Photovoltaic power station multi-system coupling control method and device

Abstract

This application relates to the fields of photovoltaic desertification control and new energy power generation technology, and provides a multi-system coupled control method and device for photovoltaic power plants. The method includes: acquiring multi-dimensional state data of different areas of the photovoltaic power plant; performing fusion decision-making on the multi-dimensional state data based on regional differentiated weights to obtain decision results; and generating control commands based on a preset priority strategy and the decision results to adjust the operating status of photovoltaic modules and / or vegetation management parameters. This application achieves full-chain coupled quantification and dynamic coordination of temperature field, wind and sand accumulation, carbon sequestration benefits, and power generation efficiency, avoiding hot spot damage and sudden power drop, solving the problems of multi-system conflicts and single-benefit optimization, and significantly improving the comprehensive benefits of photovoltaic power plants.

Description

Technical Field

[0001] This application relates to the fields of photovoltaic desertification control and new energy power generation technology, and more specifically, to a method and device for multi-system coupling control of a photovoltaic power station. Background Technology

[0002] Currently, photovoltaic desertification control technology has achieved an organic combination of desertification control and clean energy production through the model of "generating electricity on the panel and fixing sand underneath the panel".

[0003] However, the construction of photovoltaic power plants can disturb native vegetation and soil crusting, and the array layout may cause a "narrowing effect," leading to abnormal local wind speeds. At the same time, the accumulation of windblown sand causes dust to accumulate on the photovoltaic panels, seriously affecting power generation efficiency. While planting vegetation can stabilize sand, it may also cause new problems such as changes in the temperature field and shading of photovoltaic modules.

[0004] Existing technologies mainly address wind and sand problems by using methods such as straw checkerboard sand barriers, gravel covering, manual cleaning, or superhydrophobic coatings. Among these, straw checkerboard sand barriers are easily submerged by shifting sand, manual cleaning is costly and limited by water resources, and superhydrophobic coating technology is still immature. Although tracking photovoltaic systems can optimize the utilization of sunlight, they do not consider the impact of vegetation on the near-surface airflow field and cannot fundamentally solve the power attenuation problem caused by sand accumulation.

[0005] In terms of ecological restoration, the planting of drought-resistant sand-fixing plants has achieved the effect of sand fixation, but the impact of temperature field changes caused by vegetation growth on the heat dissipation of photovoltaic modules has not been considered, nor has a synergistic optimization mechanism for vegetation carbon sequestration benefits and power generation efficiency been established, resulting in the problem of "emphasizing ecology and neglecting power generation" or the separation of the two.

[0006] In addition, existing technologies are mostly focused on the optimization of single systems, such as the impact analysis of photovoltaic arrays on microclimate and the improvement of soil quality by vegetation restoration. However, they lack research on the entire chain coupling of vegetation-temperature field-wind sand accumulation-carbon sequestration benefits-power generation efficiency. As a result, a quantitative model and dynamic control strategy integrating multiple factors have not been formed, making it difficult to maximize the comprehensive benefits of photovoltaic power plants. Summary of the Invention

[0007] To address the problems of existing technologies where vegetation, microenvironment, and power generation system optimization are disconnected and lack dynamic collaborative control mechanisms, this application proposes a multi-system coupling control method and device for photovoltaic power plants to achieve a comprehensive and synergistic improvement in ecological restoration and power generation efficiency.

[0008] In a first aspect, the present invention provides a multi-system coupling control method for a photovoltaic power plant, comprising: Acquire multi-dimensional status data of different areas of a photovoltaic power station; The multi-dimensional state data is fused and a decision is made based on the regional difference weights to obtain the decision result; Control commands are generated based on a preset priority strategy and the decision results to adjust the operating status of photovoltaic modules and / or vegetation management parameters.

[0009] In an optional implementation, the multi-dimensional state data includes vegetation state data, microenvironment state data, and power generation state data.

[0010] In an optional implementation, the different areas of the photovoltaic power station include the area under the panels, the area between the panels, and the area outside the array; The regional differentiation weights are dynamically adjusted according to the region type. The region under the plate focuses on the weights of temperature field optimization and carbon sequestration benefits, the region outside the array focuses on the weights of wind and sand accumulation control, and the region between the plates maintains a balance of weights for each factor.

[0011] In an optional implementation, the preset priority strategy includes a security guarantee priority and a benefit optimization priority, wherein the execution order of the security guarantee priority is before that of the benefit optimization priority.

[0012] In an optional implementation, the security priority includes: When the sand density exceeds the first preset threshold, a control command is generated to start the cleaning device; When the surface temperature of the photovoltaic module exceeds the second preset threshold, a control command is generated to adjust the tilt angle of the photovoltaic module.

[0013] In an optional implementation, the benefit optimization priority includes: Based on the synergistic relationship between carbon sequestration and power generation, control commands are generated to adjust vegetation irrigation.

[0014] In an optional implementation, adjusting the operating status of the photovoltaic module includes adjusting the tilt angle of the photovoltaic module and / or activating the cleaning device.

[0015] In an optional implementation, the vegetation management parameters include vegetation irrigation amount.

[0016] In an optional implementation, the method further includes: Shade-loving and drought-resistant plants are planted in the area under the boards, medium and tall sand-fixing plants are planted in the area between the boards, and vegetation sand barrier belts are set up in the area outside the array.

[0017] Secondly, the present invention provides a multi-system coupling control device for a photovoltaic power station, comprising: The data acquisition module is used to acquire multi-dimensional status data of different areas of the photovoltaic power station; The fusion decision module is used to perform fusion decision-making on the multi-dimensional state data based on regional differential weights to obtain decision results; The instruction generation module is used to generate control instructions based on a preset priority strategy and the decision results, so as to adjust the operating status of photovoltaic modules and / or vegetation management parameters.

[0018] Beneficial effects: This application provides a multi-system coupled control method and device for photovoltaic power plants. By acquiring multi-dimensional state data of different areas of the photovoltaic power plant, the method performs fusion decision-making on the multi-dimensional state data based on regional differentiated weights, and generates control commands based on preset priority strategies and decision results. This breaks the current situation where vegetation, microenvironment and power generation system optimization are mutually isolated.

[0019] On the one hand, through the fusion decision-making mechanism of regional differentiated weights, it is possible to carry out differentiated control on the micro-environmental characteristics and ecological functions of different regions such as under the plate, between the plates, and outside the array, and realize the full-chain coupling quantification and dynamic coordination of temperature field, wind and sand accumulation, carbon sequestration benefits and power generation efficiency. On the other hand, by pre-setting priority strategies, cleaning or tilt adjustment is prioritized when safety priority is triggered to avoid hot spot damage and power drop. Under benefit optimization priority, vegetation management parameters are dynamically adjusted based on the synergistic relationship between carbon sequestration and power generation. This achieves a comprehensive improvement in power generation efficiency, wind and sand control, and carbon sequestration income, solves the problems of multi-system conflicts and single benefit optimization, and significantly improves the overall benefits of photovoltaic power plants. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart of a multi-system coupling control method for a photovoltaic power plant according to an embodiment of this application; Figure 2 This is a schematic diagram of the structure of a photovoltaic power plant multi-system coupling control device according to an embodiment of this application. Detailed Implementation

[0022] The technical solutions in the embodiments of this application will now be described with reference to the accompanying drawings.

[0023] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0024] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the specification of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application.

[0025] This embodiment provides a multi-coupling control method for photovoltaic power plants. For example... Figure 1 As shown, this method mainly includes steps such as acquiring multi-dimensional state data, making fusion decisions, and generating control commands. By constructing a full-chain coupled quantification and dynamic control mechanism, it breaks the current situation where vegetation, microenvironment, and power generation are isolated from each other.

[0026] Step S110: Obtain multi-dimensional status data for different areas of the photovoltaic power station.

[0027] Specifically, multi-dimensional status data refers to a comprehensive set of parameters that can reflect the internal ecology, physical environment, and operating status of power generation equipment in a photovoltaic power station.

[0028] For example, multi-dimensional status data can include vegetation status data (such as vegetation coverage, plant height, and canopy width), microenvironment status data (such as temperature field parameters, wind speed, sand particle mobility, and sand accumulation density), and power generation status data (such as photovoltaic power generation capacity, current, and voltage parameters). By deploying distributed monitoring nodes in different areas of the photovoltaic power station, the above-mentioned multi-dimensional status data can be collected in real time.

[0029] It should be understood that multidimensional state data is not limited to the specific physical quantities listed above, but can also include other parameters that can characterize the operating state, such as soil moisture and light radiation intensity, as long as they can provide data support for subsequent multi-coupling decision-making.

[0030] Step S120: Based on the regional differentiated weights, perform fusion decision on the multi-dimensional state data to obtain the decision result.

[0031] Specifically, since photovoltaic power plants face different primary challenges in different spatial locations—for example, the area under the panels mainly focuses on the impact of vegetation shading on module temperature, while the area outside the array mainly focuses on the blocking effect against wind and sand—it is necessary to introduce regionally differentiated weights for integrated decision-making. The integrated decision-making process involves assigning different weight coefficients to the acquired multi-dimensional state data according to the characteristics of their respective regions, and then comprehensively calculating a quantitative decision result.

[0032] For example, algorithms such as the Analytic Hierarchy Process (AHP), entropy weight method, or fuzzy comprehensive evaluation can be used to process the data. This embodiment, through a regionally differentiated weighting fusion decision-making mechanism, can achieve differentiated control based on the micro-environmental characteristics and ecological functions of different regions, realizing the full-chain coupling quantification and dynamic coordination of temperature field, wind and sand accumulation, carbon sequestration benefits, and power generation efficiency. This embodiment is for illustrative purposes only and not restrictive; the specific algorithm for fusion decision-making can be flexibly selected based on the actual power plant's computing power level and control accuracy requirements.

[0033] Step S130: Generate control instructions based on preset priority strategies and decision results to adjust the operating status of photovoltaic modules and / or vegetation management parameters.

[0034] Specifically, in multi-process collaborative operation, a conflict often arises between equipment safety and ecological benefits. The preset priority strategy is a logical judgment mechanism designed to resolve such conflicts. When the decision result indicates a dangerous or sub-optimal state, safety-related control commands will be triggered first; when within the safety threshold, benefit-optimization-related control commands will be triggered. The generated control commands are used to adjust the operating status of photovoltaic modules and / or vegetation management parameters.

[0035] For example, adjusting the operating status of photovoltaic modules may include adjusting the tilt angle of the photovoltaic modules, starting the cleaning device, and adjusting the spacing between the modules; adjusting vegetation management parameters may include adjusting the amount of vegetation irrigation, adjusting the frequency of vegetation pruning, and adjusting the amount of fertilizer.

[0036] Through the above scheme, this embodiment not only considers a single optimization, but also takes a global perspective to coordinate ecological sand fixation, carbon sequestration income and power generation efficiency. By intervening with a preset priority strategy, it avoids the problem of equipment hot spot damage or power drop caused by blindly pursuing ecological benefits, and achieves optimal control under multiple conflicts, significantly improving the comprehensive benefits of photovoltaic power plants.

[0037] In some embodiments, multi-dimensional state data may include vegetation state data, microenvironment state data, and power generation state data.

[0038] Specifically, vegetation status data reflects the ecological restoration and plant growth within the photovoltaic power station area. This data may include parameters such as vegetation type, vegetation coverage, plant height, canopy width, and plant carbon sequestration. Microenvironment status data characterizes the physical and climatic conditions surrounding the photovoltaic array. This data may include parameters such as photovoltaic module surface temperature, near-surface temperature beneath the panels, wind speed, sand migration rate, and sand accumulation density. Sand accumulation density can be monitored by classifying dust particles (4–60 μm) and sand particles (60–2000 μm). Power generation status data reflects the power generation efficiency of the photovoltaic power station. This data may include parameters such as photovoltaic power generation capacity, current, and voltage. Power generation capacity can be measured in zones below the panels, between panels, and outside the array. By integrating these status data, a comprehensive set of parameters representing the internal ecological and physical environment of the photovoltaic power station and the operating status of the power generation equipment can be obtained, providing detailed data support for subsequent multi-coupling decision-making.

[0039] Furthermore, the photovoltaic power station includes different areas such as the under-panel area, the inter-panel area, and the external array area; the regional differentiation weights are dynamically adjusted according to the regional type, with the under-panel area focusing on the weight of temperature field optimization and carbon sequestration benefits, the external array area focusing on the weight of wind and sand accumulation control, and the inter-panel area maintaining a balance of weights for all factors.

[0040] Specifically, the area under the panel usually refers to the shaded area directly below the photovoltaic module. This area is less illuminated by the photovoltaic panel and has less water evaporation. Planting vegetation can easily cause changes in the temperature field, which can affect the heat dissipation of the photovoltaic module. Therefore, this area focuses on the weight of temperature field optimization and carbon sequestration benefits. For example, the weight of temperature optimization is increased to 0.3 to focus on controlling the surface temperature of the module.

[0041] The outer region of the array usually refers to the windward area around the photovoltaic array. This area is the main channel for wind and sand input, so the weight of wind and sand accumulation control is emphasized. For example, the weight of wind and sand control is increased to 0.4 to prioritize reducing regional wind speed and sand migration rate.

[0042] The inter-panel area usually refers to the open space between two adjacent rows of photovoltaic modules. This area is affected by sunlight and wind relatively evenly, so it is important to maintain a balance of weights for each factor and not neglect any single factor.

[0043] By dynamically adjusting the weights according to the region type, differentiated control can be carried out for the microenvironmental characteristics and ecological functions of different regions, realizing the full-chain coupling quantification and dynamic coordination of temperature field, wind and sand accumulation, carbon sequestration benefits and power generation efficiency.

[0044] It should be understood that the dynamic adjustment algorithm for regional differential weights is not limited to the analytic hierarchy process (AHP), but can also be implemented using algorithms such as entropy weighting, fuzzy comprehensive evaluation, or machine learning models. For example, when using the entropy weighting method, weights can be objectively assigned based on the degree of variation of each state data over time; when using the fuzzy comprehensive evaluation method, multiple factors can be fuzzified and comprehensively evaluated by combining an expert experience base.

[0045] Based on the above embodiments, this embodiment further explains the logic for generating control commands. The preset priority strategy includes security priority and benefit optimization priority, with security priority being executed before benefit optimization priority.

[0046] Specifically, in the multi-coupling operation of photovoltaic power plants, there are often conflicts between control objectives of vegetation, microenvironment, and power generation. For example, to improve carbon sequestration efficiency, it may be necessary to increase vegetation irrigation to promote plant growth, but excessive vegetation growth may shade photovoltaic modules or cause changes in the temperature field under the panels, thereby affecting the heat dissipation of photovoltaic modules and causing the risk of hot spots. Similarly, to increase power generation, it may be necessary to frequently activate cleaning devices to remove accumulated sand, but this will increase water consumption and equipment wear and tear. Therefore, this embodiment establishes decision-making criteria for multiple conflicts by setting safety assurance priorities and efficiency optimization priorities.

[0047] Prioritizing safety means that when faced with urgent risks such as equipment damage or a sharp drop in power generation efficiency, control commands will skip the conventional benefit optimization logic and directly execute safety-assurance actions. From a microscopic perspective, photovoltaic modules, as core assets, require safe operation as a prerequisite for generating any benefits. If the module surface temperature is too high or the sand accumulation density is too high, it will not only lead to a sudden drop in power but also cause irreversible physical damage. At this point, any ecological or economic benefit optimization becomes meaningless. Therefore, the execution order of safety assurance must take precedence over benefit optimization priority. This order is irreplaceable and represents the bottom-line logic for ensuring the basic survivability of the power plant.

[0048] Prioritizing efficiency optimization is an advanced control logic triggered when the system is within a safe threshold. Only after confirming there is no risk of equipment damage and that basic power generation efficiency is guaranteed will parameters such as vegetation irrigation be dynamically adjusted based on the synergistic relationship between carbon sequestration and power generation, aiming to maximize both ecological and economic benefits. This layered and progressive control logic effectively avoids the risks arising from blindly pursuing a single benefit.

[0049] It should be understood that the preset priority strategy in this embodiment is not limited to the simple division of the above two levels, and can also be extended to a multi-level priority system according to the actual operation and maintenance needs of the power plant. For example, a buffer early warning priority can be added between the safety assurance priority and the benefit optimization priority, which is used to perform flexible load reduction or preparatory actions before the risk threshold is approached. The embodiments are only illustrative and not restrictive. As long as the execution order of safety assurance takes precedence over benefit optimization, they are all within the protection scope of this application.

[0050] In some embodiments, the safety priority includes: generating a control command to start the cleaning device when the sand density exceeds a first preset threshold; and generating a control command to adjust the tilt angle of the photovoltaic module when the surface temperature of the photovoltaic module exceeds a second preset threshold.

[0051] Specifically, sand accumulation density and photovoltaic module surface temperature are two core indicators reflecting the safe operating status of the equipment. When the sand accumulation density exceeds the first preset threshold, it means that the photovoltaic panel surface is covered with excessive sand and dust, which not only severely blocks sunlight and causes a sharp drop in power generation, but may also trigger localized hot spot effects, potentially burning out the module. At this point, a control command to activate the cleaning device will be generated directly to remove the sand through water washing or mechanical cleaning. When the photovoltaic module surface temperature exceeds the second preset threshold, it means that the module is not dissipating heat properly. A control command to adjust the tilt angle of the photovoltaic module will be generated, which enhances air convection heat dissipation by changing the module's windward angle or increasing the gap between the module and the ground, thereby avoiding irreversible physical damage caused by high temperatures.

[0052] In a preferred embodiment, the first preset threshold can be set to 40 g / m², and the second preset threshold can be set to 45°C. It should be understood that these thresholds are not absolutely fixed single values, but rather dynamic ranges that can be adjusted according to the wind and sand intensity and the temperature resistance level of the modules in the desert region where the photovoltaic power station is located. For example, the first preset threshold can be within the range of 30 g / m² to 50 g / m², and the second preset threshold can be within the range of 40°C to 50°C. In specific applications, the lower limit of 30 g / m² and 40°C, or the middle value of 40 g / m² and 45°C, or the upper limit of 50 g / m² and 50°C can be used.

[0053] If the threshold is set too high, for example, the first preset threshold is set to 80g / m² and the second preset threshold is set to 60℃, although the frequency of starting the cleaning device and the number of tilt angle adjustments can be reduced, when the sand density reaches 80g / m², the local current of the photovoltaic module is already severely blocked, and the hot spot temperature often exceeds the limit, which can easily cause the cell to break or the encapsulation material to melt. At this time, it is too late to trigger the safety protection action and the damage to the equipment cannot be recovered.

[0054] Conversely, if the thresholds are set too low, for example, setting the first preset threshold to 10g / m² and the second preset threshold to 30℃, while maximizing equipment safety, even minor sandstorms in the desert region can frequently trigger the cleaning device, leading to significant water waste and accelerated wear of mechanical parts. This excessive defense not only contradicts the initial goal of efficiency optimization but also causes the control system to oscillate meaninglessly. Therefore, setting the first preset threshold between 30g / m² and 50g / m², especially 40g / m², and the second preset threshold between 40℃ and 50℃, especially 45℃, falls precisely at the inflection point of the power decay curve and just before the critical point of hot spot triggering. This effectively avoids hot spot damage and sudden power drops while maximizing water conservation and equipment wear, achieving a precise balance between safety and maintenance costs.

[0055] In some embodiments, the triggering and control logic for prioritizing benefit optimization is further elaborated. Prioritizing benefit optimization includes generating control instructions for adjusting vegetation irrigation based on the synergistic relationship between carbon sequestration and power generation.

[0056] Specifically, once it's confirmed that the safety priority has not been triggered—meaning both sand density and photovoltaic module surface temperature are within safe thresholds—the control logic enters the benefit optimization phase. At this point, the primary objective is no longer to avoid equipment damage, but rather to maximize both ecological and economic benefits. The synergistic relationship between carbon sequestration and power generation refers to the dynamic balance between the carbon sequestration benefits from vegetation growth and the photovoltaic power generation efficiency. Increased vegetation cover can increase carbon sequestration, but excessive vegetation growth may shade photovoltaic modules or cause changes in the temperature field beneath the panels, thereby affecting heat dissipation and reducing power generation. Therefore, it is necessary to dynamically adjust vegetation irrigation based on this synergistic relationship to achieve simultaneous improvement in both.

[0057] For example, when the monthly growth of carbon sequestration by vegetation under the photovoltaic panel is detected to be less than 5% and the photovoltaic power generation is unaffected, a control command is generated to increase the vegetation irrigation amount, such as increasing the irrigation amount by 5%, to promote vegetation growth and improve carbon sequestration efficiency. Conversely, when the vegetation height is detected to be close to the edge of the photovoltaic panel, potentially causing shading risk, a control command is generated to reduce or suspend the vegetation irrigation amount to inhibit vegetation vegetative growth and ensure stable power generation. This embodiment, through the above-mentioned collaborative control logic, effectively avoids the problem of risks caused by blindly pursuing a single benefit, and achieves a dynamic balance between ecology and power generation.

[0058] It should be understood that the synergistic relationship between carbon sequestration and power generation is not limited to the simple threshold judgment and ratio adjustment mentioned above. It can also be achieved by constructing multi-objective optimization functions or machine learning models. For example, a multi-objective optimization function can be constructed with the objectives of maximizing carbon sequestration and maximizing power generation, and the optimal vegetation irrigation amount can be obtained through Pareto optimal solution; or, a neural network model can be trained using historical monitoring data, and the model can directly output the optimal irrigation adjustment strategy based on the current multi-dimensional state data.

[0059] In some embodiments, the specific execution actions of the control commands and the spatial layout of the vegetation in the photovoltaic power station are further elaborated. Adjusting the operating status of the photovoltaic modules includes adjusting the tilt angle of the photovoltaic modules and / or activating the cleaning device. Vegetation management parameters include vegetation irrigation volume.

[0060] Specifically, once control instructions are generated based on a preset priority strategy, these instructions need to be implemented by a specific execution mechanism.

[0061] Adjusting the tilt angle of photovoltaic modules is usually performed by the drive motor of the tracking bracket. For example, when a command to adjust the tilt angle is received, the drive motor drives the photovoltaic module to rotate a specific angle, such as increasing the tilt angle by 5 degrees, to change the windward angle of the module or increase the gap with the ground, thereby enhancing air convection and heat dissipation, or avoiding shading by excessively growing vegetation.

[0062] Activating the cleaning system can be achieved by triggering a smart cleaning robot or sprayer installed on the photovoltaic array, using water washing or mechanical sweeping to remove surface sand. Adjusting the amount of irrigation for vegetation is achieved by controlling the opening of the solenoid valve of the drip irrigation system or the operating frequency of the water pump. For example, when an increase in irrigation is needed, the opening time of the solenoid valve is increased, thereby precisely controlling the water supply for vegetation growth.

[0063] This embodiment ensures that the multi-coupling control strategy can be effectively executed at the hardware level by binding control commands to specific physical actuators.

[0064] In some embodiments, the method further includes: planting shade-loving and drought-resistant plants in the area under the boards, planting medium-to-tall sand-fixing plants in the area between the boards, and setting up a vegetation sand barrier belt in the area outside the array.

[0065] Specifically, a reasonable spatial layout of vegetation is the physical basis for optimizing the coupling of vegetation, microenvironment, and power generation. In the area under the photovoltaic panels, where sunlight is weak due to shading by the photovoltaic modules and water evaporation is low, shade-loving and drought-tolerant plants should be planted. For example, white thorn can be planted in the area under the panels.

[0066] It should be understood that shade-loving and drought-tolerant plants are not limited to white thorn; alfalfa, artemisia, and other plants with similar ecological habits can also be used. These plants can survive in low-light environments, and their vegetation cover can reduce the near-ground temperature under the photovoltaic panels, thereby optimizing the temperature field of the photovoltaic modules. Simultaneously, they achieve carbon sequestration benefits through photosynthesis. In the inter-panel area, i.e., the open space between two adjacent rows of photovoltaic modules, the impact of sunlight and wind is relatively balanced; therefore, medium-to-tall sand-fixing plants can be planted. For example, saxaul can be planted.

[0067] Similarly, medium-to-tall sand-fixing plants can also include sand willow and *Caragana korshinskii*. These plants can effectively reduce surface wind speed and inhibit sand particle migration, while their height can be controlled to not exceed half the height of the photovoltaic panel support, avoiding shading of the photovoltaic modules. In the area outside the array, i.e., the windward area surrounding the photovoltaic array, which is the main channel for wind and sand input, a vegetation sand barrier belt should be set up. For example, *Elaeagnus angustifolia* forests can be planted to form a vegetation sand barrier belt. In addition, windbreak and sand-fixing forest belts such as *Tamarix chinensis* forests can also be used for vegetation sand barrier belts.

[0068] By setting up tall vegetation sand barriers outside the array, regional wind speeds can be significantly reduced, decreasing the input of sand particles into the array and controlling sand accumulation at its source. This embodiment establishes a robust physical defense layer by constructing a three-dimensional vegetation layout strategy under the panels, between the panels, and outside the array, and by introducing a variety of species alternatives. This effectively prevents infringers from circumventing the technical solutions of this application by simply replacing plant species, while simultaneously achieving a comprehensive improvement in ecological sand fixation, carbon sequestration, and power generation efficiency.

[0069] This embodiment provides a multi-system coupling control device for a photovoltaic power station. For example... Figure 2 As shown, the device includes a data acquisition module 201, a fusion decision module 202, and an instruction generation module 203. The data acquisition module 201, the fusion decision module 202, and the instruction generation module 203 are sequentially connected in communication to realize the flow of multi-dimensional status data and the generation of control instructions.

[0070] The data acquisition module 201 is used to acquire multi-dimensional status data of different areas of the photovoltaic power station.

[0071] Specifically, the data acquisition module 201 is the sensing front end for the device to interact with the physical environment. Its hardware entity can be a distributed sensor network deployed in the under-panel area, the inter-panel area, and the area outside the array. For example, the data acquisition module 201 may include temperature sensors, wind speed sensors, sand accumulation density monitors, vegetation growth monitors, and photovoltaic power meters. These sensor nodes collect multi-dimensional status data through wired or wireless means (such as RS485 bus, LoRa, or 5G communication modules) and aggregate it to a local gateway, which then transmits it to subsequent modules.

[0072] It should be understood that the data acquisition module 201 is not limited to the aforementioned discrete sensors; it can also be an integrated weather station or UAV inspection image acquisition device that integrates multiple monitoring functions, as long as it can acquire data on vegetation, microenvironment, and power generation status. This embodiment, through the hardware deployment of the data acquisition module 201, provides high-precision data source support for multi-system coupled control.

[0073] The fusion decision module 202 is used to perform fusion decision-making on multi-dimensional state data based on regional differentiated weights to obtain decision results.

[0074] Specifically, the fusion decision module 202 is the core computing unit of the device, and its hardware entity can be a programmable logic controller (PLC), a digital signal processor (DSP), a field-programmable gate array (FPGA), or a local edge computing server. The fusion decision module 202 is pre-loaded with a weight allocation algorithm and fusion decision logic. Upon receiving multi-dimensional status data transmitted by the data acquisition module 201, the fusion decision module 202 dynamically assigns corresponding weight coefficients based on the region type of the data (e.g., under-board, between-board, or outside the array) and performs comprehensive calculations. For example, the fusion decision module 202 can use its internally integrated microprocessor to perform analytic hierarchy process (AHP) or entropy weighting calculations, outputting quantitative decision results characterizing the current system security status and benefit potential. It should be understood that the fusion decision module 202 is not limited to local hardware computing; when computing power requirements are high, it can also be a cloud server, receiving data and distributing decision results through an IoT platform. This embodiment ensures the real-time performance and reliability of complex multi-factor coupled calculations through the hardware computing power binding of the fusion decision module 202.

[0075] The instruction generation module 203 is used to generate control instructions based on preset priority strategies and decision results to adjust the operating status of photovoltaic modules and / or vegetation management parameters.

[0076] Specifically, the instruction generation module 203 is the execution logic center of the device, and its hardware entity can be a microcontroller (MCU), an industrial control computer, or a remote terminal unit (RTU). The instruction generation module 203 receives the decision results output by the fusion decision module 202 and has built-in judgment logic for safety assurance priority and benefit optimization priority. When the decision result indicates that a safety threshold has been triggered, the instruction generation module 203 immediately generates corresponding hardware control instructions, such as generating a PWM signal to drive the motor of the tracking bracket to adjust the tilt angle of the photovoltaic modules, or generating a relay closing signal to start the cleaning device. When the system is within the safety threshold, the instruction generation module 203 generates benefit optimization instructions, such as outputting an analog signal to control the opening of the solenoid valve of the drip irrigation system to adjust the irrigation amount for vegetation. It should be understood that the instruction generation module 203 can not only directly drive the actuators, but also send control instructions to the existing SCADA system of the photovoltaic power station via standard industrial communication protocols (such as Modbus or OPC UA), where the SCADA system will execute them. This embodiment ensures that the preset priority strategy can be effectively implemented at the physical level by binding the instruction generation module 203 with the hardware of the specific execution mechanism, providing a safety net for method innovation on the device side.

[0077] The examples are for illustrative purposes only and are not restrictive. The specific hardware selection for each module can be flexibly adjusted according to the scale of the power plant and the level of automation.

[0078] The following example, a 100 MW photovoltaic desertification control power station on the edge of a desert, illustrates the entire application process of the control method described in this application. The power station covers a total area of ​​5,000 mu (approximately 333 hectares), with photovoltaic panel supports reaching a height of 1.8 meters. The initial vegetation cover was less than 5%, the average annual wind speed was 3.2 m / s, and the average annual sand accumulation density was 35 g / m². It should be understood that this embodiment is illustrative only and not restrictive; the technical solution of this application is also applicable to the construction of large-scale photovoltaic bases in other desert and Gobi areas.

[0079] During the monitoring deployment phase, 20 distributed monitoring nodes were set up in each of the following areas: under the panels, between the panels, and outside the array. These nodes were used to acquire multi-dimensional status data for different areas of the photovoltaic power station. The multi-dimensional status data included vegetation status data, microenvironment status data, and power generation status data. Specifically, the monitoring nodes collected data on temperature, wind speed, sand accumulation density, vegetation growth, and photovoltaic power generation every hour, and then aggregated the data to the local edge computing gateway via a wireless network for preprocessing.

[0080] During the vegetation planting phase, the power station planted shade-loving and drought-resistant plants in the area beneath the photovoltaic panels, medium-to-tall sand-fixing plants in the area between the panels, and a vegetation sand barrier belt outside the array. Specifically, white thorn was planted in the area beneath the panels, saxaul in the area between the panels, and oleaster forests were planted outside the array, thus constructing a three-dimensional sand control system. The height of the saxaul was strictly controlled to not exceed half the height of the photovoltaic panel support, i.e., less than 0.9 meters, to avoid shading the photovoltaic modules.

[0081] In the coupled model application and control strategy execution phase, the system performs fusion decision-making on multi-dimensional state data based on regionally differentiated weights to obtain the decision results. Different regions of the photovoltaic power station include the under-panel region, the inter-panel region, and the external array region. The regionally differentiated weights are dynamically adjusted according to the region type. The under-panel region emphasizes the weight of temperature field optimization and carbon sequestration benefits, the external array region emphasizes the weight of wind and sand accumulation control, and the inter-panel region maintains a balanced weight for all factors. Specifically, the system calculates dynamic weights based on monitoring data, increasing the weight of temperature optimization in the under-panel region to 0.3 and the weight of wind and sand control in the external array region to 0.4. Subsequently, the system generates control commands based on preset priority strategies and decision results to adjust the operating status of the photovoltaic modules and / or vegetation management parameters.

[0082] Regarding safety priorities, when the sand accumulation density exceeds a first preset threshold, a control command is generated to activate the cleaning device; when the surface temperature of the photovoltaic module exceeds a second preset threshold, a control command is generated to adjust the tilt angle of the photovoltaic module. Specifically, when the temperature under the panel is greater than 35 degrees Celsius, the system generates a command to adjust the module tilt angle to 30 degrees to enhance air convection heat dissipation; when the sand accumulation density between the panels is greater than 40 grams per square meter, the system triggers a cleaning device activation command for automatic cleaning. Adjusting the operating status of the photovoltaic module includes adjusting the tilt angle of the photovoltaic module and / or activating the cleaning device.

[0083] Regarding the priority of benefit optimization, control instructions for adjusting vegetation irrigation are generated based on the synergistic relationship between carbon sequestration and power generation. Vegetation management parameters include vegetation irrigation amount. Specifically, when a 10% monthly increase in carbon sequestration is detected without triggering a shading risk, the system generates a control instruction to increase irrigation by 5% to promote vegetation growth and improve carbon sequestration benefits.

[0084] Through the implementation of the aforementioned multi-system coupled control strategy, the power station achieved significant comprehensive benefits. In terms of ecological benefits, regional vegetation coverage increased to over 40%, sand migration rate decreased by 65%, and annual carbon sequestration reached 800 tons. Regarding power generation benefits, the sand accumulation density of photovoltaic modules was effectively controlled below 20 grams per square meter, the power attenuation rate decreased from 33% to 8%, and annual power generation increased by 16.7%. This embodiment, through detailed scenario data and quantitative results, demonstrates that this application, by constructing a zone-differentiated multi-factor collaborative control mechanism, can effectively solve the problems of multi-system conflicts and single-benefit optimization, achieving a comprehensive and synergistic improvement in ecological restoration and power generation efficiency.

[0085] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A multi-system coupling control method for a photovoltaic power station, characterized in that, include: Acquire multi-dimensional status data of different areas of a photovoltaic power station; The multi-dimensional state data is fused and a decision is made based on regionally differentiated weights to obtain the decision result; Control commands are generated based on a preset priority strategy and the decision results to adjust the operating status of photovoltaic modules and / or vegetation management parameters.

2. The method according to claim 1, characterized in that, The multi-dimensional status data includes vegetation status data, microenvironment status data, and power generation status data.

3. The method according to claim 2, characterized in that, The photovoltaic power station includes different areas such as the area under the panels, the area between the panels, and the area outside the array; The regional differentiation weights are dynamically adjusted according to the region type. The region under the plate focuses on the weights of temperature field optimization and carbon sequestration benefits, the region outside the array focuses on the weights of wind and sand accumulation control, and the region between the plates maintains a balance of weights for each factor.

4. The method according to claim 1, characterized in that, The preset priority strategy includes security priority and benefit optimization priority, and the execution order of the security priority is before that of the benefit optimization priority.

5. The method according to claim 4, characterized in that, The security priority includes: When the sand density exceeds the first preset threshold, a control command is generated to start the cleaning device; When the surface temperature of the photovoltaic module exceeds the second preset threshold, a control command is generated to adjust the tilt angle of the photovoltaic module.

6. The method according to claim 4, characterized in that, The priority for optimizing benefits includes: Based on the synergistic relationship between carbon sequestration and power generation, control commands are generated to adjust vegetation irrigation.

7. The method according to claim 1, characterized in that, Adjusting the operating status of the photovoltaic modules includes adjusting the tilt angle of the photovoltaic modules and / or starting the cleaning device.

8. The method according to claim 1, characterized in that, The vegetation management parameters include vegetation irrigation amount.

9. The method according to claim 1, characterized in that, The method further includes: Shade-loving and drought-resistant plants are planted in the area under the boards, medium and tall sand-fixing plants are planted in the area between the boards, and vegetation sand barrier belts are set up in the area outside the array.

10. A multi-system coupling control device for a photovoltaic power station, characterized in that, include: The data acquisition module is used to acquire multi-dimensional status data of different areas of the photovoltaic power station; The fusion decision module is used to perform fusion decision-making on the multi-dimensional state data based on regional differential weights to obtain decision results; The instruction generation module is used to generate control instructions based on a preset priority strategy and the decision results, so as to adjust the operating status of photovoltaic modules and / or vegetation management parameters.

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