Wave power generation control system and method based on immune genetic algorithm

Abstract

This invention discloses a wave power generation control system based on an immune genetic algorithm. The outer loop control loop includes: an immune memory bank, a nonlinear error feedback control law, a primary dual-buoy object, a secondary dual-buoy object, and a mirror image of the secondary buoy object. The inner loop control loop includes: a vertical velocity control module and a process model. The output of the mirror image of the secondary buoy object is connected to the input of the nonlinear feedback control law. The output of the nonlinear error feedback control law is connected to the input of the vertical velocity control module. The output of the vertical velocity control module is connected to the input of the process model, the input of the secondary dual-buoy object, and the input of the mirror image of the secondary dual-buoy object. The output of the secondary dual-buoy object is connected to the input of the primary dual-buoy object. The output of the process model is connected to the input of the vertical velocity control module. This invention solves the problem of interference from unmeasurable state variables, optimizes the wavelength and wave height of the waves, and improves the stability of the system.

Description

Technical Field

[0001] This invention relates to the field of coaxial dual-buoy wave power generation control technology, specifically to a wave power generation control system and method based on an immune genetic algorithm. Background Technology

[0002] The ocean contains abundant wave energy, and generating electricity from wave energy is a very promising technological approach. However, due to the continuous changes in wavelength, wave height, and wave period, wave energy is unstable and discontinuous. Therefore, wave energy power generation devices need to convert the unstable and discontinuous wave input into a stable electrical output.

[0003] Currently, wave power generation devices in China are highly diverse. Some devices employ energy storage and voltage stabilization technology, which captures and stores energy while absorbing the instantaneous impact of wave energy and providing temporary energy replenishment. However, this still cannot guarantee the stability of the output voltage or the matching of the output energy with the load. For example, a patent that has been granted, "A Ship Wave Power Generation Anti-roll Device and Wave Power Generation System" (Patent No.: CN105275724B), does not consider the issue of parameter tuning and optimization for wave energy. Summary of the Invention

[0004] This invention provides a wave power generation control system and method based on an immune genetic algorithm to solve the problem that existing technologies cannot guarantee the stability of the output voltage of wave power generation devices.

[0005] This invention provides a wave power generation control system based on an immune genetic algorithm, comprising: an outer loop control loop and an inner loop control loop, wherein the outer loop control loop and the inner loop control loop constitute a cascade control loop;

[0006] The outer loop control circuit includes: the main controller and a dual-buoy object;

[0007] The main controller includes: an immune memory bank and a nonlinear error feedback control law;

[0008] A dual-buoy object includes: a primary dual-buoy object, a secondary dual-buoy object, and a mirror image of the secondary dual-buoy object;

[0009] The inner loop control circuit includes: a vertical speed control module and a process model;

[0010] The output of the mirror image of the secondary buoy is connected to the input of the nonlinear feedback control law; the output of the nonlinear error feedback control law is connected to the input of the vertical speed control module; the output of the vertical speed control module is connected to the input of the process model, the input of the secondary dual buoy, and the input of the mirror image of the secondary dual buoy; the output of the secondary dual buoy is connected to the input of the primary dual buoy; and the output of the process model is connected to the input of the vertical speed control module.

[0011] Furthermore, the aforementioned outer loop control loop also includes: a state observer;

[0012] The input of the state observer is connected to the output of the main dual-buoy object and the input of the mirror image of the secondary dual-buoy object, respectively; the output of the state observer is connected to the input of the nonlinear error feedback control law and the input of the vertical speed control module, respectively.

[0013] This invention also provides a control method for a wave power generation control system based on an immune genetic algorithm, comprising: an outer loop control process and an inner loop control process.

[0014] The outer loop control process includes the following steps:

[0015] Step A1: Obtain the dominant wavelength and wave height signals;

[0016] Step A2: Compare the dominant wavelength and wave height signals with the standard wavelength and wave height signals, and calculate the deviation and rate of change of the dominant wavelength and wave height from the standard wavelength and wave height;

[0017] Step A3: The genetic algorithm of the immune memory bank tunes the parameters of the nonlinear error feedback control law based on the deviation change rate obtained in step A2 until the parameters of the nonlinear error feedback control law reach the optimal level.

[0018] Step A4: The nonlinear error feedback control law outputs the outer loop control signal according to the control parameters to complete the outer loop control.

[0019] Furthermore, the inner loop control process includes the following steps:

[0020] Step B1: Collect information from the vertical speed control module using the process model to obtain the vertical speed control signal;

[0021] Step B2: The process model generates the inner loop control signal based on the outer loop control signal and the vertical speed control signal.

[0022] Furthermore, step A2 also includes: the state observer generating wavelength and wave height feedback compensation signals based on the dominant wavelength and wave height signals;

[0023] Step B1: The vertical speed control module generates a vertical speed control signal based on the wavelength and wave height feedback compensation signal.

[0024] Furthermore, in step A3, the parameters of the genetic algorithm for the immune memory bank are tuned by using the absolute error integral criterion through the constraints of the control action and the maximum dynamic deviation.

[0025] Furthermore, the objective function of the genetic algorithm for the immune memory bank is:

[0026]

[0027] In the formula, e(t) is the current system error, u(t) is the controller output, σ(t) is the system overshoot, and t u It is the rise time, t d The actual impact time of the sea wave disturbance, w1>0, w2>0, w3>0, w4>0, w5 are weighted coefficients, w0>0 is a weighted coefficient and w0>>w1, can be adjusted as needed.

[0028] Furthermore, the fitness probability of the genetic algorithm for the immune memory bank is:

[0029]

[0030] The beneficial effects of this invention are:

[0031] This invention solves the problem of interference from unmeasurable state variables in the field of wave power generation. It improves the stability of the system by optimizing the wavelength and wave height of waves using a genetic algorithm based on an immune memory library.

[0032] The inner-loop control circuit described in this invention can autonomously adjust the response of the acquired vertical velocity control signal to achieve optimal control performance and exhibits good robustness in system control. Furthermore, it can effectively address the issue of multiple wavelength and wave height signal variables in system control, thereby achieving synchronous and coordinated control of multiple wavelength and wave height signal variables.

[0033] This invention utilizes a genetic algorithm based on an immune memory bank to accurately optimize parameters and achieve good transient response. Furthermore, it addresses the interference problem of some unmeasurable state variables, improving upon the inability of conventional PI controllers to guarantee required high-performance indicators in feedback control. Attached Figure Description

[0034] The features and advantages of the invention will be more clearly understood by referring to the accompanying drawings, which are schematic and should not be construed as limiting the invention in any way. In the drawings:

[0035] Figure 1This is a system block diagram of a specific embodiment of the present invention. Detailed Implementation

[0036] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0037] like Figure 1 As shown, this embodiment of the invention provides a wave power generation control system based on an immune genetic algorithm, including: an outer loop control loop and an inner loop control loop, wherein the outer loop control loop and the inner loop control loop constitute a cascade control loop;

[0038] The outer control loop includes: the main controller, the dual buoy object, and the state observer (ESO);

[0039] The main controller includes: Immune Memory Library (IGA) and Nonlinear Error Feedback Control Law (NLSEF);

[0040] The dual-buoy object includes: the primary dual-buoy object G. p1 Sub-dual buoy object G p2 and the mirror image G of the secondary buoy object p3 ;

[0041] The inner loop control circuit includes: vertical speed control module G IMC Process model G m2 ;

[0042] The output of the nonlinear error feedback control law NLSEF is connected to the vertical velocity control module G. IMC Input connection; Vertical speed control module G IMC The output terminals are respectively connected to the process model G m2 The input terminal, the secondary dual buoy object G p2 The input terminal, the mirror image G of the secondary dual buoy object p3 Input connection; secondary dual buoy object G p2 The output terminal is connected to the main dual buoy object G p1 Input connection; process model G m2 The output terminal is connected to the vertical speed control module G. IMC The input terminals are connected; the input terminals of the state observer ESO are respectively connected to the main dual-buoy object G. p1 The output end, the mirror image G of the secondary dual buoy object p3The input terminals are connected; the output terminals of the state observer ESO are connected to the input terminals of the nonlinear error feedback control law NLSEF and the vertical velocity control module G, respectively. IMC The input terminal is connected.

[0043] The primary dual-buoy signal X1(t) is time-sampled to obtain the wave height and wavelength sampled signal X1(kh). The obtained wave height and wavelength sampled signal X1(kh) is compared with the standard wave height and wavelength sampled signal r(kh) by the first comparator to obtain the wavelength and wave height deviation change e. After the wavelength and wave height deviation change e is output to the immune memory bank, the immune genetic algorithm begins tuning and adjustment, outputting the optimal parameters. The control signal u1(kh) output by the nonlinear error feedback control law NLSEF serves as the vertical speed control module G. IMC The input terminal of the vertical speed control module G IMC The output control signal u2(kh) controls the process model G. m2 Mirror image G of the secondary dual buoy object p3 Action, process model G m2 The sampled wave height and wavelength signal X2(kh) is output to the vertical velocity control module G. IMC The mirror image G of the secondary dual buoy object p3 The sampled wave height and wavelength signal X3(kh) is output to the vertical velocity control module G. IMC Vertical speed control module G IMC The output wave height and wave height signal y2(t) are used as inputs to adjust the wavelength and wave height of the main dual buoys.

[0044] This invention also provides a control method for a wave power generation control system based on an immune genetic algorithm, comprising: an outer loop control process and an inner loop control process.

[0045] The outer loop control process includes the following steps:

[0046] Step A1: Obtain the dominant wavelength and wave height signals;

[0047] Step A2: Compare the dominant wavelength and wave height signals with the standard wavelength and wave height signals, and calculate the deviation and rate of change of the dominant wavelength and wave height from the standard wavelength and wave height; the state observer generates wavelength and wave height feedback compensation signals based on the dominant wavelength and wave height signals.

[0048] Step A3: The genetic algorithm of the immune memory bank tunes the parameters of the nonlinear error feedback control law based on the deviation change rate obtained in step A2 until the parameters of the nonlinear error feedback control law reach the optimal level.

[0049] Due to the interference problem of unmeasurable state variables, the genetic algorithm of immune memory bank is used to search for the optimal solution in large-scale waves and improve the robustness of the system. The parameters of the genetic algorithm of immune memory bank are tuned by the absolute error integral criterion through the constraints of control action and the maximum dynamic deviation, so that the system output is more stable in the dynamic changes of the main wavelength and wave height during system operation.

[0050] To address the disturbances present in actual ocean waves, the objective function of the genetic algorithm for the immune memory bank was optimized. The objective function is as follows:

[0051]

[0052] In the formula, e(t) is the current system error, u(t) is the controller output, σ(t) is the system overshoot, and t u It is the rise time, t d The actual impact time of the sea wave disturbance, w1>0, w2>0, w3>0, w4>0, w5 are weighted coefficients, w0>0 is a weighted coefficient and w0>>w1, can be adjusted as needed.

[0053] The immune control library's immune system possesses mechanisms to maintain immune homeostasis. Through the suppression and promotion of gentle wavelengths and heights, it allows individuals to evolve within an environment of fluctuating wave-like conditions, preventing individual homogenization and thus greatly enhancing the local search capability of the immune genetic algorithm. The suppression and promotion modules for gentle wavelengths and heights select individuals to participate in the genetic operation, choosing the concept p and the fitness probability p of the antibody. f and concentration probability p d Related, i.e., p = αp f +(1-α)p d , where α is a constant adjustment factor, 0≤α≤1.

[0054] Let the solution to be optimized be x. i The fitness function of the solution is f(x) i ), f(x) j Let be the fitness function value of the j-th solution in the population, and N be the number of antibodies in set X. Then, the fitness probability of the genetic algorithm for the immune memory bank is:

[0055]

[0056] The concentration probability is defined based on a vector matrix, where n is the distance parameter, and the antibody f(x) is defined as follows: i The distance on set X is:

[0057]

[0058] Let n iLet m be the number of antigens bound by the i-th antibody, and m be the volume ratio of the antibody to the antigen. Then the concentration of the i-th antibody can be expressed by the following formula:

[0059]

[0060] From this, a probabilistic selection formula based on antibody concentration can be derived:

[0061]

[0062] Step A4: The nonlinear error feedback control law outputs the outer loop control signal according to the control parameters to complete the outer loop control.

[0063] The inner loop control process includes the following steps:

[0064] Step B1: The vertical speed control module generates a vertical speed control signal based on the wavelength and wave height feedback compensation signal. The vertical speed control module is used to collect information through the process model to obtain the vertical speed control signal.

[0065] Step B2: The process model generates the inner loop control signal based on the outer loop control signal and the vertical speed control signal.

[0066] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A control method for a wave power generation control system based on an immune genetic algorithm, characterized in that, include: The outer loop control process and the inner loop control process are defined. The outer loop control process includes the following steps: Step A1: Obtain the dominant wavelength and wave height signals; Step A2: Compare the dominant wavelength and wave height signals with the standard wavelength and wave height signals, and calculate the deviation and rate of change of the dominant wavelength and wave height from the standard wavelength and wave height; the state observer generates wavelength and wave height feedback compensation signals based on the dominant wavelength and wave height signals. Step A3: The genetic algorithm for the immune memory bank tunes the parameters of the nonlinear error feedback control law based on the deviation change rate obtained in Step A2, until the parameters of the nonlinear error feedback control law reach their optimum. The objective function of the genetic algorithm for the immune memory bank is: ； In the formula, This is the current system error. It is the controller output. For system overshoot, It's the rising time. The actual duration of the impact of ocean wave disturbances. , , , , These are the weighting coefficients. The weighting coefficients and It can be adjusted as needed; Step A4: The nonlinear error feedback control law outputs the outer loop control signal according to the control parameters to complete the outer loop control. The inner loop control process includes the following steps: Step B1: The vertical speed control module generates a vertical speed control signal based on the wavelength and wave height feedback compensation signal. The vertical speed control module is used to collect information through the process model to obtain the vertical speed control signal. Step B2: The process model generates the inner loop control signal based on the outer loop control signal and the vertical speed control signal.

2. The control method for the wave power generation control system based on the immune genetic algorithm as described in claim 1, characterized in that, In step A3, the parameters of the genetic algorithm for the immune memory bank are tuned using the absolute error integral criterion through the constraints of control action and the maximum dynamic deviation.

3. The control method for the wave power generation control system based on immune genetic algorithm as described in claim 1 or 2, characterized in that, The fitness probability of the genetic algorithm for the immune memory bank is: 。 4. A wave power generation control system based on an immune genetic algorithm, used to implement the control method of the wave power generation control system based on an immune genetic algorithm as described in any one of claims 1-3, the control system comprising: An outer loop control loop and an inner loop control loop, the outer loop control loop and the inner loop control loop forming a cascade control loop; characterized in that the outer loop control loop includes: a main controller, a dual-buoy object and a state observer; The main controller includes: an immune memory bank and a nonlinear error feedback control law; A dual-buoy object includes: a primary dual-buoy object, a secondary dual-buoy object, and a mirror image of the secondary dual-buoy object; The inner loop control circuit includes: a vertical speed control module and a process model; The output of the nonlinear error feedback control law is connected to the input of the vertical speed control module; the output of the vertical speed control module is connected to the input of the process model, the input of the secondary dual buoy object, and the input of the mirror image of the secondary dual buoy object; the output of the secondary dual buoy object is connected to the input of the primary dual buoy object; the output of the process model is connected to the input of the vertical speed control module; the input of the state observer is connected to the output of the primary dual buoy object and the input of the mirror image of the secondary dual buoy object; the output of the state observer is connected to the input of the nonlinear error feedback control law and the input of the vertical speed control module.

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