A photovoltaic field area device positioning method and system, related devices and media

Abstract

This invention discloses a method, system, related equipment, and medium for locating equipment in a photovoltaic (PV) power generation field. Specifically, it includes: first, obtaining the coordinates of PV strings, the number of inverters, the capacity parameters of the box-type transformer, and the unit price of AC / DC cables in the PV power generation field; then, using a clustering algorithm to divide the PV array area and determining the centroid of the area as candidate inverter locations; next, using the cable unit price as a weight, calculating the candidate locations through an optimization algorithm to select the equipment location combination with the lowest total cable cost; finally, outputting this combination containing the target inverter and the box-type transformer location. This invention, by introducing a clustering algorithm and weighted optimization based on cable price ratios, achieves automation and a relatively optimal cost solution for PV power generation field equipment layout, significantly improving design efficiency and reducing cable usage and construction costs.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic power generation technology, and more specifically to a method, system, related equipment and medium for locating equipment in a photovoltaic field. Background Technology

[0002] In the design and construction of photovoltaic (PV) power plants, the division of PV arrays and the positioning of core equipment such as inverters and box-type transformers directly affect the amount of cables used and the project construction cost. Currently, the positioning of PV arrays and equipment mainly relies on the personal experience of designers. This method is not only time-consuming and labor-intensive but also highly susceptible to subjective human factors, failing to yield a relatively optimal solution for cable usage and cost.

[0003] While some photovoltaic (PV) design software possesses the function of quickly dividing PV arrays and locating equipment, the software's equipment layout logic connects PV strings and deploys equipment in a fixed sequence, making it impossible to flexibly adjust according to the actual layout of the PV field. This also makes it difficult to optimize cable usage and costs. Furthermore, existing patents' methods for dividing PV strings either only complete the PV array division through preset path finding or fail to provide clear explanations of the specific implementation methods. Neither method considers the price ratio of DC and AC cables to adjust equipment layout, failing to achieve a relatively optimal solution for equipment positioning from a cost perspective. This results in excessive cable usage and high construction costs in PV field construction.

[0004] Therefore, there is an urgent need for a device positioning method that can achieve automated calculation, optimize cable price ratios, and adapt to the actual layout of photovoltaic fields, in order to overcome the shortcomings of existing technologies. Summary of the Invention

[0005] In view of the above problems, the present invention is proposed to provide a method and apparatus for locating photovoltaic field equipment to overcome or at least partially solve the above problems. The purpose is to solve the technical problems of existing photovoltaic field equipment positioning relying on manual experience, professional software being unable to flexibly optimize, and not combining cable price ratio to achieve optimal cost, so as to realize the automation, accuracy, and cost optimization design of photovoltaic field equipment positioning.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] In a first aspect, embodiments of the present invention provide a method for locating equipment in a photovoltaic field, comprising the following steps: S1. Obtain the coordinate point data of all photovoltaic strings in the photovoltaic field, determine the number of inverters and the capacity parameters of the box-type transformer; and obtain the unit price of DC cable and AC cable. S2. Based on the coordinate point data, a clustering algorithm is used to divide the photovoltaic string into several photovoltaic array regions, and the centroid of each region is determined; the centroid is the candidate position of the inverter corresponding to each photovoltaic array region. S3. Using the unit price of the DC cable and the unit price of the AC cable as weights, the candidate locations of each inverter and the candidate locations of the box transformer are combined and calculated using an optimization algorithm. The target equipment location combination that minimizes the total cable cost is determined from multiple equipment location combinations. The total cable cost includes the cost of the DC cable from the photovoltaic string to the inverter and the cost of the AC cable from the inverter to the box transformer. S4. Output the target equipment location combination, which includes the target inverter location and the target box transformer location.

[0008] Furthermore, in step S2, a clustering algorithm is used to divide the photovoltaic string into several photovoltaic array regions, and the centroid of each region is determined; this includes the following steps: S21. Determine the required number of clusters based on the number of inverters, and initialize the centroid of each cluster. S22. Calculate the distance from the coordinate point of each photovoltaic string to each centroid point, and assign it to the cluster corresponding to the nearest centroid point; S23. Update the centroid of each cluster based on the coordinates of all photovoltaic strings in each cluster, and repeat steps S22 and S23 until the preset iteration stop condition is met.

[0009] Furthermore, in step S21, the formula for calculating the centroid is:

[0010] in, Let j be the cluster center, j be the j-th cluster, and (t+1) be the (t+1)-th iteration number; This is the t-th data point set of the j-th cluster; Let i be the i-th data point.

[0011] Furthermore, in step S2, a clustering algorithm is used to divide the photovoltaic string into several photovoltaic array regions, and the centroid of each region is determined; it also includes: S24. Visualize the coordinates of the photovoltaic strings; based on the clustering iteration results, convert the coordinates into a photovoltaic matrix according to the actual size of the photovoltaic strings, and generate a region division map of the photovoltaic array according to the final iteration version.

[0012] Furthermore, the optimization algorithm in step S3 is an exhaustive search method.

[0013] Furthermore, in step S3, the total cost of the cable is calculated as follows: Let point A be the location of the photovoltaic array, point B be the location of the inverter, and point C be the location of the box-type transformer; S31. Calculate the cable length between photovoltaic array point A and inverter location point B using Manhattan distance:

[0014] The cable length between inverter location point B and box-type transformer location point C is calculated using Manhattan distance:

[0015] Where, x A Let be the coordinates of photovoltaic array A on the x-axis, and y be the coordinates of the array A on the x-axis. A Let x be the coordinate position of photovoltaic array A on the y-axis; B Let be the x-coordinate of inverter point B on the x-axis, and y be the y-coordinate of the inverter point B on the x-axis. B Here is the coordinate position of inverter point B on the y-axis; x C Let x be the coordinate position of the box-type transformer C on the x-axis. C Let C be the coordinate position of the box-type transformer C on the x-axis; S32. Calculate the total cost of the cable connecting photovoltaic array point A and inverter location point B:

[0016] Calculate the total cable cost between inverter location point B and box-type transformer location point C:

[0017] in, For DC cable prices, Price of AC cable; S33. Determine the minimum total cost of the cable:

[0018] Where K is the number of photovoltaic strings connected to a single inverter, x A,k y A,k Let x be the coordinates of the k-th photovoltaic string; B y B x represents the planar coordinates of a single inverter; N represents the number of inverters connected to a single box-type transformer. B,n y B,n Let x be the coordinates of the nth inverter group. C y C Here are the plane coordinates of the box-type transformer.

[0019] Furthermore, in step S4, the output information also includes: the photovoltaic array partitioning result, the path and total length of the DC and AC cables.

[0020] In a second aspect, embodiments of the present invention provide a photovoltaic field equipment positioning system, which applies the photovoltaic field equipment positioning method of the first aspect embodiment described above. The system includes: Data preparation module: used to obtain the coordinate point data of all photovoltaic strings in the photovoltaic field, determine the number of inverters and the capacity parameters of the box transformer; and obtain the unit price of DC cable and AC cable; Photovoltaic array partitioning module: used to divide the photovoltaic string into several photovoltaic array regions based on the coordinate point data using a clustering algorithm, and determine the centroid of each region; the centroid is the candidate inverter position corresponding to each photovoltaic array region; Equipment location optimization module: It is used to combine and calculate the candidate locations of each inverter and the candidate locations of the box transformer by using the unit price of the DC cable and the unit price of the AC cable as weights and through optimization algorithm, and determine the target equipment location combination that minimizes the total cable cost from multiple equipment location combinations. The total cable cost includes the DC cable cost from the photovoltaic string to the inverter and the AC cable cost from the inverter to the box transformer. Result output module: used to output the target equipment location combination, which includes the target inverter location and the target box transformer location.

[0021] Thirdly, embodiments of the present invention provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the photovoltaic field equipment positioning method of the first aspect embodiment described above.

[0022] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the photovoltaic field equipment positioning method of the first aspect embodiment described above.

[0023] The beneficial effects of the above-described technical solutions provided in the embodiments of the present invention include at least the following: 1. Improved design efficiency: This invention can automate the division of photovoltaic arrays, equipment positioning, cable length and cost calculation by using photovoltaic string coordinates. It eliminates the need for manual experience, greatly reduces design time, and solves the problem of time-consuming and labor-intensive traditional manual division. 2. Achieving optimal cost: This invention combines the price ratio of DC and AC cables, iteratively optimizes the candidate locations of equipment through the K-means clustering algorithm, and uses a weighted exhaustive method to find the minimum total cable cost, thus achieving a relatively optimal solution for equipment positioning from a cost perspective; 3. Strong adaptability and high flexibility: This invention is based on the actual layout coordinates of photovoltaic strings in the photovoltaic field. It can be flexibly adjusted according to different terrains and layout schemes of the photovoltaic field. At the same time, it supports the verification of optimization effects. If the preset target is not achieved, it can be iterated and optimized again, which solves the problem of fixed-order layout and inflexible adjustment in existing software. 4. Convenient implementation and easy promotion: The specific implementation of this invention can directly import Excel coordinate files, and the operation steps are clear. Designers do not need to have advanced algorithm knowledge to complete the operation. At the same time, it can display various calculation results and diagrams in real time, which is convenient for engineering implementation and has good promotion and application value. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0025] Figure 1 This is a flowchart provided in an embodiment of the present invention.

[0026] Figure 2 This refers to the string number provided in the embodiments of the present invention.

[0027] Figure 3 An example diagram of clustering group results is provided for embodiments of the present invention.

[0028] Figure 4 This is an overview diagram of the photovoltaic field equipment numbers provided in the embodiments of the present invention.

[0029] Figure 5 This is a schematic diagram of the photovoltaic power station power distribution system provided in an embodiment of the present invention.

[0030] Figure 6 This is a region division diagram of the photovoltaic array provided in an embodiment of the present invention.

[0031] Figure 7 This is a structural diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0032] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0033] Example 1: like Figure 1 As shown in the figure, an embodiment of the present invention discloses a method for locating equipment in a photovoltaic field, including the following steps: Step S1: Obtain the coordinate point data of all photovoltaic strings in the photovoltaic field, determine the number of inverters and the capacity parameters of the box-type transformer; and obtain the unit price of DC cable and AC cable.

[0034] During implementation, the first step is to obtain the basic design data and unit price data for the photovoltaic (PV) power plant. The coordinate data of the PV strings is typically stored in spreadsheet format, such as an Excel file. Each string corresponds to a set of planar coordinates (x, y), derived from the preliminary layout map of the PV power plant or geographic information system (GIS) data. This coordinate data is read through a program interface to provide input for subsequent cluster analysis. Simultaneously, based on the total installed capacity of the PV power plant, the rated power of the selected inverters, and the capacity specifications of the box-type transformers, the required number of inverters and box-type transformers is calculated.

[0035] Step S2: Based on the coordinate point data, a clustering algorithm is used to divide the photovoltaic string into several photovoltaic array regions, and the centroid of each region is determined; the centroid is the candidate position of the inverter corresponding to each photovoltaic array region.

[0036] First, using the number of inverters as the cluster number k, k cluster centers are randomly initialized, i.e., initial centroids. Then, the Manhattan distance from the coordinates of each photovoltaic string to each cluster center is calculated, and the string is assigned to the category represented by the nearest cluster center. After one partitioning, the geometric center of each category is recalculated, i.e., the average of the coordinates of all strings in that category, as the new centroid. The centroid is the candidate inverter location corresponding to each photovoltaic array region. This allocation and update process is repeated until the change in centroid is less than a preset threshold or the maximum number of iterations is reached, at which point the algorithm converges. The final k centroids represent the center positions of each photovoltaic array region. These centroids reflect the spatial distribution characteristics of the photovoltaic strings, providing a reference for the initial selection of inverter locations. Furthermore, during the clustering process, the coordinate points can be converted into a virtual photovoltaic matrix based on the actual dimensions of the photovoltaic strings, such as length and width, facilitating the subsequent generation of a visualized region partitioning map.

[0037] Step S3: Using the unit price of the DC cable and the unit price of the AC cable as weights, the optimization algorithm is used to perform combination calculations on each candidate inverter location and candidate box transformer location. From multiple equipment location combinations, the target equipment location combination that minimizes the total cable cost is determined. The total cable cost includes the DC cable cost from the photovoltaic string to the inverter and the AC cable cost from the inverter to the box transformer.

[0038] This step is crucial for cost optimization. Based on the geographical boundaries of the photovoltaic field and the distribution range of the photovoltaic array, a reasonable search range for the box-type transformer is determined. Within this range, several initial candidate locations for the box-type transformer are generated, and invalid candidate locations that spatially overlap with the photovoltaic array are eliminated, resulting in a set of effective candidate locations for the box-type transformer. Using the unit price of DC cables and AC cables as cost weights, a weighted exhaustive search method is used to perform full combination matching between all inverter candidate locations and effective candidate locations for the box-type transformer. For each location combination, the Manhattan algorithm is used to calculate the total length of the DC cable from the photovoltaic string to the corresponding inverter and the total length of the AC cable from all inverters to the box-type transformer. The DC cable cost and AC cable cost under this combination are calculated based on the corresponding weights, and the sum of these two costs yields the total cable cost for that location combination. This combination matching and cost calculation process is repeated to complete the calculation of the total cable cost for all effective equipment location combinations. Finally, from all the calculation results, the equipment location combination with the lowest total cable cost is selected as the target equipment location combination. This combination balances the price difference between AC and DC cables and the actual usage, achieving optimal control of cable costs. In addition, during the cost accounting process, the cable length and individual cost data of each combination can be recorded in real time, which facilitates subsequent cost comparison and solution traceability.

[0039] Step S4: Output the target equipment location combination, which includes the target inverter location and the target box transformer location.

[0040] First, the core data of the target equipment location combinations selected through cost optimization is extracted, accurately outputting the spatial coordinates of the target inverters corresponding to each photovoltaic array area and the optimal spatial coordinates of the target box-type transformers for the entire field, thus clarifying the precise spatial positioning of each device. Then, all supporting data for this target location combination is output simultaneously, including the total length of the DC cable from the photovoltaic string to the inverter, the total length of the AC cable from the inverter to the box-type transformer, and the specific values ​​for DC cable cost, AC cable cost, and total cable cost. Simultaneously, a matching table between the photovoltaic array area and the target inverter is output, along with the spatial relative position information of each device location to the geographical boundaries of the photovoltaic field and the photovoltaic array. Finally, based on the above data, a visualized photovoltaic field equipment location map, a photovoltaic array area division map, and a cable path diagram are automatically generated, transforming abstract coordinate data into intuitive engineering drawings. Furthermore, basic parameter files for equipment positioning, including equipment number, coordinate information, and cable usage, can be output, facilitating import into photovoltaic professional design software for subsequent engineering design and verification.

[0041] This invention constructs a complete method for locating photovoltaic (PV) equipment in a PV field through the four steps described above. Based on the actual layout coordinates of the PV strings, this method utilizes the K-means clustering algorithm to automatically divide the PV array, avoiding the subjectivity and inefficiency of traditional manual division. More importantly, this invention innovatively introduces the cable price ratio as an optimization weight. Through iterative optimization and weighted exhaustive search, it simultaneously considers the costs of DC and AC cables during equipment positioning, thereby minimizing the total cable cost globally. This invention is highly automated; from data import to result output, no manual intervention is required. Designers only need to provide coordinate files and equipment parameters for automatic division, positioning, and calculation, significantly shortening the design cycle and improving work efficiency.

[0042] Furthermore, it offers a relatively cost-optimized solution by combining a weighted exhaustive search method with price comparison to achieve economic optimization of equipment layout. It is highly adaptable, as this method does not rely on fixed equipment layout templates but dynamically adjusts according to the actual terrain and string distribution, making it suitable for photovoltaic fields with various complex terrains. It also supports data interaction with professional design software, facilitating project implementation.

[0043] This invention also features visual output, with the system generating intuitive results such as area division maps and equipment location maps, which facilitate review by designers and reference by construction personnel, thereby improving the understandability and feasibility of the solution.

[0044] In summary, this invention solves the problems of existing photovoltaic field equipment positioning methods relying on manual experience and insufficient cost optimization, and provides an effective technical means for the refined design of photovoltaic power plants, which has high practical value and promising prospects for promotion.

[0045] The positioning of the photovoltaic field equipment of the present invention will be described in detail below: Step S1: Obtain the coordinate data of the photovoltaic string and determine the basic parameters of the equipment. The planar coordinates (x, y) of all photovoltaic strings in the photovoltaic field are organized into an Excel worksheet according to a standardized format. The coordinate data comes from the preliminary layout map of the photovoltaic field or measured data from a geographic information system. Figure 2 The image shows the imported photovoltaic string numbers. Each row corresponds to the string number and its coordinates (x, y). This data will provide input for subsequent cluster analysis. Obtain the unit price of DC and AC cables. Based on the total installed capacity of the photovoltaic field, combined with the selected inverter rated power and box-type transformer capacity specifications, the number of inverters, the number of photovoltaic strings that can be connected to each box-type transformer, and the number of inverters are calculated and determined. This parameter will serve as the core constraint for subsequent clustering and equipment positioning, and will be entered into the algorithm calculation system after data import.

[0046] Step S2: Divide the photovoltaic array regions based on the K-means clustering algorithm and determine the centroids of the regions: Using the coordinate data and equipment parameters from step S1 as input, the spatial clustering of the photovoltaic strings is completed through the K-means clustering algorithm, the centroid of each cluster is determined, and a clustering map is formed, providing a geographical reference benchmark for subsequent inverter positioning.

[0047] The K-means clustering algorithm includes the following steps: Step S21: Determine the number of clusters and initialize centroids: The centroids of the cluster are represented as:

[0048] In this invention, given the requirement for a complex number of centroids, the centroid is represented as follows:

[0049] in, The cluster center is the inverter or box-type transformer, representing its relative advantage within the cluster. j represents the j-th cluster, and (t+1) represents the (t+1)-th iteration number. This is the t-th data point set of the j-th cluster; Let i be the i-th data point.

[0050] Step S22: Calculate the distance and divide the photovoltaic string clusters: The algorithm automatically calculates the distance from the coordinate point of each photovoltaic string to each initial centroid point. In this invention, the distance calculation is adapted to the actual laying scenario of the project and adopts the Manhattan distance algorithm. Each photovoltaic string is assigned to the cluster corresponding to the nearest centroid, completing the first cluster division. The system then visualizes the imported photovoltaic string coordinates in the form of a scatter plot, intuitively presenting the spatial distribution characteristics of the photovoltaic strings and providing a visual reference for cluster division.

[0051] Step S23: Iteratively update the centroid until the stopping condition is met: The geometric center is recalculated based on the coordinates of all photovoltaic strings in each cluster, and used as the new centroid of the cluster, replacing the initial centroid. Repeat sub-steps S22 and S23 until the position change of the centroid is less than a preset threshold, or the maximum number of iterations set by the system is reached. The algorithm then converges, yielding the final centroids of the k photovoltaic array regions. Figure 3The image shows an example of K-means spatial clustering results. The colored dots represent different photovoltaic strings, and each color corresponds to an independent clustering partition. The numbered labels G1-G10 represent the centroids of each clustering partition, which are the optimal locations for inverters within that partition, intuitively reflecting the regional division of the photovoltaic array.

[0052] Step S24: Visualize the coordinates of the photovoltaic strings; based on the clustering iteration results, convert the coordinates into a photovoltaic matrix according to the actual size of the photovoltaic strings, and generate a region partitioning map of the photovoltaic array according to the final iteration version. For example... Figure 4 As shown, the spatial clustering results of photovoltaic strings based on the K-means clustering algorithm are clearly displayed. Using lines of different colors and grouping, the photovoltaic strings in the entire photovoltaic field are divided into multiple independent photovoltaic array regions. Each large region is further subdivided into specific string numbers, such as G1-A1, G1-A2, etc., providing a clear geographical reference for subsequent determination of inverter candidate locations, optimization of equipment layout, and operation and maintenance management. This numbered overview map can be used to generate the final regional division map of the photovoltaic array.

[0053] Step S3: Optimize the location of the inverter and the box-type transformer based on the cable price ratio. This step is the core of cost optimization in this invention. Using the cluster centroids from step S2 as a reference, and considering the price ratio of DC and AC cables, the inverter location is determined through iterative optimization, and then the location of the box-type transformer is determined through a weighted exhaustive method. For example... Figure 5 As shown, the red triangle represents the box-type transformer, the black square represents the inverter, and the colored dots represent the photovoltaic strings. Different colors are used to distinguish the photovoltaic strings in different zones, indicating the optimized positions of the inverter and the box-type transformer.

[0054] The total cost of the cable is calculated as follows: Let point A be the location of the photovoltaic array, point B be the location of the inverter, and point C be the location of the box-type transformer; Step S31: Calculate the cable length between photovoltaic array point A and inverter location point B using Manhattan distance:

[0055] The cable length between inverter location point B and box-type transformer location point C is calculated using Manhattan distance:

[0056] Where, x A Let be the coordinates of photovoltaic array A on the x-axis, and y be the coordinates of the array A on the x-axis. A Let x be the coordinate position of photovoltaic array A on the y-axis; B Let be the x-coordinate of inverter point B on the x-axis, and y be the y-coordinate of the inverter point B on the x-axis. BHere is the coordinate position of inverter point B on the y-axis; x C Let x be the coordinate position of the box-type transformer C on the x-axis. C Let C be the coordinate position of the box-type transformer C on the x-axis; Step S32: Calculate the total cable cost between photovoltaic array point A and inverter location point B:

[0057] Calculate the total cable cost between inverter location point B and box-type transformer location point C:

[0058] in, For DC cable prices, Price of AC cable; Step S33: Determine the minimum total cost of the cable:

[0059] Where K is the number of photovoltaic strings connected to a single inverter, x A,k y A,k Let x be the coordinates of the k-th photovoltaic string; B y B x represents the planar coordinates of a single inverter; N represents the number of inverters connected to a single box-type transformer. B,n y B,n Let x be the coordinates of the nth inverter group. C y C Here are the plane coordinates of the box-type transformer.

[0060] Step S4 calculates the total cable length and outputs equipment layout information. This step is the output stage. Based on the final locations of the inverter and box-type transformer determined in step S3, the cable length is automatically calculated, and the complete information on the feasible equipment layout for the project is output. For example... Figure 6 As shown, the coordinates of the photovoltaic strings are visualized. Based on the clustering iteration results, the coordinates are transformed into a photovoltaic matrix according to the actual size of the photovoltaic strings. The colored dashed boxes represent different clustering partitions, with each color corresponding to an independent region. Green dots represent box-type transformers, and the numbered string labels (such as G1-A1, G1-A2, etc.) represent specific photovoltaic strings. The connecting lines represent the cable connection paths from the strings to the corresponding cluster centroids, intuitively presenting the relationship between strings and equipment. The colored dashed boxes clearly show the partitions of the photovoltaic power station, facilitating system design and operation and maintenance management. The clustering partitions calculated based on algorithm iteration can minimize cable length, reduce line loss and construction costs, and provide a direct basis for subsequent cable path planning and system topology design.

[0061] Cable length calculation: The system iterates through all photovoltaic string-inverter and inverter-box transformer connections, accumulating the total DC cable length and AC cable length. Simultaneously, it calculates the total cost and total price of both AC and DC cables based on the cable unit price. Key data can be displayed in real-time on the system interface, such as the number of photovoltaic strings, the number of inverters, the box transformer coordinates, the average distance from A to B and B to C, the total line length, the itemized cost of AC and DC cables, the total cost, and comparative data on cost optimization, such as the amount of cable saved, the amount of cost savings, and the percentage. Figure 6 As shown, the system generates unique numbers for all equipment within the photovoltaic field. Simultaneously, the system generates an independent number for each inverter, clearly identifying each inverter within the entire field, greatly facilitating construction wiring, operation and maintenance, and digital record management.

[0062] The output information also includes: photovoltaic array partitioning results, DC and AC cable paths and total lengths.

[0063] Engineering verification is also possible: the system supports exporting the output equipment layout information into a general format and importing it back into SketchUp for secondary verification. The cable path and length can be seen intuitively, and the total engineering quantity can be summarized. If the verification result does not meet the preset cost target, parameters such as cable unit price and iteration step size can be readjusted, and steps S2~S4 can be executed again to achieve iterative optimization.

[0064] The four steps S1 to S4 of this photovoltaic field equipment positioning method constitute a complete automated, precise, and cost-optimized photovoltaic field equipment positioning process that is progressive, interconnected, and logically rigorous. From data preparation and array division to cost-oriented positioning of core equipment and then to the output of engineering results, it realizes a comprehensive upgrade of photovoltaic field equipment positioning design in photovoltaic power generation projects from traditional manual experience-based to algorithmic, standardized, and cost-optimized. It effectively solves the industry pain points of existing technologies, such as equipment positioning relying on manual labor, fixed logic of professional software layout, and failure to combine cable prices to achieve cost optimization.

[0065] The four steps form a tight logical connection and data loop: Step S1 serves as the foundation, providing accurate basic data and clear equipment parameter boundaries for all subsequent steps. It is a prerequisite for the implementation of the entire method; if there are deviations in data acquisition or parameter determination, the results of all subsequent algorithm calculations will lose their practical engineering significance. Step S2, based on the basic data from S1, completes the scientific clustering and division of photovoltaic strings, transforming the scattered coordinates of photovoltaic strings into photovoltaic array regions that match the number of inverters, and determining the centroid of the regions. This provides a reasonable geographical reference benchmark for equipment positioning in Step S3. At the same time, visualization processing ensures that the division results conform to the actual site layout, solving the subjectivity and inefficiency problems of traditional manual division. Step S3 is the core innovation of the entire method, building upon the clustering of S2... Using the centroid as a reference, a combination of iterative optimization and weighted exhaustive algorithms, combined with the price ratio of AC and DC cables, achieves the cost-optimal positioning of inverters and box-type transformers. This breaks through the limitations of existing technologies that only arrange equipment based on geometric location, taking cable cost as the core optimization objective. At the same time, Manhattan distance is used to calculate cable length, making the positioning results more consistent with the actual engineering laying scenario, taking into account both cost optimization and the technical specifications of equipment operation. Step S4 serves as the output end, transforming the equipment positioning results of S3 into cable length data and equipment layout information that can be implemented in the project. This completes the transformation from algorithm calculation to actual design scheme, while supporting reverse verification and iterative optimization of the scheme. The final output design scheme not only meets the requirements of cost optimization but also has strong engineering feasibility, forming a complete system.

[0066] Compared to traditional photovoltaic (PV) field equipment positioning methods, this method automates the entire process of PV array division, equipment positioning, cable length calculation, and cost calculation through algorithmic intervention. This significantly reduces the manual workload of designers and greatly improves design efficiency. More importantly, this method incorporates the price ratio of AC / DC cables into the equipment positioning optimization system. By combining algorithms such as K-means clustering, iterative optimization, and weighted exhaustive search, it achieves a relatively optimal solution for equipment positioning from a cost perspective, effectively reducing the construction cost of PV fields. Furthermore, this method possesses strong adaptability and flexibility. Based on the actual string coordinates of the PV field, the calculations can be flexibly adjusted according to the terrain conditions and string layout schemes of different fields, without being limited by fixed layout logic. It is also easy to operate, supporting direct import of coordinate data in common formats such as Excel. Designers do not need advanced algorithmic knowledge to complete the operation, and the output visualization results and engineering data facilitate the implementation and promotion of the solution.

[0067] Example 2: Based on the same inventive concept, embodiments of the present invention also provide a photovoltaic field equipment positioning system, the system comprising: Data preparation module: used to obtain the coordinate point data of all photovoltaic strings in the photovoltaic field, determine the number of inverters and the capacity parameters of the box transformer; and obtain the unit price of DC cable and AC cable; Photovoltaic array partitioning module: used to divide the photovoltaic string into several photovoltaic array regions based on the coordinate point data using a clustering algorithm, and determine the centroid of each region; the centroid is the candidate inverter position corresponding to each photovoltaic array region; Equipment location optimization module: It is used to combine and calculate the candidate locations of each inverter and the candidate locations of the box transformer by using the unit price of the DC cable and the unit price of the AC cable as weights and through optimization algorithm, and determine the target equipment location combination that minimizes the total cable cost from multiple equipment location combinations. The total cable cost includes the DC cable cost from the photovoltaic string to the inverter and the AC cable cost from the inverter to the box transformer. Result output module: used to output the target equipment location combination, which includes the target inverter location and the target box transformer location.

[0068] Since these systems and the principles they address are similar to the aforementioned photovoltaic field equipment positioning methods, the implementation of these systems can refer to the implementation of the aforementioned methods, and the repetitions will not be repeated.

[0069] Example 3: Based on the same inventive concept, the present invention also provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; When the processor executes the program stored in the memory, it is able to implement the photovoltaic field equipment positioning method as described in any one of Embodiment 1.

[0070] like Figure 7 As shown, the electronic device may include: a processor 10, a communication interface 20, a memory 30, and a communication bus 40, wherein the processor 10, the communication interface 20, and the memory 30 communicate with each other via the communication bus 40. The processor 10 can call logical instructions in the memory 30 to execute a photovoltaic field equipment positioning method, which includes: S1. Obtain the coordinate point data of all photovoltaic strings in the photovoltaic field, determine the number of inverters and the capacity parameters of the box-type transformer; and obtain the unit price of DC cable and AC cable. S2. Based on the coordinate point data, a clustering algorithm is used to divide the photovoltaic string into several photovoltaic array regions, and the centroid of each region is determined; the centroid is the candidate position of the inverter corresponding to each photovoltaic array region. S3. Using the unit price of the DC cable and the unit price of the AC cable as weights, the candidate locations of each inverter and the candidate locations of the box transformer are combined and calculated using an optimization algorithm. The target equipment location combination that minimizes the total cable cost is determined from multiple equipment location combinations. The total cable cost includes the cost of the DC cable from the photovoltaic string to the inverter and the cost of the AC cable from the inverter to the box transformer. S4. Output the target equipment location combination, which includes the target inverter location and the target box transformer location.

[0071] Example 4: This invention also provides a computer-readable storage medium, in which a program stored is used to execute the photovoltaic field equipment positioning method of Embodiment 1 above, and the program can be executed on a processor.

[0072] Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0073] The program stored on this medium is loaded into the processor's memory and executed to perform various functions. This storage medium, connected to hardware devices, enables the computer to perform the steps of Embodiment 1 described above.

[0074] 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 apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0075] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for locating equipment in a photovoltaic field, characterized in that, Includes the following steps: S1. Obtain the coordinate point data of all photovoltaic strings in the photovoltaic field, determine the number of inverters and the capacity parameters of the box-type transformer; and obtain the unit price of DC cable and AC cable. S2. Based on the coordinate point data, a clustering algorithm is used to divide the photovoltaic string into several photovoltaic array regions, and the centroid of each region is determined. The centroid point is the candidate inverter position corresponding to each of the photovoltaic array regions; S3. Using the unit price of the DC cable and the unit price of the AC cable as weights, the candidate locations of each inverter and the candidate locations of the box transformer are combined and calculated using an optimization algorithm. The target equipment location combination that minimizes the total cable cost is determined from multiple equipment location combinations. The total cable cost includes the cost of the DC cable from the photovoltaic string to the inverter and the cost of the AC cable from the inverter to the box transformer. S4. Output the target equipment location combination, which includes the target inverter location and the target box transformer location.

2. The method as described in claim 1, characterized in that, In step S2, a clustering algorithm is used to divide the photovoltaic string into several photovoltaic array regions, and the centroid of each region is determined; this includes the following steps: S21. Determine the required number of clusters based on the number of inverters, and initialize the centroid of each cluster. S22. Calculate the distance from the coordinate point of each photovoltaic string to each centroid point, and assign it to the cluster corresponding to the nearest centroid point; S23. Update the centroid of each cluster based on the coordinates of all photovoltaic strings in each cluster, and repeat steps S22 and S23 until the preset iteration stop condition is met.

3. The method as described in claim 2, characterized in that, In step S21, the formula for calculating the centroid is: in, Let j be the cluster center, j be the j-th cluster, and (t+1) be the (t+1)-th iteration number; This is the t-th data point set of the j-th cluster; Let i be the i-th data point.

4. The method as described in claim 2, characterized in that, In step S2, a clustering algorithm is used to divide the photovoltaic string into several photovoltaic array regions, and the centroid of each region is determined; it also includes: S24. Visualize the coordinates of the photovoltaic strings; based on the clustering iteration results, convert the coordinates into a photovoltaic matrix according to the actual size of the photovoltaic strings, and generate a region division map of the photovoltaic array according to the final iteration version.

5. The method as described in claim 1, characterized in that, The optimization algorithm in step S3 is an exhaustive search method.

6. The method as described in claim 1, characterized in that, In step S3, the total cost of the cable is calculated as follows: Let point A be the location of the photovoltaic array, point B be the location of the inverter, and point C be the location of the box-type transformer; S31. Calculate the cable length between photovoltaic array point A and inverter location point B using Manhattan distance: The cable length between inverter location point B and box-type transformer location point C is calculated using Manhattan distance: Where, x A Let be the coordinates of photovoltaic array A on the x-axis, and y be the coordinates of the array A on the x-axis. A Let x be the coordinate position of photovoltaic array A on the y-axis; B Let be the x-coordinate of inverter point B on the x-axis, and y be the y-coordinate of the inverter point B on the x-axis. B Here is the coordinate position of inverter point B on the y-axis; x C Let x be the coordinate position of the box-type transformer C on the x-axis. C Let C be the coordinate position of the box-type transformer C on the x-axis; S32. Calculate the total cost of the cable connecting photovoltaic array point A and inverter location point B: Calculate the total cable cost between inverter location point B and box-type transformer location point C: in, For DC cable prices, Price of AC cable; S33. Determine the minimum total cost of the cable: Where K is the number of photovoltaic strings connected to a single inverter, x A,k y A,k Let x be the coordinates of the k-th photovoltaic string; B y B x represents the planar coordinates of a single inverter; N represents the number of inverters connected to a single box-type transformer. B,n y B,n Let x be the coordinates of the nth inverter group. C y C Here are the plane coordinates of the box-type transformer.

7. The method as described in claim 1, characterized in that, In step S4, the output information also includes: photovoltaic array partitioning results, DC and AC cable paths and total lengths.

8. A photovoltaic field equipment positioning system, characterized in that, The system comprises: Data preparation module: used to obtain the coordinate point data of all photovoltaic strings in the photovoltaic field, determine the number of inverters and the capacity parameters of the box transformer; and obtain the unit price of DC cable and AC cable; Photovoltaic array partitioning module: used to divide the photovoltaic string into several photovoltaic array regions based on the coordinate point data using a clustering algorithm, and determine the centroid of each region; the centroid is the candidate inverter position corresponding to each photovoltaic array region; Equipment location optimization module: It is used to combine and calculate the candidate locations of each inverter and the candidate locations of the box transformer by using the unit price of the DC cable and the unit price of the AC cable as weights and through optimization algorithm, and determine the target equipment location combination that minimizes the total cable cost from multiple equipment location combinations. The total cable cost includes the DC cable cost from the photovoltaic string to the inverter and the AC cable cost from the inverter to the box transformer. Result output module: used to output the target equipment location combination, which includes the target inverter location and the target box transformer location.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the photovoltaic field equipment positioning method as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the photovoltaic field equipment positioning method as described in any one of claims 1 to 7.

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