A control method and system for photovoltaic tracking bracket

Abstract

The present invention relates to the field of photovoltaic tracking bracket control, and the present invention relates to a control method and system for a photovoltaic tracking bracket. The cuckoo algorithm is improved by using the Levy flight with an adaptive step size, and the DBSCAN clustering algorithm is further used to perform secondary optimization of the solution, thereby reducing the generation of invalid solutions and further improving the efficiency of the algorithm. Finally, by dynamically adjusting the duty cycle of the PWM signal and integrating the SPA solar position algorithm, the inclination angle of the photovoltaic tracking bracket is optimized to ensure that the photovoltaic module is always at the optimal light receiving angle and maximize the power generation efficiency. The present invention realizes the maximum power point tracking function by adopting the cuckoo search algorithm combined with the Levy flight mechanism with an adaptive step size, thereby dynamically adjusting the PWM duty cycle and the inclination angle of the photovoltaic tracking bracket, avoiding the local optimal problem, and maximizing the power generation efficiency. At the same time, the DBSCAN clustering algorithm is used to optimize the update of the solution, thereby improving the optimization speed and accuracy of the system.

Description

Technical Field

[0001] The present invention relates to the field of photovoltaic tracking bracket control, and in particular to a control method and system for a photovoltaic tracking bracket. Background Art

[0002] Photovoltaic power generation is the process of converting sunlight into electrical energy. Photovoltaic brackets are an important part of photovoltaic power generation systems. They are supporting structures that arrange photovoltaic modules in a certain direction and angle and fix the spacing according to the specific geographical location, climate and solar energy resource conditions of the photovoltaic power generation system. Photovoltaic tracking brackets have a separate control system. In different latitudes and different solar altitudes, the tilt angle can be adjusted through electromechanical or hydraulic devices to maximize the energy generation efficiency of the solar panel so as to maximize the utilization of solar energy. During the operation of the photovoltaic array, the output power of the photovoltaic array will change due to changes in the surrounding environment and weather, including sunrise and sunset, especially local shadows. However, under certain light and temperature conditions, there is a unique voltage that makes the system work at this voltage with the maximum output power. This voltage is called the maximum power point voltage under this condition. In order to improve the output power and power generation efficiency of the photovoltaic power generation system, it is necessary to track the maximum power point voltage under different environmental conditions.

[0003] The current photovoltaic tracking bracket system generally adopts the maximum power point tracking MPPT algorithm. However, traditional MPPT algorithms, such as the perturbation observation method and the incremental conductance method, are prone to fall into the local optimal point when encountering local shadows, resulting in the inability to track the global maximum power point, thereby affecting the power generation efficiency. There are also some methods that achieve maximum power point tracking by improving the adaptive step size of the cuckoo algorithm to seek the optimal output power under different conditions. For example, the Chinese patent application document with publication number CN115933804A discloses a photovoltaic MPPT control method and system based on an improved cuckoo algorithm, which improves the step size factor by introducing Gaussian perturbations and elite reverse learning strategies to obtain an adaptive step size factor. The improved scheme can reduce the power oscillation problem when the system tends to stabilize. However, this method has some technical defects: it is difficult to balance accuracy and speed when dealing with complex local shadows under multi-peak conditions. Summary of the invention

[0004] In view of the above-mentioned problem of difficulty in balancing algorithm accuracy and speed, in the first aspect, the present invention proposes a control method for a photovoltaic tracking bracket, comprising: initializing the duty cycle of a PWM converter in a photovoltaic power generation system and inputting it into an improved cuckoo algorithm, and outputting a global optimal solution; adjusting the PWM signal according to the duty cycle determined by the global optimal solution, changing the inclination angle of the photovoltaic tracking bracket, and maximizing the power generation efficiency of the photovoltaic power generation system; the improved cuckoo algorithm includes an adaptive step size factor, and the acquisition process is:

[0005]

[0006] in Indicates After the iteration Non-optimal solution Adaptive step size factor of ; Indicates the set step size scaling factor; Indicates the number of current iterations; Indicates the maximum number of iterations set; Indicates After the iteration Non-optimal solution The location and optimal solution The distance of the location; Indicates Non-optimal solution after iterations and the optimal solution The corresponding fitness ratios.

[0007] The present invention realizes the maximum power point tracking MPPT of the photovoltaic power generation system under local shadow conditions by introducing the cuckoo search algorithm and the adaptive step size factor, which effectively avoids the problem that the traditional MPPT algorithm is prone to fall into the local optimal solution. The cuckoo algorithm combined with the Levy flight mechanism not only improves the search efficiency of the global optimal solution, but also enables the system to respond quickly to changes in lighting conditions by dynamically adjusting the step size factor, ensuring that the photovoltaic components are always at the optimal inclination angle and maximizing the power generation efficiency. Compared with the traditional MPPT algorithm, which is inefficient under local shadow conditions, the present invention realizes dynamic optimization in the search process by introducing an adaptive step size factor, so that the algorithm can still accurately track the maximum power point under complex lighting conditions, significantly improving the overall power generation efficiency of the system.

[0008] Furthermore, the improved cuckoo algorithm also includes:

[0009] All solutions after each iteration are clustered, and the cluster with the greatest landing point effectiveness among the cluster cluster where the target non-optimal solution is located and the cluster cluster where the new solution is located after the iterative update position is located is taken as the target cluster cluster; the target cluster cluster is the cluster cluster where the position of the target non-optimal solution is located after the secondary update; the target cluster cluster is divided into windows according to the set number, and the window containing the least number of solutions is taken as the final position of the target non-optimal solution after the secondary update; the landing point effectiveness refers to the ratio of the number of all non-optimal solutions contained in the cluster cluster to the number of new solutions after the non-optimal solution position is iteratively updated; the target non-optimal solution refers to any non-optimal solution after any iteration.

[0010] The present invention clusters all solutions after each iteration and selects clusters for secondary update based on the effectiveness of the landing points. The present invention can effectively avoid the generation of duplicate solutions or invalid solutions, significantly improve the diversity of solutions and the convergence efficiency of the algorithm. The window containing the least number of solutions is used as the final position of the non-optimal solution after the secondary update, which further optimizes the search space and makes the new solution closer to the global optimal solution.

[0011] Furthermore, the clustering algorithm is a DBSCAN clustering algorithm.

[0012] The DBSCAN clustering algorithm is used to cluster the new solutions generated in each iteration, avoiding repeated searches for the same solution or invalid solutions, further improving search efficiency. The adaptive clustering feature of the DBSCAN algorithm enables the algorithm to adaptively adjust the clustering results according to the distribution characteristics of the data, thereby ensuring that the system always searches near the optimal solution.

[0013] Furthermore, the cutoff condition of the cuckoo algorithm includes: in response to the current number of iterations not reaching the set number of iterations, the range of all solutions within the current number of iterations is less than a set threshold, the iteration ends, and the optimal solution of the current number of iterations is output as the global optimal solution; in response to the current number of iterations reaching the set number of iterations, the optimal solution of the current number of iterations is output as the global optimal solution.

[0014] The present invention sets the iteration cutoff condition of the cuckoo algorithm to ensure that the algorithm can be terminated in time under the premise of meeting the accuracy requirements, thereby reducing unnecessary computational overhead. This dynamic termination mechanism not only improves the computational efficiency, but also outputs the current optimal solution as the global optimal solution when the set maximum number of iterations is reached, thereby ensuring the adaptability and flexibility of the algorithm under complex lighting conditions. The number of iterations of traditional algorithms is usually fixed, without considering the distribution and accuracy of the solution. The present invention flexibly adjusts the termination condition of the algorithm through the dual judgment mechanism of the extreme difference threshold and the maximum number of iterations, reduces unnecessary calculations, and improves the real-time performance and accuracy of the algorithm.

[0015] Furthermore, the inclination angle of the photovoltaic tracking bracket needs to be adjusted according to the real-time azimuth and altitude angle of the sun and the local latitude.

[0016] Furthermore, the real-time azimuth and altitude angle of the sun are obtained through a SPA sun position algorithm.

[0017] By using the SPA solar position algorithm, the real-time position of the sun, including the azimuth and altitude, can be accurately calculated, thereby providing data support for the calculation of the optimal inclination angle of the photovoltaic tracking bracket, so that the photovoltaic modules are always in the best light-receiving state.

[0018] Furthermore, the duty cycle adjustment range of the PWM converter is .

[0019] In a second aspect, the present invention provides a control system for a photovoltaic tracking bracket, comprising a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the control method for the photovoltaic tracking bracket of the present invention is implemented.

[0020] The technical effects of the present invention are:

[0021] The present invention realizes the maximum power point tracking function by adopting the cuckoo search algorithm combined with the Levy flight mechanism of adaptive step length, thereby dynamically adjusting the PWM duty cycle and the inclination angle of the photovoltaic tracking bracket, avoiding the local optimal problem, and maximizing the power generation efficiency. At the same time, the DBSCAN clustering algorithm is used to optimize the solution update, which improves the optimization speed and accuracy of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] By reading the following detailed description with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

[0023] Figure 1 is a flow chart schematically showing a control method of a photovoltaic tracking bracket in an embodiment of the present invention;

[0024] Figure 2 is a schematic diagram schematically showing the grayscale of a photovoltaic array in an embodiment of the present invention;

[0025] Figure 3 is a schematic diagram schematically showing the grayscale of a photovoltaic tracking bracket in an embodiment of the present invention;

[0026] Figure 4 The figure schematically shows a control system block diagram of a photovoltaic tracking bracket according to an embodiment of the present invention. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0028] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0029] Photovoltaic tracking bracket control method embodiment:

[0030] like Figure 1 As shown, a control method of a photovoltaic tracking bracket of the present invention includes:

[0031] S1. Determine an adaptive maximum power point tracking control strategy under partial shadow.

[0032] Since local shadows have a significant impact on the output power of photovoltaic power generation systems, maximum power point tracking is required. The algorithm is used to find the maximum power point. Maximum Power Point Tracking Using Cuckoo Algorithm The control strategy enables it to realize the maximum power point tracking function of the photovoltaic power generation system under local shadow conditions, avoid the possibility of falling into the local optimum, and improve the power generation efficiency.

[0033] The cuckoo search algorithm is based on the special reproduction method of cuckoos and the evolution of Levy flight, and is a heuristic algorithm that aims to quickly find the global optimal solution. The high efficiency of the cuckoo search algorithm stems from the special Levy flight mechanism. In the optimization process, if the step size factor is always large, the algorithm has a strong exploration ability, but cannot obtain a high-precision global optimal solution; if the step size factor is always small, the algorithm requires more iterations to achieve the target accuracy. Therefore, in this embodiment, the step size factor is adaptively adjusted based on the difference and connection between each solution and the optimal solution after each iteration, thereby achieving a more accurate and faster global optimal solution optimization process.

[0034] In photovoltaic power generation systems, photovoltaic arrays are the main components, which consist of multiple solar panels that absorb sunlight and use the photovoltaic effect to generate direct current, such as Figure 2 As shown. The photovoltaic tracking bracket is a bracket system that can automatically track the position of the sun. It is fixed on the back of multiple solar panels, such as Figure 3 As shown in the figure, the photovoltaic array is mainly adjusted to maintain the best incident angle by adjusting the angle of the photovoltaic module, so as to maximize the absorption of solar energy. The interface between the photovoltaic array and the load is usually Type converter, Duty cycle of the signal is a control variable, and its value range is generally ; At the same time, by adjusting the duty cycle, the output effective voltage can be affected, thereby achieving control of the output voltage.

[0035] The cuckoo search algorithm simulates the reproduction method of the cuckoo. After the cuckoo lays an egg in a randomly selected bird's nest, it will keep the bird's nest with the highest fitness. If the bird's nest is discovered by the owner of the bird's nest, a new bird's nest will be recreated, and the cuckoo will lay eggs again in the new bird's nest. In summary, in the cuckoo search algorithm in this embodiment, the initialized bird's nest position is the initial value of the duty cycle, and the fitness of each bird's nest is the output power corresponding to the duty cycle it represents. At the same time, the cuckoo adopts the Levy flight mechanism when randomly selecting a bird's nest for laying eggs. Because the Levy function has infinite variance, the Levy flight search step length combines a large number of small steps and a small number of large steps, so that the cuckoo algorithm generates new solutions through Levy flight during the search. There is a step factor in the update process of the Levy flight position. , used to control the range of random search.

[0036] S2. Obtain the adaptive step size factor in the Levy flight position update.

[0037] In this embodiment, an adaptive step size factor is obtained so that the algorithm has a larger step size factor in the initial stage of optimization, and the step size is reduced during the optimization process, thereby expanding the algorithm's early search space and improving the algorithm's global and local search performance. After the iteration, we get a set of bird nest positions, that is, the duty cycle value. At the same time, we can also call the position of each bird nest a solution. All solutions of the iterations are taken as a data set: ; Then calculate the data set The fitness of each bird's nest in the equation is the output power corresponding to each duty cycle value; if the If the set iteration cutoff condition has not been reached in the iteration, the solution with the largest fitness is taken as the optimal solution, which is recorded as: , and there is , and the Iterative optimal solution Keep until iterations; for non-optimal solutions, the iterative process is as follows:

[0038] The first All non-optimal solutions in the iteration are taken as another data set, recorded as: , and there is For the Iteration non-optimal solution dataset Middle Solution Perform iterative update, the initial iterative update formula is:

[0039]

[0040] in Indicates After the iteration The location of a non-optimal solution; Indicates After the iteration The location of a non-optimal solution; represents the step size factor; represents the Levy distribution function, which is a well-known technology and will not be described here any more.

[0041] In this embodiment, the fitness ratio of each non-optimal solution to the optimal solution is first counted. After the iteration The fitness of a non-optimal solution is recorded as , that is, The output power corresponding to the duty cycle value; the fitness of the optimal solution is recorded as , then the fitness ratio is Then calculate the distance between each non-optimal solution and the optimal solution, where After the iteration The distance between a non-optimal solution and the optimal solution is , and there is . The final After the iteration Adaptive step size factor in the position update formula of a non-optimal solution for:

[0042]

[0043] in represents the step length scaling factor, which can be taken as an empirical value of 0.001 in this embodiment, and can be adjusted by the implementer according to the actual situation; Indicates the number of current iterations; Indicates the maximum number of iterations set by the cuckoo algorithm. In this embodiment, the empirical value 1000 can be taken. The implementer can also set it to 2000, 3000 or 5000 according to the actual situation; Indicates After the iteration Non-optimal solution With the optimal solution distance; Indicates Non-optimal solution after iterations and the optimal solution The corresponding fitness ratios.

[0044] when The larger the value, the After the iteration Non-optimal solution Distance Optimal Solution The farther away, the more the step size factor should be increased to expand the search area. The step size factor The bigger; when The smaller the time, the more After the iteration Non-optimal solution The closer to the optimal solution , the more the step size factor should be reduced to increase the local search accuracy, the step size factor The smaller.

[0045] when The larger the value, the After the iteration Non-optimal solution The corresponding output power The closer to the peak power , then the step size of the next iteration needs to be reduced to improve the search accuracy to approach the peak value, so the step size factor The smaller; when The smaller the time, the more After the iteration Non-optimal solution The corresponding output power Distance Peak Power Farther away, the step size of the next iteration needs to be increased to quickly avoid the area with a low probability of the maximum power point. The step size factor The bigger.

[0046] when The larger the value, the closer the current number of iterations is to the maximum number of iterations. The step factor needs to be reduced to increase the search accuracy. The smaller; when The smaller the value, the more likely the current iteration is an initial iteration, and the step factor needs to be increased to expand the search area. The bigger.

[0047] Then the final Non-optimal solution after iterations The position update formula is:

[0048]

[0049] in Indicates After the iteration The location of a non-optimal solution; Indicates After the iteration The location of a non-optimal solution; represents the adaptive step size factor; represents the Levy distribution function.

[0050] The present invention uses an adaptive step-size factor acquisition method, and the step-size factor is dynamically adjusted according to the distance between the optimal solution and the non-optimal solution, the fitness ratio, and the current number of iterations, thereby ensuring that the step-size factor is larger in the early stage to expand the search range, and gradually decreases in the later stage to improve the search accuracy. This dynamic adjustment mechanism can greatly improve the optimization speed and accuracy of the algorithm, especially in scenes with rapidly changing lighting, and can quickly find the global optimal solution. The present invention effectively improves the response speed and accuracy of the system through adaptive adjustment of the step-size factor, especially under local shadow conditions.

[0051] S3. Cluster the solutions after each iteration to achieve secondary update of the new solutions.

[0052] In order to improve the search efficiency, when generating a new solution in each iteration, it is necessary to avoid obtaining a solution that is repeated or similar to the previous iteration, and reduce invalid solutions to improve the convergence efficiency. Therefore, in this embodiment, the new solution generated in each iteration is updated twice. Examples of iterations are: After iterations, any historical duty cycle The corresponding output power The two-dimensional data points are: . The two-dimensional data points formed in the iteration and all previous iterations are clustered using the DBSCAN algorithm. The input of the algorithm includes the two-dimensional data points formed by the data of previous iterations, the neighborhood radius And the minimum number of points , where the neighborhood radius You can take the experience value 0.01, the minimum number of points The empirical value can be 3, and the implementer can also adjust it according to the actual situation. The output of the algorithm includes the cluster label of the data point, the core point of each cluster, and the noise point. After obtaining the clustering result, in order to ensure that any subsequent two-dimensional data point has a corresponding label, all noise points that do not belong to the cluster are also merged into a noise cluster. The above DBSCAN clustering algorithm belongs to the well-known technology and will not be described here.

[0053] The DBSCAN clustering algorithm is used to cluster the new solutions generated in each iteration, avoiding repeated searches for the same solution or invalid solutions, further improving search efficiency. The adaptive clustering feature of the DBSCAN algorithm enables the algorithm to adaptively adjust the clustering results according to the distribution characteristics of the data, thereby ensuring that the system always searches near the optimal solution.

[0054] First, count the In-cluster The number of solutions produced by the iteration , and then use the adaptively improved Levy flight to perform the next iteration, and count the The new solution generated by the iteration is The number of clusters , here we define The effectiveness of the cluster's location ,for:

[0055]

[0056] when The larger the value, the There are more new solutions than old solutions in a cluster, and the probability of generating a peak point in the cluster is high. When a new solution falls in the cluster, its corresponding power is more likely to be close to the peak value, and the landing point of the cluster is more effective. The bigger; when The value of is small, indicating that There are more old solutions than new solutions in a cluster, and the probability of generating a peak point in the cluster is small. When a new solution falls into the cluster, the less likely its corresponding power is to be the peak power, the more effective the point of the cluster is. Then the new solution Cluster, old solution Compare the effectiveness of the points of all clusters passing through the connecting line between the cluster and the center points of the two clusters, and select the cluster with the largest effectiveness as the point cluster for the second update of the new solution .

[0057] In this embodiment, the cluster The number of windows can be 60, 70 or 100, etc. The implementer can adjust it according to the actual situation. The number of historical duty cycles in each window is counted as the density of all old solutions in the window. , with the As an example, the density of the old solution is , then the probability that the new solution falls within the window is for:

[0058]

[0059] in Represents the new solution point cluster The effectiveness of implementation; Indicates the maximum value of the drop point effectiveness of all clusters. The larger the value, the The greater the density of old solutions in a window, the smaller the probability that the new solution falls in the window. The smaller; when The larger the value, the more new solution points will be. The effectiveness of the placement Maximum value of the drop point effectiveness The larger the ratio is, the more likely it is that the new solution will fall in the The greater the probability of a window, the The larger the value, the more new solutions fall into the cluster. The probability of each window in the new solution Generated in cluster In the window with the highest probability, the second update of the new solution is completed.

[0060] After the iteration generates new solutions for secondary update, the fitness of each new solution, i.e., the output power, is calculated, and the old solution is replaced by the new solution with high fitness, and the new solution is updated according to the discovery probability. Eliminate some solutions, and further use preference random walk to generate new solutions to replace the eliminated solutions, where the probability of finding The empirical value can be 0.25. At the same time, in this embodiment, the algorithm termination condition can also be set as: if the set number of iterations is not reached, The duty cycle range is less than the threshold When , the optimal solution after the current iteration is output as the global optimal solution, where the threshold An empirical value of 0.1 may be used, and implementers may also select other values ​​such as 0.2, 0.3 or 0.5 according to actual conditions.

[0061] The present invention clusters all solutions after each iteration and selects clusters for secondary update based on the effectiveness of the landing points. The present invention can effectively avoid the generation of duplicate solutions or invalid solutions, significantly improve the diversity of solutions and the convergence efficiency of the algorithm. The window containing the least number of solutions is used as the final position of the non-optimal solution after the secondary update, which further optimizes the search space and makes the new solution closer to the global optimal solution.

[0062] In summary, the steps of the improved cuckoo algorithm are as follows: first, set the initial parameters of the algorithm and initialize the solution, that is, the location of the bird's nest; then calculate the fitness of all solutions, retain the solution with the highest fitness, use the adaptive step-length Levy flight to generate a new solution for each non-optimal solution, update the landing point of the new solution based on the clustering effectiveness of the historical solution, calculate the fitness of the new solution, and replace the old solution with the new solution with the high fitness; then, according to the discovery probability Randomly eliminate some solutions, and use preference random walk to generate new solutions to replace the eliminated solutions; finally, calculate the fitness of each solution, and determine whether the termination condition is met after each iteration. If so, output the optimal solution and its corresponding fitness, that is, the maximum output power; otherwise, repeat the above steps to generate new solutions until the iteration termination condition is met.

[0063] The present invention sets the iteration cutoff condition of the cuckoo algorithm to ensure that the algorithm can be terminated in time under the premise of meeting the accuracy requirements, thereby reducing unnecessary computational overhead. This dynamic termination mechanism not only improves the computational efficiency, but also outputs the current optimal solution as the global optimal solution when the set maximum number of iterations is reached, thereby ensuring the adaptability and flexibility of the algorithm under complex lighting conditions. The number of iterations of traditional algorithms is usually fixed, without considering the distribution and accuracy of the solution. The present invention flexibly adjusts the termination condition of the algorithm through the dual judgment mechanism of the extreme difference threshold and the maximum number of iterations, reduces unnecessary calculations, and improves the real-time performance and accuracy of the algorithm.

[0064] S4. Based on the duty cycle corresponding to the optimal solution, the inclination angle of the photovoltaic tracking bracket is adjusted to maximize the output power of the photovoltaic power generation system.

[0065] After obtaining the optimal duty cycle, At the appropriate signal frequency, the output voltage is changed according to the optimal duty cycle to maximize the output power of the photovoltaic array. When the ambient temperature of the photovoltaic array remains unchanged but the light intensity changes, that is, when local shadows occur, the output current of the photovoltaic cell will change with the light intensity, but the maximum power point voltage remains basically unchanged. The light intensity can be changed by adjusting the inclination angle of the photovoltaic tracking bracket. Therefore, after determining the real-time maximum power point voltage, the inclination angle can be adjusted to maximize the light intensity and thus the output power.

[0066] After adjusting the output voltage to the voltage value corresponding to the maximum power point obtained in step S3, the SPA solar position algorithm can be used to calculate the real-time azimuth and altitude of the sun. Then, based on the solar azimuth and altitude and the local latitude, the optimal inclination angle of the photovoltaic tracking bracket can be calculated, and the motor of the photovoltaic tracking bracket can be driven to make real-time adjustments so that the sunlight is vertically irradiated to the surface of the photovoltaic module to achieve the maximum output power of the photovoltaic power generation system. The above-mentioned SPA solar position algorithm is a well-known technology and will not be repeated here.

[0067] By using the SPA solar position algorithm, the real-time position of the sun, including the azimuth and altitude, can be accurately calculated, thereby providing data support for the calculation of the optimal inclination angle of the photovoltaic tracking bracket, so that the photovoltaic modules are always in the best light-receiving state.

[0068] The present invention realizes the maximum power point tracking MPPT of the photovoltaic power generation system under local shadow conditions by introducing the cuckoo search algorithm and the adaptive step size factor, which effectively avoids the problem that the traditional MPPT algorithm is prone to fall into the local optimal solution. The cuckoo algorithm combined with the Levy flight mechanism not only improves the search efficiency of the global optimal solution, but also enables the system to respond quickly to changes in lighting conditions by dynamically adjusting the step size factor, ensuring that the photovoltaic components are always at the optimal inclination angle and maximizing the power generation efficiency. Compared with the traditional MPPT algorithm, which is inefficient under local shadow conditions, the present invention realizes dynamic optimization in the search process by introducing an adaptive step size factor, so that the algorithm can still accurately track the maximum power point under complex lighting conditions, significantly improving the overall power generation efficiency of the system.

[0069] Implementation example of control system for photovoltaic tracking bracket:

[0070] On the other hand, the present invention also provides a control system for a photovoltaic tracking bracket. Figure 4 As shown, the control system of the photovoltaic tracking bracket includes a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a control method for the photovoltaic tracking bracket according to the first aspect of the present invention is implemented.

[0071] The control system of the photovoltaic tracking bracket also includes other components well known to those skilled in the art, such as a communication interface, and the configuration and functions of these components are known in the art, so they will not be described in detail here.

[0072] In the present invention, the aforementioned memory may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, apparatus or device. For example, a computer-readable storage medium may be any appropriate magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory RRAM (Resistive Random Access Memory), a dynamic random access memory DRAM (Dynamic Random Access Memory), a static random access memory SRAM (Static Random-Access Memory), an enhanced dynamic random access memory EDRAM (Enhanced Dynamic Random Access Memory), a high-bandwidth memory HBM (High-Bandwidth Memory), a hybrid memory cube HMC (Hybrid Memory Cube), etc., or any other medium that can be used to store the required information and can be accessed by an application, a module, or both. Any such computer storage medium may be part of a device or accessible or connectable to a device. Any application or module described in the present invention may be implemented using computer-readable / executable instructions that may be stored or otherwise maintained by such a computer-readable medium.

[0073] In the description of this specification, "plurality" or "several" means at least two, such as two, three or more, etc., unless otherwise clearly and specifically defined.

[0074] Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

Claims

1. A control method for a photovoltaic tracking bracket, characterized in that: The method comprises: Initializing the duty cycle of the PWM converter in the photovoltaic power generation system and inputting it into the improved cuckoo algorithm to output a global optimal solution; adjusting the PWM signal according to the duty cycle determined by the global optimal solution, changing the inclination angle of the photovoltaic tracking bracket, and maximizing the power generation efficiency of the photovoltaic power generation system; The improved cuckoo algorithm includes an adaptive step size factor, which is obtained as follows: ； in Indicates After the iteration Non-optimal solution Adaptive step size factor of ; Indicates the set step size scaling factor; Indicates the number of current iterations; Indicates the maximum number of iterations set; Indicates After the iteration Non-optimal solution The location and optimal solution The distance of the location; Indicates Non-optimal solution after iterations and the optimal solution The corresponding fitness ratios.

2. A control method for a photovoltaic tracking bracket according to claim 1, characterized in that: The improved cuckoo algorithm also includes: Cluster all solutions after each iteration, and take the cluster with the greatest landing point effectiveness among the clusters where the target non-optimal solution is located and the clusters where the new solutions are located after the iterative update position as the target cluster; the target cluster is the cluster where the location of the target non-optimal solution is located after the second update; divide the target cluster into windows according to the set number, and take the window containing the least number of solutions as the final location of the target non-optimal solution after the second update; the landing point effectiveness refers to the ratio of the number of all non-optimal solutions contained in the cluster to the number of new solutions after the iterative update position of the non-optimal solution; the target non-optimal solution refers to any non-optimal solution after any iteration; Count the number of historical duty cycles in each window as the density of all old solutions in the window, and calculate the probability that the new solution falls within the window. The calculation formula is: in For the The density of the old solution corresponding to the window, Represents the new solution point cluster The effectiveness of implementation; Indicates the maximum value of the drop point effectiveness of all clusters; New statistical solutions fall into clusters The probability of each window in the cluster In the window with the highest probability, the second update of the new solution is completed.

3. A control method for a photovoltaic tracking bracket according to claim 2, characterized in that: The clustering algorithm is the DBSCAN clustering algorithm.

4. A control method for a photovoltaic tracking bracket according to claim 1, characterized in that: The cut-off conditions of the cuckoo algorithm include: In response to the current number of iterations not reaching the set number of iterations, the range of all solutions within the current number of iterations is less than the set threshold, the iteration ends, and the optimal solution of the current number of iterations is output as the global optimal solution; in response to the current number of iterations reaching the set number of iterations, the optimal solution of the current number of iterations is output as the global optimal solution.

5. The control method of a photovoltaic tracking bracket according to claim 1, characterized in that: The inclination angle of the photovoltaic tracking bracket also needs to be adjusted according to the real-time azimuth and altitude angle of the sun and the local latitude.

6. A control method for a photovoltaic tracking bracket according to claim 5, characterized in that: The real-time azimuth and altitude angle of the sun are obtained through the SPA sun position algorithm.

7. A control method for a photovoltaic tracking bracket according to claim 1, characterized in that: The duty cycle adjustment range of the PWM converter is .

8. A control system for a photovoltaic tracking bracket, characterized in that: The method comprises a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the control method of the photovoltaic tracking bracket according to any one of claims 1 to 7 is implemented.

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