PID-GA optimization control system of water turbine governor

Abstract

The invention discloses a PID-GA optimization control system of a water turbine governor. The PID-GA optimization control system comprises a hydroelectric generating set, a PID controller, a genetic algorithm optimization module and a load monitoring module. The hydroelectric generating set comprises a water turbine, a synchronous generator and a guide vane servo mechanism; the input end of the PID controller is connected with the output of a rotating speed error signal of the synchronous generator, and the output end of the PID controller is connected with a guide vane servo mechanism through a signal; the genetic algorithm optimization module is connected with the PID controller, and the genetic algorithm optimization module is used for dynamically optimizing PID parameters and obtaining an optimal PID parameter combination; the load monitoring module is connected with the PID controller, collects load power data in real time and feeds back the load power data to the PID controller. According to the invention, the self-adaptive optimization control of the rotating speed of the water turbine of the hydroelectric generating set is realized, and the problems of adjustment lag and overshoot caused by fixed traditional PID (Proportion Integration Differentiation) parameters are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of hydroelectric generator set control, in particular to a PID-GA optimization control system for a turbine speed governor. Background Art

[0002] Traditional turbine speed governors often use fixed-parameter PID control. In actual operation, when the load changes suddenly, the fixed PID parameters make it difficult for the governor to respond quickly and accurately, resulting in frequency overshoot or regulation lag, making it difficult to meet the IEEE 50Hz±1% frequency deviation standard.

[0003] In existing technologies, determining PID parameters often relies on manual debugging. This approach is not only inefficient, but the debugged parameters are also poorly adaptable to varying load conditions, making it impossible to guarantee optimal system operation. Furthermore, independent hydropower stations, lacking the support of a larger power grid, face a more significant impact of load fluctuations on frequency stability. When the load suddenly increases or decreases, the generator speed changes rapidly, causing significant fluctuations in the output frequency, severely impacting power supply quality. Summary of the Invention

[0004] To solve the above problems, the present invention provides a PID-GA optimization control system for a turbine speed governor, which realizes adaptive optimization control of the turbine speed of a hydroelectric generator set and solves the regulation lag and overshoot problems caused by the fixed traditional PID parameters.

[0005] The present invention provides a PID-GA optimization control system for a turbine speed governor, and the specific technical solution is as follows: The system includes a hydroelectric generator set, a PID controller, a genetic algorithm optimization module, and a load monitoring module; The hydroelectric generator set includes a turbine, a synchronous generator and a guide vane servo mechanism; The input end of the PID controller is connected to the output of the speed error signal of the synchronous generator, and the output end of the PID controller is connected to the guide vane servo mechanism signal; The genetic algorithm optimization module is connected to the PID controller, and the genetic algorithm optimization module is used to dynamically optimize the PID parameters to obtain the optimal PID parameter combination; The load monitoring module is connected to the PID controller, collects load power data in real time and feeds back the data to the PID controller.

[0006] Furthermore, the genetic algorithm optimization module minimizes the frequency deviation based on the objective function to generate optimal PID parameters.

[0007] Furthermore, the specific process of generating the optimal PID parameters is as follows: S1: Generate K p , K i , K d Parameter chromosome, construct the initial population; S2: Construct nonlinear speed regulator model; S3: decoding each chromosome into PID parameters, and applying them to the nonlinear speed regulator model to obtain a system response curve and calculate a fitness function; S4: Through selection, crossover, mutation and local search enhancement operations, the population is iteratively optimized to output the optimal PID parameter combination.

[0008] Furthermore, the fitness function is expressed as follows:

[0009] Among them, ITAE is the time-weighted absolute error, ; t is the system running time, e ( t ) is the difference between the generator output frequency and the standard frequency, OS is the overshoot percentage, TV is the control variable change rate, ; u t is the output of the controller at time t, u t-1 is the output of the controller at time t-1, and ω1, ω2, and ω3 are weight coefficients.

[0010] Furthermore, the transfer function of the PID controller is:

[0011] in, 、 and They represent proportion, integration, and differentiation respectively, and s represents the complex variable of Laplace transform.

[0012] Furthermore, the synchronous generator adopts a 1.5kW salient pole synchronous generator with a rated voltage of 380V and a rotation speed of 1500rpm.

[0013] Furthermore, the opening range of the guide vane servo mechanism is 0.01 pu to 0.975 pu, and the time constant of the guide vane servo mechanism is set to 0.07 seconds.

[0014] The beneficial effects of the present invention are as follows: The present invention designs a PID-GA optimization control system, in which a load monitoring module is connected to a PID controller, and a genetic algorithm optimization module is connected to the PID control. The load monitoring module obtains real-time load power. When the load changes, the genetic algorithm optimization module dynamically adjusts the PID parameters according to the current system state through iterative calculation of the genetic algorithm, thereby solving the adjustment lag and overshoot problems caused by the fixed traditional PID parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a logical flow diagram of the genetic algorithm optimization module of the present invention. DETAILED DESCRIPTION

[0016] The following description clearly and completely describes the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts are within the scope of protection of the present invention.

[0017] In the description of the embodiments of the present invention, it should be noted that the indicated orientations or positional relationships are based on the orientations or positional relationships shown in the accompanying drawings, or the orientations or positional relationships in which the inventive product is typically placed when in use, or the orientations or positional relationships commonly understood by those skilled in the art, or the orientations or positional relationships in which the inventive product is typically placed when in use. These are merely for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore should not be understood as limiting the present invention. In addition, the terms "first" and "second" are used only to distinguish descriptions and should not be understood as indicating or implying relative importance.

[0018] In describing the embodiments of the present invention, it should be noted that, unless otherwise specified or limited, the terms "disposed" and "connected" should be understood broadly. For example, they may refer to fixed connections, detachable connections, or integral connections; they may refer to direct connections or indirect connections through an intermediary. Those skilled in the art will understand the specific meanings of these terms in the present invention based on the specific circumstances.

[0019] Example 1 Example 1 of the present invention discloses a PID-GA optimization control system for a turbine speed governor, such as Figure 1 As shown, the details are as follows: The system includes a hydroelectric generator set, a PID controller, a genetic algorithm (GA) optimization module, and a load monitoring module; The hydroelectric generator set includes a turbine, a synchronous generator and a guide vane servo mechanism. The turbine is used to convert water energy into mechanical energy to drive the synchronous generator to generate electricity. The guide vane servo mechanism can adjust the guide vane opening of the turbine to control the water flow. The input end of the PID controller is connected to the output of the speed error signal of the synchronous generator, and the output end of the PID controller is connected to the guide vane servo mechanism signal; a control signal is output based on the error signal to adjust the guide vane opening. Specifically, the error signal is obtained by comparing the actual speed of the generator with the set speed; The genetic algorithm optimization module is connected to the PID controller, and is used to dynamically optimize the PID parameters and obtain the optimal PID parameter combination through continuous iterative calculation to improve the control performance of the system; As a preferred embodiment, the genetic algorithm optimization module minimizes frequency deviation based on an objective function that comprehensively considers factors such as the difference between the generator output frequency and the standard frequency to generate optimal PID parameters to ensure stable operation of the system under different loads.

[0020] In PID parameter optimization based on GA, we encode the three PID parameters Kp, Ki, and Kd as chromosomes, i.e. individuals. Common encoding methods include binary encoding and real number encoding; the fitness function is usually selected as an evaluation indicator of the control system performance, such as ISE (integrated square error), IAE (integrated absolute error), integrated time absolute error (ITAE), etc.; then, through the iterative process of the genetic algorithm, we find the PID parameters that minimize the fitness function.

[0021] The specific process is as follows: S1: Use Latin hypercube sampling to generate K p , K i , K d Parameter chromosome, construct the initial population, ensure spatial uniformity, where K p is the proportional gain coefficient, K i is the integral gain coefficient, K d is the differential gain coefficient; S2: Construct nonlinear speed regulator model; S3: Decode each chromosome into PID parameters and apply them to the simulation model. Run the simulation model to obtain the system response curve and calculate the fitness function. The fitness function is:

[0022] Among them, ITAE is the time-weighted absolute error, , t is the system running time, e ( t) is the difference between the generator output frequency and the standard frequency; OS is the overshoot percentage; TV is the control variable change rate, , u t is the output of the controller at time t, i.e. the control quantity, u t-1 is the output of the controller at time t-1; ω1, ω2, ω3 are weight coefficients, which can be ω1=0.7, ω2=0.2, ω3=0.1; S4: Based on the fitness value of the individual, through a combination of tournament selection and elite retention system, the top 10% of the best individuals are directly promoted to the next generation; S5: Randomly select two individuals in the population for crossover operation to generate new individuals, using a crossover method that combines arithmetic crossover and single-point crossover; S6: Perform mutation operation on the new individuals, introduce new genes, use Gaussian mutation, and the standard deviation σ decreases linearly from 0.1 to 0.01 as the number of generations increases; S7: Use simulated annealing hybrid strategy to perform neighborhood search with a predetermined probability value after mutation; S8: Repeat steps S3-S7 until the termination condition is met; the termination condition is that the maximum number of evolutionary generations is reached or the fitness reaches a preset threshold; S9: Output the optimal PID parameter combination.

[0023] Specifically, the transfer function of the PID controller is:

[0024] in, 、 and They represent proportion, integration, and differentiation respectively, and s represents the complex variable of Laplace transform.

[0025] The load monitoring module is connected to the PID controller, collects load power data in real time and feeds it back to the PID controller, providing load change information for system adjustment.

[0026] As a preferred embodiment, the synchronous generator adopts a 1.5kW salient pole synchronous generator with a rated voltage of 380V and a rotation speed of 1500rpm.

[0027] As a preferred embodiment, the opening range of the guide vane servo mechanism is 0.01pu to 0.975pu. This opening range can ensure that the turbine operates in a safe and efficient state, avoiding damage to the turbine or reduced efficiency due to excessive or insufficient opening. The time constant of the guide vane servo mechanism is set to 0.07 seconds to ensure the timeliness and accuracy of the guide vane opening adjustment.

[0028] In the control system described in this embodiment, the PID controller outputs a guide vane opening signal to the servo mechanism based on the optimal PID parameters provided by the genetic algorithm optimization module, regulating the turbine flow rate. The PID controller also processes the generator speed error signal and, in combination with the optimized PID parameters, calculates an appropriate guide vane opening control signal to ensure the generator speed remains stable near the set value. This regulation process stabilizes the generator output frequency at 50 Hz ± 0.5 Hz, meeting the power system's frequency stability requirements.

[0029] The present invention is not limited to the aforementioned specific embodiments, but extends to any new features or any new combination disclosed in this specification, as well as any new method or process steps or any new combination disclosed.

Claims

1. A PID-GA optimization control system for a turbine speed governor, characterized in that: It includes hydroelectric generator set, PID controller, genetic algorithm optimization module and load monitoring module; The hydroelectric generator set includes a turbine, a synchronous generator and a guide vane servo mechanism; The input end of the PID controller is connected to the output of the speed error signal of the synchronous generator, and the output end of the PID controller is connected to the guide vane servo mechanism signal; The genetic algorithm optimization module is connected to the PID controller, and the genetic algorithm optimization module is used to dynamically optimize the PID parameters to obtain the optimal PID parameter combination; The load monitoring module is connected to the PID controller, collects load power data in real time and feeds back the data to the PID controller.

2. The PID-GA optimization control system for a turbine speed governor according to claim 1, characterized in that: The genetic algorithm optimization module minimizes the frequency deviation based on the objective function and generates the optimal PID parameters.

3. The PID-GA optimization control system for a turbine speed governor according to claim 2, characterized in that: Generate the optimal PID parameters. The specific process is as follows: S1: Generate K p , K i , K d Parameter chromosome, construct the initial population; S2: Construct nonlinear speed regulator model; S3: decoding each chromosome into PID parameters and applying them to the nonlinear speed regulator model to obtain a system response curve and calculate a fitness function; S4: Through selection, crossover, mutation and local search enhancement operations, the population is iteratively optimized to output the optimal PID parameter combination.

4. The PID-GA optimization control system for a turbine speed governor according to claim 3, characterized in that: The fitness function is expressed as follows: Among them, ITAE is the time-weighted absolute error, ; t is the system running time, e ( t ) is the difference between the generator output frequency and the standard frequency, OS is the overshoot percentage, TV is the control variable change rate, ; u t is the output of the controller at time t, u t-1 is the output of the controller at time t-1, and ω1, ω2, and ω3 are weight coefficients.

5. The PID-GA optimization control system for a turbine speed governor according to claim 1, characterized in that: The transfer function of the PID controller is: in, 、 and They represent proportion, integration, and differentiation respectively, and s represents the complex variable of Laplace transform.

6. The PID-GA optimization control system for a turbine speed governor according to claim 1, characterized in that: The synchronous generator is a 1.5kW salient pole synchronous generator with a rated voltage of 380V and a rotation speed of 1500rpm.

7. The PID-GA optimization control system for a turbine speed governor according to claim 1, characterized in that: The opening range of the guide vane servo mechanism is 0.01 pu to 0.975 pu, and the time constant of the guide vane servo mechanism is set to 0.07 seconds.

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