A photovoltaic array dynamic reconfiguration adaptive control method and system

Abstract

The application discloses a kind of photovoltaic array dynamic reconstruction adaptive control method and system, comprising: the relevant data of photovoltaic array is collected, edge preliminary analysis relevant data, identify photovoltaic panel state;Through perturbation observation method, MPPT control is carried out to each photovoltaic panel;Photovoltaic array end is detected by clustering analysis, assesses photovoltaic array state and does dynamic reconstruction.This application method is combined with the advantage of edge computing and centralized control, realizes fast, accurate shadow detection and efficient dynamic reconstruction control.This method belongs to photovoltaic power generation system control technical field, has the advantages such as improving system overall performance, enhancing environmental adaptability and improving scalability.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic power generation, and in particular to a method and system for dynamic reconstruction adaptive control of a photovoltaic array. Background Art

[0002] Photovoltaic power generation, as a clean and renewable form of energy, plays an increasingly important role in the transformation of the global energy structure. With the continuous advancement of photovoltaic technology, the efficiency and reliability of photovoltaic arrays have been significantly improved. However, in practical applications, photovoltaic arrays still face many challenges, the most prominent of which is the performance degradation under partial shading and uneven lighting conditions. Traditional fixed-topology photovoltaic arrays often perform poorly in these conditions, resulting in a significant reduction in the overall output power of the system. To address this problem, researchers have proposed dynamic reconstruction technology to optimize system performance by adjusting the connection method of photovoltaic arrays in real time. Early dynamic reconstruction methods were mainly based on preset rules or simple heuristic algorithms. Although they can improve system performance in some cases, they still have shortcomings such as slow response speed and poor adaptability.

[0003] With the development of artificial intelligence and big data technologies, machine learning-based dynamic reconfiguration methods for photovoltaic arrays have gradually become a research hotspot. These methods analyze large amounts of historical data and establish complex mathematical models to predict the optimal reconfiguration strategy. However, existing intelligent reconfiguration methods still have some limitations: First, most methods require extensive computing resources and complex central control systems, making them difficult to widely apply in practical engineering projects; second, these methods often ignore the real-time status of individual components in the photovoltaic array and cannot quickly respond to local environmental changes; third, existing methods lack scalability when dealing with large-scale photovoltaic arrays, making it difficult to meet the needs of photovoltaic power plants of different sizes. In addition, existing technologies still have room for improvement in the accuracy of shadow detection and the optimization of reconfiguration strategies, especially in complex and changing environmental conditions, making it difficult to achieve true adaptive control. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is that the existing photovoltaic array dynamic reconstruction technology mainly has problems such as slow response speed, poor adaptability, insufficient scalability and poor performance in complex environments.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: a method for adaptive control of dynamic reconstruction of a photovoltaic array, comprising: collecting relevant data of the photovoltaic array, preliminarily analyzing the relevant data at the edge end, and identifying the status of the photovoltaic panel; performing MPPT control on each photovoltaic panel through the perturbation observation method; and performing shadow detection through cluster analysis at the photovoltaic array end, evaluating the status of the photovoltaic array, and performing dynamic reconstruction.

[0007] As a preferred solution of the photovoltaic array dynamic reconstruction adaptive control method described in the present invention, the relevant data includes illumination data, temperature data, electrical data, and system topology and reconstruction status data.

[0008] As a preferred solution of the photovoltaic array dynamic reconstruction adaptive control method of the present invention, the edge end corresponds to a single photovoltaic panel, is responsible for data collection and calculation of performance ratio to determine the status of the photovoltaic panel, which is expressed as:

[0009]

[0010] Where PR is performance ratio; P is output power; G is light intensity; P0 is the nominal power of the photovoltaic panel; calculate the difference between the current PR and the average PR of the same period in the past 7 days, C Z , and the difference rate C between the current panel PR and the average PR of the adjacent panels X , when C Z <-20% and C X >-20%, it is considered as shadow occlusion; when C Z <-20% and C X When the value is less than -20%, it is judged as a photovoltaic panel failure, the photovoltaic panel is immediately isolated, and a fault alarm is sent to the photovoltaic array end; when C Z >-20% and C X When the power consumption is less than -20%, it is judged that the photovoltaic panel has been in low power for a long time, and no isolation measures are taken, and a fault alarm is sent to the photovoltaic array end.

[0011] As a preferred solution of the photovoltaic array dynamic reconstruction adaptive control method described in the present invention, the MPPT control includes: performing MPPT control at the edge through the perturbation observation method, adjusting the photovoltaic panel operating point in real time, calculating the power P(k) based on the collected voltage V(k) and current I(k), performing a small amplitude perturbation on the voltage, collecting the disturbed voltage V(k+1) and current I(k+1) to calculate the power P(k+1) after the perturbation, comparing the power with the power after the perturbation, and determining the next perturbation direction, which is expressed as:

[0012] ΔP=P(k+1)-P(k)

[0013] ΔV=V(k+1)-V(k)

[0014] Where AP represents the power disturbance change; ΔV represents the voltage disturbance change; when AP / ΔV>0, V(k+2)=V(k+1)+ΔV; when AP / ΔV<0, V(k+2)=V(k+1)-ΔV; the step size is adjusted through an adaptive mechanism, increasing when away from the maximum power point and decreasing when approaching the maximum power point, expressed as:

[0015] ΔV(k+1)=ΔV(k)×(1+α×|ΔP / ΔV|)

[0016] Wherein, α represents the step adjustment parameter; the voltage, current, power, temperature, MPPT efficiency and accumulated energy output data of the photovoltaic panel are regularly uploaded to the photovoltaic array end.

[0017] As a preferred embodiment of the photovoltaic array dynamic reconfiguration adaptive control method of the present invention, the shadow detection includes comprehensively analyzing the output of all panels in the photovoltaic array. When there are shadowed panels or panels with long-term low power, the shadow detection and identification of the photovoltaic array is performed using a clustering algorithm. Based on the data uploaded by each edge end in the photovoltaic array, the voltage, current deviation rate and temperature deviation of each panel are calculated, which can be expressed as:

[0018]

[0019]

[0020] T d =TT e

[0021] Among them, V d Indicates voltage deviation rate; V indicates photovoltaic panel voltage; V0 indicates photovoltaic panel nominal voltage; I d Indicates the current deviation rate; I indicates the photovoltaic panel current; I0 ​​indicates the photovoltaic panel nominal current; T d Indicates temperature deviation; T indicates photovoltaic panel temperature; T e Indicates the ambient temperature;

[0022] Create a 4D feature vector for each photovoltaic panel

[0023] Set the number of clusters K = 3, representing no shadow, partial shadow, and full shadow, initialize the center point, and assign each panel to the nearest center point:

[0024] cluster(i)=argmin j ||X i -c j || 2

[0025] Where cluster(i) represents the cluster number to which panel i is assigned; X i represents the eigenvector of panel i; c j Represents the center point of cluster j; recalculate the center point of each cluster:

[0026]

[0027] Among them, cluster j Represents cluster j; the iteration is terminated when the center point moves less than the set threshold or reaches the maximum number of iterations, and the clusters are sorted according to the PR value of the cluster center, and marked as no shadow, partial shadow, and full shadow from high to low according to the PR value; a photovoltaic panel position matrix is ​​created, and the row and column positions of each photovoltaic panel are recorded. For each panel in the cluster, the adjacent panels are checked and the proportion of adjacent panels belonging to the same cluster is calculated. The spatial continuity score of the panels within each cluster is calculated, which is expressed as:

[0028]

[0029] Among them, S k represents the spatial continuity score; n j represents the number of adjacent panels in the same cluster j; n represents the total number of adjacent panels; when S k When Z is greater than 0.7, the confidence of the shadow judgment of cluster j is increased by z1; the clustering results of the last three time points are saved. When the clustering results are the same for three consecutive times, the confidence of the shadow judgment is increased by z2; for the basic confidence of each cluster j, the overall average PR of all panels in the photovoltaic array is calculated, and the absolute difference between the average PR of cluster center j and the overall average is calculated. The absolute difference is normalized as the basic confidence z0, and the total confidence Z = z0 + z1 + z2. When Z is less than 0.8, it is judged that the clustering confidence is too low and clustering is performed again. When Z is greater than or equal to 0.8, a photovoltaic array shadow status report is generated, and the shadow status of each panel and the number and proportion of panels in each shadow status are output. The clustering results are visualized in combination with the created photovoltaic panel position matrix.

[0030] As a preferred solution of the photovoltaic array dynamic reconstruction adaptive control method described in the present invention, the dynamic reconstruction includes: when a faulty photovoltaic panel or shadow is detected, dynamic reconstruction is performed, and the photovoltaic panels affected by the shadow are divided into partial shadow groups and full shadow groups according to the partial shadows and full shadows marked by the shadow detection. The photovoltaic panels are connected in parallel in each shadow group, and the photovoltaic panels judged as faulty at the edge are removed from the series group and isolated separately; for photovoltaic panels without shadows, all panels are arranged in descending order according to the PR value, starting from the high PR value, and the panels are allocated to the series group one by one until the voltage upper limit is reached or the photovoltaic panels are used up. If there are still photovoltaic panels remaining when the voltage upper limit is reached, a new series group is started.

[0031] As a preferred embodiment of the photovoltaic array dynamic reconfiguration adaptive control method of the present invention, the dynamic reconfiguration further includes defining the domain operation of the unshaded photovoltaic panel as exchanging the positions of two unshaded panels and moving one unshaded panel to another series group, taking the total output power as the primary objective and the inter-string performance balance as the secondary objective to construct a fitness function, which is expressed as:

[0032]

[0033] Where D represents the fitness score; w represents the weight function; M represents the total number of unshaded panel series groups; P j represents the power of the jth series group; Represents the power mean of all series groups; initialized by simulated annealing idea, set the initial temperature T init and the termination temperature T final , cooling rate a, iteration N times at each temperature; at temperature T>T final When , a new solution is generated through domain operation and the fitness of the new solution D is calculated new , when D new >D, accept the new solution. new ≤D with probability (D new -D) / T accepts the new solution. If the fitness score of the new solution is greater than the highest fitness score in history, the new solution is updated as the optimal solution. Repeat the process of generating new solutions and calculating fitness N times, and cool down T new =T×a; when the termination temperature is reached, the optimal solution is output, and based on the optimal solution, a minimized reconstruction operation sequence from the current state to the target state is generated.

[0034] In the second aspect, the present invention also provides a photovoltaic array dynamic reconstruction adaptive control system, including a data acquisition module, which collects relevant data of each photovoltaic panel at the edge end and analyzes it to preliminarily determine whether there is a fault or shadow on the photovoltaic panel; a disturbance control module, which performs MPPT control on each photovoltaic panel through the disturbance observation method, adjusts the working point of the photovoltaic panel in real time, and uploads the data of the photovoltaic panel to the photovoltaic array end; a dynamic reconstruction module, which identifies shadows based on the data uploaded by the edge end, and dynamically reconstructs based on the detected shadows to improve the performance of the photovoltaic system.

[0035] In a third aspect, the present invention further provides a computing device, comprising: a memory and a processor;

[0036] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the photovoltaic array dynamic reconstruction adaptive control method are implemented.

[0037] In a fourth aspect, the present invention further provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the photovoltaic array dynamic reconstruction adaptive control method.

[0038] The present invention's beneficial effects: By combining the advantages of edge computing and centralized control, the method achieves fast and accurate shadow detection and efficient dynamic reconstruction control. This method, which belongs to the field of photovoltaic power generation system control technology, has the advantages of improving overall system performance, enhancing environmental adaptability, and improving scalability. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0040] Figure 1 This is an overall flow chart of a photovoltaic array dynamic reconfiguration adaptive control method provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0041] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0042] Example 1, with reference to Figure 1 , as one embodiment of the present invention, provides a photovoltaic array dynamic reconfiguration adaptive control method, comprising:

[0043] S1: Collect relevant data of the photovoltaic array, and the edge end preliminarily analyzes the relevant data to identify the status of the photovoltaic panel.

[0044] A photovoltaic array is a system consisting of multiple solar panels (also called photovoltaic panels or photovoltaic modules) connected in a specific way. These solar panels are usually installed in a certain arrangement to maximize the absorption and conversion efficiency of solar energy.

[0045] Dynamic Reconfiguration of Photovoltaic Arrays During the operation of a photovoltaic system, the electrical connections between photovoltaic panels are dynamically changed according to real-time conditions, so as to optimize the performance of the entire system when part of the array is blocked or the efficiency of some components decreases.

[0046] Furthermore, relevant data include illumination data, temperature data, electrical data, and system topology and reconstruction status data. The temperature data is divided into the temperature of each photovoltaic panel and the ambient temperature of the photovoltaic field. The temperature data and electrical data of each photovoltaic panel are collected through sensors installed on each photovoltaic panel.

[0047] Light data mainly includes light intensity and light incident angle. Light sensors (such as silicon photocells or thermopile pyranometers) are installed near photovoltaic panels or integrated into the panels to measure light intensity and incident angle in real time.

[0048] The system topology and reconstruction status data are not directly measured data, but status information maintained within the system. After each reconstruction operation, the system will update the current connection topology and working status information.

[0049] Furthermore, the edge corresponds to a single photovoltaic panel, responsible for data collection and calculating the performance ratio to determine the status of the photovoltaic panel, which is expressed as:

[0050]

[0051] Among them, PR represents performance ratio; P represents output power; G represents light intensity; and P0 represents the nominal power of the photovoltaic panel.

[0052] Calculate the difference C between the current PR and the average PR of the same period in the past 7 days Z , and the difference rate C between the current panel PR and the average PR of the adjacent panels X , expressed as:

[0053]

[0054]

[0055] Among them, PR Z It represents the average PR of the same period in the past 7 days; PRX represents the average PR of adjacent panels.

[0056] When C Z <-20% and C X When it is greater than -20%, it is considered as shadow occlusion.

[0057] When C Z <-20% and C X When the value is less than -20%, it is judged as a photovoltaic panel failure, the photovoltaic panel is immediately isolated, and a fault alarm is sent to the photovoltaic array end.

[0058] When C Z >-20% and C X When the power consumption is less than -20%, it is judged that the photovoltaic panel has been in low power for a long time, and no isolation measures are taken, and a fault alarm is sent to the photovoltaic array end.

[0059] It should be noted that when C Z When the value is less than -20%, it means that the performance of the current photovoltaic panel is much lower than the average performance of the same period in history. There may be two situations: shadow blocking or photovoltaic panel failure. If the performance of adjacent panels is also reduced, that is, C X If the performance of the adjacent panels remains basically unchanged and only this photovoltaic panel has declined, it is likely that the photovoltaic panel has failed and needs to be isolated and an early warning issued, and staff should be dispatched to inspect and repair it.

[0060] When C Z >-20% and C X When the value is less than -20%, it indicates that the performance of this PV panel has been declining over a period of time. In this case, the performance of adjacent PV panels is generally not affected, so isolation measures are not required. The impact can be reduced through dynamic reconstruction of the PV array end, but manual inspection is required to determine whether there are any causes such as aging or failure.

[0061] S2: Perform MPPT control on each photovoltaic panel through the perturbation and observation method.

[0062] The edge end uses the perturbation and observation method to perform MPPT control and adjust the operating point of the photovoltaic panel in real time. The basic principle of the perturbation and observation (P&O) MPPT method is to determine the maximum power point by making a small perturbation on the operating voltage of the photovoltaic panel and then observing the change in output power.

[0063] The edge end uses the perturbation observation method to perform MPPT control, adjust the photovoltaic panel operating point in real time, calculate the power P(k) based on the collected voltage V(k) and current I(k), perform a small amplitude perturbation on the voltage, collect the disturbed voltage V(k+1) and current I(k+1) to calculate the perturbed power P(k+1), compare the power with the perturbed power, and decide the next perturbation direction, which is expressed as:

[0064] ΔP=P(k+1)-P(k)

[0065] ΔV=V(k+1)-V(k)

[0066] Where AP represents the power disturbance change; ΔV represents the voltage disturbance change.

[0067] When AP / ΔV>0, V(k+2)=V(k+1)+ΔV; when AP / ΔV<0, V(k+2)=V(k+1)-ΔV.

[0068] The step size is adjusted through an adaptive mechanism. The step size increases when it is far away from the maximum power point and decreases when it is close to the maximum power point. It is expressed as:

[0069] ΔV(k+1)=ΔV(k)×(1+α×|ΔP / ΔV|)

[0070] Wherein, α represents the step adjustment parameter; the voltage, current, power, temperature, MPPT efficiency and accumulated energy output data of the photovoltaic panel are regularly uploaded to the photovoltaic array end.

[0071] The sampling frequency is 100Hz-1kHz, and the voltage regulation is achieved by adjusting the duty cycle of the DC-DC converter. The MPPT data is used to assist in fault detection. When the power P(k) is lower than the preset threshold P min After a period of time, a fault warning is triggered, P min Set to the theoretical minimum output power of the panel under specific lighting and temperature conditions. If the actual output power P(k) is lower than this minimum value for a long time, it is likely that the panel has a fault or abnormality. The judgment lasting for a period of time can rule out the impact of short-term environmental fluctuations (such as cloud cover). The specific duration can be set according to the conditions of the photovoltaic site.

[0072] When |ΔP / ΔV|<ε for a period of time, it means that the panel is aging or needs cleaning. |ΔP / ΔV| represents the rate of change of power to voltage, that is, the slope of the PV curve. Under normal circumstances, the PV curve should have a clear slope, and ε is the minimum acceptable slope.

[0073] When the panel ages, the efficiency of the photovoltaic panel will gradually decrease over time. Aging will cause the PV curve to become flatter and the maximum power point will no longer be so obvious. The accumulation of dust and dirt will reduce the photoelectric conversion efficiency of the panel, which will also cause the PV curve to become flatter. The change in the slope of the PV curve can detect the efficiency drop problem early and distinguish between the need for replacement (aging) and the need for maintenance (cleaning), avoiding the output power dropping to P min the following.

[0074] S3: The photovoltaic array performs shadow detection through cluster analysis, evaluates the photovoltaic array status and performs dynamic reconstruction.

[0075] Furthermore, considering the output of all panels in the photovoltaic array, when there are shadows or panels with long-term low power, a clustering algorithm is used to detect and identify the shadows of the photovoltaic array. Based on the data uploaded by each edge end in the photovoltaic array, the voltage, current deviation rate and temperature deviation of each panel are calculated, which can be expressed as:

[0076]

[0077]

[0078] T d =TT e

[0079] Among them, V d Indicates voltage deviation rate; V indicates photovoltaic panel voltage; V0 indicates photovoltaic panel nominal voltage; I d Indicates the current deviation rate; I indicates the photovoltaic panel current; I0 ​​indicates the photovoltaic panel nominal current; T d Indicates temperature deviation; T indicates photovoltaic panel temperature; T e Indicates the ambient temperature.

[0080] Create a 4D feature vector for each photovoltaic panel

[0081] Set the number of clusters K = 3, representing no shadow, partial shadow, and full shadow, initialize the center point, and assign each panel to the nearest center point:

[0082] cluster(i)=argmin j ||X i -c j || 2

[0083] Where cluster(i) represents the cluster number to which panel i is assigned; X i represents the eigenvector of panel i; c jRepresents the center point of cluster j, and recalculate the center point of each cluster:

[0084]

[0085] Among them, cluster j represents cluster j.

[0086] The iteration is terminated when the center point moves less than the set threshold or reaches the maximum number of iterations. The clusters are sorted according to the PR value of the cluster center and marked as no shadow, partial shadow and full shadow from high to low according to the PR value.

[0087] Create a photovoltaic panel position matrix, record the row and column positions of each photovoltaic panel, and for each panel in the cluster, check the adjacent panels and calculate the proportion of adjacent panels that belong to the same cluster. Calculate the spatial continuity score of the panels within each cluster, expressed as:

[0088]

[0089] Among them, S k represents the spatial continuity score; n j represents the number of adjacent panels in the same cluster j; n represents the total number of adjacent panels; when S k When >0.7, the confidence of the shadow judgment of cluster j increases by z1.

[0090] The clustering results of the last three time points are saved. When the clustering results are the same for three consecutive times, the confidence of the shadow judgment increases by z2.

[0091] For the basic confidence of each cluster j, the overall average PR of all panels in the photovoltaic array is calculated, the absolute difference between the average PR of cluster center j and the overall average is calculated, and the absolute difference is normalized as the basic confidence z0. The total confidence Z = z0 + z1 + z2. When Z < 0.8, it is judged that the cluster confidence is too low and clustering is performed again. When Z ≥ 0.8, a photovoltaic array shadow status report is generated, and the shadow status of each panel and the number and proportion of panels in each shadow status are output. The clustering results are visualized in combination with the created photovoltaic panel position matrix.

[0092] It should be noted that z1 and z2 can be simply set to a small constant, such as 0.1 and 0.2, when S k When the value of z1 is greater than 0.7, the total confidence is calculated based on the value of z1. Similarly, z2 is included in the total confidence only when the clustering results are the same for three consecutive times.

[0093] Furthermore, when a faulty photovoltaic panel or shadow is detected, dynamic reconstruction is performed. According to the partial shadow and full shadow marked by the shadow detection, the photovoltaic panels affected by the shadow are divided into partial shadow groups and full shadow groups. Within each shadow group, the photovoltaic panels are connected in parallel, and the photovoltaic panels judged as faulty at the edge end are removed from the series group and isolated separately.

[0094] For unshaded PV panels, arrange all panels in descending order of PR value, starting from the highest PR value, and assign the panels to the series group one by one until the voltage limit is reached or the PV panels are used up. If there are still PV panels left when the voltage limit is reached, start a new series group.

[0095] It should be noted that the series connection of photovoltaic panels means that the panels are connected end to end, the voltage is added, and the current remains unchanged. This can increase the system voltage, reduce the current, and thus reduce line losses. However, if the performance of one panel deteriorates, it will affect the performance of the entire series chain. Therefore, when there is no shadow or fault, the photovoltaic panels are kept in series to form multiple series groups to reduce line losses and maximize system performance.

[0096] The parallel connection of photovoltaic panels is to connect the positive pole of the panel to the positive pole, and the negative pole to the negative pole, add the current, and keep the voltage unchanged. Problems with a single panel will not significantly affect the overall output. It is necessary to handle larger currents, which may increase line losses.

[0097] Connecting shaded panels in parallel is primarily to avoid the "short panel effect" and maximize remaining output. In a series configuration, the weakest panel limits the current of the entire chain. If a panel is severely shaded, it can significantly reduce the output of the entire series group. By connecting in parallel, each shaded panel can independently output its maximum possible current without being limited by other panels.

[0098] Furthermore, the domain operations of unshaded photovoltaic panels are defined as exchanging the positions of two unshaded panels and moving one unshaded panel to another series group. The total output power is taken as the primary goal and the performance balance between strings is taken as the secondary goal to construct the fitness function, which is expressed as:

[0099]

[0100] Where D represents the fitness score; w represents the weight function; M represents the total number of unshaded panel series groups; P j represents the power of the jth series group; Represents the average power of all series groups.

[0101] Initialize with the simulated annealing idea and set the initial temperature T init and the termination temperature T final , cooling rate a, iterate N times at each temperature.

[0102] At temperature T>T final When , a new solution is generated through domain operation and the fitness of the new solution D is calculated new , when D new >D, accept the new solution. new ≤D with probability (D new -D) / T accepts the new solution. If the fitness score of the new solution is greater than the highest fitness score in history, the new solution is updated as the optimal solution. Repeat the process of generating new solutions and calculating fitness N times, and cool down T new =T×a.

[0103] When the termination temperature is reached, the optimal solution is output, and based on the optimal solution, a minimized reconstruction operation sequence from the current state to the target state is generated.

[0104] This embodiment also provides a photovoltaic array dynamic reconstruction adaptive control system, including a data acquisition module, where the edge end collects relevant data of each photovoltaic panel and analyzes it to preliminarily determine whether the photovoltaic panel has a fault or shadow; a disturbance control module, which performs MPPT control on each photovoltaic panel through a disturbance observation method, adjusts the photovoltaic panel working point in real time, and uploads the photovoltaic panel data to the photovoltaic array end; and a dynamic reconstruction module, where the photovoltaic array end identifies shadows based on the data uploaded by the edge end, and dynamically reconstructs based on the detected shadows to improve the performance of the photovoltaic system.

[0105] This embodiment also provides a computing device, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the photovoltaic array dynamic reconstruction adaptive control method proposed in the above embodiment.

[0106] This embodiment further provides a storage medium storing a computer program, which, when executed by a processor, implements the photovoltaic array dynamic reconfiguration adaptive control method proposed in the above embodiment.

[0107] The storage medium proposed in this embodiment and the photovoltaic array dynamic reconstruction adaptive control method proposed in the above embodiment belong to the same inventive concept. For technical details not fully described in this embodiment, please refer to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0108] Through the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented with the help of software and necessary general-purpose hardware, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer's floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods of various embodiments of the present invention.

[0109] Example 2: The following is an embodiment of the present invention, which provides a method for dynamic reconstruction adaptive control of a photovoltaic array. In order to verify the beneficial effects of the present invention, a scientific demonstration is carried out through simulation experiments.

[0110] In order to verify the effectiveness and superiority of the proposed method for dynamic reconstruction of photovoltaic arrays, a 30-day experiment was conducted in a photovoltaic power station. The simulation experiment was carried out using 20 photovoltaic panels in the photovoltaic power station, with a nominal power of 350W for each panel.

[0111] The experiment is divided into three stages: the first stage (1-10 days) uses the traditional MPPT control method; the second stage (11-20 days) uses the edge MPPT control method of the present invention, but does not perform shadow detection and dynamic reconstruction; the third stage (21-30 days) uses the complete dynamic reconstruction adaptive control method of the present invention.

[0112] In the first phase, traditional perturbation-and-observe MPPT control was implemented with a fixed step size of 0.5V. In the second phase, edge computing devices were installed on each photovoltaic panel to implement the edge-end MPPT control method described in this invention, including adaptive step size adjustment and performance ratio calculation. In the third phase, a complete dynamically reconfigurable adaptive control system was deployed, including shadow detection and dynamic reconfiguration capabilities.

[0113] To simulate the actual working environment, some abnormal conditions were artificially created at different time periods: on the 15th day, a thin film was covered on one panel to simulate mild pollution; on the 25th day, two panels were covered with a sunshade cloth to simulate partial shadow; on the 28th day, two panels were artificially caused to malfunction. Data was collected every 10 minutes, and the average value of the data for that day was calculated at the end of each day. Some experimental data were rounded and displayed, as shown in Table 1.

[0114] Table 1 Experimental data table

[0115]

[0116] The data shows that under similar light intensity and temperature conditions, the dynamic reconfiguration adaptive control method of the present invention significantly improves total power generation. For example, under similar light intensities on the fifth day (traditional MPPT) and the 30th day (dynamic reconfiguration), total power generation increased from 1180 kWh to 1248 kWh, an increase of approximately 5.8%. This demonstrates that the present method can more effectively utilize photovoltaic resources and improve the overall power generation efficiency of the system.

[0117] The efficiency of the traditional MPPT method is 95.2%, but it increases to 97.8% with edge MPPT. Finally, after using the complete dynamic reconstruction method, the MPPT efficiency stabilizes at over 98.5%. This significant efficiency improvement is due to the adaptive step size adjustment mechanism and real-time edge control strategy proposed in this paper, which enables the system to track the maximum power point faster and more accurately.

[0118] In summary, the experimental results of this example fully demonstrate the effectiveness and superiority of this method for dynamic reconfiguration of photovoltaic arrays. This method not only improves the system's power generation efficiency but also significantly enhances the accuracy of fault and shadow detection. Furthermore, the dynamic reconfiguration mechanism enhances the system's adaptability and stability. These advantages give this method significant potential for practical application, providing strong support for performance optimization and intelligent management of photovoltaic power generation systems.

[0119] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A photovoltaic array dynamic reconfiguration adaptive control method, characterized in that: include: Collect relevant data of the photovoltaic array, conduct preliminary analysis of the relevant data at the edge end, and identify the status of the photovoltaic panels; MPPT control of each photovoltaic panel is performed using the perturbation and observation method; The photovoltaic array side performs shadow detection through cluster analysis, evaluates the photovoltaic array status and performs dynamic reconstruction; The edge end corresponds to a single photovoltaic panel and is responsible for data collection and calculation of performance ratio to determine the status of the photovoltaic panel, which is expressed as: Among them, PR represents performance ratio; P represents output power; G represents light intensity; P0 represents the nominal power of the photovoltaic panel; Calculate the difference C between the current PR and the average PR of the same period in the past 7 days Z , and the difference rate C between the current panel PR and the average PR of the adjacent panels X , when C Z <-20% and C X When it is >-20%, it is considered as shadow occlusion; When C Z <-20% and C X When the value is less than -20%, it is judged as a photovoltaic panel failure, the photovoltaic panel is immediately isolated, and a fault alarm is sent to the photovoltaic array end; When C Z >-20% and C X When the power consumption is less than -20%, it is judged that the photovoltaic panel has been underpowered for a long time, and no isolation measures are taken. A fault alarm is sent to the photovoltaic array end; The MPPT control includes performing MPPT control at the edge through the perturbation observation method, adjusting the photovoltaic panel operating point in real time, calculating the power P(k) based on the collected voltage V(k) and current I(k), performing a small amplitude perturbation on the voltage, collecting the disturbed voltage V(k+1) and current I(k+1) to calculate the perturbed power P(k+1), comparing the power with the perturbed power, and determining the next perturbation direction, which is expressed as: ΔP=P(k+1)-P(k) ΔV=V(k+1)-V(k) Among them, ΔP represents the power disturbance change; ΔV represents the voltage disturbance change; When ΔP / ΔV>0, V(k+2)=V(k+1)+ΔV; When ΔP / ΔV<0, V(k+2)=V(k+1)-ΔV; The step size is adjusted through an adaptive mechanism. The step size increases when it is far away from the maximum power point and decreases when it is close to the maximum power point. It is expressed as: ΔV(k+1)=ΔV(k)×(1+α×|ΔP / ΔV|) Where α represents the step size adjustment parameter; the voltage, current, power, temperature, MPPT efficiency and accumulated energy output data of the photovoltaic panel are regularly uploaded to the photovoltaic array end; The dynamic reconstruction includes, when a fault or shadow of a faulty photovoltaic panel is detected, performing dynamic reconstruction, dividing the photovoltaic panels affected by the shadow into a partial shadow group and a full shadow group according to the partial shadow and full shadow marked by the shadow detection, connecting the photovoltaic panels in parallel within each shadow group, and removing the photovoltaic panels judged to be faulty at the edge end from the series group and isolating them separately; For unshaded PV panels, arrange all panels in descending order of PR value, starting from the highest PR value, and assign the panels to the series group one by one until the voltage limit is reached or the PV panels are used up. If there are still PV panels left when the voltage limit is reached, start a new series group.

2. The photovoltaic array dynamic reconfiguration adaptive control method according to claim 1, wherein: The relevant data includes illumination data, temperature data, electrical data, and system topology and reconstruction status data.

3. The photovoltaic array dynamic reconfiguration adaptive control method according to claim 2, wherein: The shadow detection includes comprehensively analyzing the output of all panels in the photovoltaic array. When there is a shadow or a panel with long-term low power, the shadow detection and identification of the photovoltaic array is performed through a clustering algorithm. Based on the data uploaded by each edge end in the photovoltaic array, the voltage, current deviation rate and temperature deviation of each panel are calculated, which can be expressed as: T d =T-T e Among them, V d Indicates voltage deviation rate; V indicates photovoltaic panel voltage; V0 indicates photovoltaic panel nominal voltage; I d Indicates the current deviation rate; I indicates the photovoltaic panel current; I0 ​​indicates the photovoltaic panel nominal current; T d Indicates temperature deviation; T indicates photovoltaic panel temperature; T e Indicates the ambient temperature; Create a 4D feature vector for each photovoltaic panel Set the number of clusters K = 3, representing no shadow, partial shadow, and full shadow, initialize the center point, and assign each panel to the nearest center point: cluster(i)=argmin j ||X i -c j || 2 Where cluster(i) represents the cluster number to which panel i is assigned; X i represents the eigenvector of panel i; c j represents the center point of cluster j; Recalculate the centroid of each cluster: Among them, cluster j represents cluster j; When the center point moves less than the set threshold or reaches the maximum number of iterations, the iteration is terminated, and the clusters are sorted according to the PR value of the cluster center, and marked as no shadow, partial shadow, and full shadow from high to low according to the PR value; Create a photovoltaic panel position matrix, record the row and column positions of each photovoltaic panel, and for each panel in the cluster, check the adjacent panels and calculate the proportion of adjacent panels that belong to the same cluster. Calculate the spatial continuity score of the panels within each cluster, expressed as: Among them, S k represents the spatial continuity score; n j represents the number of adjacent panels in the same cluster j; n represents the total number of adjacent panels; when S k When >0.7, the confidence of the shadow judgment of cluster j increases by z1; Save the clustering results of the last three time points. When the clustering results are the same for three consecutive times, the confidence of the shadow judgment increases by z2. For the basic confidence of each cluster j, the overall average PR of all panels in the photovoltaic array is calculated, and the absolute difference between the average PR of cluster center j and the overall average is calculated. The absolute difference is normalized as the basic confidence z0, and the total confidence Z = z0 + z1 + z2. When Z < 0.8, it is judged that the cluster confidence is too low and clustering is performed again. When Z ≥ 0.8, a photovoltaic array shadow status report is generated, and the shadow status of each panel and the number and proportion of panels in each shadow status are output. The clustering results are visualized in combination with the created photovoltaic panel position matrix.

4. The photovoltaic array dynamic reconfiguration adaptive control method according to claim 3, wherein: The dynamic reconfiguration further includes defining the neighborhood operation of the unshaded photovoltaic panel as exchanging the positions of two unshaded panels and moving one unshaded panel to another series group, taking the total output power as the primary goal and the inter-string performance balance as the secondary goal to construct a fitness function, which is expressed as: Where D represents the fitness score; w represents the weight function; M represents the total number of unshaded panel series groups; P j represents the power of the jth series group; represents the average power of all series groups; Initialize with the simulated annealing idea and set the initial temperature T init and the termination temperature T final , cooling rate a, iteration N times at each temperature; At temperature T>T final When , a new solution is generated through neighborhood operation and the fitness of the new solution D is calculated new , when D new >D, accept the new solution, when D new ≤D with probability (D new -D) / T accepts the new solution. If the fitness score of the new solution is greater than the highest fitness score in history, the new solution is updated as the optimal solution. Repeat the process of generating new solutions and calculating fitness N times, and cool down T new =T×a; When the termination temperature is reached, the optimal solution is output, and based on the optimal solution, a minimized reconstruction operation sequence from the current state to the target state is generated.

5. A photovoltaic array dynamic reconfiguration adaptive control system using the method according to any one of claims 1 to 4, characterized in that: include, The data acquisition module collects and analyzes the relevant data of each photovoltaic panel at the edge to preliminarily determine whether there is a fault or shadow on the photovoltaic panel; The disturbance control module performs MPPT control on each photovoltaic panel through the disturbance observation method, adjusts the working point of the photovoltaic panel in real time, and uploads the data of the photovoltaic panel to the photovoltaic array end; Dynamic reconstruction module: The photovoltaic array end identifies shadows based on the data uploaded by the edge end, and dynamically reconstructs according to the detected shadows to improve the performance of the photovoltaic system.

6. A computing device comprising: memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the method according to any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions, when executed by a processor, implement the steps of the method according to any one of claims 1 to 4.

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