Photovoltaic array parameterized design and dynamic electrical reconstruction method and system

Abstract

This invention relates to the field of photovoltaic array parameter design, specifically to a method and system for parametric design and dynamic electrical reconfiguration of photovoltaic arrays. The method involves acquiring multi-dimensional characteristic parameters of the photovoltaic array to construct a photovoltaic array model; generating photovoltaic array samples based on the model; performing fluid dynamics and electrical simulations on the samples to obtain the amount of sand deposition on the surface of each photovoltaic module and the total power generation efficiency; using minimizing deposition and maximizing total power generation efficiency as optimization objectives, globally optimizing the multi-dimensional characteristic parameters, and obtaining the optimal photovoltaic array layout parameters after iterative convergence; constructing the photovoltaic array based on the optimal layout parameters; monitoring the operating parameters of the photovoltaic array in real time; and triggering dynamic electrical reconfiguration when the operating parameters exceed a preset threshold. This invention achieves dual optimization of the photovoltaic array's form and electrical connections, significantly reducing sand accumulation losses and electrical mismatch losses in harsh desert environments.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic array parameter design, and specifically to a method and system for photovoltaic array parametric design and dynamic electrical reconfiguration. Background Technology

[0002] Distributed photovoltaic (PV) power generation systems are power generation facilities that use photovoltaic modules to directly convert solar energy into electrical energy. They are mainly used in power grid access scenarios, building-integrated power generation, and desert power stations. The layout design of the PV array directly determines the power generation efficiency and resistance to environmental disturbances. The geometric parameters such as the horizontal spacing, vertical spacing, and tilt angle of the PV modules need to be precisely set according to the solar altitude angle, azimuth angle, and local climate conditions to balance the loss of shading, dust deposition and wear, and land use efficiency.

[0003] When a photovoltaic array is in operation, solar radiation strikes the surface of the photovoltaic modules at a specific angle, converting light energy into direct current (DC) electricity. However, two main problems exist in actual operation: (1) Electrical mismatch problem: Due to the transient changes in light caused by cloud movement, static shadows caused by the growth of surrounding buildings and vegetation, uneven local irradiance often occurs on the surface of the photovoltaic array. The output current of the components in the shaded area is significantly reduced, resulting in current mismatch with the normal components. This leads to frequent conduction of bypass diodes and the generation of hot spot effect, resulting in a significant decrease in power generation efficiency.

[0004] (2) Dust deposition and wear problem: In desert areas, dust particles in the wind and sand flow collide and deposit on the surface of photovoltaic modules, causing a decrease in light transmittance and an aggravation of hot spot effect. At the same time, long-term scouring leads to wear on the surface of the modules, further reducing power generation efficiency and increasing operation and maintenance costs. Summary of the Invention

[0005] To address the problems mentioned in the prior art, this invention proposes a parametric design and dynamic electrical reconfiguration method and system for photovoltaic arrays. By co-simulating gas-solid two-phase flow models and electrophysical models, and achieving the dual optimization objectives of minimizing deposition amount and maximizing total power generation efficiency, the optimal photovoltaic array layout parameters are designed rapidly. The photovoltaic array is constructed based on the optimal photovoltaic array layout parameters, and the electrical topology of the photovoltaic modules within the photovoltaic array is adjusted in real time to solve the problems in the background art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: This invention proposes a method for parametric design and dynamic electrical reconfiguration of photovoltaic arrays, comprising the following steps: S1. Obtain the multi-dimensional feature parameters of the photovoltaic array, which is composed of multiple photovoltaic modules arranged in a row; fit the array arrangement to the multi-dimensional feature parameters to obtain the parameter driving relationship, and construct the photovoltaic array model based on the parameter driving relationship. S2. Generate multiple sets of photovoltaic array samples with different parameter driving relationships based on the photovoltaic array model, perform fluid dynamics simulation on the photovoltaic array samples to obtain the amount of sand deposited on the surface of each photovoltaic module; calculate the light transmittance loss on the surface of each photovoltaic module based on the amount of sand deposited, and perform electrical simulation on the photovoltaic array samples based on the light transmittance loss to obtain the total power generation efficiency under different operating conditions. S3. With the optimization objectives of minimizing sedimentation and maximizing total power generation efficiency, the ocean predator algorithm is used to globally optimize the multidimensional feature parameters based on the optimization objectives, and the optimal photovoltaic array layout parameters are obtained after iterative convergence. S4. Construct a photovoltaic array based on the optimal photovoltaic array layout parameters; monitor the operating parameters of the photovoltaic array in real time, and trigger dynamic electrical reconfiguration when the operating parameters exceed a preset threshold; the dynamic electrical reconfiguration includes bypassing and isolating photovoltaic modules affected by shading or sand accumulation from the series branch by changing the electrical topology connection method between photovoltaic modules inside the photovoltaic array, and / or recombining multiple photovoltaic modules affected by shading or sand accumulation with similar low current output characteristics in parallel.

[0007] As a further improvement of the present invention, in step S1, the multidimensional feature parameters of the photovoltaic array are obtained, and the array arrangement is fitted to the multidimensional feature parameters to obtain the parameter driving relationship, including: The multidimensional characteristic parameters include the length, width, thickness, horizontal spacing, vertical spacing, incoming flow angle, and tilt angle of the photovoltaic module; The arrangement curve of the photovoltaic array is fitted using a fourth-order Bézier curve. The multidimensional feature parameters are used as control variables to establish logical constraints between the multidimensional feature parameters, forming a parameter-driven relationship. The parameter-driven relationship is used to make the geometric shape of the photovoltaic array updated in real time as the multidimensional feature parameters are adjusted.

[0008] As a further improvement of the present invention, in step S2, a fluid dynamics simulation is performed on the photovoltaic array sample to obtain the amount of sand deposited on the surface of each photovoltaic module, including: The fluid dynamics simulation uses a gas-solid two-phase flow model. The computational domain of the gas-solid two-phase flow model is divided into the inlet region, the core array region, and the wake region. The inlet region, core array region and wake region are spatially segmented and boundary conditions are defined. Unsteady numerical simulations are performed under multiple working conditions with different sand content, flow velocity and inlet direction angle to extract the amount of sand deposited on the surface of each photovoltaic module.

[0009] As a further improvement of the present invention, in step S2, the transmittance loss of each photovoltaic module surface is calculated based on the deposition amount, and electrical simulation is performed on the photovoltaic array sample based on the transmittance loss to obtain the total power generation efficiency under different operating conditions, including: The formula for calculating the transmittance loss is as follows:

[0010] In the formula: This is due to the loss of light transmittance; The amount of sand deposited on the surface of the photovoltaic module; is the base of the natural logarithm; The electrical simulation is based on an electrical physical model, using transmittance loss, radiance, and temperature as inputs to calculate the basic power generation efficiency, as shown in the following formula:

[0011] In the formula: Based on power generation efficiency; This represents the actual output power of the photovoltaic array; Rated power; The radiance of the photovoltaic module; The irradiance under standard test conditions is taken as 1000 W / m. 2 ; The power temperature coefficient of the photovoltaic module is -0.40% / ℃. The temperature of the photovoltaic module; The temperature under standard test conditions is taken as 25℃. Based on the baseline power generation efficiency and incorporating the mismatch loss rate, the total power generation efficiency under different operating conditions is calculated using the following formula:

[0012] In the formula: The total power generation efficiency under different operating conditions; The mismatch loss rate; The formula for calculating the mismatch loss rate is as follows:

[0013] In the formula: This is the sum of the maximum output power of all photovoltaic modules when they operate independently; N The number of photovoltaic modules; For the first i The maximum power of each photovoltaic module when it operates independently; The maximum total power output of a photovoltaic array when the photovoltaic modules are connected in series and parallel, limited by the lowest current component.

[0014] As a further improvement to the present invention, the expression in S3 with the optimization objectives of minimizing deposition amount and maximizing total power generation efficiency is as follows:

[0015] In the formula: To minimize the amount of sediment; To maximize overall power generation efficiency; For the first The amount of sand deposited on the surface of each photovoltaic module; For the first Efficiency loss of individual photovoltaic modules; This is the rated power.

[0016] As a further improvement of the present invention, in step S3, the marine predator algorithm is used to globally optimize the multidimensional feature parameters based on the optimization objective, and the optimal photovoltaic array layout parameters are obtained after iterative convergence, including: Step 1: Create an initial population X of size N. The initial position vector of the individuals in the initial population is shown in the following formula:

[0017] In the formula: This represents the position vector of individuals in the initial population. Indicates the upper limit of the search space; Indicates the lower bound of the search space; ; Each individual in the population corresponds to a set of photovoltaic array layout parameters; Step 2: Update the population individuals in the current population in stages according to the iteration process: The first phase employs the Levy flight strategy to perform a global search of individuals within the current population. The position update formula is as follows:

[0018] In the formula: For individuals in the population at iteration t+1 I The position vector; For the population individuals at the t-th iteration I The position vector; Let be a constant, taken as 0.5; Let Lévy's flight be a random vector; This is the current optimal solution; The second phase employs a hybrid approach combining the Lévy flight strategy and the Brownian motion strategy. The current population is divided into two parts: one part uses the Lévy flight strategy, and the other part uses the Brownian motion strategy. The position update formula is as follows:

[0019] In the formula Solve for the elite in the current population; Let be a random vector of Brownian motion; Otherwise; if indicates a conditional statement; The third stage employs a Brownian motion strategy to perform a local fine-grained search of individuals within the current population. The calculation formula is as follows:

[0020] In the formula: This is the adaptive convergence factor; Step 3: Introduce the FADs effect during the iteration process to simulate environmental changes, and randomly reset the individuals in the population. The calculation formula is as follows:

[0021] In the formula: It is a binary vector, where each element is either 0 or 1; Step 4: Repeat steps 2 to 3 until the number of iterations is reached or the convergence condition is met, and output the optimal photovoltaic array layout parameters.

[0022] As a further improvement of the present invention, after performing S4, a verification step is also included, the verification step comprising: A dynamic evaluation index system is constructed, which includes voltage deviation rate, photovoltaic output change rate, and sand accumulation coverage rate; the dynamic evaluation index system is used to evaluate the performance of the photovoltaic array after dynamic electrical reconfiguration.

[0023] This invention proposes a photovoltaic array parametric design and dynamic electrical reconfiguration system, comprising: The acquisition module is used to acquire the multi-dimensional feature parameters of the photovoltaic array, which is composed of multiple photovoltaic modules arranged in a row; the multi-dimensional feature parameters are fitted to the array arrangement to obtain the parameter driving relationship, and the photovoltaic array model is constructed based on the parameter driving relationship. The simulation module is used to generate multiple sets of photovoltaic array samples with different parameter driving relationships based on the photovoltaic array model, perform fluid dynamics simulation on the photovoltaic array samples to obtain the amount of sand deposited on the surface of each photovoltaic module, calculate the light transmittance loss on the surface of each photovoltaic module based on the amount of sand deposited, and perform electrical simulation on the photovoltaic array samples based on the light transmittance loss to obtain the total power generation efficiency under different operating conditions. The iterative optimization module is used to optimize the multidimensional feature parameters with the objectives of minimizing sedimentation and maximizing total power generation efficiency. Based on the optimization objectives, the ocean predator algorithm is used to perform global optimization on the multidimensional feature parameters. After iterative convergence, the optimal photovoltaic array layout parameters are obtained. The dynamic electrical reconfiguration module is used to construct a photovoltaic array based on the optimal photovoltaic array layout parameters; monitor the operating parameters of the photovoltaic array in real time, and trigger dynamic electrical reconfiguration when the operating parameters exceed a preset threshold; the dynamic electrical reconfiguration includes changing the electrical topology connection between photovoltaic modules inside the photovoltaic array, bypassing and isolating photovoltaic modules affected by shading or sand accumulation from the series branch, and / or recombining multiple photovoltaic modules affected by shading or sand accumulation with similar low current output characteristics in parallel.

[0024] This invention proposes a photovoltaic array parametric design and dynamic electrical reconfiguration device, characterized in that it includes a processor and a memory, wherein the processor executes a computer program stored in the memory to implement the photovoltaic array parametric design and dynamic electrical reconfiguration method as described above.

[0025] This invention proposes a computer-readable storage medium, characterized in that it is used to store a computer program, wherein the computer program, when executed by a processor, implements the photovoltaic array parametric design and dynamic electrical reconfiguration method as described above.

[0026] Compared with the prior art, the present invention achieves the following technical effects: This invention couples fluid dynamics simulation with electrical simulation. It obtains the amount of sand deposited on the surface of photovoltaic modules based on a gas-solid two-phase flow model, and calculates the light transmittance loss based on the amount of sediment before inputting it into the electrical physical model for simulation. This transforms the layout optimization of photovoltaic arrays from traditional empirical design to data-driven decision-making. In addition, this invention uses the optimization objectives of minimizing sedimentation and maximizing total power generation efficiency, and employs the marine predator algorithm for global optimization. This achieves an optimal trade-off between sand accumulation resistance and high power generation efficiency, avoiding the problems of traditional single-objective optimization.

[0027] Based on obtaining the optimal photovoltaic array layout parameters, this invention dynamically changes the electrical topology connection through bypass isolation and / or parallel reconfiguration, enabling the photovoltaic array to actively adapt to instantaneous environmental changes such as cloud movement and sandstorms, filling the technological gap between static layout design and real-time operation control. Finally, through the synergistic cooperation of optimal photovoltaic array layout parameter design and dynamic electrical reconfiguration, this invention achieves dual optimization of photovoltaic array form and electrical connection, which can significantly reduce sand accumulation loss and electrical mismatch loss of photovoltaic arrays in harsh desert environments, effectively improve power generation efficiency and operational stability throughout the entire life cycle, and reduce power plant operation and maintenance costs. Attached Figure Description

[0028] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0029] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present invention, and not all of the structures.

[0030] See Figure 1 This embodiment proposes a method for parametric design and dynamic electrical reconfiguration of photovoltaic arrays, including the following steps: S1. Obtain the multi-dimensional feature parameters of the photovoltaic array, which is composed of multiple photovoltaic modules arranged in a row; fit the array arrangement to the multi-dimensional feature parameters to obtain the parameter driving relationship, and construct the photovoltaic array model based on the parameter driving relationship. S2. Generate multiple sets of photovoltaic array samples with different parameter driving relationships based on the photovoltaic array model, perform fluid dynamics simulation on the photovoltaic array samples to obtain the amount of sand deposited on the surface of each photovoltaic module; calculate the light transmittance loss on the surface of each photovoltaic module based on the amount of sand deposited, and perform electrical simulation on the photovoltaic array samples based on the light transmittance loss to obtain the total power generation efficiency under different operating conditions. S3. With the optimization objectives of minimizing sedimentation and maximizing total power generation efficiency, the ocean predator algorithm is used to globally optimize the multidimensional feature parameters based on the optimization objectives, and the optimal photovoltaic array layout parameters are obtained after iterative convergence. S4. Construct a photovoltaic array based on the optimal photovoltaic array layout parameters; monitor the operating parameters of the photovoltaic array in real time, and trigger dynamic electrical reconfiguration when the operating parameters exceed a preset threshold; the dynamic electrical reconfiguration includes bypassing and isolating photovoltaic modules affected by shading or sand accumulation from the series branch by changing the electrical topology connection method between photovoltaic modules inside the photovoltaic array, and / or recombining multiple photovoltaic modules affected by shading or sand accumulation with similar low current output characteristics in parallel.

[0031] The present invention will be further explained below with reference to the accompanying drawings and specific embodiments: Step 1: This embodiment first obtains the multi-dimensional characteristic parameters of the photovoltaic array. These parameters include the length, width, thickness, horizontal spacing, vertical spacing, incoming flow angle, and tilt angle of the photovoltaic modules. The length and width of the photovoltaic modules determine the light-receiving area of ​​a single module; the thickness affects the structural strength and wind resistance of the modules; the horizontal and vertical spacing together determine the overall footprint of the photovoltaic modules and the mutual shading relationship between them; the tilt angle directly affects the light incidence angle on the surface of the photovoltaic modules and the natural sliding effect of accumulated sand; and the incoming flow angle characterizes the deflection attitude of the photovoltaic modules relative to the direction of the incoming sand flow, significantly influencing the deposition and distribution of sand particles on the array surface.

[0032] After obtaining the multidimensional feature parameters, the preferred embodiment uses a fourth-order Bézier curve to fit the arrangement curve of the photovoltaic array. In this embodiment, the multidimensional feature parameters are used as control variables, and the shape change of the Bézier curve is driven by adjusting the position coordinates of the control points, thereby controlling the arrangement of the entire photovoltaic array and realizing the description and flexible control of complex photovoltaic array arrangements.

[0033] Subsequently, logical constraints are established between multi-dimensional feature parameters. For example, the tilt angle and longitudinal spacing must meet the geometric constraint condition of no shadow shading on the winter solstice, that is, the shadow of the front row photovoltaic modules cannot block the rear row photovoltaic modules when the solar altitude angle is the lowest on the winter solstice; or, the lateral horizontal spacing of photovoltaic modules must meet the structural safety requirements to ensure that there is no collision between photovoltaic modules under extreme wind load conditions, and so on.

[0034] This embodiment enables the photovoltaic array structure to have a complete parameter-driven relationship by logically constraining multi-dimensional feature parameters. When the parameter-driven relationship changes, it will automatically link other related parameters to ensure that the geometric shape of the photovoltaic array is always within a reasonable range. Finally, a three-dimensional CAD (Computer-Aided Design) platform is preferably used to construct a photovoltaic array model based on the parameter-driven relationship and the fitting results of the Bézier curve. The photovoltaic array model can not only intuitively display the three-dimensional geometric shape of the photovoltaic array, but also realize the real-time association between the geometric shape and the parameter-driven relationship. That is, when any parameter changes, the photovoltaic array model will automatically update the geometric shape without the need for manual remodeling.

[0035] Step 2: This step first generates multiple sets of photovoltaic array samples corresponding to different parameter driving relationships based on the photovoltaic array model constructed in Step 1. Each set of photovoltaic array samples corresponds to a set of geometric parameter combinations, such as different horizontal spacing, vertical spacing and tilt angle.

[0036] Fluid dynamics simulations were performed on each photovoltaic array sample. In this embodiment, a gas-solid two-phase flow model was used for the fluid dynamics simulation. The computational domain of the gas-solid two-phase flow model was divided into three regions: the inlet region, the core array region, and the wake region. The inlet region is located at the front of the computational domain and is used to establish fully developed inlet boundary conditions; the core array region is the area where the photovoltaic array is located and is the focus of the simulation; the wake region is located downstream of the photovoltaic array and is used to capture the influence of the photovoltaic array on the downstream flow field. In this embodiment, the length of the inlet region is set to 50m, specifically for a 2-column × 2-row photovoltaic array, and the length of the wake region is set to 100m. The total size of the computational domain is 200m × 120m × 30m.

[0037] In this embodiment, the gas-solid two-phase flow model is calculated using the Euler-Lagrange method. The RANS (Reynolds-Averaged Navier-Stokes equations) are solved through the continuous phase, employing k... The turbulence model was solved in a closed loop, with the inlet boundary set as a velocity inlet and the turbulence intensity set to 5%. The discrete phase (dust particles) was tracked using the DPM (Deformable Part Model) model, with the particle size distribution set to 50–200 μm and the mass flow rate set to 0.1 kg / s, thereby simulating the characteristics of wind and sand flow in the Gobi Desert environment.

[0038] This embodiment uses the above-mentioned fluid dynamics simulation to extract the amount of sand deposited on the surface of each photovoltaic module. The amount of sand deposited reflects the degree of sand accumulation on photovoltaic modules under different geometric layouts.

[0039] In this embodiment, the transmittance loss of each photovoltaic module surface is calculated based on the deposition amount. The formula for calculating the transmittance loss is as follows:

[0040] In the formula: This is due to the loss of light transmittance; The amount of sand deposited on the surface of the photovoltaic module; is the base of the natural logarithm.

[0041] Based on this, electrical simulations were performed using transmittance loss, irradiance, and temperature as inputs to the electrical physical model. The electrical physical model was implemented on the Matlab (Matrix Laboratory) platform. Specifically, the basic power generation efficiency of the photovoltaic module was calculated by mapping the transmittance loss on the surface of the photovoltaic module to the corresponding irradiance attenuation, as shown in the following formula:

[0042]

[0043]

[0044] In the formula: Based on power generation efficiency; This represents the actual output power of the photovoltaic array; Rated power; The radiance of the photovoltaic module; Irradiance under standard test conditions, 1000 W / m 2 ; The power temperature coefficient of the photovoltaic module is -0.40% / ℃; The temperature of the photovoltaic module; The temperature under standard test conditions is 25°C.

[0045] By further introducing the mismatch loss rate, the total power generation efficiency under different operating conditions is calculated:

[0046] In the formula: The mismatch loss rate is determined by the degree of difference in the output current of each photovoltaic module in the photovoltaic array. When the current of the photovoltaic module is significantly reduced due to sand accumulation or shading, the mismatch loss rate increases. This represents the total power generation efficiency under different operating conditions.

[0047] The formula for calculating the mismatch loss rate is as follows:

[0048] In the formula: This is the sum of the maximum output power of all photovoltaic modules when they operate independently; N The number of photovoltaic modules; For the first i The maximum power of each photovoltaic module when it operates independently; The maximum total power output of a photovoltaic array when the photovoltaic modules are connected in series and parallel, limited by the lowest current component.

[0049] Step 3: This step aims to minimize the amount of sediment and maximize the overall power generation efficiency. Its expression is as follows:

[0050] In the formula: To minimize the amount of sediment; To maximize overall power generation efficiency; For the first The amount of sand deposited on the surface of each photovoltaic module; For the first Efficiency loss of individual photovoltaic modules; This is the rated power.

[0051] This embodiment also includes constraints, including that the wind pressure on the photovoltaic array does not exceed 1000Pa and that the front row of photovoltaic modules does not cast shadows on the rear row of photovoltaic modules under the conditions of the winter solstice.

[0052] Step 3: In this embodiment, the marine predator algorithm is preferably used to solve the above optimization objective. The specific process is as follows: Create an initial population X of size N, where N is 30, the maximum number of iterations is 50, and the dimension D is set to 3, corresponding to the three parameters to be optimized: horizontal spacing, vertical spacing, and tilt angle. The initial position vector of the individuals in the initial population is shown in the following formula:

[0053] In the formula: This represents the position vector of individuals in the initial population. Indicates the upper limit of the search space; Indicates the lower bound of the search space; ; Each individual in the population corresponds to a set of photovoltaic array layout parameters, namely a set of horizontal spacing, vertical spacing and tilt angle values.

[0054] Update the population individuals in the current population in stages according to the iterative process: In the first stage (corresponding to iterations 1-17), the Lévy flight strategy is used to perform a global search of individuals within the current population, focusing on exploring areas with a horizontal spacing greater than 4m. When the horizontal spacing increases, the airflow channels within the photovoltaic array become wider, effectively reducing the wake disturbance of the front-row photovoltaic modules on the rear-row photovoltaic modules and reducing the probability of dust particle deposition. The position update formula is as follows:

[0055] In the formula: For individuals in the population at iteration t+1 I The position vector; For the population individuals at the t-th iteration I The position vector; Let be a constant, taken as 0.5; Let Lévy's flight be a random vector; This is the current optimal solution.

[0056] Where the random vector for:

[0057]

[0058] In the formula: These are random numbers that follow a standard normal distribution. v These are random numbers that follow a normal distribution. For scale parameters; The Levi Flight Index is set to 1.5. is the gamma function; sin is the sine function.

[0059] The second stage (corresponding to iterations 18-34) employs a hybrid strategy combining Lévy flight and Brownian motion, shifting the search focus to optimizing the tilt angle. The tilt angle is a key parameter affecting sediment deposition. When the tilt angle is small, dust particles tend to remain and accumulate on the photovoltaic module surface; when the tilt angle is large, the accumulated sand is more likely to slide off the photovoltaic module surface under gravity, leading to increased wind load and decreased land use efficiency. Through a balanced search using the hybrid strategy, the tilt angle that optimizes the natural sliding of sediment is found.

[0060] This embodiment uses To determine the optimal strategy, the current population is divided into two parts: one part uses the Lévy flight strategy, and the other part uses the Brownian motion strategy. This effectively balances global exploration with local development, preventing the algorithm from getting stuck in local optima too early or from having excessively low search efficiency. The position update formula is as follows:

[0061] In the formula: Solve for the elite in the current population; Let be a random vector of Brownian motion; Otherwise; if indicates a conditional judgment.

[0062] The third stage (corresponding to iterations 35-50) employs a Brownian motion strategy to perform a local fine-grained search for the horizontal spacing, vertical spacing, and tilt angle. The calculation formula is as follows:

[0063]

[0064] In the formula: This is the adaptive convergence factor; This represents the current iteration number; This represents the maximum number of iterations.

[0065] This embodiment introduces the FADs (Fish Aggregating Devices, optimal solution aggregation points) effect during the iteration process to simulate environmental changes. A random reset is applied to some individuals in the population with a probability of 0.2, randomly reassigning their positions to new locations within the search space. The calculation formula is as follows:

[0066] In the formula: It is a binary vector, where each element is either 0 or 1.

[0067] Repeat the above steps until the number of iterations is reached or the convergence condition is met, and output the optimal photovoltaic array layout parameters. In the example, the photovoltaic array constructed based on the optimal photovoltaic array layout parameters has a 45% reduction in the amount of dust deposited on the photovoltaic array and a 20% reduction in wind pressure. This shows that the photovoltaic array obtained by the optimal photovoltaic array layout parameters not only effectively suppresses the deposition of sand and dust on the surface of the photovoltaic modules, but also improves the flow field distribution around the photovoltaic array.

[0068] Step 4: During the operation of the photovoltaic array, the current output of each photovoltaic module in the array is monitored in real time through a module-level power optimizer, while environmental parameters such as irradiance, wind speed, and sand concentration are also monitored. When the power generation efficiency decreases from the initial value by more than a set threshold (15%), or the irradiance change rate exceeds 20%, or the wind speed change rate exceeds 30%, dynamic electrical reconfiguration is triggered. The threshold values ​​in this embodiment can be flexibly set according to specific engineering scenarios and are not limited to these.

[0069] Dynamic electrical reconfiguration alters the electrical topology connections between photovoltaic modules within a photovoltaic array using a controllable switching device. In this embodiment, the controllable switching device is a solid-state switch matrix or a smart junction box, located at the row or module level of the photovoltaic array, with a switching time of less than 10 milliseconds, enabling rapid adjustment of the topology.

[0070] Dynamic electrical reconfiguration includes two specific implementation methods. The first method is bypass isolation: when the output power of a photovoltaic module affected by shading or sand accumulation is lower than 30% of its rated power, the photovoltaic module is bypassed from the original series branch to prevent its low current from dragging down the output of the entire series branch. The second method is parallel reconfiguration: when the output power of a photovoltaic module affected by shading or sand accumulation is between 30% and 50% of its rated power, multiple shaded modules with similar low current output characteristics are decoupled from their respective series branches and recombined into a new parallel branch to avoid a severe current bottleneck effect in the entire series branch.

[0071] In actual implementation, one of the two methods can be chosen, or both can be used simultaneously depending on the specific shading status of each row, to achieve precise control by using different reconstruction strategies for different rows. For example, in rows with severe sand accumulation, a bypass isolation strategy is preferred to avoid hot spot risks, while in rows with dynamically changing shadows, a parallel reconfiguration strategy is preferred to maintain a higher voltage level and grid connection stability.

[0072] After the dynamic electrical reconfiguration is completed, the operating status of the photovoltaic array before and after the reconfiguration is quantitatively compared using a dynamic evaluation index system consisting of voltage deviation rate, photovoltaic output change rate, and sand accumulation coverage rate, in order to determine the effectiveness of the dynamic electrical reconfiguration.

[0073] Based on the same inventive concept, this invention also provides a photovoltaic array parametric design and dynamic electrical reconfiguration system. Since the principle of this photovoltaic array parametric design and dynamic electrical reconfiguration system in solving problems is similar to that of the aforementioned photovoltaic array parametric design and dynamic electrical reconfiguration method, the implementation of this photovoltaic array parametric design and dynamic electrical reconfiguration system can refer to the implementation of the photovoltaic array parametric design and dynamic electrical reconfiguration method, and the repeated parts will not be described again.

[0074] In specific implementation, the photovoltaic array parametric design and dynamic electrical reconfiguration system provided in this embodiment of the invention specifically includes: The acquisition module is used to acquire the multi-dimensional feature parameters of the photovoltaic array, which is composed of multiple photovoltaic modules arranged in a row; the multi-dimensional feature parameters are fitted to the array arrangement to obtain the parameter driving relationship, and the photovoltaic array model is constructed based on the parameter driving relationship. The simulation module is used to generate multiple sets of photovoltaic array samples with different parameter driving relationships based on the photovoltaic array model, perform fluid dynamics simulation on the photovoltaic array samples to obtain the amount of sand deposited on the surface of each photovoltaic module, calculate the light transmittance loss on the surface of each photovoltaic module based on the amount of sand deposited, and perform electrical simulation on the photovoltaic array samples based on the light transmittance loss to obtain the total power generation efficiency under different operating conditions. The iterative optimization module is used to optimize the multidimensional feature parameters with the objectives of minimizing sedimentation and maximizing total power generation efficiency. Based on the optimization objectives, the ocean predator algorithm is used to perform global optimization on the multidimensional feature parameters. After iterative convergence, the optimal photovoltaic array layout parameters are obtained. The dynamic electrical reconfiguration module is used to construct a photovoltaic array based on the optimal photovoltaic array layout parameters; monitor the operating parameters of the photovoltaic array in real time, and trigger dynamic electrical reconfiguration when the operating parameters exceed a preset threshold; the dynamic electrical reconfiguration includes changing the electrical topology connection between photovoltaic modules inside the photovoltaic array, bypassing and isolating photovoltaic modules affected by shading or sand accumulation from the series branch, and / or recombining multiple photovoltaic modules affected by shading or sand accumulation with similar low current output characteristics in parallel.

[0075] Accordingly, this invention also provides a photovoltaic array parametric design and dynamic electrical reconfiguration device, including a processor and a memory, wherein the processor executes a computer program stored in the memory to implement the photovoltaic array parametric design and dynamic electrical reconfiguration method provided in this invention.

[0076] For more detailed information on the above methods, please refer to the relevant content disclosed in the foregoing embodiments, which will not be repeated here.

[0077] Accordingly, embodiments of the present invention also provide a computer-readable storage medium for storing a computer program, wherein the computer program, when executed by a processor, implements the photovoltaic array parametric design and dynamic electrical reconfiguration method described above in embodiments of the present invention.

[0078] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems, devices, and storage media disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0079] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0080] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0081] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0082] The foregoing has provided a detailed description of the photovoltaic array parametric design and dynamic electrical reconfiguration method, system, equipment, and storage medium provided by the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for parametric design and dynamic electrical reconfiguration of a photovoltaic array, characterized in that, Includes the following steps: S1. Obtain the multi-dimensional feature parameters of the photovoltaic array, which is composed of multiple photovoltaic modules arranged in a row; fit the array arrangement to the multi-dimensional feature parameters to obtain the parameter driving relationship, and construct the photovoltaic array model based on the parameter driving relationship. S2. Generate multiple sets of photovoltaic array samples with different parameter driving relationships based on the photovoltaic array model, perform fluid dynamics simulation on the photovoltaic array samples to obtain the amount of sand deposited on the surface of each photovoltaic module; calculate the light transmittance loss on the surface of each photovoltaic module based on the amount of sand deposited, and perform electrical simulation on the photovoltaic array samples based on the light transmittance loss to obtain the total power generation efficiency under different operating conditions. S3. With the optimization objectives of minimizing sedimentation and maximizing total power generation efficiency, the ocean predator algorithm is used to globally optimize the multidimensional feature parameters based on the optimization objectives, and the optimal photovoltaic array layout parameters are obtained after iterative convergence. S4. Construct a photovoltaic array based on the optimal photovoltaic array layout parameters; The operating parameters of the photovoltaic array are monitored in real time. When the operating parameters exceed the preset threshold, dynamic electrical reconfiguration is triggered. The dynamic electrical reconfiguration includes changing the electrical topology connection between photovoltaic modules inside the photovoltaic array, bypassing and isolating photovoltaic modules affected by shading or sand accumulation from the series branch, and / or recombining multiple photovoltaic modules affected by shading or sand accumulation with similar low current output characteristics in parallel.

2. The method for parametric design and dynamic electrical reconfiguration of a photovoltaic array according to claim 1, characterized in that, In step S1, multidimensional feature parameters of the photovoltaic array are obtained, and the array arrangement is fitted to the multidimensional feature parameters to obtain the parameter driving relationship, including: The multidimensional characteristic parameters include the length, width, thickness, horizontal spacing, vertical spacing, incoming flow angle, and tilt angle of the photovoltaic module; The arrangement curve of the photovoltaic array is fitted using a fourth-order Bézier curve. The multidimensional feature parameters are used as control variables to establish logical constraints between the multidimensional feature parameters, forming a parameter-driven relationship. The parameter-driven relationship is used to make the geometric shape of the photovoltaic array updated in real time as the multidimensional feature parameters are adjusted.

3. The method for parametric design and dynamic electrical reconfiguration of a photovoltaic array according to claim 1, characterized in that, In step S2, a fluid dynamics simulation is performed on the photovoltaic array sample to obtain the amount of sand deposited on the surface of each photovoltaic module, including: The fluid dynamics simulation uses a gas-solid two-phase flow model. The computational domain of the gas-solid two-phase flow model is divided into the inlet region, the core array region, and the wake region. The inlet region, core array region and wake region are spatially segmented and boundary conditions are defined. Unsteady numerical simulations are performed under multiple working conditions with different sand content, flow velocity and inlet direction angle to extract the amount of sand deposited on the surface of each photovoltaic module.

4. The method for parametric design and dynamic electrical reconfiguration of a photovoltaic array according to claim 1, characterized in that, In step S2, the transmittance loss of each photovoltaic module surface is calculated based on the deposition amount. Electrical simulation is then performed on the photovoltaic array sample based on this transmittance loss to obtain the total power generation efficiency under different operating conditions, including: The formula for calculating the transmittance loss is as follows: In the formula: This is due to the loss of light transmittance; The amount of sand deposited on the surface of the photovoltaic module; is the base of the natural logarithm; The electrical simulation is based on an electrical physical model, using transmittance loss, radiance, and temperature as inputs to calculate the basic power generation efficiency, as shown in the following formula: In the formula: Based on power generation efficiency; This represents the actual output power of the photovoltaic array; Rated power; The radiance of the photovoltaic module; The irradiance under standard test conditions is taken as 1000 W / m. 2 ; The power temperature coefficient of the photovoltaic module is -0.40% / ℃. The temperature of the photovoltaic module; The temperature under standard test conditions is taken as 25℃. Based on the baseline power generation efficiency and incorporating the mismatch loss rate, the total power generation efficiency under different operating conditions is calculated using the following formula: In the formula: The total power generation efficiency under different operating conditions; The mismatch loss rate; The formula for calculating the mismatch loss rate is as follows: In the formula: This is the sum of the maximum output power of all photovoltaic modules when they operate independently; N The number of photovoltaic modules; For the first i The maximum power of each photovoltaic module when it operates independently; The maximum total power output of a photovoltaic array when the photovoltaic modules are connected in series and parallel, limited by the lowest current component.

5. The method for parametric design and dynamic electrical reconfiguration of a photovoltaic array according to claim 1, characterized in that, The expression in S3, which aims to minimize deposition and maximize total power generation efficiency, is as follows: In the formula: To minimize the amount of sediment; To maximize overall power generation efficiency; For the first The amount of sand deposited on the surface of each photovoltaic module; For the first Efficiency loss of individual photovoltaic modules; This is the rated power.

6. The method for parametric design and dynamic electrical reconfiguration of a photovoltaic array according to claim 1, characterized in that, In step S3, the marine predator algorithm is used to globally optimize the multidimensional feature parameters based on the optimization objective. After iterative convergence, the optimal photovoltaic array layout parameters are obtained, including: Step 1: Create an initial population X of size N. The initial position vector of the individuals in the initial population is shown in the following formula: In the formula: This represents the position vector of individuals in the initial population. Indicates the upper limit of the search space; Indicates the lower bound of the search space; ; Each individual in the population corresponds to a set of photovoltaic array layout parameters; Step 2: Update the population individuals in the current population in stages according to the iteration process: The first phase employs the Levy flight strategy to perform a global search of individuals within the current population. The position update formula is as follows: In the formula: For individuals in the population at iteration t+1 I The position vector; For the population individuals at the t-th iteration I The position vector; Let be a constant, taken as 0.5; Let Lévy's flight be a random vector; This is the current optimal solution; The second phase employs a hybrid approach combining the Lévy flight strategy and the Brownian motion strategy. The current population is divided into two parts: one part uses the Lévy flight strategy, and the other part uses the Brownian motion strategy. The position update formula is as follows: In the formula Solve for the elite in the current population; Let be a random vector of Brownian motion; Otherwise; if indicates a conditional statement; The third stage employs a Brownian motion strategy to perform a local fine-grained search of individuals within the current population. The calculation formula is as follows: In the formula: This is the adaptive convergence factor; Step 3: Introduce the FADs effect during the iteration process to simulate environmental changes, and randomly reset the individuals in the population. The calculation formula is as follows: In the formula: It is a binary vector, where each element is either 0 or 1; Step 4: Repeat steps 2 to 3 until the number of iterations is reached or the convergence condition is met, and output the optimal photovoltaic array layout parameters.

7. The method for parametric design and dynamic electrical reconfiguration of a photovoltaic array according to claim 1, characterized in that, After S4, a verification step is also included, which includes: A dynamic evaluation index system is constructed, which includes voltage deviation rate, photovoltaic output change rate, and sand accumulation coverage rate; the dynamic evaluation index system is used to evaluate the performance of the photovoltaic array after dynamic electrical reconfiguration.

8. A photovoltaic array parametric design and dynamic electrical reconfiguration system, characterized in that, include: The acquisition module is used to acquire multi-dimensional feature parameters of a photovoltaic array, which is composed of multiple photovoltaic modules arranged in a row. The multidimensional feature parameters are fitted to the array arrangement to obtain the parameter driving relationship, and a photovoltaic array model is constructed based on the parameter driving relationship. The simulation module is used to generate multiple sets of photovoltaic array samples with different parameter driving relationships based on the photovoltaic array model, perform fluid dynamics simulation on the photovoltaic array samples to obtain the amount of sand deposited on the surface of each photovoltaic module, calculate the light transmittance loss on the surface of each photovoltaic module based on the amount of sand deposited, and perform electrical simulation on the photovoltaic array samples based on the light transmittance loss to obtain the total power generation efficiency under different operating conditions. The iterative optimization module is used to optimize the multidimensional feature parameters with the objectives of minimizing sedimentation and maximizing total power generation efficiency. Based on the optimization objectives, the ocean predator algorithm is used to perform global optimization on the multidimensional feature parameters. After iterative convergence, the optimal photovoltaic array layout parameters are obtained. The dynamic electrical reconfiguration module is used to construct a photovoltaic array based on the optimal photovoltaic array layout parameters; monitor the operating parameters of the photovoltaic array in real time, and trigger dynamic electrical reconfiguration when the operating parameters exceed a preset threshold; the dynamic electrical reconfiguration includes changing the electrical topology connection between photovoltaic modules inside the photovoltaic array, bypassing and isolating photovoltaic modules affected by shading or sand accumulation from the series branch, and / or recombining multiple photovoltaic modules affected by shading or sand accumulation with similar low current output characteristics in parallel.

9. A photovoltaic array parametric design and dynamic electrical reconfiguration device, characterized in that, It includes a processor and a memory, wherein the processor executes a computer program stored in the memory to implement the photovoltaic array parametric design and dynamic electrical reconfiguration method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, Used to store a computer program, wherein the computer program, when executed by a processor, implements the photovoltaic array parametric design and dynamic electrical reconfiguration method as described in any one of claims 1 to 7.

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