CN115825756A - Distributed energy storage power station fault multi-stage diagnosis method and system - Google Patents
Distributed energy storage power station fault multi-stage diagnosis method and system Download PDFInfo
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Abstract
The application provides a distributed energy storage power station fault multistage diagnosis method and a system, wherein the method comprises the following steps: collecting operation data of each battery in a distributed energy storage power station, and preprocessing the operation data; respectively determining multistage diagnostic data corresponding to the distributed energy storage power station according to the preprocessed operating data; and predicting the fault of the distributed energy storage power station according to the multistage diagnosis data. The technical scheme provided by the application has higher compatibility, and can accurately diagnose the fault of the distributed energy storage power station.
Description
Technical Field
The application relates to the technical field of energy storage, in particular to a distributed energy storage power station fault multi-stage diagnosis method and system.
Background
In recent years, the installation of lithium battery energy storage systems at home and abroad is rapidly increased, because the internal and external characteristics of battery energy storage power station equipment are different from those of conventional electrical equipment, the battery energy storage power station equipment has typical electrochemical dynamic evolution characteristics, performance maintenance cannot move the operation maintenance rules of the conventional electrical equipment, the problems of battery performance attenuation, large energy consumption, serious life loss and the like can be caused by improper operation maintenance, and even potential safety hazards can exist, so that the evaluation and prediction of the health state of a battery are vital to the healthy and stable operation of the system, accurate state evaluation and trend prediction are helpful to find out a problem battery, the system maintenance is convenient, and the healthy and stable of the energy storage system is ensured.
The relevant scholars have conducted relevant studies such as state of charge (SOC) estimation, battery internal resistance estimation, remaining service life (RUL) prediction, etc., however, there are few studies on state detection in real-time state of a lithium battery power station. The battery energy storage power station can produce a large amount of data in the operation and maintenance process, and from the perspective of improving the operation and maintenance effect, how to efficiently utilize the various and large-quantity storage data, and the accurate diagnosis of the fault of the energy storage power station is a technical problem which needs to be solved urgently at present.
Disclosure of Invention
The application provides a distributed energy storage power station fault multistage diagnosis method and system, which aim to at least solve the technical problem of accurately diagnosing faults of an energy storage power station by efficiently utilizing various and large-quantity storage data.
The embodiment of the first aspect of the application provides a distributed energy storage power station fault multi-stage diagnosis method, which comprises the following steps:
collecting operation data of each battery in a distributed energy storage power station, and preprocessing the operation data;
respectively determining multistage diagnostic data corresponding to the distributed energy storage power station according to the preprocessed operating data;
and predicting the fault of the distributed energy storage power station according to the multistage diagnosis data.
Preferably, the determining of the multi-stage diagnosis data corresponding to the distributed energy storage power stations according to the preprocessed operation data includes
Screening out characteristic values of all batteries in the distributed energy storage power station from the operation data, and determining basic data index values of all the batteries based on the characteristic values of all the batteries;
screening primary diagnostic data of each battery in the distributed energy storage power station from the data, and determining a primary diagnostic data index value of each battery based on the primary diagnostic data of each battery;
determining the voltage standard score, the coulombic efficiency and the IC curve peak value of each battery according to the operation data, and determining a secondary diagnosis data index value of each battery based on the voltage standard score, the coulombic efficiency and the IC curve peak value;
determining a weight coefficient of a secondary diagnostic data index value of each battery respectively, and determining a tertiary diagnostic data index value of each battery based on the weight coefficient;
wherein the multi-level diagnostic data comprises: a base data index value, a primary diagnostic data index value, a secondary diagnostic data index value, and a tertiary diagnostic data index value for each battery.
Further, the calculation formula of the voltage standard score of each battery is as follows:
in the formula (I), the compound is shown in the specification,
for the voltage standard of the ith battery in the distributed energy storage power station,
for the voltage value of the ith battery in the distributed energy storage power station,
the average value of the voltage of the batteries in the decentralized energy storage plant,
is the standard deviation of the voltage;
the coulombic efficiency of each cell is calculated as follows:
in the formula (I), the compound is shown in the specification,
for the coulomb efficiency of the ith battery in a decentralized energy storage plant,
for the discharge capacitance of the ith battery in the decentralized energy storage plant,
the charging capacitor is the charging capacitor of the ith battery in the distributed energy storage power station.
Further, the calculation formula of the weight coefficient of the secondary diagnostic data index value of each battery is as follows:
in the formula (I), the compound is shown in the specification,
for the value of the d index in the index values of the secondary diagnostic data of the ith battery in the decentralized energy storage power station,
the standard deviation of the d index in the index values of the secondary diagnostic data of the ith battery in the distributed energy storage power station,
the average value of the d index in the index values of the secondary diagnosis data of the ith battery in the distributed energy storage power station,
the number of secondary diagnostic data indexes is shown.
Further, the determining the index value of the three-level diagnostic data of each battery based on the weight coefficient includes:
determining a single-factor diagnostic value of each index in the secondary diagnostic data indexes of each battery;
and determining the three-level diagnostic data index value of each battery according to the weight coefficient and the single-factor diagnostic value of each index.
Further, the calculation formula of the three-level diagnostic data index value of each cell is as follows:
in the formula (I), the compound is shown in the specification,
the three-level diagnostic data index value of the ith battery in the distributed energy storage power station,
for the single-factor diagnostic value matrix of the ith battery in the distributed energy storage power station,
is a matrix of weight coefficients.
Further, the predicting the fault of the distributed energy storage power station according to the multi-stage diagnosis data comprises:
carrying out standardization processing on the basic data index value, the primary diagnosis data index value, the secondary diagnosis data index value and the tertiary diagnosis data index value of each battery;
and substituting the standardized basic data index value, the first-stage diagnosis data index value, the second-stage diagnosis data index value and the third-stage diagnosis data index value of each battery into a pre-constructed comprehensive prediction model to obtain a fault prediction result of the distributed energy storage power station.
Further, the method further comprises:
and displaying the fault prediction result of the distributed energy storage power station.
The embodiment of the second aspect of the present application provides a distributed energy storage power station fault multistage diagnosis system, where the system includes:
the acquisition module is used for acquiring the operation data of each battery in the distributed energy storage power station and preprocessing the operation data;
the determining module is used for respectively determining the multi-stage diagnosis data corresponding to the distributed energy storage power station according to the preprocessed operation data;
and the diagnosis module is used for predicting the fault of the distributed energy storage power station according to the multi-stage diagnosis data.
Preferably, the determination module comprises
The first determining unit is used for screening out the characteristic values of all batteries in the distributed energy storage power station from the operation data and determining the basic data index values of all the batteries based on the characteristic values of all the batteries;
the second determining unit is used for screening out primary diagnostic data of each battery in the distributed energy storage power station from the data and determining a primary diagnostic data index value of each battery based on the primary diagnostic data of each battery;
the third determining unit is used for determining the voltage standard score, the coulombic efficiency and the IC curve peak value of each battery according to the operation data, and determining the secondary diagnosis data index value of each battery based on the voltage standard score, the coulombic efficiency and the IC curve peak value;
a fourth determination unit configured to determine a weight coefficient of a secondary diagnostic data index value of each battery, respectively, and determine a tertiary diagnostic data index value of each battery based on the weight coefficient;
wherein the multi-level diagnostic data comprises: a base data index value, a primary diagnostic data index value, a secondary diagnostic data index value, and a tertiary diagnostic data index value for each battery.
The technical scheme provided by the embodiment of the application at least has the following beneficial effects:
the application provides a distributed energy storage power station fault multistage diagnosis method and a system, wherein the method comprises the following steps: collecting operation data of each battery in a distributed energy storage power station, and preprocessing the operation data; respectively determining multistage diagnostic data corresponding to the distributed energy storage power station according to the preprocessed operating data; and predicting the fault of the distributed energy storage power station according to the multistage diagnosis data. The technical scheme provided by the application has higher compatibility, and can accurately diagnose the fault of the distributed energy storage power station.
Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.
Drawings
The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a flow chart of a distributed energy storage power station fault multi-stage diagnostic method provided according to an embodiment of the present application;
FIG. 2 is a schematic diagram of connection of electrical appliances in a distributed energy storage power station fault multi-stage diagnosis method according to an embodiment of the present application;
fig. 3 is a first structural diagram of a distributed energy storage power station fault multi-stage diagnosis system according to an embodiment of the present application;
FIG. 4 is a block diagram of a determination module provided in accordance with one embodiment of the present application;
fig. 5 is a second structural diagram of a distributed energy storage power station fault multi-stage diagnosis system according to an embodiment of the present application.
Detailed Description
Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary and intended to be used for explaining the present application and should not be construed as limiting the present application.
The application provides a distributed energy storage power station fault multistage diagnosis method and system, and the method comprises the following steps: collecting operation data of each battery in a distributed energy storage power station, and preprocessing the operation data; respectively determining multi-stage diagnosis data corresponding to the distributed energy storage power station according to the preprocessed operation data; and predicting the fault of the distributed energy storage power station according to the multi-stage diagnosis data. The technical scheme provided by the application has higher compatibility, and can accurately diagnose the fault of the distributed energy storage power station.
The distributed energy storage power station fault multi-stage diagnosis method and system according to the embodiment of the application are described below with reference to the accompanying drawings.
Example one
Fig. 1 is a flowchart of a distributed energy storage power station fault multi-stage diagnosis method according to an embodiment of the present application, as shown in fig. 1, the method includes:
step 1: collecting operation data of each battery in a distributed energy storage power station, and preprocessing the operation data;
in the embodiment of the disclosure, the operation data of each Battery in the distributed energy storage power station is acquired in real time from a multilevel Battery Data Acquisition System (BDAS);
it should be noted that, as shown in fig. 2, the BDAS is connected with the battery cells, the modules and the clusters one by one, the BDAS connected with the battery cells is a primary BDAS, the BDAS connected with the battery modules is a secondary BDAS, the BDAS connected with the battery clusters is a tertiary BDAS, and the primary BDAS is collected in the secondary and then collected in the tertiary, so as to form a multi-level BDAS.
Further, the preprocessing the operation data includes:
and (3) cleaning operation is carried out on the operation data of each battery: processing missing value data, processing noise data and format arrangement of data;
and for the missing value or the obvious and unreasonable abnormal value, replacing and assigning by adopting the number average value of a period of time before and after the value.
Step 2: respectively determining multistage diagnostic data corresponding to the distributed energy storage power station according to the preprocessed operating data;
wherein the multi-level diagnostic data comprises: a base data index value, a primary diagnostic data index value, a secondary diagnostic data index value, and a tertiary diagnostic data index value for each battery.
In an embodiment of the present disclosure, the step 2 specifically includes:
step 2-1: screening out characteristic values of all batteries in the distributed energy storage power station from the operation data, and determining basic data index values of all the batteries based on the characteristic values of all the batteries;
it should be noted that the characteristic values include: one or more of battery charge-discharge capacity, voltage, current, charge-discharge time, power, battery internal resistance and temperature.
And taking one or more of the charge-discharge capacity, voltage, current, charge-discharge time, power, internal resistance and temperature of the battery as basic data index values of each battery.
Step 2-2: screening primary diagnostic data of each battery in the distributed energy storage power station from the data, and determining a primary diagnostic data index value of each battery based on the primary diagnostic data of each battery;
it should be noted that, according to the index definition, a threshold of the first-level diagnostic data index is obtained through relevant standards and statistics, threshold detection is performed, whether the first-level diagnostic data exceeds the threshold is determined, and if the first-level diagnostic data exceeds the threshold, the first-level diagnostic data is regarded as abnormal.
Step 2-3: and determining the voltage standard score, the coulombic efficiency and the IC curve peak value of each battery according to the operation data, and determining a secondary diagnosis data index value of each battery based on the voltage standard score, the coulombic efficiency and the IC curve peak value, wherein the voltage standard score, the coulombic efficiency and the IC curve peak value are used as the secondary diagnosis data index value of each battery.
Further, the calculation formula of the voltage standard score of each battery is as follows:
in the formula (I), the compound is shown in the specification,
for the voltage standard of the ith battery in the distributed energy storage power station,
for the voltage value of the ith battery in the distributed energy storage power station,
the average value of the voltage of the batteries in the decentralized energy storage plant,
is the standard deviation of the voltage;
the coulombic efficiency of each cell is calculated as follows:
in the formula (I), the compound is shown in the specification,
for the coulomb efficiency of the ith cell in a decentralized energy storage plant,
for the discharge capacitance of the ith battery in the decentralized energy storage plant,
the charging capacitor is the charging capacitor of the ith battery in the distributed energy storage power station.
The determination of the peak value of the IC curve of each battery comprises the following steps: obtaining an OCV curve of the battery according to the first-order equivalent circuit model to obtain dV, obtaining dQ according to combined ampere-hour integration and Kalman filtering calculation, finally obtaining a relation curve of which the IC curve is dQ/dV-SOC, and determining the peak value of the IC curve of the battery in the relation curve of which the IC curve is dQ/dV-SOC.
It should be noted that consistency analysis is performed on the voltage standard score, the coulomb efficiency and the IC curve peak value of each battery, whether a battery with poor consistency exists is judged, and if yes, the battery is regarded as abnormal;
illustratively, a voltage standard score median is determined according to the voltage standard score, and if the voltage standard score median is less than 1, the battery is healthy; if the voltage standard of 1< | is divided into median values of | <2, the batteries have inconsistency; if the 2< | voltage standard is divided into median values | <3, the battery fault phenomenon begins to worsen; if the 3< | voltage standard is divided into median | values, the battery needs to intervene on a fault site.
Step 2-4: a weight coefficient of a secondary diagnostic data index value of each battery is determined, and a tertiary diagnostic data index value of each battery is determined based on the weight coefficient.
Wherein the calculation formula of the weight coefficient of the secondary diagnostic data index value of each battery is as follows:
in the formula (I), the compound is shown in the specification,
the value of the d index in the index values of the secondary diagnosis data of the ith battery in the distributed energy storage power station,
the standard deviation of the d index in the index values of the secondary diagnostic data of the ith battery in the distributed energy storage power station,
the average value of the d index in the index values of the secondary diagnosis data of the ith battery in the distributed energy storage power station,
the number of secondary diagnosis data indexes is shown.
Further, the determining the index value of the three-level diagnostic data of each battery based on the weight coefficient includes:
determining a single-factor diagnostic value of each index in the secondary diagnostic data indexes of each battery;
and determining the three-level diagnostic data index value of each battery according to the weight coefficient and the single-factor diagnostic value of each index.
It should be noted that the determining of the single-factor diagnostic value of each index in the secondary diagnostic data index of each battery includes:
determining the membership function of each index in the secondary diagnostic data indexes, and calculating the membership function value of each index;
and taking the membership function value of each index as a single-factor diagnostic value of each index.
The membership function of the voltage standard index is parabolic, the membership function of the coulombic efficiency index is S-shaped, and the membership function of the IC curve peak index is linear.
The parabolic membership function
The index is most beneficial to the battery state in a certain range, and the battery health is not benefited when the index deviates from the range and increases or decreases, and the calculation formula is as follows:
the S-type membership function
The index is related to the battery state in a positive or negative way in a certain range, the influence on the battery state is not large when the index exceeds the range, the index is mostly used for beneficial or harmful factors, and the calculation formula is as follows:
the linear type membership function
The index has no obvious upper and lower limits, and is in linear relation with the battery health state along with the change of the index, and the calculation formula is as follows:
；
Further, the calculation formula of the three-level diagnostic data index value of each battery is as follows:
in the formula (I), the compound is shown in the specification,
the index value of the three-level diagnosis data of the ith battery in the distributed energy storage power station,
for the single-factor diagnostic value matrix of the ith battery in the distributed energy storage power station,
is a matrix of weight coefficients.
And step 3: and predicting the fault of the distributed energy storage power station according to the multi-stage diagnosis data.
In an embodiment of the present disclosure, the step 3 specifically includes:
step 3-1: carrying out standardization processing on the basic data index value, the primary diagnosis data index value, the secondary diagnosis data index value and the tertiary diagnosis data index value of each battery;
step 3-2: and substituting the standardized basic data index value, the first-stage diagnosis data index value, the second-stage diagnosis data index value and the third-stage diagnosis data index value of each battery into a pre-constructed comprehensive prediction model to obtain a fault prediction result of the distributed energy storage power station.
The pre-constructed comprehensive prediction model can be obtained by fusing one or more models of a machine learning model (KNN, k-Means, SVM, logistic regression), an integrated learning model (XGboost, lightGBM, catBoost) and a deep learning model (LSTM, BP, CNN).
It should be noted that the training of the model includes:
acquiring basic data index values, primary diagnostic data index values, secondary diagnostic data index values, tertiary diagnostic data index values and corresponding fault results of each battery in a historical period, and performing dimensionless standardization to obtain a model building data set;
and carrying out training by bringing the model construction data set into the comprehensive prediction model, and outputting a fault prediction model result after the loss function is converged through iterative optimization to obtain the trained comprehensive prediction model.
In an embodiment of the disclosure, the method further comprises:
and sending the fault prediction result of the distributed energy storage power station to an EMS system for displaying, as shown in FIG. 2.
It should be noted that, in fig. 2, the PCS is a distributed energy storage power station, the BDAS is used for acquiring and analyzing real-time data of the battery cells, the modules and the cluster levels, the database platform is used for storing data, and the diagnosis center is used for comprehensively summarizing and analyzing data uploaded by the BDAS at each level and feeding back results to the EMS system.
In summary, according to the distributed energy storage power station fault multi-stage diagnosis method provided by the embodiment, the BDAS is connected with the battery, so that data acquisition is more accurate, and meanwhile, the first-stage diagnosis threshold value judgment speed is high, and as the first-stage abnormality detection, the method can be used for quickly judging a relatively serious shallow fault exceeding the threshold value; the secondary diagnosis is based on the consistency of the battery, the health state of the energy storage power station is analyzed systematically from various single-factor angles, and the battery performance and the fault condition can be comprehensively detected in various aspects and at multiple angles; the three-level diagnosis is based on a battery operation experience model, the actual operation condition of the battery is more accurately predicted and judged by combining the chemical characteristics and the data characteristics of the battery, and the method is higher in compatibility, can flexibly match different project requirements, and is very friendly in interface.
Example two
Fig. 3 is a block diagram of a distributed energy storage power station fault multi-stage diagnosis system according to an embodiment of the present application, and as shown in fig. 3, the system includes:
the acquisition module 100 is used for acquiring operation data of each battery in the distributed energy storage power station and preprocessing the operation data;
a determining module 200, configured to determine, according to the preprocessed operating data, multi-stage diagnostic data corresponding to the distributed energy storage power station respectively;
and the diagnosis module 300 is configured to predict the fault of the distributed energy storage power station according to the multi-stage diagnosis data.
In the disclosed embodiment, as shown in fig. 4, the determining module 200 includes
A first determining unit 201, configured to screen out a characteristic value of each battery in the distributed energy storage power station from the operation data, and determine a basic data index value of each battery based on the characteristic value of each battery;
the second determining unit 202 is used for screening out primary diagnostic data of each battery in the distributed energy storage power station from the data, and determining a primary diagnostic data index value of each battery based on the primary diagnostic data of each battery;
a third determining unit 203, configured to determine a voltage standard score, a coulombic efficiency, and an IC curve peak value of each battery according to the operation data, and determine a secondary diagnostic data index value of each battery based on the voltage standard score, the coulombic efficiency, and the IC curve peak value;
a fourth determination unit 204 for determining a weight coefficient of a secondary diagnostic data index value of each battery, respectively, and determining a tertiary diagnostic data index value of each battery based on the weight coefficient;
wherein the multi-level diagnostic data comprises: a base data index value, a primary diagnostic data index value, a secondary diagnostic data index value, and a tertiary diagnostic data index value for each battery.
Further, the calculation formula of the voltage standard score of each battery is as follows:
in the formula (I), the compound is shown in the specification,
for the voltage standard of the ith battery in the distributed energy storage power station,
for the voltage value of the ith battery in the distributed energy storage power station,
is divided intoThe average voltage of the cells in a distributed energy storage plant,
is the standard deviation of the voltage;
the coulombic efficiency of each cell is calculated as follows:
in the formula (I), the compound is shown in the specification,
for the coulomb efficiency of the ith battery in a decentralized energy storage plant,
the discharge capacitance of the ith battery in the distributed energy storage power station,
the charging capacitor is the charging capacitor of the ith battery in the distributed energy storage power station.
Wherein the calculation formula of the weight coefficient of the secondary diagnostic data index value of each battery is as follows:
in the formula (I), the compound is shown in the specification,
the value of the d index in the index values of the secondary diagnosis data of the ith battery in the distributed energy storage power station,
the standard deviation of the d index in the index values of the secondary diagnosis data of the ith battery in the distributed energy storage power station,
the average value of the d index in the index values of the secondary diagnosis data of the ith battery in the distributed energy storage power station,
the number of secondary diagnostic data indexes is shown.
The determining of the index value of the three-level diagnostic data for each battery based on the weight coefficient includes:
determining a single-factor diagnostic value of each index in the secondary diagnostic data indexes of each battery;
and determining the three-level diagnostic data index value of each battery according to the weight coefficient and the single-factor diagnostic value of each index.
Wherein the calculation formula of the three-level diagnostic data index value of each battery is as follows:
in the formula (I), the compound is shown in the specification,
the index value of the three-level diagnosis data of the ith battery in the distributed energy storage power station,
for the single-factor diagnostic value matrix of the ith battery in the distributed energy storage power station,
is a matrix of weight coefficients.
In an embodiment of the present disclosure, the diagnostic module is specifically configured to:
carrying out standardization processing on the basic data index value, the primary diagnosis data index value, the secondary diagnosis data index value and the tertiary diagnosis data index value of each battery;
and substituting the basic data index value, the primary diagnosis data index value, the secondary diagnosis data index value and the tertiary diagnosis data index value of each battery subjected to standardization treatment into a pre-constructed comprehensive prediction model to obtain a fault prediction result of the distributed energy storage power station.
In the embodiment of the present disclosure, as shown in fig. 5, the system further includes: the display module 400 is configured to display the fault prediction result of the distributed energy storage power station.
In summary, the distributed energy storage power station fault multi-stage diagnosis system provided by the embodiment has high compatibility, and can accurately diagnose faults of the distributed energy storage power station.
In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present application.
Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.
Claims (10)
1. A distributed energy storage power station fault multi-stage diagnosis method is characterized by comprising the following steps:
collecting operation data of each battery in a distributed energy storage power station, and preprocessing the operation data;
respectively determining multi-stage diagnosis data corresponding to the distributed energy storage power station according to the preprocessed operation data;
and predicting the fault of the distributed energy storage power station according to the multistage diagnosis data.
2. Method according to claim 1, wherein the determining of the respective multi-stage diagnostic data for the decentralized energy storage power station from the preprocessed operating data comprises
Screening out the characteristic value of each battery in the distributed energy storage power station from the operation data, and determining the basic data index value of each battery based on the characteristic value of each battery;
screening primary diagnostic data of each battery in the distributed energy storage power station from the data, and determining a primary diagnostic data index value of each battery based on the primary diagnostic data of each battery;
determining the voltage standard score, the coulombic efficiency and the IC curve peak value of each battery according to the operation data, and determining a secondary diagnosis data index value of each battery based on the voltage standard score, the coulombic efficiency and the IC curve peak value;
determining a weight coefficient of a secondary diagnostic data index value of each battery respectively, and determining a tertiary diagnostic data index value of each battery based on the weight coefficient;
wherein the multi-level diagnostic data comprises: a base data index value, a primary diagnostic data index value, a secondary diagnostic data index value, and a tertiary diagnostic data index value for each battery.
3. The method of claim 2, wherein the voltage criteria score for each cell is calculated as follows:
in the formula (I), the compound is shown in the specification,
for the voltage standard of the ith battery in the distributed energy storage power station,
for the voltage value of the ith battery in the distributed energy storage power station,
the average value of the voltage of the batteries in the decentralized energy storage plant,
is the standard deviation of the voltage;
the coulombic efficiency of each cell is calculated as follows:
in the formula (I), the compound is shown in the specification,
for the coulomb efficiency of the ith battery in a decentralized energy storage plant,
for the discharge capacitance of the ith battery in the decentralized energy storage plant,
the charging capacitor is the charging capacitor of the ith battery in the distributed energy storage power station.
4. The method according to claim 2, wherein the weight coefficient of the secondary diagnostic data index value for each battery is calculated by:
in the formula (I), the compound is shown in the specification,
the value of the d index in the index values of the secondary diagnosis data of the ith battery in the distributed energy storage power station,
the standard deviation of the d index in the index values of the secondary diagnosis data of the ith battery in the distributed energy storage power station,
the average value of the d index in the index values of the secondary diagnosis data of the ith battery in the distributed energy storage power station,
the number of secondary diagnosis data indexes is shown.
5. The method of claim 2, wherein determining a three-level diagnostic data index value for each cell based on the weight coefficients comprises:
determining a single-factor diagnostic value of each index in the secondary diagnostic data indexes of each battery;
and determining the three-level diagnostic data index value of each battery according to the weight coefficient and the single-factor diagnostic value of each index.
6. The method of claim 5, wherein the calculation of the three-level diagnostic data index value for each cell is as follows:
in the formula (I), the compound is shown in the specification,
the index value of the three-level diagnosis data of the ith battery in the distributed energy storage power station,
for the single-factor diagnostic value matrix of the ith battery in the distributed energy storage power station,
is a matrix of weight coefficients.
7. The method according to claim 2, wherein the predicting of the fault of the decentralized energy storage power station from the multi-level diagnostic data comprises:
carrying out standardization processing on the basic data index value, the primary diagnosis data index value, the secondary diagnosis data index value and the tertiary diagnosis data index value of each battery;
and substituting the standardized basic data index value, the first-stage diagnosis data index value, the second-stage diagnosis data index value and the third-stage diagnosis data index value of each battery into a pre-constructed comprehensive prediction model to obtain a fault prediction result of the distributed energy storage power station.
8. The method of claim 7, wherein the method further comprises:
and displaying the fault prediction result of the distributed energy storage power station.
9. A distributed energy storage power station fault multi-stage diagnostic system, comprising:
the acquisition module is used for acquiring the operation data of each battery in the distributed energy storage power station and preprocessing the operation data;
the determining module is used for respectively determining the multi-stage diagnosis data corresponding to the distributed energy storage power station according to the preprocessed operation data;
and the diagnosis module is used for predicting the fault of the distributed energy storage power station according to the multi-stage diagnosis data.
10. The system of claim 9, wherein the determination module comprises
The first determining unit is used for screening out the characteristic values of all batteries in the distributed energy storage power station from the operation data and determining the basic data index values of all the batteries based on the characteristic values of all the batteries;
the second determining unit is used for screening out primary diagnostic data of each battery in the distributed energy storage power station from the data and determining a primary diagnostic data index value of each battery based on the primary diagnostic data of each battery;
the third determining unit is used for determining the voltage standard score, the coulombic efficiency and the IC curve peak value of each battery according to the operation data, and determining the secondary diagnosis data index value of each battery based on the voltage standard score, the coulombic efficiency and the IC curve peak value;
a fourth determination unit configured to determine a weight coefficient of a secondary diagnostic data index value of each battery, respectively, and determine a tertiary diagnostic data index value of each battery based on the weight coefficient;
wherein the multi-level diagnostic data comprises: a base data index value, a primary diagnostic data index value, a secondary diagnostic data index value, and a tertiary diagnostic data index value for each battery.
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