Marine Photovoltaic MPPT Control Method and System Based on Improved Variable Step Size Conductivity Increment Method

Abstract

This invention belongs to the field of marine photovoltaic power generation technology, specifically relating to a marine photovoltaic MPPT control method and system based on an improved variable step-size conductance incremental method. The method first uses the Long-Nosed Raccoon optimization algorithm to optimize the duty cycle of the DC-DC converter in the photovoltaic system, obtaining a globally optimal individual whose output power satisfies the switching conditions. Then, the duty cycle and output power corresponding to this globally optimal individual are used as the initial conditions for the variable step-size conductance incremental method. An initial variable step size is set, and the duty cycle is searched again by gradually decreasing the step size change. The final duty cycle and output power obtained are used for photovoltaic MPPT control. This invention combines the Long-Nosed Raccoon optimization algorithm with the variable step-size conductance incremental method to form a hybrid algorithm, which not only shortens the time for the algorithm to search for the global optimum but also effectively reduces power fluctuations during the algorithm's convergence process.

Description

Technical Field

[0001] This invention belongs to the field of marine photovoltaic power generation technology, specifically relating to a marine photovoltaic MPPT control method and system based on an improved variable step size conductance incremental method. Background Technology

[0002] In recent years, with the increasing demand for energy and the growing scarcity of traditional fuels, the research and development of renewable energy has become a hot topic. Among these, solar energy is highly favored due to its unique advantages such as abundant resources, lack of geographical limitations, and clean operation. However, solar power generation is costly, making research focused on improving the conversion efficiency of photovoltaic systems and reducing the cost of photovoltaic power generation particularly necessary.

[0003] To improve marine photovoltaic technology, reduce fuel consumption, decrease exhaust emissions, and promote green ships, it is necessary to operate photovoltaic cells at their maximum power point as much as possible, achieving maximum power point tracking (MPPT). This method of maximizing the conversion of light energy into electrical energy is known as MPPT. Mature MPPT methods include the constant voltage method, the perturbation and observation method, and the variable step-size conductance incremental method. The traditional variable step-size conductance incremental method has a slow response speed during the startup phase and significant power fluctuations during the tracking convergence process. Summary of the Invention

[0004] The purpose of this invention is to address the aforementioned problems in the existing technology by providing a marine photovoltaic MPPT control method and system based on an improved variable step size conductance incremental method, which features fast response speed and low power fluctuation.

[0005] To achieve the above objectives, the technical solution of the present invention is as follows:

[0006] In a first aspect, this invention proposes a marine photovoltaic MPPT control method based on an improved variable step-size conductance incremental method, the control method comprising:

[0007] S1. Use the Long-Nosed Raccoon optimization algorithm to optimize the duty cycle of the DC-DC converter in the photovoltaic system; determine whether the output power corresponding to the global optimal individual in the current iteration meets the switching condition. If it does, proceed to S2; otherwise, continue iterating.

[0008] S2. The duty cycle and output power corresponding to the globally optimal individual obtained in S1 are used as the initial conditions of the variable step size conductance incremental method. The initial variable step size is set and the duty cycle is searched by gradually decreasing the step size change.

[0009] S3. Utilize the final duty cycle and output power output from S2 for photovoltaic MPPT control.

[0010] S1 includes:

[0011] S11. Initialize the population size of long-nosed raccoons and the location of individual long-nosed raccoons, wherein the location of individual long-nosed raccoons corresponds to the duty cycle of the DC-DC converter in the photovoltaic system.

[0012] S12. Calculate the fitness value of individual long-nosed raccoons, obtain the globally optimal individual, and update the position of long-nosed raccoon individuals in the population; the fitness value of a long-nosed raccoon individual corresponds to the output power obtained by multiplying the load current of the photovoltaic system and the voltage across its terminals; the globally optimal individual refers to the long-nosed raccoon individual with the highest fitness value; updating the position of long-nosed raccoon individuals in the population means: setting the fitness value of long-nosed raccoon individual i in the current iteration. Fitness value corresponding to the position after updating according to the position update strategy To make a comparison, if Then the position of individual long-nosed raccoon i will be changed from Updated to Conversely, the position of individual long-nosed raccoon i remains unchanged;

[0013] S13. Determine whether the output power corresponding to the globally optimal individual in the current iteration meets the switching condition. If it does, proceed to S2; otherwise, return to S12 to continue the iteration.

[0014] The location update strategy for individual long-nosed raccoons is as follows: location updates are performed sequentially during the hunting and attack phase and the escape from predators phase. During the hunting and attack phase, long-nosed raccoon individuals in the population are sorted in descending order of fitness value. For the top-ranked long-nosed raccoon individuals in the population... The position update formula for an individual long-nosed raccoon is:

[0015]

[0016] In the above formula, Let represent the positions of individual long-nosed raccoon i in the (t+1)th and tth iterations, respectively; α is the step size control factor. This is a point-to-point multiplication operation; Iguana t Let be the globally optimal individual obtained in the t-th iteration; I represents a random number in the set {1, 2}; Levy(λ) is the Levy flight function; N is the number of long-nosed raccoon individuals;

[0017] For the later stages of the long-nosed raccoon population The position update formula for an individual long-nosed raccoon is:

[0018]

[0019] In the above formula, r is a random number in the interval (0, 1); A random number within the interval [0.2, 0.9]; for The corresponding fitness value; Let be the fitness value of individual long-nosed raccoon i at its position in the current iteration.

[0020] The switching condition is:

[0021]

[0022] In the above formula, P1 is the output power corresponding to the globally optimal individual in the current iteration, and P2 is the output power corresponding to the globally optimal individual in the previous iteration.

[0023] In S2, the expression for searching the duty cycle using the variable step size conductance increment method is:

[0024]

[0025] p = e -k·(iter-1)

[0026] In the above formula, D old D new dI and dU represent the duty cycles before and after the search, respectively; I and U represent the current and voltage corresponding to the duty cycle after the search, respectively; dI is the difference between the current corresponding to the duty cycle after the search and the current corresponding to the duty cycle before the search; dU is the difference between the voltage corresponding to the duty cycle after the search and the voltage corresponding to the duty cycle before the search; p is the shrinkage factor. is the initial duty cycle change; e is the natural logarithm; k is a constant controlling the rate of decrease of the search factor; iter is the current iteration number of the variable step size conductance increment method.

[0027] Secondly, the present invention proposes a marine photovoltaic MPPT control system based on an improved variable step size conductance incremental method, wherein the control method includes a first optimization module, a second optimization module, and an MPPT control module;

[0028] The first optimization module is used to optimize the duty cycle of the DC-DC converter in the photovoltaic system using the Long-nosed Raccoon optimization algorithm. When the output power corresponding to the global optimal individual in the current iteration meets the switching conditions, the module outputs the duty cycle and output power corresponding to the global optimal individual in the current iteration.

[0029] The second optimization module is used to take the duty cycle and output power corresponding to the globally optimal individual obtained by the first optimization module as the initial conditions of the variable step size conductance incremental method, set the initial variable step size and continue to search for the duty cycle by gradually reducing the step size change, and output the final duty cycle and output power.

[0030] The MPPT control module is used to perform photovoltaic MPPT control based on the final duty cycle and output power output by the second optimization module.

[0031] The first optimization module optimizes the duty cycle of the DC-DC converter in the photovoltaic system according to the following steps:

[0032] S11. Initialize the population size of long-nosed raccoons and the location of individual long-nosed raccoons, wherein the location of individual long-nosed raccoons corresponds to the duty cycle of the DC-DC converter in the photovoltaic system.

[0033] S12. Calculate the fitness value of individual long-nosed raccoons, obtain the globally optimal individual, and update the position of long-nosed raccoon individuals in the population; the fitness value of a long-nosed raccoon individual corresponds to the output power obtained by multiplying the load current of the photovoltaic system and the voltage across its terminals; the globally optimal individual refers to the long-nosed raccoon individual with the highest fitness value; updating the position of long-nosed raccoon individuals in the population means: setting the fitness value of long-nosed raccoon individual i in the current iteration. Fitness value corresponding to the position after updating according to the position update strategy To make a comparison, if Then the position of individual long-nosed raccoon i will be changed from Updated to Conversely, the position of individual long-nosed raccoon i remains unchanged;

[0034] S13. Determine whether the output power corresponding to the globally optimal individual in the current iteration meets the switching condition. If it does, proceed to S2; otherwise, return to S12 to continue the iteration.

[0035] The location update strategy for individual long-nosed raccoons is as follows: location updates are performed sequentially during the hunting and attack phase and the escape from predators phase. During the hunting and attack phase, long-nosed raccoon individuals in the population are sorted in descending order of fitness value. For the top-ranked long-nosed raccoon individuals in the population... The position update formula for an individual long-nosed raccoon is:

[0036]

[0037] In the above formula, Let represent the positions of individual long-nosed raccoon i in the (t+1)th and tth iterations, respectively; α is the step size control factor. This is a point-to-point multiplication operation; Iguana t Let be the globally optimal individual obtained in the t-th iteration; I represents a random number in the set {1, 2}; Levy(λ) is the Levy flight function; N is the number of long-nosed raccoon individuals;

[0038] For the later stages of the long-nosed raccoon population The position update formula for an individual long-nosed raccoon is:

[0039]

[0040] In the above formula, r is a random number in the interval (0, 1); A random number within the interval [0.2, 0.9]; for The corresponding fitness value; Let be the fitness value of individual long-nosed raccoon i at its position in the current iteration.

[0041] The switching condition is:

[0042]

[0043] In the above formula, P1 is the output power corresponding to the globally optimal individual in the current iteration, and P2 is the output power corresponding to the globally optimal individual in the previous iteration.

[0044] The expression for searching the duty cycle using the variable step size conductance increment method is as follows:

[0045]

[0046] p = ek·(iter-1)

[0047] In the above formula, D old D new dI and dU represent the duty cycles before and after the search, respectively; I and U represent the current and voltage corresponding to the duty cycle after the search, respectively; dI is the difference between the current corresponding to the duty cycle after the search and the current corresponding to the duty cycle before the search; dU is the difference between the voltage corresponding to the duty cycle after the search and the voltage corresponding to the duty cycle before the search; p is the shrinkage factor. is the initial duty cycle change; e is the natural logarithm; k is a constant controlling the rate of decrease of the search factor; iter is the current iteration number of the variable step size conductance increment method.

[0048] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0049] The present invention discloses a marine photovoltaic MPPT control method based on an improved variable step-size conductance incremental method. First, the Long-Nosed Raccoon optimization algorithm is used to optimize the duty cycle of the DC-DC converter in the photovoltaic system, obtaining a globally optimal individual whose output power satisfies the switching conditions. The duty cycle and output power corresponding to this globally optimal individual are used as the initial conditions for the variable step-size conductance incremental method. An initial variable step size is set, and the duty cycle is searched by gradually decreasing the step size change. The final duty cycle and output power obtained are used for photovoltaic MPPT control. This method combines the Long-Nosed Raccoon optimization algorithm with the variable step-size conductance incremental method to form a hybrid algorithm, which can not only shorten the time for the algorithm to search for the global optimum, but also effectively reduce power fluctuations during the algorithm's convergence process. Attached Figure Description

[0050] Figure 1 This is a flowchart of the control method described in Embodiment 1 of the present invention.

[0051] Figure 2 The PU characteristic curves of the photovoltaic system under different illumination modes set for Example 1.

[0052] Figure 3 The output power curve of the photovoltaic array obtained by applying the control method described in Example 1 under illumination mode P1.

[0053] Figure 4 The output power curve of the photovoltaic array obtained by applying the control method described in Example 1 when the illumination mode changes from P2 to P3.

[0054] Figure 5 This is a structural block diagram of the control system described in Embodiment 2 of the present invention. Detailed Implementation

[0055] The present invention will now be described in further detail with reference to specific embodiments and accompanying drawings.

[0056] Example 1:

[0057] See Figure 1 A marine photovoltaic MPPT control method based on an improved variable step size conductance incremental method is implemented according to the following steps:

[0058] S1. Optimize the duty cycle of the DC-DC converter in the photovoltaic system using the Long-Nosed Raccoon optimization algorithm:

[0059] S11. Initialize the population size and individual positions of long-nosed raccoons. The individual positions of the long-nosed raccoons correspond to the duty cycle of the DC-DC converter in the photovoltaic system. The positions of the long-nosed raccoons are set to random values ​​within the interval [0.2, 0.9].

[0060] S12. Calculate the fitness value of an individual long-nosed raccoon. The fitness value of an individual long-nosed raccoon corresponds to the output power obtained by multiplying the load current and the voltage across the terminals after the output voltage and current of the photovoltaic system have stabilized. Then, obtain the globally optimal individual, which is the long-nosed raccoon individual with the highest fitness value. Then, update the position of long-nosed raccoon individuals in the long-nosed raccoon population. Updating the position of long-nosed raccoon individuals in the long-nosed raccoon population means: setting the fitness value of long-nosed raccoon individual i in the current iteration. Fitness value corresponding to the position after updating according to the position update strategy To make a comparison, if Then the position of individual long-nosed raccoon i will be changed from Updated to Conversely, the position of individual raccoon i remains unchanged; the position update strategy for the individual raccoon is as follows: position updates are performed sequentially in the hunting and attack phase and the escape from predators phase; in order to better explore the search space and accelerate the search speed, this embodiment uses the Lévy flight function to improve the position update formula for the hunting and attack phases, resulting in the improved raccoon optimization algorithm (ICOA algorithm):

[0061] Individual long-nosed raccoons in the population were sorted in descending order of fitness value. For the top-ranked long-nosed raccoons in the population... The position update formula for an individual long-nosed raccoon is:

[0062]

[0063] In the above formula, Let represent the positions of individual long-nosed raccoon i in the (t+1)th and tth iterations, respectively; α is the step size control factor. This is a point-to-point multiplication operation; Iguana t Let be the globally optimal individual obtained in the t-th iteration; I represents a random number in the set {1, 2}; N is the number of individuals in the long-nosed raccoon population, and N is an even number; Levy(λ) is the Levy flight function;

[0064] Levy(λ) can be expressed as Where β is the exponential coefficient of the Lévy flight function, and u and v follow a normal distribution, calculated as follows:

[0065]

[0066] In the above formula, Γ is the standard gamma function; Represents the variances of u and v;

[0067] For the later stages of the long-nosed raccoon population The position update formula for an individual long-nosed raccoon is:

[0068]

[0069] In the above formula, r is a random number in the interval (0, 1); A random number within the interval [0.2, 0.9]; for The corresponding fitness value; Let be the fitness value of individual long-nosed raccoon i at its position in the current iteration;

[0070] S13. Determine whether the output power corresponding to the globally optimal individual in the current iteration meets the switching condition. If it does, it means that the ICOA has been used to search for the vicinity of the final duty cycle, and it is necessary to enter S2 for precise approach; otherwise, return to S12 to continue the iteration. The switching condition is:

[0071]

[0072] In the above formula, P1 is the output power corresponding to the global best individual in the current iteration, and P2 is the output power corresponding to the global best individual in the previous iteration;

[0073] S2. Using the duty cycle, output power, and voltage corresponding to the globally optimal individual obtained in S1 as the initial duty cycle, output power, and voltage of the Variable Step Size Incremental Conductivity (VSCI) algorithm, an initial variable step size is set, and the duty cycle is searched more accurately by gradually decreasing the step size change until the algorithm converges. The expression for searching the duty cycle using the Variable Step Size Incremental Conductivity is as follows:

[0074]

[0075] p = ek·(iter-1)

[0076] In the above formula, D old D new dI and dU represent the duty cycles before and after the search, respectively; I and U represent the current and voltage corresponding to the duty cycle after the search, respectively; dI is the difference between the current corresponding to the duty cycle after the search and the current corresponding to the duty cycle before the search; dU is the difference between the voltage corresponding to the duty cycle after the search and the voltage corresponding to the duty cycle before the search; p is the shrinkage factor. is the initial duty cycle change; e is the natural logarithm; k is a constant controlling the rate of decrease of the search factor; iter is the current iteration number of the variable step size conductance incremental method;

[0077] Use the duty cycle obtained in S1 as D old If the current and voltage of the duty cycle after the search satisfy Then D new Reduce to If satisfied Then D new Increase to In the above process, D new Each time the value is updated, the iteration count is incremented by one, and the duty cycle changes. It will decrease as the number of iterations increases, and so on, until D. new With D old When they are equal, D new D no longer changes, at this point new This is the duty cycle at the point of maximum power.

[0078] S3. Perform photovoltaic MPPT control using the final duty cycle and output power output from S2. During this process, to address changes in the final duty cycle caused by external environmental changes, it is necessary to continuously monitor the photovoltaic output power periodically. If the output power of the photovoltaic system before and after the changes meet the restart conditions, return to step S1 and restart the calculation. The restart conditions are:

[0079]

[0080] In the above formula, P3 and P4 are the output power of the photovoltaic system before and after monitoring, respectively.

[0081] To verify the effectiveness of the control method described in this invention, a photovoltaic system on a ship's deck is used as the research object. A photovoltaic system simulation model is built in the MATLAB / Simulink environment. Through simulation calculations, the P (power)-U (voltage) characteristic curves of the photovoltaic system under different illumination modes are shown below. Figure 2 As shown; the output power curve of the photovoltaic array obtained by applying the control method of the present invention under illumination mode P1 is shown in the figure. Figure 3 As shown, when the illumination mode changes from P2 to P3, the output power curve of the photovoltaic array obtained by applying the control method described in this invention is as follows: Figure 4 As shown. From Figure 3 , Figure 4 It can be seen that the control method described in this invention has a fast response speed and small power fluctuation, and can quickly recover to steady state in the face of sudden changes in illumination conditions.

[0082] Example 2:

[0083] See Figure 5 A marine photovoltaic MPPT control system based on an improved variable step size conductance incremental method includes a first optimization module, a second optimization module, and an MPPT control module. The first optimization module is used to optimize the duty cycle of the DC-DC converter in the photovoltaic system using the Long-Nosed Raccoon optimization algorithm. The specific steps are as follows:

[0084] S11. Initialize the population size of long-nosed raccoons and the location of individual long-nosed raccoons, wherein the location of individual long-nosed raccoons corresponds to the duty cycle of the DC-DC converter in the photovoltaic system.

[0085] S12. Calculate the fitness value of individual long-nosed raccoons, obtain the globally optimal individual, and update the position of long-nosed raccoon individuals in the population; the fitness value of a long-nosed raccoon individual corresponds to the output power obtained by multiplying the load current of the photovoltaic system and the voltage across its terminals; the globally optimal individual refers to the long-nosed raccoon individual with the highest fitness value; updating the position of long-nosed raccoon individuals in the population means: setting the fitness value of long-nosed raccoon individual i in the current iteration. Fitness value corresponding to the position after updating according to the position update strategy To make a comparison, if Then the position of individual long-nosed raccoon i will be changed from Updated to Conversely, the position of individual long-nosed raccoon i remains unchanged; the position update strategy for long-nosed raccoon individuals is as follows: position updates are performed sequentially in the hunting and attack phase and the escape from predators phase; in the hunting and attack phase, long-nosed raccoon individuals in the population are sorted in descending order of fitness value, and for the top long-nosed raccoon individuals in the population... The position update formula for an individual long-nosed raccoon is:

[0086]

[0087] In the above formula, Let represent the positions of individual long-nosed raccoon i in the (t+1)th and tth iterations, respectively; α is the step size control factor. This is a point-to-point multiplication operation; Iguana t Let be the globally optimal individual obtained in the t-th iteration; I represents a random number in the set {1, 2}; Levy(λ) is the Levy flight function; N is the number of long-nosed raccoon individuals;

[0088] For the later stages of the long-nosed raccoon population The position update formula for an individual long-nosed raccoon is:

[0089]

[0090] In the above formula, r is a random number in the interval (0, 1); A random number within the interval [0.2, 0.9]; for The corresponding fitness value; Let be the fitness value of individual long-nosed raccoon i at its position in the current iteration;

[0091] S13. Determine whether the output power corresponding to the globally optimal individual in the current iteration meets the switching condition. If it does, proceed to S2; otherwise, return to S12 to continue the iteration. The switching condition is:

[0092]

[0093] In the above formula, P1 is the output power corresponding to the global best individual in the current iteration, and P2 is the output power corresponding to the global best individual in the previous iteration;

[0094] The second optimization module uses the duty cycle and output power corresponding to the globally optimal individual obtained by the first optimization module as the initial conditions for the variable step size conductance incremental method. It sets an initial variable step size and continues to search for the duty cycle by gradually decreasing the step size change, outputting the final duty cycle and output power. The expression for searching the duty cycle using the variable step size conductance incremental method is as follows:

[0095]

[0096] p = e -k·(iter-1)

[0097] In the above formula, D old D new dI and dU represent the duty cycles before and after the search, respectively; I and U represent the current and voltage corresponding to the duty cycle after the search, respectively; dI is the difference between the current corresponding to the duty cycle after the search and the current corresponding to the duty cycle before the search; dU is the difference between the voltage corresponding to the duty cycle after the search and the voltage corresponding to the duty cycle before the search; p is the shrinkage factor. is the initial duty cycle change; e is the natural logarithm; k is a constant controlling the rate of decrease of the search factor; iter is the current iteration number of the variable step size conductance incremental method;

[0098] The MPPT control module is used to perform photovoltaic MPPT control based on the final duty cycle and output power output by the second optimization module.

[0099] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1A device that provides the functions specified in one or more boxes.

[0100] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0101] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0102] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0103] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A marine photovoltaic MPPT control method based on an improved variable step size conductance incremental method, characterized in that: The control method includes: S1. Use the Long-Nosed Raccoon optimization algorithm to optimize the duty cycle of the DC-DC converter in the photovoltaic system; determine whether the output power corresponding to the global optimal individual in the current iteration meets the switching condition. If it does, proceed to S2; otherwise, continue iterating. S2. The duty cycle and output power corresponding to the globally optimal individual obtained in S1 are used as the initial conditions of the variable step size conductance incremental method. The initial variable step size is set and the duty cycle is searched by gradually decreasing the step size change. S3. Utilize the final duty cycle and output power output from S2 for photovoltaic MPPT control; S1 includes: S11. Initialize the population size of long-nosed raccoons and the location of individual long-nosed raccoons, wherein the location of individual long-nosed raccoons corresponds to the duty cycle of the DC-DC converter in the photovoltaic system. S12. Calculate the fitness value of individual long-nosed raccoons, obtain the globally optimal individual, and update the position of long-nosed raccoon individuals in the population; the fitness value of a long-nosed raccoon individual corresponds to the output power obtained by multiplying the load current of the photovoltaic array and the voltage across its terminals; the globally optimal individual refers to the long-nosed raccoon individual with the highest fitness value; updating the position of long-nosed raccoon individuals in the population means: setting the fitness value F of long-nosed raccoon individual i in the current iteration. i t The fitness value F corresponding to the position after updating according to the position update strategy i t+1 For comparison, if F i t+1 >F i t Then the position of individual long-nosed raccoon i will be changed from F i t Updated to F i t+1 Conversely, the position of individual long-nosed raccoon i remains unchanged. S13. Determine whether the output power corresponding to the global optimal individual in the current iteration meets the switching condition. If it does, proceed to S2; otherwise, return to S12 to continue the iteration. In S2, the expression for searching the duty cycle using the variable step size conductance increment method is: p=e -k·(iter-1) In the above formula, D old D new dI and dU represent the duty cycles before and after the search, respectively; I and U represent the current and voltage corresponding to the duty cycle after the search, respectively; dI is the difference between the current corresponding to the duty cycle after the search and the current corresponding to the duty cycle before the search; dU is the difference between the voltage corresponding to the duty cycle after the search and the voltage corresponding to the duty cycle before the search; p is the shrinkage factor. is the initial duty cycle change; e is the natural logarithm; k is a constant controlling the rate of decrease of the search factor; iter is the current iteration number of the variable step size conductance increment method.

2. The marine photovoltaic MPPT control method based on the improved variable step size conductance incremental method according to claim 1, characterized in that: The location update strategy for individual long-nosed raccoons is as follows: location updates are performed sequentially during the hunting and attack phase and the escape from predators phase. During the hunting and attack phase, long-nosed raccoon individuals in the population are sorted in descending order of fitness value. For the top-ranked long-nosed raccoon individuals in the population... The position update formula for an individual long-nosed raccoon is: In the above formula, Let represent the positions of individual long-nosed raccoon i in the (t+1)th and tth iterations, respectively; α is the step size control factor. This is a point-to-point multiplication operation; Iguana t Let be the globally optimal individual obtained in the t-th iteration; I represents a random number in the set {1, 2}; Levy(λ) is the Levy flight function; N is the number of long-nosed raccoon individuals; For the later stages of the long-nosed raccoon population The position update formula for an individual long-nosed raccoon is: In the above formula, r is a random number in the interval (0, 1); A random number within the interval [0.2, 0.9]; for The corresponding fitness value; F i t Let be the fitness value of individual long-nosed raccoon i at its position in the current iteration.

3. The marine photovoltaic MPPT control method based on the improved variable step size conductance incremental method according to claim 1, characterized in that: The switching condition is: In the above formula, P1 is the output power corresponding to the globally optimal individual in the current iteration, and P2 is the output power corresponding to the globally optimal individual in the previous iteration.

4. The marine photovoltaic MPPT control system based on the improved variable step size conductance incremental method according to any one of claims 1-3, characterized in that: The control system includes a first optimization module, a second optimization module, and an MPPT control module; The first optimization module is used to optimize the duty cycle of the DC-DC converter in the photovoltaic system using the Long-nosed Raccoon optimization algorithm. When the output power corresponding to the global optimal individual in the current iteration meets the switching conditions, the module outputs the duty cycle and output power corresponding to the global optimal individual in the current iteration. The second optimization module is used to take the duty cycle and output power corresponding to the globally optimal individual obtained by the first optimization module as the initial conditions of the variable step size conductance incremental method, set the initial variable step size and continue to search for the duty cycle by gradually reducing the step size change, and output the final duty cycle and output power. The MPPT control module is used to perform photovoltaic MPPT control based on the final duty cycle and output power output by the second optimization module.

5. The marine photovoltaic MPPT control system based on the improved variable step size conductance incremental method according to claim 4, characterized in that: The first optimization module optimizes the duty cycle of the DC-DC converter in the photovoltaic system according to the following steps: S11. Initialize the population size of long-nosed raccoons and the location of individual long-nosed raccoons, wherein the location of individual long-nosed raccoons corresponds to the duty cycle of the DC-DC converter in the photovoltaic system. S12. Calculate the fitness value of individual long-nosed raccoons, obtain the globally optimal individual, and update the position of long-nosed raccoon individuals in the population; the fitness value of a long-nosed raccoon individual corresponds to the output power obtained by multiplying the load current of the photovoltaic array and the voltage across its terminals; the globally optimal individual refers to the long-nosed raccoon individual with the highest fitness value; updating the position of long-nosed raccoon individuals in the population means: setting the fitness value F of long-nosed raccoon individual i in the current iteration. i t The fitness value F corresponding to the position after updating according to the position update strategy i t+1 For comparison, if F i t+1 >F i t Then the position of individual long-nosed raccoon i will be changed from F i t Updated to F i t+1 ; Conversely, the position of individual long-nosed raccoon i remains unchanged; S13. Determine whether the output power corresponding to the globally optimal individual in the current iteration meets the switching condition. If it does, proceed to S2; otherwise, return to S12 to continue the iteration.

6. The marine photovoltaic MPPT control system based on the improved variable step size conductance incremental method according to claim 5, characterized in that: The location update strategy for the individual long-nosed raccoon is as follows: The location update strategy for individual long-nosed raccoons is as follows: location updates are performed sequentially during the hunting and attack phase and the escape from predators phase. During the hunting and attack phase, long-nosed raccoon individuals in the population are sorted in descending order of fitness value. For the top-ranked long-nosed raccoon individuals in the population... The position update formula for an individual long-nosed raccoon is: In the above formula, Let represent the positions of individual long-nosed raccoon i in the (t+1)th and tth iterations, respectively; α is the step size control factor. This is a point-to-point multiplication operation; Iguana t Let be the globally optimal individual obtained in the t-th iteration; I represents a random number in the set {1, 2}; Levy(λ) is the Levy flight function; N is the number of long-nosed raccoon individuals; For the later stages of the long-nosed raccoon population The position update formula for an individual long-nosed raccoon is: In the above formula, r is a random number in the interval (0, 1); A random number within the interval [0.2, 0.9]; for The corresponding fitness value; F i t Let be the fitness value of individual long-nosed raccoon i at its position in the current iteration.

7. The marine photovoltaic MPPT control system based on the improved variable step size conductance incremental method according to claim 4, characterized in that: The switching condition is: In the above formula, P1 is the output power corresponding to the globally optimal individual in the current iteration, and P2 is the output power corresponding to the globally optimal individual in the previous iteration.

8. The marine photovoltaic MPPT control system based on the improved variable step size conductance incremental method according to claim 4, characterized in that: The expression for searching the duty cycle using the variable step size conductance increment method is as follows: p=e -k·(iter-1) In the above formula, D old D new , respectively, are the duty cycles before and after the search; I and U are the current and voltage corresponding to the duty cycle after the search, respectively; dI is the difference between the current corresponding to the duty cycle after the search and the current corresponding to the duty cycle before the search. dU is the difference between the voltage corresponding to the duty cycle after the search and the voltage corresponding to the duty cycle before the search. p is the contraction factor; e is the natural logarithm; k is a constant that controls the rate at which the search factor decreases; iter is the current iteration number of the variable step size conductance increment method.

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