Wave power generation device approximate optimal reference speed generation method based on simulated annealing particle swarm algorithm

Abstract

The application provides a wave power generation device approximate optimal reference speed generation method based on a simulated annealing particle swarm algorithm, and comprises the following steps: based on a point absorption direct drive wave power generation device, a linear motion equation is established and frequency domain transformation is performed to obtain an equivalent circuit model; a simulated annealing particle swarm algorithm is used to optimize a motor output power and the damping coefficient to obtain an approximate optimal damping coefficient; and according to a reference speed expression and the approximate optimal damping coefficient, an approximate optimal reference speed is obtained. The application uses the basic theory of impedance matching, adopts the simulated annealing particle swarm algorithm which combines the particle swarm and the simulated annealing, overcomes the disadvantages of premature convergence of the particle swarm algorithm, improves the slow convergence speed of the simulated annealing algorithm, and improves the overall performance of the algorithm; meanwhile, the strategy does not need to predict future waves, significantly reduces the calculation complexity, and greatly enhances the adaptability of the device to complex sea conditions.

Description

Technical Field

[0001] This invention belongs to the technical field of renewable energy power generation, and specifically relates to a method for generating an approximate optimal reference velocity for wave power generation devices based on simulated annealing particle swarm optimization algorithm. Background Technology

[0002] In the vast field of renewable energy power generation, wave energy generation, as a highly promising clean energy technology, has received widespread attention from the global academic and industrial communities in recent years. However, the efficiency and stability of wave energy generation systems largely depend on the optimization of their control system parameters, especially the generation strategies for the optimal damping coefficient and optimal reference velocity. The complexity, variability, and nonlinearity of the wave environment make solving this problem challenging. Simulated annealing and particle swarm optimization (PSO), two commonly used intelligent optimization algorithms, are applied separately to the parameter optimization of wave energy generation systems. Simulated annealing avoids getting trapped in local optima by probabilistically accepting poor solutions, but its convergence speed is relatively slow, especially when dealing with high-dimensional complex problems, where efficiency becomes a significant issue. On the other hand, PSO finds the global optimum through information sharing and cooperation among particles, offering advantages such as fast convergence and ease of implementation. However, PSO is prone to premature convergence, meaning it locks into a local optimum too early, making it difficult to further explore the solution space, which limits its application in complex optimization problems. Summary of the Invention

[0003] Objective of the Invention: The technical problem to be solved by the present invention is to address the shortcomings of the prior art by providing a method for generating an approximately optimal reference velocity for a wave power generation device based on a simulated annealing particle swarm optimization algorithm, comprising the following steps:

[0004] Step 1: Based on the point absorption direct-drive wave power generation device, establish the system frequency domain model according to the linear motion equation;

[0005] Step 2: Treat the force component in the system frequency domain model as the electrical component in the circuit, and establish an equivalent circuit model using the force-electric analogy method;

[0006] Step 3: Use the operating condition optimization algorithm to construct the operating optimization objective function for the motor output power;

[0007] Step 4: Based on the equivalent circuit model, configure the initial parameters for the simulated annealing particle swarm optimization and randomly generate an initial particle population. The initial parameters include random control parameters r1 and r2, population size D, and maximum iteration step size dB. rmax Maximum number of iterations K, initial annealing temperature T0, minimum annealing temperature T end The annealing coefficient α and learning factors c1 and c2; r1 and r2 are random numbers between 0 and 1;

[0008] Step 5: Determine whether to accept the current solution based on the Monte Carlo (Metropolis) criterion;

[0009] Step 6: Update the velocity and position of particles in the population based on the global optimal alternative solution, and update the individual output power and global optimal output power of particles in the population.

[0010] Step 7: When the annealing temperature is lower than the termination temperature and the maximum number of iterations has been reached, output the optimal output power of the objective function and the corresponding approximate optimal damping coefficient.

[0011] Step 8: Obtain the approximate optimal reference speed based on the reference speed expression and the approximate optimal damping coefficient.

[0012] In step 1, the point absorption direct-drive wave power generation device includes a buoy and a permanent magnet synchronous generator. The permanent magnet synchronous generator includes a coil and a permanent magnet. The permanent magnet synchronous generator is used as the power output shaft, and the buoy is coupled to the power output shaft in the vertical direction to form a motion device.

[0013] Considering only the vertical force, the permanent magnet synchronous generator reciprocates under the influence of the buoy. The coil cuts the magnetic lines of force, generating an induced current. The amplitude of the generator force is adjusted by regulating the induced current. Based on Newton's second law and the Cummins equation, the linear motion equation is:

[0014]

[0015] Where M and m ∞ These are the mass of the motion device and the additional mass at infinite frequency, K. r (t) is the kernel function convolved with the velocity at time t, K h It is the sum of the hydrostatic stiffness coefficient and the spring stiffness coefficient, F e (t) is the excitation force generated by the incident wave at time t, F g x(t) is the anti-electromagnetic force of the permanent magnet synchronous generator at time t, and x(t) and v(t) are the vertical displacement and velocity of the moving device at time t, respectively.

[0016] In step 1, by performing a frequency domain transformation on formula (1), the following system frequency domain model is obtained:

[0017]

[0018] Where j is the imaginary unit, ω is the angular frequency, V(ω) is the velocity of the converter and the float at angular frequency ω, and F e (ω) is the excitation force generated by the incident wave at angular frequency ω, F g (ω) is the anti-electromagnetic force of the generator at angular frequency ω, Kr (ω) is the kernel function convolved with the velocity at angular frequency ω, K r (ω)=B r (ω)+jωA r (ω), A r (ω) and B r (ω) represents the so-called radiation-added mass and radiation damping at angular frequency ω, respectively.

[0019] In step 2, the velocity V(ω) in formula (2) is used as the current component in the circuit; the excitation force is used as the voltage source; the anti-electromagnetic force is used as the impedance, which can be used as the adjustable output load; the coefficient K r (ω), ω(M+m) ∞ )and As inherent resistive and inductive loads in the circuit, an equivalent circuit model is obtained; according to Ohm's law of the circuit, the expression for the velocity V(ω) is:

[0020]

[0021] Among them, Z w (ω) represents the system's inherent equivalent impedance at angular frequency ω for the power output shaft. When the impedance of the anti-electromagnetic force is adjusted to the system resonance, the generator output power is at its maximum, and at this time the speed and the excitation force are in phase.

[0022] In step 3, the objective function for optimizing the motor output power is the average output power model derived from the point absorption direct-drive wave generator model, expressed as:

[0023]

[0024] in For the average output power of a point-absorption direct-drive wave generator at angular frequency ω, Re[] means taking the real part of the expression within the square brackets, V * (ω) represents the conjugate of the velocity of the power output shaft system at angular frequency ω;

[0025] The operating condition optimization algorithm ensures that the speed and the excitation force are in phase, thus making the equivalent circuit exhibit resistive characteristics. This requires the control force applied to the power output shaft to conform to the following equation:

[0026]

[0027] in Z at angular frequency ω w The conjugate of (ω), Z g (ω) represents the output impedance of the motor at angular frequency ω;

[0028] With the goal of maximizing output power, an optimization objective function is established that satisfies:

[0029] max[f(x)]=max[P(B r (6)

[0030] in For the equivalent circuit with a damping coefficient of B r The expression for the maximum output power at time B r R is the damping coefficient in the equivalent circuit. g For the equivalent output resistance, It is the conjugate of the damping coefficient in the equivalent circuit.

[0031] In step 5, the constraints for accepting the current solution are:

[0032] P(B r(k+1) )>P(B r(k) (7)

[0033] Among them B r(k+1) Let be the damping coefficient selected in the (k+1)th iteration; if the constraints are met, the current solution is accepted; otherwise, the current solution is accepted with a specific probability, expressed as:

[0034]

[0035] Where T k It is the annealing temperature after performing k annealing operations, and exp refers to the natural exponential function.

[0036] In step 6, the velocities and positions of particles in the population are updated based on the globally optimal alternative solution, satisfying:

[0037] dB rpq (k+1)=dB rpq (k)+c1r1(k)[P pq (k)-B rpq (k)]+c2r2(k)[P gq (k)-B rpq (k)] (9)

[0038] B rpq (k+1)=B rpq (k)+dB rpq (k+1) (10)

[0039] Where p, q = 1, 2, ..., D; D is the selected particle dimension; dB rpq (k) is the iteration step size of each particle in the k-th iteration, dB. rpq (k)∈[-dB rmax dB rmax ], Brpq (k) represents the approximate maximum damping coefficient of each particle in the k-th iteration, dB. rmax Indicates dB rpq (k) Upper limit of value, P pq (k) is when B is added at iteration step k. rpq Substitute the value into the formula The obtained component value of the particle's own optimal position in the q-th dimension, P gq (k) is when B is added at iteration step k. rpq Substitute the value into the formula The obtained global optimal position of the population is the component value in the q-th dimension. Refers to B rpq The conjugate of , c1 and c2 are two learning factors with different values, and r1(k) and r2(k) are two uniform random numbers in the range [0,1] with different values ​​at the k-th iteration; the individual optimal output power and the global optimal output power of each particle are determined again based on the global optimal position update results of the particles in the population.

[0040] In step 7, determine whether the annealing temperature satisfies the following constraint:

[0041] T k <T end (11)

[0042] Among them, T end It is the annealing termination temperature;

[0043] If constraint (11) is not satisfied, then perform temperature annealing and update the temperature rule expression as follows:

[0044] T k+1 =α·T k (12)

[0045] Where α is the annealing coefficient;

[0046] If constraint (11) is satisfied, then determine whether the number of iterations satisfies the following constraint:

[0047] k <K (13)

[0048] Where k is the number of annealing operations already performed, and K is the maximum number of annealing operations set; if constraint (13) is satisfied, the probability calculation is repeated through formula (8) until the maximum number of iterations is reached, and the optimal output power of the objective function and the corresponding approximate optimal damping are output.

[0049] In step 8, when the equivalent circuit exhibits resistive characteristics, the reference speed expression for the power output shaft system is:

[0050]

[0051] Among them, V ref (ω) is the reference velocity at angular frequency ω, and the corresponding reference velocity is the approximate optimal reference velocity.

[0052] The present invention also provides an electronic device, including a processor and a memory, the memory storing program code that, when executed by the processor, causes the processor to perform the steps of the method.

[0053] This invention transforms the damping coefficient in a point absorption direct-drive wave power generation device into a group of particles to be optimized. The output power of the device is used as the optimization objective function. Each particle undergoes a simulated annealing process in parallel. During the annealing process, the Metropolis criterion is used to selectively accept the new state of each particle and use its jumping characteristics to jump out of the local optimum, finally converging to the global optimum.

[0054] The present invention has the following beneficial effects: The method of the present invention uses simulated annealing particle swarm optimization algorithm to generate the optimal reference velocity. Compared with the standard particle swarm optimization algorithm or simulated annealing algorithm, the method of the present invention overcomes the drawback of premature convergence of particle swarm optimization algorithm, and improves the disadvantage of slow convergence speed of simulated annealing algorithm, thereby improving the overall performance of the algorithm and enhancing the adaptability of the device to complex sea conditions. Attached Figure Description

[0055] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments, and the advantages of the present invention in the above and / or other aspects will become clearer.

[0056] Figure 1 This is a flowchart of the simulated annealing algorithm of the present invention.

[0057] Figure 2 This is a schematic diagram illustrating the optimization of the damping coefficient in an embodiment of the present invention.

[0058] Figure 3 This refers to the output power during the parameter adjustment process in this embodiment of the invention.

[0059] Figure 4 This is a comparison chart of control performance under inconsistent parameters in the embodiments of the present invention.

[0060] Figure 5 This is the convergence curve of the output power objective function when using this method in an embodiment of the present invention.

[0061] Figure 6 This is a comparison chart showing the generator output energy obtained using this method in an embodiment of the present invention compared with fixed parameters. Detailed Implementation

[0062] In this embodiment, a point absorption direct-drive wave power generation device is first provided, including a buoy and a permanent magnet synchronous generator. The permanent magnet synchronous generator includes a coil and a permanent magnet. The permanent magnet synchronous generator serves as the power output shaft, and the buoy is coupled to the power output shaft in the vertical direction to form a motion device. Considering only the force in the vertical direction, the permanent magnet synchronous generator reciprocates under the drive of the buoy. The coil cuts the magnetic lines of force, generating an induced current. The induced current is regulated to adjust the amplitude of the generator force.

[0063] Based on this, embodiments of the present invention provide a method for generating an approximately optimal reference velocity for a wave power generation device based on a simulated annealing particle swarm optimization algorithm, such as... Figure 1 As shown, the method includes the following steps:

[0064] Step 1: Based on the point absorption direct-drive wave power generation device, establish the system frequency domain model according to its linear motion equation.

[0065] Step 2: Treat the force components in the system model as electrical components in the circuit, and establish an equivalent circuit model using the force-electric analogy method.

[0066] Step 3: Use the operating condition optimization algorithm to construct the operating optimization objective function for the motor output power;

[0067] Step 4: Based on the equivalent circuit model, configure the initial parameters for the simulated annealing particle swarm optimization, and randomly generate an initial particle population, including random control parameters r1 and r2, population size D, and maximum iteration step size dB. rmax Maximum number of iterations K, initial annealing temperature T0, minimum annealing temperature T end The initial parameters set in this embodiment are: annealing coefficient α and learning factors c1 and c2; wherein, the initial parameters are: population size D = 600, maximum number of iterations K = 100, initial annealing temperature T0 = 1000, and minimum annealing temperature T... end =0.01, annealing coefficient α=0.6, learning factor c1=2.05, c2=2.05, and the initial population is generated randomly.

[0068] Step 5: Determine whether to accept the current solution based on the Monte Carlo (Metropolis) criterion;

[0069] Step 6: Update the velocity and position of particles in the population according to the global optimal alternative solution, and update the individual output power and global optimal output power of particles in the population.

[0070] Step 7: When the annealing temperature is lower than the termination temperature and the maximum number of iterations has been reached, output the optimal output power of the objective function and the corresponding approximate optimal damping coefficient.

[0071] Step 8: Based on the reference speed expression and the above-mentioned approximate optimal damping coefficient, obtain the approximate optimal reference speed.

[0072] Specifically, in step 1, the linear motion relationship is as follows:

[0073]

[0074] Where M and m ∞ These are the mass of the motion device and the additional mass at infinite frequency, respectively, with a value of M = 773254 kg, m ∞ = 400073 kg, K r F(t) is the kernel function convolved with the velocity at time t. e (t) is the excitation force generated by the incident wave at time t, F g (t) represents the anti-electromagnetic force of the permanent magnet synchronous generator at time t, x(t) and v(t) represent the vertical displacement and velocity of the moving device at time t, respectively, and K h It is the sum of the hydrostatic stiffness coefficient and the spring stiffness coefficient, K h Satisfying the expression:

[0075] K h =ρ e Sg (2)

[0076] Wherein, seawater density ρ e =1025kg / m 3 Cross-sectional area S = 16πm 2 The acceleration due to gravity g is taken as 9.81 m / s². 2 .

[0077] By performing a frequency domain transformation on equation (1), the frequency domain expression is obtained as follows:

[0078]

[0079] Where j is the imaginary unit, ω is the angular frequency, V(ω) is the velocity of the converter and the buoy at the angular frequency ω, and F e (ω) is the excitation force generated by the incident wave at angular frequency ω, F g (ω) is the anti-electromagnetic force of the generator at angular frequency ω, K r (ω) is the kernel function convolved with the velocity at angular frequency ω, K r (ω)=B r (ω)+jωA r (ω), A r (ω) and B r (ω) represents the so-called radiation-added mass and radiation damping at different frequencies at angular frequency ω.

[0080] In step 2, the velocity component in formula (3) is considered as the current component in the circuit; the excitation force is considered as a voltage source; the anti-electromagnetic force is considered as an output load with adjustable impedance; the coefficient K r (ω), ω(M+m) ∞ )and As inherent resistive and inductive loads in the circuit, an equivalent circuit model is obtained. Based on Ohm's fundamental law for circuits, the expression for speed can be derived as follows:

[0081]

[0082] In the formula, Z w (ω) represents the system's inherent equivalent impedance at angular frequency ω for the power output shaft. When the impedance of the anti-electromagnetic force is adjusted to the system resonance, the generator output power is at its maximum, and at this time the speed and the excitation force are in phase.

[0083] In step 3, the objective function of the motor output power operation optimization problem is the average output power model derived from the point absorption direct-drive wave power generation device model:

[0084]

[0085] in, For the average output power of a point-absorption direct-drive wave generator at angular frequency ω, Re[] means taking the real part of the expression within the square brackets, V * It is the conjugate of the speed of the power output shaft system.

[0086] Inspired by photovoltaic (PV) energy capture strategies, this method considers the operating point of PV modules as primarily dependent on impedance matching relative to the connected load. The output characteristics of the DDWEC system are also related to both system and load impedances. The algorithm ensures that the velocity and excitation force are in phase, thus making the equivalent circuit exhibit resistive characteristics. This requires the control force applied to the power output shaft to conform to the following equation:

[0087]

[0088] in, Z at angular frequency ω w The conjugate of Z g (ω) represents the output impedance of the motor at angular frequency ω.

[0089] With the goal of maximizing output power, an optimization objective function is established that satisfies:

[0090] max[f(x)]=max[P(B r (7)

[0091] in, For the equivalent circuit with a damping coefficient of B r The expression for the maximum output power at time B r is the damping coefficient in the equivalent circuit.

[0092] Among them, R g For the equivalent output resistance, This is the conjugate of the damping coefficient in the equivalent circuit. The proposed method makes R... g =B r B r This does not represent the true value of the system damping coefficient. When R g With actual damping coefficient When the values ​​are equal, the system output power reaches its optimal level. Because... It remains an unknown parameter in the system, so it is necessary to find its true value.

[0093] In step 5, the simulated annealing algorithm uses a thermodynamic system to represent the optimization process by gradually cooling the system to its lowest energy state. By borrowing the mechanism of the simulated annealing algorithm, in this embodiment, the expression for the constraint condition accepting the current solution is:

[0094] P(B r(k+1) )>P(B r(k) (8)

[0095] Among them B r(k+1) This is the damping coefficient selected in the (k+1)th iteration. If the constraints are met, the current solution is accepted.

[0096] If the constraints are not met, the Metropolis criterion is used to determine whether to accept the current solution. Metropolis is an efficient key sampling method, and its algorithm is as follows: when the system changes from one energy state to another, the corresponding energy changes from P(B) to P(B). r(k) ) changes to P(B) r(k+1) Its probability expression is:

[0097]

[0098] Where T k It is the annealing temperature after performing k annealing operations, and exp refers to the natural exponential function.

[0099] In step 6, the velocities and positions of particles in the population are updated based on the globally optimal alternative solution, satisfying:

[0100] dB rpq (l+1)=dB rpq (k)+c1r1(k)[P pq (k)-B rpq(k)]+c2r2(k)[P gq (k)-B rpq (k)] (10)

[0101] B rpq (k+1)=B rpq (k)+dB rpq (k+1) (11)

[0102] Where p, q = 1, 2, ..., D; D is the selected particle dimension; dB rpq (k) is the iteration step size of each particle in the k-th iteration, dB. rpq (k)∈[-dB rmax dB rmax ], B rpq (k) represents the approximate maximum damping coefficient of each particle in the k-th iteration, dB. rmax Indicates dB rpq (k) Upper limit of value, P pq (k) is when B is added at iteration step k. rpq Substitute the value into the formula The obtained component value of the particle's own optimal position in the q-th dimension, P gq (k) is when B is added at iteration step k. rpq Substitute the value into the formula The obtained global optimal position of the population is the component value in the q-th dimension. Refers to B rpq The conjugate of , c1 and c2 are two learning factors with different values, and r1(k) and r2(k) are two uniform random numbers in the range [0,1] with different values ​​at the k-th iteration; the individual optimal output power and the global optimal output power of each particle are determined again based on the global optimal position update results of the particles in the population.

[0103] In this embodiment, when the damping coefficient is below the optimized value, even a small change in it will lead to significant fluctuations in output power; conversely, when the damping coefficient exceeds the optimized threshold, its impact on output power is significantly reduced, such as... Figure 2 As shown, the ordinate P g (pu) represents the per-unit value of output power, with the horizontal axis R. g (pu) represents the per-unit value of the equivalent output resistance in the equivalent circuit. Therefore, a logarithmic transformation of the damping coefficient is performed to improve control performance. The new lnBr-P curve is shown below. Figure 3 As shown, the ordinate P g (pu) represents the per-unit value of output power, with the horizontal axis being lnB. r (pu) means the per-unit value of the equivalent circuit damping coefficient after the logarithmic transformation of ln.

[0104] Specifically, dB rpq The expression is:

[0105]

[0106] Where ΔP is the change in output power, and ΔlnB rpq It is the change in the ln logarithmic transformation of the damping coefficient of each particle, and λ is an adjustable parameter.

[0107] The right side of equation (10) consists of three parts: the first part is the "inertia" or "momentum" part, which reflects the particle's motion "habit" and represents the particle's tendency to maintain its previous velocity; the second part is the "cognition" part, which reflects the particle's memory or recollection of its own historical experience and represents the particle's tendency to approach its own historical best position; the third part is the "social" part, which reflects the collective historical experience of cooperation and knowledge sharing among particles and represents the particle's tendency to approach the collective or neighborhood historical best position. Based on the update results of the global best position of particles in the population, the individual optimal output power and the global optimal output power of each particle are determined again.

[0108] In step 7, it is determined whether the annealing temperature satisfies the constraint equation:

[0109] T k <T end (13)

[0110] Among them, T k It is the annealing temperature after performing k annealing operations, T end This is the annealing termination temperature. If constraint (13) is not satisfied, then a temperature annealing operation is performed, where the update temperature rule expression is:

[0111] T k+1 =α·T k (14)

[0112] Where α is the annealing coefficient. If constraint (13) is satisfied, then determine whether the number of iterations satisfies the constraint:

[0113] k <K (15)

[0114] Where k is the number of annealing operations performed, and K is the maximum number of annealing operations set. If constraint (15) is satisfied, the probability calculation is repeated until the maximum number of iterations is reached, and the optimal output power of the objective function and the corresponding approximate optimal damping are output.

[0115] In step 8, when the equivalent circuit exhibits resistive characteristics, the reference speed expression for the power output shaft system is:

[0116]

[0117] Among them, V ref (ω) is the reference velocity at angular frequency ω. The corresponding reference velocity at this time is the approximate optimal reference velocity.

[0118] Based on the proposed Simulated Annealing Particle Swarm Optimization (SAPSO) algorithm, convergence results for the near-optimal damping coefficient were obtained, such as... Figure 4 As shown, the vertical axis B r (pu) represents the per-unit value of the equivalent circuit damping coefficient; the convergence curve of the output power objective function is as follows: Figure 5 As shown, a horizontal comparison diagram of the generator output energy obtained using this method is presented for the device and its equivalent circuit of a point absorption direct-drive wave power generation device. Figure 6 As shown.

[0119] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0120] This invention provides a method for generating an approximately optimal reference velocity for a wave power generation device based on a simulated annealing particle swarm optimization algorithm. Many methods and approaches exist for implementing this technical solution; the above description is merely a preferred embodiment of this invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of this invention, and these improvements and modifications should also be considered within the scope of protection of this invention. All components not explicitly stated in this embodiment can be implemented using existing technologies.

Claims

1. A method for generating an approximately optimal reference velocity for a wave power generation device based on simulated annealing particle swarm optimization algorithm, characterized in that, Includes the following steps: Step 1: Based on the point absorption direct-drive wave power generation device, establish the system frequency domain model according to the linear motion equation; Step 2: Treat the force component in the system frequency domain model as the electrical component in the circuit, and establish an equivalent circuit model using the force-electric analogy method; Step 3: Use the operating condition optimization algorithm to construct the operating optimization objective function for the motor output power; Step 4: Based on the equivalent circuit model, configure the initial parameters for the simulated annealing particle swarm optimization and randomly generate an initial particle population. The initial parameters include random control parameters r1 and r2, population size D, and maximum iteration step size dB. rmax Maximum number of iterations K, initial annealing temperature T0, minimum annealing temperature T end The annealing coefficient α and learning factors c1 and c2; r1 and r2 are random numbers between 0 and 1; Step 5: Determine whether to accept the current solution based on the Monte Carlo criterion; Step 6: Update the velocity and position of particles in the population based on the global optimal alternative solution, and update the individual output power and global optimal output power of particles in the population. Step 7: When the annealing temperature is lower than the termination temperature and the maximum number of iterations has been reached, output the optimal output power of the objective function and the corresponding approximate optimal damping coefficient. Step 8: Obtain the approximate optimal reference speed based on the reference speed expression and the approximate optimal damping coefficient.

2. The method according to claim 1, characterized in that, In step 1, the point absorption direct-drive wave power generation device includes a buoy and a permanent magnet synchronous generator. The permanent magnet synchronous generator includes a coil and a permanent magnet. The permanent magnet synchronous generator is used as the power output shaft, and the buoy is coupled to the power output shaft in the vertical direction to form a motion device. Considering only the vertical force, the permanent magnet synchronous generator reciprocates under the influence of the buoy. The coil cuts the magnetic lines of force, generating an induced current. The amplitude of the generator force is adjusted by regulating the induced current. Based on Newton's second law and the Cummins equation, the linear motion equation is: Where M and m ∞ These are the mass of the motion device and the additional mass at infinite frequency, K. r (t) is the kernel function convolved with the velocity at time t, K h It is the sum of the hydrostatic stiffness coefficient and the spring stiffness coefficient, F e (t) is the excitation force generated by the incident wave at time t, F g x(t) is the anti-electromagnetic force of the permanent magnet synchronous generator at time t, and x(t) and v(t) are the vertical displacement and velocity of the moving device at time t, respectively.

3. The method according to claim 2, characterized in that, In step 1, by performing a frequency domain transformation on formula (1), the following system frequency domain model is obtained: Where j is the imaginary unit, ω is the angular frequency, V(ω) is the velocity of the converter and the float at angular frequency ω, and F e (ω) is the excitation force generated by the incident wave at angular frequency ω, F g (ω) is the anti-electromagnetic force of the generator at angular frequency ω, K r (ω) is the kernel function convolved with the velocity at angular frequency ω, K r (ω)=B r (ω)+jωA r (ω), A r (ω) and B r (ω) represents the so-called radiation-added mass and radiation damping at angular frequency ω, respectively.

4. The method according to claim 3, characterized in that, In step 2, the velocity V(ω) in formula (2) is taken as the current component in the circuit; the excitation force is taken as the voltage source; and the anti-electromagnetic force is taken as the impedance. coefficient K r (ω), ω(M+m) ∞ )and As inherent resistive and inductive loads in the circuit, an equivalent circuit model is obtained; according to Ohm's law of the circuit, the expression for the velocity V(ω) is: Among them, Z w (ω) represents the system's inherent equivalent impedance at angular frequency ω for the power output shaft.

5. The method according to claim 4, characterized in that, In step 3, the objective function for optimizing the motor output power is the average output power model derived from the point absorption direct-drive wave generator model, expressed as: in For the average output power of a point-absorption direct-drive wave generator at angular frequency ω, Re[] means taking the real part of the expression within the square brackets, V * (ω) represents the conjugate of the velocity of the power output shaft system at angular frequency ω; The operating condition optimization algorithm ensures that the speed and excitation force are in phase, thus making the equivalent circuit exhibit resistive characteristics. This requires the control force applied to the power output shaft to conform to the following equation: in Z at angular frequency ω w The conjugate of (ω), Z g (ω) represents the output impedance of the motor at angular frequency ω; With the goal of maximizing output power, an optimization objective function is established that satisfies: max[f(x)]=max[P(B r )] (6) in For the equivalent circuit with a damping coefficient of B r The expression for the maximum output power at time B r R is the damping coefficient in the equivalent circuit; g This is the equivalent output resistance. It is the conjugate of the damping coefficient in the equivalent circuit.

6. The method according to claim 5, characterized in that, In step 5, the constraints for accepting the current solution are: P(B r(k+1) )>P(B r(k) ) (7) Among them B r(k+1) Let be the damping coefficient selected in the (k+1)th iteration; if the constraints are met, the current solution is accepted; otherwise, the current solution is accepted with a specific probability, expressed as: Where T k It is the annealing temperature after performing k annealing operations, and exp refers to the natural exponential function.

7. The method according to claim 6, characterized in that, In step 6, the velocities and positions of particles in the population are updated based on the globally optimal alternative solution, satisfying: dB rpq (k+1)=dB rpq (k)+c1r1(k)[P pq (k)-B rpq (k)]+c2r2(k)[P gq (k)-B rpq (k)] (9) B rpq (k+1)=B rpq (k)+dB rpq (k+1) (10) Where p, q = 1, 2, ..., D; D is the selected particle dimension; dB rpq (k) is the iteration step size of each particle in the k-th iteration, dB. rpq (k)∈[-dB rmax dB rmax ], B rpq (k) represents the approximate maximum damping coefficient of each particle in the k-th iteration, dB. rmax Indicates dB rpq (k) Upper limit of value, P pq (k) is when B is added at iteration step k. rpq Substitute the value into the formula The obtained component value of the particle's own optimal position in the q-th dimension, P gq (k) is when B is added at iteration step k. rpq Substitute the value into the formula The obtained global optimal position of the population is the component value in the q-th dimension. Refers to B rpq The conjugate of , c1 and c2 are two learning factors with different values, and r1(k) and r2(k) are two uniform random numbers in the range [0,1] with different values ​​at the k-th iteration; the individual optimal output power and the global optimal output power of each particle are determined again based on the global optimal position update results of the particles in the population.

8. The method according to claim 7, characterized in that, In step 7, determine whether the annealing temperature satisfies the following constraint: T k <T end (11) Among them, T end It is the annealing termination temperature; If constraint (11) is not satisfied, then perform temperature annealing and update the temperature rule expression as follows: T k+1 =α·T k (12) Where α is the annealing coefficient; If constraint (11) is satisfied, then determine whether the number of iterations satisfies the following constraint: k <K (13) Where k is the number of annealing operations already performed, and K is the maximum number of annealing operations set; if constraint (13) is satisfied, the probability calculation is repeated through formula (8) until the maximum number of iterations is reached, and the optimal output power of the objective function and the corresponding approximate optimal damping are output.

9. The method according to claim 8, characterized in that, In step 8, when the equivalent circuit exhibits resistive characteristics, the reference speed expression for the power output shaft system is: Among them, V ref (ω) is the reference velocity at angular frequency ω.

10. An electronic device, characterized in that, It includes a processor and a memory, the memory storing program code that, when executed by the processor, causes the processor to perform the steps of the method as described in any one of claims 1 to 9.

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