Marine photovoltaic MPPT control method based on chaotic fishing and perturbation observation method

Abstract

A marine photovoltaic MPPT control method based on a chaos fishing and perturbation observation method comprises the following steps that the population number of fishers is determined in a preset area through Tent chaos mapping, then the positions of the fishers are initialized, and the positions of the fishers are calculated in combination with different duty ratios to obtain corresponding fitness values; carrying out global search in a preset region according to the fitness value by utilizing a large step length strategy of a fishing optimization algorithm to obtain a region with a global optimal duty ratio, then switching to local tracking, and carrying out local search on the region with the global optimal duty ratio by adopting a small step length strategy to obtain a local optimal duty ratio; the local optimal duty ratio is used as the initial duty ratio of the perturbation and observation method, and then further search is carried out to obtain the optimal duty ratio, namely the maximum power point. The method can track the global maximum power point, and is suitable for different photovoltaic systems and illumination conditions.

Description

Technical Field

[0001] The present invention relates to an improvement of a marine photovoltaic MPPT control method, belonging to the field of marine photovoltaic power generation, and particularly relates to a marine photovoltaic MPPT control method based on chaotic fishing and perturbation observation method. Background Art

[0002] During the navigation of a ship, the lighting conditions may change rapidly. For example, cloud cover or the change of the sun's angle. If the sampling frequency of the maximum power point tracking (MPPT) algorithm is too low, it will not be able to capture the changes in the parameters of the photovoltaic system in time, resulting in low tracking efficiency. This will cause the photovoltaic system to be unable to respond quickly to the lighting changes and unable to work at the maximum power point for a long time, thus causing energy loss. When traditional MPPT control methods are applied to the photovoltaic MPPT control under shading conditions, it is not easy to find the maximum power point, resulting in a large amount of energy waste.

[0003] The Chinese patent application with the application number CN202210220560.2 and the application date of March 8, 2022 discloses a tracking control method and system for the maximum power point of a photovoltaic under non-uniform illumination. First, a photovoltaic cell model under non-uniform illumination is established to obtain the photovoltaic power generation curve under non-uniform illumination. Then, the maximum power point of photovoltaic power generation is tracked in real time through the simulated annealing algorithm to obtain the voltage value and current value of the maximum power point. According to the DC-DC circuit, a mathematical model is established as the prediction model of the model predictive control algorithm. At the same time, the obtained voltage value and current value of the maximum power point are used as the reference values of the objective function of the model predictive control algorithm to obtain the switching signal for controlling the switching state of the DC-DC circuit. The simulated annealing algorithm is used in this invention, which can jump out of the local maximum value of the photovoltaic power generation curve under non-uniform illumination and perform a global search for the maximum power point of the photovoltaic; it improves the adaptability of the mathematical model to the switching action of the system. The above solution only solves the problem of tracking the photovoltaic power under non-uniform illumination, but the above solution does not solve the problem of being unable to accurately track the global maximum power point.

[0004] Disclosing the information of this background art section is only intended to increase the understanding of the overall background of this patent application, and should not be regarded as an admission or any form of suggestion that this information constitutes the prior art already known to those of ordinary skill in the art. Summary of the Invention

[0005] The object of the present invention is to overcome the problem in the prior art that the global maximum power point cannot be accurately tracked, and provides a marine photovoltaic MPPT control method based on chaotic fishing and perturbation observation method that can accurately track the global maximum power point.

[0006] To achieve the above object, the technical solution of the present invention is: a marine photovoltaic MPPT control method based on chaotic fishing and perturbation observation method, and the marine photovoltaic MPPT control method based on chaotic fishing and perturbation observation method includes the following steps:

[0007] First step, first use Tent chaotic mapping to determine the number of fishermen in a preset area, and initialize the position of each fisherman, that is, initialize the duty cycle of the DC-DC converter, and then calculate the position of each fisherman in combination with different duty cycles to obtain the corresponding fitness value;

[0008] Second step, first use the large step size strategy of the fishing optimization algorithm according to the fitness value to perform global search in the preset area to narrow the distance between the fisherman and the global optimal duty cycle to obtain the area of the global optimal duty cycle, and then switch the fishing optimization algorithm to local tracking, and use the small step size strategy to perform local search on the area of the global optimal duty cycle to obtain the local optimal duty cycle, and at this time terminate the fishing optimization algorithm;

[0009] Third step, first use the local optimal duty cycle as the initial duty cycle of the perturbation observation method, and then use the perturbation observation method to further search for the initial duty cycle to obtain the optimal duty cycle, that is, the maximum power point.

[0010] The marine photovoltaic MPPT control method based on chaotic fishing and perturbation observation method further includes: judging whether to restart the fishing algorithm according to the change of the external light condition;

[0011] If the external light condition does not change, continue to output the current optimal duty cycle;

[0012] If the external light condition changes, causing the optimal duty cycle to also change, at this time restart the fishing algorithm and re-search for the optimal duty cycle according to the new light condition.

[0013] When the external light condition changes, the optimal duty cycle of the DC-DC converter in the photovoltaic system will also change accordingly. At this time, it is necessary to restart the fishing algorithm, and use the photovoltaic power change rate as the condition for whether to restart the fishing algorithm: if the photovoltaic power change rate between the previous and current moments is greater than 5%, then restart the fishing algorithm.

[0014] In the first step, first use Tent chaotic mapping to determine the number of fishermen in a preset area, specifically: set the population number pop and the maximum number of iterations T, randomly select a number in the interval (0,1) as X1, and initialize the population position with Tent chaotic mapping;

[0015] The formula of the Tent chaotic mapping function is specifically:

[0016]

[0017] Among them, i is an integer between 1 and pop, and a = 0.5.

[0018] In the second step, first use the fishing optimization algorithm with a large step size strategy to perform a global search within a preset area to narrow the distance between the fisherman and the global optimal duty cycle, so as to obtain the area of the global optimal duty cycle. Specifically:

[0019] S1. In the fishing optimization algorithm, at the beginning of fishing, the fish are the most abundant and the catch rate is the highest. However, as the fisherman's catch gradually decreases, the catch rate drops. Specifically, it is expressed as:

[0020]

[0021] Among them, EFs is the currently evaluated quantity, and MaxEFs is the maximum evaluated quantity;

[0022] S2. According to the strategy, the independent search is carried out using the following formula:

[0023]

[0024] Among them, the value range of Exp is (-1, 1), fit max and fit min are the worst and best fitness values after the t-th complete position update respectively; t represents the current iteration number of the fisherman's position; Fisher i t and Fisher i t+1 represent the current iteration and the position of the i-th fisherman in its next generation; r s is a random number between (0, 1), Dis is the Euclidean distance between the individual and the reference object, and R is less than or equal to Dis;

[0025] S3. According to the strategy, the group capture is carried out using the following formula:

[0026]

[0027] Among them, c is a group of 3 - 4 people whose positions are not updated, Centre c is the target point of the c-group encirclement; Fisher i t+1 and Fisher i t represent the positions of the i-th fisherman in the c-group after the (t + 1)-th and t-th updates; r 2 is the speed at which the fisherman approaches the center, and its value range is (0, 1), r 3is the offset of movement, whose value range is (-1, 1) and gradually decreases as the EFs increase.

[0028] The fisherman can freely choose independent search or group capture. However, when the capture rate α is high, the fisherman's capture strategy tends to the independent search method. When the capture rate α decreases, the fisherman turns to the group capture method. We use a random number (0, 1) for simulation. When p < α, independent search is selected, and when p ≥ α, group capture is selected. p is the probability, a random number between (0, 1).

[0029] The small-step strategy is adopted to perform local search on the region of the global optimal duty cycle to obtain the local optimal duty cycle. At this time, the fishing optimization algorithm is terminated. Specifically:

[0030]

[0031] where GD is the Gaussian distribution function, the overall mean μ is at 0, and the overall variance σ increases from 0 - 1 as the number of evaluations increases, Fisher i t+1 is the position of the i-th fisherman after the (t + 1)-th update, mean(Fisher) is the matrix of the mean values of each dimension of the fisherman center, Gbest is the global optimal position, r 4 is a random number in the set {1, 2, 3}.

[0032] The switch to local tracking is specifically:

[0033]

[0034] where d old and d new are the duty cycles before and after perturbation respectively, P pv(old) and P pv are the photovoltaic powers corresponding to the respective duty cycles, is the perturbation change amount; the duty cycle Gbest obtained by tracking with the chaotic fishing optimization algorithm is used as the initial d old for the perturbation observation method. If the photovoltaic power after the duty cycle increases is greater than the photovoltaic power before the increase, the duty cycle continues to increase; if the photovoltaic power becomes smaller, the duty cycle decreases old on the basis of d Repeat the above steps until the change in photovoltaic power is less than an absolute value close to 0.

[0035] The termination of the fishing optimization algorithm is specifically: Set the switching conditions of the fishing algorithm. When one of the following conditions is met, the fishing optimization algorithm is terminated;

[0036] A. The current duty cycle falls within the threshold range of the global optimal duty cycle;

[0037] B. Reach the maximum number of iterations or the change in duty cycle for consecutive multiple iterations is less than the set threshold.

[0038] It is set according to the scale and complexity of the photovoltaic system to determine the number of fisher populations.

[0039] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0040] 1. In the marine photovoltaic MPPT control method based on chaotic fishing and perturbation observation method of the present invention, firstly, through the Tent chaotic mapping and the large-step strategy of the fishing optimization algorithm, effective global search can be carried out within the preset area to generate initial solutions evenly distributed in the search space, which can ensure that the initial population covers various local maximum power points that may appear in the shaded environment; then after determining the area of the global optimal duty cycle, switch to the small-step strategy for local search to improve the search accuracy; then use the perturbation observation method to accurately track the global optimal duty cycle to obtain the maximum power point; then combine the global and local search capabilities of the fishing optimization algorithm with the accurate tracking ability of the perturbation observation method, which is applicable to various photovoltaic systems and lighting conditions and has strong adaptability; it can effectively cope with the changes in environmental conditions such as light and temperature, thereby improving the energy conversion efficiency and power generation of the photovoltaic system. Therefore, the present invention can accurately track the global maximum power point and is applicable to different photovoltaic systems and lighting conditions.

[0041] 2. In the marine photovoltaic MPPT control method based on chaotic fishing and perturbation observation method of the present invention, it combines the advantages of the fishing optimization algorithm and the perturbation observation method and can converge to the optimal solution quickly in a short time; the fishing optimization algorithm can quickly narrow the search range, while the perturbation observation method can make fine adjustments within the local range, improving the convergence speed of the algorithm. Therefore, the present invention is convenient to adjust and improves the convergence speed of the algorithm.

[0042] 3. In the marine photovoltaic MPPT control method based on chaotic fishing and perturbation observation method of the present invention, by adjusting the algorithm parameters and strategies, various complex environmental changes can be dealt with to ensure that the photovoltaic system always operates near the maximum power point. By accurately tracking the maximum power point, the light energy can be converted into electrical energy to the greatest extent, thereby improving the energy conversion efficiency of the photovoltaic system, reducing energy loss, and lowering the operating cost of the system. Therefore, the present invention reduces energy loss and lowers the cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is the shaded photovoltaic array diagram of the present invention.

[0044] Figure 2 It is the schematic diagram of the optimal duty cycle in the present invention.

[0045] Figure 3 is the flowchart of the present invention.

[0046] Figure 4 is the simulation model diagram of the present invention.

[0047] Figure 5 is the output power curve diagram of the present invention. Detailed implementation manners

[0048] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific implementation manners.

[0049] Refer to Figures 1 to 5 , a marine photovoltaic MPPT control method based on chaotic fishing and perturbation observation method, the marine photovoltaic MPPT control method based on chaotic fishing and perturbation observation method includes the following steps:

[0050] First step, first determine the number of fishermen population within a preset area by using Tent chaotic mapping, and initialize the position of each fisherman, that is, initialize the duty cycle of the DC-DC converter, and then calculate the position of each fisherman in combination with different duty cycles to obtain the corresponding fitness value;

[0051] Second step, first perform a global search within a preset area according to the fitness value by using the large step size strategy of the fishing optimization algorithm to narrow the distance between the fisherman and the global optimal duty cycle to obtain the area of the global optimal duty cycle, and then switch the fishing optimization algorithm to local tracking, and use the small step size strategy to perform local search on the area of the global optimal duty cycle to obtain the local optimal duty cycle, and at this time, terminate the fishing optimization algorithm;

[0052] Third step, first use the local optimal duty cycle as the initial duty cycle of the perturbation observation method, and then use the perturbation observation method to further search for the initial duty cycle to obtain the optimal duty cycle, that is, the maximum power point.

[0053] The marine photovoltaic MPPT control method based on chaotic fishing and perturbation observation method further includes: judging whether to restart the fishing algorithm according to the change of the external light condition;

[0054] If the external light condition does not change, continue to output the current optimal duty cycle;

[0055] If the external light condition changes, causing the optimal duty cycle to also change, at this time, restart the fishing algorithm, and find the optimal duty cycle again according to the new light condition.

[0056] When the external light condition changes, the optimal duty ratio of the DC-DC converter in the photovoltaic system also changes accordingly. At this time, the fishing algorithm needs to be restarted, and the photovoltaic power change rate is used as the condition for whether to restart the fishing algorithm: if the photovoltaic power change rate between the previous and current moments is greater than 5%, the fishing algorithm is restarted.

[0057] In the first step, the population number of fishermen is determined by using Tent chaotic mapping in a preset area. Specifically: set the population number pop and the maximum number of iterations T, randomly select a number in the interval (0,1) as X1, and initialize the population position with Tent chaotic mapping;

[0058] The formula of the Tent chaotic mapping function is specifically:

[0059]

[0060] Among them, i is an integer between 1 and pop, and a = 0.5.

[0061] In the second step, first use the large step size strategy to perform a global search in the preset area according to the fishing optimization algorithm to narrow the distance between the fishermen and the global optimal duty ratio, so as to obtain the area of the global optimal duty ratio. Specifically:

[0062] S1. In the fishing optimization algorithm, at the beginning of fishing, the fish are the most abundant and the fishing catch rate is the highest. However, as the fishermen's catch gradually decreases, the fishing catch rate decreases. Specifically, it is expressed as:

[0063]

[0064] Among them, EFs is the currently evaluated quantity, and MaxEFs is the maximum evaluated quantity;

[0065] S2. According to the strategy, the independent search is carried out using the following formula:

[0066]

[0067] Among them, the value range of Exp is (-1,1), fit max and fit min are the worst and best fitness values after the t-th complete position update respectively; t represents the current iteration number of the fishermen's position; Fisher i t and Fisher i t+1 represent the current iteration and the position of the next generation of the i-th fisherman; r s is a random number between (0,1), Dis is the Euclidean distance between the individual and the reference object, and R is less than or equal to Dis;

[0068] S3. According to the strategy, the swarm capture performs search using the following formula:

[0069]

[0070]

[0071] where c is a group of 3 - 4 individuals whose positions are not updated, Centre c is the target point of the c - group's encirclement, Fisher i t+1 and Fisher i t represent the positions of the i - th fisherman in group c after the (t + 1) - th and t - th updates; r 2 is the speed at which the fisherman approaches the center, with a value range of (0, 1), r 3 is the offset of the movement, with a value range of (-1, 1), which gradually decreases as the number of EFs increases.

[0072] The fisherman can freely choose independent search or swarm capture. However, when the capture rate α is high, the fisherman's capture strategy tends to the independent search method. When the capture rate α decreases, the fisherman turns to the swarm capture method. We use a random number (0, 1) for simulation. When p < α, independent search is selected, and when p ≥ α, swarm capture is selected. p is the probability, a random number between (0, 1).

[0073] The small - step - size strategy is adopted to perform local search on the region of the global - optimal duty cycle to obtain the local - optimal duty cycle. At this time, the fishing optimization algorithm is terminated. Specifically:

[0074]

[0075] where GD is the Gaussian distribution function, the overall mean μ is at 0, the overall variance σ increases from 0 - 1 as the number of evaluations increases, Fisher i t+1 is the position of the i - th fisherman after the (t + 1) - th update, mean(Fisher) is the matrix of the mean values of each dimension of the fisherman center, Gbest is the global - optimal position, r 4 is a random number in the set {1, 2, 3}.

[0076] The switching to local tracking is specifically as follows:

[0077]

[0078] where d old and d new are the duty cycles before and after perturbation respectively, P pv(old) and P pv are the photovoltaic powers corresponding to the respective duty cycles, is the disturbance change amount; the duty cycle Gbest obtained by tracking the chaotic fishing optimization algorithm is used as the initial d of the disturbance observation method old , if the photovoltaic power after the duty cycle increases is greater than the photovoltaic power before the increase, then continue to increase the duty cycle; if the photovoltaic power becomes smaller, then decrease the duty cycle based on d old Repeat the above steps until the change in photovoltaic power is less than an absolute value close to 0.

[0079] The termination of the fishing optimization algorithm is specifically as follows: Set the switching conditions of the fishing algorithm, and terminate the fishing optimization algorithm when one of the following conditions is met;

[0080] A. The current duty cycle falls within the threshold range of the global optimal duty cycle;

[0081] B. Reach the maximum number of iterations or the change amount of the duty cycle is less than the set threshold for multiple consecutive iterations.

[0082] It is set according to the scale and complexity of the photovoltaic system to determine the number of fishermen in the population.

[0083] The supplementary description of the present invention is as follows:

[0084] According to the steps of the fishing optimization algorithm, first perform a global search to narrow the distance from the new global optimal duty cycle; then switch to local tracking to perform a fine search in the area of the global optimal duty cycle until a new local optimal duty cycle is found. After finding the new local optimal duty cycle, use it as the initial duty cycle of the disturbance observation method, and continue to use the disturbance observation method for precise tracking to cope with the small changes in light conditions and maintain the stable operation of the photovoltaic system at the maximum power point.

[0085] Embodiment 1:

[0086] A marine photovoltaic MPPT control method based on chaotic fishing and disturbance observation method, the marine photovoltaic MPPT control method based on chaotic fishing and disturbance observation method includes the following steps:

[0087] The first step is to use the Tent chaotic map to determine the number of fishermen in the population within a preset area, and initialize the position of each fisherman, that is, initialize the duty cycle of the DC-DC converter. Then, calculate the position of each fisherman in combination with different duty cycles to obtain the corresponding fitness value;

[0088] ​Step 2: First, according to the fitness value, use the large step size strategy of the fish swarm optimization algorithm to perform global search within the preset area to narrow the distance between the fisherman and the global optimal duty cycle, so as to obtain the area of the global optimal duty cycle. Then, switch the fish swarm optimization algorithm to local tracking, and use the small step size strategy to perform local search on the area of the global optimal duty cycle to obtain the local optimal duty cycle. At this time, terminate the fish swarm optimization algorithm;

[0089] Step 3: First, use the local optimal duty cycle as the initial duty cycle of the perturbation observation method, and then use the perturbation observation method to further search the initial duty cycle to obtain the optimal duty cycle, that is, the maximum power point.

[0090] Embodiment 2:

[0091] Embodiment 2 is basically the same as Embodiment 1, and the difference lies in:

[0092] The marine photovoltaic MPPT control method based on chaotic fish swarm and perturbation observation method further includes: judging whether to restart the fish swarm algorithm according to the change of the external light condition; if the external light condition does not change, continue to output the current optimal duty cycle; if the external light condition changes, causing the optimal duty cycle to change accordingly, at this time, restart the fish swarm algorithm, and find the optimal duty cycle again according to the new light condition.

[0093] When the external light condition changes, the optimal duty cycle of the DC-DC converter in the photovoltaic system will also change accordingly. At this time, it is necessary to restart the fish swarm algorithm, and use the photovoltaic power change rate as the condition for whether to restart the fish swarm algorithm: if the photovoltaic power change rate between the previous and current moments is greater than 5%, then restart the fish swarm algorithm, and set the threshold of the photovoltaic power change rate to 5%. This means that when the change of the photovoltaic power between two adjacent moments exceeds 5%, it is considered that the external light condition has changed significantly. The setting of this threshold needs to be determined according to the actual operation of the photovoltaic system and the requirements for tracking accuracy. 5% is a commonly used reference value, but it can also be adjusted according to specific conditions;

[0094] The output power of the photovoltaic system is closely related to the external light condition; when the light intensity changes, the volt-ampere characteristic curve of the photovoltaic array will also change accordingly, resulting in the movement of the position of the maximum power point. Since the optimal duty cycle of the DC-DC converter corresponds to the maximum power point of the photovoltaic array, the change of the light condition will also cause the optimal duty cycle to change accordingly;

[0095] After the light condition changes, the current optimal duty cycle may no longer be the optimal solution. Continuing to use this duty cycle will cause the photovoltaic system to not operate at the maximum power point, reducing the energy conversion efficiency and power generation of the system. Therefore, it is necessary to restart the fishing algorithm and perform global search and local tracking again to find a new optimal duty cycle to ensure that the photovoltaic system always operates near the maximum power point.

[0096] Embodiment 3:

[0097] Embodiment 3 is basically the same as Embodiment 1, and the differences are as follows:

[0098] In the first step, first determine the population number of fishermen within a preset area using the Tent chaotic map. Specifically: set the population number pop and the maximum number of iterations T. Randomly select a number X1 within the interval (0,1), and initialize the population position using the Tent chaotic map, and initialize the fishermen population using the Tent chaotic map to avoid the problem of single population position;

[0099] The formula of the Tent chaotic map function is specifically:

[0100]

[0101] where i is an integer between 1 and pop, and a = 0.5;

[0102] In the second step, first perform a global search within a preset area using the large step size strategy according to the fishing optimization algorithm to narrow the distance between the fishermen and the global optimal duty cycle to obtain the area of the global optimal duty cycle. Specifically:

[0103] S1. In the fishing optimization algorithm, at the beginning of fishing, the fish are the most abundant and the catch rate is the highest. However, as the fishermen's catch gradually decreases, the catch rate decreases. Specifically, it is expressed as:

[0104]

[0105] where EFs is the currently evaluated quantity and MaxEFs is the maximum evaluated quantity;

[0106] The fishermen can freely choose independent search or group capture. However, when the capture rate α is relatively high, the fishermen's capture strategy tends to the independent search method. When the capture rate α decreases, the fishermen turn to the group capture method. We use a random number (0,1) for simulation. When p < α, independent search is selected, and when p ≥ α, group capture is selected. p is the probability, a random number between (0,1);

[0107] S2. According to the strategy, the independent search uses the following formula for search:

[0108]

[0109] Among them, the value range of Exp is (-1, 1), and fit max and fit min are the worst and best fitness values respectively after the t-th complete position update; t represents the current iteration number of the fisherman's position; Fisher i t and Fisher i t+1 represent the positions of the i-th fisherman in the current iteration and its next generation; r s is a random number between (0, 1), Dis is the Euclidean distance between the individual and the reference object, and R is less than or equal to Dis;

[0110] S3. According to the strategy, the group capture uses the following formula for search:

[0111]

[0112]

[0113] Among them, c is a group of 3 - 4 people whose positions are not updated, and Centre c is the target point of the c-group's encirclement, and Fisher i t+1 and Fisher i t represent the positions of the i-th fisherman in the c-group after the (t + 1)-th and t-th updates; r 2 is the speed at which the fisherman approaches the center, with a value range of (0, 1), and r 3 is the offset of the movement, with a value range of (-1, 1), which gradually decreases as EFs increases.

[0114] The local search for the region of the global optimal duty cycle is carried out by adopting a small step size strategy to obtain the local optimal duty cycle. At this time, the fishing optimization algorithm is terminated, specifically:

[0115]

[0116] Among them, GD is the Gaussian distribution function, the overall mean μ is at 0, and the overall variance σ increases from 0 - 1 as the number of evaluations increases. Fisher i t+1 is the position of the i-th fisherman after the (t + 1)-th update, mean(Fisher) is the matrix of the mean values of each dimension of the fisherman center, Gbest is the global optimal position, and r 4 is a random number in the set {1, 2, 3}.

[0117] Example 4:

[0118] Example 4 is basically the same as Example 1, except that:

[0119] The switching condition is that the power change rate between the previous and current moments is less than 3%. If this condition is met, it indicates that the global optimal duty cycle has been tracked near by the chaotic fishing optimization algorithm at this time, then the tracking of the chaotic fishing optimization algorithm is terminated, and the tracked duty cycle value is used as the initial duty cycle value of the perturbation observation method, and the perturbation observation method is used to continue tracking the global optimal duty cycle. The specific switching to local tracking is as follows:

[0120]

[0121] where d old and d new are the duty cycles before and after perturbation respectively, P pv(old) and P pv are the photovoltaic powers corresponding to the respective duty cycles. is the perturbation change amount; the duty cycle Gbest obtained by tracking the chaotic fishing optimization algorithm is used as the initial d old of the perturbation observation method. If the photovoltaic power after the duty cycle increases is greater than the photovoltaic power before the increase, the duty cycle is continuously increased; if the photovoltaic power becomes smaller, the duty cycle is decreased on the basis of d old The above steps are repeated until the change in photovoltaic power is less than an absolute value close to 0.

[0122] The specific termination of the fishing optimization algorithm is as follows: Set the switching condition of the fishing algorithm, and terminate the fishing optimization algorithm when one of the following conditions is met;

[0123] A. The current duty cycle falls within the threshold range of the global optimal duty cycle;

[0124] B. The maximum number of iterations is reached or the change amount of the duty cycle in consecutive multiple iterations is less than the set threshold;

[0125] It is set according to the scale and complexity of the photovoltaic system to determine the number of fisher populations.

[0126] Example 5:

[0127] Example 5 is basically the same as Example 1, except that:

[0128] ​Within a preset region, determine the population size of fishermen according to the problem scale and complexity to ensure full coverage of the search space. Initialize the position of each fisherman, i.e., the duty cycle of the DC-DC converter, using the Tent chaotic map. The Tent chaotic map can generate an initial population with good randomness and uniform distribution characteristics, which helps improve the global search ability of the algorithm. For each fisherman's position, calculate its fitness value, i.e., the output power of the photovoltaic system at this duty cycle. The fitness value reflects the performance of the current duty cycle and is the basis for the subsequent optimization process. Then, apply the large step size strategy of the fish swarm optimization algorithm to conduct a global search within the preset region. Through exploration with a larger step size, quickly narrow the distance between the fishermen and the global optimal duty cycle, and preliminarily determine the region where the global optimal duty cycle is located. When the global search phase approaches the neighborhood of the global optimal duty cycle, switch to the local tracking phase and use the small step size strategy to conduct a fine search in this region. The small step size strategy can improve the local search accuracy of the algorithm and fine-tune the global optimal duty cycle to obtain the local optimal duty cycle. When certain termination conditions are met, such as reaching the maximum number of iterations, the fitness value converges, or the change amount of the local optimal duty cycle is less than the set threshold, terminate the fish swarm optimization algorithm;

[0129] Use the local optimal duty cycle obtained by the fish swarm optimization algorithm as the initial duty cycle of the perturbation observation method, providing a better starting point for the perturbation observation method, so that the perturbation observation method can start searching from a position relatively close to the maximum power point. This greatly reduces the number of iterations required by the perturbation observation method and improves the search efficiency. Use the perturbation observation method to further search and optimize the local optimal duty cycle. The perturbation observation method fine-tunes the local optimal duty cycle by slightly perturbing the duty cycle and comparing the power values to achieve precise tracking of the optimal duty cycle and ensure that the photovoltaic system always operates near the maximum power point. The perturbation observation method dynamically adjusts the perturbation step size and direction by comparing the power value after perturbation with the power value after the previous perturbation. In a shaded environment, this dynamic adjustment mechanism can better adapt to the change of the power curve, avoid oscillation near the local maximum power point, and finally find the maximum power point.

[0130] The above is only the preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiment. Any equivalent modification or change made by those of ordinary skill in the art according to the disclosure of the present invention shall be included in the protection scope recorded in the claims.

Claims

1. A marine photovoltaic MPPT control method based on chaos fishing and disturbance observation method, characterized in that: The marine photovoltaic MPPT control method based on chaos fishing and disturbance observation method comprises the following steps: The first step is to use Tent chaotic mapping to determine the number of fishermen in the preset area and initialize the position of each fisherman, that is, initialize the duty cycle of the DC-DC converter, and then calculate the position of each fisherman with different duty cycles to obtain the corresponding fitness value; The second step is to use the large step length strategy of the fishing optimization algorithm according to the fitness value to perform a global search in the preset area, reduce the distance between the fisherman and the global optimal duty cycle, and obtain the area of ​​the global optimal duty cycle. Then, the fishing optimization algorithm is switched to local tracking, and a small step length strategy is used to perform a local search on the area of ​​the global optimal duty cycle to obtain the local optimal duty cycle. At this time, the fishing optimization algorithm is terminated; The third step is to first use the local optimal duty cycle as the initial duty cycle of the perturbation and observation method, and then use the perturbation and observation method to further search the initial duty cycle to obtain the optimal duty cycle, that is, the maximum power point.

2. According to claim 1, a marine photovoltaic MPPT control method based on chaotic fishing and perturbation observation method is characterized in that: The marine photovoltaic MPPT control method based on chaotic fishing and perturbation observation method also includes: judging whether to restart the fishing algorithm according to the change of external lighting conditions; If the external lighting conditions do not change, continue to output the current optimal duty cycle; If the external lighting conditions change, causing the optimal duty cycle to change accordingly, the fishing algorithm is restarted to search for the optimal duty cycle again according to the new lighting conditions.

3. According to claim 2, a marine photovoltaic MPPT control method based on chaotic fishing and perturbation observation method is characterized in that: When the external lighting conditions change, the optimal duty cycle of the DC-DC converter in the photovoltaic system will also change accordingly. At this time, the fishing algorithm needs to be restarted, and the photovoltaic power change rate is used as the condition for restarting the fishing algorithm: if the photovoltaic power change rate before and after is greater than 5%, the fishing algorithm is restarted.

4. According to claim 1, a marine photovoltaic MPPT control method based on chaotic fishing and perturbation observation method is characterized in that: In the first step, the population size of fishermen is determined in a preset area using the Tent chaotic map, specifically: the population size pop and the maximum number of iterations T are set, a number is randomly selected as X1 in the interval (0,1), and the population position is initialized using the Tent chaotic map; The Tent chaos mapping function formula is as follows: Wherein, i is an integer between 1 and pop, and a=0.

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5. According to claim 1, a marine photovoltaic MPPT control method based on chaotic fishing and perturbation observation method is characterized in that: In the second step, a global search is first performed in a preset area using a large step length strategy according to the fishing optimization algorithm to reduce the distance between the fisherman and the global optimal duty cycle, so as to obtain the area of ​​the global optimal duty cycle, specifically: S1. In the fishing optimization algorithm, at the beginning of fishing, fish are most abundant and the catch rate is the highest, but as the fisherman's catch gradually decreases, the catch rate decreases, which is specifically expressed as: Among them, EFs is the number of current evaluations, and MaxEFs is the maximum number of evaluations; S2. According to the strategy, independent search uses the following formula to search: Among them, the range of Exp is (-1,1), f i t max and fit min are the worst and best fitness values ​​after the tth complete position update, respectively; t represents the current iteration number of the fisherman's position; and Represents the position of the current iteration of the i-th fisherman and its next generation; r s is a random number between (0,1), Dis is the Euclidean distance between the individual and the reference object, and R is less than or equal to Dis; S3. According to the strategy, group capture uses the following formula to search: Among them, c is a group of 3-4 people, their positions are not updated, Centre c is the target point of the c group encirclement, and It represents the position of the i-th fisherman in group c after t+1 and t updates; r2 is the speed at which the fisherman approaches the center, with a value range of (0,1); r3 is the offset of the movement, with a value range of (-1,1), which gradually decreases with the increase of EFs.

6. A marine photovoltaic MPPT control method based on chaotic fishing and disturbance observation method according to claim 5, characterized in that: The fisherman can freely choose independent search or group capture, but when the capture rate α is high, the fisherman's capture strategy tends to be an independent search method. When the capture rate α decreases, the fisherman turns to the group capture method. We use random numbers (0,1) for simulation. When p < α, independent search is selected. When p ≥ α, group capture is selected. p is a probability, a random number between (0,1).

7. The marine photovoltaic MPPT control method based on chaos fishing and disturbance observation method according to claim 1 is characterized in that: The small step size strategy is used to locally search the area of ​​the global optimal duty cycle to obtain the local optimal duty cycle, and the fishing optimization algorithm is terminated at this time, specifically: Among them, GD is the Gaussian distribution function, the population mean μ is at 0, and the population variance σ increases from 0 to 1 as the number of evaluations increases. is the position of the i-th fisherman after the t+1th update, mean(Fisher) is the matrix of the means of each dimension of the fisherman center, Gbest is the global optimal position, and r4 is a random number in the set {1,2,3}.

8. A marine photovoltaic MPPT control method based on chaos fishing and disturbance observation method according to claim 7, characterized in that: The switching to local tracking is specifically as follows: Among them, d old and d new are the duty cycles before and after the disturbance, P pv(old) and P pv are the photovoltaic power under the corresponding duty cycle, is the disturbance variation; the duty cycle Gbest tracked by the chaotic fishing optimization algorithm is used as the initial d of the disturbance observation method old , if the duty cycle increases If the photovoltaic power after the increase is greater than the photovoltaic power before the increase, the duty cycle will continue to increase; if the photovoltaic power decreases, then old The duty cycle is reduced based on Repeat the above steps until the PV power change is less than an absolute value close to 0.

9. The marine photovoltaic MPPT control method based on chaos fishing and disturbance observation method according to claim 1 is characterized in that: The specific method of terminating the fishing optimization algorithm is: setting a switching condition of the fishing algorithm, and terminating the fishing optimization algorithm when one of the following conditions is met; A. The current duty cycle falls within the threshold range of the global optimal duty cycle; B. The maximum number of iterations is reached or the duty cycle change for multiple consecutive iterations is less than the set threshold.

10. The marine photovoltaic MPPT control method based on chaos fishing and disturbance observation method according to claim 1 is characterized in that: The settings are made according to the size and complexity of the photovoltaic system to determine the number of fisher populations.

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