Intelligent prediction method for photovoltaic short-term generation power combination

Abstract

The invention belongs to the technical field of power generation power prediction, and particularly relates to an intelligent prediction method for photovoltaic short-term power generation power combination, which comprises a plurality of photovoltaic modules and a power generation power combination prediction system, and air cleanliness testers are arranged around the plurality of photovoltaic modules. An air dust detection module is arranged in the air cleanliness tester, and the power generation power combined prediction system comprises an intelligent network end module, an intelligent prediction module and a block chain center module. The intelligent network end module comprises a haze unit, a wind power unit and a geographical environment unit, the intelligent network end module is electrically connected with the intelligent prediction module, and the intelligent prediction module is electrically connected with the block chain center module. And due to the fact that dust and dirt are accumulated on the photovoltaic surface, the sunlight absorption capacity of the photovoltaic system is reduced, and predicted generated power data are not accurate.

Description

Technical Field

[0001] The present invention belongs to the technical field of power generation prediction, and in particular relates to an intelligent prediction method for a photovoltaic short-term power generation combination. Background Art

[0002] After entering the new century, environmental protection and energy crises have become increasingly severe. Solar power generation has been widely used around the world due to its advantages of cleanliness, renewable energy and easy distributed promotion. With the proposal of carbon peak and carbon neutrality strategies, my country's photovoltaic power generation industry will usher in a new development opportunity, and the proportion of photovoltaic power generation in total power generation will increase significantly. However, photovoltaic power generation is closely related to meteorological factors. It has strong intermittent, volatile and random characteristics. Large-scale photovoltaic power generation grid connection not only has a strong impact on the power grid, but also affects the quality of electric energy, and also has an adverse effect on the stable operation and effective dispatch of the power system. Reasonable prediction of photovoltaic power generation can effectively reduce the impact of grid connection on the power grid, improve the stability of power station operation, and enhance the main grid's acceptance of photovoltaic power. Therefore, effective prediction of photovoltaic power generation is of great significance to promoting the application of solar power generation and the safe, efficient and economical operation of the entire power grid.

[0003] In the process of photovoltaic module power generation, dust and dirt will hinder the absorption of sunlight and affect the power generation efficiency. Dust and dirt mostly come from dust in the air and dust particles in haze. These fall on photovoltaic modules and affect the prediction accuracy of power generation. This phenomenon has become a problem that people in this field need to solve urgently. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent prediction method for photovoltaic short-term power generation combination to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for intelligent prediction of photovoltaic short-term power generation combination, comprising a plurality of photovoltaic components and a power generation combination prediction system, air cleanliness testers are arranged around the plurality of photovoltaic components, and an air dust detection module is arranged inside the air cleanliness tester, the power generation combination prediction system comprises an intelligent network terminal module, an intelligent prediction module and a blockchain center module; the intelligent network terminal module comprises a haze unit, a wind power unit and a geographical environment unit, the intelligent network terminal module is electrically connected to the intelligent prediction module, the intelligent prediction module is electrically connected to the blockchain center module, and the air dust detection module is electrically connected to the wind power unit and the intelligent prediction module respectively.

[0006] The present invention further illustrates that the air dust detection module is used to detect the dust content in the air near the photovoltaic components, the intelligent network terminal module is used to identify climate data through networking, the haze unit is used to identify haze weather data, the wind unit is used to identify the weather wind force, and the geographical environment unit is used to identify the ambient humidity of the photovoltaic setting area. The intelligent prediction module is used to comprehensively predict the photovoltaic power generation through the haze unit, the wind unit, the geographical environment unit and the air dust detection module. The blockchain center module is used to record the predicted power generation data each time and store it through blockchain technology.

[0007] The present invention further illustrates that the intelligent prediction method includes the following steps: step S1, the intelligent network module and the air dust detection module run calculations simultaneously; step S2, the haze weather data, the weather wind force data, the environmental humidity data of the photovoltaic setting area and the air dust content data near the photovoltaic module are comprehensively calculated; step S3, the surface cleanliness of a single photovoltaic module is measured, and the sunlight absorption efficiency of the photovoltaic module is calculated based on the cleanliness, so as to measure the power generation, and the power generation is combined with the power generation generated by other factors; step S4, the total power generation of all photovoltaic modules is calculated through the intelligent prediction module, and the data is imported into the blockchain center module to protect the data through blockchain technology.

[0008] The present invention further illustrates that in step S2, the effect of haze on the surface cleanliness of photovoltaic modules is as follows: Among them, W is the total amount of haze dust, W1 is the total amount of dust plus the mass of the evaporation dish, and W a is the mass of the dried evaporating dish, W b is the mass of ethylene glycol solution after evaporation to dryness, S is the area of ​​dust collecting cylinder mouth, N is the number of sampling days; wind dilution of haze: Among them, W 稀 is the amount of haze and dust diluted by wind, Q is the wind force, and Q max is the highest wind force level; then W 后 =WW 稀 , W 后 It is the amount of haze and dust after being diluted by wind.

[0009] The present invention further illustrates that in step S2, the amount of dust normally dropped from the surface of the photovoltaic module by the wind is: Therefore, the amount of dust remaining after the wind blows away the dust that normally falls on the surface of the photovoltaic module is: W 剩 =W 空 -W 清 , W 空 is the normal dust content in the air caused by factors such as automobile exhaust, W 清 W is the amount of dust removed by wind force.剩 It is the amount of dust remaining after the wind blows away the normally fallen dust.

[0010] The present invention further illustrates that in step S2, the environmental humidity data of the photovoltaic setting area is When , it means that the photovoltaic installation area is a wetland, and the environmental humidity data of the photovoltaic installation area is When , it means that the photovoltaic installation area is a dry area. is the ambient humidity, I normal ambient humidity; and when When is the maximum ambient humidity, W 湿 is the ratio of haze dustfall coefficient caused by ambient humidity; When

[0011] The present invention further illustrates that in step S3, when hour, when hour, W 综 is the comprehensive amount of dust on the surface of a single PV module; W min is the surface of the photovoltaic module without dust, F is the power generation of a single photovoltaic module, F max is the maximum power generation of a single photovoltaic module; F 组 =F+F1+F2+…+F n , n is the number of photovoltaic modules, F 组 It is the sum of the predicted short-term power generation of all PV panels.

[0012] The present invention further illustrates that in step S4, the data of the total short-term power generation of the photovoltaic components is imported into the blockchain, and the data is protected by the blockchain.

[0013] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention dilutes the haze through the dust in the haze and the wind, thereby obtaining the total amount of dust in the haze. Due to the wind, the influence of the environmental humidity on the haze increases, thereby causing an increase in the amount of dust. Before the dust falls on the photovoltaic module, part of it is blown away by the wind, and the remaining dust plus the original dust in the air fall on the photovoltaic module, causing the photovoltaic module to change its absorption intensity of sunlight. The predicted photovoltaic power generation data is more accurate, and it can fully avoid the dust and dirt that hinder the absorption of sunlight and affect the power generation efficiency, thereby causing the predicted power generation data to be inaccurate, and the prediction quality can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0015] Figure 1 It is a schematic diagram of the module connection relationship of the power generation combination prediction system of the present invention;

[0016] Figure 2 It is a schematic diagram of the units included in the intelligent network terminal module of the present invention;

[0017] Figure 3 It is a schematic diagram of the photovoltaic power generation power prediction process of the present invention. DETAILED DESCRIPTION

[0018] The following is a further non-limiting detailed description of the technical solution of the present invention in conjunction with the preferred embodiments and the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] See also Figure 1-3 The present invention provides a technical solution: a method for intelligent prediction of photovoltaic short-term power generation combination, including a plurality of photovoltaic modules and a power generation combination prediction system, air cleanliness testers are arranged around the plurality of photovoltaic modules, and an air dust detection module is arranged inside the air cleanliness tester, and the power generation combination prediction system includes an intelligent network terminal module, an intelligent prediction module and a blockchain center module;

[0020] The intelligent network terminal module includes a haze unit, a wind power unit and a geographical environment unit. The intelligent network terminal module is electrically connected to the intelligent prediction module, the intelligent prediction module is electrically connected to the blockchain center module, and the air dust detection module is electrically connected to the wind power unit and the intelligent prediction module respectively.

[0021] The air dust detection module is used to detect the dust content in the air near the photovoltaic components. The intelligent network terminal module is used to identify climate data through networking. The haze unit is used to identify haze weather data. The wind unit is used to identify the weather wind force. The geographical environment unit is used to identify the environmental humidity of the photovoltaic setting area. The intelligent prediction module is used to comprehensively predict the photovoltaic power generation through the haze unit, wind unit, geographical environment unit and air dust detection module. The blockchain center module is used to record the predicted power generation data for each time and store it through blockchain technology.

[0022] The intelligent prediction method includes the following steps:

[0023] Step S1, the intelligent network terminal module and the air dust detection module run calculations simultaneously;

[0024] Step S2, comprehensively calculating the haze weather data, the weather wind force data, the environmental humidity data of the photovoltaic installation area, and the air dust content data near the photovoltaic module;

[0025] Step S3, measuring the surface cleanliness of a single photovoltaic module, and calculating the sunlight absorption efficiency of the photovoltaic module based on the cleanliness, thereby measuring the power generation, and combining the power generation with the power generation generated by other factors;

[0026] Step S4: Calculate the total power generation of all photovoltaic modules through the intelligent prediction module, import the data into the blockchain center module, and protect the data through blockchain technology.

[0027] In step S2, the effect of haze on the surface cleanliness of photovoltaic modules is as follows:

[0028] Among them, W is the total amount of haze dust, W1 is the total amount of dust plus the mass of the evaporation dish, and W a is the mass of the dried evaporating dish, W b is the mass of ethylene glycol solution after evaporation to dryness, S is the area of ​​the dust collecting cylinder mouth, and N is the number of sampling days;

[0029] Wind dilution of haze: Among them, W 稀 is the amount of haze and dust diluted by wind, Q is the wind force, and Q max The highest wind force level;

[0030] Then W 后 =WW 稀 , W 后 It is the amount of haze and dust after being diluted by wind.

[0031] In step S2, the amount of dust normally dropped from the surface of the photovoltaic module by wind is:

[0032] Therefore, the amount of dust remaining after the wind blows away the dust that normally falls on the surface of the photovoltaic module is: W 剩 =W 空 -W 清 , W 空 is the normal dust content in the air caused by factors such as automobile exhaust, W 清 W is the amount of dust removed by wind force. 剩 It is the amount of dust remaining after the wind blows away the normally fallen dust.

[0033] In step S2, the environmental humidity data of the photovoltaic setting area is When , it means that the photovoltaic installation area is a wetland, and the environmental humidity data of the photovoltaic installation area is When , it means that the photovoltaic installation area is a dry area. is the ambient humidity, My normal ambient humidity;

[0034] When When is the maximum ambient humidity, W 湿 is the ratio of haze dustfall coefficient caused by ambient humidity;

[0035] When When

[0036] In step S3,

[0037] when hour,

[0038] when hour, W 综 is the comprehensive amount of dust on the surface of a single PV module;

[0039] W min is the surface of the photovoltaic module without dust, F is the power generation of a single photovoltaic module, F max The maximum power generation of a single photovoltaic module;

[0040] F 组 =F+F1+F2+…+F n , n is the number of photovoltaic modules, F 组 is the sum of the predicted short-term power generation of all PV panels;

[0041] The total amount of dust in the haze is obtained by the dust in the haze and the dilution of the haze by wind. Due to the wind and the increased impact of environmental humidity on the haze, the amount of dust increases. Before the dust falls on the photovoltaic modules, part of it is blown away by the wind. The remaining dust plus the original dust in the air falls on the photovoltaic modules, causing the photovoltaic modules to change their absorption intensity of sunlight. The predicted photovoltaic power generation data is more accurate, and can fully avoid dust and dirt that hinder the absorption of sunlight and affect the power generation efficiency, thereby causing inaccurate predicted power generation data, which can improve the prediction quality.

[0042] In step S4, the data of the total short-term power generation of the photovoltaic modules is imported into the blockchain, and the data is protected by the blockchain;

[0043] By setting up blockchain technology, it can play the role of decentralization, immutability, transparency, security and programmability. Each data block is linked to the previous block to form a continuous chain, which ensures the integrity of the data. This technology combines distributed storage, point-to-point transmission, consensus mechanism and cryptography to record transactions and information through a growing data block chain to ensure data security and transparency.

[0044] In the description of the present invention, it is necessary to understand that the terms "up", "down", "front", "back", "left", "right", etc. indicate directions or positional relationships based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as a limitation on the present invention.

[0045] Finally, it should be pointed out that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them. Although the present invention has been described in detail with reference to the above embodiments, a person skilled in the art should understand that the technical solutions described in the above embodiments can still be modified, or some of the technical features can be replaced by equivalents, and these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent prediction method for photovoltaic short-term power generation combination, comprising a plurality of photovoltaic modules and a power generation combination prediction system, characterized in that: Air cleanliness testers are arranged around the photovoltaic modules, and air dust detection modules are arranged inside the air cleanliness testers. The power generation combination prediction system includes an intelligent network terminal module, an intelligent prediction module and a blockchain center module. The intelligent network terminal module includes a haze unit, a wind power unit and a geographical environment unit. The intelligent network terminal module is electrically connected to the intelligent prediction module, the intelligent prediction module is electrically connected to the blockchain center module, and the air dust detection module is electrically connected to the wind power unit and the intelligent prediction module respectively.

2. The method for intelligent prediction of photovoltaic short-term power generation combination according to claim 1, characterized in that: The air dust detection module is used to detect the dust content in the air near the photovoltaic components, the intelligent network terminal module is used to identify climate data through networking, the haze unit is used to identify haze weather data, the wind unit is used to identify the weather wind force, and the geographical environment unit is used to identify the environmental humidity of the photovoltaic setting area. The intelligent prediction module is used to comprehensively predict the photovoltaic power generation through the haze unit, wind unit, geographical environment unit and air dust detection module. The blockchain center module is used to record the predicted power generation data each time and store it through blockchain technology.

3. The intelligent prediction method of photovoltaic short-term power generation combination according to claim 2 is characterized by: The intelligent prediction method comprises the following steps: Step S1, the intelligent network terminal module and the air dust detection module run calculations simultaneously; Step S2, comprehensively calculating the haze weather data, the weather wind force data, the environmental humidity data of the photovoltaic installation area, and the air dust content data near the photovoltaic module; Step S3, measuring the surface cleanliness of a single photovoltaic module, and calculating the sunlight absorption efficiency of the photovoltaic module based on the cleanliness, thereby measuring the power generation, and combining the power generation with the power generation generated by other factors; Step S4: Calculate the total power generation of all photovoltaic modules through the intelligent prediction module, import the data into the blockchain center module, and protect the data through blockchain technology.

4. The intelligent prediction method of photovoltaic short-term power generation combination according to claim 3 is characterized by: In step S2, the effect of haze on the surface cleanliness of photovoltaic modules is as follows: Among them, W is the total amount of haze dust, W1 is the total amount of dust plus the mass of the evaporation dish, and W a is the mass of the dried evaporating dish, W b is the mass of ethylene glycol solution after evaporation to dryness, S is the area of ​​the dust collecting cylinder mouth, and N is the number of sampling days; Wind dilution of haze: Among them, W 稀 is the amount of haze and dust diluted by wind, Q is the wind force, and Q max The highest wind force level; Then W 后 =WW 稀 , W 后 It is the amount of haze and dust after being diluted by wind.

5. The intelligent prediction method of photovoltaic short-term power generation combination according to claim 4 is characterized by: In step S2, the amount of dust normally dropped from the surface of the photovoltaic module by wind is: Therefore, the amount of dust remaining after the wind blows away the dust that normally falls on the surface of the photovoltaic module is: W 剩 =W 空 -W 清 , W 空 is the normal dust content in the air caused by factors such as automobile exhaust, W 清 W is the amount of dust removed by wind force. 剩 It is the amount of dust remaining after the wind blows away the normally fallen dust.

6. The intelligent prediction method of photovoltaic short-term power generation combination according to claim 5, characterized in that: In step S2, the environmental humidity data of the photovoltaic setting area is When , it means that the photovoltaic installation area is a wetland, and the environmental humidity data of the photovoltaic installation area is When , it means that the photovoltaic installation area is a dry area. is the ambient humidity, My normal ambient humidity; When When is the maximum ambient humidity, W 湿 is the ratio of haze dustfall coefficient caused by ambient humidity; When When 7. The intelligent prediction method of photovoltaic short-term power generation combination according to claim 6 is characterized by: In the step S3, when hour, when hour, W 综 is the comprehensive amount of dust on the surface of a single PV module; W min is the surface of the photovoltaic module without dust, F is the power generation of a single photovoltaic module, F max The maximum power generation of a single photovoltaic module; F 组 =F+F1+F2+…+F n , n is the number of photovoltaic modules, F 组 It is the sum of the predicted short-term power generation of all PV panels.

8. The intelligent prediction method of photovoltaic short-term power generation combination according to claim 7 is characterized by: In step S4, the data of the total short-term power generation of the photovoltaic modules is imported into the blockchain, and the data is protected by the blockchain.

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