CN116666785A - Energy storage battery system safety early warning method and device, electronic equipment and medium - Google Patents
Energy storage battery system safety early warning method and device, electronic equipment and medium Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 101
- 238000004146 energy storage Methods 0.000 title claims abstract description 71
- 239000007789 gas Substances 0.000 claims abstract description 964
- 238000001514 detection method Methods 0.000 claims abstract description 74
- 239000011159 matrix material Substances 0.000 claims description 67
- 238000012545 processing Methods 0.000 claims description 21
- 238000004590 computer program Methods 0.000 claims description 19
- 238000007621 cluster analysis Methods 0.000 claims description 14
- 238000004364 calculation method Methods 0.000 claims description 12
- 238000012935 Averaging Methods 0.000 claims description 3
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 abstract description 3
- 229910001416 lithium ion Inorganic materials 0.000 abstract description 3
- 238000010586 diagram Methods 0.000 description 8
- 230000006870 function Effects 0.000 description 7
- GELKBWJHTRAYNV-UHFFFAOYSA-K lithium iron phosphate Chemical compound [Li+].[Fe+2].[O-]P([O-])([O-])=O GELKBWJHTRAYNV-UHFFFAOYSA-K 0.000 description 5
- 238000012544 monitoring process Methods 0.000 description 5
- 230000002159 abnormal effect Effects 0.000 description 3
- 239000011261 inert gas Substances 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- BVKZGUZCCUSVTD-UHFFFAOYSA-L Carbonate Chemical compound [O-]C([O-])=O BVKZGUZCCUSVTD-UHFFFAOYSA-L 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 238000012098 association analyses Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000010219 correlation analysis Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 239000000428 dust Substances 0.000 description 1
- 239000008151 electrolyte solution Substances 0.000 description 1
- 229940021013 electrolyte solution Drugs 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000004880 explosion Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 239000002341 toxic gas Substances 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Chemical compound O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Classifications
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- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M10/00—Secondary cells; Manufacture thereof
- H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
- H01M10/425—Structural combination with electronic components, e.g. electronic circuits integrated to the outside of the casing
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- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M10/00—Secondary cells; Manufacture thereof
- H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
- H01M10/48—Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte
Abstract
The invention belongs to the field of lithium ion battery energy storage systems, and discloses a safety early warning method, a safety early warning device, electronic equipment and a safety early warning medium for an energy storage battery system; the method comprises the following steps: acquiring gas concentration detection values and alarm concentration values w of a plurality of gases in an energy storage battery system s，gas The method comprises the steps of carrying out a first treatment on the surface of the Calculating the gray correlation average value mu of each gas according to the gas concentration detection values of a plurality of gases gas Sum of variances sigma gas The method comprises the steps of carrying out a first treatment on the surface of the Predicting a gas concentration predicted value x of each gas at the next moment according to the gas concentration detected values of a plurality of gases * gas Sum of variances delta * gas The method comprises the steps of carrying out a first treatment on the surface of the Based on the detected gas concentration values of several gases and the predicted gas concentration value x of each gas at the next moment * gas Calculating gray correlation average value mu' of each gas sample detection value and predicted value constituting data set gas The method comprises the steps of carrying out a first treatment on the surface of the The safety pre-warning of the energy storage battery system is carried out,obtaining early warning information; and outputting the early warning information. The invention can predict the content of the gas in thermal runaway, thereby accurately and effectively carrying out grading early warning and guaranteeing the application safety of the energy storage system.
Description
Technical Field
The invention belongs to the field of lithium ion battery energy storage systems, and particularly relates to a safety early warning method, a safety early warning device, electronic equipment and a safety early warning medium for an energy storage battery system.
Background
In recent years, energy storage of lithium ion batteries represented by lithium iron phosphate batteries has been rapidly increased, but safety problems frequently become bottleneck problems restricting industrial scale development. Most of lithium iron phosphate batteries commercially used at present adopt flammable carbonate substances as electrolyte solutions, thermal runaway reactions inevitably occur under the condition that internal and external triggering conditions are met, and when the batteries are in thermal runaway, a large amount of flammable and toxic gases are released into the environment through a safety valve or a defect of a battery shell, and once the gases encounter an ignition source, fires and even explosions can occur.
Lithium iron phosphate battery fires produce a mixed gas of complex multi-component combustible gases rather than a single type of combustible gas; lithium iron phosphate battery thermal runaway generated gas comprising H 2 、CH 4 、CO、C 2 H 4 、C 2 H 6 Concentration of combustible gas and CO 2 The inert gases are equal, the content of the combustible gas with a single component can be accurately detected and early-warned through a special gas detector, but the accuracy is not enough, and the gas state in the energy storage system cannot be accurately early-warned; and because the environmental condition of the energy storage system changes greatly, the components of the monitoring system are affected by various interference factors such as temperature, dust, water vapor and the like, and in the process of collecting, transmitting, storing and processing monitoring data, sensor faults, storage medium faults, network transmission faults, electromagnetic interference and the influence of human management problems can also exist.
The method for early warning the safety of the energy storage battery system is necessary to provide, and can effectively monitor the concentration of gas generated by thermal runaway of the lithium iron phosphate battery at an observation point, early warn in real time and ensure the safe operation of the energy storage battery system.
Disclosure of Invention
The invention aims to provide a safety early warning method, a safety early warning device, electronic equipment and a safety early warning medium for an energy storage battery system, and aims to solve the technical problem that the prior art cannot effectively monitor the multi-component mixed combustible gas released based on battery thermal runaway.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
in a first aspect, the present invention provides a method for early warning of energy storage battery system, including:
acquiring gas concentration detection values and alarm concentration values w of a plurality of gases in an energy storage battery system s，gas ；
Calculating the gray correlation average value mu of each gas according to the gas concentration detection values of a plurality of gases gas Sum of variances sigma gas The method comprises the steps of carrying out a first treatment on the surface of the Predicting the next time of each gas according to the gas concentration detection values of a plurality of gasesIs the gas concentration predictive value x of (2) * gas Sum of variances delta * gas The method comprises the steps of carrying out a first treatment on the surface of the Based on the detected gas concentration values of several gases and the predicted gas concentration value x of each gas at the next moment * gas Calculating gray correlation average value mu' of each gas sample detection value and predicted value constituting data set gas ；
Mean value mu of gray correlation according to each gas gas Sum of variances sigma gas Predicted value x of gas concentration of each gas * gas Sum of variances delta * gas Alarm concentration value w s，gas Performing safety early warning on the energy storage battery system to obtain early warning information;
and outputting the early warning information.
The invention is further improved in that: the method comprises the steps of obtaining gas concentration detection values and alarm concentration values w of a plurality of gases in the energy storage battery system s，gas In the step (a), the plurality of gases specifically include: h 2 、CH 4 、CO、C 2 H 4 、C 2 H 6 CO 2 。
The invention is further improved in that: the gas concentration predicted value x of each gas at the next moment is predicted according to the gas concentration detected values of a plurality of gases * gas Sum of variances delta * gas Specifically comprises the following steps:
according to a certain gas concentration sample value x at time t t,gas By using the principle of Gaussian process regression model, the gas concentration time sequence length N is used x X for each gas concentration t+1 Predicting the concentration at the moment to obtain a gas concentration predicted value x at the t+1 moment of each gas * gas Sum of variances delta * gas The prediction interval is [ x ] * gas -δ * gas ,x * gas +δ * gas ]。
The invention is further improved in that: calculating the average value mu of gray correlation degree of each gas according to the gas concentration detection values of a plurality of gases gas Sum of variances sigma gas Specifically comprises the following steps:
Extracting N concentration detection values before a certain gas time t and a gas detection value x at a time t+1 t+1,gas N+1 data are composed, and a series of: { x 1 ,x 2 ,…,x N ,x N+1 Reconstructing a sample space for n+1 data to obtain a sample space data set x= { X j ,j＝1,2,Λ,N x },X j ＝{x j ,x j+1 ,Λ,x j+m-1 },N x =n-m+1, m is the dimension of X; x is X j The columns, x, formed for data reconstruction j Is the concentration value of a certain gas;
and (3) carrying out standardized method processing on each data in the matrix:
calculate X i,(i,j) And X j,(i,j) Is a correlation coefficient of (a):
wherein delta is ij ＝|X i,(i,j) -X j,(i,j) I, ρ is a coefficient, m=max (#) ij )，m＝min(△ ij )；
X i,(i,j) And X j,(i,j) The degree of association of (2) is:
for all ofi≤j，X i,(i,j) And X j,(i,j) The gray correlation matrix of (2) is represented as an upper triangular matrix; obtaining a correlation matrix by gray correlation cluster analysis:
calculating the average value mu of gray correlation of gas concentration data by calculating the average value of all elements in the correlation matrix R gas Sum of variances sigma gas ；
The invention is further improved in that: the gas concentration detection value according to a plurality of gases and the gas concentration prediction value x of each gas at the next moment * gas Calculating gray correlation average value mu' of each gas sample detection value and predicted value constituting data set gas Specifically comprises the following steps:
extracting N concentration detection values x before a certain gas time t t,gas And a gas prediction value x at time t+1 * gas N+1 data are composed, constituting a series: { x 1 ,x 2 ,…,x N ,x * gas Reconstructing a sample space for the n+1 data to obtain a sample space data set X;
and (3) carrying out standardized method processing on each data in the matrix:
calculate X i,(i,j) And X j,(i,j) Is a correlation coefficient of (a):
wherein delta is ij ＝|X i,(i,j) -X j,(i,j) I, ρ is a coefficient, m=max (#) ij )，m＝min(△ ij )；
X i,(i,j) And X j,(i,j) The degree of association of (2) is:
for all i.ltoreq.j, X i,(i,j) And X j,(i,j) The gray correlation matrix of (2) is represented as an upper triangular matrix; obtaining a correlation matrix by gray correlation cluster analysis:
calculating the average value of all elements in the correlation matrix R to obtain a gas concentration predicted value x * gas Average value mu' of gray correlation degree between belonging gas concentration sample and other samples gas 。
The invention is further improved in that: the average value mu of gray correlation degree according to each gas gas Sum of variances sigma gas Predicted value x of gas concentration of each gas * gas Sum of variances delta * gas Alarm concentration value w s，gas Performing safety early warning of an energy storage battery system and obtaining early warning information, wherein the method specifically comprises the following steps of:
when x is * gas +δ * gas ≤μ gas +σ gas When the method is used, early warning and warning are not carried out; at x * gas <w s，gas On the premise of (1) when x * gas +δ * gas ∈[μ gas +σ gas ,μ gas +1.44σ gas ]When the method is used, early warning and warning are not carried out;
when mu gas +1.44σ gas <x * gas +δ * gas <μ gas +1.96σ gas At this time, the predicted value x of the gas concentration in 1 minute continuously was calculated * gas For a period of time t h When t h >At 0.5 minutes, and the presence of gas causes μ' gas <μ gas Generating I-level early warning information;
when a certain gas x is present * gas +δ * gas >μ gas +1.96σ gas If x * gas <w s，gas Detecting a gas concentration predicted value x within 2min * gas For a period of time t h Setting a warning time t w ，t h <t w <2 minutes, at alert time t w If gas is present in the interior so that t h >At 1min, if mu' gas <μ gas Generating III-level early warning information, otherwise, generating II-level early warning information; if 0.5min<t h <1min and the presence of a gas makes mu' gas <μ gas And generating II-level early warning information, or else, generating I-level early warning information.
In a second aspect, the present invention provides a safety pre-warning device for an energy storage battery system, including:
the acquisition module is used for acquiring gas concentration detection values and alarm concentration values w of a plurality of gases in the energy storage battery system s，gas ；
A calculation module for calculating gray correlation average value mu of each gas according to the gas concentration detection values of the plurality of gases gas Sum of variances sigma gas The method comprises the steps of carrying out a first treatment on the surface of the Predicting a gas concentration predicted value x of each gas at the next moment according to the gas concentration detected values of a plurality of gases * gas Sum of variances delta * gas The method comprises the steps of carrying out a first treatment on the surface of the Based on the detected gas concentration values of several gases and the predicted gas concentration value x of each gas at the next moment * gas Calculating gray correlation average value mu' of each gas sample detection value and predicted value constituting data set gas ；
The early warning module is used for averaging mu according to the gray correlation degree of each gas gas Sum of variances sigma gas Predicted value x of gas concentration of each gas * gas Sum of variances delta * gas Alarm concentration value w s，gas Performing safety early warning on the energy storage battery system to obtain early warning information;
and the output module is used for outputting the early warning information.
The invention is further improved in that: the calculation module predicts a gas concentration predicted value x of each gas at the next moment according to the gas concentration detected values of a plurality of gases * gas Sum of variances delta * gas Specifically comprises the following steps:
according to a certain gas concentration sample value x at time t t,gas By using the principle of Gaussian process regression model, the gas concentration time sequence length N is used x X for each gas concentration t+1 Predicting the concentration at the moment to obtain a gas concentration predicted value x at the t+1 moment of each gas * gas Sum of variances delta * gas The prediction interval is [ x ] * gas -δ * gas ,x * gas +δ * gas ]。
The invention is further improved in that: the calculation module calculates the gray correlation average value mu of each gas according to the gas concentration detection values of a plurality of gases gas Sum of variances sigma gas Specifically comprises the following steps:
extracting N concentration detection values before a certain gas time t and a gas detection value x at a time t+1 t+1,gas N+1 data are composed, and a series of: { x 1 ,x 2 ,…,x N ,x N+1 Reconstructing a sample space for n+1 data to obtain a sample spaceData set x= { X j ,j＝1,2,Λ,N x },X j ＝{x j ,x j+1 ,Λ,x j+m-1 },N x =n-m+1, m is the dimension of X; x is X j The columns, x, formed for data reconstruction j Is the concentration value of a certain gas;
and (3) carrying out standardized method processing on each data in the matrix:
calculate X i,(i,j) And X j,(i,j) Is a correlation coefficient of (a):
wherein delta is ij ＝|X i,(i,j) -X j,(i,j) I, ρ is a coefficient, m=max (#) ij )，m＝min(△ ij )；
X i,(i,j) And X j,(i,j) The degree of association of (2) is:
for all i.ltoreq.j, X i,(i,j) And X j,(i,j) The gray correlation matrix of (2) is represented as an upper triangular matrix; obtaining a correlation matrix by gray correlation cluster analysis:
calculating the average value mu of gray correlation of gas concentration data by calculating the average value of all elements in the correlation matrix R gas Sum of variances sigma gas ；
The invention is further improved in that: the calculation module calculates the gas concentration detection values of a plurality of gases and the gas concentration prediction value x of each gas at the next moment * gas Calculating gray correlation average value mu' of each gas sample detection value and predicted value constituting data set gas Specifically comprises the following steps:
extracting N concentration detection values x before a certain gas time t t,gas And a gas prediction value x at time t+1 * gas N+1 data are composed, constituting a series: { x 1 ,x 2 ,…,x N ,x * gas Reconstructing a sample space for the n+1 data to obtain a sample space data set X;
and (3) carrying out standardized method processing on each data in the matrix:
Calculate X i,(i,j) And X j,(i,j) Is a correlation coefficient of (a):
wherein delta is ij ＝|X i,(i,j) -X j,(i,j) I, ρ is a coefficient, m=max (#) ij )，m＝min(△ ij )；
X i,(i,j) And X j,(i,j) The degree of association of (2) is:
for all i.ltoreq.j, X i,(i,j) And X j,(i,j) The gray correlation matrix of (2) is represented as an upper triangular matrix; obtaining a correlation matrix by gray correlation cluster analysis:
calculating the average value of all elements in the correlation matrix R to obtain a gas concentration predicted value x * gas Average value mu' of gray correlation degree between belonging gas concentration sample and other samples gas 。
The invention is further improved in that: the early warning module is used for carrying out early warning according to the gray correlation degree average mu of each gas gas Sum of variances sigma gas Predicted value x of gas concentration of each gas * gas Sum of variances delta * gas Alarm concentration value w s，gas Performing safety early warning of an energy storage battery system and obtaining early warning information, wherein the method specifically comprises the following steps of:
when x is * gas +δ * gas ≤μ gas +σ gas When the method is used, early warning and warning are not carried out; at x * gas <w s，gas On the premise of (1) when x * gas +δ * gas ∈[μ gas +σ gas ,μ gas +1.44σ gas ]When not doingEarly warning and warning;
when mu gas +1.44σ gas <x * gas +δ * gas <μ gas +1.96σ gas At this time, the predicted value x of the gas concentration in 1 minute continuously was calculated * gas For a period of time t h When t h >At 0.5 minutes, and the presence of gas causes μ' gas <μ gas Generating I-level early warning information;
when a certain gas x is present * gas +δ * gas >μ gas +1.96σ gas If x * gas <w s，gas Detecting a gas concentration predicted value x within 2min * gas For a period of time t h Setting a warning time t w ，t h <t w <2 minutes, at alert time t w If gas is present in the interior so that t h >At 1min, if mu' gas <μ gas Generating III-level early warning information, otherwise, generating II-level early warning information; if 0.5min<t h <1min and the presence of a gas makes mu' gas <μ gas And generating II-level early warning information, or else, generating I-level early warning information.
In a third aspect, the present invention provides an electronic device, including a processor and a memory, where the processor is configured to execute a computer program stored in the memory to implement the energy storage battery system safety precaution method.
In a fourth aspect, the present invention is a computer readable storage medium storing at least one instruction that when executed by a processor implements the energy storage battery system security pre-warning method.
Compared with the prior art, the invention has the following beneficial effects:
the invention provides a safety early warning method and device for an energy storage battery system, electronic equipment and a medium; the method comprises the following steps: acquiring gas concentration detection values and alarm concentration values w of a plurality of gases in an energy storage battery system s，gas The method comprises the steps of carrying out a first treatment on the surface of the From gas concentration measurements of several gasesCalculating the gray correlation average mu of each gas gas Sum of variances sigma gas The method comprises the steps of carrying out a first treatment on the surface of the Predicting a gas concentration predicted value x of each gas at the next moment according to the gas concentration detected values of a plurality of gases * gas Sum of variances delta * gas The method comprises the steps of carrying out a first treatment on the surface of the Based on the detected gas concentration values of several gases and the predicted gas concentration value x of each gas at the next moment * gas Calculating gray correlation average value mu' of each gas sample detection value and predicted value constituting data set gas The method comprises the steps of carrying out a first treatment on the surface of the Mean value mu of gray correlation according to each gas gas Sum of variances sigma gas Predicted value x of gas concentration of each gas * gas Sum of variances delta * gas Alarm concentration value w s，gas Performing safety early warning on the energy storage battery system to obtain early warning information; and outputting the early warning information. The invention provides a safety pre-warning method of an energy storage system based on multi-component mixed combustible gas released by thermal runaway of a battery, which predicts the content of the thermal runaway gas, so that the safety of the application of the energy storage system is ensured.
According to the invention, 5 kinds of battery thermal runaway released combustible gases and 1 kind of battery thermal runaway released inert gases are considered for battery safety early warning, so that the result is more accurate, and the prediction precision is improved. According to the invention, the gas concentration prediction and early warning of the energy storage system are carried out by utilizing the Gaussian process regression model principle and gray correlation cluster analysis, so that the advanced sensing of the abnormal condition of the gas quantity in the energy storage system is realized, and the occurrence of large-scale safety accidents is effectively prevented.
Drawings
The accompanying drawings, which are included to provide a further understanding 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 the invention. In the drawings:
FIG. 1 is a schematic flow chart of a method for safety pre-warning of an energy storage battery system according to the present invention;
FIG. 2 is a schematic diagram of a safety pre-warning device of an energy storage battery system according to the present invention;
fig. 3 is a block diagram of an electronic device according to the present invention.
Detailed Description
The invention will be described in detail below with reference to the drawings in connection with embodiments. It should be noted that, without conflict, the embodiments of the present invention and features of the embodiments may be combined with each other.
The following detailed description is exemplary and is intended to provide further details of the invention. Unless defined otherwise, all technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments in accordance with the invention.
Example 1
The invention provides a safety pre-warning method of an energy storage battery system, which comprises the following steps:
Acquiring H in energy storage battery system detected in early warning period T 2 、CH 4 、CO、C 2 H 4 、C 2 H 6 CO 2 Is a gas concentration value of (1);
the processing of the six gas concentration values detected in real time specifically comprises the following steps: the concentration prediction and association analysis of the multi-component gas generate and output early warning information;
and carrying out early warning and alarming according to the early warning information.
The steps of multi-component gas concentration prediction and correlation analysis specifically comprise:
assume that a certain gas concentration sample value at time t is x t,gas Predicting a certain gas concentration at the next time t+1 by using a Gaussian process regression model principle and using the gas concentration time sequence length N x X for each gas concentration t+1 Predicting the concentration at the moment to obtain the predicted value x of each gas * gas Prediction interval [ x ] * gas -δ * gas ,x * gas +δ * gas ]。
For H 2 、CH 4 、CO、C 2 H 4 、C 2 H 6 CO 2 Gas concentration detection value x at time t of (2) t,gas And a gas detection value x at time t+1 t+1,gas Extracting N concentration detection values and a gas detection value x before a certain gas time t t+1,gas The n+1 data is composed, and the number sequence of the n+1 data can be written as: { x 1 ,x 2 ,…,x N ,x N+1 Reconstructing the sample space for the n+1 data to obtain a sample space data set x= { X j ,j＝1,2,Λ,N x },X j ＝{x j ,x j+1 ,Λ,x j+m-1 },N x N-m+1, m is the dimension of X, typically m takes the value N/2; x is X j The columns, x, formed for data reconstruction j Is the concentration value of a certain gas. Then
And (3) carrying out standardized method processing on each data in the matrix:
i represents the number of rows in the matrix;
j represents the number of columns in the matrix.
Thereby obtaining a mean normalized sample, calculating X i,(i,j) And X j,(i,j) Is a correlation coefficient of (a):
wherein delta is ij ＝|X i,(i,j) -X j,(i,j) Valuep is 0.5, m=max ([ delta ]) ij )，m＝min(△ ij )
Then X i,(i,j) And X j,(i,j) The degree of association of (2) is:
for all i.ltoreq.j, X i,(i,j) And X j,(i,j) The gray correlation matrix of (c) may be represented as an upper triangular matrix:
obtaining a correlation matrix by gray correlation cluster analysis:
calculating the average value mu of gray correlation of gas concentration data by calculating the average value of all elements in the correlation matrix R gas Sum of variances sigma gas 。
Wherein k is a number from 1 to m, and has no physical meaning; l is a number from 1 to m, and has no physical meaning.
Also for H 2 、CH 4 、CO、C 2 H 4 、C 2 H 6 CO 2 Is a gas concentration detection value x of (2) t,gas And a gas predictive value x * gas Extracting N concentration data values and a gas predicted value x before a certain gas time t * gas The n+1 data is composed, and the number sequence of the n+1 data can be written as: { x 1 ,x 2 ,…,x N ,x * gas }，
According to the above steps, gas concentration detection is performed on a plurality of gasesValue and predicted value x of gas concentration at the next time of each gas * gas Calculating gray correlation average value mu' of each gas sample detection value and predicted value constituting data set gas And the method is used for subsequent early warning judgment.
The step of generating the early warning information specifically comprises the following steps:
based on the gas concentration anomaly analysis of the monitoring points, according to H 2 、CH 4 、CO、C 2 H 4 、C 2 H 6 CO 2 Predicted value x of concentration of each gas at time t+1 in gas * gas And prediction interval x * gas -δ * gas ,x * gas +δ * gas ]Mean value mu of time series of gas concentration of N samples in time t gas Sum of variances sigma gas Duration t of greater continuous predictive value h，gas The method comprises the steps of carrying out a first treatment on the surface of the Gas concentration time series sample correlation average mu gas Average value mu 'of gray correlation degree of gas concentration sample to which predicted value belongs and other samples' gas The method comprises the steps of carrying out a first treatment on the surface of the Let the gas concentration alarm concentration at a certain monitoring point specified by the regulations be w s，gas And determining a certain gas concentration early warning threshold value of the monitoring point according to the following method and dividing the early warning grade.
(1) Early warning level I
When x is * gas +δ * gas ≤μ gas +σ gas And when the alarm is considered as normal, the alarm is not given. At x * gas <w s，gas On the premise of (1) when x * gas +δ * gas ∈[μ gas +σ gas ,μ gas +1.44σ gas ]And when the alarm is considered as normal, the alarm is not given. When mu gas +1.44σ gas <x * gas +δ * gas <μ gas +1.96σ gas Regarding the gas concentration as being larger, calculating the predicted value of the gas concentration in 1 minute continuously for a larger time t h When t h >At 0.5 min, if μ' gas <μ gas Belonging to abnormal conditionsThe early warning level I is determined by t w Indicating the warning time, setting the warning time t h <t w <1 minute; if mu' gas >μ gas Continuing to judge other gases, if no gas exists so as to lead mu' gas <μ gas Regarding as normal condition, no warning is made, if gas exists to make mu' gas <μ gas And (5) determining the early warning grade I.
(2) Early warning class II
When mu gas +1.96σ gas <x * gas +δ * gas If x * gas <w s，gas When the gas concentration does not reach the alarm limit value, detecting the predicted value of the gas concentration within 1 minute for a longer time t h If t h >0.5min and μ' gas <μ gas The predicted value of the gas concentration has a continuous trend, belongs to abnormal conditions, is determined as early warning II level, and sets the warning time t h <t w <1 minute, continuing to judge, t w For a period of time of mu' gas >μ gas If the predicted value of the gas concentration has a continuous trend, but the correlation between the sample to which the predicted value belongs and other sample data is strong, the predicted value is determined as an early warning I level, and if other gases exist, the predicted value of the gas concentration continuously increases, so that x is caused by the existence of the other gases * gas +δ * gas >μ gas +1.96σ gas ,t h >0.5min, and μ' gas <μ gas And (5) determining the early warning grade II.
(3) Early warning class III
When a certain gas x is present * gas +δ * gas >μ gas +1.96σ gas If x * gas <w s，gas When the concentration of a certain gas does not reach the alarm limit value, calculating the duration time t within 2min h If t h >1min and mu' gas <μ gas Setting early warning level III and setting warning time t h <t w <2 minutes, at alert time t w If gas is present in the interior so that t h >At 1min, if mu' gas <μ gas Then defining the early warning class III, otherwise, defining the early warning class II; if 0.5min<t h <1min and the presence of a gas makes mu' gas <μ gas And if not, the early warning stage II is set, otherwise, the early warning stage I is set.
Example 2
Referring to fig. 1, the present invention provides a safety pre-warning method for an energy storage battery system, comprising:
s1, acquiring gas concentration detection values and alarm concentration values w of a plurality of gases in an energy storage battery system s，gas ；
S2, calculating a gray correlation average value mu of each gas according to the gas concentration detection values of the plurality of gases gas Sum of variances sigma gas The method comprises the steps of carrying out a first treatment on the surface of the Predicting a gas concentration predicted value x of each gas at the next moment according to the gas concentration detected values of a plurality of gases * gas Sum of variances delta * gas The method comprises the steps of carrying out a first treatment on the surface of the Based on the detected gas concentration values of several gases and the predicted gas concentration value x of each gas at the next moment * gas Calculating gray correlation average value mu' of each gas sample detection value and predicted value constituting data set gas ；
S3, according to the gray correlation average value mu of each gas gas Sum of variances sigma gas Predicted value x of gas concentration of each gas * gas Sum of variances delta * gas Alarm concentration value w s，gas Performing safety early warning on the energy storage battery system to obtain early warning information;
s4, outputting the early warning information. In a specific embodiment, the early warning information is output in an acousto-optic and electric mode; the early warning information can also be output by means of screen display information, short messages, mails or telephones.
In a specific embodiment, the method includes obtaining gas concentration detection values and alarm concentration values w of a plurality of gases in the energy storage battery system s，gas In the step (a), the plurality of gases specifically include: h 2 、CH 4 、CO、C 2 H 4 、C 2 H 6 CO 2 。
In one embodiment, the gas concentration prediction value x of each gas at the next moment is predicted according to the gas concentration detection values of a plurality of gases * gas Sum of variances delta * gas Specifically comprises the following steps:
according to a certain gas concentration sample value x at time t t,gas By using the principle of Gaussian process regression model, the gas concentration time sequence length N is used x X for each gas concentration t+1 Predicting the concentration at the moment to obtain a gas concentration predicted value x at the t+1 moment of each gas * gas Sum of variances delta * gas The prediction interval is [ x ] * gas -δ * gas ,x * gas +δ * gas ]。
In one embodiment, the gray correlation average value mu of each gas is calculated according to the gas concentration detection values of a plurality of gases gas Sum of variances sigma gas Specifically comprises the following steps:
extracting N concentration detection values before a certain gas time t and a gas detection value x at a time t+1 t+1,gas N+1 data are composed, and a series of: { x 1 ,x 2 ,…,x N ,x N+1 Reconstructing a sample space for n+1 data to obtain a sample space data set x= { X j ,j＝1,2,Λ,N x },X j ＝{x j ,x j+1 ,Λ,x j+m-1 },N x =n-m+1, m is the dimension of X; x is X j The columns, x, formed for data reconstruction j Is the concentration value of a certain gas;
and (3) carrying out standardized method processing on each data in the matrix:
/>
calculate X i,(i,j) And X j,(i,j) Is a correlation coefficient of (a):
wherein delta is ij ＝|X i,(i,j) -X j,(i,j) I, ρ is a coefficient, m=max (#) ij )，m＝min(△ ij )；
X i,(i,j) And X j,(i,j) The degree of association of (2) is:
for all i.ltoreq.j, X i,(i,j) And X j,(i,j) The gray correlation matrix of (2) is represented as an upper triangular matrix; obtaining a correlation matrix by gray correlation cluster analysis:
calculating the average value mu of gray correlation of gas concentration data by calculating the average value of all elements in the correlation matrix R gas Sum of variances sigma gas ；
In one embodiment, the gas concentration detection value according to a plurality of gases and the gas concentration prediction value x of each gas at the next moment * gas Structured data setCalculating gray correlation average value mu' of data set formed by detection value and predicted value of each gas sample gas Specifically comprises the following steps:
extracting N concentration detection values x before a certain gas time t t,gas And a gas prediction value x at time t+1 * gas N+1 data are composed, constituting a series: { x 1 ,x 2 ,…,x N ,x * gas Reconstructing a sample space for the n+1 data to obtain a sample space data set X;
and (3) carrying out standardized method processing on each data in the matrix:
calculate X i,(i,j) And X j,(i,j) Is a correlation coefficient of (a):
wherein delta is ij ＝|X i,(i,j) -X j,(i,j) I, ρ is a coefficient, m=max (#) ij )，m＝min(△ ij )；
X i,(i,j) And X j,(i,j) The degree of association of (2) is:
for all i.ltoreq.j, X i,(i,j) And X j,(i,j) The gray correlation matrix of (2) is represented as an upper triangular matrix; benefit (benefit)Obtaining a correlation matrix by gray correlation cluster analysis:
calculating the average value of all elements in the correlation matrix R to obtain a gas concentration predicted value x * gas Average value mu' of gray correlation degree between belonging gas concentration sample and other samples gas 。
In one embodiment, the average value mu of gray correlation according to each gas gas Sum of variances sigma gas Predicted value x of gas concentration of each gas * gas Sum of variances delta * gas Alarm concentration value w s，gas Performing safety early warning of an energy storage battery system and obtaining early warning information, wherein the method specifically comprises the following steps of:
when x is * gas +δ * gas ≤μ gas +σ gas When the method is used, early warning and warning are not carried out; at x * gas <w s，gas On the premise of (1) when x * gas +δ * gas ∈[μ gas +σ gas ,μ gas +1.44σ gas ]When the method is used, early warning and warning are not carried out;
when mu gas +1.44σ gas <x * gas +δ * gas <μ gas +1.96σ gas At this time, the predicted value x of the gas concentration in 1 minute continuously was calculated * gas For a period of time t h When t h >At 0.5 minutes, and the presence of gas causes μ' gas <μ gas Generating I-level early warning information;
when a certain gas x is present * gas +δ * gas >μ gas +1.96σ gas If x * gas <w s，gas Detecting a gas concentration predicted value x within 2min * gas For a period of time t h Setting a warning time t w ，t h <t w <For 2 minutes, atAlert time t w If gas is present in the interior so that t h >At 1min, if mu' gas <μ gas Generating III-level early warning information, otherwise, generating II-level early warning information; if 0.5min <t h <1min and the presence of a gas makes mu' gas <μ gas And generating II-level early warning information, or else, generating I-level early warning information.
Example 3
Referring to fig. 2, the present invention provides a safety pre-warning device of an energy storage battery system, comprising:
the acquisition module is used for acquiring gas concentration detection values and alarm concentration values w of a plurality of gases in the energy storage battery system s，gas The method comprises the steps of carrying out a first treatment on the surface of the In a specific embodiment, the plurality of gases specifically includes: h 2 、CH 4 、CO、C 2 H 4 、C 2 H 6 CO 2 。
A calculation module for calculating gray correlation average value mu of each gas according to the gas concentration detection values of the plurality of gases gas Sum of variances sigma gas The method comprises the steps of carrying out a first treatment on the surface of the Predicting a gas concentration predicted value x of each gas at the next moment according to the gas concentration detected values of a plurality of gases * gas Sum of variances delta * gas The method comprises the steps of carrying out a first treatment on the surface of the Based on the detected gas concentration values of several gases and the predicted gas concentration value x of each gas at the next moment * gas Calculating gray correlation average value mu' of each gas sample detection value and predicted value constituting data set gas ；
The early warning module is used for averaging mu according to the gray correlation degree of each gas gas Sum of variances sigma gas Predicted value x of gas concentration of each gas * gas Sum of variances delta * gas Alarm concentration value w s，gas Performing safety early warning on the energy storage battery system to obtain early warning information;
And the output module is used for outputting the early warning information.
In one embodiment, the calculation module predicts the gas concentration pre-determined at the next time of each gas based on the gas concentration detection values of the plurality of gasesMeasurement value x * gas Sum of variances delta * gas Specifically comprises the following steps:
according to a certain gas concentration sample value x at time t t,gas By using the principle of Gaussian process regression model, the gas concentration time sequence length N is used x X for each gas concentration t+1 Predicting the concentration at the moment to obtain a gas concentration predicted value x at the t+1 moment of each gas * gas Sum of variances delta * gas The prediction interval is [ x ] * gas -δ * gas ,x * gas +δ * gas ]。
In one embodiment, the calculation module calculates the gray correlation average value mu of each gas according to the gas concentration detection values of the plurality of gases gas Sum of variances sigma gas Specifically comprises the following steps:
extracting N concentration detection values before a certain gas time t and a gas detection value x at a time t+1 t+1,gas N+1 data are composed, and a series of: { x 1 ,x 2 ,…,x N ,x N+1 Reconstructing a sample space for n+1 data to obtain a sample space data set x= { X j ,j＝1,2,Λ,N x },X j ＝{x j ,x j+1 ,Λ,x j+m-1 },N x =n-m+1, m is the dimension of X; x is X j The columns, x, formed for data reconstruction j Is the concentration value of a certain gas;
and (3) carrying out standardized method processing on each data in the matrix:
Calculate X i,(i,j) And X j,(i,j) Is a correlation coefficient of (a):
wherein delta is ij ＝|X i,(i,j) -X j,(i,j) I, ρ is a coefficient, m=max (#) ij )，m＝min(△ ij )；
X i,(i,j) And X j,(i,j) The degree of association of (2) is:
for all i.ltoreq.j, X i,(i,j) And X j,(i,j) The gray correlation matrix of (2) is represented as an upper triangular matrix; obtaining a correlation matrix by gray correlation cluster analysis:
calculating the average value mu of gray correlation of gas concentration data by calculating the average value of all elements in the correlation matrix R gas Sum of variances sigma gas ；
In one embodiment, the calculation module calculates a plurality of gas concentration values according to the detected gas concentration values of the plurality of gases and the predicted gas concentration value x of each gas at the next time * gas Calculating gray correlation average value mu' of each gas sample detection value and predicted value constituting data set gas In the step (a) of the step (c),the method specifically comprises the following steps:
extracting N concentration detection values x before a certain gas time t t,gas And a gas prediction value x at time t+1 * gas N+1 data are composed, constituting a series: { x 1 ,x 2 ,…,x N ,x * gas Reconstructing a sample space for the n+1 data to obtain a sample space data set X;
and (3) carrying out standardized method processing on each data in the matrix:
calculate X i,(i,j) And X j,(i,j) Is a correlation coefficient of (a):
wherein delta is ij ＝|X i,(i,j) -X j,(i,j) I, ρ is a coefficient, m=max (#) ij )，m＝min(△ ij )；
X i,(i,j) And X j,(i,j) The degree of association of (2) is:
/>
for all i.ltoreq.j, X i,(i,j) And X j,(i,j) The gray correlation matrix of (2) is represented as an upper triangular matrix; obtaining a correlation matrix by gray correlation cluster analysis:
Calculating the average value of all elements in the correlation matrix R to obtain a gas concentration predicted value x * gas Average value mu' of gray correlation degree between belonging gas concentration sample and other samples gas 。
In one embodiment, the pre-warning module averages μ according to the gray correlation of each gas gas Sum of variances sigma gas Predicted value x of gas concentration of each gas * gas Sum of variances delta * gas Alarm concentration value w s，gas Performing safety early warning of an energy storage battery system and obtaining early warning information, wherein the method specifically comprises the following steps of:
when x is * gas +δ * gas ≤μ gas +σ gas When the method is used, early warning and warning are not carried out; at x * gas <w s，gas On the premise of (1) when x * gas +δ * gas ∈[μ gas +σ gas ,μ gas +1.44σ gas ]When the method is used, early warning and warning are not carried out;
when mu gas +1.44σ gas <x * gas +δ * gas <μ gas +1.96σ gas At this time, the predicted value x of the gas concentration in 1 minute continuously was calculated * gas For a period of time t h When t h >At 0.5 minutes, and the presence of gas causes μ' gas <μ gas Generating I-level early warning information;
when a certain gas x is present * gas +δ * gas >μ gas +1.96σ gas If x * gas <w s，gas Detecting a gas concentration predicted value x within 2min * gas For a period of time t h Setting a warning time t w ，t h <t w <2 minutes, at alert time t w If gas is present in the interior so that t h >At 1min, if mu' gas <μ gas Generating III-level early warning information, otherwise, generating II-level early warning information; if 0.5min<t h <1min and the presence of a gas makes mu' gas <μ gas And generating II-level early warning information, or else, generating I-level early warning information.
Example 4
Referring to fig. 3, the present invention further provides an electronic device 100 for implementing the method of safety precaution of the energy storage battery system; the electronic device 100 comprises a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104.
The memory 101 may be used to store the computer program 103, and the processor 102 implements the steps of the energy storage battery system safety precaution method described in embodiment 1 or 2 by running or executing the computer program stored in the memory 101 and invoking the data stored in the memory 101. The memory 101 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like; the storage data area may store data (such as audio data) created according to the use of the electronic device 100, and the like. In addition, the memory 101 may include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card), at least one disk storage device, a Flash memory device, or other non-volatile solid state storage device.
The at least one processor 102 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 circuits (Application Specific Integrated Circuit, ASIC), field-programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. The processor 102 may be a microprocessor or the processor 102 may be any conventional processor or the like, the processor 102 being a control center of the electronic device 100, the various interfaces and lines being utilized to connect various portions of the overall electronic device 100.
The memory 101 in the electronic device 100 stores a plurality of instructions to implement a method for energy storage battery system security pre-warning, and the processor 102 can execute the plurality of instructions to implement:
acquiring gas concentration detection values and alarm concentration values w of a plurality of gases in an energy storage battery system s，gas ；
Calculating the gray correlation average value mu of each gas according to the gas concentration detection values of a plurality of gases gas Sum of variances sigma gas The method comprises the steps of carrying out a first treatment on the surface of the Predicting a gas concentration predicted value x of each gas at the next moment according to the gas concentration detected values of a plurality of gases * gas Sum of variances delta * gas The method comprises the steps of carrying out a first treatment on the surface of the Based on the detected gas concentration values of several gases and the predicted gas concentration value x of each gas at the next moment * gas Calculating gray correlation average value mu' of each gas sample detection value and predicted value constituting data set gas ；
Mean value mu of gray correlation according to each gas gas Sum of variances sigma gas Predicted value x of gas concentration of each gas * gas Sum of variances delta * gas Alarm concentration value w s，gas Performing safety early warning on the energy storage battery system to obtain early warning information;
and outputting the early warning information.
Example 5
The modules/units integrated in the electronic device 100 may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as a stand alone product. Based on such understanding, the present invention may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, and a Read-Only Memory (ROM).
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. 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 instructions. These computer program instructions may be provided to a processor 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 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 instructions may also be stored in a computer-readable memory 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 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 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.
Finally, it should be noted that: the above embodiments are only for illustrating the technical aspects of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.
Claims (13)
1. The energy storage battery system safety pre-warning method is characterized by comprising the following steps:
acquiring gas concentration detection values and alarm concentration values w of a plurality of gases in an energy storage battery system s，gas ；
Calculating the gray correlation average value mu of each gas according to the gas concentration detection values of a plurality of gases gas Sum of variances sigma gas The method comprises the steps of carrying out a first treatment on the surface of the Predicting a gas concentration predicted value x of each gas at the next moment according to the gas concentration detected values of a plurality of gases * gas Sum of variances delta * gas The method comprises the steps of carrying out a first treatment on the surface of the Based on the detected gas concentration values of several gases and the predicted gas concentration value x of each gas at the next moment * gas Calculating gray correlation average value mu' of each gas sample detection value and predicted value constituting data set gas ；
Mean value mu of gray correlation according to each gas gas Sum of variances sigma gas Predicted value x of gas concentration of each gas * gas Sum of variances delta * gas Alarm concentration value w s，gas Performing safety early warning on the energy storage battery system to obtain early warning information;
and outputting the early warning information.
2. The method for safety precaution of an energy storage battery system according to claim 1, wherein the method is characterized in that the gas concentration detection values and the alarm concentration value w of a plurality of gases in the energy storage battery system are obtained s，gas In the step (a), the plurality of gases specifically include: h 2 、CH 4 、CO、C 2 H 4 、C 2 H 6 CO 2 。
3. The energy storage battery system safety pre-warning method according to claim 1, wherein the gas concentration prediction value x of each gas at the next moment is predicted according to the gas concentration detection values of a plurality of gases * gas Sum of variances delta * gas Specifically comprises the following steps:
according to a certain gas concentration sample value x at time t t,gas By using the principle of Gaussian process regression model, the gas concentration time sequence length N is used x X for each gas concentration t+1 Predicting the concentration at the moment to obtain a gas concentration predicted value x at the t+1 moment of each gas * gas Sum of variances delta * gas The prediction interval is [ x ] * gas -δ * gas ,x * gas +δ * gas ]。
4. The energy storage battery system safety pre-warning method according to claim 1, wherein the gray correlation average μ of each gas is calculated according to the gas concentration detection values of the plurality of gases gas Sum of variances sigma gas Specifically comprises the following steps:
extracting N concentration detection values before a certain gas time t and a gas detection value x at a time t+1 t+1,gas N+1 data are composed, and a series of: { x 1 ,x 2 ,…,x N ,x N+1 Reconstruction of sample space for n+1 dataTo the sample space data set x= { X j ,j＝1,2,Λ,N x },X j ＝{x j ,x j+1 ,Λ,x j+m-1 },N x =n-m+1, m is the dimension of X; x is X j The columns, x, formed for data reconstruction j Is the concentration value of a certain gas;
And (3) carrying out standardized method processing on each data in the matrix:
calculate X i,(i,j) And X j,(i,j) Is a correlation coefficient of (a):
wherein delta is ij ＝|X i,(i,j) -X j,(i,j) I, ρ is a coefficient, m=max (#) ij )，m＝min(△ ij )；
X i,(i,j) And X j,(i,j) The degree of association of (2) is:
for all i.ltoreq.j, X i,(i,j) And X j,(i,j) The gray correlation matrix of (2) is represented as an upper triangular matrix; obtaining a correlation matrix by gray correlation cluster analysis:
calculating the average value mu of gray correlation of gas concentration data by calculating the average value of all elements in the correlation matrix R gas Sum of variances sigma gas ；
5. The energy storage battery system safety pre-warning method according to claim 1, wherein the gas concentration detection value according to a plurality of gases and the gas concentration prediction value x at the next time of each gas * gas Calculating gray correlation average value mu' of each gas sample detection value and predicted value constituting data set gas Specifically comprises the following steps:
extracting N concentration detection values x before a certain gas time t t,gas And a gas prediction value x at time t+1 * gas N+1 data are composed, constituting a series: { x 1 ,x 2 ,…,x N ,x * gas Reconstructing a sample space for the n+1 data to obtain a sample space data set X;
and (3) carrying out standardized method processing on each data in the matrix:
calculate X i,(i,j) And X j,(i,j) Is a correlation coefficient of (a):
Wherein delta is ij ＝|X i,(i,j) -X j,(i,j) I, ρ is a coefficient, m=max (#) ij )，m＝min(△ ij )；
X i,(i,j) And X j,(i,j) The degree of association of (2) is:
for all i.ltoreq.j, X i,(i,j) And X j,(i,j) The gray correlation matrix of (2) is represented as an upper triangular matrix; obtaining a correlation matrix by gray correlation cluster analysis:
calculating the average value of all elements in the correlation matrix R to obtain a gas concentration predicted value x * gas Average value mu' of gray correlation degree between belonging gas concentration sample and other samples gas 。
6. The energy storage battery system safety pre-warning method according to claim 1, wherein the gray correlation average μ according to each gas gas Sum of variances sigma gas Predicted value x of gas concentration of each gas * gas Sum of variances delta * gas Alarm concentration value w s，gas Safety pre-warning of energy storage battery systemThe step of obtaining the early warning information specifically comprises the following steps:
when x is * gas +δ * gas ≤μ gas +σ gas When the method is used, early warning and warning are not carried out; at x * gas <w s，gas On the premise of (1) when x * gas +δ * gas ∈[μ gas +σ gas ,μ gas +1.44σ gas ]When the method is used, early warning and warning are not carried out;
when mu gas +1.44σ gas <x * gas +δ * gas <μ gas +1.96σ gas At this time, the predicted value x of the gas concentration in 1 minute continuously was calculated * gas For a period of time t h When t h >At 0.5 minutes, and the presence of gas causes μ' gas <μ gas Generating I-level early warning information;
when a certain gas x is present * gas +δ * gas >μ gas +1.96σ gas If x * gas <w s，gas Detecting a gas concentration predicted value x within 2min * gas For a period of time t h Setting a warning time t w ，t h <t w <2 minutes, at alert time t w If gas is present in the interior so that t h >At 1min, if mu' gas <μ gas Generating III-level early warning information, otherwise, generating II-level early warning information; if 0.5min<t h <1min and the presence of a gas makes mu' gas <μ gas And generating II-level early warning information, or else, generating I-level early warning information.
7. Energy storage battery system safety precaution device, its characterized in that includes:
the acquisition module is used for acquiring gas concentration detection values and alarm concentration values w of a plurality of gases in the energy storage battery system s，gas ；
A calculation module for calculating gray correlation average value mu of each gas according to the gas concentration detection values of the plurality of gases gas Sum of variances sigma gas The method comprises the steps of carrying out a first treatment on the surface of the Predicting a gas concentration predicted value x of each gas at the next moment according to the gas concentration detected values of a plurality of gases * gas Sum of variances delta * gas The method comprises the steps of carrying out a first treatment on the surface of the Based on the detected gas concentration values of several gases and the predicted gas concentration value x of each gas at the next moment * gas Calculating gray correlation average value mu' of each gas sample detection value and predicted value constituting data set gas ；
The early warning module is used for averaging mu according to the gray correlation degree of each gas gas Sum of variances sigma gas Predicted value x of gas concentration of each gas * gas Sum of variances delta * gas Alarm concentration value w s，gas Performing safety early warning on the energy storage battery system to obtain early warning information;
And the output module is used for outputting the early warning information.
8. The energy storage battery system safety precaution device according to claim 7, wherein the calculation module predicts a predicted value x of the gas concentration at the next moment of each gas according to the detected values of the gas concentrations of the plurality of gases * gas Sum of variances delta * gas Specifically comprises the following steps:
according to a certain gas concentration sample value x at time t t,gas By using the principle of Gaussian process regression model, the gas concentration time sequence length N is used x X for each gas concentration t+1 Predicting the concentration at the moment to obtain a gas concentration predicted value x at the t+1 moment of each gas * gas Sum of variances delta * gas The prediction interval is [ x ] * gas -δ * gas ,x * gas +δ * gas ]。
9. The energy storage battery system safety precaution device according to claim 7, wherein the calculation module calculates a gray correlation average μ for each gas according to the gas concentration detection values of the plurality of gases gas Sum of variances sigma gas Specifically comprises the following steps:
extracting N concentration detection values before a certain gas time t and a gas detection value x at a time t+1 t+1,gas N+1 data are composed, and a series of: { x 1 ,x 2 ,…,x N ,x N+1 Reconstructing a sample space for n+1 data to obtain a sample space data set x= { X j ,j＝1,2,Λ,N x },X j ＝{x j ,x j+1 ,Λ,x j+m-1 },N x =n-m+1, m is the dimension of X; x is X j The columns, x, formed for data reconstruction j Is the concentration value of a certain gas;
and (3) carrying out standardized method processing on each data in the matrix:
calculate X i,(i,j) And X j,(i,j) Is a correlation coefficient of (a):
wherein delta is ij ＝|X i,(i,j) -X j,(i,j) I, ρ is a coefficient, m=max (#) ij )，m＝min(△ ij )；
X i,(i,j) And X j,(i,j) The degree of association of (2) is:
for all i.ltoreq.j, X i,(i,j) And X j,(i,j) The gray correlation matrix of (2) is represented as an upper triangular matrix; obtaining a correlation matrix by gray correlation cluster analysis:
calculating the average value mu of gray correlation of gas concentration data by calculating the average value of all elements in the correlation matrix R gas Sum of variances sigma gas ；
10. The energy storage battery system safety precaution device according to claim 7, wherein the calculation module calculates the gas concentration prediction value x of each gas according to the detected gas concentration values of the plurality of gases and the predicted gas concentration value x of each gas at the next moment * gas Calculating gray correlation average value mu' of each gas sample detection value and predicted value constituting data set gas Specifically comprises the following steps:
extracting N concentration detection values x before a certain gas time t t,gas And a gas prediction value x at time t+1 * gas N+1 data are composed, constituting a series: { x 1 ,x 2 ,…,x N ,x * gas Reconstructing a sample space for the n+1 data to obtain a sample space data set X;
and (3) carrying out standardized method processing on each data in the matrix:
Calculate X i,(i,j) And X j,(i,j) Is a correlation coefficient of (a):
wherein delta is ij ＝|X i,(i,j) -X j,(i,j) I, ρ is a coefficient, m=max (#) ij )，m＝min(△ ij )；
X i,(i,j) And X j,(i,j) The degree of association of (2) is:
for all i.ltoreq.j, X i,(i,j) And X j,(i,j) The gray correlation matrix of (2) is represented as an upper triangular matrix; obtaining a correlation matrix by gray correlation cluster analysis:
calculating the average value of all elements in the correlation matrix R to obtain a gas concentration predicted value x * gas Average value mu' of gray correlation degree between belonging gas concentration sample and other samples gas 。
11. The energy storage of claim 7The battery system safety pre-warning device is characterized in that the pre-warning module averages mu according to gray correlation degree of each gas gas Sum of variances sigma gas Predicted value x of gas concentration of each gas * gas Sum of variances delta * gas Alarm concentration value w s，gas Performing safety early warning of an energy storage battery system and obtaining early warning information, wherein the method specifically comprises the following steps of:
when x is * gas +δ * gas ≤μ gas +σ gas When the method is used, early warning and warning are not carried out; at x * gas <w s，gas On the premise of (1) when x * gas +δ * gas ∈[μ gas +σ gas ,μ gas +1.44σ gas ]When the method is used, early warning and warning are not carried out;
when mu gas +1.44σ gas <x * gas +δ * gas <μ gas +1.96σ gas At this time, the predicted value x of the gas concentration in 1 minute continuously was calculated * gas For a period of time t h When t h >At 0.5 minutes, and the presence of gas causes μ' gas <μ gas Generating I-level early warning information;
when a certain gas x is present * gas +δ * gas >μ gas +1.96σ gas If x * gas <w s，gas Detecting a gas concentration predicted value x within 2min * gas For a period of time t h Setting a warning time t w ，t h <t w <2 minutes, at alert time t w If gas is present in the interior so that t h >At 1min, if mu' gas <μ gas Generating III-level early warning information, otherwise, generating II-level early warning information; if 0.5min<t h <1min and the presence of a gas makes mu' gas <μ gas And generating II-level early warning information, or else, generating I-level early warning information.
12. An electronic device comprising a processor and a memory, the processor configured to execute a computer program stored in the memory to implement the energy storage battery system safety precaution method of any one of claims 1 to 6.
13. A computer readable storage medium storing at least one instruction that when executed by a processor implements the energy storage battery system safety warning method of any one of claims 1 to 6.
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| CN116896139B (en) * | 2023-09-11 | 2023-12-12 | 中国铁塔股份有限公司四川省分公司 | Novel BMS system based on communication power supply system |
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