PID (Proportion Integration Differentiation) setting method and equipment based on Chinese pangolin optimization algorithm and medium
By using a PID tuning method based on the Chinese Pangolin optimization algorithm and combining it with H∞ control theory, the PID parameters are optimized, which solves the problem of simultaneously satisfying the requirements of speed, stability and robustness in the synchronous generator control system, and realizes efficient and stable operation of the synchronous generator.
Patent Information
- Application Number
- CN202510965605.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-14
- Publication Date
- 2025-10-28
AI Technical Summary
The existing PID controller parameter tuning method is difficult to simultaneously meet the performance requirements of speed, stability and robustness in synchronous generator control systems, and is prone to falling into local optimality.
A PID tuning method based on the Chinese pangolin optimization algorithm is adopted. By simulating the predation behavior of pangolins, an odor diffusion model is established. Combined with the H∞ control theory, the PID parameters are optimized, global search and local optimization are achieved, and the positions of the optimal solution and suboptimal solution are updated until the power system control requirements are met.
The rapidity, stability and robustness of the synchronous generator control system under various working conditions are achieved, ensuring the efficient and stable operation of the power system, avoiding falling into local optimal solutions, and improving the accuracy and efficiency of PID parameter tuning.
Smart Images
Figure CN120855503A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of generator control technology, and in particular to a PID tuning method, device and medium based on the Chinese pangolin optimization algorithm. Background Art
[0002] With the continuous expansion and increasing complexity of power systems, synchronous generators, as core equipment in power systems, directly affect the safe and efficient operation of the entire power grid due to their operational stability and control performance. Synchronous generators typically employ PID (Proportional-Integral-Derivative) controllers to precisely regulate key variables such as speed and excitation current, meeting the power grid's high-quality requirements for frequency and voltage. Furthermore, PID controllers are widely used in industrial control due to their simple structure, strong robustness, and wide applicability.
[0003] However, due to the complex factors such as nonlinearity, uncertainty and external disturbances in power systems, the existing PID controller parameter tuning methods have low tuning efficiency and are prone to getting trapped in local optima. Therefore, it is difficult to meet the performance requirements of speed, stability and robustness in synchronous generator control systems at the same time. Summary of the Invention
[0004] In response to the aforementioned problems and technical requirements, the applicant proposes a PID tuning method, device, and medium based on the Chinese pangolin optimization algorithm. This method aims to solve the problem that existing PID control cannot simultaneously meet the performance requirements of speed, stability, and robustness, and to achieve the goal of simultaneously meeting these performance requirements by tuning the PID controller parameters.
[0005] This application provides a PID tuning method based on the Chinese pangolin optimization algorithm, the method comprising:
[0006] Acquire the data to be processed, wherein the data to be processed includes: generator operating parameters, grid disturbance parameters, PID parameter space, and an optimal solution and multiple suboptimal solutions. The PID space includes multiple PID parameters and the position of each PID parameter. The optimal solution and suboptimal solutions are determined from the multiple PID parameters.
[0007] Iteratively perform the following update operations:
[0008] Based on the generator's operating parameters, grid disturbance parameters, and a pre-defined odor diffusion mathematical model, an odor concentration factor is obtained, and the fitness of each PID parameter is evaluated based on the generator's operating parameters and grid disturbance parameters; based on the odor concentration factor and the fitness, the positions of the optimal and suboptimal solutions are updated.
[0009] If the updated optimal solution meets the preset power system control requirements and belongs to the PID parameter space, the optimal solution is output.
[0010] According to the PID tuning method based on the Chinese pangolin optimization algorithm provided in the embodiments of this application, the position of the optimal solution and the position of the suboptimal solution are updated based on the odor concentration factor and the fitness, including:
[0011] First, update the positions of the optimal and suboptimal solutions based on the fitness.
[0012] Then, based on the odor concentration factor, update the positions of the optimal and suboptimal solutions.
[0013] According to the PID tuning method based on the Chinese pangolin optimization algorithm provided in the embodiments of this application, the data to be processed further includes: the current solution;
[0014] Based on the odor concentration factor, the positions of the optimal and suboptimal solutions are updated, including:
[0015] If the odor concentration factor is less than the preset concentration factor, keep the position of the current solution unchanged, and update the position of the suboptimal solution based on the position of the suboptimal solution, the position of the current solution, the position of the optimal solution and the preset first position update formula.
[0016] According to the PID tuning method based on the Chinese pangolin optimization algorithm provided in the embodiments of this application, the position of the optimal solution and the position of the suboptimal solution are updated based on the odor concentration factor, including:
[0017] When the odor concentration factor is greater than or equal to the preset concentration factor, the position of the optimal solution is updated and obtained based on the position of the current solution, the position of the optimal solution, and the preset second position update formula.
[0018] According to the PID tuning method based on the Chinese pangolin optimization algorithm provided in the embodiments of this application, based on the generator's operating parameters, power grid disturbance parameters, and a preset odor diffusion mathematical model, the odor concentration factor is obtained, including:
[0019] The generator's operating parameters and grid disturbance parameters are input into the odor diffusion mathematical model to obtain the model output results;
[0020] Input the model output into the preset odor concentration calculation formula to obtain the odor concentration factor output by the odor concentration calculation formula.
[0021] According to the PID tuning method based on the Chinese pangolin optimization algorithm provided in the embodiments of this application, the fitness of each PID parameter is evaluated based on the generator's operating parameters and grid disturbance parameters, including:
[0022] The fitness of each PID parameter is evaluated based on a preset fitness calculation formula;
[0023] The fitness calculation formula includes:
[0024] itness=α·ISE+β·Overshoot+γ·Settlingtime;
[0025] Wherein, Fine represents fitness, α, β and γ represent varying weighting coefficients, ISE represents the integral exponent of error, Overshoot represents overshoot, and Settling time represents settling time. The integral exponent of error, overshoot and settling time are obtained based on the generator's operating parameters and the grid disturbance parameters, respectively.
[0026] According to the PID tuning method based on the Chinese pangolin optimization algorithm provided in the embodiments of this application, based on H ∞ Control theory determines the boundaries of the PID parameter space.
[0027] According to the PID tuning method based on the Chinese pangolin optimization algorithm provided in the embodiments of this application, the PID parameter range includes:
[0028]
[0029] Among them, T i T represents the integration constant. d Let represent the differential constant, K represent the synchronous generator controller coefficient, and v represent the delay time, which is a constant.
[0030] This application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps of the PID tuning method based on the Chinese pangolin optimization algorithm as described in any of the preceding claims.
[0031] This application also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the PID tuning method based on the Chinese pangolin optimization algorithm as described in any of the preceding claims.
[0032] The PID tuning method, device, and medium based on the Chinese pangolin optimization algorithm provided in this application embodiment acquire data to be processed, including generator operating parameters, grid disturbance parameters, PID parameter space, and one optimal solution and multiple suboptimal solutions. The PID space includes multiple PID parameters and the position of each PID parameter. The optimal and suboptimal solutions are determined from the multiple PID parameters. Iterative execution is performed based on the generator operating parameters, grid disturbance parameters, and a preset odor diffusion mathematical model to obtain an odor concentration factor. The fitness of each PID parameter is evaluated based on the generator operating parameters and grid disturbance parameters. Based on the odor concentration factor and fitness, the positions of the optimal and suboptimal solutions are updated until the updated optimal solution meets the preset power system control requirements and belongs to the PID parameter space. The optimal solution is then output. This achieves rapid and accurate acquisition of the optimal PID parameters that conform to the current state, enabling the synchronous generator control system to simultaneously meet the performance requirements of speed, stability, and robustness. Attached Figure Description
[0033] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0034] Figure 1 This is one of the flowcharts illustrating the PID tuning method based on the Chinese pangolin optimization algorithm provided in the embodiments of this application;
[0035] Figure 2 This is the second flowchart of the PID tuning method based on the Chinese pangolin optimization algorithm provided in the embodiments of this application;
[0036] Figure 3 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. DETAILED DESCRIPTION
[0037] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.
[0038] This application provides a PID tuning method based on the Chinese pangolin optimization algorithm. This method can be applied to smart terminals, servers, and power systems. This application uses the application of this method in a server as an example for illustration; this is illustrative and not intended to limit the scope of protection of this application. Other descriptions in the embodiments are also illustrative and will not be elaborated further. Figure 1 As shown, the method includes:
[0039] Step 101: Obtain the data to be processed.
[0040] The data to be processed includes: generator operating parameters, grid disturbance parameters, PID parameter space, and one optimal solution and multiple suboptimal solutions. The PID space includes multiple PID parameters and the position of each PID parameter. The optimal and suboptimal solutions are determined from the multiple PID parameters.
[0041] Step 102, iteratively perform the following update operations: based on the generator's operating parameters, grid disturbance parameters, and a preset odor diffusion mathematical model, obtain the odor concentration factor, and based on the generator's operating parameters and grid disturbance parameters, evaluate the fitness of each PID parameter; based on the odor concentration factor and fitness, update the position of the optimal solution and the position of the suboptimal solution.
[0042] Step 103: If the updated optimal solution meets the preset power system control requirements and belongs to the PID parameter space, output the optimal solution.
[0043] Specifically, the optimization algorithm for the Chinese pangolin is derived from the pangolin's unique predation behavior. This algorithm establishes a scent diffusion model and simulates the pangolin's attraction and phased predation behaviors to create a multi-stage optimization mechanism. Based on this mechanism, a global search is performed to obtain the optimal solution.
[0044] The PID tuning method based on the Chinese pangolin optimization algorithm provided in this application obtains data to be processed, including generator operating parameters, grid disturbance parameters, PID parameter space, and one optimal solution and multiple suboptimal solutions. The PID space includes multiple PID parameters and the position of each PID parameter. The optimal and suboptimal solutions are determined from the multiple PID parameters. The method iteratively executes a mathematical model based on the generator operating parameters, grid disturbance parameters, and a preset odor diffusion model to obtain an odor concentration factor. It also evaluates the fitness of each PID parameter based on the generator operating parameters and grid disturbance parameters. Based on the odor concentration factor and fitness, the method updates the positions of the optimal and suboptimal solutions until the updated optimal solution meets the preset power system control requirements and belongs to the PID parameter space. Finally, it outputs the optimal solution. This method achieves fast and accurate acquisition of the optimal PID parameters that meet the current state, enabling the synchronous generator control system to simultaneously meet the performance requirements of speed, stability, and robustness.
[0045] This application leverages the superior global search capability and optimization efficiency of the Chinese pangolin optimization algorithm, while exhibiting better robustness in handling complex multidimensional nonlinear optimization problems, to address the issues encountered in PID controller tuning in existing technologies. Specifically, by simulating the attraction and predation mechanisms in pangolin predation behavior, the global search capability is enhanced. The dynamic performance indicators of the synchronous generator (e.g., overshoot, rise time, settling time, etc.) are closely integrated with the optimization objective, ensuring that the power system maintains stable and efficient operation under various operating conditions.
[0046] In one specific embodiment, based on H ∞ Control theory determines the boundaries of the PID parameter space.
[0047] Specifically, the boundaries of PID parameters, including the proportional coefficient, are determined based on robust stability adjustment. Integral coefficient Differential coefficients
[0048] The data within this boundary range belongs to the PID parameter space.
[0049] Specifically, after obtaining the PID parameter space, an initial optimal solution (pangolin) and multiple initial suboptimal solutions (ants) are determined from the PID parameter space. Subsequently, based on the initial optimal and suboptimal solutions, the solution is iteratively updated until the final optimal solution is determined.
[0050] In one specific embodiment, the PID parameter range includes:
[0051]
[0052] Among them, T i T represents the integration constant. d Let represent the differential constant, K represent the synchronous generator controller coefficient, and v represent the delay time, which is a constant.
[0053] In one specific embodiment, the specific implementation of updating the position of the optimal solution and the position of the suboptimal solution based on the odor concentration factor and the fitness includes:
[0054] First, update the positions of the optimal and suboptimal solutions based on fitness; then, update the positions of the optimal and suboptimal solutions based on the odor concentration factor.
[0055] In one specific embodiment, the data to be processed further includes: the current solution.
[0056] Based on the odor concentration factor, the specific implementation of updating the positions of the optimal and suboptimal solutions includes:
[0057] If the odor concentration factor is less than the preset concentration factor, keep the position of the current solution unchanged, and update the position of the suboptimal solution based on the position of the suboptimal solution, the position of the current solution, and the position of the optimal solution.
[0058] The current solution's position is the pangolin's position. During this process, the pangolin remains stationary, attracting ants to approach. That is, what changes is the position of the suboptimal solution.
[0059] Specifically, if the odor concentration factor is less than the preset concentration factor, the update strategy corresponding to the induction phase is executed.
[0060] The luring phase is divided into two behaviors: attracting and capturing, and moving and feeding.
[0061] The update strategy for attraction-capture is as follows: keep the position of the current solution unchanged, and update the position of the suboptimal solution to make it closer to the position of the current solution. The position X of the suboptimal solution is obtained during the update process. A (t+1).
[0062] Specifically, the positions of the suboptimal solution, the current solution, and the optimal solution are input into the first position update formula to obtain the position of the suboptimal solution output by the first position update formula.
[0063] Specifically, the formula for updating the first position is shown in formula (1):
[0064] X A (t+1)=X(t)+x A (t)-A1(D A (t))……………(1)
[0065] Among them, X A(t+1) represents the position of the updated suboptimal solution, X A X(t) represents the position of the suboptimal solution before the update, X(t) represents the position of the current solution at the t-th iteration, and D... A (t) represents the relative positions of the optimal and suboptimal solutions, D A (t)=|aX A (t)-X M (t)|, A1 is a constant, a is the aroma trajectory, X M (t) represents the position of the optimal solution before the update.
[0066] Where A1 = 2 × E × rand() - E,
[0067] Where λ is a constant, V o2 0.2 represents a random number, which is a constant. T represents the maximum number of iterations, and t represents the current number of iterations.
[0068] In one specific embodiment, the specific implementation of updating the positions of the optimal and suboptimal solutions based on the odor concentration factor includes:
[0069] When the odor concentration factor is greater than or equal to the preset concentration factor, the position of the optimal solution is updated and obtained based on the position of the current solution, the position of the optimal solution, and the preset second position update formula (including any one or more of formulas (2), (4), and (5)).
[0070] Specifically, when the odor concentration factor is greater than or equal to the preset concentration factor, the update strategy corresponding to the predation phase is executed.
[0071] The predation phase is divided into three behaviors: search and location, rapid approach, and digging for prey.
[0072] Specifically, when the odor concentration factor is greater than or equal to the preset concentration factor and less than the first threshold, the update strategy for the optimal solution corresponding to the search and positioning phase is executed.
[0073] If the odor concentration factor is greater than or equal to the first threshold and less than the second threshold, the update strategy for the optimal solution corresponding to the rapid approach phase is executed.
[0074] If the odor concentration factor is greater than or equal to the third threshold, the update strategy for the optimal solution corresponding to the predation phase is executed.
[0075] The update strategy for search and positioning is as follows: update the position of the optimal solution according to the Levy flight mechanism, as detailed in formula (2):
[0076] X M(t+1)=sin(C1·X(t)+A1·|X M (t)-L levy ·D M (t)|)……(2)
[0077] Among them, D M (t)=|L levy ·X M (t)-X(t)|.
[0078] Among them, X M (t+1) represents the position of the updated optimal solution, C1 represents the coefficient of the position update rate, and X(t) represents the position of the current solution at the t-th iteration. M (t) represents the position of the optimal solution before the update.
[0079] Among them, the coefficient of the position update rate (rapid descent parameter) is a variable, which is determined by formula (3):
[0080]
[0081] The update strategy for rapid approach is to update the position of the optimal solution based on the preset aroma trajectory calculation formula.
[0082] The location of the updated optimal solution is shown in formula (4):
[0083] X M (t+1)=X(t)-A1·|X M (t)-e -a ·sin(rand·π)·D M (t)|…(4)
[0084] Among them, D M (t)=|a·X M (t)-X(t)|.
[0085] Where 'a' represents the aroma trajectory.
[0086] The update strategy for predation is to update the position of the optimal solution based on formula (5).
[0087] X M (t+1)=X(t)+A1·|X M (t)-D M (t)|……………(5)
[0088] Among them, D M (t)=|C1·X M (t)-X(t)|.
[0089] In one specific embodiment, the mathematical model for odor diffusion is shown in formula (6):
[0090]
[0091] Where Q represents the gas source intensity (the degree of aggregation of multiple PID parameters in the PID parameter space), u represents the wind speed factor (a randomly determined value), and σ y σ represents the operating parameters of the generator. z The parameters represent grid disturbances, H represents the load condition corresponding to the generator, and u represents the introduced random factor (introducing randomness to avoid getting trapped in local optima).
[0092] Based on the generator's operating parameters, grid disturbance parameters, and a pre-defined mathematical model for odor diffusion, the specific implementation of the odor concentration factor includes:
[0093] The generator's operating parameters and grid disturbance parameters are input into the odor diffusion mathematical model to obtain the model output results; the model output results are then input into the preset odor concentration calculation formula to obtain the odor concentration factor output by the odor concentration calculation formula.
[0094] The formula for calculating odor concentration is shown in formula (7):
[0095]
[0096] Among them, C M Let M(t) represent the odor concentration factor, and M(t) represent the model output result obtained in the t-th iteration.
[0097] In one specific embodiment, the specific implementation of evaluating the fitness of each PID parameter based on the generator's operating parameters and grid disturbance parameters is shown in formula (8):
[0098] Fitness=α·ISE+β·Overshoot+γ·Settlingtime…………(8)
[0099] Wherein, Fine represents fitness, α, β and γ represent varying weighting coefficients, ISE represents the integral exponent of error, Overshoot represents overshoot, and Settling time represents settling time. The integral exponent of error, overshoot and settling time are obtained based on the generator's operating parameters and the grid disturbance parameters, respectively.
[0100] Below, through Figure 2 This application will be described in detail as follows:
[0101] Step 201, according to H ∞ Control theory imposes restrictions on the search space of particle swarm optimization.
[0102] Among them, the population of the PID parameters of the initial synchronous generator includes the population size and location.
[0103] Step 202: Evaluate the fitness of each parameter and calculate the fitness value of all initialized particles in the population according to the fitness calculation formula.
[0104] Step 203 specifies that the optimal position of the synchronous generator PID parameters is the position of the Chinese pangolin, and the second optimal position of the synchronous generator PID parameters is the position of the ant.
[0105] Step 204 updates the dynamic equilibrium parameters required for each stage of calculation. Specifically, this includes updating the odor concentration factor using the odor diffusion model; updating the rapid descent parameters; updating the generated random Brownian motion trajectory using the aroma diffusion trajectory calculation formula; and updating the random step size Llevy generated by the Levy flight mechanism.
[0106] The formulas for calculating the aroma diffusion trajectory are shown in formulas (8) and (9):
[0107]
[0108] Among them, a x Let represent the aroma trajectory in the X direction, and let x(t) represent the aroma position in the X direction at the t-th iteration. c a represents the aroma diffusion coefficient. y Let y(t) represent the aroma trajectory in the Y direction, and y(t) represent the aroma position in the Y direction at the t-th iteration. z Let z(t) represent the aroma trajectory in the Z direction, z(t) represent the aroma position in the Z direction at the t-th iteration, r2 represent a random number, and a represent a random Brownian motion trajectory (aroma trajectory).
[0109] Step 205: Determine the stage to be executed based on the odor concentration value.
[0110] Lure phase: Perform two-stage position update; Predation phase: Search and localization phase (global exploration), rapid approach phase (convergence acceleration), predation phase (local optimization).
[0111] Step 206: Determine whether the obtained optimal synchronous generator PID parameters meet the system control requirements and the boundary requirements. If yes, proceed to step 207; otherwise, return to step 202.
[0112] Step 207: Output the optimal PID parameters for the synchronous generator.
[0113] This application introduces H ∞By incorporating control theory and reducing the search range of the population, and by employing the Chinese pangolin optimization algorithm, the algorithm gains a stronger ability to escape local optima, resulting in more reliable optimization performance and avoiding getting trapped in local optima, especially in complex, multi-modal search spaces. In ordinary particle swarm optimization (PSO) PID tuning algorithms, the accuracy of optimization results may be limited due to insufficient population diversity. However, by optimizing the local search strategy using the Chinese pangolin optimization algorithm, the optimal solution can be found more accurately, resulting in superior control performance in PID parameter tuning, such as smaller overshoot, shorter rise time, and lower steady-state error. Therefore, the PID tuning method for synchronous generator control systems based on the Chinese pangolin optimization algorithm comprehensively surpasses ordinary PSO methods in terms of global search capability, convergence speed, optimization accuracy, and adaptability, providing a more efficient and stable PID parameter tuning scheme for complex control systems.
[0114] Figure 3 An example of a physical structure diagram of an electronic device is shown below. Figure 3 As shown, the electronic device may include a processor 301, a communication interface 302, a memory 303, and a communication bus 304. The processor 301, communication interface 302, and memory 303 communicate with each other via the communication bus 304. The processor 301 can call logic instructions from the memory 303 to execute a PID tuning method based on the Chinese pangolin optimization algorithm.
[0115] Furthermore, the logical instructions in the aforementioned memory 303 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0116] On the other hand, the present invention also provides a computer program product, the computer program product including a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions, and when the program instructions are executed by a computer, the computer is able to execute the PID tuning method based on the Chinese pangolin optimization algorithm provided by the above methods.
[0117] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the PID tuning method based on the Chinese pangolin optimization algorithm provided in the above embodiments.
[0118] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.
[0119] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, or of course, by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the existing technology can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or certain parts of the embodiments.
[0120] Finally, it should be noted that the above descriptions are merely preferred embodiments of this application, and this application is not limited to the above embodiments. It is understood that other improvements and variations directly derived or conceived by those skilled in the art without departing from the spirit and concept of this application should be considered to be included within the protection scope of this application.
Claims
1. A PID tuning method based on the Chinese pangolin optimization algorithm, characterized in that, The method comprises: Acquire the data to be processed, wherein the data to be processed includes: generator operating parameters, grid disturbance parameters, PID parameter space, and an optimal solution and multiple suboptimal solutions. The PID space includes multiple PID parameters and the position of each PID parameter. The optimal solution and suboptimal solutions are determined from the multiple PID parameters. Iteratively perform the following update operations: Based on the generator's operating parameters, grid disturbance parameters, and a pre-defined odor diffusion mathematical model, an odor concentration factor is obtained, and the fitness of each PID parameter is evaluated based on the generator's operating parameters and grid disturbance parameters; based on the odor concentration factor and the fitness, the positions of the optimal and suboptimal solutions are updated. If the updated optimal solution meets the preset power system control requirements and belongs to the PID parameter space, the optimal solution is output.
2. The PID tuning method based on the Chinese pangolin optimization algorithm according to claim 1, characterized in that, Based on the odor concentration factor and the fitness, the positions of the optimal and suboptimal solutions are updated, including: First, update the positions of the optimal and suboptimal solutions based on the fitness. Then, based on the odor concentration factor, update the positions of the optimal and suboptimal solutions.
3. The PID tuning method based on the Chinese pangolin optimization algorithm according to claim 2, characterized in that, The data to be processed also includes: the current solution; Based on the odor concentration factor, the positions of the optimal and suboptimal solutions are updated, including: If the odor concentration factor is less than the preset concentration factor, keep the position of the current solution unchanged, and update the position of the suboptimal solution based on the position of the suboptimal solution, the position of the current solution, the position of the optimal solution and the preset first position update formula.
4. The PID tuning method based on the Chinese pangolin optimization algorithm according to claim 2, characterized in that, Based on the odor concentration factor, the positions of the optimal and suboptimal solutions are updated, including: When the odor concentration factor is greater than or equal to the preset concentration factor, the position of the optimal solution is updated and obtained based on the position of the current solution, the position of the optimal solution, and the preset second position update formula.
5. The PID tuning method based on the Chinese pangolin optimization algorithm according to any one of claims 1-4, characterized in that, Based on the generator's operating parameters, grid disturbance parameters, and a pre-defined mathematical model for odor diffusion, the odor concentration factor is obtained, including: The generator's operating parameters and grid disturbance parameters are input into the odor diffusion mathematical model to obtain the model output results; Input the model output into the preset odor concentration calculation formula to obtain the odor concentration factor output by the odor concentration calculation formula.
6. The PID tuning method based on the Chinese pangolin optimization algorithm according to any one of claims 1-4, characterized in that, Based on the generator's operating parameters and grid disturbance parameters, the fitness of each PID parameter is evaluated, including: The fitness of each PID parameter is evaluated based on a preset fitness calculation formula; The fitness calculation formula includes: itness=α·ISE+β·Overshoot+γ·Settlingtime; Where Fitness represents fitness, α, β and γ represent varying weighting coefficients, ISE represents the integral exponent of error, Overshoot represents overshoot, and Settling time represents settling time. The integral exponent of error, overshoot and settling time are obtained based on the generator's operating parameters and the grid disturbance parameters, respectively.
7. The PID tuning method based on the Chinese pangolin optimization algorithm according to any one of claims 1-4, characterized in that, Based on H ∞ Control theory determines the boundaries of the PID parameter space.
8. The PID tuning method based on the Chinese pangolin optimization algorithm according to claim 7, characterized in that, The PID parameter range includes: Among them, T i T represents the integration constant. d Let represent the differential constant, K represent the synchronous generator controller coefficient, and v represent the delay time, which is a constant.
9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the PID tuning method based on the Chinese pangolin optimization algorithm as described in any one of claims 1 to 8.
10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the PID tuning method based on the Chinese pangolin optimization algorithm as described in any one of claims 1 to 8.