the interval [0,1];
[0105] In the optimization of coefficient K, the objective function should be to minimize the adjustment time of the system. The constraint condition is that the guide vane and blade openings are within the acceptable range of the speed control system and cannot exceed the safety value specified by the system. The evaluation index is the percentage of the adjustment time t of the optimized system compared to the original adjustment time t0:
[0106]
[0107] When the operating conditions of the axial-flow propeller turbine change, the original optimization coefficient K can no longer guarantee the rationality of the signal distribution at this time, because the K value at this time is optimized for the previous operating conditions, so the coefficient K needs to be adjusted according to the changes in the operating conditions, and the value of Δt% can be adjusted according to actual needs; when Δt% is less than the set value, the signal distribution device will recalculate the optimization coefficient K so that the guide vane opening and blade angle allocated by the device can meet the operating conditions at this time; therefore, the evaluation index Δt% can ensure that the signal distribution device can perform optimally under different conditions.
[0108] In the optimization of the axial flow propeller turbine speed control system, the particle swarm size N is generally 30. A smaller swarm size is prone to fall into the local optimum. A larger swarm size can improve convergence and find the global optimal solution faster, but the amount of calculation for each iteration will increase accordingly. The particle dimension D is 1 because there is only one independent variable, namely the optimization coefficient K. The number of iterations k is recommended to be 70. If the number of iterations is too small, the solution obtained by the algorithm is unstable. If the number of iterations is too large, the algorithm requires a long calculation time. The learning factors c1 and c2 are recommended to be 2. If the learning factor is too low, the particles will wander outside the target area, while if it is too high, the particles will cross the target area. It is recommended to adopt an adaptive linear change strategy for the inertia weight ω. First, a global search is used to make the search space converge quickly to a certain area, and then a local fine search is used to obtain a high-precision solution. The particle speed is between [-1,1] to prevent the particle speed from exceeding the available range and skipping the optimal solution.
[0109] The above embodiments are only preferred technical solutions of the present invention and should not be regarded as limiting the present invention. The protection scope of the present invention shall be the technical solutions recorded in the claims, including equivalent replacement solutions of the technical features in the technical solutions recorded in the claims. That is, equivalent replacement improvements within this scope are also within the protection scope of the present invention.
Claims
1. A speed control optimization method for an axial flow propeller turbine based on coordinated control of guide vanes and blades, characterized in that: The following steps are involved: Step 1: Establish models of various parts of the axial flow propeller turbine speed control system, including PID controller, actuator, water diversion system, turbine and generator-load; Step 2: Add a signal distribution device between the PID controller and the actuator of the model, and the PID controller outputs the control signal y pid The actuator receives the signal and converts it into the guide vane opening signal y, and then calculates the blade angle signal z through the association curve; in the model, the signal distribution device first receives the control signal y pid , the signal distribution device decomposes it into a guide vane opening signal y and a blade angle signal z, and then transmits the guide vane opening signal and the blade opening angle to the actuator respectively, so as to speed up the actuator action time and reduce the system adjustment time; Step 3: Use the particle swarm algorithm to optimize the coefficient K. Take different optimization coefficients K under different working conditions to optimize the axial flow propeller turbine under different working conditions. Input the coefficient K corresponding to the working condition into the signal distribution device to enable the signal distribution device to exert its optimal performance.
2. The method for optimizing the speed regulation of an axial flow propeller turbine based on coordinated control of guide vanes and blades according to claim 1, characterized in that: In step 1, the PID controller model construction process is as follows: There are three important parameters K in the PID controller: p , K i and K d , where K p Represents the proportional gain, which affects the response speed of the speed control system and the accuracy of the system after it is in a steady state. K i Indicates the integral gain, which is used to eliminate the steady-state error and make the output value consistent with the given value. d It represents the differential gain, which can alleviate the hysteresis of the controlled object and reduce the overshoot and oscillation of the speed control system. The transfer function of the PID controller linear model is: Where x is the grid frequency, c is the given frequency, σ is the controller output signal, T 1v is the differential time constant, b p is the permanent slip coefficient, Δ is the relative deviation, and s is a complex variable, which represents the frequency response when the real part is zero and the imaginary part is the angular frequency.
3. The method for optimizing the speed regulation of an axial flow propeller turbine based on coordinated control of guide vanes and blades according to claim 1, characterized in that: In step 1, the process of building the actuator model is as follows: The actuator has two major functions: one is to convert the received electrical signal into a mechanical signal, that is, to generate the corresponding mechanical displacement; the other is to amplify the electrical signal so that the signal can be recognized by the system more effectively. The actuator is simplified to a first-order system, and the linear model transfer function of the axial-flow propeller turbine actuator is: Among them, y is the guide vane opening, y pid is the PID controller output, T y is the inertia time constant; in actual situations, the actuator contains dead zone and saturated nonlinear links, which are caused by the mechanical dead zone, guide vane opening limit and rate limit caused by the mechanical link.
4. The method for optimizing the speed regulation of an axial-flow propeller turbine based on coordinated control of guide vanes and blades according to claim 1, characterized in that: In step 1, the water diversion system model building process is as follows: The task of the water diversion system is to guide the water from the upstream reservoir to the turbine, and drive the turbine to rotate by the water flow impacting the blades, and finally flow to the downstream reservoir; the water diversion system includes the water diversion pipeline, the pressure surge chamber, the pressure pipeline and the tailwater pipe; the pressure water diversion system model is divided into two categories: rigid water hammer model and elastic water hammer model; in small hydropower stations with small water flow fluctuations in the water diversion pipeline and a pipeline length of less than 800 meters, the water flow inertia can be ignored and the model can be regarded as a rigid water hammer model; when the water diversion pipeline is more than 800 meters long, the water flow inertia is large, and the elasticity between the water and the pipeline wall needs to be considered, and the elastic water hammer model is adopted; The transfer function of the pipeline rigid water hammer model is: F1(s)=-T w s; Among them, T w is the water time constant; The transfer function of the pipeline elastic water hammer model is: Among them, T e Allow time for water to spread.
5. The method for optimizing the speed regulation of an axial-flow propeller turbine based on coordinated control of guide vanes and blades according to claim 1, characterized in that: In step 1, the turbine model construction process is as follows: The turbine uses the kinetic energy and potential energy of water flow to drive the impeller to rotate, thereby driving the generator to generate electricity; water flows from high to low, the height difference provides potential energy, and the speed of the water flow provides kinetic energy; the expression of the axial flow propeller turbine model is: Among them, m t is the turbine torque, q is the flow rate, y is the guide vane opening, z is the blade angle, h is the water head, x is the speed, e is the x 、e y 、e h 、e qx 、e qy and e qh represents the transfer coefficient of the turbine, e z is the transmission coefficient of turbine torque to blade angle, e qz is the transfer coefficient of turbine flow to blade angle.
6. The method for optimizing the speed regulation of an axial flow propeller turbine based on coordinated control of guide vanes and blades according to claim 1, characterized in that: In step 1, the generator-load model construction process is as follows: The generator is the core component that converts mechanical energy into electrical energy. It plays a key role in the production of electrical energy. Its design and operation need to take into account the performance of the turbine, the requirements of the power grid, and the overall reliability of the system. The generator-load is its first-order model, and the transfer function is: Among them, m g Indicates load torque, T a represents the total constant taking into account the inertia time constant of the generator and the inertia time constant of the load, e g Indicates the regulation factor of the generator.
7. The method for optimizing the speed regulation of an axial flow propeller turbine based on coordinated control of guide vanes and blades according to claim 1, characterized in that: The specific process of step 2 is: After receiving the control signal output by the PID controller, pid Finally, the original guide vane opening y′ is calculated according to the original actuator algorithm, the original guide vane opening y′ is input into the original symmetric curve, the original blade angle z′ is calculated, the original blade angle z′ is multiplied by an optimization coefficient K, the range of K is between 0 and 1, and the blade angle z is obtained. The original blade angle z′ is subtracted from the current blade angle z to obtain the change in blade angle Δz, and this part of the blade angle change is converted into the guide vane opening according to a certain ratio, so as to obtain the change in guide vane opening Δy, and the change in guide vane opening Δy plus the original guide vane opening y′ can obtain the current guide vane opening y, and finally the guide vane opening y and the blade angle z are input into the guide vane actuator and the blade actuator respectively.
8. The method for optimizing the speed regulation of an axial-flow propeller turbine based on coordinated control of guide vanes and blades according to claim 1, characterized in that: The specific process of optimizing the coefficient K using the particle swarm algorithm in step 3 is as follows: The specific steps of the particle swarm algorithm include initializing the position and speed of the particle swarm, evaluating the fitness of each particle according to the objective function, updating the speed and position of the particle, and determining whether to terminate the algorithm according to certain stopping conditions; assuming that in a D-dimensional search space, there are N particles forming a colony, then the speed and position update formula of the i-th particle is: Among them, k is the number of iterations, d is the particle dimension number, is the velocity vector of particle i in the kth iteration, is the position vector of particle i in the dth dimension in the kth iteration, is the historical optimal position of particle i in the dth dimension in the kth iteration, that is, the optimal solution searched by the i-th particle after the kth iteration, is the historical optimal position of the swarm in the dth dimension in the kth iteration, that is, the optimal solution in the entire particle swarm after the kth iteration, ω is the inertia weight, c1 is the individual learning factor, c2 is the group learning factor, r1 and r2 are random numbers in the interval [0,1]; In the optimization of coefficient K, the objective function should be to minimize the adjustment time of the system. The constraint condition is that the guide vane and blade openings are within the acceptable range of the speed control system and cannot exceed the safety value specified by the system. The evaluation index is the percentage of the adjustment time t of the optimized system compared to the original adjustment time t0: When the operating conditions of the axial-flow propeller turbine change, the original optimization coefficient K can no longer guarantee the rationality of the signal distribution at this time, because the K value at this time is optimized for the previous operating conditions, so the coefficient K needs to be adjusted according to the changes in the operating conditions, and the value of Δt% can be adjusted according to actual needs; when Δt% is less than the set value, the signal distribution device will recalculate the optimization coefficient K so that the guide vane opening and blade angle allocated by the device can meet the operating conditions at this time; therefore, the evaluation index Δt% can ensure that the signal distribution device can perform optimally under different conditions.