Distributed photovoltaic data dynamic mapping method

Abstract

The invention relates to a distributed photovoltaic data dynamic mapping method. The method comprises the following steps: acquiring ledger information of photovoltaic equipment; collecting real-time monitoring data of the photovoltaic equipment and surrounding environment data of the photovoltaic equipment; taking the machine account information as static data of the photovoltaic equipment, combining the real-time monitoring data of the photovoltaic equipment and the surrounding environment data of the photovoltaic equipment into dynamic data of the photovoltaic equipment, and associating the static data with the dynamic data through the serial number of the photovoltaic equipment; a big data analysis and processing technology is utilized to synthesize the collected real-time monitoring data of the photovoltaic equipment into a dynamic model in real time; and acquiring real-time dynamic data of the photovoltaic equipment, superposing the position, the operation state and the environmental parameters of the photovoltaic equipment on a map based on the association relationship between the static data and the dynamic data, and performing real-time communication with the dynamic model to dynamically update the panoramic view. According to the invention, the distributed photovoltaic equipment can be monitored in real time.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic data monitoring, and in particular to a distributed photovoltaic data dynamic mapping method. Background Art

[0002] Photovoltaic is a technology that uses solar energy to generate electricity by installing solar panels on roofs, walls or other building surfaces. With the increasing demand for sustainable energy and technological advances, distributed photovoltaics have become an important source of clean energy. However, due to the decentralized nature of distributed photovoltaics, how to manage and monitor them has become a challenge. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a distributed photovoltaic data dynamic mapping method, which can monitor distributed photovoltaic equipment in real time.

[0004] The technical solution adopted by the present invention to solve the technical problem is: to provide a distributed photovoltaic data dynamic mapping method, comprising the following steps:

[0005] Obtain the ledger information of photovoltaic equipment;

[0006] Collect real-time monitoring data of photovoltaic equipment and surrounding environment data of photovoltaic equipment;

[0007] The ledger information is used as static data of the photovoltaic equipment, the real-time monitoring data of the photovoltaic equipment and the surrounding environment data of the photovoltaic equipment are combined into dynamic data of the photovoltaic equipment, and the static data and the dynamic data are associated by the number of the photovoltaic equipment;

[0008] Using big data analysis and processing technology, the real-time monitoring data of photovoltaic equipment collected is synthesized into a real-time dynamic model;

[0009] Real-time dynamic data of photovoltaic equipment is obtained, and based on the correlation between the static data and the dynamic data, the location, operating status and environmental parameters of the photovoltaic equipment are superimposed on the map, and real-time communication is performed with the dynamic model to dynamically update the panoramic view.

[0010] The inventory information of the photovoltaic equipment includes coordinate information, number, user number, photovoltaic access point number, photovoltaic access point category, photovoltaic measurement point number, photovoltaic type, photovoltaic structure, photovoltaic manufacturer and photovoltaic rated output power.

[0011] The real-time monitoring data of the photovoltaic device includes light energy conversion rate, current, voltage and output power.

[0012] The ambient environment data of the photovoltaic device includes solar radiation intensity, temperature, illumination time, humidity, PM2.5 value and PM10 value.

[0013] The use of big data analysis and processing technology to synthesize a real-time dynamic model for the collected real-time monitoring data of photovoltaic equipment also includes: compressing the static data and dynamic data in a serialized format to form a time-space integrated data set; and when obtaining the real-time dynamic data of the photovoltaic equipment, parsing the real-time dynamic data based on the compression format of the time-space integrated data set.

[0014] When the location, operating status and environmental parameters of the photovoltaic equipment are superimposed on the map, the superposition is performed based on a Web-based front-end interface.

[0015] When the real-time communication with the dynamic model is performed, the WebSocket technology is used to realize the real-time communication.

[0016] The distributed photovoltaic data dynamic mapping method also includes:

[0017] The static data and dynamic data of the photovoltaic equipment are generated in pairs, and the operation characteristics and abnormal conditions of the photovoltaic equipment are revealed through photovoltaic power generation prediction and power flow calculation, thereby achieving a visual analysis of the panoramic view.

[0018] Beneficial Effects

[0019] Due to the adoption of the above-mentioned technical scheme, the present invention has the following advantages and positive effects compared with the prior art: the present invention combines the photovoltaic equipment ledger with the topological information, dynamically superimposes the photovoltaic real-time monitoring data, and forms a model dynamic mapping technology for distributed photovoltaics, which solves the practical problem that traditional mapping methods are difficult to cover large-scale photovoltaic power stations, and can help managers to more accurately grasp the operation of photovoltaic equipment, and monitor and optimize it in real time. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a flow chart of a method for dynamically mapping distributed photovoltaic data according to an embodiment of the present invention. DETAILED DESCRIPTION

[0021] The present invention will be further described below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms fall within the scope limited by the appended claims of the application equally.

[0022] The embodiment of the present invention relates to a distributed photovoltaic data dynamic mapping method, such as Figure 1As shown, the following steps are included:

[0023] Step 1: Obtain PV equipment ledger information. Team members can use the mobile app to enter the coordinate information, number, user number, PV access point number, PV access point category, PV measurement point number, PV type, PV structure, PV manufacturer, and PV rated output power of medium-voltage PV and low-voltage PV into the system and store them in the PostgreSQL database.

[0024] Step 2: Collect real-time monitoring data of the photovoltaic equipment. The light energy conversion rate, current, voltage and output power of the photovoltaic equipment can be collected into the system through the collection terminal of the photovoltaic equipment.

[0025] Step 3: Collect the surrounding environment data of the photovoltaic equipment. The surrounding environment data can be obtained from the meteorological supplier. Through the coordinates of the photovoltaic equipment, the real-time solar radiation intensity, temperature, illumination time, humidity, PM2.5 value, PM10 value, and the short-term predicted solar radiation intensity, temperature, illumination time, humidity, PM2.5 value, PM10 value are obtained.

[0026] Step 4: Static data and dynamic data are superimposed. According to the definition of distributed photovoltaic static data and dynamic data in IEC61970, in this step, the ledger information can be used as the static data of the photovoltaic equipment, and the real-time monitoring data of the photovoltaic equipment and the surrounding environment data of the photovoltaic equipment can be combined into the dynamic data of the photovoltaic equipment, and then the static data and dynamic data can be associated through the number of the photovoltaic equipment.

[0027] Step 5: Data compression. In this step, Google's Protocol Buffers (protobuf) technology can be used to follow the superposition model of step 4 to efficiently compress static and dynamic data, reduce the burden of data transmission and storage, and ensure data integrity and recoverability.

[0028] Step 6: Real-time synthesis of graphs. Using the big data analysis database clickhouse and the streaming computing framework flink, the real-time monitoring data of photovoltaic equipment collected is synthesized into a dynamic model in real time.

[0029] Step 7: Web-based real-time data overlay. Develop a Web-based front-end interface to obtain real-time dynamic data of photovoltaic equipment, and overlay the location, operating status and environmental parameters of photovoltaic equipment on the map according to the overlay model defined in step 4 (i.e. the association between static data and dynamic data). Use WebSocket technology to achieve real-time communication between the front-end Web interface and the back-end dynamic model, thereby dynamically updating the panoramic view.

[0030] Step 8: Visualize and analyze the panoramic view generated by data pairing. This step is generated based on the static data and dynamic data of photovoltaic equipment. Through algorithms such as photovoltaic power generation prediction and power flow calculation, the operating characteristics and abnormal conditions of photovoltaic equipment are revealed, which facilitates managers to conduct targeted analysis and decision-making.

[0031] It is not difficult to find that the present invention combines the photovoltaic equipment ledger with topological information, dynamically superimposes photovoltaic real-time monitoring data, and forms a model dynamic mapping technology for distributed photovoltaics, which solves the practical problem that traditional mapping methods are difficult to cover large-scale photovoltaic power stations. It can help managers to more accurately grasp the operation of photovoltaic equipment, and monitor and optimize it in real time.

Claims

1. A distributed photovoltaic data dynamic mapping method, characterized in that: The following steps are involved: Obtain the ledger information of photovoltaic equipment; Collect real-time monitoring data of photovoltaic equipment and surrounding environment data of photovoltaic equipment; The ledger information is used as static data of the photovoltaic equipment, the real-time monitoring data of the photovoltaic equipment and the surrounding environment data of the photovoltaic equipment are combined into dynamic data of the photovoltaic equipment, and the static data and the dynamic data are associated by the number of the photovoltaic equipment; Using big data analysis and processing technology, the real-time monitoring data of photovoltaic equipment collected is synthesized into a real-time dynamic model; Real-time dynamic data of photovoltaic equipment is obtained, and based on the correlation between the static data and the dynamic data, the location, operating status and environmental parameters of the photovoltaic equipment are superimposed on the map, and real-time communication is performed with the dynamic model to dynamically update the panoramic view.

2. The distributed photovoltaic data dynamic mapping method according to claim 1, characterized in that: The inventory information of the photovoltaic equipment includes coordinate information, number, user number, photovoltaic access point number, photovoltaic access point category, photovoltaic measurement point number, photovoltaic type, photovoltaic structure, photovoltaic manufacturer and photovoltaic rated output power.

3. The distributed photovoltaic data dynamic mapping method according to claim 1, characterized in that: The real-time monitoring data of the photovoltaic device includes light energy conversion rate, current, voltage and output power.

4. The distributed photovoltaic data dynamic mapping method according to claim 1, characterized in that: The ambient environment data of the photovoltaic device includes solar radiation intensity, temperature, illumination time, humidity, PM2.5 value and PM10 value.

5. The distributed photovoltaic data dynamic mapping method according to claim 1, characterized in that: The use of big data analysis and processing technology to synthesize a real-time dynamic model for the collected real-time monitoring data of photovoltaic equipment also includes: compressing the static data and dynamic data in a serialized format to form a time-space integrated data set; and when obtaining the real-time dynamic data of the photovoltaic equipment, parsing the real-time dynamic data based on the compression format of the time-space integrated data set.

6. The distributed photovoltaic data dynamic mapping method according to claim 1, characterized in that: When the location, operating status and environmental parameters of the photovoltaic equipment are superimposed on the map, the superposition is performed based on a Web-based front-end interface.

7. The distributed photovoltaic data dynamic mapping method according to claim 1, characterized in that: When the real-time communication with the dynamic model is performed, the WebSocket technology is used to realize the real-time communication.

8. The distributed photovoltaic data dynamic mapping method according to claim 1, characterized in that: Also includes: The static data and dynamic data of the photovoltaic equipment are generated in pairs, and the operation characteristics and abnormal conditions of the photovoltaic equipment are revealed through photovoltaic power generation prediction and power flow calculation, thereby achieving a visual analysis of the panoramic view.

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