CN113537525A - Self-adaptive early warning method for fault state of battery energy storage system - Google Patents

Self-adaptive early warning method for fault state of battery energy storage system Download PDF

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CN113537525A
CN113537525A CN202110836171.8A CN202110836171A CN113537525A CN 113537525 A CN113537525 A CN 113537525A CN 202110836171 A CN202110836171 A CN 202110836171A CN 113537525 A CN113537525 A CN 113537525A
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CN113537525B (en
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肖先勇
陈智凡
汪颖
韦凌霄
李瑛�
席嫣娜
鞠力
陶以彬
冯鑫振
曹天植
易姝娴
陈瑞
李烜
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China Electric Power Research Institute Co Ltd CEPRI
Electric Power Research Institute of State Grid Jibei Electric Power Co Ltd
State Grid Beijing Electric Power Co Ltd
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Electric Power Research Institute of State Grid Jibei Electric Power Co Ltd
State Grid Beijing Electric Power Co Ltd
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Abstract

The invention discloses a self-adaptive early warning method for the fault state of a battery energy storage system, which is characterized in that an NJW clustering algorithm is used for reducing the dimension of high-dimensional monitoring data, a pull-type matrix of heterogeneous data is constructed to obtain a characteristic vector of the heterogeneous data, and the characteristic vector is used for uniquely replacing original data to perform clustering analysis, so that the problem of singularity frequently occurring in non-convex data clustering by using a traditional method is solved; the DTW is used for dynamically regulating the time axes of the asynchronous monitoring data, and two groups of monitoring data are mapped to the synchronous time axes, so that the observation error caused by asynchronous sampling of the monitoring data is overcome, and the problem that the time axes of the heterogeneous data of the sampling points cannot be in one-to-one correspondence in the traditional method is solved; and finally, a sliding window model is constructed to inhibit the influence of a small number of outliers in the monitoring data, clustering analysis is carried out based on the DTM distance, and fault clustering points are objectively selected through a sparse coefficient LSR and a fault threshold FT, so that over-estimation or under-estimation of the fault state by a traditional fault clustering method is avoided, and self-adaptive alarm of the BESS fault state is realized.

Description

Self-adaptive early warning method for fault state of battery energy storage system
Technical Field
The invention relates to the technical field of battery energy storage system detection, in particular to a fault state self-adaptive early warning method for a battery energy storage system.
Background
Most of safety problems encountered by a Battery Energy Storage System (BESS) come from a single cell level, mainly include overcharge, overdischarge, internal short circuit and external short circuit, in the conventional BESS fault early warning, a parameter estimation method realized by establishing an accurate electric heating simulation model or a threshold value limiting method realized based on an empirical estimation method only identifies abnormal data in the operation process of the BESS, and the BESS fault early warning plays a reference correction role by depending on normal data rather than ignoring observation errors and process noise in the normal battery circulation process. However, a large-capacity battery energy storage device is generally formed by connecting hundreds of battery cells in series and in parallel, the number of monitoring units which work together with a battery box is dozens, different monitoring units perform asynchronous measurement aiming at different battery boxes, large observation errors and process noises often exist among acquired heterogeneous data, sampling time axes recorded by different monitoring units are different from a battery circulation process, data are difficult to compare, and influences of the factors on BESS fault identification precision are not negligible.
The high-capacity BESS generally divides batteries into regions, and coordinates the battery boxes of each region according to different requirements to adopt different charging and discharging strategies, for example, part of the battery boxes are deeply charged and deeply discharged to meet the peak regulation and valley filling requirements of a power plant, and the other battery boxes possibly work in a shallow charging and shallow discharging mode at the same time to inhibit user harmonic waves and solve the problem of power quality; the same battery box also has completely different working modes in different stages of charging and discharging, trickle charging can be used for protecting the safety of the battery in the initial charging stage and the later charging stage, high-rate quick charging is adopted in the middle stage, and voltage and current curves in different stages have great difference. In order to achieve sufficient identification accuracy, the fault identification method based on the measured data of the monitoring unit must jump out of a laboratory environment, and the problem that asynchronous data of battery boxes of different battery partitions are not matched in different cycle stages in actual work is solved.
Firstly, when the BESS fault state early warning is carried out by the existing method, observation errors and process noises of different source data of different battery box monitoring units are generally directly ignored or processed according to the same source noise, and no literature is provided for deeply researching a matching method of the different source data. Actual BMS (Battery Management System) monitoring data and theoretical research show that the time axes of data sequences recorded by different monitoring units are often asynchronous and difficult to directly compare, so that misjudgment is likely to occur in the fault early warning if mismatching of heterogeneous data is ignored during BESS fault state early warning.
Secondly, in the prior art, when fault identification is realized based on actually measured data of the monitoring unit, a charging and discharging strategy and a battery circulation form are generally directly defined according to the type of a battery. The high-capacity BESS used in the industrial process generally adopts different charging and discharging strategies in battery boxes of each subarea, and the same battery box also has completely different working modes in different stages of charging and discharging. If the characteristic extraction and time axis regulation are not carried out on the monitoring data, the problem that asynchronous data of different battery partition battery boxes in different cycle stages are not matched in actual work is solved, underestimation or overestimation of different degrees can easily occur in the fault state early warning, and the BESS fault state early warning cannot reflect the real safety state of the energy storage equipment.
In addition, when the existing data is used for identifying abnormal data through fault clustering, a distance method is often adopted to directly determine fault data, however, a clustering distance threshold value of a fault point is generally given through empirical estimation, so that great contingency exists, and once a clustering point set is defined too large or too small, false alarm of fault early warning is easily caused.
Interpretation of terms:
a battery energy storage system: the battery is used as an energy storage carrier and is formed by combining hundreds of battery cell monomers, and the energy storage system for storing electric energy and supplying the electric energy generally comprises a battery cabinet for storing the energy and a control cabinet for monitoring, regulating and controlling the energy.
A battery management system: the battery management system is a system for monitoring and controlling the safety and the running state of the energy storage battery, saves and processes the monitored battery information and feeds the monitored battery information back to a user in real time, and each parameter is regulated and controlled according to the acquired information to protect the battery from running reliably and stably.
NJW spectral clustering algorithm: a spectral clustering algorithm acquires a corresponding Laplacian matrix by monitoring a data similarity matrix, selects eigenvectors corresponding to a plurality of previous maximum eigenvalues as a one-to-one corresponding substitution matrix of original data, and then clusters the eigenvectors according to rows.
Dynamic time warping algorithm (DTW): an algorithm compares asynchronous time series similarity by curving a monitoring data time axis and dynamically integrates asynchronous time series.
Disclosure of Invention
Aiming at the problems, the invention aims to provide a self-adaptive early warning method for the fault state of a battery energy storage system, which is used for processing heterogeneous data based on an NJW spectral clustering algorithm, classifying similar working conditions in battery boxes capable of being compared with the fault state, solving the problem of inconsistent time axes of asynchronous data by using a dynamic warping algorithm, constructing a sliding window model to inhibit the influence of a small number of outliers in monitoring data, carrying out clustering analysis based on DTW distance, and objectively selecting fault clustering points through sparse coefficients and fault thresholds so as to realize BESS fault state self-adaptive early warning. The technical scheme is as follows:
a self-adaptive early warning method for a fault state of a battery energy storage system comprises the following steps:
step 1: reducing the dimension of high-dimensional battery box monitoring data by using an NJW clustering algorithm, obtaining a safety characteristic standard matrix by constructing a pull-type matrix of heterogeneous data, and performing clustering analysis by only replacing original data of target monitoring parameters of the battery box with the safety characteristic standard matrix;
step 2: the time axes of the safety characteristic standard matrix are regulated through a dynamic time regulation algorithm, and two groups of monitoring data are mapped to a synchronous time axis to compare the similarity degree of the asynchronous monitoring data;
and step 3: and constructing a sliding window model to inhibit the influence of a small number of outliers in the monitoring data, carrying out cluster analysis based on the DTW distance, objectively selecting fault cluster points through a sparse coefficient LSR and a fault threshold FT, and further determining the fault battery box.
Further, the step 1 specifically includes the following steps:
step 1.1: setting a target monitoring parameter of a battery management system monitoring battery box as V, acquiring m groups of battery box monitoring data, wherein each group of data comprises n sampling points, and expressing the to-be-clustered monitoring data as follows:
U＝{u1,u2,…,un}∈Vm
step 1.2: extracting battery box monitoring data to construct similarity matrix W ═ Wij∣i≤m,j≤n}∈Vn×nThe following were used:
Figure BDA0003177126030000031
wherein u isiAnd ujRepresenting two heterogeneous battery box target monitoring data; sigmaiAnd σjTo adaptively identify parameters, [ sigma ]iIs a cell box target monitoring parameter uiAverage value of r sampling points with minimum Euclidean distance in the rest monitoring data, sigmajIs a cell box target monitoring parameter ujAverage value of r sampling points with minimum Euclidean distance in the rest monitoring data;
step 1.3: calculating to obtain a measurement matrix D ═ { D ═ according to the similarity matrix Wij∣i≤m,j≤n}∈Vn×n：
Figure BDA0003177126030000032
Step 1.4: calculating a Laplace matrix L by the similarity matrix W and the measurement matrix D:
Figure BDA0003177126030000033
step 1.5: calculating the eigenvalue and eigenvector corresponding to the pull-type matrix L, and arranging the characteristics in descending orderValue and eigenvector, and taking the first K eigenvectors to form a security feature matrix S ═ S1,s2,…,sK]∈Vn×KIn the method, the security feature matrix S is normalized line by line to form a security feature standard matrix Y ═ Yij∣i≤m,j≤n}∈Vn×K：
Figure BDA0003177126030000034
In the formula, sijIs the ith row and j columns of elements of the S matrix;
step 1.6: and each row of the full-characteristic standard matrix Y corresponds to a battery box target monitoring parameter sequence and uniquely replaces original sampling data.
Further, the step 2 specifically includes the following steps:
step 2.1: in a safety characteristic standard matrix Y, the characteristic vectors of the monitoring data corresponding to two asynchronous battery boxes a and b are respectively Ya＝{ya1,ya2,…,yanAnd Yb＝{yb1,yb2,…,ybnN is the number of sample points in each row of the safety characteristic standard matrix, namely the number of sampling points contained in the monitoring data of each battery box, and the distance matrix R of the battery boxes a and b is obtained by solving the distance matrix R ═ Rij∣i≤n,j≤n}：
Figure BDA0003177126030000041
MIN＝min{ri-1,j,ri,j-1,ri-1,j-1}
In the formula, d (y)ai,ybj) Is a sample point yaiAnd ybjThe Euclidean distance of; d (y)ai,ybj) + MIN is the sum of the minimum Euclidean distance between the current sample point and each adjacent sample point;
step 2.2: after forming the distance matrix, the DTW distance of the battery boxes a, b is:
DTW(Ya,Yb)＝rnn a,b≤m
in the same way, the DTW distance of any two battery boxes measured according to the monitoring parameter V can be calculated, and when a is equal to b, the DTW distance is zero.
Further, the step 3 specifically includes the following steps:
step 3.1: sliding window of length d, d<<n, placing the initial position t of the monitoring data U to be clustered1Moving the sliding increment backwards continuously along with the calculation of the NJW-DTW clustering algorithm to obtain sampling interval time q; calculating and storing the distance between the a-th battery box and the b-th battery box in each window until an n-d + 1-th subsequence with the length of d is formed; and so on, obtaining n-d +1 sliding windows and corresponding distance matrixes in total, and expressing as:
DTWj＝{dtw1,dtw2,…,dtwn-d+1}∈Vm
the fault condition occurs in one or more of the time windows;
step 3.2: computing sparse coefficients LSR
Defining the sparse coefficient LSR (a) of the a-th battery box as the reciprocal of the average distance of the battery box within the remaining distance k:
Figure BDA0003177126030000042
Figure BDA0003177126030000043
in the formula, size (DTW)k(Ya) The number of the battery boxes within the distance k is defined as the number of the battery boxes a; DTW (Y)a,Yb) The actual distance between the battery boxes within the k distance between the battery boxes a and b; without loss of generality, the k distance is taken to be half of the farthest DTW distance;
step 3.3: the failure threshold FT is defined as the inverse of the average distance of all battery compartments within its k distance:
Figure BDA0003177126030000051
step 3.4: and arranging the sparse coefficients LSR (a) of all the m battery boxes, and taking the fault threshold value FT as a judgment basis, and identifying all the battery boxes with the sparse coefficients lower than FT as fault battery boxes.
Furthermore, the battery box target monitoring parameter is battery box terminal voltage, branch current, battery temperature, insulation resistance or battery capacity.
The invention has the beneficial effects that:
1) the invention applies NJW clustering algorithm to reduce the dimension of high-dimensional monitoring data, obtains the characteristic vector of the high-dimensional monitoring data by constructing a pull-type matrix of heterogeneous data, and uniquely replaces the original data with the characteristic vector to perform clustering analysis. The NJW spectral clustering overcomes the limitation of the traditional method on the data dimension and the sequence length, and solves the singularity problem of the traditional method on the non-convex data clustering;
2) according to the method, the DTW is used for dynamically regulating the time axis of the asynchronous monitoring data, and two groups of monitoring data are mapped to the synchronous time axis, so that the observation error caused by asynchronous sampling of the monitoring data is overcome, the accuracy of a clustering result is greatly improved, and the problem that the time axes of the heterogeneous data of sampling points cannot be in one-to-one correspondence in the traditional method is solved;
3) the method constructs a sliding window model to inhibit the influence of a small number of outliers in the monitoring data, carries out clustering analysis based on the DTW distance, and objectively selects the fault clustering points through the sparse coefficient LSR and the fault threshold FT, thereby avoiding over-estimation or under-estimation of the fault state by the traditional fault clustering method, being capable of automatically adapting to parameters and actual working conditions of different BESS and realizing the self-adaptive alarm of the BESS fault state.
Drawings
Fig. 1 is a flow chart of a self-adaptive early warning method for a fault state of a battery energy storage system according to the invention.
FIG. 2 is a circular curve of the heterogeneous data on the same time axis.
Fig. 3 is a schematic view of a sliding window model.
Fig. 4 is a schematic diagram of fault clustering.
Detailed Description
The invention is described in further detail below with reference to the figures and specific embodiments. The technical scheme of the invention mainly comprises three major steps, namely heterogeneous data clustering, asynchronous sequence dynamic normalization and fault state identification, and a flow chart is shown in figure 1, wherein each major step and the minor steps thereof are elaborated as follows:
NJW spectral clustering algorithm for heterogeneous data
The NJW spectral clustering algorithm is an algorithm for carrying out dimensionality reduction clustering on high-dimensional heterogeneous data, and obtains characteristic vectors of the heterogeneous data by constructing a pull-type matrix of the heterogeneous data, and then uniquely replaces original data with the characteristic vectors to carry out clustering analysis. NJW spectral clustering has no limit on data dimensionality, and the singularity problem which often occurs in heterogenous non-convex shapes is effectively avoided. In this embodiment, the fault state early warning based on the battery box terminal voltage is taken as an example, and the steps are as follows (the rest monitoring data also have completely consistent calculation steps):
1. setting the voltage of a battery box terminal monitored by a battery management system as V, acquiring m groups of battery box monitoring data, wherein each group of data comprises n sampling points, and representing the to-be-clustered monitoring data as follows:
U＝{u1,u2,…,un}∈Vm (1)
2. extracting monitoring data to construct similarity matrix W ═ Wij∣i≤m,j≤n}∈Vn×nThe following were used:
Figure BDA0003177126030000061
wherein: u. ofiAnd ujThe terminal voltage of the battery box with two different sources is shown, and sigma is an adaptive identification parameter. SigmaiIs a cell box target monitoring parameter uiAverage value of r sampling points with minimum Euclidean distance in the rest monitoring data, sigmajIs a cell box target monitoring parameter ujAnd taking the average value of r sampling points with the minimum Euclidean distance in the rest monitoring data, wherein r is generally 3-7 in order to ensure the clustering accuracy. .
3. According to the similarityThe degree matrix W is calculated to obtain a measurement matrix D ═ Dij∣i≤m,j≤n}∈Vn×n：
Figure BDA0003177126030000062
4. Calculating a Laplace matrix L by the similarity matrix W and the measurement matrix D:
Figure BDA0003177126030000063
5. calculating the eigenvalue and eigenvector corresponding to the pull-type matrix L, arranging the eigenvalue and eigenvector in descending order, and taking the first K eigenvectors to form a security eigenvector matrix S ═ S1,s2,…,sK]∈Vn×KIn the method, S array is normalized line by line to form security feature standard matrix Y ═ Yij∣i≤m,j≤n}∈Vn×K：
Figure BDA0003177126030000064
In the formula, sijIs the ith row and j columns of elements of the S matrix.
6. Each row of the safety characteristic standard matrix Y corresponds to a battery box terminal voltage sequence and can uniquely replace original sampling data. And then performing dynamic time-warping clustering on the obtained data.
Dynamic time warping algorithm
As shown in fig. 2, there is a problem that the time axis of the obtained asynchronous monitoring data of different battery compartment boxes in different cycle stages in actual operation is not matched. The method improves a K mean value clustering algorithm used by a heterogeneous data NJW spectral clustering algorithm, and improves Euclidean distance into DTW distance for K mean value clustering analysis. The DTW distance compares the similarity of asynchronous monitoring data through the time axis of a regular safety feature standard matrix Y, and the steps are as follows:
1. setting two asynchronous battery boxes a and b in a safety characteristic standard matrix Y to be correspondingly monitoredThe measured data feature vectors are respectively Ya＝{ya1,ya2,…,yanAnd Yb＝{yb1,yb2,…,ybnN is the number of sample points in each row of the safety characteristic standard matrix, that is, the number of sample points included in the monitoring data of each group of battery boxes, and the distance matrix R of the battery boxes a and b can be obtained by solving the distance matrix R ═ Rij∣i≤n,j≤n}：
Figure BDA0003177126030000071
MIN＝min{ri-1,j,ri,j-1,ri-1,j-1} (7)
In the formula, d (y)ai,ybj) Is a sample point yaiAnd ybjThe Euclidean distance of; d (y)ai,ybj) + MIN is the sum of the minimum Euclidean distance between the current sample point and each adjacent sample point;
2. after forming the distance matrix, the DTW distance of the battery boxes a, b is
DTW(Ya,Yb)＝rnn a,b≤m (8)
In the same way, the DTW distance of any two battery boxes measured according to the monitoring parameter V can be calculated, and when a is equal to b, the DTW distance is zero.
On the basis of an NJW safety characteristic standard matrix Y, the DTW method dynamically normalizes the time axis of one sequence by finding the numerical similarity of two groups of monitoring data, avoids observation errors caused by data asynchronous recording, and can map the two sequences onto a synchronous time axis, so that the similarity of the two sequences is calculated, the accuracy of a clustering result is greatly improved, and the defect that the time axes of different sampling point data cannot correspond to one another in the traditional method is overcome.
Third, fault state recognition algorithm
In order to find out abnormal fault data in monitoring data, the invention provides a fault state identification method based on an NJW-DTW clustering algorithm, which comprises the following steps, wherein in order to simplify the description, the following distance refers to a DTW distance:
1. sliding window model
And inhibiting the influence of a small number of outliers in the monitoring data by adopting a sliding window model. BMS monitoring battery box terminal voltage V acquires m groups of battery box monitoring data, and every group of data includes n sampling points, can show the monitoring data of waiting to cluster as: u ═ U1,u2,…,un}∈VmT is more than or equal to 1 and less than or equal to n, and the length d (d)<<n) is arranged at the initial position t of the monitoring data U to be clustered1And moving backward with the computation of the NJW-DTW clustering algorithm, the sliding increment is the sampling interval time q, as shown in FIG. 3. Calculating and storing the distance between the a-th battery box and the b-th battery box in each window until an n-d + 1-th subsequence with the length of d is formed; by analogy, n-d +1 sliding windows and corresponding distance matrixes can be obtained in total, and are represented as follows: DTWj＝{dtw1,dtw2,…,dtwn-d+1}∈VmThe fault condition occurs in one or more of the time windows.
2. Sparse coefficient LSR (LSR)
Defining the sparse coefficient LSR (a) of the a-th battery box as the reciprocal of the average distance of the battery box within the remaining distance k:
Figure BDA0003177126030000081
in the formula, size (DTW)k(Ya) The number of the battery boxes within the distance k is defined as the number of the battery boxes a; DTW (Y)a,Yb) The actual distance between the battery boxes within the k distance between the battery boxes a and b; without loss of generality, the k distance is taken to be half of the farthest DTW distance;
the sparse coefficient LSR (i) reflects the distribution density of the safety states of all the battery boxes around the battery box i, and the smaller the local sparse coefficient is, the higher the failure probability of the battery box i is, and vice versa.
The failure threshold FT (FT) is defined as the inverse of the average distance of all battery compartments within its k distance:
Figure BDA0003177126030000082
if a battery box i fails, its sparseness factor should be smaller than the failure factor FT, because the battery box i is now located a greater safety distance from all other battery boxes than all other battery boxes. All the battery boxes with sparse coefficients LSR smaller than the fault factor FT are taken as candidate fault battery boxes, and fault clustering is shown in FIG. 4.
3. Fault state identification
And (3) arranging the sparse coefficients LSR (a) of all the m battery boxes, and taking the fault threshold value FT as a judgment basis, wherein all the battery boxes with the sparse coefficients lower than FT can be regarded as fault battery boxes.
Starting from heterogeneous monitoring data, the method applies an NJW clustering algorithm to reduce the dimension of high-dimensional data, extracts the characteristic vector of the monitoring data, and eliminates observation errors and process noise influence; the DTW method is used for dynamically integrating the asynchronous data, so that the problem of mismatching of heterogeneous data is solved; the method has the advantages that fault clustering analysis is carried out based on objective indexes, and the method has strong self-adaptability to different systems, so that the safe operation state of the battery energy storage system can be objectively reflected, and fault early warning can be accurately sent out.

Claims (5)

1. A self-adaptive early warning method for a fault state of a battery energy storage system is characterized by comprising the following steps:
step 1: reducing the dimension of high-dimensional battery box monitoring data by using an NJW clustering algorithm, obtaining a safety characteristic standard matrix by constructing a pull-type matrix of heterogeneous data, and performing clustering analysis by only replacing original data of target monitoring parameters of the battery box with the safety characteristic standard matrix;
step 2: the time axes of the safety characteristic standard matrix are regulated through a dynamic time regulation algorithm, and two groups of monitoring data are mapped to a synchronous time axis to compare the similarity degree of the asynchronous monitoring data;
and step 3: and constructing a sliding window model to inhibit the influence of a small number of outliers in the monitoring data, carrying out cluster analysis based on the DTW distance, objectively selecting fault cluster points through a sparse coefficient LSR and a fault threshold FT, and further determining the fault battery box.
2. The battery energy storage system fault state adaptive early warning method according to claim 1, wherein the step 1 specifically comprises the following steps:
step 1.1: setting a target monitoring parameter of a battery management system monitoring battery box as V, acquiring m groups of battery box monitoring data, wherein each group of data comprises n sampling points, and expressing the to-be-clustered monitoring data as follows:
U＝{u1,u2,…,un}∈Vm
step 1.2: extracting battery box monitoring data to construct similarity matrix W ═ Wij∣i≤m,j≤n}∈Vn×nThe following were used:
Figure FDA0003177126020000011
wherein u isiAnd ujRepresenting two heterogeneous battery box target monitoring data; sigmaiAnd σjTo adaptively identify parameters, [ sigma ]iIs a cell box target monitoring parameter uiAverage value of r sampling points with minimum Euclidean distance in the rest monitoring data, sigmajIs a cell box target monitoring parameter ujAverage value of r sampling points with minimum Euclidean distance in the rest monitoring data;
step 1.3: calculating to obtain a measurement matrix D ═ { D ═ according to the similarity matrix Wij∣i≤m,j≤n}∈Vn×n：
Figure FDA0003177126020000012
Step 1.4: calculating a Laplace matrix L by the similarity matrix W and the measurement matrix D:
Figure FDA0003177126020000013
step 1.5: calculating the eigenvalue and eigenvector corresponding to the pull-type matrix L, arranging the eigenvalue and eigenvector in descending order, and taking the first K eigenvectors to form a security eigenvector matrix S ═ S1,s2,…,sK]∈Vn×KIn the method, the security feature matrix S is normalized line by line to form a security feature standard matrix Y ═ Yij∣i≤m,j≤n}∈Vn×K：
Figure FDA0003177126020000021
In the formula, sijIs the ith row and j columns of elements of the S matrix;
step 1.6: and each row of the full-characteristic standard matrix Y corresponds to a battery box target monitoring parameter sequence and uniquely replaces original sampling data.
3. The battery energy storage system fault state adaptive early warning method according to claim 2, wherein the step 2 specifically comprises the following steps:
step 2.1: in a safety characteristic standard matrix Y, the characteristic vectors of the monitoring data corresponding to two asynchronous battery boxes a and b are respectively Ya＝{ya1,ya2,…,yanAnd Yb＝{yb1,yb2,…,ybnN is the number of sample points in each row of the safety characteristic standard matrix, namely the number of sampling points contained in the monitoring data of each battery box, and the distance matrix R of the battery boxes a and b is obtained by solving the distance matrix R ═ Rij∣i≤n,j≤n}：
Figure FDA0003177126020000022
MIN＝min{ri-1,j,ri,j-1,ri-1,j-1}
In the formula, d (y)ai,ybj) Is a sample point yaiAnd ybjThe Euclidean distance of; d(yai,ybj) + MIN is the sum of the minimum Euclidean distance between the current sample point and each adjacent sample point;
step 2.2: after forming the distance matrix, the DTW distance of the battery boxes a, b is:
DTW(Ya,Yb)＝rnn a,b≤m
in the same way, the DTW distance of any two battery boxes measured according to the monitoring parameter V can be calculated, and when a is equal to b, the DTW distance is zero.
4. The battery energy storage system fault state adaptive early warning method according to claim 3, wherein the step 3 specifically comprises the following steps:
step 3.1: sliding window of length d, d<<n, placing the initial position t of the monitoring data U to be clustered1Moving the sliding increment backwards continuously along with the calculation of the NJW-DTW clustering algorithm to obtain sampling interval time q; calculating and storing the distance between the a-th battery box and the b-th battery box in each window until an n-d + 1-th subsequence with the length of d is formed; and so on, obtaining n-d +1 sliding windows and corresponding distance matrixes in total, and expressing as:
DTWj＝{dtw1,dtw2,…,dtwn-d+1}∈Vm
the fault condition occurs in one or more of the time windows;
step 3.2: computing sparse coefficients LSR
Defining the sparse coefficient LSR (a) of the a-th battery box as the reciprocal of the average distance of the battery box within the remaining distance k:
Figure FDA0003177126020000031
Figure FDA0003177126020000032
in the formula, size (DTW)k(Ya))The number of the battery boxes within the distance k is the battery box a; DTW (Y)a,Yb) The actual distance between the battery boxes within the k distance between the battery boxes a and b; without loss of generality, the k distance is taken to be half of the farthest DTW distance;
step 3.3: the failure threshold FT is defined as the inverse of the average distance of all battery compartments within its k distance:
Figure FDA0003177126020000033
step 3.4: and arranging the sparse coefficients LSR (a) of all the m battery boxes, and taking the fault threshold value FT as a judgment basis, and identifying all the battery boxes with the sparse coefficients lower than FT as fault battery boxes.
5. The adaptive battery energy storage system fault state early warning method according to any one of claims 1-4, wherein the target battery box monitoring parameter is a battery box terminal voltage, a branch current, a battery temperature, an insulation resistance or a battery capacity.
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Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114280352A (en) * 2021-12-27 2022-04-05 杭州电子科技大学 Current-based large instrument man-hour calculation method
CN115267589A (en) * 2022-09-26 2022-11-01 陕西汽车集团股份有限公司 Multi-parameter joint diagnosis method for battery faults of electric vehicle
CN115308631A (en) * 2022-10-09 2022-11-08 湖北工业大学 Fault diagnosis method and system for new energy automobile power battery pack
CN116577671A (en) * 2023-07-12 2023-08-11 中国华能集团清洁能源技术研究院有限公司 Battery system abnormality detection method and device
CN116775408A (en) * 2023-06-19 2023-09-19 上海启斯云计算有限公司 Intelligent monitoring method for operation state of energy storage equipment
CN117171588A (en) * 2023-11-02 2023-12-05 吉林省有继科技有限公司 Method for detecting gradient utilization faults of power battery
CN117331921A (en) * 2023-09-28 2024-01-02 石家庄铁道大学 Bearing monitoring multisource data processing method

Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100121587A1 (en) * 2006-11-30 2010-05-13 The Boeing Company Health Management of Rechargeable Batteries
CN104897403A (en) * 2015-06-24 2015-09-09 北京航空航天大学 Self-adaption fault diagnosis method based on permutation entropy (PE) and manifold-based dynamic time warping (MDTW)
CN108414896A (en) * 2018-06-04 2018-08-17 西南交通大学 A kind of electric network failure diagnosis method
CN108960321A (en) * 2018-07-02 2018-12-07 国电南瑞科技股份有限公司 A kind of large size lithium battery energy storage battery power station battery failures prediction technique
CN109002781A (en) * 2018-07-02 2018-12-14 国电南瑞科技股份有限公司 A kind of energy accumulation current converter failure prediction method
CN111046942A (en) * 2019-12-09 2020-04-21 交控科技股份有限公司 Turnout fault judgment method and device
CN111516548A (en) * 2020-04-23 2020-08-11 华南理工大学 Cloud platform-based charging pile system for realizing power battery fault diagnosis
US20200271725A1 (en) * 2019-02-25 2020-08-27 Toyota Research Institute, Inc. Systems, methods, and storage media for predicting a discharge profile of a battery pack
CN112946522A (en) * 2021-02-05 2021-06-11 四川大学 On-line monitoring method for short-circuit fault in battery energy storage system caused by low-temperature working condition

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100121587A1 (en) * 2006-11-30 2010-05-13 The Boeing Company Health Management of Rechargeable Batteries
CN104897403A (en) * 2015-06-24 2015-09-09 北京航空航天大学 Self-adaption fault diagnosis method based on permutation entropy (PE) and manifold-based dynamic time warping (MDTW)
CN108414896A (en) * 2018-06-04 2018-08-17 西南交通大学 A kind of electric network failure diagnosis method
CN108960321A (en) * 2018-07-02 2018-12-07 国电南瑞科技股份有限公司 A kind of large size lithium battery energy storage battery power station battery failures prediction technique
CN109002781A (en) * 2018-07-02 2018-12-14 国电南瑞科技股份有限公司 A kind of energy accumulation current converter failure prediction method
US20200271725A1 (en) * 2019-02-25 2020-08-27 Toyota Research Institute, Inc. Systems, methods, and storage media for predicting a discharge profile of a battery pack
CN111046942A (en) * 2019-12-09 2020-04-21 交控科技股份有限公司 Turnout fault judgment method and device
CN111516548A (en) * 2020-04-23 2020-08-11 华南理工大学 Cloud platform-based charging pile system for realizing power battery fault diagnosis
CN112946522A (en) * 2021-02-05 2021-06-11 四川大学 On-line monitoring method for short-circuit fault in battery energy storage system caused by low-temperature working condition

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
胡安平等: "基于电力电子接口的储能系统惯性特征研究", 《中国电机工程学报》 *

Cited By (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114280352A (en) * 2021-12-27 2022-04-05 杭州电子科技大学 Current-based large instrument man-hour calculation method
CN114280352B (en) * 2021-12-27 2024-02-13 杭州电子科技大学 Current-based large instrument working hour calculation method
CN115267589A (en) * 2022-09-26 2022-11-01 陕西汽车集团股份有限公司 Multi-parameter joint diagnosis method for battery faults of electric vehicle
CN115267589B (en) * 2022-09-26 2023-01-06 陕西汽车集团股份有限公司 Multi-parameter joint diagnosis method for battery faults of electric vehicle
CN115308631A (en) * 2022-10-09 2022-11-08 湖北工业大学 Fault diagnosis method and system for new energy automobile power battery pack
CN116775408A (en) * 2023-06-19 2023-09-19 上海启斯云计算有限公司 Intelligent monitoring method for operation state of energy storage equipment
CN116775408B (en) * 2023-06-19 2024-02-09 上海启斯云计算有限公司 Intelligent monitoring method for operation state of energy storage equipment
CN116577671A (en) * 2023-07-12 2023-08-11 中国华能集团清洁能源技术研究院有限公司 Battery system abnormality detection method and device
CN116577671B (en) * 2023-07-12 2023-09-29 中国华能集团清洁能源技术研究院有限公司 Battery system abnormality detection method and device
CN117331921A (en) * 2023-09-28 2024-01-02 石家庄铁道大学 Bearing monitoring multisource data processing method
CN117171588A (en) * 2023-11-02 2023-12-05 吉林省有继科技有限公司 Method for detecting gradient utilization faults of power battery

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