Intelligent communication method and system for distributed photovoltaic power generation data

Abstract

The present invention relates to the field of photovoltaic power generation technology, and specifically to an intelligent communication method and system for distributed photovoltaic power generation data. The invention analyzes the environmental data of photovoltaic panels at each moment within the neighborhood range of each moment, clusters the data points in three-dimensional coordinates, and obtains multiple data clusters; obtains the state output power of each data cluster according to the output power of the photovoltaic panel at the corresponding moment of each data point in each data cluster; obtains the degree of operation change between adjacent moments according to the relative distance of the data points between adjacent moments and the difference in the state output power of the corresponding data cluster; and then obtains the power change coefficient of the photovoltaic panel at the next moment of the real time; and communicates the data intelligently. The present invention improves the effect of intelligent data communication by accurately analyzing the change of the output power of the photovoltaic panel at the next moment, determining the data that needs to be transmitted first.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic power generation, and in particular to an intelligent communication method and system for distributed photovoltaic power generation data. Background Art

[0002] As a flexible energy supply method, distributed power sources can better adapt to changes in energy demand and reduce dependence on traditional power generation. Due to the small-scale and decentralized characteristics of distributed power sources, they can better adapt to the intermittent and volatile nature of energy. In distributed photovoltaic power generation, photovoltaic panels are one of the important sources of electricity, and their performance directly affects the power generation capacity and stability of the entire system. Through intelligent communication methods for photovoltaic power generation-related data, real-time monitoring and analysis of the operating status of distributed photovoltaic power generation, dynamic adjustment and optimization can be carried out, thereby improving the stability and reliability of energy management.

[0003] In the prior art, the output power change threshold between the real time moment and the previous moment is obtained through experience to analyze the change in actual output power. If there is a large change, priority communication transmission is required. However, since changes in the external environment will affect the output power, the state changes of the external environment are not taken into account. The judgment of the output power change size only through the empirical threshold is inaccurate, and the intelligent communication effect is poor. Summary of the invention

[0004] In order to solve the technical problem that the output power change is inaccurately judged only by empirical threshold without considering the state change of the external environment, the purpose of the present invention is to provide an intelligent communication method and system for distributed photovoltaic power generation data. The technical solution adopted is as follows:

[0005] The present invention proposes an intelligent communication method for distributed photovoltaic power generation data, the method comprising:

[0006] Obtaining photovoltaic panel environmental data and photovoltaic panel output power of distributed photovoltaic power generation at each moment, wherein the photovoltaic panel environmental data includes temperature, humidity and light intensity, forming data points in three-dimensional coordinates;

[0007] In the neighborhood range of each moment, all data points in the three-dimensional coordinates are clustered according to the position distribution of the data points in the three-dimensional coordinates to obtain multiple data clusters at each moment; the state output power of each data cluster is obtained according to the output power of the photovoltaic panel at the corresponding moment of each data point in each data cluster; the degree of operation change between adjacent moments is obtained according to the relative distance of the data points between adjacent moments and the difference in the state output power of the data clusters between the corresponding moments;

[0008] According to the change of the output power of the photovoltaic panel between different adjacent moments within the neighborhood of the real time moment, and the degree of operation change between the corresponding adjacent moments, the power change coefficient of the photovoltaic panel at the next moment of the real time moment is obtained;

[0009] The data is intelligently communicated according to the power variation coefficient at the next moment.

[0010] Furthermore, the method for obtaining the state output power includes:

[0011] The average value of the photovoltaic panel output power at the corresponding time of all data points in each data cluster is obtained as the state output power of each data cluster.

[0012] Furthermore, the method for obtaining the degree of operation change includes:

[0013] Calculate the Euclidean distance of data points between adjacent moments as relative distance;

[0014] Obtain the difference in state output power of the data clusters where the data points are located between adjacent moments as the moment state difference;

[0015] The moment state difference and the relative distance between adjacent moments are merged to obtain the degree of operation change between adjacent moments.

[0016] Furthermore, the method for obtaining the power variation coefficient includes:

[0017] Obtain the difference in the photovoltaic panel output power between each moment and the previous adjacent moment as the moment power difference;

[0018] According to the power difference between each moment and the previous adjacent moment within the neighborhood of the real time moment, and the corresponding degree of operation change, the power change coefficient of the next moment of the real time moment is obtained.

[0019] Furthermore, the method for obtaining the power variation coefficient includes:

[0020] Calculate the product of the power difference between each moment and the previous adjacent moment within the neighborhood of the real time moment and the degree of operation change as the first product;

[0021] The first products between different moments within the neighborhood of the real time moment and the previous adjacent moment are accumulated, and positive correlation normalization mapping is performed, and the mapped value is used as the power change coefficient of the next moment of the real time moment.

[0022] Further, the intelligent communication of data according to the power change coefficient at the next moment includes:

[0023] If the absolute value of the power change coefficient at the next moment is greater than the preset change threshold, it is determined that the data at the real time moment is to be given priority in communication transmission.

[0024] Furthermore, the method for obtaining the data clustering clusters includes:

[0025] In the neighborhood range at each moment, according to the position distribution of the data points in the three-dimensional coordinates, DBSCAN clustering is performed on all the data points in the three-dimensional coordinates to obtain multiple data clustering clusters.

[0026] Furthermore, the preset change threshold is set to 0.8.

[0027] Furthermore, the method for obtaining the neighborhood range at each moment includes:

[0028] Taking each moment as the benchmark, the range formed by all historical moments is taken as the neighborhood range of each moment.

[0029] The present invention also proposes an intelligent communication system for distributed photovoltaic power generation data, comprising a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements any one of the steps of the intelligent communication method for distributed photovoltaic power generation data.

[0030] The present invention has the following beneficial effects:

[0031] The present invention takes into account that the operating state of the photovoltaic panel is affected by different environmental states. In the neighborhood range of each moment, all data points in the three-dimensional coordinates are clustered according to the position distribution of the data points in the three-dimensional coordinates to obtain multiple data clustering clusters, and similar environmental states are classified into one group to reflect the situations of different environmental states; according to the photovoltaic panel output power at the corresponding moment of each data point in each data clustering cluster, the state output power of each data clustering cluster is obtained to reflect the influence of the environmental state represented by the data clustering cluster on the energy output of the photovoltaic panel; according to the relative distance between the data points at adjacent moments and the difference in the state output power of the corresponding data clustering cluster, the state output power of each data clustering cluster is obtained. The degree of operation change between adjacent moments, combined with the relative distance and output power difference, can more comprehensively evaluate the change in the operating state of the system between adjacent moments, reflecting the direct change in physical quantities and the potential transformation of the internal state of the system; according to the change in the output power of the photovoltaic panel between different adjacent moments within the neighborhood of the real-time moment, and the degree of operation change between the corresponding adjacent moments, the power change coefficient of the photovoltaic panel at the next moment of the real-time moment is obtained, and the historical operation data and real-time status information are comprehensively considered to more accurately predict the power change of the photovoltaic panel at the future moment; the data is intelligently communicated to ensure the fast and accurate transmission of the data, while reducing the communication cost and energy consumption. The present invention improves the effect of data intelligent communication by accurately analyzing the change in the output power of the photovoltaic panel at the next moment, determining the data that needs to be transmitted first. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0033] Figure 1 A flow chart of an intelligent communication method for distributed photovoltaic power generation data provided by one embodiment of the present invention;

[0034] Figure 2 A flow chart of a method for obtaining the degree of operation variation provided by an embodiment of the present invention;

[0035] Figure 3 A schematic diagram of the distribution of data clusters where data points at adjacent moments are located is provided in one embodiment of the present invention. DETAILED DESCRIPTION

[0036] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the intelligent communication method and system for distributed photovoltaic power generation data proposed by the present invention, its specific implementation, structure, characteristics and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0037] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0038] The specific scheme of the intelligent communication method and system for distributed photovoltaic power generation data provided by the present invention is described in detail below with reference to the accompanying drawings.

[0039] See also Figure 1 , which shows a flow chart of an intelligent communication method for distributed photovoltaic power generation data provided by an embodiment of the present invention, and the specific method includes:

[0040] Step S1: Obtain the photovoltaic panel environmental data and photovoltaic panel output power of distributed photovoltaic power generation at each moment. The photovoltaic panel environmental data includes temperature, humidity and light intensity, which constitute data points in three-dimensional coordinates.

[0041] In order to more accurately communicate distributed photovoltaic power generation data intelligently, the changes in the external environment of the photovoltaic panel are considered for analysis. When analyzing the photovoltaic panel, various types of environmental influencing factors are included, which in turn affect the power generation efficiency of the photovoltaic panel. Since temperature, humidity and light intensity are interrelated and jointly affect the power generation efficiency of the photovoltaic panel, in the embodiment of the present invention, temperature, humidity and light intensity are used as environmental influencing factors of the photovoltaic panel. When the temperature, humidity and light intensity change, the power generation efficiency of the photovoltaic panel will also change accordingly; first, a photovoltaic environment monitor composed of a temperature sensor, a humidity sensor and a light intensity measuring instrument is placed around the photovoltaic panel to obtain the temperature, humidity and light intensity at each moment in the photovoltaic panel environment, forming data points in three-dimensional coordinates; and a power sensor is connected inside the photovoltaic panel to obtain the output power of the photovoltaic panel, and the photovoltaic panel environmental data and the photovoltaic panel output power at each moment are obtained. The photovoltaic panel environmental data includes temperature, humidity and light intensity, which constitute data points in three-dimensional coordinates, wherein the temperature, humidity and light intensity are different coordinate axis data in the three-dimensional coordinates respectively.

[0042] In one embodiment of the present invention, in order to facilitate the subsequent data processing, the photovoltaic panel environmental data and the photovoltaic panel output power are standardized to eliminate the dimension of the data so that indicators of different units or orders of magnitude can be comprehensively analyzed and compared. Standardization can adopt existing methods such as Z-score standardization and maximum and minimum standardization. The specific means are technical means well known to those skilled in the art and will not be described in detail here.

[0043] It should be noted that, in one embodiment of the present invention, the time interval is 5 seconds; in other embodiments of the present invention, the size of the time interval can be set according to specific circumstances, and is not limited or elaborated here.

[0044] Step S2: within the neighborhood range at each moment, cluster all data points in the three-dimensional coordinates according to the position distribution of the data points in the three-dimensional coordinates to obtain multiple data clusters at each moment; obtain the state output power of each data cluster according to the output power of the photovoltaic panel at the corresponding moment of each data point in each data cluster; obtain the degree of operation change between adjacent moments according to the relative distance of the data points between adjacent moments and the difference in the state output power of the corresponding data cluster.

[0045] The operating state of photovoltaic panels is affected by different environmental conditions. Through clustering algorithms, similar environmental conditions within the neighborhood can be classified into a group to reflect the conditions of different environmental conditions, which is convenient for targeted analysis. Therefore, within the neighborhood at each moment, all data points in the three-dimensional coordinates are clustered according to the position distribution of the data points in the three-dimensional coordinates to obtain multiple data clusters at each moment.

[0046] Preferably, in one embodiment of the present invention, the method for obtaining data clusters includes:

[0047] In the neighborhood range at each moment, according to the position distribution of the data points in the three-dimensional coordinates, DBSCAN clustering is performed on all the data points in the three-dimensional coordinates to obtain multiple data clusters at each moment.

[0048] The DBSCAN clustering algorithm can effectively identify high-density areas in data points and divide them into clusters, while eliminating the influence of noise points. It should be noted that the specific DBSCAN clustering is a technical means well known to those skilled in the art, and will not be described in detail here.

[0049] It should be noted that, in one embodiment of the present invention, the neighborhood range of each moment is based on each moment, and the range formed by the historical moments is used as the neighborhood range of each moment; in other embodiments of the present invention, the size of the neighborhood range is a technical means well known to those skilled in the art, and is not limited or elaborated here.

[0050] Different environmental conditions will have different effects on the output power of photovoltaic panels. For example, the stronger the light intensity, the more solar energy the photovoltaic components can capture, and the more electricity the photovoltaic panels generate. Within a certain range, rising temperatures will promote the power generation efficiency of photovoltaic panels, but too high a temperature will cause the output power of photovoltaic panels to decrease, affecting the amount of power generated. Weather with high humidity, such as rain or heavy snow, will cause solar radiation to weaken, thereby reducing the amount of power generated by photovoltaic panels. By analyzing the overall level of the output power of photovoltaic panels in each data cluster, it can be reflected that the environmental state represented by the data cluster has an impact on the energy output of photovoltaic panels. The greater the output power of photovoltaic panels at the corresponding moment of the data point in the data cluster, the greater the overall level, the greater the state output power, and the more conducive the environmental state is to the energy output of photovoltaic panels. Therefore, according to the output power of photovoltaic panels at the corresponding moment of each data point in each data cluster, the state output power of each data cluster is obtained.

[0051] Preferably, in one embodiment of the present invention, the method for obtaining the state output power includes:

[0052] The mean value of the photovoltaic panel output power at the corresponding time of all data points in each data cluster is obtained as the state output power of each data cluster.

[0053] In one embodiment of the present invention, the formula for obtaining the state output power is expressed as:

[0054] ;

[0055] in, Indicates The state output power of each data cluster; Indicates The first The output power of the photovoltaic panel at the time corresponding to the data point; Indicates The number of data points in a cluster.

[0056] In the formula for state output power, It means calculating the cumulative output power of the photovoltaic panels at the corresponding time of all data points in each data cluster. The larger the cumulative result, the greater the output power of the photovoltaic panels at the corresponding time of each data point, the greater the state output power, and the more favorable the environmental distribution of energy output.

[0057] Since different environmental states correspond to different operating states of photovoltaic panels, data points are grouped into clusters with similar characteristics through cluster analysis. Data points in the same cluster represent similar system states or behaviors. Therefore, the difference in the state output power of the corresponding data clustering cluster is analyzed to reflect the magnitude of the change in the environmental state. When the output power of the photovoltaic panel is greatly affected at the moment of mutation and for a period of time thereafter, the greater the environmental change, the greater the degree of operation change; the relative distance reflects the rate of change or trend of the data in the time dimension. The larger the distance between data points at adjacent moments, it means that the system state has changed significantly within the corresponding time; therefore, the degree of operation change between adjacent moments is obtained based on the relative distance of data points between adjacent moments and the difference in the state output power of the corresponding data clustering cluster.

[0058] Preferably, in one embodiment of the present invention, the method for obtaining the degree of operation change can be found in Figure 2 , which shows a flow chart of a method for obtaining the degree of operation variation, including:

[0059] Step S201: Calculate the Euclidean distance between data points at adjacent moments as the relative distance.

[0060] Among them, the larger the relative distance, the farther the data points between adjacent moments are, reflecting that the environmental state changes greatly between moments, which will have a greater impact on the output power of the photovoltaic panel.

[0061] It should be noted that the specific Euclidean distance is a technical means well known to those skilled in the art and will not be elaborated here.

[0062] Step S202: obtaining the difference in state output power of the data clusters where the data points are located between adjacent moments as the moment state difference.

[0063] It should be noted that since data points may be affected by different environmental conditions at different times, the clusters may also change. Therefore, for each moment, after obtaining the data points obtained from each detection of the photovoltaic panel, clustering is performed to analyze the data clusters in which the data points are located, so as to more intuitively understand the changes in environmental conditions between adjacent moments, such as Figure 3 , showing a distribution diagram of data clusters where data points at adjacent moments are located; by analyzing the difference in state output power of the data clusters where the data points are located, it reflects the state change of the photovoltaic panel within the corresponding time. The greater the difference, the greater the state change.

[0064] Step S203: The moment state difference and relative distance between adjacent moments are integrated to obtain the operation change degree between adjacent moments.

[0065] It should be noted that in some embodiments of the present invention, the moment state difference and relative distance between corresponding adjacent moments are fused by addition or multiplication to obtain the degree of operation change between corresponding adjacent moments; wherein, the greater the moment state difference and the greater the relative distance, the greater the corresponding environmental state change and the greater the degree of operation change.

[0066] In one embodiment of the present invention, the formula for the degree of operation variation is expressed as:

[0067] ;

[0068] in, Indicates Time and the degree of operational variability between moments; Indicates The state output power of the data cluster where the data point at the moment is located; Indicates The state output power of the data cluster where the data point at the moment is located; Indicates Time and The relative distance of data points between moments; Indicates taking the absolute value.

[0069] In the formula for the degree of running change, Indicates the calculation Moment and The difference in state output power of the data clusters where the data points are located between moments is called the moment state difference. The smaller the moment state difference, the closer the state output power of the data clusters where the data points are located between adjacent moments, which indicates that the external state influence on the data during monitoring is more consistent. The smaller the relative distance, the closer the data points are between adjacent moments, the closer their positions are, and the more similar the environmental states are. Therefore, the smaller the moment state difference, the smaller the relative distance, and the smaller the degree of operation change.

[0070] Step S3: According to the change of the photovoltaic panel output power between different adjacent moments within the neighborhood of the real time moment, and the degree of operation change between the corresponding adjacent moments, the power change coefficient of the photovoltaic panel at the next moment of the real time moment is obtained.

[0071] Since different environmental conditions often have a certain duration, the impact on the working status of the photovoltaic panel will also last for a period of time. When the operating conditions of the photovoltaic panel change significantly in a certain trend over a continuous period of time, the possibility of affecting the operating conditions of the photovoltaic panel in a subsequent short period of time is greater; when the environment changes suddenly, the output power of the photovoltaic panel also changes accordingly. The greater the change, the greater the power change coefficient at the next moment; therefore, according to the change in the output power of the photovoltaic panel between different adjacent moments within the neighborhood of the real-time moment, and the degree of operation change between the corresponding adjacent moments, the power change coefficient of the photovoltaic panel at the next moment of the real-time moment is obtained.

[0072] Preferably, in one embodiment of the present invention, the method for obtaining the power variation coefficient includes:

[0073] Obtain the difference in the photovoltaic panel output power between each moment and the previous adjacent moment as the moment power difference;

[0074] According to the power difference between each moment and the previous adjacent moment within the neighborhood of the real time moment, and the corresponding operation change degree, the power change coefficient of the next moment of the real time moment is obtained.

[0075] Among them, in one embodiment of the present invention, the power change coefficient of the next moment of the real-time moment is obtained, including: calculating the product of the moment power difference between each moment in the neighborhood range of the real-time moment and the previous adjacent moment and the degree of operation change as the first product; accumulating the first product between different moments in the neighborhood range of the real-time moment and the previous adjacent moment, and performing positive correlation normalization mapping, and the mapped value is used as the power change coefficient of the next moment of the real-time moment.

[0076] In one embodiment of the present invention, the formula of the power variation coefficient is expressed as:

[0077] ;

[0078] in, Indicates real time The power change coefficient at the next moment; Indicates Time and the degree of operational variability between moments; Indicates The photovoltaic panel output power at the moment; Indicates The photovoltaic panel output power at the moment; Indicates real time The number of moments in the neighborhood of ; represents the hyperbolic tangent function.

[0079] In the formula for the power variation coefficient, The function will Mapping is in the range -1 to 1; Indicates the calculation Time and The difference in the output power of the photovoltaic panels between the times, the greater the difference is than 0, the The greater the photovoltaic panel output power at the moment is than the The greater the positive change of the photovoltaic panel output power at the moment, the greater the power change coefficient, the smaller the difference is than 0, and the The smaller the photovoltaic panel output power at the moment is, the The more negative the change in the photovoltaic panel output power at the moment, the smaller the power change coefficient, the closer the difference is to 0, and the The closer the photovoltaic panel output power is to the The output power of the photovoltaic panel at the moment, the smaller the power change, the closer the power change coefficient is to 0; the power difference at the moment is used as the weight, and the degree of operation change between adjacent moments is weighted. The larger the power difference at the moment, the greater the degree of operation change, and the larger the power change coefficient.

[0080] It should be noted that the neighborhood range of the real-time moment is the time range consisting of the real-time moment and all historical moments.

[0081] Step S4: Intelligently communicate data according to the power change coefficient at the next moment.

[0082] The power change coefficient at the next moment reflects the degree and direction of change in the operation of the photovoltaic panel. Different changes can be analyzed to give priority to communication and transmission of data with larger changes to avoid energy waste of the photovoltaic panel. The data can be intelligently communicated based on the power change coefficient at the next moment.

[0083] Preferably, in one embodiment of the present invention, intelligently communicating data according to the power change coefficient at the next moment includes:

[0084] If the absolute value of the power change coefficient at the next moment is greater than the preset change threshold, it is determined that the data at the real time moment is to be given priority in communication transmission.

[0085] Among them, the real-time data includes the photovoltaic panel environmental data, photovoltaic panel output power and power change coefficient obtained in real time during the method processing; by giving priority to communication transmission of characteristic data whose changes are significantly different from normal conditions, the operating status of the system can be understood in a timely manner.

[0086] It should be noted that, in one embodiment of the present invention, the preset change threshold is set to 0.8; in other embodiments of the present invention, the size of the preset change threshold can be set according to specific circumstances, which is not limited or elaborated here.

[0087] It should be noted that in order to avoid waste of energy output of photovoltaic panels, in another embodiment of the present invention, the DS-FP algorithm is used to perform priority communication transmission on the data, and then the data is transmitted to the photovoltaic panel data processing system, and the load of the photovoltaic panel is adjusted, including: if the power change coefficient when priority communication transmission is required is a positive number, the output power of the photovoltaic panel has been increasing, and the load of the photovoltaic panel should increase; if the power change coefficient when priority communication transmission is required is a negative number, the output power of the photovoltaic panel has been decreasing, and the load of the photovoltaic panel should be reduced; by adjusting the load, it is ensured that the photovoltaic panel outputs higher energy efficiency during operation.

[0088] It should be noted that the DS-FP algorithm can process and transmit data immediately when the data is generated or updated, so that the data processing system can respond in a timely manner, significantly improving the real-time and reliability of data transmission; the specific DS-FP algorithm is a technical means well known to technical personnel in this field and will not be described in detail here.

[0089] In summary, the present invention analyzes a variety of environmental data of the photovoltaic panel at each moment to form data points in three-dimensional coordinates; in the neighborhood range at each moment, according to the position distribution of the data points in the three-dimensional coordinates, all data points in the three-dimensional coordinates are clustered to obtain multiple data clustering clusters; according to the output power of the photovoltaic panel at the corresponding moment of each data point in each data clustering cluster, the state output power of each data clustering cluster is obtained; according to the relative distance of the data points between adjacent moments and the difference in the state output power of the corresponding data clustering cluster, the degree of operation change between adjacent moments is obtained; and then the power change coefficient of the photovoltaic panel at the next moment of the real time is obtained; and the data is intelligently communicated. The present invention improves the effect of intelligent data communication by accurately analyzing the change of the output power of the photovoltaic panel at the next moment, determining the data that needs to be transmitted first.

[0090] The present invention also proposes an intelligent communication system for distributed photovoltaic power generation data, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements any step of an intelligent communication method for distributed photovoltaic power generation data.

[0091] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0092] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. An intelligent communication method for distributed photovoltaic power generation data, characterized in that: The method comprises: Obtaining photovoltaic panel environmental data and photovoltaic panel output power of distributed photovoltaic power generation at each moment, wherein the photovoltaic panel environmental data includes temperature, humidity and light intensity, forming data points in three-dimensional coordinates; In the neighborhood range of each moment, all data points in the three-dimensional coordinates are clustered according to the position distribution of the data points in the three-dimensional coordinates to obtain multiple data clusters at each moment; the state output power of each data cluster is obtained according to the output power of the photovoltaic panel at the corresponding moment of each data point in each data cluster; the degree of operation change between adjacent moments is obtained according to the relative distance of the data points between adjacent moments and the difference in the state output power of the data clusters between the corresponding moments; According to the change of the output power of the photovoltaic panel between different adjacent moments within the neighborhood of the real time moment, and the degree of operation change between the corresponding adjacent moments, the power change coefficient of the photovoltaic panel at the next moment of the real time moment is obtained; Intelligently communicate data based on the power change coefficient at the next moment; The intelligent communication of data according to the power change coefficient at the next moment includes: If the absolute value of the power change coefficient at the next moment is greater than the preset change threshold, it is determined that the data at the real time moment is to be given priority in communication transmission.

2. The intelligent communication method for distributed photovoltaic power generation data according to claim 1, characterized in that: The method for obtaining the state output power includes: The average value of the photovoltaic panel output power at the corresponding time of all data points in each data cluster is obtained as the state output power of each data cluster.

3. The intelligent communication method for distributed photovoltaic power generation data according to claim 1, characterized in that: The method for obtaining the degree of operation change includes: Calculate the Euclidean distance of data points between adjacent moments as relative distance; Obtain the difference in state output power of the data clusters where the data points are located between adjacent moments as the moment state difference; The moment state difference and the relative distance between adjacent moments are merged to obtain the degree of operation change between adjacent moments.

4. The intelligent communication method for distributed photovoltaic power generation data according to claim 1, characterized in that: The method for obtaining the power variation coefficient includes: Obtain the difference in the photovoltaic panel output power between each moment and the previous adjacent moment as the moment power difference; According to the power difference between each moment and the previous adjacent moment within the neighborhood of the real time moment, and the corresponding degree of operation change, the power change coefficient of the next moment of the real time moment is obtained.

5. The intelligent communication method for distributed photovoltaic power generation data according to claim 4, characterized in that: The method for obtaining the power variation coefficient includes: Calculate the product of the power difference between each moment and the previous adjacent moment within the neighborhood of the real time moment and the degree of operation change as the first product; The first products between different moments within the neighborhood of the real time moment and the previous adjacent moment are accumulated, and positive correlation normalization mapping is performed, and the mapped value is used as the power change coefficient of the next moment of the real time moment.

6. The intelligent communication method for distributed photovoltaic power generation data according to claim 1, characterized in that: The method for obtaining the data clustering clusters includes: In the neighborhood range at each moment, according to the position distribution of the data points in the three-dimensional coordinates, DBSCAN clustering is performed on all the data points in the three-dimensional coordinates to obtain multiple data clusters at each moment.

7. The intelligent communication method for distributed photovoltaic power generation data according to claim 1, characterized in that: The preset change threshold is set to 0.

8.

8. The intelligent communication method for distributed photovoltaic power generation data according to claim 1, characterized in that: The method for obtaining the neighborhood range at each moment includes: Taking each moment as the benchmark, the range formed by all historical moments is taken as the neighborhood range of each moment.

9. An intelligent communication system for distributed photovoltaic power generation data, the system comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of an intelligent communication method for distributed photovoltaic power generation data as described in any one of claims 1 to 8 are implemented.

Images