CN115098737A - Energy storage power station data processing method and system, storage medium and terminal - Google Patents
Energy storage power station data processing method and system, storage medium and terminal Download PDFInfo
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Abstract
The invention provides a data processing method and system for an energy storage power station, a storage medium and a terminal, and the method comprises the following steps: collecting battery data of an energy storage power station in real time; preprocessing the battery data; and sending the preprocessed battery data to a Kafka stream data platform so that a distributed server can acquire the preprocessed battery data from the Kafka and analyze the battery data. The data processing method and system, the storage medium and the terminal of the energy storage power station can process a large amount of energy storage power station data in real time, diagnose the safety performance of the battery in real time, monitor the running state of the battery and ensure the safety of the battery and the energy storage power station.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a data processing method and system for an energy storage power station, a storage medium and a terminal.
Background
The battery-related accidents are mostly caused by the temperature rise of the battery due to the internal short circuit of the battery, thereby causing the occurrence of combustion, explosion and the like. The accident has short time, generally only a few minutes. For operation and maintenance of the energy storage power station, the safety of the battery can be ensured only by monitoring and processing the data of the battery in real time.
At present, an energy storage power station is newly built at many times, such as several megawatts, dozens of megawatts or even hundreds of megawatts. A large number of batteries are used in each energy storage plant. These batteries produce a large amount of battery data during operation. Only by analyzing the data in real time, the safety of the battery operation and the safety of the energy storage power station can be ensured. However, since a large number of batteries are connected to one energy storage power station, the amount of battery data generated is huge. For example, a 20MW energy storage power station includes approximately 1400 battery boxes, each of which includes 24 cells and 8 temperature probes, for a total of approximately 67200 cells and 11200 temperature probes. From this calculation, voltage and temperature data of hundreds of thousands of batteries can be generated every second, so that a monitoring or processing platform of the energy storage power station needs to analyze and process so much data and store corresponding data.
Therefore, how to analyze and store the data with high frequency and huge data volume becomes a difficult problem to be solved at present. In the prior art, it is a common and direct way to expand the capacity of a data acquisition server and a data processing server. However, the method of expanding the memory can only solve a part of the problems, and cannot perform the unregulated expansion, so that the above problems cannot be fundamentally solved.
Disclosure of Invention
In view of the above-mentioned shortcomings of the prior art, an object of the present invention is to provide a method and a system for processing data of an energy storage power station, a storage medium, and a terminal, which can process a large amount of data of the energy storage power station in real time, and ensure the safety of a battery and the energy storage power station.
In order to achieve the above and other related objects, the present invention provides a data processing method for an energy storage power station, comprising the following steps: collecting battery data of an energy storage power station in real time; preprocessing the battery data; and sending the preprocessed battery data to a Kafka stream data platform so that a distributed server can acquire the preprocessed battery data from the Kafka and analyze the battery data.
In an embodiment of the invention, the battery data includes one or more combinations of battery voltage, battery temperature, and battery current.
In an embodiment of the present invention, the pre-processing the battery data includes the following steps:
integrating the battery data of the battery box under each battery cluster in the battery stack;
for each battery box, sorting battery data of the battery cells in the battery box;
for each battery cluster, sorting battery data of battery boxes in the battery cluster;
and sequencing the cell data of the cell clusters in the cell stack aiming at the cell stack.
In one embodiment of the present invention, the sorting modes selected for sorting the battery data of the battery cells in the battery box, sorting the battery data of the battery boxes in the battery cluster, and sorting the battery data of the battery cluster are the same or different; the sorting mode comprises the following steps:
sorting based on the voltage magnitude;
sorting based on the temperature data size;
sorting based on the size of the standard deviation of the voltage in the battery box;
sorting is carried out based on the voltage variance in the battery box;
sorting is carried out based on the voltage range in the battery box;
sorting is carried out based on the size of the standard deviation of the temperature in the battery box;
sorting is carried out based on the temperature variance in the battery box;
and sequencing is carried out based on the temperature range difference in the battery box.
The invention provides an energy storage power station data processing system which comprises an acquisition module, a preprocessing module and a transmission module;
the acquisition module is used for acquiring the battery data of the energy storage power station in real time;
the preprocessing module is used for preprocessing the battery data;
the transmission module is used for sending the preprocessed battery data to a Kafka stream data platform so that the distributed server can obtain the preprocessed battery data from the Kafka and analyze and process the preprocessed battery data.
The invention provides a storage medium on which a computer program is stored which, when executed by a processor, implements the energy storage plant data processing method described above.
The invention provides a data processing terminal of an energy storage power station, which comprises: a processor and a memory;
the memory is used for storing a computer program;
the processor is used for executing the computer program stored in the memory so as to enable the energy storage power station data processing terminal to execute the energy storage power station data processing method.
The invention provides an energy storage power station data processing system, which comprises the energy storage power station data processing terminal, a Kafka stream data platform and a distributed server;
the Kafka stream data platform is used for receiving battery data provided by the energy storage power station data processing terminal;
and the distributed server is used for acquiring the battery data from the Kafka stream data platform and analyzing and processing the battery data.
In an embodiment of the present invention, when the resources of the distributed servers are sufficient, the distributed servers sequentially analyze and process the battery data.
In an embodiment of the present invention, when the resources of the distributed server are in shortage, the distributed server selects the battery data of the battery box in front of or behind to perform analysis processing.
As described above, the energy storage power station data processing method and system, the storage medium and the terminal according to the present invention have the following beneficial effects:
(1) by preprocessing the data of the energy storage power station, the times of data transmission are effectively reduced, and the data volume of each transmission is increased;
(2) the Kafka stream data platform is adopted to realize the high-efficiency transmission of data; by a distributed data processing mode, the occurrence of data blockage is avoided;
(3) the method can process the battery data in time, and ensures the safety of the battery and the safety of the energy storage power station.
Drawings
FIG. 1 is a flow chart of a data processing method for an energy storage power station according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an embodiment of a data processing system of an energy storage power station;
FIG. 3 is a schematic structural diagram of an energy storage power station data processing terminal according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of another embodiment or configuration of the energy storage power station data processing system according to the present invention.
Description of the element reference numerals
21 acquisition module
22 preprocessing module
23 Transmission Module
31 processor
32 memory
41 energy storage power station data processing terminal
42 Kafka stream data platform
43 distributed server
Detailed Description
The embodiments of the present invention are described below with specific examples, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and its several details are capable of modifications and variations in various obvious respects, all without departing from the spirit of the invention. It should be noted that the features in the following embodiments and examples may be combined with each other without conflict.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention, and the drawings only show the components related to the present invention rather than the number, shape and size of the components in practical implementation, and the type, number and proportion of the components in practical implementation may be changed freely, and the layout of the components may be more complicated.
The method and the system for processing the data of the energy storage power station, the storage medium and the terminal can realize real-time processing of a large amount of data of the energy storage power station through preprocessing of battery data and distributed storage analysis, ensure the safety of the battery and the energy storage power station and have high practicability.
As shown in fig. 1, in an embodiment, the energy storage power station data processing method of the present invention includes the following steps:
and step S1, collecting the battery data of the energy storage power station in real time.
Specifically, in order to realize timely processing of battery data of the energy storage power station, the data processing terminal of the energy storage power station acquires the battery data in real time. In an embodiment of the invention, the battery data includes one or more combinations of battery voltage, battery temperature, and battery current.
And step S2, preprocessing the battery data.
Specifically, the preprocessing of the battery data comprises the following steps:
21) cell data for the cell boxes under each cell cluster in the stack is integrated.
Specifically, the battery stack includes a plurality of battery clusters, each battery cluster including a plurality of battery cases, each battery case including a plurality of battery cells. Therefore, the cell data of the cell boxes under each cell cluster in the cell stack needs to be integrated together to form a record, so that the primary classification of the cell data is realized.
22) And sequencing the battery data of the electric cores in the battery boxes aiming at each battery box.
23) And sequencing the battery data of the battery boxes in the battery cluster aiming at each battery cluster.
24) And sequencing the cell data of the cell clusters in the cell stack aiming at the cell stack.
Therefore, when sorting is performed, the battery data of the battery cells need to be sorted first, then the battery data of the battery box needs to be sorted, and finally the battery data of the battery cluster needs to be sorted. The sorting method is convenient for quickly reading the battery data of the battery boxes meeting the threshold requirement, reduces the frequency of data transmission, increases the data volume of single transmission, and simultaneously roughly distinguishes the quality of the battery boxes. In the charging process, the battery box in which the electric core with higher voltage is located can reach a charging cut-off state firstly. This situation may lead to other cells, other battery boxes not yet being full. During discharging, the battery box where the battery cell with lower voltage is located can reach the cut-off voltage first, and other battery cells do not discharge the internal electricity. Thus, for batteries, the higher the temperature, the more likely problems generally occur. The high temperature tends to cause thermal runaway of the battery. The likelihood of battery transmission problems during standing is generally low. Generally, the failure of the battery is caused during the charging and discharging process and during the large-current charging and discharging process.
In an embodiment of the present invention, the sorting manners selected for sorting the battery data of the battery cells in the battery box, sorting the battery data of the battery boxes in the battery cluster, and sorting the battery data of the battery cluster are the same or different. For example, the voltage data of the battery cells are not sorted, and the temperatures of the battery cells are sorted from high to low. The battery data of the plurality of battery boxes under one battery cluster may be sorted according to the magnitude of the pole difference of the voltage of each battery box. The voltage data of the plurality of cell clusters under the cell stack may be sorted by the maximum standard deviation of each cell box within each cell cluster.
Preferably, the sorting manner includes:
a) sorting based on the voltage magnitude;
b) sorting based on the temperature data size;
c) sorting based on the size of the standard deviation of the voltage in the battery box;
d) sorting is carried out based on the voltage variance in the battery box;
e) sorting is carried out based on the voltage range in the battery box;
f) sorting is carried out based on the size of the standard deviation of the temperature in the battery box;
g) sorting is carried out based on the temperature variance in the battery box;
h) and sequencing is carried out based on the temperature range difference in the battery box.
And step S3, sending the preprocessed battery data to a Kafka stream data platform, so that the distributed server acquires the preprocessed battery data from the Kafka and analyzes and processes the battery data.
Specifically, the Kafka streaming data platform is adopted to realize data collection and sending, and the Kafka streaming data platform has the advantages of high throughput, low delay, good durability, strong expansibility, low fault tolerance and the like. And in cooperation with a distributed strategy, the Kafka flow data platform can process a large amount of data generated in the operation process of the battery in the energy storage power station in real time.
The energy storage power station data processing terminal serves as a producer, collected battery data are sent to a certain specific subject on the Kafka flow data platform, and a large amount of battery data can be processed only by one collection program. Other services can acquire the battery data of the part as a consumer to analyze and process.
According to the invention, the distributed server is configured to consume the data of the specified subject on the Kafka stream data platform, acquire all the battery data in the corresponding battery cluster, and analyze and process the battery data. If the distributed server resources are enough, the battery data can be analyzed and processed in sequence; if the resources of the distributed server are in shortage, the battery data of the battery boxes at the front or the back in the sorted battery data can be processed, so that the analysis and the processing of a large amount of normal battery data are avoided.
As shown in fig. 2, in an embodiment, the energy storage power station data processing system of the present invention includes an acquisition module 21, a preprocessing module 22, and a transmission module 23.
The acquisition module 21 is used for acquiring the battery data of the energy storage power station in real time.
Specifically, in order to realize timely processing of battery data of the energy storage power station, the data processing terminal of the energy storage power station acquires the battery data in real time. In an embodiment of the invention, the battery data includes one or more combinations of battery voltage, battery temperature, and battery current.
The preprocessing module 22 is connected to the acquisition module 21 and configured to preprocess the battery data.
Specifically, the preprocessing of the battery data comprises the following steps:
21) cell data for the cell boxes under each cell cluster in the stack is integrated.
Specifically, the battery stack includes a plurality of battery clusters, each battery cluster including a plurality of battery boxes, each battery box including a plurality of cells. Therefore, the cell data of the cell boxes under each cell cluster in the cell stack needs to be integrated together to form a record, so that the primary classification of the cell data is realized.
22) And sequencing the battery data of the electric cores in the battery boxes aiming at each battery box.
23) And sequencing the battery data of the battery boxes in the battery cluster aiming at each battery cluster.
24) And sequencing the cell data of the cell clusters in the cell stack aiming at the cell stack.
Therefore, when sorting is performed, the battery data of the battery cells need to be sorted first, then the battery data of the battery box needs to be sorted, and finally the battery data of the battery cluster needs to be sorted. The sorting method is convenient for quickly reading the battery data of the battery boxes meeting the threshold requirement, reduces the frequency of data transmission, increases the data volume of single transmission and simultaneously roughly distinguishes the quality of the battery boxes. In the charging process, the battery box in which the electric core with higher voltage is located can reach a charging cut-off state firstly. This situation may lead to other cells, other battery boxes not yet being fully charged. During discharging, the battery box where the battery cell with lower voltage is located can reach the cut-off voltage first, and other battery cells do not discharge the internal electricity. Thus, for batteries, the higher the temperature, the more likely problems generally occur. The high temperature tends to cause thermal runaway of the battery. The likelihood of battery transmission problems during standing is generally low. Generally, the failure of the battery is caused during the charging and discharging process and during the large-current charging and discharging process.
In an embodiment of the present invention, the sorting manners selected for sorting the battery data of the battery cells in the battery box, sorting the battery data of the battery boxes in the battery cluster, and sorting the battery data of the battery cluster are the same or different. For example, the voltage data of the battery cells are not sorted, and the temperatures of the battery cells are sorted from high to low. The battery data of a plurality of battery boxes under one battery cluster can be sorted according to the range of the voltage of each battery box. The voltage data for the plurality of cell clusters under the cell stack may be sorted by the maximum standard deviation of each cell box within each cell cluster.
Preferably, the sorting manner includes:
a) sorting based on the voltage magnitude;
b) sorting based on the temperature data size;
c) sorting is carried out based on the size of the standard deviation of the voltage in the battery box;
d) sorting based on the voltage variance in the battery box;
e) sorting is carried out based on the voltage range in the battery box;
f) sorting based on the size of the standard deviation of the temperature in the battery box;
g) sorting is carried out based on the temperature variance in the battery box;
h) and sequencing is carried out based on the temperature range difference in the battery box.
The transmission module 23 is connected to the preprocessing module 22, and configured to send the preprocessed battery data to a Kafka streaming data platform, so that the distributed server obtains the preprocessed battery data from the Kafka and performs analysis processing on the battery data.
Specifically, the Kafka streaming data platform is adopted to realize data collection and sending, and the Kafka streaming data platform has the advantages of high throughput, low delay, good durability, strong expansibility, low fault tolerance and the like. And in cooperation with a distributed strategy, the Kafka flow data platform can process a large amount of data generated in the operation process of the battery in the energy storage power station in real time.
The Kafka stream data platform is a platform in a message queue form with information stream issuing and subscribing functions, the energy storage power station data processing terminal serves as a producer, collected battery data are sent to a specific subject on the Kafka stream data platform, and a large amount of battery data can be processed only by one collection program. Other services can acquire the battery data of the part as a consumer to analyze and process.
According to the invention, the distributed server is configured to consume the data of the specified theme on the Kafka stream data platform, acquire all the battery data in the corresponding battery cluster, and analyze and process the battery data. If the distributed server resources are enough, the battery data can be analyzed and processed in sequence; if the resources of the distributed server are in shortage, the battery data of the battery boxes at the front or the back in the sorted battery data can be processed, so that the analysis and the processing of a large amount of normal battery data are avoided.
It should be noted that the division of the modules of the above apparatus is only a logical division, and the actual implementation may be wholly or partially integrated into one physical entity, or may be physically separated. And the modules can be realized in a form that all the modules are called by the processing element through software, can also be realized in a form that all the modules are called by the hardware, can also be realized in a form that part of the modules are called by the processing element through software, and can also be realized in a form that part of the modules are called by the hardware. For example: the x module can be a processing element which is established independently, and can also be integrated in one chip of the device. Furthermore, the x-module may be stored in the memory of the apparatus in the form of program code, and may be called by a certain processing element of the apparatus to execute the functions of the x-module. The other modules are implemented similarly. All or part of the modules can be integrated together or can be independently realized. The processing element described herein may be an integrated circuit having signal processing capabilities. In implementation, each step of the above method or each module above may be implemented by an integrated logic circuit of hardware in a processor element or an instruction in the form of software. The above modules may be one or more integrated circuits configured to implement the above methods, such as: one or more Application Specific Integrated Circuits (ASICs), one or more microprocessors (DSPs), one or more Field Programmable Gate Arrays (FPGAs), and the like. When some of the above modules are implemented in the form of a Processing element scheduler code, the Processing element may be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor capable of calling program code. These modules may be integrated together and implemented in the form of a System-on-a-chip (SOC).
The storage medium of the present invention has stored thereon a computer program which, when executed by a processor, implements the energy storage plant data processing method described above. Preferably, the storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic disk, U-disk, memory card, or optical disk.
As shown in fig. 3, in an embodiment, the energy storage power station data processing terminal of the present invention includes: a processor 31 and a memory 32.
The memory 32 is used for storing computer programs.
The memory 32 includes: various media that can store program codes, such as ROM, RAM, magnetic disk, U-disk, memory card, or optical disk.
The processor 31 is connected to the memory 32 and configured to execute the computer program stored in the memory, so that the energy storage power station data processing terminal executes the energy storage power station data processing method.
Preferably, the Processor 31 may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; the Integrated Circuit may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, or discrete hardware components.
As shown in fig. 4, in an embodiment, the energy storage power station data processing system of the present invention includes the energy storage power station data processing terminal 41, the Kafka streaming data platform 42, and the distribution server 43.
The Kafka stream data platform 42 is connected to the energy storage power station data processing terminal 41, and is configured to receive the battery data provided by the energy storage power station data processing terminal 41.
The distributed server 43 is connected to the Kafka streaming data platform 42, and is configured to obtain the battery data from the Kafka streaming data platform 42, and perform analysis processing on the battery data.
In an embodiment of the present invention, when the resources of the distributed servers are sufficient, the distributed servers sequentially analyze and process the battery data; when the resources of the distributed server are in shortage, the distributed server selects the battery data of the battery box at the front or the back to perform analysis processing.
In summary, the data processing method and system, the storage medium and the terminal for the energy storage power station of the invention effectively reduce the times of data transmission and increase the data volume of each transmission by preprocessing the data of the energy storage power station; the Kafka stream data platform is adopted to realize the high-efficiency transmission of data; by a distributed data processing mode, the occurrence of data blockage is avoided; the method can process the battery data in time, and ensures the safety of the battery and the safety of the energy storage power station. Therefore, the invention effectively overcomes various defects in the prior art and has high industrial utilization value.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.
Claims (10)
1. The data processing method for the energy storage power station is characterized by comprising the following steps of:
collecting battery data of an energy storage power station in real time;
preprocessing the battery data;
and sending the preprocessed battery data to a Kafka stream data platform so that a distributed server can acquire the preprocessed battery data from the Kafka and analyze the battery data.
2. The energy storage power station data processing method of claim 1, wherein the battery data comprises one or more combinations of battery voltage, battery temperature, and battery current.
3. The energy storage power station data processing method of claim 1, wherein pre-processing the battery data comprises the steps of:
integrating the battery data of the battery box under each battery cluster in the battery stack;
for each battery box, sorting battery data of the battery cells in the battery box;
for each battery cluster, sorting battery data of battery boxes in the battery cluster;
and sequencing the cell data of the cell clusters in the cell stack aiming at the cell stack.
4. The energy storage power station data processing method of claim 3, characterized in that the sorting modes selected for sorting the battery data of the cells in the battery box, sorting the battery data of the battery box in the battery cluster, and sorting the battery data of the battery cluster are the same or different; the sorting mode comprises the following steps:
sorting based on the voltage magnitude;
sorting based on the temperature data size;
sorting is carried out based on the size of the standard deviation of the voltage in the battery box;
sorting based on the voltage variance in the battery box;
sorting is carried out based on the voltage range in the battery box;
sorting is carried out based on the size of the standard deviation of the temperature in the battery box;
sorting based on the temperature variance in the battery box;
and sequencing based on the temperature range in the battery box.
5. The data processing system of the energy storage power station is characterized by comprising an acquisition module, a preprocessing module and a transmission module;
the acquisition module is used for acquiring the battery data of the energy storage power station in real time;
the preprocessing module is used for preprocessing the battery data;
the transmission module is used for sending the preprocessed battery data to a Kafka stream data platform so that the distributed server can obtain the preprocessed battery data from the Kafka and analyze and process the preprocessed battery data.
6. A storage medium on which a computer program is stored, characterized in that the program, when being executed by a processor, carries out the energy storage plant data processing method of any one of claims 1 to 4.
7. An energy storage power station data processing terminal, comprising: a processor and a memory;
the memory is used for storing a computer program;
the processor is configured to execute the computer program stored in the memory to cause the energy storage power station data processing terminal to execute the energy storage power station data processing method of any one of claims 1 to 4.
8. An energy storage power station data processing system, characterized by comprising the energy storage power station data processing terminal of claim 7, a Kafka streaming data platform and a distributed server;
the Kafka flow data platform is used for receiving battery data provided by the energy storage power station data processing terminal;
and the distributed server is used for acquiring the battery data from the Kafka stream data platform and analyzing and processing the battery data.
9. The energy storage power station data processing system of claim 8, wherein the distributed servers analyze the battery data sequentially when the distributed servers are sufficiently resourced.
10. The energy storage power station data processing system of claim 8, wherein when the distributed server resources are in short supply, the distributed server selects the battery data of the front or rear battery box for analysis processing.
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