Distributed photovoltaic meteorological resource real-time monitoring station site selection method and system

Abstract

The invention discloses a distributed photovoltaic meteorological resource real-time monitoring station group site selection method and system, and the method comprises the steps: 1, obtaining photovoltaic historical resource data of a target region, and constructing a historical data set; 2, based on the historical data set, analyzing space-time distribution characteristics of distributed photovoltaic radiation resources in a target area, and dividing clusters with consistent radiation resource changes; 3, respectively selecting an optimal center point of each cluster; and step 4, performing micro-region site selection based on each cluster center point to determine an optimal site establishment position. According to the method, the existing distributed layout condition is considered, the future distributed development planning is also considered, and the method has the characteristics of full coverage of regional monitoring information, high representativeness of monitoring stations, scientific and reasonable site selection and excellent economical efficiency of a monitoring layout scheme.

Description

Technical Field

[0001] The present invention belongs to the technical field of new energy power generation, and in particular relates to a site selection method and system for a distributed photovoltaic meteorological resource real-time monitoring station group. Background Art

[0002] As distributed photovoltaic installed capacity grows, its output has an increasing impact on the power grid. Because distributed photovoltaic sites are numerous and widespread, individual monitoring is difficult. Therefore, reliable, efficient, and cost-effective regional distributed photovoltaic meteorological resource monitoring is urgently needed. Currently, distributed photovoltaic regional meteorological resource monitoring has numerous blind spots, and a mature monitoring layout plan has yet to be established. For example:

[0003] 1. Chinese patent application number CN202211117492.3 proposes a photovoltaic base site selection method that takes into account the distribution characteristics of solar energy resources. This method selects original meteorological parameters related to solar energy resource assessment, establishes a solar radiation conversion model based on horizontal total solar radiation, and uses slope total solar radiation, temperature at a height of 2 meters from the ground, and wind speed at a height of 10 meters measured by meteorological satellites to construct a solar resource power generation power model. Then, a photovoltaic power generation potential assessment system is established based on photovoltaic AC output power. Infeasible locations are eliminated, and locations with photovoltaic power capacity coefficients exceeding a set threshold are selected to determine the final photovoltaic base site selection plan. However, this method is only suitable for power station site selection and not for meteorological monitoring station site selection. This is because the data used in this method does not take into account the regional consistency of meteorological resources. Its meteorological station site selection suffers from problems such as duplicate site construction, excessive site selection, and poor economic efficiency.

[0004] 2. Chinese patent application number CN201910029741.5 proposes a method for selecting meteorological monitoring points based on photovoltaic power generation output clustering. It uses cluster analysis to divide user photovoltaics under each weather type by region, analyzes the output consistency of different regions, optimizes the clustering location of user photovoltaics, calculates the correlation coefficient between each photovoltaic power station in the region and the regional photovoltaic output, selects the power station with a correlation greater than the set value as the representative power station in the region, and selects the location of the most superior representative power station as the meteorological monitoring point. However, this method obtains station construction information based on the output data clustering of existing power stations, does not take into account areas where no stations have been built, and has blind spots in site selection and layout, resulting in missed selection issues.

[0005] 3. Chinese patent application number CN202311617863.9 proposes a regional resource monitoring layout method and system for distributed photovoltaics. The method includes constructing a distributed photovoltaic region containing several grids based on the longitude and latitude information of the distributed photovoltaic region; extracting multi-source information from each grid within the distributed photovoltaic region using interpolation to obtain quantitative information for each grid; classifying the quantitative information of each grid using weight allocation and feature clustering methods based on the quantitative information of each grid to obtain several types of regions; and determining the optimal monitoring points within each type of region based on the distributed photovoltaic location and installed capacity information within each type of region. However, this method also obtains station construction information by clustering the output data of existing power stations, which is prone to layout blind spots and omissions.

[0006] In summary, existing monitoring site deployment schemes are overly simplistic, often relying on empirical or indirect methods to infer weather monitoring stations based on power plant information. Some methods focus solely on site selection for existing distributed systems, failing to fully consider the inherent characteristics of weather monitoring information, the development and planning characteristics of distributed power generation, and the cost-effectiveness of monitoring sites. Therefore, current distributed PV regional weather resource monitoring schemes require further optimization. Summary of the Invention

[0007] In order to address the deficiencies in the prior art, the present invention provides a method and system for site selection of a distributed photovoltaic meteorological resource real-time monitoring station group. By fully considering the consistency of local meteorological resources and the spatiotemporal distribution characteristics of distributed photovoltaics, it not only takes into account the existing distributed layout, but also takes into account future distributed development plans. It has the characteristics of full coverage of regional monitoring information, strong representativeness of monitoring sites, scientific and reasonable site selection, and excellent economic efficiency of monitoring layout plans.

[0008] The present invention adopts the following technical solutions.

[0009] In a first aspect, the present invention provides a method for site selection of a distributed photovoltaic meteorological resource real-time monitoring station group, characterized in that the method comprises:

[0010] Step 1: Collect historical photovoltaic resource data in the target area and build a historical data set;

[0011] Step 2: Based on the historical data set, analyze the spatiotemporal distribution characteristics of distributed photovoltaic radiation resources in the target area and divide the clusters with consistent radiation resource changes;

[0012] Step 3: Select the best center point of each cluster;

[0013] Step 4: Perform micro-region site selection based on the center points of each cluster to determine the optimal site location.

[0014] Optionally, in step 1, the historical resource data obtained includes historical numerical weather forecast data and historical meteorological resource observation data within a set time.

[0015] Optionally, in step 1, the steps of constructing the historical dataset include:

[0016] Based on the missing and missed measurements of historical photovoltaic resource data, the abnormal data periods are extracted, and the historical numerical weather forecast data are used to improve the abnormal data periods to form a complete historical meteorological data matrix.

[0017] Optionally, in step 2, the step of dividing clusters with consistent radiation resource changes includes:

[0018] Step 2.1: Process the historical meteorological data matrix into a standardized anomaly matrix;

[0019] Step 2.2: Separate the normalized anomaly matrix into time field and space field by empirical orthogonal function decomposition (EOF);

[0020] Step 2.3: Calculate the contribution rate of eigenvector variance and cumulative variance contribution rate based on the eigenvalues;

[0021] Step 2.4: According to the variance contribution rate Screen the significant modes and select the main mode with the largest variance contribution; where N is the number of samples;

[0022] Step 2.5: Perform the rotational empirical orthogonal function decomposition REOF on each selected main mode, calculate the standard deviation σ of the spatial field, and divide the clusters with consistent irradiance changes based on the standard of greater than (less than) ±σ.

[0023] Optionally, in step 2.1, the expression of the standardized deviation matrix is ​​as follows:

[0024]

[0025] Where, X represents the historical meteorological data matrix; x mn represents the observation value of the mth meteorological observation station in the nth period; n represents the total observation period, m represents the total number of meteorological observation stations; M represents the mean matrix of the historical meteorological data matrix X; m i Represents the data value of the i-th row in the mean matrix M, i = 1, 2…m; where m i The calculation formula is as follows:

[0026]

[0027] Where x ij represents the observation value of the i-th meteorological observation station in the j-th period.

[0028] Optionally, step 3 includes: using a fuzzy c-means FCM clustering algorithm to select the best center point of each cluster.

[0029] Optionally, in step 4, the optimal station location determined meets the following constraints: the surrounding environment of the meteorological resource monitoring station should satisfy the requirement that no obstacles are projected onto the light-receiving surface of the monitoring instrument, and there is no interference from artificial radiation sources; there are no obstacles such as tall buildings and trees, and the distance to a single obstacle should be greater than 3 times the height of the obstacle, and the distance to a row of obstacles should be greater than 10 times the maximum height of the obstacle.

[0030] In a second aspect, the present invention provides a distributed photovoltaic meteorological resource real-time monitoring station cluster site selection system, which runs the steps of any method described in the first aspect of the present invention, and the system includes:

[0031] The acquisition module is used to collect historical photovoltaic resource data in the target area and build a historical data set;

[0032] a partitioning module for analyzing the spatiotemporal distribution characteristics of distributed photovoltaic radiation resources in a target area based on the historical data set, and partitioning clusters with consistent radiation resource changes;

[0033] The selection module is used to select the best center point of each cluster;

[0034] The site selection module is used to select micro-area sites based on the center points of each cluster to determine the optimal site location.

[0035] In a third aspect, the present invention provides a terminal including a processor and a storage medium;

[0036] The storage medium is used to store instructions;

[0037] The processor is configured to operate according to the instructions to execute the steps of any one of the methods described in the first aspect of the present invention.

[0038] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any one of the methods described in the first aspect of the present invention.

[0039] The beneficial effect of the present invention is that, compared with the prior art,

[0040] Compared with other existing technical methods, the present invention first has more complete basic data, including historical numerical weather forecast data, historical meteorological resource observation data, and analyzes the missing and missed measurements of historical meteorological resource observation data, extracts abnormal data periods, and uses historical numerical weather forecast data to improve abnormal data periods to form a complete historical data matrix. Then the site selection is scientific and representative. Based on the historical data matrix of the target area, the distribution characteristics and fluctuation characteristics of meteorological resources such as irradiance are fully considered to perform cluster division, taking into account distributed planning and development, and can effectively achieve full coverage monitoring of regional distributed photovoltaics. Each cluster is clustered to find the optimal scientific representative point. With the center point as a reference, combined with reality, considering the site selection principles such as terrain, landform, and communication, it can effectively ensure that it is the best monitoring point. This method fully considers the consistency of local meteorological resources and the spatiotemporal distribution characteristics of distributed photovoltaics. It takes into account both the existing distributed layout and the future distributed development plan. It has the characteristics of full coverage of regional monitoring information, strong representativeness of monitoring sites, scientific methods, and excellent economic efficiency of monitoring layout plans. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 A schematic diagram of the process of site selection for a distributed photovoltaic meteorological resource real-time monitoring station group in the present invention;

[0042] Figure 2 This is a schematic diagram of the Ningxia regional photovoltaic resource monitoring network construction in the present invention;

[0043] Figure 3 This is a schematic diagram of the layout of Class A photovoltaic resource monitoring stations in Ningxia region in the present invention;

[0044] Figure 4 This is a schematic diagram of the B-level photovoltaic resource monitoring station layout in Ningxia region in the present invention;

[0045] Figure 5 This is a structural principle block diagram of the distributed photovoltaic meteorological resource real-time monitoring station group site selection system of the present invention. DETAILED DESCRIPTION

[0046] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. The embodiments described in the present invention are only part of the embodiments of the present invention, not all of the embodiments. Based on the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without making any creative efforts are all within the scope of protection of the present invention.

[0047] Example 1:

[0048] Reference Figure 1The embodiment of the present invention provides a method for selecting a distributed photovoltaic meteorological resource real-time monitoring station group, which specifically includes the following steps:

[0049] Step 1: Collect historical photovoltaic resource data in the target area and build a historical data set;

[0050] In this embodiment, historical photovoltaic resource data for the target area over the past 10 years is collected, quality control is performed on the data, and a historical data matrix is ​​constructed; the historical resource data includes historical numerical weather forecast data and historical meteorological resource observation data; based on the missing or omission of historical meteorological resource observation data, abnormal data periods are extracted, and the abnormal data periods are supplemented using historical numerical weather forecast data to form a complete historical meteorological data matrix.

[0051] Step 2: Based on the historical data set, analyze the spatiotemporal distribution characteristics of distributed photovoltaic radiation resources in the target area and divide them into clusters with consistent radiation resource changes. The specific steps include:

[0052] Step 2.1: Process the historical meteorological data matrix into a standardized anomaly matrix;

[0053] Step 2.2: Separate the normalized anomaly matrix into time field and space field by empirical orthogonal function decomposition (EOF);

[0054] Step 2.3: Calculate the contribution rate of eigenvector variance and cumulative variance contribution rate based on the eigenvalues;

[0055] Step 2.4: According to the variance contribution rate (N is the number of samples) Screen the significant modes and select the main mode with the largest variance contribution;

[0056] Step 2.5: Perform the rotational empirical orthogonal function decomposition REOF on each selected main mode, calculate the standard deviation σ of the spatial field, and divide the clusters with consistent irradiance changes based on the standard of greater than (less than) ±σ.

[0057] As an embodiment of the present invention, in step 2, the spatiotemporal distribution characteristics of the radiation resources in the distributed photovoltaic area are analyzed based on EOF; the historical data matrix is ​​processed into standardized anomalies, and the time field and the space field are separated by empirical orthogonal function decomposition (EOF), the spatiotemporal variation characteristics of the irradiance in the target area are analyzed, the main mode with the largest variance contribution is selected, and then the rotated empirical orthogonal function decomposition (REOF) is performed to divide the cluster areas with relatively consistent irradiance changes.

[0058] The EOF method transforms interrelated spatial data into a new coordinate system. The coordinate axes in the new coordinate system are linear combinations of the original data, and are independent of each other (i.e., orthogonal) and have certain physical meanings. This method has been widely used in meteorology, oceanography, geography, etc. It has the following characteristics: (1) Interrelated data are decomposed into independent patterns, each of which can be analyzed separately; (2) The largest independent variables can be sorted by their size, that is, the eigenvalues ​​and eigenvectors are sorted. This sorting reflects the relative importance of different patterns. The EOF method is described as follows:

[0059] First, based on the original information and data, the following historical data matrix can be obtained:

[0060]

[0061] Where X represents the historical meteorological data matrix, x mn represents the observation value of the mth meteorological observation station in the nth period; n represents the total observation period, m represents the total number of meteorological observation stations; the time resolution is one week. The purpose of OF decomposition is to decompose X into a spatial matrix S and a corresponding time coefficient matrix T, that is, X = ST. The specific algorithm is as follows:

[0062] ① Data preprocessing: Subtract the mean matrix from the matrix X to form a square matrix

[0063]

[0064] Where, X represents the historical meteorological data matrix; x mn represents the observation value of the mth meteorological observation station in the nth period; n represents the total observation period, m represents the total number of meteorological observation stations; M represents the mean matrix of the historical meteorological data matrix X; m i Represents the data value of the i-th row in the mean matrix M, i = 1, 2…m; where m i The calculation formula is as follows:

[0065]

[0066] Where xij represents the observation value of the i-th meteorological observation station in the j-th period.

[0067] ②Calculate the covariance matrix: (X T is the rotation rank of X) From matrix theory, we know that C is a symmetric matrix.

[0068] ③Calculate the eigenvalues ​​of C (λ1,λ2,...,λ m ) and eigenvector V: satisfies CV=VΛ, where:

[0069]

[0070] The matrix V is an orthogonal matrix, and the j-th column element of the matrix V is the eigenvalue λ j The corresponding eigenvector.

[0071] ④ Calculate the principal components: Calculate the eigenvector variance contribution rate and the cumulative variance contribution rate based on the eigenvalues. Generally, the eigenvalues ​​λ1, λ2, ..., λ1 whose cumulative variance contribution rate reaches 85-95% are taken. p The corresponding first, second, ..., pth (p≤m) eigenvectors are the main modes.

[0072] REOF can reduce spatially correlated regions to a few, allowing for objective identification of spatial patterns. Each spatial point in the spatial field has a variable that is highly correlated with only one principal component, so REOF allows for objective clustering.

[0073] Step 3: Select the best center point of each cluster;

[0074] Step 3: The optimal center selection method for each cluster first determines the objective function of the cluster center selection algorithm.

[0075] Divide n vectors xi (i = 1, 2, ..., n) into c groups and find the cluster center of each group so that the value function of the dissimilarity index is minimized. Each given data point uses a membership degree between 0 and 1 to determine the degree to which it belongs to each group. The membership matrix u is allowed to have elements with values ​​between 0 and 1. Combined with the normalization requirement, the sum of the membership degrees of a data set is always equal to 1:

[0076]

[0077] Then, the value function (or objective function) of the clustering algorithm is:

[0078]

[0079] Here uij is between 0 and 1; ci is the cluster center of fuzzy group i, dij = ||ci-xj|| is the Euclidean distance between the i-th cluster center and the j-th data point; and m∈[1,∞) is a weighted index.

[0080] By constructing the following new objective function, we can find the necessary conditions for formula (7.2) to reach the minimum value:

[0081]

[0082] Here λj, j = 1 to n, is the Lagrange multiplier of the n constraints in equation (7.1). The necessary condition for minimizing equation (7.2) by taking the derivative of all input parameters is:

[0083]

[0084] and

[0085]

[0086] Given the two necessary conditions above, the clustering algorithm is a simple iterative process. When running in batch mode, the following steps are used to determine the cluster centers ci and the membership matrix U[1]:

[0087] Step 1: Initialize the membership matrix U with random numbers between 0 and 1 so that it satisfies the constraints in formula (7.1);

[0088] Step 2: Select the initial cluster centers. For n vectors xi (i = 1, 2, ..., n), standardize the same meteorological element for each vector to obtain n standardized vectors xxi. Calculate the length of each vector in the Euclidean space formed by their combination. Sort the vector group xx by length from smallest to largest. If the vector with the smallest length is xxk, then use vector xk as the first cluster center. Similarly, use the vector with the largest length as the last cluster center. Evenly distribute the remaining cluster centers according to vector length to obtain the initial cluster centers.

[0089] Step 3: Use formula (7.4) to calculate c cluster centers ci, i = 1,…,c.

[0090] Step 4: Calculate the value function according to formula (7.2). If it is less than a certain threshold, or the change in the value function value relative to the last time is less than a certain threshold, the algorithm stops.

[0091] Step 5: Use (7.5) to calculate the new U matrix. Return to step 3.

[0092] Step 4: Perform micro-region site selection based on the center points of each cluster to determine the optimal site location.

[0093] The surrounding environment of the meteorological resource monitoring station should meet the requirements of no obstacles projecting onto the light-receiving surface of the monitoring instrument, and no interference from artificial radiation sources; there should be no obstacles such as tall buildings and trees, and the distance to a single obstacle should be greater than 3 times the height of the obstacle, and the distance to a row of obstacles should be greater than 10 times the maximum height of the obstacle.

[0094] In summary, this paper proposes a downscaling-based site selection method for distributed photovoltaic meteorological resource real-time monitoring stations. By constructing a regionally refined meteorological dataset, analyzing the spatiotemporal distribution characteristics of distributed photovoltaic regional radiation resources, and dividing them into clusters with consistent radiation resource changes, this method addresses the problem that the traditional fuzzy c-means (FCM) clustering algorithm is extremely dependent on the selection of an initial center point. A fuzzy c-means clustering algorithm based on initial representative points is used to achieve stable cluster center point selection. Finally, using this center point as a reference, the monitoring station layout is selected in combination with the actual topography and communication conditions.

[0095] Take Ningxia, for example. The region boasts a high altitude, little rain, good atmospheric transparency, high radiation intensity, and long sunshine hours. According to the national solar resource classification (categorized as rich, relatively rich, average, and poor), Ningxia is among the regions with relatively abundant solar energy resources, joining Tibet, Qinghai, Xinjiang, and Gansu. The average annual total solar radiation is 4,800 to 5,700 MJ / m², with 2,250 to 3,100 hours of sunshine per year, making it one of the regions with the most abundant sunshine resources in my country. The average annual solar radiation is higher in the north than in the south, increasing from 4,947.3 MJ / m² in the Liupan Mountains in the south to 6,000 MJ / m² in the north-central region. Solar radiation resources vary significantly across the region, with the northern region receiving more than the southern, with a north-south difference exceeding 1,000 MJ / m². Lingwu and Tongxin are relatively high-value centers, exceeding 6,100 MJ / m². Solar energy is highest and more evenly distributed in the Yellow River Irrigation Area and in Yanchi and Tongxin, located in the central arid zone, with annual values ​​ranging from 5,864 to 6,100 MJ / m². This makes Guyuan a region rich in solar energy and one of my country's high-energy solar radiation areas. Annual radiation is relatively low in Guyuan, with total solar radiation ranging from 4,947 to 5,641 MJ / m². Regional variations are also evident, decreasing from north to south, with annual total radiation decreasing by 50 MJ / m² for every 10 km southward.

[0096] Based on the Ningxia regional photovoltaic information and the above-mentioned site selection method, after theoretical calculations, the collaborative networking of two types of monitoring stations of different levels, 21 sets of Class A stations (collection of full-factor information, including cloud map collection and advanced intelligent analysis functions) and 125 sets of Class B stations (collection of key factor information such as radiation, temperature, and wind speed), can efficiently achieve full coverage of regional photovoltaic resource monitoring information.

[0097] The beneficial effect of the present invention is that, compared with the prior art,

[0098] Compared with other existing technical methods, the present invention first has more complete basic data, including historical numerical weather forecast data, historical meteorological resource observation data, and analyzes the missing and missed measurements of historical meteorological resource observation data, extracts abnormal data periods, and uses historical numerical weather forecast data to improve abnormal data periods to form a complete historical data matrix. Then the site selection is scientific and representative. Based on the historical data matrix of the target area, the distribution characteristics and fluctuation characteristics of meteorological resources such as irradiance are fully considered to perform cluster division, taking into account distributed planning and development, and can effectively achieve full coverage monitoring of regional distributed photovoltaics. Each cluster is clustered to find the optimal scientific representative point. With the center point as a reference, combined with reality, considering the site selection principles such as terrain, landform, and communication, it can effectively ensure that it is the best monitoring point. This method fully considers the consistency of local meteorological resources and the spatiotemporal distribution characteristics of distributed photovoltaics. It takes into account both the existing distributed layout and the future distributed development plan. It has the characteristics of full coverage of regional monitoring information, strong representativeness of monitoring sites, scientific methods, and excellent economic efficiency of monitoring layout plans.

[0099] Example 2:

[0100] like Figure 5 As shown, the present invention provides a distributed photovoltaic meteorological resource real-time monitoring station group site selection system, which is used to implement the steps of the method in the above embodiment 1, and specifically includes:

[0101] The acquisition module is used to collect historical photovoltaic resource data in the target area and build a historical data set;

[0102] a partitioning module for analyzing the spatiotemporal distribution characteristics of distributed photovoltaic radiation resources in a target area based on the historical data set, and partitioning clusters with consistent radiation resource changes;

[0103] The selection module is used to select the best center point of each cluster;

[0104] The site selection module is used to select micro-area sites based on the center points of each cluster to determine the optimal site location.

[0105] The distributed photovoltaic meteorological resource real-time monitoring station group site selection system provided in the embodiment of the present invention and the distributed photovoltaic meteorological resource real-time monitoring station group site selection method provided in Example 1 are based on the same technical concept and can produce the beneficial effects described in Example 1. For the contents not described in detail in this embodiment, please refer to Example 1.

[0106] Example 3:

[0107] An embodiment of the present invention provides a terminal including a processor and a storage medium;

[0108] The storage medium is used to store instructions;

[0109] The processor is configured to operate according to the instruction to execute the steps of the method according to any one of the first embodiments.

[0110] Example 4:

[0111] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the steps of the method described in any one of the first embodiments are implemented.

[0112] The present invention may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present invention.

[0113] A computer-readable storage medium can be a tangible device that can hold and store instructions for use by an instruction execution device. A computer-readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination thereof. As used herein, a computer-readable storage medium is not to be construed as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through an electrical wire.

[0114] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions to be stored in the computer-readable storage medium in each computing / processing device.

[0115] The computer program instructions for performing the operation of the present invention can be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, and conventional procedural programming languages ​​such as "C" language or similar programming languages. The computer readable program instructions can be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), is personalized by utilizing the state information of the computer readable program instructions, and the electronic circuit can execute the computer readable program instructions, thereby realizing various aspects of the present invention.

[0116] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A method for site selection of distributed photovoltaic meteorological resource real-time monitoring station group, characterized in that: Methods include: Step 1: Collect historical photovoltaic resource data in the target area and build a historical data set; Step 2: Based on the historical data set, analyze the spatiotemporal distribution characteristics of distributed photovoltaic radiation resources in the target area, and divide clusters with consistent changes in radiation resources; Step 3: Select the best center point of each cluster; Step 4: Perform micro-region site selection based on the center points of each cluster to determine the optimal site location.

2. The method for selecting a distributed photovoltaic meteorological resource real-time monitoring station group according to claim 1, characterized in that: In step 1, the historical resource data obtained includes historical numerical weather forecast data and historical meteorological resource observation data within a set time.

3. The method for site selection of distributed photovoltaic meteorological resource real-time monitoring station group according to claim 2 is characterized in that: In step 1, the steps to construct the historical data set include: According to the missing and missed measurements of historical photovoltaic resource data, the abnormal data periods are extracted, and the historical numerical weather forecast data are used to improve the abnormal data periods to form a complete historical meteorological data matrix.

4. The method for site selection of distributed photovoltaic meteorological resource real-time monitoring station group according to claim 3 is characterized in that: In step 2, the step of dividing the clusters with consistent radiation resource changes includes: Step 2.1: Processing the historical meteorological data matrix into a standardized anomaly matrix; Step 2.2: Separate the normalized anomaly matrix into time field and space field by empirical orthogonal function decomposition (EOF); Step 2.3: Calculate the contribution rate of eigenvector variance and cumulative variance contribution rate according to the eigenvalues; Step 2.4: According to the variance contribution rate Screen the significant modes and select the main modes with larger variance contribution; where N is the number of samples; Step 2.5: Perform rotational empirical orthogonal function decomposition REOF on each selected main mode, calculate the standard deviation σ of the spatial field, and divide the clusters with consistent irradiance changes based on the standard of being greater than (less than) ±σ.

5. The method for site selection of distributed photovoltaic meteorological resource real-time monitoring station group according to claim 4 is characterized in that: In step 2.1, the expression of the standardized anomaly matrix is ​​as follows: Where X represents the historical meteorological data matrix; mn represents the observation value of the mth meteorological observation station in the nth period; n represents the total observation period, m represents the total number of meteorological observation stations; M represents the mean matrix of the historical meteorological data matrix X; m i Represents the data value of the i-th row in the mean matrix M, i = 1, 2…m; where m i The calculation formula is as follows: In the formula, x ij Represents the observation value of the i-th meteorological observation station in the j-period.

6. The method for site selection of distributed photovoltaic meteorological resource real-time monitoring station group according to claim 1, characterized in that: The step 3 includes: using the fuzzy c-means FCM clustering algorithm to select the best center point of each cluster.

7. The method for site selection of distributed photovoltaic meteorological resource real-time monitoring station group according to claim 1, characterized in that: In step 4, the optimal station location determined meets the following constraints: the surrounding environment of the meteorological resource monitoring station should satisfy the requirement that no obstacles are projected onto the light-receiving surface of the monitoring instrument, and there is no interference from artificial radiation sources; there are no obstacles such as tall buildings and trees, and the distance to a single obstacle should be greater than 3 times the height of the obstacle, and the distance to a row of obstacles should be greater than 10 times the maximum height of the obstacle.

8. A distributed photovoltaic meteorological resource real-time monitoring station group site selection system, running the distributed photovoltaic meteorological resource real-time monitoring station group site selection method according to any one of claims 1 to 7, characterized in that: The system includes: The acquisition module is used to collect the historical photovoltaic resource data of the target area and construct a historical data set; A partitioning module is used to analyze the spatiotemporal distribution characteristics of distributed photovoltaic radiation resources in the target area based on the historical data set, and partition the clusters with consistent changes in radiation resources; A selection module is used to select the best center point of each cluster; The site selection module is used to select micro-area sites based on the center points of each cluster to determine the optimal site location.

9. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

Images