CN118350688A - Energy storage power station health state assessment method and system - Google Patents
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Abstract
The invention provides a method and a system for evaluating the health state of an energy storage power station, and belongs to the technical field of energy storage power stations. The method comprises the following steps: acquiring preprocessed energy storage power station operation data transmitted from an end side, and converting the preprocessed energy storage power station operation data into preset characteristic parameters to obtain the characteristic parameters of the energy storage power station; based on the characteristic parameters of the energy storage power station and the actual environment working condition data of the energy storage power station, carrying out data fusion to obtain fusion characteristic information of the energy storage power station; and transmitting the fusion characteristic information of the energy storage power station to the cloud side so that the cloud side can evaluate the health state of the energy storage power station based on the health state evaluation model of the energy storage power station and the fusion characteristic information of the energy storage power station, and recovering the health state evaluation result of the energy storage power station fed back by the cloud side. The method and the device realize the purpose of rapidly evaluating and predicting the health state of the energy storage power station by mining and analyzing the operation data of the energy storage power station. And the problems of huge monitoring data volume and few effective data monitoring points of the energy storage power station are effectively solved.
Description
Technical Field
The invention relates to the technical field of energy storage power stations, in particular to an energy storage power station health state assessment method, an energy storage power station health state assessment system, a machine-readable storage medium and electronic equipment.
Background
The lithium ion battery has the advantages of high energy density, low self-discharge rate, long cycle life and the like, and is widely applied to super-large scale energy storage power stations. However, due to the difference of internal resistances and the difference of discharge depths of the batteries when the batteries leave the factory, the difference of health states SOH (state of health) between the batteries is larger and larger along with the working time. In order to prolong the overall service life of the battery of the energy storage power station, reduce the investment cost of the energy storage power station of the ultra-large battery, maximize the utilization of the existing resources, improve the operation reliability of the energy storage system and quickly evaluate and predict the health state of the aged energy storage battery on line, which is a problem to be solved urgently.
The data size of the energy storage power station is large, and the number of points to be monitored in the operation of the energy storage power station is about 30 ten thousand points by taking an electrochemical energy storage power station with the power of 100MW/200MWh as an example. Therefore, how to efficiently store and utilize huge energy storage power station operation data to quickly evaluate and predict the health status of the energy storage power station is a current urgent need to be solved.
Disclosure of Invention
The embodiment of the invention aims to provide a method and a system for evaluating the health state of an energy storage power station, which at least solve the problems of how to efficiently utilize huge energy storage power station operation data and rapidly evaluate and predict the health state of the energy storage power station.
To achieve the above object, a first aspect of the present invention provides a method for evaluating a health status of an energy storage power station, the method being performed by a side, the side being communicatively connected to a cloud side and an end side, the method comprising:
Acquiring preprocessed energy storage power station operation data transmitted from an end side, and converting the preprocessed energy storage power station operation data into preset characteristic parameters to obtain corresponding energy storage power station characteristic parameters;
based on the characteristic parameters of the energy storage power station and the actual environment working condition data of the energy storage power station, carrying out data fusion to obtain fusion characteristic information of the energy storage power station;
and transmitting the fusion characteristic information of the energy storage power station to the cloud side so that the cloud side can evaluate the health state of the energy storage power station based on the health state evaluation model of the energy storage power station and the fusion characteristic information of the energy storage power station, and recovering the health state evaluation result of the energy storage power station fed back by the cloud side.
Optionally, the characteristic parameter of the energy storage power station is a parameter for representing and reflecting the characteristic of the energy storage power station, and the characteristic parameter of the energy storage power station at least comprises a current change rate, a voltage change rate, a temperature change rate, an SOC parameter, an SOH parameter and/or an impedance parameter.
Optionally, the actual environmental condition data of the energy storage power station includes a battery type, an operation condition and an operation environment of the energy storage power station;
The above-mentioned based on energy storage power station characteristic parameter and energy storage power station's actual environment operating mode data, carry out data fusion and obtain energy storage power station and fuse characteristic information, include:
and fusing the battery type, the operation condition, the operation environment, the current change rate, the voltage change rate, the temperature change rate, the SOC parameter, the SOH parameter and/or the impedance parameter of the energy storage power station to obtain the fusion characteristic information of the energy storage power station.
Optionally, the cloud side is configured to:
Determining a corresponding initial model for estimating the health state of each energy storage power station from a pre-established initial model matrix for estimating the health state of the energy storage battery according to the battery type of the energy storage power station;
based on historical recycling data of the energy storage power stations, evaluating the initial model of the health state evaluation of each energy storage power station to obtain evaluation deviation corresponding to the initial model of the health state evaluation of each energy storage power station;
And determining an initial energy storage power station health state evaluation model with minimum evaluation deviation as an energy storage power station health state evaluation model.
Optionally, the rule for establishing the initial model matrix for estimating the state of health of the energy storage battery includes:
Acquiring health data of the full life cycle of the energy storage batteries of various types based on an accelerated aging test;
Aiming at various types of energy storage batteries, based on health data of the whole life cycle of the energy storage batteries, combining an energy storage battery health state mechanism model and various data modeling modes, respectively establishing a plurality of energy storage power station health state evaluation initial models corresponding to the various types of energy storage batteries;
Based on the actual operation characteristics of the energy storage power stations, correcting the initial model of the health state evaluation of each energy storage power station corresponding to the energy storage batteries of various types in sequence; the actual operation characteristics represent the operation characteristics of the energy storage power station in different operation scenes;
and establishing an initial model matrix for estimating the health state of the energy storage battery based on the initial model for estimating the health state of each energy storage power station after the correction corresponding to the energy storage batteries of various types.
Optionally, the above multiple data modeling modes include:
Based on a plurality of AI algorithms, carrying out data modeling; wherein,
The plurality of AI algorithms include at least decision trees, random forests, support vector machines, and/or neural networks.
Optionally, the historical recycling data of the energy storage power station comprises a plurality of groups of historical operation data and energy storage power station health state values corresponding to each group of historical operation data, and the evaluation deviation comprises energy storage power station health state square deviation data and energy storage power station health state standard deviation data;
The above-mentioned based on the historical cyclic utilization data of energy storage power station, evaluate each energy storage power station health state evaluation initial model, obtain the evaluation deviation that each energy storage power station health state evaluation initial model corresponds, include:
The method comprises the steps of evaluating an initial model aiming at the health state of each energy storage power station, taking each group of historical operation data of the energy storage power station as input, and testing the initial model for evaluating the health state of the energy storage power station to obtain a health state test value of the energy storage power station corresponding to each group of historical operation data;
And evaluating an initial model aiming at the health state of each energy storage power station, and obtaining corresponding energy storage power station health state square error data and energy storage power station health state standard deviation data based on the health state value of the energy storage power station corresponding to each group of historical operation data and the corresponding health state test value of the energy storage power station.
Optionally, the end side is configured to:
Collecting operation data of the energy storage power station from an energy storage battery compartment and a boosting converter compartment of the energy storage power station; the energy storage power station operation data at least comprises battery numbers, voltages, temperatures, currents and/or charge and discharge power;
preprocessing the operation data of the energy storage power station, and transmitting the preprocessed operation data of the energy storage power station to the side; wherein the preprocessing includes data cleansing and data screening.
Optionally, the end side is further configured to:
based on the recovered health state evaluation result of the energy storage power station, performing fault judgment of the energy storage power station;
based on the fault judgment result, whether to send out alarm information is determined.
Optionally, after acquiring the preprocessed energy storage power station operation data transmitted by the end side, the method further includes:
storing the preprocessed operation data of the energy storage power station to a data storage platform; wherein,
The data storage platform adopts a time sequence database and a relational database to carry out distributed storage on the operation data of the energy storage power station after pretreatment.
The second aspect of the present invention provides an energy storage power station health status evaluation system deployed on an edge side, the edge side being communicatively connected with a cloud side and an end side, the system comprising:
The characteristic parameter conversion module is used for acquiring the preprocessed energy storage power station operation data transmitted by the end side, and converting the preprocessed energy storage power station operation data into preset characteristic parameters to obtain corresponding characteristic parameters of the energy storage power station;
The data fusion module is used for carrying out data fusion based on the characteristic parameters of the energy storage power station and the actual environment working condition data of the energy storage power station to obtain fusion characteristic information of the energy storage power station;
The health state evaluation module is used for transmitting the fusion characteristic information of the energy storage power station to the cloud side so that the cloud side can evaluate the health state of the energy storage power station based on the health state evaluation model of the energy storage power station and the fusion characteristic information of the energy storage power station, and recovering the health state evaluation result of the energy storage power station fed back by the cloud side.
Optionally, a positive isolation gatekeeper is arranged between the side and the end side.
In a third aspect the invention provides a machine readable storage medium having stored thereon instructions which, when executed by a processor, cause the processor to be configured to perform the energy storage power station health assessment method described above.
In a fourth aspect of the present invention, an electronic device is provided, the electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the method for evaluating the health status of an energy storage power station when executing the computer program.
Through the technical scheme, the energy storage power station health state assessment method and the energy storage power station health state assessment system are provided, a cloud-side-end collaborative data processing architecture is adopted for data analysis, the side receives the preprocessed energy storage power station operation data transmitted by the side, and the preprocessed energy storage power station operation data is converted into energy storage power station characteristic parameters capable of reflecting the health state characteristics of the energy storage power station. And carrying out data fusion on the characteristic parameters of the energy storage power station and the actual environment working condition data of the energy storage power station to obtain fusion characteristic information of the energy storage power station, wherein the fusion characteristic information of the energy storage power station reserves various characteristic information of the energy storage power station so as to provide support for later decision analysis. And transmitting the fusion characteristic information of the energy storage power station to a cloud side, and inputting the fusion characteristic information of the energy storage power station to a corresponding energy storage power station health state evaluation model on the cloud side to evaluate the health state of the energy storage power station, so as to obtain an evaluation result of the health state of the energy storage power station. And recovering the health state evaluation result of the energy storage power station fed back by the cloud side by the side. Therefore, the purpose of rapidly evaluating and predicting the health state of the energy storage power station is achieved through mining and analyzing the operation data of the energy storage power station. In addition, the method and the system adopt a cloud-side-end three-level architecture to develop multi-element data fusion of the energy storage power station, effectively solve the problems of huge monitoring data quantity and few monitoring points of effective data of the energy storage power station, and reduce the pressure of cloud data transmission and storage.
Additional features and advantages of embodiments of the invention will be set forth in the detailed description which follows.
Drawings
The accompanying drawings are included to provide a further understanding of embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain, without limitation, the embodiments of the invention. In the drawings:
FIG. 1 is a flow chart of a method for evaluating the health status of an energy storage power station according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of data interaction of an energy storage power station according to an embodiment of the present invention;
FIG. 3 is a block diagram of an energy storage power station health assessment system provided in accordance with one embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to a preferred embodiment of the present invention.
Description of the reference numerals
10-Electronic device, 100-processor, 101-memory, 102-computer program.
Detailed Description
The following describes specific embodiments of the present invention in detail with reference to the drawings. It should be understood that the detailed description and specific examples, while indicating and illustrating the invention, are not intended to limit the invention.
FIG. 1 is a flow chart of a method for evaluating the health status of an energy storage power station according to an embodiment of the present invention. As shown in fig. 1, an embodiment of the present invention provides a method for evaluating a health status of an energy storage power station, where the method is performed by a side, and the side is communicatively connected to a cloud side and an end side, and the method includes:
s110: acquiring preprocessed energy storage power station operation data transmitted from an end side, and converting the preprocessed energy storage power station operation data into preset characteristic parameters to obtain corresponding energy storage power station characteristic parameters;
The characteristic parameters of the energy storage power station are parameters for representing and reflecting the characteristics of the energy storage power station, and at least comprise a current change rate, a voltage change rate, a temperature change rate, an SOC parameter, an SOH parameter and/or an impedance parameter.
Further, the end side is configured to: collecting operation data of the energy storage power station from an energy storage battery compartment and a boosting converter compartment of the energy storage power station; the energy storage power station operation data at least comprises battery numbers, voltages, temperatures, currents and/or charge and discharge power; preprocessing the operation data of the energy storage power station, and transmitting the preprocessed operation data of the energy storage power station to the side; wherein the preprocessing includes data cleansing and data screening.
Specifically, a cloud-side-end cooperative data processing architecture is adopted to perform data analysis, a data acquisition function and a data cleaning and data screening function are configured at the end side, so that the operation data of the energy storage power station are acquired at the end side, and the operation data of the energy storage power station are cleaned and screened so as to be convenient for analysis application. And the end side transmits the operation data of the energy storage power station after cleaning and screening to the side. The regional energy storage power station characteristic fusion function is configured on the side, so that after the side receives the washed and screened energy storage power station operation data, the washed and screened energy storage power station operation data (namely, the data of current, voltage, temperature and the like which can be directly measured by the energy storage power station) are converted into characteristic parameters reflecting the health state of the energy storage battery, and the characteristic parameters (such as current change rate, voltage change rate, temperature change rate, SOC, SOH, impedance and the like) of the energy storage power station are obtained.
S120: based on the characteristic parameters of the energy storage power station and the actual environment working condition data of the energy storage power station, carrying out data fusion to obtain fusion characteristic information of the energy storage power station;
Further, the actual environmental condition data of the energy storage power station comprises battery types, operation conditions and operation environments of the energy storage power station; the above-mentioned based on energy storage power station characteristic parameter and energy storage power station's actual environment operating mode data, carry out data fusion and obtain energy storage power station and fuse characteristic information, include: and fusing the battery type, the operation condition, the operation environment, the current change rate, the voltage change rate, the temperature change rate, the SOC parameter, the SOH parameter and/or the impedance parameter of the energy storage power station to obtain the fusion characteristic information of the energy storage power station.
Specifically, at the side, the characteristic parameters of the energy storage power station are associated with the battery type, the operation working condition and the operation environment condition of the energy storage power station, namely, the characteristic parameters of the charge and discharge power, the time, the operation temperature of the energy storage battery and the like, and data fusion is carried out to obtain fusion characteristic information of the energy storage power station. The energy storage power station fuses the characteristic information and reserves various characteristic information of the energy storage power station so as to provide support for later decision analysis.
S130: and transmitting the fusion characteristic information of the energy storage power station to the cloud side so that the cloud side can evaluate the health state of the energy storage power station based on the health state evaluation model of the energy storage power station and the fusion characteristic information of the energy storage power station, and recovering the health state evaluation result of the energy storage power station fed back by the cloud side.
In detail, an energy storage power station health state evaluation function is configured on the cloud side, fusion characteristic information of the energy storage power station uploaded to the cloud side by the side is input into a corresponding energy storage power station health state evaluation model to perform energy storage power station health state evaluation, and the cloud side feeds back an energy storage power station health state evaluation result to the side.
Specifically, the method adopts a cloud-side-end cooperative data processing architecture to perform data analysis, the side receives the preprocessed energy storage power station operation data transmitted from the side, and the preprocessed energy storage power station operation data is converted into energy storage power station characteristic parameters capable of reflecting the health state characteristics of the energy storage power station. And carrying out data fusion on the characteristic parameters of the energy storage power station and the actual environment working condition data of the energy storage power station to obtain fusion characteristic information of the energy storage power station, wherein the fusion characteristic information of the energy storage power station reserves various characteristic information of the energy storage power station so as to provide support for later decision analysis. And transmitting the fusion characteristic information of the energy storage power station to a cloud side, and inputting the fusion characteristic information of the energy storage power station to a corresponding energy storage power station health state evaluation model on the cloud side to evaluate the health state of the energy storage power station, so as to obtain an evaluation result of the health state of the energy storage power station. And recovering the health state evaluation result of the energy storage power station fed back by the cloud side by the side. Therefore, the purpose of rapidly evaluating and predicting the health state of the energy storage power station is achieved through mining and analyzing the operation data of the energy storage power station. In addition, the method adopts a cloud-side-end three-level architecture to develop multi-data fusion of the energy storage power station, so that the problems of huge monitoring data quantity and few monitoring points of effective data of the energy storage power station are effectively solved, and the pressure of cloud data transmission and storage is reduced.
In some implementations of this embodiment, the cloud side is configured to: determining a corresponding initial model for estimating the health state of each energy storage power station from a pre-established initial model matrix for estimating the health state of the energy storage battery according to the battery type of the energy storage power station; based on historical recycling data of the energy storage power stations, evaluating the initial model of the health state evaluation of each energy storage power station to obtain evaluation deviation corresponding to the initial model of the health state evaluation of each energy storage power station; and determining an initial energy storage power station health state evaluation model with minimum evaluation deviation as an energy storage power station health state evaluation model.
Specifically, when the cloud side is triggered to evaluate the health state of the energy storage power station, the cloud side retrieves each energy storage power station health state evaluation initial model matched with the battery type of the energy storage power station from a pre-established energy storage battery health state evaluation initial model matrix according to the battery type of the energy storage power station contained in the fusion characteristic information of the energy storage power station, and utilizes historical recycling data of the energy storage power station as test data to evaluate and test each energy storage power station health state evaluation initial model, and takes the energy storage power station health state evaluation initial model with the smallest evaluation deviation as the energy storage power station health state evaluation model. The method takes the historical cyclic use data of the energy storage power station as a reference, and ensures the pertinence and the effectiveness of the selected energy storage power station health state evaluation model. The method and the device realize data testing and modeling training through the initial model matrix for estimating the state of health of the energy storage battery, and obtain the energy storage power station state of health estimation model with optimal effect so as to estimate and predict the state of health of the energy storage power station.
Further, the historical recycling data of the energy storage power station comprises a plurality of groups of historical operation data and energy storage power station health state values corresponding to the historical operation data, and the evaluation deviation comprises energy storage power station health state square deviation data and energy storage power station health state standard deviation data; the above-mentioned based on the historical cyclic utilization data of energy storage power station, evaluate each energy storage power station health state evaluation initial model, obtain the evaluation deviation that each energy storage power station health state evaluation initial model corresponds, include: the method comprises the steps of evaluating an initial model aiming at the health state of each energy storage power station, taking each group of historical operation data of the energy storage power station as input, and testing the initial model for evaluating the health state of the energy storage power station to obtain a health state test value of the energy storage power station corresponding to each group of historical operation data; and evaluating an initial model aiming at the health state of each energy storage power station, and obtaining corresponding energy storage power station health state square error data and energy storage power station health state standard deviation data based on the health state value of the energy storage power station corresponding to each group of historical operation data and the corresponding health state test value of the energy storage power station.
Specifically, in the process of selecting the health state evaluation model of the energy storage power station, according to a pre-laboratory test result (it is to be noted that, the initial model matrix for health state evaluation of the energy storage battery is obtained on the basis of an accelerated aging test, and then the pre-laboratory test result is the accelerated aging test result), and by combining multiple sets of historical operation data of the energy storage power station and health state values (for example, the previous 100 actual cycle data) of the energy storage power station corresponding to each set of historical operation data, taking the square variance and standard deviation of the health state of the energy storage battery as judgment basis, the initial model for health state evaluation of the energy storage power station with the smallest deviation is selected as the health state evaluation model of the energy storage power station.
In some implementations of the present embodiments, after the energy storage power station health status assessment model is selected, the model assessment result is assessed periodically in an actual application process of the energy storage power station health status assessment model, and the energy storage power station health status assessment model is dynamically updated by using real-time operation data of the energy storage power station, so as to ensure accuracy of the energy storage power station health status assessment model.
In some implementations of this embodiment, the rule for establishing the initial model matrix for estimating the state of health of the energy storage battery includes: acquiring health data of the whole life cycle of the energy storage batteries of various types in a laboratory based on an accelerated aging test; aiming at various types of energy storage batteries, based on health data of the whole life cycle of the energy storage batteries, combining an energy storage battery health state mechanism model and various data modeling modes, respectively establishing a plurality of energy storage power station health state evaluation initial models corresponding to the various types of energy storage batteries; based on the actual operation characteristics of the energy storage power stations, correcting the initial models of the health state evaluation of each energy storage power station corresponding to the energy storage batteries of various types in sequence, so that the initial models of the health state evaluation of each energy storage power station after correction are available energy storage power station health state evaluation models which accord with the actual operation characteristics of the energy storage power stations, and the pertinence and the effectiveness of the initial models of the health state evaluation of each energy storage power station after correction are ensured; the actual operation characteristics represent the operation characteristics of the energy storage power station in different operation scenes; and establishing an initial model matrix for estimating the health state of the energy storage battery based on the initial model for estimating the health state of each energy storage power station after the correction corresponding to the energy storage batteries of various types. The method adopts a mode of combining a data model and a mechanism model to establish an initial model matrix for evaluating the health state of the energy storage battery.
It should be noted that, the energy storage battery health state mechanism model is: reactants of electrochemical reactions involved in the energy storage battery are in a limited space system at the macroscopic and microscopic levels, and the reactants are not added continuously along with the execution of the reactions, namely the reactions are always in a continuous evolution process in the execution process.
In some implementations of the present embodiment, the plurality of data modeling modes include: based on a plurality of AI algorithms, carrying out data modeling; wherein the plurality of AI algorithms include at least decision trees, random forests, support vector machines, and/or neural networks.
In some implementations of this embodiment, the end side is further configured to: based on the recovered health state evaluation result of the energy storage power station, performing fault judgment of the energy storage power station; based on the fault judgment result, whether to send out alarm information is determined. Therefore, the running health state of the energy storage power station can be timely found and early warned, so that power station staff can take measures conveniently.
In some implementations of this embodiment, after obtaining the preprocessed energy storage power station operational data transmitted by the end side, the method further includes: storing the preprocessed operation data of the energy storage power station to a data storage platform; the data storage platform adopts a time sequence database and a relational database to perform distributed storage on the preprocessed operation data of the energy storage power station.
Further, the data storage platform comprises two parts, namely local storage and cloud storage, wherein the local storage is used for storing the preprocessed operation data of the energy storage power station at the side, and the cloud storage is used for storing fusion characteristic information of the energy storage power station. According to the method, the preprocessed operation data of the energy storage power station is stored by utilizing the side, so that the pressure of cloud data transmission and storage is further effectively reduced.
Referring to fig. 2, fig. 2 is a schematic diagram illustrating data interaction of an energy storage power station according to an embodiment of the invention. An energy storage battery data acquisition device and an application server are arranged in a production area of an energy storage power station as end sides related to the method, the data acquisition device can acquire full data in an energy storage battery cabin and a boosting converter cabin, and in order to ensure the reliability of the data acquisition device, two data networks (namely an energy storage power station data network A and an energy storage power station data network B) are generally adopted for acquisition and are mutually backed up. The application server arranged at the end side is mainly used for cleaning and screening operation data of the energy storage power station, and a fault diagnosis and alarm function is configured in the application server so as to analyze the operation state of the energy storage power station in real time and perform early warning on possible faults. And the data after the cleaning treatment is transmitted to an office area of the energy storage power station through a forward isolation barrier for data analysis and treatment. An application server and a data storage server are arranged in an office area of the energy storage power station as the side related to the method, and the operation data of the energy storage power station are stored in the data storage server at the side for a long time; the feature fusion function is configured in an application server at the side, data such as current, voltage, temperature and the like which can be directly measured by the energy storage power station are converted into parameters reflecting the characteristics of the energy storage battery, such as current change rate, voltage change rate, temperature change rate, SOC, SOH, impedance and the like, the parameters are associated with operation working conditions and operation environment conditions, namely, the parameters such as charge and discharge power, time and the operation temperature of the energy storage battery are associated, data fusion is carried out, and characteristic information obtained by data fusion is shown in a table 1:
TABLE 1 characteristic information obtained by data fusion
The integrated data are sent to a cloud side through a network, an application server and a data storage server are arranged on the cloud side, the data storage server is mainly used for storing the integrated data, the application server is mainly used for training development and health state assessment of a health state model, and health state assessment results are sent to an office area of an energy storage power station through the network.
In the training development process of the health state model, selecting an energy storage battery of the same type as the corresponding energy storage power station, and firstly, developing an accelerated aging test in a laboratory to obtain health data in the whole life cycle of the battery; secondly, establishing an initial model matrix for estimating the health state of the energy storage battery based on digital-analog driving by utilizing the acquired laboratory data and combining an energy storage battery health state mechanism model and adopting various data modeling modes (such as artificial intelligence, a neural network, a random forest and a support vector machine); finally, checking and evaluating the corresponding energy storage power station health state evaluation initial models in the energy storage battery health state evaluation initial model matrix by using the actual operation data of the energy storage power stations, and selecting the energy storage power station health state evaluation initial model with the minimum evaluation deviation as an energy storage power station health state evaluation model for actual application; in the actual application process, the model evaluation result is evaluated regularly, and the model is updated by using the operation data.
FIG. 3 is a block diagram of an energy storage power station health assessment system provided in one embodiment of the present invention. As shown in fig. 3, an embodiment of the present invention provides a health status evaluation system of an energy storage power station, disposed on an edge side, where the edge side is communicatively connected with a cloud side and an end side, and the system includes:
The characteristic parameter conversion module is used for acquiring the preprocessed energy storage power station operation data transmitted by the end side, and converting the preprocessed energy storage power station operation data into preset characteristic parameters to obtain corresponding characteristic parameters of the energy storage power station;
The data fusion module is used for carrying out data fusion based on the characteristic parameters of the energy storage power station and the actual environment working condition data of the energy storage power station to obtain fusion characteristic information of the energy storage power station;
The health state evaluation module is used for transmitting the fusion characteristic information of the energy storage power station to the cloud side so that the cloud side can evaluate the health state of the energy storage power station based on the health state evaluation model of the energy storage power station and the fusion characteristic information of the energy storage power station, and recovering the health state evaluation result of the energy storage power station fed back by the cloud side.
Specifically, the system adopts a cloud-side-end cooperative data processing architecture to perform data analysis, the side receives the preprocessed energy storage power station operation data transmitted from the side, and the preprocessed energy storage power station operation data is converted into energy storage power station characteristic parameters capable of reflecting the health state characteristics of the energy storage power station. And carrying out data fusion on the characteristic parameters of the energy storage power station and the actual environment working condition data of the energy storage power station to obtain fusion characteristic information of the energy storage power station, wherein the fusion characteristic information of the energy storage power station reserves various characteristic information of the energy storage power station so as to provide support for later decision analysis. And transmitting the fusion characteristic information of the energy storage power station to a cloud side, and inputting the fusion characteristic information of the energy storage power station to a corresponding energy storage power station health state evaluation model on the cloud side to evaluate the health state of the energy storage power station, so as to obtain an evaluation result of the health state of the energy storage power station. And recovering the health state evaluation result of the energy storage power station fed back by the cloud side by the side. Therefore, the purpose of rapidly evaluating and predicting the health state of the energy storage power station is achieved through mining and analyzing the operation data of the energy storage power station. The system adopts a cloud-side-end three-level architecture to develop multi-data fusion of the energy storage power station, so that the problems of huge monitoring data quantity and few monitoring points of effective data of the energy storage power station are effectively solved, and the pressure of cloud data transmission and storage is reduced.
In some implementations of this embodiment, a positive isolation barrier is disposed between the side and end sides.
Embodiments of the present invention also provide a machine-readable storage medium having stored thereon instructions that, when executed by the processor 100, cause the processor 100 to be configured to perform the energy storage power station health status assessment method described above.
Machine-readable storage media include both permanent and non-permanent, removable and non-removable media, and information storage may be implemented by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
The embodiment of the present invention further provides an electronic device 10, where the electronic device 10 includes a memory 101, a processor 100, and a computer program 102 stored in the memory 101 and capable of running on the processor 100, and the processor 100 implements the above-mentioned health status assessment method of the energy storage power station when executing the computer program 102.
Fig. 4 is a schematic diagram of an electronic device according to an embodiment of the present invention. As shown in fig. 4, the electronic device 10 of this embodiment includes: a processor 100, a memory 101, and a computer program 102 stored in the memory 101 and executable on the processor 100. The steps of the method embodiments described above are implemented by the processor 100 when executing the computer program 102. Or the processor 100, when executing the computer program 102, performs the functions of the modules/units of the apparatus embodiments described above.
By way of example, computer program 102 may be partitioned into one or more modules/units that are stored in memory 101 and executed by processor 100 to accomplish the present invention. One or more of the modules/units may be a series of computer program instruction segments capable of performing particular functions to describe the execution of the computer program 102 in the electronic device 10. For example, the computer program 102 may be partitioned into a feature parameter transformation module, a data fusion module, and a health status assessment module.
The electronic device 10 may be a desktop computer, a notebook computer, a palm computer, a cloud server, or the like. The electronic device 10 may include, but is not limited to, a processor 100, a memory 101. It will be appreciated by those skilled in the art that fig. 4 is merely an example of the electronic device 10 and is not intended to limit the electronic device 10, and may include more or fewer components than shown, or may combine certain components, or different components, e.g., the electronic device may further include an input-output device, a network access device, a bus, etc.
The Processor 100 may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (DIGITAL SIGNAL Processor, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), off-the-shelf Programmable gate array (Field-Programmable GATE ARRAY, FPGA) or other Programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 101 may be an internal storage unit of the electronic device 10, such as a hard disk or a memory of the electronic device 10. The memory 101 may also be an external storage device of the electronic device 10, such as a plug-in hard disk provided on the electronic device 10, a smart memory card (SMART MEDIA CARD, SMC), a Secure Digital (SD) card, a flash memory card (FLASH CARD), or the like. Further, the memory 101 may also include both internal storage units and external storage devices of the electronic device 10. The memory 101 is used to store computer programs and other programs and data required by the electronic device 10. The memory 101 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, the specific names of the functional units and modules are only for distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program 102 product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program 102 product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program 102 products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program 102 instructions. These computer program 102 instructions may be provided to a processor 100 of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor 100 of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program 102 instructions may also be stored in a computer-readable memory 101 that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory 101 produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program 102 instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises an element.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and variations of the present application will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the application are to be included in the scope of the claims of the present application.
Claims (14)
1. A method for evaluating the health status of an energy storage power station, the method being performed by a side, the side being communicatively coupled to a cloud side and an end side, the method comprising:
Acquiring preprocessed energy storage power station operation data transmitted from an end side, and converting the preprocessed energy storage power station operation data into preset characteristic parameters to obtain corresponding energy storage power station characteristic parameters;
based on the characteristic parameters of the energy storage power station and the actual environment working condition data of the energy storage power station, carrying out data fusion to obtain fusion characteristic information of the energy storage power station;
and transmitting the fusion characteristic information of the energy storage power station to the cloud side so that the cloud side can evaluate the health state of the energy storage power station based on the health state evaluation model of the energy storage power station and the fusion characteristic information of the energy storage power station, and recovering the health state evaluation result of the energy storage power station fed back by the cloud side.
2. The energy storage power station health assessment method of claim 1, wherein the energy storage power station characteristic parameter is a parameter for characterizing and reflecting the energy storage power station characteristic, and the energy storage power station characteristic parameter at least comprises a current change rate, a voltage change rate, a temperature change rate, an SOC parameter, an SOH parameter and/or an impedance parameter.
3. The energy storage power station health assessment method of claim 2, wherein the actual environmental condition data of the energy storage power station comprises a battery type, an operating condition and an operating environment of the energy storage power station;
the method for obtaining the fusion characteristic information of the energy storage power station by data fusion based on the characteristic parameters of the energy storage power station and the actual environment working condition data of the energy storage power station comprises the following steps:
and fusing the battery type, the operation condition, the operation environment, the current change rate, the voltage change rate, the temperature change rate, the SOC parameter, the SOH parameter and/or the impedance parameter of the energy storage power station to obtain the fusion characteristic information of the energy storage power station.
4. The energy storage power plant health assessment method of claim 1, wherein the cloud side is configured to:
Determining a corresponding initial model for estimating the health state of each energy storage power station from a pre-established initial model matrix for estimating the health state of the energy storage battery according to the battery type of the energy storage power station;
based on historical recycling data of the energy storage power stations, evaluating the initial model of the health state evaluation of each energy storage power station to obtain evaluation deviation corresponding to the initial model of the health state evaluation of each energy storage power station;
And determining an initial energy storage power station health state evaluation model with minimum evaluation deviation as an energy storage power station health state evaluation model.
5. The method of claim 4, wherein the establishing rules of the initial model matrix for estimating the state of health of the energy storage battery comprise:
Acquiring health data of the full life cycle of the energy storage batteries of various types based on an accelerated aging test;
Aiming at various types of energy storage batteries, based on health data of the whole life cycle of the energy storage batteries, combining an energy storage battery health state mechanism model and various data modeling modes, respectively establishing a plurality of energy storage power station health state evaluation initial models corresponding to the various types of energy storage batteries;
Based on the actual operation characteristics of the energy storage power stations, correcting the initial model of the health state evaluation of each energy storage power station corresponding to the energy storage batteries of various types in sequence; the actual operation characteristics represent the operation characteristics of the energy storage power station in different operation scenes;
and establishing an initial model matrix for estimating the health state of the energy storage battery based on the initial model for estimating the health state of each energy storage power station after the correction corresponding to the energy storage batteries of various types.
6. The method of claim 5, wherein the plurality of data modeling approaches comprise:
Based on a plurality of AI algorithms, carrying out data modeling; wherein,
The plurality of AI algorithms include at least decision trees, random forests, support vector machines, and/or neural networks.
7. The energy storage power station health state assessment method according to claim 4, wherein the historical recycling data of the energy storage power station comprises a plurality of groups of historical operation data and energy storage power station health state values corresponding to the groups of historical operation data, and the assessment deviation comprises energy storage power station health state square error data and energy storage power station health state standard deviation data;
Based on the historical cyclic use data of the energy storage power stations, the initial model of the health state evaluation of each energy storage power station is evaluated to obtain the evaluation deviation corresponding to the initial model of the health state evaluation of each energy storage power station, and the method comprises the following steps:
The method comprises the steps of evaluating an initial model aiming at the health state of each energy storage power station, taking each group of historical operation data of the energy storage power station as input, and testing the initial model for evaluating the health state of the energy storage power station to obtain a health state test value of the energy storage power station corresponding to each group of historical operation data;
And evaluating an initial model aiming at the health state of each energy storage power station, and obtaining corresponding energy storage power station health state square error data and energy storage power station health state standard deviation data based on the health state value of the energy storage power station corresponding to each group of historical operation data and the corresponding health state test value of the energy storage power station.
8. The energy storage power plant health assessment method of claim 1, wherein the end side is configured to:
collecting operation data of the energy storage power station from an energy storage battery compartment and a boosting converter compartment of the energy storage power station; the energy storage power station operation data at least comprise battery numbers, voltages, temperatures, currents and/or charge and discharge power;
preprocessing the operation data of the energy storage power station, and transmitting the preprocessed operation data of the energy storage power station to the side; wherein the preprocessing includes data cleansing and data screening.
9. The energy storage power plant health assessment method of claim 1, wherein the end side is further configured to:
based on the recovered health state evaluation result of the energy storage power station, performing fault judgment of the energy storage power station;
based on the fault judgment result, whether to send out alarm information is determined.
10. The energy storage power station health assessment method of claim 1, further comprising, after obtaining the preprocessed energy storage power station operational data transmitted by the end side:
storing the preprocessed operation data of the energy storage power station to a data storage platform; wherein,
And the data storage platform adopts a time sequence database and a relational database to perform distributed storage on the preprocessed operation data of the energy storage power station.
11. An energy storage power station health state assessment system, characterized by being deployed on an edge side, the edge side being communicatively connected with a cloud side and an end side, the system comprising:
The characteristic parameter conversion module is used for acquiring the preprocessed energy storage power station operation data transmitted by the end side, and converting the preprocessed energy storage power station operation data into preset characteristic parameters to obtain corresponding characteristic parameters of the energy storage power station;
The data fusion module is used for carrying out data fusion based on the characteristic parameters of the energy storage power station and the actual environment working condition data of the energy storage power station to obtain fusion characteristic information of the energy storage power station;
The health state evaluation module is used for transmitting the fusion characteristic information of the energy storage power station to the cloud side so that the cloud side can evaluate the health state of the energy storage power station based on the health state evaluation model of the energy storage power station and the fusion characteristic information of the energy storage power station, and recovering the health state evaluation result of the energy storage power station fed back by the cloud side.
12. The energy storage power plant health assessment system of claim 11, wherein a forward isolation barrier is disposed between the side and end sides.
13. A machine-readable storage medium having instructions stored thereon, which when executed by a processor cause the processor to be configured to perform the energy storage power station health assessment method of any of claims 1 to 10.
14. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the energy storage power station health assessment method of any one of claims 1 to 10 when the computer program is executed by the processor.
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